code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _A ( _a ):
"""simple docstring"""
UpperCAmelCase : str = """naver-clova-ix/donut-base-finetuned-docvqa"""
UpperCAmelCase : Tuple = (
"""This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
"""should be the document containing the information, as well as a `question` that is the question about the """
"""document. It returns a text that contains the answer to the question."""
)
UpperCAmelCase : List[str] = """document_qa"""
UpperCAmelCase : str = AutoProcessor
UpperCAmelCase : Optional[int] = VisionEncoderDecoderModel
UpperCAmelCase : int = ["""image""", """text"""]
UpperCAmelCase : int = ["""text"""]
def __init__( self : Tuple , *__UpperCAmelCase : Union[str, Any] , **__UpperCAmelCase : Any):
if not is_vision_available():
raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool.")
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase)
def __snake_case ( self : Tuple , __UpperCAmelCase : "Image" , __UpperCAmelCase : str):
a : Any = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
a : Union[str, Any] = task_prompt.replace("{user_input}" , __UpperCAmelCase)
a : Optional[Any] = self.pre_processor.tokenizer(
__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_tensors="pt").input_ids
a : Any = self.pre_processor(__UpperCAmelCase , return_tensors="pt").pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def __snake_case ( self : int , __UpperCAmelCase : int):
return self.model.generate(
inputs["pixel_values"].to(self.device) , decoder_input_ids=inputs["decoder_input_ids"].to(self.device) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__UpperCAmelCase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__UpperCAmelCase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__UpperCAmelCase , ).sequences
def __snake_case ( self : str , __UpperCAmelCase : List[Any]):
a : Union[str, Any] = self.pre_processor.batch_decode(__UpperCAmelCase)[0]
a : Optional[Any] = sequence.replace(self.pre_processor.tokenizer.eos_token , "")
a : Any = sequence.replace(self.pre_processor.tokenizer.pad_token , "")
a : Optional[Any] = re.sub(r"<.*?>" , "" , __UpperCAmelCase , count=1).strip() # remove first task start token
a : List[str] = self.pre_processor.tokenajson(__UpperCAmelCase)
return sequence["answer"]
| 40 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
def __init__( self , __UpperCAmelCase) ->Any:
a_ = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same number of values, each of which must be of type "
"int or float.")
if len(__UpperCAmelCase) != 0:
a_ = len(rows[0])
if cols == 0:
raise error
for row in rows:
if len(__UpperCAmelCase) != cols:
raise error
for value in row:
if not isinstance(__UpperCAmelCase , (int, float)):
raise error
a_ = rows
else:
a_ = []
def UpperCAmelCase__ ( self) ->list[list[int]]:
return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]
@property
def UpperCAmelCase__ ( self) ->int:
return len(self.rows)
@property
def UpperCAmelCase__ ( self) ->int:
return len(self.rows[0])
@property
def UpperCAmelCase__ ( self) ->tuple[int, int]:
return (self.num_rows, self.num_columns)
@property
def UpperCAmelCase__ ( self) ->bool:
return self.order[0] == self.order[1]
def UpperCAmelCase__ ( self) ->Matrix:
a_ = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
for row_num in range(self.num_rows)
]
return Matrix(__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->int:
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0])
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]))
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns))
def UpperCAmelCase__ ( self) ->bool:
return bool(self.determinant())
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase) ->int:
a_ = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns)
if other_column != column
]
for other_row in range(self.num_rows)
if other_row != row
]
return Matrix(__UpperCAmelCase).determinant()
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase) ->int:
if (row + column) % 2 == 0:
return self.get_minor(__UpperCAmelCase , __UpperCAmelCase)
return -1 * self.get_minor(__UpperCAmelCase , __UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Matrix:
return Matrix(
[
[self.get_minor(__UpperCAmelCase , __UpperCAmelCase) for column in range(self.num_columns)]
for row in range(self.num_rows)
])
def UpperCAmelCase__ ( self) ->Matrix:
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns)
]
for row in range(self.minors().num_rows)
])
def UpperCAmelCase__ ( self) ->Matrix:
a_ = [
[self.cofactors().rows[column][row] for column in range(self.num_columns)]
for row in range(self.num_rows)
]
return Matrix(__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Matrix:
a_ = self.determinant()
if not determinant:
raise TypeError("Only matrices with a non-zero determinant have an inverse")
return self.adjugate() * (1 / determinant)
def __repr__( self) ->str:
return str(self.rows)
def __str__( self) ->str:
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0])) + "]]"
return (
"["
+ "\n ".join(
[
"[" + ". ".join([str(__UpperCAmelCase) for value in row]) + ".]"
for row in self.rows
])
+ "]"
)
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase = None) ->None:
a_ = TypeError("Row must be a list containing all ints and/or floats")
if not isinstance(__UpperCAmelCase , __UpperCAmelCase):
raise type_error
for value in row:
if not isinstance(__UpperCAmelCase , (int, float)):
raise type_error
if len(__UpperCAmelCase) != self.num_columns:
raise ValueError(
"Row must be equal in length to the other rows in the matrix")
if position is None:
self.rows.append(__UpperCAmelCase)
else:
a_ = self.rows[0:position] + [row] + self.rows[position:]
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase = None) ->None:
a_ = TypeError(
"Column must be a list containing all ints and/or floats")
if not isinstance(__UpperCAmelCase , __UpperCAmelCase):
raise type_error
for value in column:
if not isinstance(__UpperCAmelCase , (int, float)):
raise type_error
if len(__UpperCAmelCase) != self.num_rows:
raise ValueError(
"Column must be equal in length to the other columns in the matrix")
if position is None:
a_ = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
else:
a_ = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows)
]
def __eq__( self , __UpperCAmelCase) ->bool:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase):
return NotImplemented
return self.rows == other.rows
def __ne__( self , __UpperCAmelCase) ->bool:
return not self == other
def __neg__( self) ->Matrix:
return self * -1
def __add__( self , __UpperCAmelCase) ->Matrix:
if self.order != other.order:
raise ValueError("Addition requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
])
def __sub__( self , __UpperCAmelCase) ->Matrix:
if self.order != other.order:
raise ValueError("Subtraction requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
])
def __mul__( self , __UpperCAmelCase) ->Matrix:
if isinstance(__UpperCAmelCase , (int, float)):
return Matrix(
[[int(element * other) for element in row] for row in self.rows])
elif isinstance(__UpperCAmelCase , __UpperCAmelCase):
if self.num_columns != other.num_rows:
raise ValueError(
"The number of columns in the first matrix must "
"be equal to the number of rows in the second")
return Matrix(
[
[Matrix.dot_product(__UpperCAmelCase , __UpperCAmelCase) for column in other.columns()]
for row in self.rows
])
else:
raise TypeError(
"A Matrix can only be multiplied by an int, float, or another matrix")
def __pow__( self , __UpperCAmelCase) ->Matrix:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase):
raise TypeError("A Matrix can only be raised to the power of an int")
if not self.is_square:
raise ValueError("Only square matrices can be raised to a power")
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
"Only invertable matrices can be raised to a negative power")
a_ = self
for _ in range(other - 1):
result *= self
return result
@classmethod
def UpperCAmelCase__ ( cls , __UpperCAmelCase , __UpperCAmelCase) ->int:
return sum(row[i] * column[i] for i in range(len(__UpperCAmelCase)))
if __name__ == "__main__":
import doctest
doctest.testmod() | 243 | 0 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
__snake_case : Dict = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase_ )
class __UpperCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[str]:
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
self.check_model_type(_UpperCAmelCase )
def __A ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> Dict:
A_ ,A_ = {}, {}
if padding is not None:
A_ = padding
if truncation is not None:
A_ = truncation
if top_k is not None:
A_ = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE ) -> Dict:
if isinstance(_UpperCAmelCase , (Image.Image, str) ) and isinstance(_UpperCAmelCase , _UpperCAmelCase ):
A_ = {'''image''': image, '''question''': question}
else:
A_ = image
A_ = super().__call__(_UpperCAmelCase , **_UpperCAmelCase )
return results
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ) -> int:
A_ = load_image(inputs['''image'''] )
A_ = self.tokenizer(
inputs['''question'''] , return_tensors=self.framework , padding=_UpperCAmelCase , truncation=_UpperCAmelCase )
A_ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework )
model_inputs.update(_UpperCAmelCase )
return model_inputs
def __A ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
A_ = self.model(**_UpperCAmelCase )
return model_outputs
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=5 ) -> List[str]:
if top_k > self.model.config.num_labels:
A_ = self.model.config.num_labels
if self.framework == "pt":
A_ = model_outputs.logits.sigmoid()[0]
A_ ,A_ = probs.topk(_UpperCAmelCase )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
A_ = scores.tolist()
A_ = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase , _UpperCAmelCase )]
| 366 | '''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __UpperCAmelCase :
'''simple docstring'''
pass
| 18 | 0 |
import math
def UpperCamelCase__( UpperCamelCase__ : int )->bool:
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
A__ = range(3 , int(math.sqrt(UpperCamelCase__ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def UpperCamelCase__( UpperCamelCase__ : Any , UpperCamelCase__ : List[Any]=1 , **UpperCamelCase__ : List[str] )->str:
A__ = factor * value
A__ = value
while not is_prime(UpperCamelCase__ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **UpperCamelCase__ )
return value
| 193 |
import numpy as np
def UpperCamelCase__( UpperCamelCase__ : np.array )->np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 193 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict , __lowerCAmelCase : int ) -> Tuple:
# Initialise PyTorch model
snake_case = MobileBertConfig.from_json_file(__lowerCAmelCase )
print(F'''Building PyTorch model from configuration: {config}''' )
snake_case = MobileBertForPreTraining(__lowerCAmelCase )
# Load weights from tf checkpoint
snake_case = load_tf_weights_in_mobilebert(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __lowerCAmelCase )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 3 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : int ) -> int:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
snake_case = 0
snake_case = str(__lowerCAmelCase )
while len(__lowerCAmelCase ) != 1:
snake_case = [int(__lowerCAmelCase ) for i in num_string]
snake_case = 1
for i in range(0 , len(__lowerCAmelCase ) ):
total *= numbers[i]
snake_case = str(__lowerCAmelCase )
steps += 1
return steps
def __lowerCamelCase ( __lowerCAmelCase : int ) -> int:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
snake_case = 0
snake_case = str(__lowerCAmelCase )
while len(__lowerCAmelCase ) != 1:
snake_case = [int(__lowerCAmelCase ) for i in num_string]
snake_case = 0
for i in range(0 , len(__lowerCAmelCase ) ):
total += numbers[i]
snake_case = str(__lowerCAmelCase )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 3 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ , ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = parent
UpperCAmelCase_ : Any = 13
UpperCAmelCase_ : Optional[int] = 7
UpperCAmelCase_ : Tuple = True
UpperCAmelCase_ : int = True
UpperCAmelCase_ : Any = True
UpperCAmelCase_ : Optional[int] = 99
UpperCAmelCase_ : Tuple = 32
UpperCAmelCase_ : Union[str, Any] = 2
UpperCAmelCase_ : Optional[Any] = 4
UpperCAmelCase_ : List[str] = 37
UpperCAmelCase_ : Optional[int] = "gelu"
UpperCAmelCase_ : Union[str, Any] = 0.1
UpperCAmelCase_ : Optional[int] = 0.1
UpperCAmelCase_ : Dict = 512
UpperCAmelCase_ : str = 16
UpperCAmelCase_ : Any = 2
UpperCAmelCase_ : int = 0.02
UpperCAmelCase_ : str = 3
UpperCAmelCase_ : Optional[Any] = 4
UpperCAmelCase_ : List[str] = None
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase_ : str = None
if self.use_input_mask:
UpperCAmelCase_ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase_ : List[str] = None
UpperCAmelCase_ : Tuple = None
UpperCAmelCase_ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase_ : Dict = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ ( self ):
"""simple docstring"""
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase_ : List[str] = True
UpperCAmelCase_ : Optional[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase_ : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = TFEsmModel(config=lowercase_ )
UpperCAmelCase_ : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
UpperCAmelCase_ : Union[str, Any] = model(lowercase_ )
UpperCAmelCase_ : Optional[Any] = [input_ids, input_mask]
UpperCAmelCase_ : Tuple = model(lowercase_ )
UpperCAmelCase_ : List[str] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ):
"""simple docstring"""
UpperCAmelCase_ : Dict = True
UpperCAmelCase_ : str = TFEsmModel(config=lowercase_ )
UpperCAmelCase_ : str = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
UpperCAmelCase_ : Any = model(lowercase_ )
UpperCAmelCase_ : Optional[Any] = [input_ids, input_mask]
UpperCAmelCase_ : str = model(lowercase_ , encoder_hidden_states=lowercase_ )
# Also check the case where encoder outputs are not passed
UpperCAmelCase_ : Optional[int] = model(lowercase_ , attention_mask=lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Dict = TFEsmForMaskedLM(config=lowercase_ )
UpperCAmelCase_ : List[Any] = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.num_labels
UpperCAmelCase_ : Union[str, Any] = TFEsmForTokenClassification(config=lowercase_ )
UpperCAmelCase_ : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
UpperCAmelCase_ : List[str] = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : int = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) : List[str] = config_and_inputs
UpperCAmelCase_ : str = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A_ (lowercase__ ,lowercase__ ,unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
{
"""feature-extraction""": TFEsmModel,
"""fill-mask""": TFEsmForMaskedLM,
"""text-classification""": TFEsmForSequenceClassification,
"""token-classification""": TFEsmForTokenClassification,
"""zero-shot""": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE__ : int = False
SCREAMING_SNAKE_CASE__ : List[str] = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : int = TFEsmModelTester(self )
UpperCAmelCase_ : Optional[Any] = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_ )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ : Optional[Any] = TFEsmModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@unittest.skip("Protein models do not support embedding resizing." )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("Protein models do not support embedding resizing." )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : Union[str, Any] = model_class(lowercase_ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
UpperCAmelCase_ : List[str] = model.get_bias()
assert isinstance(lowercase_ , lowercase_ )
for k, v in name.items():
assert isinstance(lowercase_ , tf.Variable )
else:
UpperCAmelCase_ : Union[str, Any] = model.get_output_embeddings()
assert x is None
UpperCAmelCase_ : Optional[int] = model.get_bias()
assert name is None
@require_tf
class A_ (unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
UpperCAmelCase_ : int = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase_ : Dict = model(lowercase_ )[0]
UpperCAmelCase_ : List[Any] = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , lowercase_ )
# compare the actual values for a slice.
UpperCAmelCase_ : Tuple = tf.constant(
[
[
[8.92_15_18, -10.58_98_14, -6.4_67_13_07],
[-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
[-7.78_12_47, -13.95_15_57, -3.74_05_92],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
UpperCAmelCase_ : Optional[int] = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
UpperCAmelCase_ : Union[str, Any] = model(lowercase_ )[0]
# compare the actual values for a slice.
UpperCAmelCase_ : Tuple = tf.constant(
[
[
[0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
[0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
[0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 61 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a = 'src/diffusers'
# Matches is_xxx_available()
_a = re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
_a = re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
_a = '\n{0} = None\n'
_a = '\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n'
_a = '\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n'
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : int = _re_backend.findall(__lowerCamelCase )
if len(__lowerCamelCase ) == 0:
return None
return "_and_".join(__lowerCamelCase )
def __a ( ):
with open(os.path.join(__lowerCamelCase, "__init__.py" ), "r", encoding="utf-8", newline="\n" ) as f:
UpperCAmelCase_ : Optional[int] = f.readlines()
# Get to the point we do the actual imports for type checking
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : Optional[int] = {}
# Go through the end of the file
while line_index < len(__lowerCamelCase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
UpperCAmelCase_ : Union[str, Any] = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith("else:" ):
line_index += 1
line_index += 1
UpperCAmelCase_ : List[str] = []
# Until we unindent, add backend objects to the list
while line_index < len(__lowerCamelCase ) and len(lines[line_index] ) > 1:
UpperCAmelCase_ : Union[str, Any] = lines[line_index]
UpperCAmelCase_ : Optional[Any] = _re_single_line_import.search(__lowerCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", " ) )
elif line.startswith(" " * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(__lowerCamelCase ) > 0:
UpperCAmelCase_ : Optional[int] = objects
else:
line_index += 1
return backend_specific_objects
def __a ( __lowerCamelCase, __lowerCamelCase ):
if name.isupper():
return DUMMY_CONSTANT.format(__lowerCamelCase )
elif name.islower():
return DUMMY_FUNCTION.format(__lowerCamelCase, __lowerCamelCase )
else:
return DUMMY_CLASS.format(__lowerCamelCase, __lowerCamelCase )
def __a ( __lowerCamelCase=None ):
if backend_specific_objects is None:
UpperCAmelCase_ : Tuple = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
UpperCAmelCase_ : str = {}
for backend, objects in backend_specific_objects.items():
UpperCAmelCase_ : int = "[" + ", ".join(f"""\"{b}\"""" for b in backend.split("_and_" ) ) + "]"
UpperCAmelCase_ : Dict = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(__lowerCamelCase, __lowerCamelCase ) for o in objects] )
UpperCAmelCase_ : int = dummy_file
return dummy_files
def __a ( __lowerCamelCase=False ):
UpperCAmelCase_ : Optional[Any] = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
UpperCAmelCase_ : Union[str, Any] = {"torch": "pt"}
# Locate actual dummy modules and read their content.
UpperCAmelCase_ : List[str] = os.path.join(__lowerCamelCase, "utils" )
UpperCAmelCase_ : Optional[int] = {
backend: os.path.join(__lowerCamelCase, f"""dummy_{short_names.get(__lowerCamelCase, __lowerCamelCase )}_objects.py""" )
for backend in dummy_files.keys()
}
UpperCAmelCase_ : Any = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(__lowerCamelCase ):
with open(__lowerCamelCase, "r", encoding="utf-8", newline="\n" ) as f:
UpperCAmelCase_ : Optional[int] = f.read()
else:
UpperCAmelCase_ : Any = ""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f"""Updating diffusers.utils.dummy_{short_names.get(__lowerCamelCase, __lowerCamelCase )}_objects.py as the main """
"__init__ has new objects." )
with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n" ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
"The main __init__ has objects that are not present in "
f"""diffusers.utils.dummy_{short_names.get(__lowerCamelCase, __lowerCamelCase )}_objects.py. Run `make fix-copies` """
"to fix this." )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
_a = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 61 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _a ( _lowerCAmelCase , unittest.TestCase ):
A = DanceDiffusionPipeline
A = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
A = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
A = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
A = False
A = False
def __snake_case (self ) -> Optional[Any]:
torch.manual_seed(0 )
UpperCAmelCase_: Dict = UNetaDModel(
block_out_channels=(32, 32, 64), extra_in_channels=16, sample_size=512, sample_rate=16000, in_channels=2, out_channels=2, flip_sin_to_cos=SCREAMING_SNAKE_CASE_, use_timestep_embedding=SCREAMING_SNAKE_CASE_, time_embedding_type="""fourier""", mid_block_type="""UNetMidBlock1D""", down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D"""), up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip"""), )
UpperCAmelCase_: Any = IPNDMScheduler()
UpperCAmelCase_: List[str] = {
"""unet""": unet,
"""scheduler""": scheduler,
}
return components
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=0 ) -> List[str]:
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCAmelCase_: List[str] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCAmelCase_: Union[str, Any] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = {
"""batch_size""": 1,
"""generator""": generator,
"""num_inference_steps""": 4,
}
return inputs
def __snake_case (self ) -> Dict:
UpperCAmelCase_: List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_: Optional[Any] = self.get_dummy_components()
UpperCAmelCase_: List[str] = DanceDiffusionPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = pipe(**SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = output.audios
UpperCAmelCase_: List[str] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
UpperCAmelCase_: List[Any] = np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def __snake_case (self ) -> Any:
return super().test_save_load_local()
@skip_mps
def __snake_case (self ) -> Any:
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def __snake_case (self ) -> Dict:
return super().test_save_load_optional_components()
@skip_mps
def __snake_case (self ) -> Dict:
return super().test_attention_slicing_forward_pass()
def __snake_case (self ) -> Optional[int]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def __snake_case (self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case (self ) -> List[Any]:
UpperCAmelCase_: Any = torch_device
UpperCAmelCase_: Tuple = DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k""" )
UpperCAmelCase_: int = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Dict = torch.manual_seed(0 )
UpperCAmelCase_: Union[str, Any] = pipe(generator=SCREAMING_SNAKE_CASE_, num_inference_steps=100, audio_length_in_s=4.0_9_6 )
UpperCAmelCase_: Optional[Any] = output.audios
UpperCAmelCase_: List[Any] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
UpperCAmelCase_: Optional[Any] = np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: Dict = torch_device
UpperCAmelCase_: Optional[Any] = DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k""", torch_dtype=torch.floataa )
UpperCAmelCase_: Dict = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Union[str, Any] = torch.manual_seed(0 )
UpperCAmelCase_: int = pipe(generator=SCREAMING_SNAKE_CASE_, num_inference_steps=100, audio_length_in_s=4.0_9_6 )
UpperCAmelCase_: int = output.audios
UpperCAmelCase_: Union[str, Any] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
UpperCAmelCase_: Dict = np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 82 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class _a ( _lowerCAmelCase ):
A = '''encodec'''
def __init__(self, SCREAMING_SNAKE_CASE_=[1.5, 3.0, 6.0, 1_2.0, 2_4.0], SCREAMING_SNAKE_CASE_=24000, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=128, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=[8, 5, 4, 2], SCREAMING_SNAKE_CASE_="weight_norm", SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="reflect", SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=1.0, SCREAMING_SNAKE_CASE_=1024, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]:
UpperCAmelCase_: List[Any] = target_bandwidths
UpperCAmelCase_: str = sampling_rate
UpperCAmelCase_: Any = audio_channels
UpperCAmelCase_: List[str] = normalize
UpperCAmelCase_: List[Any] = chunk_length_s
UpperCAmelCase_: List[Any] = overlap
UpperCAmelCase_: Any = hidden_size
UpperCAmelCase_: str = num_filters
UpperCAmelCase_: Any = num_residual_layers
UpperCAmelCase_: int = upsampling_ratios
UpperCAmelCase_: Tuple = norm_type
UpperCAmelCase_: Union[str, Any] = kernel_size
UpperCAmelCase_: str = last_kernel_size
UpperCAmelCase_: Union[str, Any] = residual_kernel_size
UpperCAmelCase_: str = dilation_growth_rate
UpperCAmelCase_: int = use_causal_conv
UpperCAmelCase_: int = pad_mode
UpperCAmelCase_: List[Any] = compress
UpperCAmelCase_: Dict = num_lstm_layers
UpperCAmelCase_: List[Any] = trim_right_ratio
UpperCAmelCase_: List[Any] = codebook_size
UpperCAmelCase_: List[Any] = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase_: Optional[Any] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' )
super().__init__(**SCREAMING_SNAKE_CASE_ )
@property
def __snake_case (self ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def __snake_case (self ) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1, int((1.0 - self.overlap) * self.chunk_length ) )
@property
def __snake_case (self ) -> int:
UpperCAmelCase_: Optional[int] = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def __snake_case (self ) -> int:
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 82 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
"""facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
"""facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
"""facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
"""facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
"""facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
"""facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}
class lowercase__ ( _UpperCAmelCase ):
A__ : int ="""xmod"""
def __init__( self : Optional[Any] , UpperCAmelCase_ : Optional[int]=30522 , UpperCAmelCase_ : Union[str, Any]=768 , UpperCAmelCase_ : List[str]=12 , UpperCAmelCase_ : List[str]=12 , UpperCAmelCase_ : List[str]=3072 , UpperCAmelCase_ : Dict="gelu" , UpperCAmelCase_ : str=0.1 , UpperCAmelCase_ : Dict=0.1 , UpperCAmelCase_ : Optional[Any]=512 , UpperCAmelCase_ : Any=2 , UpperCAmelCase_ : Optional[Any]=0.02 , UpperCAmelCase_ : Optional[Any]=1e-1_2 , UpperCAmelCase_ : Any=1 , UpperCAmelCase_ : Dict=0 , UpperCAmelCase_ : Optional[int]=2 , UpperCAmelCase_ : Tuple="absolute" , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Union[str, Any]=False , UpperCAmelCase_ : int=2 , UpperCAmelCase_ : int=False , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : Union[str, Any]=("en_XX",) , UpperCAmelCase_ : int=None , **UpperCAmelCase_ : str , ):
super().__init__(pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = type_vocab_size
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = position_embedding_type
SCREAMING_SNAKE_CASE__ = use_cache
SCREAMING_SNAKE_CASE__ = classifier_dropout
SCREAMING_SNAKE_CASE__ = pre_norm
SCREAMING_SNAKE_CASE__ = adapter_reduction_factor
SCREAMING_SNAKE_CASE__ = adapter_layer_norm
SCREAMING_SNAKE_CASE__ = adapter_reuse_layer_norm
SCREAMING_SNAKE_CASE__ = ln_before_adapter
SCREAMING_SNAKE_CASE__ = list(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = default_language
class lowercase__ ( _UpperCAmelCase ):
@property
def A_ ( self : List[Any] ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE__ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 176 |
def _lowercase ( ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 1
while len(UpperCamelCase_ ) < 1e6:
constant.append(str(UpperCamelCase_ ) )
i += 1
SCREAMING_SNAKE_CASE__ = ''.join(UpperCamelCase_ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 176 | 1 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : int = logging.get_logger()
@dataclass
class __A :
'''simple docstring'''
__lowercase: nn.Module
__lowercase: List[nn.Module] = field(default_factory=snake_case__)
__lowercase: list = field(default_factory=snake_case__)
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Tensor , UpperCAmelCase_ : Tensor ) ->List[Any]:
"""simple docstring"""
snake_case_ = len(list(m.modules() ) ) == 1 or isinstance(UpperCAmelCase_ , nn.Convad ) or isinstance(UpperCAmelCase_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCAmelCase_ )
def __call__( self : int , UpperCAmelCase_ : Tensor ) ->Optional[int]:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCAmelCase_ )
[x.remove() for x in self.handles]
return self
@property
def lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
return list(filter(lambda UpperCAmelCase_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __A :
'''simple docstring'''
__lowercase: nn.Module
__lowercase: nn.Module
__lowercase: int = 1
__lowercase: List = field(default_factory=snake_case__)
__lowercase: List = field(default_factory=snake_case__)
__lowercase: bool = True
def __call__( self : Optional[int] , UpperCAmelCase_ : Tensor ) ->List[Any]:
"""simple docstring"""
snake_case_ = Tracker(self.dest )(UpperCAmelCase_ ).parametrized
snake_case_ = Tracker(self.src )(UpperCAmelCase_ ).parametrized
snake_case_ = list(filter(lambda UpperCAmelCase_ : type(UpperCAmelCase_ ) not in self.src_skip , UpperCAmelCase_ ) )
snake_case_ = list(filter(lambda UpperCAmelCase_ : type(UpperCAmelCase_ ) not in self.dest_skip , UpperCAmelCase_ ) )
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ) and self.raise_if_mismatch:
raise Exception(
F"""Numbers of operations are different. Source module has {len(UpperCAmelCase_ )} operations while"""
F""" destination module has {len(UpperCAmelCase_ )}.""" )
for dest_m, src_m in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"""Transfered from={src_m} to={dest_m}""" )
class __A (nn.Module):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : nn.Module ) ->Union[str, Any]:
"""simple docstring"""
super().__init__()
snake_case_ = []
# - get the stem
feature_blocks.append(("""conv1""", model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith("""block""" ), F"""Unexpected layer name {k}"""
snake_case_ = len(UpperCAmelCase_ ) + 1
feature_blocks.append((F"""res{block_index}""", v) )
snake_case_ = nn.ModuleDict(UpperCAmelCase_ )
def lowerCAmelCase ( self : List[Any] , UpperCAmelCase_ : Tensor ) ->Union[str, Any]:
"""simple docstring"""
return get_trunk_forward_outputs(
UpperCAmelCase_ , out_feat_keys=UpperCAmelCase_ , feature_blocks=self._feature_blocks , )
class __A (snake_case__):
'''simple docstring'''
def lowerCAmelCase ( self : Any , UpperCAmelCase_ : str ) ->str:
"""simple docstring"""
snake_case_ = x.split("""-""" )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[int] , UpperCAmelCase_ : str ) ->Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
if x not in self:
snake_case_ = self.convert_name_to_timm(UpperCAmelCase_ )
snake_case_ = partial(lambda: (timm.create_model(UpperCAmelCase_ , pretrained=UpperCAmelCase_ ).eval(), None) )
else:
snake_case_ = super().__getitem__(UpperCAmelCase_ )
return val
class __A (snake_case__):
'''simple docstring'''
def __getitem__( self : List[str] , UpperCAmelCase_ : str ) ->Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
snake_case_ = RegNetModel
else:
snake_case_ = RegNetForImageClassification
return val
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
for from_key, to_key in keys:
snake_case_ = from_state_dict[from_key].clone()
print(f"""Copied key={from_key} to={to_key}""" )
return to_state_dict
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True , ) -> Any:
print(f"""Converting {name}...""" )
with torch.no_grad():
snake_case_ , snake_case_ = from_model_func()
snake_case_ = our_model_func(_SCREAMING_SNAKE_CASE ).eval()
snake_case_ = ModuleTransfer(src=_SCREAMING_SNAKE_CASE , dest=_SCREAMING_SNAKE_CASE , raise_if_mismatch=_SCREAMING_SNAKE_CASE )
snake_case_ = torch.randn((1, 3, 224, 224) )
module_transfer(_SCREAMING_SNAKE_CASE )
if from_state_dict is not None:
snake_case_ = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
snake_case_ = [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
snake_case_ = manually_copy_vissl_head(_SCREAMING_SNAKE_CASE , our_model.state_dict() , _SCREAMING_SNAKE_CASE )
our_model.load_state_dict(_SCREAMING_SNAKE_CASE )
snake_case_ = our_model(_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE )
snake_case_ = (
our_outputs.logits if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else our_outputs.last_hidden_state
)
snake_case_ = from_model(_SCREAMING_SNAKE_CASE )
snake_case_ = from_output[-1] if type(_SCREAMING_SNAKE_CASE ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
snake_case_ = our_outputs.hidden_states[-1]
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message="""Add model""" , use_temp_dir=_SCREAMING_SNAKE_CASE , )
snake_case_ = 224 if """seer""" not in name else 384
# we can use the convnext one
snake_case_ = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" , size=_SCREAMING_SNAKE_CASE )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message="""Add image processor""" , use_temp_dir=_SCREAMING_SNAKE_CASE , )
print(f"""Pushed {name}""" )
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True ) -> Tuple:
snake_case_ = """imagenet-1k-id2label.json"""
snake_case_ = 1_000
snake_case_ = (1, num_labels)
snake_case_ = """huggingface/label-files"""
snake_case_ = num_labels
snake_case_ = json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) ) , """r""" ) )
snake_case_ = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = partial(_SCREAMING_SNAKE_CASE , num_labels=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , labelaid=_SCREAMING_SNAKE_CASE )
snake_case_ = {
"""regnet-x-002""": ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 , layer_type="""x""" ),
"""regnet-x-004""": ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 160, 384] , groups_width=16 , layer_type="""x""" ),
"""regnet-x-006""": ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 240, 528] , groups_width=24 , layer_type="""x""" ),
"""regnet-x-008""": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 128, 288, 672] , groups_width=16 , layer_type="""x""" ),
"""regnet-x-016""": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 168, 408, 912] , groups_width=24 , layer_type="""x""" ),
"""regnet-x-032""": ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 192, 432, 1_008] , groups_width=48 , layer_type="""x""" ),
"""regnet-x-040""": ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 240, 560, 1_360] , groups_width=40 , layer_type="""x""" ),
"""regnet-x-064""": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 392, 784, 1_624] , groups_width=56 , layer_type="""x""" ),
"""regnet-x-080""": ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 240, 720, 1_920] , groups_width=120 , layer_type="""x""" ),
"""regnet-x-120""": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2_240] , groups_width=112 , layer_type="""x""" ),
"""regnet-x-160""": ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[256, 512, 896, 2_048] , groups_width=128 , layer_type="""x""" ),
"""regnet-x-320""": ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[336, 672, 1_344, 2_520] , groups_width=168 , layer_type="""x""" ),
# y variant
"""regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 ),
"""regnet-y-004""": ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 104, 208, 440] , groups_width=8 ),
"""regnet-y-006""": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 112, 256, 608] , groups_width=16 ),
"""regnet-y-008""": ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 128, 320, 768] , groups_width=16 ),
"""regnet-y-016""": ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 120, 336, 888] , groups_width=24 ),
"""regnet-y-032""": ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 216, 576, 1_512] , groups_width=24 ),
"""regnet-y-040""": ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[128, 192, 512, 1_088] , groups_width=64 ),
"""regnet-y-064""": ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[144, 288, 576, 1_296] , groups_width=72 ),
"""regnet-y-080""": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 448, 896, 2_016] , groups_width=56 ),
"""regnet-y-120""": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2_240] , groups_width=112 ),
"""regnet-y-160""": ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[224, 448, 1_232, 3_024] , groups_width=112 ),
"""regnet-y-320""": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1_392, 3_712] , groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
"""regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1_392, 3_712] , groups_width=232 ),
"""regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1_968, 4_920] , groups_width=328 ),
"""regnet-y-1280-seer""": RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1_056, 2_904, 7_392] , groups_width=264 ),
"""regnet-y-2560-seer""": RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1_696, 2_544, 5_088] , groups_width=640 ),
"""regnet-y-10b-seer""": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010 ),
# finetuned on imagenet
"""regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1_392, 3_712] , groups_width=232 ),
"""regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1_968, 4_920] , groups_width=328 ),
"""regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1_056, 2_904, 7_392] , groups_width=264 ),
"""regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1_696, 2_544, 5_088] , groups_width=640 ),
"""regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010 ),
}
snake_case_ = NameToOurModelFuncMap()
snake_case_ = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple[nn.Module, Dict]:
snake_case_ = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , model_dir=str(_SCREAMING_SNAKE_CASE ) , map_location="""cpu""" )
snake_case_ = model_func()
# check if we have a head, if yes add it
snake_case_ = files["""classy_state_dict"""]["""base_model"""]["""model"""]
snake_case_ = model_state_dict["""trunk"""]
model.load_state_dict(_SCREAMING_SNAKE_CASE )
return model.eval(), model_state_dict["heads"]
# pretrained
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch""" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch""" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch""" , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch""" , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1_010 , w_a=1_744 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch""" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch""" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch""" , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
snake_case_ = partial(
_SCREAMING_SNAKE_CASE , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch""" , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1_010 , w_a=1_744 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
_SCREAMING_SNAKE_CASE , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
_SCREAMING_SNAKE_CASE , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
return config, expected_shape
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help=(
'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=Path,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
default=True,
type=bool,
required=False,
help='If True, push model and image processor to the hub.',
)
__SCREAMING_SNAKE_CASE : str = parser.parse_args()
__SCREAMING_SNAKE_CASE : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 233 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class __A (snake_case__):
'''simple docstring'''
__lowercase: List[str] = """canine"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : str=768 , UpperCAmelCase_ : Union[str, Any]=12 , UpperCAmelCase_ : Optional[Any]=12 , UpperCAmelCase_ : Optional[Any]=3_072 , UpperCAmelCase_ : List[Any]="gelu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : List[str]=16_384 , UpperCAmelCase_ : Tuple=16 , UpperCAmelCase_ : int=0.02 , UpperCAmelCase_ : Tuple=1E-12 , UpperCAmelCase_ : str=0 , UpperCAmelCase_ : int=0XE000 , UpperCAmelCase_ : Optional[int]=0XE001 , UpperCAmelCase_ : Dict=4 , UpperCAmelCase_ : List[Any]=4 , UpperCAmelCase_ : List[Any]=8 , UpperCAmelCase_ : Dict=16_384 , UpperCAmelCase_ : Optional[int]=128 , **UpperCAmelCase_ : Any , ) ->int:
"""simple docstring"""
super().__init__(pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_ )
snake_case_ = max_position_embeddings
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = initializer_range
snake_case_ = type_vocab_size
snake_case_ = layer_norm_eps
# Character config:
snake_case_ = downsampling_rate
snake_case_ = upsampling_kernel_size
snake_case_ = num_hash_functions
snake_case_ = num_hash_buckets
snake_case_ = local_transformer_stride
| 233 | 1 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
__lowercase : int = MODEL_FOR_MASKED_LM_MAPPING
__lowercase : Optional[int] = TF_MODEL_FOR_MASKED_LM_MAPPING
def snake_case_ ( self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , top_k=2 , framework="""tf""")
__SCREAMING_SNAKE_CASE = unmasker("""My name is <mask>""")
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=6) , [
{"""sequence""": """My name is grouped""", """score""": 2.1E-05, """token""": 3_8_0_1_5, """token_str""": """ grouped"""},
{"""sequence""": """My name is accuser""", """score""": 2.1E-05, """token""": 2_5_5_0_6, """token_str""": """ accuser"""},
] , )
__SCREAMING_SNAKE_CASE = unmasker("""The largest city in France is <mask>""")
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=6) , [
{
"""sequence""": """The largest city in France is grouped""",
"""score""": 2.1E-05,
"""token""": 3_8_0_1_5,
"""token_str""": """ grouped""",
},
{
"""sequence""": """The largest city in France is accuser""",
"""score""": 2.1E-05,
"""token""": 2_5_5_0_6,
"""token_str""": """ accuser""",
},
] , )
__SCREAMING_SNAKE_CASE = unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3)
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=6) , [
{"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
{"""sequence""": """My name is Patrick""", """score""": 2E-05, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Te""", """score""": 1.9E-05, """token""": 2_9_4_1, """token_str""": """ Te"""},
] , )
@require_torch
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , top_k=2 , framework="""pt""")
__SCREAMING_SNAKE_CASE = unmasker("""My name is <mask>""")
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=6) , [
{"""sequence""": """My name is Maul""", """score""": 2.2E-05, """token""": 3_5_6_7_6, """token_str""": """ Maul"""},
{"""sequence""": """My name isELS""", """score""": 2.2E-05, """token""": 1_6_4_1_6, """token_str""": """ELS"""},
] , )
__SCREAMING_SNAKE_CASE = unmasker("""The largest city in France is <mask>""")
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=6) , [
{
"""sequence""": """The largest city in France is Maul""",
"""score""": 2.2E-05,
"""token""": 3_5_6_7_6,
"""token_str""": """ Maul""",
},
{"""sequence""": """The largest city in France isELS""", """score""": 2.2E-05, """token""": 1_6_4_1_6, """token_str""": """ELS"""},
] , )
__SCREAMING_SNAKE_CASE = unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3)
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=6) , [
{"""sequence""": """My name is Patrick""", """score""": 2.1E-05, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Te""", """score""": 2E-05, """token""": 2_9_4_1, """token_str""": """ Te"""},
{"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
] , )
__SCREAMING_SNAKE_CASE = unmasker("""My name is <mask> <mask>""" , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=6) , [
[
{
"""score""": 2.2E-05,
"""token""": 3_5_6_7_6,
"""token_str""": """ Maul""",
"""sequence""": """<s>My name is Maul<mask></s>""",
},
{"""score""": 2.2E-05, """token""": 1_6_4_1_6, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
],
[
{
"""score""": 2.2E-05,
"""token""": 3_5_6_7_6,
"""token_str""": """ Maul""",
"""sequence""": """<s>My name is<mask> Maul</s>""",
},
{"""score""": 2.2E-05, """token""": 1_6_4_1_6, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
],
] , )
@require_torch_gpu
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = pipeline("""fill-mask""" , model="""hf-internal-testing/tiny-random-distilbert""" , device=0 , framework="""pt""")
# convert model to fp16
pipe.model.half()
__SCREAMING_SNAKE_CASE = pipe("""Paris is the [MASK] of France.""")
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__)
@slow
@require_torch
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = pipeline(task="""fill-mask""" , model="""distilroberta-base""" , top_k=2 , framework="""pt""")
self.run_large_test(lowerCAmelCase__)
@slow
@require_tf
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = pipeline(task="""fill-mask""" , model="""distilroberta-base""" , top_k=2 , framework="""tf""")
self.run_large_test(lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = unmasker("""My name is <mask>""")
self.assertEqual(
nested_simplify(lowerCAmelCase__) , [
{"""sequence""": """My name is John""", """score""": 0.0_08, """token""": 6_1_0, """token_str""": """ John"""},
{"""sequence""": """My name is Chris""", """score""": 0.0_07, """token""": 1_5_7_3, """token_str""": """ Chris"""},
] , )
__SCREAMING_SNAKE_CASE = unmasker("""The largest city in France is <mask>""")
self.assertEqual(
nested_simplify(lowerCAmelCase__) , [
{
"""sequence""": """The largest city in France is Paris""",
"""score""": 0.2_51,
"""token""": 2_2_0_1,
"""token_str""": """ Paris""",
},
{
"""sequence""": """The largest city in France is Lyon""",
"""score""": 0.2_14,
"""token""": 1_2_7_9_0,
"""token_str""": """ Lyon""",
},
] , )
__SCREAMING_SNAKE_CASE = unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3)
self.assertEqual(
nested_simplify(lowerCAmelCase__) , [
{"""sequence""": """My name is Patrick""", """score""": 0.0_05, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Clara""", """score""": 0.0_00, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
{"""sequence""": """My name is Te""", """score""": 0.0_00, """token""": 2_9_4_1, """token_str""": """ Te"""},
] , )
@require_torch
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , framework="""pt""")
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
self.run_pipeline_test(lowerCAmelCase__ , [])
@require_tf
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , framework="""tf""")
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
self.run_pipeline_test(lowerCAmelCase__ , [])
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__):
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)""")
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = [
f"This is another {tokenizer.mask_token} test",
]
return fill_masker, examples
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = fill_masker.tokenizer
__SCREAMING_SNAKE_CASE = fill_masker.model
__SCREAMING_SNAKE_CASE = fill_masker(
f"This is a {tokenizer.mask_token}" , )
self.assertEqual(
lowerCAmelCase__ , [
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
] , )
__SCREAMING_SNAKE_CASE = fill_masker([f"This is a {tokenizer.mask_token}"])
self.assertEqual(
lowerCAmelCase__ , [
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
] , )
__SCREAMING_SNAKE_CASE = fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."])
self.assertEqual(
lowerCAmelCase__ , [
[
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
],
[
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
],
] , )
with self.assertRaises(lowerCAmelCase__):
fill_masker([None])
# No mask_token is not supported
with self.assertRaises(lowerCAmelCase__):
fill_masker("""This is""")
self.run_test_top_k(lowerCAmelCase__ , lowerCAmelCase__)
self.run_test_targets(lowerCAmelCase__ , lowerCAmelCase__)
self.run_test_top_k_targets(lowerCAmelCase__ , lowerCAmelCase__)
self.fill_mask_with_duplicate_targets_and_top_k(lowerCAmelCase__ , lowerCAmelCase__)
self.fill_mask_with_multiple_masks(lowerCAmelCase__ , lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = tokenizer.get_vocab()
__SCREAMING_SNAKE_CASE = sorted(vocab.keys())[:2]
# Pipeline argument
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , targets=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}")
self.assertEqual(
lowerCAmelCase__ , [
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
] , )
__SCREAMING_SNAKE_CASE = {vocab[el] for el in targets}
self.assertEqual({el["""token"""] for el in outputs} , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["""token_str"""] for el in outputs} , set(lowerCAmelCase__))
# Call argument
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , targets=lowerCAmelCase__)
self.assertEqual(
lowerCAmelCase__ , [
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
] , )
__SCREAMING_SNAKE_CASE = {vocab[el] for el in targets}
self.assertEqual({el["""token"""] for el in outputs} , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["""token_str"""] for el in outputs} , set(lowerCAmelCase__))
# Score equivalence
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , targets=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = [top_mask["""token_str"""] for top_mask in outputs]
__SCREAMING_SNAKE_CASE = [top_mask["""score"""] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(lowerCAmelCase__) == set(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , targets=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = [top_mask["""score"""] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(lowerCAmelCase__) , nested_simplify(lowerCAmelCase__))
# Raises with invalid
with self.assertRaises(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , targets=[])
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , targets=[""""""])
with self.assertRaises(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , targets="""""")
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , top_k=2)
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}")
self.assertEqual(
lowerCAmelCase__ , [
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
] , )
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , top_k=2)
self.assertEqual(
lowerCAmelCase__ , [
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
] , )
self.assertEqual(nested_simplify(lowerCAmelCase__) , nested_simplify(lowerCAmelCase__))
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = tokenizer.get_vocab()
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__)
# top_k=2, ntargets=3
__SCREAMING_SNAKE_CASE = sorted(vocab.keys())[:3]
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , top_k=2 , targets=lowerCAmelCase__)
# If we use the most probably targets, and filter differently, we should still
# have the same results
__SCREAMING_SNAKE_CASE = [el["""token_str"""] for el in sorted(lowerCAmelCase__ , key=lambda lowerCAmelCase__: x["score"] , reverse=lowerCAmelCase__)]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(lowerCAmelCase__).issubset(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = fill_masker(f"This is a {tokenizer.mask_token}" , top_k=3 , targets=lowerCAmelCase__)
# They should yield exactly the same result
self.assertEqual(nested_simplify(lowerCAmelCase__) , nested_simplify(lowerCAmelCase__))
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.get_vocab()
# String duplicates + id duplicates
__SCREAMING_SNAKE_CASE = sorted(vocab.keys())[:3]
__SCREAMING_SNAKE_CASE = [targets[0], targets[1], targets[0], targets[2], targets[1]]
__SCREAMING_SNAKE_CASE = fill_masker(f"My name is {tokenizer.mask_token}" , targets=lowerCAmelCase__ , top_k=1_0)
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(lowerCAmelCase__) , 3)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = FillMaskPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = fill_masker(
f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}" , top_k=2)
self.assertEqual(
lowerCAmelCase__ , [
[
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
],
[
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
],
[
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
{"""sequence""": ANY(lowerCAmelCase__), """score""": ANY(lowerCAmelCase__), """token""": ANY(lowerCAmelCase__), """token_str""": ANY(lowerCAmelCase__)},
],
] , )
| 100 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Tuple = ['''keras_nlp''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""keras_nlp"""])
| 100 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a : int = 16
a : str = 32
def lowercase__(A , A = 16 ) ->Optional[Any]:
"""simple docstring"""
lowercase__ : Tuple= AutoTokenizer.from_pretrained("bert-base-cased" )
lowercase__ : str= load_dataset("glue" , "mrpc" )
def tokenize_function(A ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : Any= tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=A , max_length=A )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : Optional[int]= datasets.map(
A , batched=A , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : List[str]= tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(A ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : List[Any]= 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : Dict= 16
elif accelerator.mixed_precision != "no":
lowercase__ : Dict= 8
else:
lowercase__ : Optional[int]= None
return tokenizer.pad(
A , padding="longest" , max_length=A , pad_to_multiple_of=A , return_tensors="pt" , )
# Instantiate dataloaders.
lowercase__ : List[Any]= DataLoader(
tokenized_datasets["train"] , shuffle=A , collate_fn=A , batch_size=A )
lowercase__ : str= DataLoader(
tokenized_datasets["validation"] , shuffle=A , collate_fn=A , batch_size=A )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a : Optional[Any] = mocked_dataloaders # noqa: F811
def lowercase__(A , A ) ->Tuple:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , A ) == "1":
lowercase__ : List[Any]= 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
lowercase__ : Any= Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
lowercase__ : int= Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Any= config["lr"]
lowercase__ : Tuple= int(config["num_epochs"] )
lowercase__ : Optional[Any]= int(config["seed"] )
lowercase__ : Optional[int]= int(config["batch_size"] )
set_seed(A )
lowercase__ : Tuple= get_dataloaders(A , A )
lowercase__ : str= evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
lowercase__ : List[str]= 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowercase__ : Tuple= batch_size // MAX_GPU_BATCH_SIZE
lowercase__ : Dict= MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : str= AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=A )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : Optional[Any]= model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : int= AdamW(params=model.parameters() , lr=A )
# Instantiate scheduler
lowercase__ : str= get_linear_schedule_with_warmup(
optimizer=A , num_warmup_steps=100 , num_training_steps=(len(A ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ : Tuple= accelerator.prepare(
A , A , A , A , A )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
lowercase__ : List[str]= os.path.split(A )[-1].split("." )[0]
accelerator.init_trackers(A , A )
# Now we train the model
for epoch in range(A ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
lowercase__ : Union[str, Any]= 0
for step, batch in enumerate(A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ : Any= model(**A )
lowercase__ : Union[str, Any]= outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
lowercase__ : Optional[int]= loss / gradient_accumulation_steps
accelerator.backward(A )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(A ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Optional[int]= model(**A )
lowercase__ : Dict= outputs.logits.argmax(dim=-1 )
lowercase__ : Dict= accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=A , references=A , )
lowercase__ : Any= metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , A )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(A ),
"epoch": epoch,
} , step=A , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowercase__() ->List[Any]:
"""simple docstring"""
lowercase__ : List[Any]= argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=A , default=A , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=A , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
lowercase__ : Any= parser.parse_args()
lowercase__ : Optional[Any]= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(A , A )
if __name__ == "__main__":
main()
| 370 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __UpperCAmelCase( nn.Module ):
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
super().__init__()
lowercase__ : Any= nn.Linear(3 , 4 )
lowercase__ : Tuple= nn.BatchNormad(4 )
lowercase__ : Dict= nn.Linear(4 , 5 )
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(snake_case__ ) ) )
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self , snake_case__ , *snake_case__ , **snake_case__ ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ ):
'''simple docstring'''
return output + 1
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= ModelForTest()
lowercase__ : str= ModelHook()
add_hook_to_module(snake_case__ , snake_case__ )
self.assertEqual(test_model._hf_hook , snake_case__ )
self.assertTrue(hasattr(snake_case__ , "_old_forward" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , "forward" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["x"] )
remove_hook_from_module(snake_case__ )
self.assertFalse(hasattr(snake_case__ , "_hf_hook" ) )
self.assertFalse(hasattr(snake_case__ , "_old_forward" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= ModelForTest()
lowercase__ : int= ModelHook()
add_hook_to_module(snake_case__ , snake_case__ )
add_hook_to_module(snake_case__ , snake_case__ , append=snake_case__ )
self.assertEqual(isinstance(test_model._hf_hook , snake_case__ ) , snake_case__ )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(snake_case__ , "_old_forward" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , "forward" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["x"] )
remove_hook_from_module(snake_case__ )
self.assertFalse(hasattr(snake_case__ , "_hf_hook" ) )
self.assertFalse(hasattr(snake_case__ , "_old_forward" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= ModelForTest()
lowercase__ : int= torch.randn(2 , 3 )
lowercase__ : Optional[Any]= test_model(x + 1 )
lowercase__ : Tuple= test_model(x + 2 )
lowercase__ : str= PreForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Tuple= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
lowercase__ : Tuple= PreForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Optional[Any]= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
lowercase__ : List[str]= SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Dict= test_model(snake_case__ )
assert torch.allclose(snake_case__ , snake_case__ , atol=1e-5 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Tuple= ModelForTest()
lowercase__ : Optional[int]= torch.randn(2 , 3 )
lowercase__ : Optional[int]= test_model(snake_case__ )
lowercase__ : str= PostForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Optional[int]= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , output + 1 , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
lowercase__ : Tuple= PostForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Dict= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , output + 1 , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
lowercase__ : Optional[Any]= SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : List[str]= test_model(snake_case__ )
assert torch.allclose(snake_case__ , output + 2 , atol=1e-5 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= ModelForTest()
lowercase__ : Optional[Any]= torch.randn(2 , 3 )
lowercase__ : int= test_model(snake_case__ )
lowercase__ : Union[str, Any]= PostForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Dict= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , output + 1 ) )
self.assertTrue(outputa.requires_grad )
lowercase__ : Any= True
lowercase__ : Optional[int]= test_model(snake_case__ )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
lowercase__ : int= torch.randn(2 , 3 )
lowercase__ : List[str]= model(snake_case__ )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(snake_case__ , AlignDevicesHook(io_same_device=snake_case__ ) )
lowercase__ : Tuple= torch.randn(2 , 3 ).to(0 )
lowercase__ : Optional[Any]= model(snake_case__ )
self.assertEqual(output.device , torch.device(0 ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
lowercase__ : Optional[int]= {"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
lowercase__ : Optional[int]= torch.device(hook_kwargs["execution_device"] )
self.assertEqual(model.batchnorm.running_mean.device , snake_case__ )
lowercase__ : List[Any]= torch.randn(2 , 3 )
lowercase__ : str= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
lowercase__ : Optional[int]= {
"execution_device": 0 if torch.cuda.is_available() else "cpu",
"offload": True,
"offload_buffers": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
lowercase__ : str= torch.randn(2 , 3 )
lowercase__ : str= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
lowercase__ : str= 0 if torch.cuda.is_available() else "cpu"
attach_align_device_hook(snake_case__ , execution_device=snake_case__ , offload=snake_case__ )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
lowercase__ : Dict= torch.device(snake_case__ )
self.assertEqual(model.batchnorm.running_mean.device , snake_case__ )
lowercase__ : Optional[Any]= torch.randn(2 , 3 )
lowercase__ : List[Any]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
attach_align_device_hook(snake_case__ , execution_device=snake_case__ , offload=snake_case__ , offload_buffers=snake_case__ )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
lowercase__ : List[str]= torch.randn(2 , 3 )
lowercase__ : List[Any]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Any= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
lowercase__ : Optional[Any]= 0 if torch.cuda.is_available() else "cpu"
attach_align_device_hook(
snake_case__ , execution_device=snake_case__ , offload=snake_case__ , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
lowercase__ : Tuple= torch.device(snake_case__ )
self.assertEqual(model.batchnorm.running_mean.device , snake_case__ )
lowercase__ : str= torch.randn(2 , 3 )
lowercase__ : List[Any]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
snake_case__ , execution_device=snake_case__ , offload=snake_case__ , weights_map=model.state_dict() , offload_buffers=snake_case__ , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
lowercase__ : Dict= torch.randn(2 , 3 )
lowercase__ : List[str]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
| 150 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : str = ["""image_processor""", """tokenizer"""]
a_ : int = """BlipImageProcessor"""
a_ : Union[str, Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] , a_ : List[Any] , a_ : int ):
lowerCAmelCase_ : Union[str, Any] = False
super().__init__(a_ , a_ )
lowerCAmelCase_ : Any = self.image_processor
def __call__( self : int , a_ : ImageInput = None , a_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , a_ : bool = True , a_ : Union[bool, str, PaddingStrategy] = False , a_ : Union[bool, str, TruncationStrategy] = None , a_ : Optional[int] = None , a_ : int = 0 , a_ : Optional[int] = None , a_ : Optional[bool] = None , a_ : bool = False , a_ : bool = False , a_ : bool = False , a_ : bool = False , a_ : bool = False , a_ : bool = True , a_ : Optional[Union[str, TensorType]] = None , **a_ : Dict , ):
if images is None and text is None:
raise ValueError("You have to specify either images or text." )
# Get only text
if images is None:
lowerCAmelCase_ : Tuple = self.tokenizer
lowerCAmelCase_ : int = self.tokenizer(
text=a_ , add_special_tokens=a_ , padding=a_ , truncation=a_ , max_length=a_ , stride=a_ , pad_to_multiple_of=a_ , return_attention_mask=a_ , return_overflowing_tokens=a_ , return_special_tokens_mask=a_ , return_offsets_mapping=a_ , return_token_type_ids=a_ , return_length=a_ , verbose=a_ , return_tensors=a_ , **a_ , )
return text_encoding
# add pixel_values
lowerCAmelCase_ : Union[str, Any] = self.image_processor(a_ , return_tensors=a_ )
if text is not None:
lowerCAmelCase_ : List[str] = self.tokenizer(
text=a_ , add_special_tokens=a_ , padding=a_ , truncation=a_ , max_length=a_ , stride=a_ , pad_to_multiple_of=a_ , return_attention_mask=a_ , return_overflowing_tokens=a_ , return_special_tokens_mask=a_ , return_offsets_mapping=a_ , return_token_type_ids=a_ , return_length=a_ , verbose=a_ , return_tensors=a_ , **a_ , )
else:
lowerCAmelCase_ : Optional[int] = None
if text_encoding is not None:
encoding_image_processor.update(a_ )
return encoding_image_processor
def lowerCamelCase ( self : Union[str, Any] , *a_ : Tuple , **a_ : int ):
return self.tokenizer.batch_decode(*a_ , **a_ )
def lowerCamelCase ( self : Optional[Any] , *a_ : Any , **a_ : int ):
return self.tokenizer.decode(*a_ , **a_ )
@property
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : Optional[int] = self.tokenizer.model_input_names
lowerCAmelCase_ : Optional[int] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 241 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"""facebook/maskformer-swin-base-ade""": (
"""https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowercase__ = logging.get_logger(__name__)
class __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : Optional[int] = """maskformer"""
a_ : Optional[int] = {"""hidden_size""": """mask_feature_size"""}
a_ : Optional[int] = ["""resnet""", """swin"""]
a_ : int = ["""detr"""]
def __init__( self : str , a_ : int = 2_56 , a_ : int = 2_56 , a_ : float = 0.1 , a_ : bool = False , a_ : Optional[Dict] = None , a_ : Optional[Dict] = None , a_ : float = 0.02 , a_ : float = 1.0 , a_ : float = 1.0 , a_ : float = 1.0 , a_ : float = 20.0 , a_ : Optional[bool] = None , **a_ : str , ):
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowerCAmelCase_ : Tuple = SwinConfig(
image_size=3_84 , in_channels=3 , patch_size=4 , embed_dim=1_28 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Optional[Any] = backbone_config.pop("model_type" )
lowerCAmelCase_ : Any = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ : str = config_class.from_dict(a_ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
f'''Supported model types: {",".join(self.backbones_supported )}''' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowerCAmelCase_ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowerCAmelCase_ : Optional[Any] = (
decoder_config.pop("model_type" ) if isinstance(a_ , a_ ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f'''Transformer Decoder {decoder_type} not supported, please use one of'''
f''' {",".join(self.decoders_supported )}''' )
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowerCAmelCase_ : List[Any] = config_class.from_dict(a_ )
lowerCAmelCase_ : str = backbone_config
lowerCAmelCase_ : Tuple = decoder_config
# main feature dimension for the model
lowerCAmelCase_ : str = fpn_feature_size
lowerCAmelCase_ : str = mask_feature_size
# initializer
lowerCAmelCase_ : List[Any] = init_std
lowerCAmelCase_ : Tuple = init_xavier_std
# Hungarian matcher && loss
lowerCAmelCase_ : int = cross_entropy_weight
lowerCAmelCase_ : Dict = dice_weight
lowerCAmelCase_ : int = mask_weight
lowerCAmelCase_ : Any = use_auxiliary_loss
lowerCAmelCase_ : Dict = no_object_weight
lowerCAmelCase_ : Optional[int] = output_auxiliary_logits
lowerCAmelCase_ : int = self.decoder_config.encoder_attention_heads
lowerCAmelCase_ : str = self.decoder_config.num_hidden_layers
super().__init__(**a_ )
@classmethod
def lowerCamelCase ( cls : int , a_ : PretrainedConfig , a_ : PretrainedConfig , **a_ : Tuple ):
return cls(
backbone_config=a_ , decoder_config=a_ , **a_ , )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Optional[int] = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : Optional[Any] = self.backbone_config.to_dict()
lowerCAmelCase_ : Union[str, Any] = self.decoder_config.to_dict()
lowerCAmelCase_ : List[str] = self.__class__.model_type
return output
| 241 | 1 |
from __future__ import annotations
_lowerCamelCase : List[str] = list[list[int]]
# assigning initial values to the grid
_lowerCamelCase : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
_lowerCamelCase : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def _a ( SCREAMING_SNAKE_CASE__ : Matrix , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def _a ( SCREAMING_SNAKE_CASE__ : Matrix ) -> List[str]:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def _a ( SCREAMING_SNAKE_CASE__ : Matrix ) -> Optional[int]:
'''simple docstring'''
if location := find_empty_location(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Dict = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ : List[Any] = digit
if sudoku(UpperCAmelCase_ ) is not None:
return grid
SCREAMING_SNAKE_CASE__ : List[str] = 0
return None
def _a ( SCREAMING_SNAKE_CASE__ : Matrix ) -> int:
'''simple docstring'''
for row in grid:
for cell in row:
print(UpperCAmelCase_ , end=" " )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
_lowerCamelCase : List[Any] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 353 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = '''▁'''
_lowerCamelCase : Dict = {'''vocab_file''': '''sentencepiece.bpe.model'''}
_lowerCamelCase : int = {
'''vocab_file''': {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
),
}
}
_lowerCamelCase : Optional[Any] = {
'''xlm-roberta-base''': 5_1_2,
'''xlm-roberta-large''': 5_1_2,
'''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
'''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
'''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
'''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = VOCAB_FILES_NAMES
UpperCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ = ["input_ids", "attention_mask"]
def __init__( self : Dict, _UpperCAmelCase : str, _UpperCAmelCase : Optional[int]="<s>", _UpperCAmelCase : Optional[int]="</s>", _UpperCAmelCase : Dict="</s>", _UpperCAmelCase : List[Any]="<s>", _UpperCAmelCase : Union[str, Any]="<unk>", _UpperCAmelCase : List[Any]="<pad>", _UpperCAmelCase : str="<mask>", _UpperCAmelCase : Optional[Dict[str, Any]] = None, **_UpperCAmelCase : List[Any], ) -> None:
"""simple docstring"""
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE__ : int = AddedToken(_UpperCAmelCase, lstrip=_UpperCAmelCase, rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase, _UpperCAmelCase ) else mask_token
SCREAMING_SNAKE_CASE__ : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCAmelCase, eos_token=_UpperCAmelCase, unk_token=_UpperCAmelCase, sep_token=_UpperCAmelCase, cls_token=_UpperCAmelCase, pad_token=_UpperCAmelCase, mask_token=_UpperCAmelCase, sp_model_kwargs=self.sp_model_kwargs, **_UpperCAmelCase, )
SCREAMING_SNAKE_CASE__ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_UpperCAmelCase ) )
SCREAMING_SNAKE_CASE__ : Tuple = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
SCREAMING_SNAKE_CASE__ : List[str] = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE__ : Dict = 1
SCREAMING_SNAKE_CASE__ : int = len(self.sp_model ) + self.fairseq_offset
SCREAMING_SNAKE_CASE__ : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : Dict = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : int, _UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs" ):
SCREAMING_SNAKE_CASE__ : Dict = {}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def A_ ( self : Any, _UpperCAmelCase : List[int], _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : List[str] = [self.cls_token_id]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A_ ( self : List[Any], _UpperCAmelCase : List[int], _UpperCAmelCase : Optional[List[int]] = None, _UpperCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase, token_ids_a=_UpperCAmelCase, already_has_special_tokens=_UpperCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] + ([0] * len(_UpperCAmelCase )) + [1]
def A_ ( self : Union[str, Any], _UpperCAmelCase : List[int], _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def A_ ( self : List[str] ) -> List[str]:
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def A_ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A_ ( self : List[str], _UpperCAmelCase : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_UpperCAmelCase, out_type=_UpperCAmelCase )
def A_ ( self : Optional[Any], _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.sp_model.PieceToId(_UpperCAmelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def A_ ( self : Tuple, _UpperCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def A_ ( self : Any, _UpperCAmelCase : int ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = "".join(_UpperCAmelCase ).replace(_UpperCAmelCase, " " ).strip()
return out_string
def A_ ( self : Union[str, Any], _UpperCAmelCase : str, _UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(
_UpperCAmelCase, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, _UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCAmelCase, "wb" ) as fi:
SCREAMING_SNAKE_CASE__ : Any = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (out_vocab_file,)
| 191 | 0 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[Any]:
# Initialise PyTorch model
_lowercase : Union[str, Any] = FunnelConfig.from_json_file(lowerCamelCase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
_lowercase : Tuple = FunnelBaseModel(lowerCamelCase_ ) if base_model else FunnelModel(lowerCamelCase_ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowerCamelCase_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
SCREAMING_SNAKE_CASE : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowercase ( __magic_name__ ):
'''simple docstring'''
for param in module.parameters():
UpperCAmelCase : Any = False
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : int = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCAmelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : str = plt.imshow(__magic_name__ )
fig.axes.get_xaxis().set_visible(__magic_name__ )
fig.axes.get_yaxis().set_visible(__magic_name__ )
plt.show()
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : str = datetime.now()
UpperCAmelCase : Tuple = current_time.strftime("%H:%M:%S" )
return timestamp
| 311 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : List[str] = 'xlm'
__UpperCAmelCase : int = {
'hidden_size': 'emb_dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
'n_words': 'vocab_size', # For backward compatibility
}
def __init__( self , _a=30_145 , _a=2_048 , _a=12 , _a=16 , _a=0.1 , _a=0.1 , _a=True , _a=False , _a=False , _a=False , _a=1 , _a=True , _a=512 , _a=2_048**-0.5 , _a=1E-12 , _a=0.02 , _a=0 , _a=1 , _a=2 , _a=3 , _a=5 , _a=True , _a="first" , _a=True , _a=None , _a=True , _a=0.1 , _a=5 , _a=5 , _a=0 , _a=0 , _a=2 , _a=0 , **_a , ):
__a = vocab_size
__a = emb_dim
__a = n_layers
__a = n_heads
__a = dropout
__a = attention_dropout
__a = gelu_activation
__a = sinusoidal_embeddings
__a = causal
__a = asm
__a = n_langs
__a = use_lang_emb
__a = layer_norm_eps
__a = bos_index
__a = eos_index
__a = pad_index
__a = unk_index
__a = mask_index
__a = is_encoder
__a = max_position_embeddings
__a = embed_init_std
__a = init_std
__a = summary_type
__a = summary_use_proj
__a = summary_activation
__a = summary_proj_to_labels
__a = summary_first_dropout
__a = start_n_top
__a = end_n_top
__a = mask_token_id
__a = lang_id
if "n_words" in kwargs:
__a = kwargs['''n_words''']
super().__init__(pad_token_id=_a , bos_token_id=_a , **_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def __UpperCAmelCase ( self ):
if self.task == "multiple-choice":
__a = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__a = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 11 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowercase ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Union[str, Any]=0.9_99 , lowerCAmelCase__ : List[str]="cosine" , ) -> Optional[int]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowerCAmelCase__ : int ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowerCAmelCase__ : Optional[Any] ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
__a = []
for i in range(lowerCAmelCase__ ):
__a = i / num_diffusion_timesteps
__a = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__ ) / alpha_bar_fn(lowerCAmelCase__ ) , lowerCAmelCase__ ) )
return torch.tensor(lowerCAmelCase__ , dtype=torch.floataa )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : Tuple = [e.name for e in KarrasDiffusionSchedulers]
__UpperCAmelCase : str = 2
@register_to_config
def __init__( self , _a = 1_000 , _a = 0.0_0085 , _a = 0.012 , _a = "linear" , _a = None , _a = "epsilon" , _a = "linspace" , _a = 0 , ):
if trained_betas is not None:
__a = torch.tensor(_a , dtype=torch.floataa )
elif beta_schedule == "linear":
__a = torch.linspace(_a , _a , _a , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__a = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _a , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__a = betas_for_alpha_bar(_a )
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )
__a = 1.0 - self.betas
__a = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(_a , _a , _a )
def __UpperCAmelCase ( self , _a , _a=None ):
if schedule_timesteps is None:
__a = self.timesteps
__a = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
__a = 1 if len(_a ) > 1 else 0
else:
__a = timestep.cpu().item() if torch.is_tensor(_a ) else timestep
__a = self._index_counter[timestep_int]
return indices[pos].item()
@property
def __UpperCAmelCase ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def __UpperCAmelCase ( self , _a , _a , ):
__a = self.index_for_timestep(_a )
if self.state_in_first_order:
__a = self.sigmas[step_index]
else:
__a = self.sigmas_interpol[step_index]
__a = sample / ((sigma**2 + 1) ** 0.5)
return sample
def __UpperCAmelCase ( self , _a , _a = None , _a = None , ):
__a = num_inference_steps
__a = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
__a = np.linspace(0 , num_train_timesteps - 1 , _a , dtype=_a )[::-1].copy()
elif self.config.timestep_spacing == "leading":
__a = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__a = (np.arange(0 , _a ) * step_ratio).round()[::-1].copy().astype(_a )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
__a = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__a = (np.arange(_a , 0 , -step_ratio )).round().copy().astype(_a )
timesteps -= 1
else:
raise ValueError(
f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )
__a = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
__a = torch.from_numpy(np.log(_a ) ).to(_a )
__a = np.interp(_a , np.arange(0 , len(_a ) ) , _a )
__a = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
__a = torch.from_numpy(_a ).to(device=_a )
# interpolate sigmas
__a = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
__a = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
__a = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(_a ).startswith('''mps''' ):
# mps does not support float64
__a = torch.from_numpy(_a ).to(_a , dtype=torch.floataa )
else:
__a = torch.from_numpy(_a ).to(_a )
# interpolate timesteps
__a = self.sigma_to_t(_a ).to(_a , dtype=timesteps.dtype )
__a = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
__a = torch.cat([timesteps[:1], interleaved_timesteps] )
__a = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
__a = defaultdict(_a )
def __UpperCAmelCase ( self , _a ):
# get log sigma
__a = sigma.log()
# get distribution
__a = log_sigma - self.log_sigmas[:, None]
# get sigmas range
__a = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
__a = low_idx + 1
__a = self.log_sigmas[low_idx]
__a = self.log_sigmas[high_idx]
# interpolate sigmas
__a = (low - log_sigma) / (low - high)
__a = w.clamp(0 , 1 )
# transform interpolation to time range
__a = (1 - w) * low_idx + w * high_idx
__a = t.view(sigma.shape )
return t
@property
def __UpperCAmelCase ( self ):
return self.sample is None
def __UpperCAmelCase ( self , _a , _a , _a , _a = True , ):
__a = self.index_for_timestep(_a )
# advance index counter by 1
__a = timestep.cpu().item() if torch.is_tensor(_a ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
__a = self.sigmas[step_index]
__a = self.sigmas_interpol[step_index + 1]
__a = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
__a = self.sigmas[step_index - 1]
__a = self.sigmas_interpol[step_index]
__a = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
__a = 0
__a = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
__a = sigma_hat if self.state_in_first_order else sigma_interpol
__a = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
__a = sigma_hat if self.state_in_first_order else sigma_interpol
__a = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError('''prediction_type not implemented yet: sample''' )
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
__a = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
__a = sigma_interpol - sigma_hat
# store for 2nd order step
__a = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
__a = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
__a = sigma_next - sigma_hat
__a = self.sample
__a = None
__a = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_a )
def __UpperCAmelCase ( self , _a , _a , _a , ):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
__a = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(_a ):
# mps does not support float64
__a = self.timesteps.to(original_samples.device , dtype=torch.floataa )
__a = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
__a = self.timesteps.to(original_samples.device )
__a = timesteps.to(original_samples.device )
__a = [self.index_for_timestep(_a , _a ) for t in timesteps]
__a = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
__a = sigma.unsqueeze(-1 )
__a = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 11 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Optional[int] = 1
A : str = 3
A : str = (32, 32)
A : int = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
return image
@property
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
A : int = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
return model
@property
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
A : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
return model
@property
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
A : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(SCREAMING_SNAKE_CASE__ )
@property
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
def extract(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
class A :
def __init__( self ) -> str:
"""simple docstring"""
A : str = torch.ones([0] )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
self.pixel_values.to(SCREAMING_SNAKE_CASE__ )
return self
return Out()
return extract
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
A : Tuple = self.dummy_cond_unet
A : List[str] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=SCREAMING_SNAKE_CASE__ , set_alpha_to_one=SCREAMING_SNAKE_CASE__ , )
A : Optional[Any] = self.dummy_vae
A : List[str] = self.dummy_text_encoder
A : Tuple = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# make sure here that pndm scheduler skips prk
A : List[Any] = StableDiffusionPipeline(
unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ , vae=SCREAMING_SNAKE_CASE__ , text_encoder=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ , safety_checker=SCREAMING_SNAKE_CASE__ , feature_extractor=self.dummy_extractor , )
A : Optional[Any] = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
A : str = '''A painting of a squirrel eating a burger'''
A : str = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(0 )
A : Optional[Any] = sd_pipe([prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' )
A : Dict = output.images
A : Union[str, Any] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(0 )
A : Optional[int] = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , return_dict=SCREAMING_SNAKE_CASE__ , )[0]
A : Any = image[0, -3:, -3:, -1]
A : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A : Optional[int] = np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
A : int = self.dummy_cond_unet
A : Union[str, Any] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__ )
A : List[Any] = self.dummy_vae
A : Optional[Any] = self.dummy_text_encoder
A : Union[str, Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# make sure here that pndm scheduler skips prk
A : Any = StableDiffusionPipeline(
unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ , vae=SCREAMING_SNAKE_CASE__ , text_encoder=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ , safety_checker=SCREAMING_SNAKE_CASE__ , feature_extractor=self.dummy_extractor , )
A : str = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
A : Union[str, Any] = '''A painting of a squirrel eating a burger'''
A : Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(0 )
A : Tuple = sd_pipe([prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' )
A : Optional[Any] = output.images
A : str = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(0 )
A : Optional[Any] = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , return_dict=SCREAMING_SNAKE_CASE__ , )[0]
A : Optional[int] = image[0, -3:, -3:, -1]
A : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A : int = np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : Dict = StableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-lms-pipe''' , safety_checker=SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert isinstance(pipe.scheduler , SCREAMING_SNAKE_CASE__ )
assert pipe.safety_checker is None
A : Tuple = pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(SCREAMING_SNAKE_CASE__ )
A : List[str] = StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
A : Optional[int] = pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : Any = self.dummy_cond_unet
A : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__ )
A : Dict = self.dummy_vae
A : List[Any] = self.dummy_text_encoder
A : Any = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# put models in fp16
A : Optional[int] = unet.half()
A : Dict = vae.half()
A : Optional[Any] = bert.half()
# make sure here that pndm scheduler skips prk
A : List[Any] = StableDiffusionPipeline(
unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ , vae=SCREAMING_SNAKE_CASE__ , text_encoder=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ , safety_checker=SCREAMING_SNAKE_CASE__ , feature_extractor=self.dummy_extractor , )
A : int = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
A : str = '''A painting of a squirrel eating a burger'''
A : Any = sd_pipe([prompt] , num_inference_steps=2 , output_type='''np''' ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Tuple = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' , safety_checker=SCREAMING_SNAKE_CASE__ )
A : Optional[Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
A : List[str] = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
A : str = (
'''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
''' children from bahnhof zoo, detailed '''
)
A : Union[str, Any] = 4003660346
A : Any = 7
# without safety guidance (sld_guidance_scale = 0)
A : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
A : List[Any] = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=SCREAMING_SNAKE_CASE__ , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
A : str = output.images
A : Any = image[0, -3:, -3:, -1]
A : Optional[Any] = [0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
# without safety guidance (strong configuration)
A : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
A : List[Any] = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=SCREAMING_SNAKE_CASE__ , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
A : Union[str, Any] = output.images
A : Optional[Any] = image[0, -3:, -3:, -1]
A : List[str] = [0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : int = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' , safety_checker=SCREAMING_SNAKE_CASE__ )
A : Dict = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
A : Dict = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
A : List[Any] = '''padme amidala taking a bath artwork, safe for work, no nudity'''
A : List[str] = 2734971755
A : Union[str, Any] = 7
A : int = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
A : str = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=SCREAMING_SNAKE_CASE__ , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
A : Dict = output.images
A : str = image[0, -3:, -3:, -1]
A : Dict = [0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
A : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
A : Dict = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=SCREAMING_SNAKE_CASE__ , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
A : Any = output.images
A : Union[str, Any] = image[0, -3:, -3:, -1]
A : Tuple = [0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : int = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' )
A : Tuple = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
A : Optional[int] = (
'''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
''' leyendecker'''
)
A : Dict = 1044355234
A : List[Any] = 12
A : Optional[int] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
A : Dict = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=SCREAMING_SNAKE_CASE__ , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
A : Optional[int] = output.images
A : str = image[0, -3:, -3:, -1]
A : List[str] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7
A : Tuple = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
A : int = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE__ , guidance_scale=SCREAMING_SNAKE_CASE__ , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
A : Union[str, Any] = output.images
A : Dict = image[0, -3:, -3:, -1]
A : Optional[Any] = np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 3 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class _SCREAMING_SNAKE_CASE( A ):
SCREAMING_SNAKE_CASE_ : List[str] = '''vivit'''
def __init__( self ,SCREAMING_SNAKE_CASE__=2_24 ,SCREAMING_SNAKE_CASE__=32 ,SCREAMING_SNAKE_CASE__=[2, 16, 16] ,SCREAMING_SNAKE_CASE__=3 ,SCREAMING_SNAKE_CASE__=7_68 ,SCREAMING_SNAKE_CASE__=12 ,SCREAMING_SNAKE_CASE__=12 ,SCREAMING_SNAKE_CASE__=30_72 ,SCREAMING_SNAKE_CASE__="gelu_fast" ,SCREAMING_SNAKE_CASE__=0.0 ,SCREAMING_SNAKE_CASE__=0.0 ,SCREAMING_SNAKE_CASE__=0.0_2 ,SCREAMING_SNAKE_CASE__=1E-06 ,SCREAMING_SNAKE_CASE__=True ,**SCREAMING_SNAKE_CASE__ ,) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :int = hidden_size
__SCREAMING_SNAKE_CASE :List[Any] = num_hidden_layers
__SCREAMING_SNAKE_CASE :Union[str, Any] = num_attention_heads
__SCREAMING_SNAKE_CASE :Union[str, Any] = intermediate_size
__SCREAMING_SNAKE_CASE :Any = hidden_act
__SCREAMING_SNAKE_CASE :Optional[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE :str = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE :Any = initializer_range
__SCREAMING_SNAKE_CASE :Optional[int] = layer_norm_eps
__SCREAMING_SNAKE_CASE :Optional[int] = image_size
__SCREAMING_SNAKE_CASE :List[str] = num_frames
__SCREAMING_SNAKE_CASE :Any = tubelet_size
__SCREAMING_SNAKE_CASE :str = num_channels
__SCREAMING_SNAKE_CASE :Any = qkv_bias
super().__init__(**SCREAMING_SNAKE_CASE__ ) | 191 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class __lowerCAmelCase ( A__ ):
lowerCamelCase_ : Tuple = '''timesformer'''
def __init__(self , __magic_name__=224 , __magic_name__=16 , __magic_name__=3 , __magic_name__=8 , __magic_name__=768 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3072 , __magic_name__="gelu" , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1e-6 , __magic_name__=True , __magic_name__="divided_space_time" , __magic_name__=0 , **__magic_name__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__snake_case )
snake_case_ : Any = image_size
snake_case_ : Union[str, Any] = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Dict = num_frames
snake_case_ : Tuple = hidden_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : str = num_attention_heads
snake_case_ : str = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : List[Any] = initializer_range
snake_case_ : Any = layer_norm_eps
snake_case_ : Optional[Any] = qkv_bias
snake_case_ : List[Any] = attention_type
snake_case_ : List[Any] = drop_path_rate
| 353 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowerCAmelCase_ = logging.getLogger(__name__)
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if os.path.exists(_UpperCamelCase ):
if os.path.exists(os.path.join(_UpperCamelCase , '''config.json''' ) ) and os.path.isfile(
os.path.join(_UpperCamelCase , '''config.json''' ) ):
os.remove(os.path.join(_UpperCamelCase , '''config.json''' ) )
if os.path.exists(os.path.join(_UpperCamelCase , '''pytorch_model.bin''' ) ) and os.path.isfile(
os.path.join(_UpperCamelCase , '''pytorch_model.bin''' ) ):
os.remove(os.path.join(_UpperCamelCase , '''pytorch_model.bin''' ) )
else:
os.makedirs(_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=False ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = 2
if unlogit:
snake_case_ : Any = torch.pow(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Optional[Any] = p * torch.log(_UpperCamelCase )
snake_case_ : Dict = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(_UpperCamelCase ) ) ) )
for row in range(len(_UpperCamelCase ) ):
if tensor.dtype != torch.long:
logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:d}''' for x in tensor[row].cpu().data ) )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=None , _UpperCamelCase=False ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ , snake_case_ : int = model.config.num_hidden_layers, model.config.num_attention_heads
snake_case_ : int = torch.zeros(_UpperCamelCase , _UpperCamelCase ).to(args.device )
snake_case_ : Optional[int] = torch.zeros(_UpperCamelCase , _UpperCamelCase ).to(args.device )
if head_mask is None:
snake_case_ : Tuple = torch.ones(_UpperCamelCase , _UpperCamelCase ).to(args.device )
head_mask.requires_grad_(requires_grad=_UpperCamelCase )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
snake_case_ : Dict = None
snake_case_ : Tuple = 0.0
snake_case_ : Dict = 0.0
for step, inputs in enumerate(tqdm(_UpperCamelCase , desc='''Iteration''' , disable=args.local_rank not in [-1, 0] ) ):
snake_case_ : Any = tuple(t.to(args.device ) for t in inputs )
((snake_case_) , ) : Union[str, Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
snake_case_ : List[str] = model(_UpperCamelCase , labels=_UpperCamelCase , head_mask=_UpperCamelCase )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
snake_case_ , snake_case_ , snake_case_ : int = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(_UpperCamelCase ):
snake_case_ : Dict = entropy(attn.detach() , _UpperCamelCase )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(_UpperCamelCase ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
snake_case_ : Union[str, Any] = 2
snake_case_ : Any = torch.pow(torch.pow(_UpperCamelCase , _UpperCamelCase ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20
if not args.dont_normalize_global_importance:
snake_case_ : Union[str, Any] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''' )
print_ad_tensor(_UpperCamelCase )
if compute_importance:
logger.info('''Head importance scores''' )
print_ad_tensor(_UpperCamelCase )
logger.info('''Head ranked by importance scores''' )
snake_case_ : Optional[int] = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
snake_case_ : Union[str, Any] = torch.arange(
head_importance.numel() , device=args.device )
snake_case_ : Dict = head_ranks.view_as(_UpperCamelCase )
print_ad_tensor(_UpperCamelCase )
return attn_entropy, head_importance, total_loss
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ : Optional[int] = compute_heads_importance(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase )
snake_case_ : Any = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , _UpperCamelCase , original_score * args.masking_threshold )
snake_case_ : Any = torch.ones_like(_UpperCamelCase )
snake_case_ : Union[str, Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
snake_case_ : List[Any] = original_score
while current_score >= original_score * args.masking_threshold:
snake_case_ : List[str] = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
snake_case_ : Optional[Any] = float('''Inf''' )
snake_case_ : List[Any] = head_importance.view(-1 ).sort()[1]
if len(_UpperCamelCase ) <= num_to_mask:
print('''BREAK BY num_to_mask''' )
break
# mask heads
snake_case_ : Optional[int] = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist() ) )
snake_case_ : Optional[Any] = new_head_mask.view(-1 )
snake_case_ : int = 0.0
snake_case_ : List[Any] = new_head_mask.view_as(_UpperCamelCase )
snake_case_ : List[str] = new_head_mask.clone().detach()
print_ad_tensor(_UpperCamelCase )
# Compute metric and head importance again
snake_case_ , snake_case_ , snake_case_ : str = compute_heads_importance(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase , head_mask=_UpperCamelCase )
snake_case_ : Tuple = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , _UpperCamelCase , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''' )
print_ad_tensor(_UpperCamelCase )
np.save(os.path.join(args.output_dir , '''head_mask.npy''' ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : str = datetime.now()
snake_case_ , snake_case_ , snake_case_ : List[Any] = compute_heads_importance(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase , compute_importance=_UpperCamelCase , head_mask=_UpperCamelCase )
snake_case_ : Union[str, Any] = 1 / loss
snake_case_ : Union[str, Any] = datetime.now() - before_time
snake_case_ : int = sum(p.numel() for p in model.parameters() )
snake_case_ : Tuple = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_UpperCamelCase ) )
}
for k, v in heads_to_prune.items():
if isinstance(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Any = [
v,
]
assert sum(len(_UpperCamelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(_UpperCamelCase )
snake_case_ : Union[str, Any] = sum(p.numel() for p in model.parameters() )
snake_case_ : Dict = datetime.now()
snake_case_ , snake_case_ , snake_case_ : Union[str, Any] = compute_heads_importance(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase , compute_importance=_UpperCamelCase , head_mask=_UpperCamelCase , actually_pruned=_UpperCamelCase , )
snake_case_ : Union[str, Any] = 1 / loss
snake_case_ : Optional[Any] = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , _UpperCamelCase , _UpperCamelCase , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , _UpperCamelCase , _UpperCamelCase )
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100 )
save_model(_UpperCamelCase , args.output_dir )
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=_UpperCamelCase , type=_UpperCamelCase , required=_UpperCamelCase , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=_UpperCamelCase , type=_UpperCamelCase , required=_UpperCamelCase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=_UpperCamelCase , type=_UpperCamelCase , required=_UpperCamelCase , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=_UpperCamelCase , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=_UpperCamelCase , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=_UpperCamelCase , type=_UpperCamelCase , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=_UpperCamelCase , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''' )
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''' )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''' )
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''' )
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=_UpperCamelCase , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=_UpperCamelCase , help='''Amount to heads to masking at each masking step.''' )
parser.add_argument('''--metric_name''' , default='''acc''' , type=_UpperCamelCase , help='''Metric to use for head masking.''' )
parser.add_argument(
'''--max_seq_length''' , default=128 , type=_UpperCamelCase , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=_UpperCamelCase , help='''Batch size.''' )
parser.add_argument('''--seed''' , type=_UpperCamelCase , default=42 )
parser.add_argument('''--local_rank''' , type=_UpperCamelCase , default=-1 , help='''local_rank for distributed training on gpus''' )
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''' )
parser.add_argument('''--server_ip''' , type=_UpperCamelCase , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=_UpperCamelCase , default='''''' , help='''Can be used for distant debugging.''' )
snake_case_ : Any = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_UpperCamelCase )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
snake_case_ : Tuple = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
snake_case_ : Tuple = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
snake_case_ : List[str] = torch.device('''cuda''' , args.local_rank )
snake_case_ : Union[str, Any] = 1
torch.distributed.init_process_group(backend='''nccl''' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
snake_case_ : int = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
snake_case_ : Any = nn.parallel.DistributedDataParallel(
_UpperCamelCase , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=_UpperCamelCase )
elif args.n_gpu > 1:
snake_case_ : Dict = nn.DataParallel(_UpperCamelCase )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=_UpperCamelCase )
torch.save(_UpperCamelCase , os.path.join(args.output_dir , '''run_args.bin''' ) )
logger.info('''Training/evaluation parameters %s''' , _UpperCamelCase )
# Prepare dataset
snake_case_ : str = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
snake_case_ : Any = (torch.from_numpy(_UpperCamelCase ),)
snake_case_ : Any = TensorDataset(*_UpperCamelCase )
snake_case_ : List[str] = RandomSampler(_UpperCamelCase )
snake_case_ : int = DataLoader(_UpperCamelCase , sampler=_UpperCamelCase , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
snake_case_ : List[str] = mask_heads(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
prune_heads(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
main()
| 279 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class __lowerCAmelCase :
def __init__( self :List[Any] , __magic_name__ :Union[str, Any] , __magic_name__ :Any=13 , __magic_name__ :Optional[Any]=2 , __magic_name__ :Dict=24 , __magic_name__ :Any=16 , __magic_name__ :List[str]=True , __magic_name__ :Tuple=True , __magic_name__ :Tuple=32 , __magic_name__ :int=5 , __magic_name__ :List[str]=4 , __magic_name__ :int=37 , __magic_name__ :Optional[int]="gelu" , __magic_name__ :Optional[int]=0.1 , __magic_name__ :List[Any]=0.1 , __magic_name__ :Union[str, Any]=10 , __magic_name__ :List[Any]=0.02 , __magic_name__ :Optional[int]=None , __magic_name__ :Optional[int]=2 , __magic_name__ :Dict=2 , ):
'''simple docstring'''
a = parent
a = batch_size
a = patch_size
a = max_length
a = num_mel_bins
a = is_training
a = use_labels
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = type_sequence_label_size
a = initializer_range
a = scope
a = frequency_stride
a = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
a = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
a = (self.max_length - self.patch_size) // self.time_stride + 1
a = frequency_out_dimension * time_out_dimension
a = num_patches + 2
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = self.get_config()
return config, input_values, labels
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__magic_name__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def lowerCamelCase__ ( self :Tuple , __magic_name__ :Tuple , __magic_name__ :Optional[int] , __magic_name__ :Tuple ):
'''simple docstring'''
a = ASTModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
a = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"""input_values""": input_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{'''audio-classification''': ASTForAudioClassification, '''feature-extraction''': ASTModel}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def lowerCamelCase__ ( self :Dict , __magic_name__ :Tuple , __magic_name__ :int , __magic_name__ :Dict , __magic_name__ :Union[str, Any] , __magic_name__ :Tuple ):
'''simple docstring'''
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = ASTModelTester(self )
a = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""AST does not use inputs_embeds""" )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__magic_name__ )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["""input_values"""]
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
@slow
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ASTModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def __A ( ) -> Union[str, Any]:
a = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" )
a , a = torchaudio.load(__lowerCamelCase )
return audio, sampling_rate
@require_torch
@require_torchaudio
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
return (
ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" )
if is_torchaudio_available()
else None
)
@slow
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = self.default_feature_extractor
a = ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" ).to(__magic_name__ )
a = self.default_feature_extractor
a , a = prepare_audio()
a = audio.squeeze().numpy()
a = feature_extractor(__magic_name__ , sampling_rate=__magic_name__ , return_tensors="""pt""" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
a = model(**__magic_name__ )
# verify the logits
a = torch.Size((1, 527) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
a = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1E-4 ) )
| 228 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
raise ValueError(
"""starting number must be
and integer and be more than 0""" )
if not iterations >= 1:
raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz""" )
a = """"""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__lowerCamelCase )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 228 | 1 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
__lowercase = '''base_with_context'''
def lowerCAmelCase (__UpperCamelCase : Tuple , __UpperCamelCase : List[str] ):
"""simple docstring"""
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding'''] ) )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=__UpperCamelCase )
for lyr_num, lyr in enumerate(model.encoders ):
__UpperCamelCase =weights[F"""layers_{lyr_num}"""]
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale'''] ) )
__UpperCamelCase =ly_weight['''attention''']
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale'''] ) )
return model
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : Dict ):
"""simple docstring"""
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=__UpperCamelCase )
for lyr_num, lyr in enumerate(model.encoders ):
__UpperCamelCase =weights[F"""layers_{lyr_num}"""]
__UpperCamelCase =ly_weight['''attention''']
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale'''] ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale'''] ) )
return model
def lowerCAmelCase (__UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=__UpperCamelCase )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
__UpperCamelCase =weights[F"""layers_{lyr_num}"""]
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale'''] ) )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T ) )
__UpperCamelCase =ly_weight['''self_attention''']
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCamelCase =ly_weight['''MultiHeadDotProductAttention_0''']
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale'''] ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
__UpperCamelCase =nn.Parameter(
torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale'''] ) )
__UpperCamelCase =nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T ) )
return model
def lowerCAmelCase (__UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
__UpperCamelCase =checkpoints.load_tax_checkpoint(args.checkpoint_path )
__UpperCamelCase =jnp.tree_util.tree_map(onp.array , __UpperCamelCase )
__UpperCamelCase =[
'''from __gin__ import dynamic_registration''',
'''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
'''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
'''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
]
__UpperCamelCase =os.path.join(args.checkpoint_path , '''..''' , '''config.gin''' )
__UpperCamelCase =inference.parse_training_gin_file(__UpperCamelCase , __UpperCamelCase )
__UpperCamelCase =inference.InferenceModel(args.checkpoint_path , __UpperCamelCase )
__UpperCamelCase =DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' , variance_type='''fixed_large''' )
__UpperCamelCase =SpectrogramNotesEncoder(
max_length=synth_model.sequence_length['''inputs'''] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='''gated-gelu''' , )
__UpperCamelCase =SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length['''targets_context'''] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='''gated-gelu''' , )
__UpperCamelCase =TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length['''targets_context'''] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
__UpperCamelCase =load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''] , __UpperCamelCase )
__UpperCamelCase =load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''] , __UpperCamelCase )
__UpperCamelCase =load_decoder(ta_checkpoint['''target''']['''decoder'''] , __UpperCamelCase )
__UpperCamelCase =OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder''' )
__UpperCamelCase =SpectrogramDiffusionPipeline(
notes_encoder=__UpperCamelCase , continuous_encoder=__UpperCamelCase , decoder=__UpperCamelCase , scheduler=__UpperCamelCase , melgan=__UpperCamelCase , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
parser.add_argument(
'''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
)
parser.add_argument(
'''--checkpoint_path''',
default=f'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help='''Path to the original jax model checkpoint.''',
)
__lowercase = parser.parse_args()
main(args)
| 85 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Any ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =data
__UpperCamelCase =None
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase =None
__UpperCamelCase =None
def __iter__( self : int ) -> Iterator[Any]:
'''simple docstring'''
__UpperCamelCase =self.head
while self.head:
yield node.data
__UpperCamelCase =node.next
if node == self.head:
break
def __len__( self : Union[str, Any] ) -> int:
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self : str ) -> Union[str, Any]:
'''simple docstring'''
return "->".join(str(UpperCamelCase__ ) for item in iter(self ) )
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
self.insert_nth(len(self ) , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Optional[int] , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
self.insert_nth(0 , UpperCamelCase__ )
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
if index < 0 or index > len(self ):
raise IndexError('''list index out of range.''' )
__UpperCamelCase =Node(UpperCamelCase__ )
if self.head is None:
__UpperCamelCase =new_node # first node points itself
__UpperCamelCase =__UpperCamelCase =new_node
elif index == 0: # insert at head
__UpperCamelCase =self.head
__UpperCamelCase =__UpperCamelCase =new_node
else:
__UpperCamelCase =self.head
for _ in range(index - 1 ):
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next
__UpperCamelCase =new_node
if index == len(self ) - 1: # insert at tail
__UpperCamelCase =new_node
def UpperCAmelCase_ ( self : Any ) -> Any:
'''simple docstring'''
return self.delete_nth(0 )
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def UpperCAmelCase_ ( self : int , UpperCamelCase__ : int = 0 ) -> Any:
'''simple docstring'''
if not 0 <= index < len(self ):
raise IndexError('''list index out of range.''' )
__UpperCamelCase =self.head
if self.head == self.tail: # just one node
__UpperCamelCase =__UpperCamelCase =None
elif index == 0: # delete head node
__UpperCamelCase =self.tail.next.next
__UpperCamelCase =self.head.next
else:
__UpperCamelCase =self.head
for _ in range(index - 1 ):
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next.next
if index == len(self ) - 1: # delete at tail
__UpperCamelCase =temp
return delete_node.data
def UpperCAmelCase_ ( self : str ) -> bool:
'''simple docstring'''
return len(self ) == 0
def lowerCAmelCase ():
"""simple docstring"""
__UpperCamelCase =CircularLinkedList()
assert len(__UpperCamelCase ) == 0
assert circular_linked_list.is_empty() is True
assert str(__UpperCamelCase ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(__UpperCamelCase ) == i
circular_linked_list.insert_nth(__UpperCamelCase , i + 1 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ = {
'''configuration_xlm_roberta''': [
'''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaConfig''',
'''XLMRobertaOnnxConfig''',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ['''XLMRobertaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ['''XLMRobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMRobertaForCausalLM''',
'''XLMRobertaForMaskedLM''',
'''XLMRobertaForMultipleChoice''',
'''XLMRobertaForQuestionAnswering''',
'''XLMRobertaForSequenceClassification''',
'''XLMRobertaForTokenClassification''',
'''XLMRobertaModel''',
'''XLMRobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMRobertaForCausalLM''',
'''TFXLMRobertaForMaskedLM''',
'''TFXLMRobertaForMultipleChoice''',
'''TFXLMRobertaForQuestionAnswering''',
'''TFXLMRobertaForSequenceClassification''',
'''TFXLMRobertaForTokenClassification''',
'''TFXLMRobertaModel''',
'''TFXLMRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxXLMRobertaForMaskedLM''',
'''FlaxXLMRobertaForCausalLM''',
'''FlaxXLMRobertaForMultipleChoice''',
'''FlaxXLMRobertaForQuestionAnswering''',
'''FlaxXLMRobertaForSequenceClassification''',
'''FlaxXLMRobertaForTokenClassification''',
'''FlaxXLMRobertaModel''',
'''FlaxXLMRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 235 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : np.ndarray ,__a : Union[int, Iterable[int]] ,__a : bool ,__a : int ) -> Tuple[int, int]:
"""simple docstring"""
def constraint_to_multiple_of(__a : List[str] ,__a : Dict ,__a : Any=0 ,__a : int=None ):
_a : Dict = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_a : Any = math.floor(val / multiple ) * multiple
if x < min_val:
_a : Dict = math.ceil(val / multiple ) * multiple
return x
_a : Union[str, Any] = (output_size, output_size) if isinstance(__a ,__a ) else output_size
_a , _a : List[Any] = get_image_size(__a )
_a , _a : Any = output_size
# determine new height and width
_a : Union[str, Any] = output_height / input_height
_a : Tuple = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_a : Optional[Any] = scale_width
else:
# fit height
_a : Tuple = scale_height
_a : Optional[Any] = constraint_to_multiple_of(scale_height * input_height ,multiple=__a )
_a : int = constraint_to_multiple_of(scale_width * input_width ,multiple=__a )
return (new_height, new_width)
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = ["pixel_values"]
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BILINEAR , _a = False , _a = 1 , _a = True , _a = 1 / 2_5_5 , _a = True , _a = None , _a = None , **_a , ) -> None:
super().__init__(**_a )
_a : Optional[int] = size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
_a : Optional[Any] = get_size_dict(_a )
_a : Any = do_resize
_a : Dict = size
_a : str = keep_aspect_ratio
_a : Any = ensure_multiple_of
_a : Optional[Any] = resample
_a : List[Any] = do_rescale
_a : int = rescale_factor
_a : Any = do_normalize
_a : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_a : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowercase ( self , _a , _a , _a = False , _a = 1 , _a = PILImageResampling.BICUBIC , _a = None , **_a , ) -> np.ndarray:
_a : str = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_a : Optional[Any] = get_resize_output_image_size(
_a , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_a , multiple=_a , )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a , _a = None , **_a , ) -> int:
return rescale(_a , scale=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a , _a , _a = None , **_a , ) -> np.ndarray:
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ) -> PIL.Image.Image:
_a : Optional[int] = do_resize if do_resize is not None else self.do_resize
_a : Union[str, Any] = size if size is not None else self.size
_a : str = get_size_dict(_a )
_a : List[Any] = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_a : Optional[Any] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_a : str = resample if resample is not None else self.resample
_a : str = do_rescale if do_rescale is not None else self.do_rescale
_a : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_a : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
_a : str = image_mean if image_mean is not None else self.image_mean
_a : Tuple = image_std if image_std is not None else self.image_std
_a : Dict = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_a : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
_a : int = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_rescale:
_a : Optional[Any] = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
_a : Optional[int] = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
_a : int = [to_channel_dimension_format(_a , _a ) for image in images]
_a : Tuple = {'''pixel_values''': images}
return BatchFeature(data=_a , tensor_type=_a )
def __lowercase ( self , _a , _a = None ) -> Any:
_a : Optional[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_a ) != len(_a ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_a ):
_a : List[Any] = target_sizes.numpy()
_a : str = []
for idx in range(len(_a ) ):
_a : str = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_a )
_a : Union[str, Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_a )
else:
_a : Tuple = logits.argmax(dim=1 )
_a : Union[str, Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 235 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase :
'''simple docstring'''
def __init__(self , __a , __a=13 , __a=32 , __a=2 , __a=3 , __a=16 , __a=[1, 2, 1] , __a=[2, 2, 4] , __a=2 , __a=2.0 , __a=True , __a=0.0 , __a=0.0 , __a=0.1 , __a="gelu" , __a=False , __a=True , __a=0.02 , __a=1E-5 , __a=True , __a=None , __a=True , __a=10 , __a=8 , ) -> str:
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = image_size
UpperCAmelCase__ = patch_size
UpperCAmelCase__ = num_channels
UpperCAmelCase__ = embed_dim
UpperCAmelCase__ = depths
UpperCAmelCase__ = num_heads
UpperCAmelCase__ = window_size
UpperCAmelCase__ = mlp_ratio
UpperCAmelCase__ = qkv_bias
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = drop_path_rate
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = use_absolute_embeddings
UpperCAmelCase__ = patch_norm
UpperCAmelCase__ = layer_norm_eps
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = is_training
UpperCAmelCase__ = scope
UpperCAmelCase__ = use_labels
UpperCAmelCase__ = type_sequence_label_size
UpperCAmelCase__ = encoder_stride
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCamelCase__ (self , __a , __a , __a ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = SwinvaModel(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase__ = model(__a )
UpperCAmelCase__ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
UpperCAmelCase__ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def UpperCamelCase__ (self , __a , __a , __a ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = SwinvaForMaskedImageModeling(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase__ = model(__a )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
UpperCAmelCase__ = 1
UpperCAmelCase__ = SwinvaForMaskedImageModeling(__a )
model.to(__a )
model.eval()
UpperCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase__ (self , __a , __a , __a ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.type_sequence_label_size
UpperCAmelCase__ = SwinvaForImageClassification(__a )
model.to(__a )
model.eval()
UpperCAmelCase__ = model(__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = config_and_inputs
UpperCAmelCase__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
__SCREAMING_SNAKE_CASE = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = SwinvaModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=__a , embed_dim=37 )
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
pass
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(__a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__a , nn.Linear ) )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(__a )
UpperCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ = [*signature.parameters.keys()]
UpperCAmelCase__ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __a )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = True
for model_class in self.all_model_classes:
UpperCAmelCase__ = True
UpperCAmelCase__ = False
UpperCAmelCase__ = True
UpperCAmelCase__ = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
UpperCAmelCase__ = model(**self._prepare_for_class(__a , __a ) )
UpperCAmelCase__ = outputs.attentions
UpperCAmelCase__ = len(self.model_tester.depths )
self.assertEqual(len(__a ) , __a )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
UpperCAmelCase__ = True
UpperCAmelCase__ = config.window_size**2
UpperCAmelCase__ = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
UpperCAmelCase__ = model(**self._prepare_for_class(__a , __a ) )
UpperCAmelCase__ = outputs.attentions
self.assertEqual(len(__a ) , __a )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
UpperCAmelCase__ = len(__a )
# Check attention is always last and order is fine
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
UpperCAmelCase__ = model(**self._prepare_for_class(__a , __a ) )
if hasattr(self.model_tester , 'num_hidden_states_types' ):
UpperCAmelCase__ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
UpperCAmelCase__ = 2
self.assertEqual(out_len + added_hidden_states , len(__a ) )
UpperCAmelCase__ = outputs.attentions
self.assertEqual(len(__a ) , __a )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def UpperCamelCase__ (self , __a , __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
UpperCAmelCase__ = model(**self._prepare_for_class(__a , __a ) )
UpperCAmelCase__ = outputs.hidden_states
UpperCAmelCase__ = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__a ) , __a )
# Swinv2 has a different seq_length
UpperCAmelCase__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
UpperCAmelCase__ = outputs.reshaped_hidden_states
self.assertEqual(len(__a ) , __a )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = reshaped_hidden_states[0].shape
UpperCAmelCase__ = (
reshaped_hidden_states[0].view(__a , __a , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
UpperCAmelCase__ = True
self.check_hidden_states_output(__a , __a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ = True
self.check_hidden_states_output(__a , __a , __a , __a )
def UpperCamelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = 3
UpperCAmelCase__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
UpperCAmelCase__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase__ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
UpperCAmelCase__ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
UpperCAmelCase__ = True
self.check_hidden_states_output(__a , __a , __a , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ = True
self.check_hidden_states_output(__a , __a , __a , (padded_height, padded_width) )
def UpperCamelCase__ (self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__a )
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
@slow
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = SwinvaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = _config_zero_init(__a )
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(config=__a )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@require_vision
@require_torch
class lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def UpperCamelCase__ (self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
__a )
UpperCAmelCase__ = self.default_image_processor
UpperCAmelCase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
UpperCAmelCase__ = image_processor(images=__a , return_tensors='pt' ).to(__a )
# forward pass
with torch.no_grad():
UpperCAmelCase__ = model(**__a )
# verify the logits
UpperCAmelCase__ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
UpperCAmelCase__ = torch.tensor([-0.39_47, -0.43_06, 0.00_26] ).to(__a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1E-4 ) )
| 335 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
@register_to_config
def __init__(self , *,
__a = 4 , __a = 768 , __a , __a , ) -> str:
"""simple docstring"""
super().__init__()
UpperCAmelCase__ = nn.Parameter(torch.zeros(__a ) )
# parameters for additional clip time embeddings
UpperCAmelCase__ = nn.Linear(__a , __a )
UpperCAmelCase__ = nn.Linear(__a , __a )
# parameters for encoder hidden states
UpperCAmelCase__ = clip_extra_context_tokens
UpperCAmelCase__ = nn.Linear(
__a , self.clip_extra_context_tokens * cross_attention_dim )
UpperCAmelCase__ = nn.Linear(__a , __a )
UpperCAmelCase__ = nn.LayerNorm(__a )
def UpperCamelCase__ (self , *, __a , __a , __a , __a ) -> Optional[Any]:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
UpperCAmelCase__ = image_embeddings.shape[0]
UpperCAmelCase__ = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
UpperCAmelCase__ = classifier_free_guidance_embeddings.expand(
__a , -1 )
UpperCAmelCase__ = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
UpperCAmelCase__ = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
UpperCAmelCase__ = self.embedding_proj(__a )
UpperCAmelCase__ = self.clip_image_embeddings_project_to_time_embeddings(__a )
UpperCAmelCase__ = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
UpperCAmelCase__ = self.clip_extra_context_tokens_proj(__a )
UpperCAmelCase__ = clip_extra_context_tokens.reshape(__a , -1 , self.clip_extra_context_tokens )
UpperCAmelCase__ = clip_extra_context_tokens.permute(0 , 2 , 1 )
UpperCAmelCase__ = self.encoder_hidden_states_proj(__a )
UpperCAmelCase__ = self.text_encoder_hidden_states_norm(__a )
UpperCAmelCase__ = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 335 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
def __init__( self : str , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple ):
super().__init__()
# make sure scheduler can always be converted to DDIM
lowerCAmelCase : Tuple = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=UpperCamelCase_ , scheduler=UpperCamelCase_ )
@torch.no_grad()
def __call__( self : List[str] , UpperCamelCase_ : int = 1 , UpperCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase_ : float = 0.0 , UpperCamelCase_ : int = 5_0 , UpperCamelCase_ : Optional[bool] = None , UpperCamelCase_ : Optional[str] = "pil" , UpperCamelCase_ : bool = True , ):
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size , UpperCamelCase_ ):
lowerCAmelCase : Optional[int] = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
lowerCAmelCase : List[str] = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(UpperCamelCase_ , UpperCamelCase_ ) and len(UpperCamelCase_ ) != batch_size:
raise ValueError(
F'''You have passed a list of generators of length {len(UpperCamelCase_ )}, but requested an effective batch'''
F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowerCAmelCase : Any = randn_tensor(UpperCamelCase_ , generator=UpperCamelCase_ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(UpperCamelCase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowerCAmelCase : Optional[int] = self.unet(UpperCamelCase_ , UpperCamelCase_ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
lowerCAmelCase : Optional[int] = self.scheduler.step(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , eta=UpperCamelCase_ , use_clipped_model_output=UpperCamelCase_ , generator=UpperCamelCase_ ).prev_sample
lowerCAmelCase : Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1 )
lowerCAmelCase : int = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowerCAmelCase : List[str] = self.numpy_to_pil(UpperCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase_ )
| 60 | def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ):
"""simple docstring"""
while b:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = b, a % b
return a
def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(lowerCAmelCase , a % b )
def _snake_case ( ):
"""simple docstring"""
print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}' )
print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}' )
print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}' )
print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}' )
print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}' )
print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}' )
print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}' )
print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}' )
print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}' )
print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}' )
if __name__ == "__main__":
main()
| 18 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "unispeech-sat"
def __init__( self : Optional[int] ,lowercase_ : Optional[Any]=3_2 ,lowercase_ : List[Any]=7_6_8 ,lowercase_ : int=1_2 ,lowercase_ : Tuple=1_2 ,lowercase_ : List[Any]=3_0_7_2 ,lowercase_ : Dict="gelu" ,lowercase_ : Any=0.1 ,lowercase_ : str=0.1 ,lowercase_ : Optional[Any]=0.1 ,lowercase_ : List[Any]=0.0 ,lowercase_ : str=0.0 ,lowercase_ : Tuple=0.1 ,lowercase_ : Optional[int]=0.1 ,lowercase_ : Tuple=0.02 ,lowercase_ : str=1E-5 ,lowercase_ : Any="group" ,lowercase_ : Optional[Any]="gelu" ,lowercase_ : int=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,lowercase_ : str=(5, 2, 2, 2, 2, 2, 2) ,lowercase_ : Tuple=(1_0, 3, 3, 3, 3, 2, 2) ,lowercase_ : Optional[int]=False ,lowercase_ : Tuple=1_2_8 ,lowercase_ : Any=1_6 ,lowercase_ : int=False ,lowercase_ : Dict=True ,lowercase_ : List[Any]=0.05 ,lowercase_ : str=1_0 ,lowercase_ : Dict=2 ,lowercase_ : Dict=0.0 ,lowercase_ : str=1_0 ,lowercase_ : int=0 ,lowercase_ : List[Any]=3_2_0 ,lowercase_ : Union[str, Any]=2 ,lowercase_ : Optional[Any]=0.1 ,lowercase_ : Tuple=1_0_0 ,lowercase_ : Tuple=2_5_6 ,lowercase_ : int=2_5_6 ,lowercase_ : str=0.1 ,lowercase_ : Optional[int]="mean" ,lowercase_ : str=False ,lowercase_ : List[Any]=False ,lowercase_ : Any=2_5_6 ,lowercase_ : str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) ,lowercase_ : Optional[int]=(5, 3, 3, 1, 1) ,lowercase_ : str=(1, 2, 3, 1, 1) ,lowercase_ : List[str]=5_1_2 ,lowercase_ : List[str]=0 ,lowercase_ : Optional[Any]=1 ,lowercase_ : List[str]=2 ,lowercase_ : Any=5_0_4 ,**lowercase_ : Optional[int] ,):
super().__init__(**lowercase_ ,pad_token_id=lowercase_ ,bos_token_id=lowercase_ ,eos_token_id=lowercase_ )
lowerCAmelCase__ : Any = hidden_size
lowerCAmelCase__ : Union[str, Any] = feat_extract_norm
lowerCAmelCase__ : Dict = feat_extract_activation
lowerCAmelCase__ : Optional[Any] = list(lowercase_ )
lowerCAmelCase__ : str = list(lowercase_ )
lowerCAmelCase__ : Optional[int] = list(lowercase_ )
lowerCAmelCase__ : Dict = conv_bias
lowerCAmelCase__ : Dict = num_conv_pos_embeddings
lowerCAmelCase__ : List[Any] = num_conv_pos_embedding_groups
lowerCAmelCase__ : Dict = len(self.conv_dim )
lowerCAmelCase__ : str = num_hidden_layers
lowerCAmelCase__ : Optional[Any] = intermediate_size
lowerCAmelCase__ : Any = hidden_act
lowerCAmelCase__ : Any = num_attention_heads
lowerCAmelCase__ : str = hidden_dropout
lowerCAmelCase__ : List[str] = attention_dropout
lowerCAmelCase__ : Any = activation_dropout
lowerCAmelCase__ : Optional[Any] = feat_proj_dropout
lowerCAmelCase__ : Any = final_dropout
lowerCAmelCase__ : Tuple = layerdrop
lowerCAmelCase__ : Optional[int] = layer_norm_eps
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Optional[Any] = vocab_size
lowerCAmelCase__ : Any = num_clusters
lowerCAmelCase__ : str = do_stable_layer_norm
lowerCAmelCase__ : Dict = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCAmelCase__ : List[str] = apply_spec_augment
lowerCAmelCase__ : Optional[int] = mask_time_prob
lowerCAmelCase__ : Tuple = mask_time_length
lowerCAmelCase__ : Union[str, Any] = mask_time_min_masks
lowerCAmelCase__ : List[Any] = mask_feature_prob
lowerCAmelCase__ : Optional[Any] = mask_feature_length
lowerCAmelCase__ : List[Any] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowerCAmelCase__ : Dict = num_codevectors_per_group
lowerCAmelCase__ : Tuple = num_codevector_groups
lowerCAmelCase__ : int = contrastive_logits_temperature
lowerCAmelCase__ : Dict = feat_quantizer_dropout
lowerCAmelCase__ : Any = num_negatives
lowerCAmelCase__ : Dict = codevector_dim
lowerCAmelCase__ : Dict = proj_codevector_dim
lowerCAmelCase__ : Union[str, Any] = diversity_loss_weight
# ctc loss
lowerCAmelCase__ : List[str] = ctc_loss_reduction
lowerCAmelCase__ : Optional[Any] = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowerCAmelCase__ : List[Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowerCAmelCase__ : Dict = list(lowercase_ )
lowerCAmelCase__ : int = list(lowercase_ )
lowerCAmelCase__ : Dict = list(lowercase_ )
lowerCAmelCase__ : Tuple = xvector_output_dim
@property
def __lowerCAmelCase ( self : Dict ):
return functools.reduce(operator.mul ,self.conv_stride ,1 ) | 364 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCamelCase : int = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __SCREAMING_SNAKE_CASE ( A_ ):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
lowerCAmelCase__ : str = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(A_ , id=A_ )
| 74 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
A : List[str] = MobileBertConfig.from_json_file(snake_case__ )
print(F'Building PyTorch model from configuration: {config}' )
A : Optional[Any] = MobileBertForPreTraining(snake_case__ )
# Load weights from tf checkpoint
A : Optional[int] = load_tf_weights_in_mobilebert(snake_case__ , snake_case__ , snake_case__ )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , snake_case__ )
if __name__ == "__main__":
lowercase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--mobilebert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained MobileBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
lowercase : Optional[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 3 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : List[Any] = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 3 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' ,'False' ) ) is not True ,reason='Skipping test because should only be run when releasing minor transformers version' ,)
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding='''utf-8''' , check=lowerCAmelCase_ , )
assert hasattr(self , '''env''' )
def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : str ) -> Dict:
"""simple docstring"""
_a = F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
_a = {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=lowerCAmelCase_ , instance_count=lowerCAmelCase_ , instance_type=self.instance_type , debugger_hook_config=lowerCAmelCase_ , hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=lowerCAmelCase_ , py_version='''py36''' , )
def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[str] ) -> Any:
"""simple docstring"""
TrainingJobAnalytics(lowerCAmelCase_ ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def __lowerCAmelCase ( self : str , lowerCAmelCase_ : List[str] ) -> List[Any]:
"""simple docstring"""
_a = self.create_estimator(lowerCAmelCase_ )
# run training
estimator.fit()
# result dataframe
_a = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
_a = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
_a = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
_a = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 99_99_99 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'{estimator.latest_training_job.name}.json' , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , lowerCAmelCase_ )
| 179 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class A :
def __init__( self : Any , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str]=13 , lowerCAmelCase_ : Optional[Any]=10 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Dict=32 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : int=37 , lowerCAmelCase_ : int="gelu" , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : Dict=10 , lowerCAmelCase_ : int=0.0_2 , lowerCAmelCase_ : Union[str, Any]=0.9 , lowerCAmelCase_ : str=None , ) -> int:
"""simple docstring"""
_a = parent
_a = batch_size
_a = image_size
_a = num_channels
_a = patch_size
_a = tubelet_size
_a = num_frames
_a = is_training
_a = use_labels
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = type_sequence_label_size
_a = initializer_range
_a = mask_ratio
_a = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
_a = (image_size // patch_size) ** 2
_a = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
_a = int(mask_ratio * self.seq_length )
def __lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
_a = VideoMAEModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_a = model(lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = VideoMAEForPreTraining(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_a = torch.ones((self.num_masks,) )
_a = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
_a = mask.expand(self.batch_size , -1 ).bool()
_a = model(lowerCAmelCase_ , lowerCAmelCase_ )
# model only returns predictions for masked patches
_a = mask.sum().item()
_a = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a = config_and_inputs
_a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A ( _a ,_a ,unittest.TestCase ):
lowercase_ = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowercase_ = (
{'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_a = VideoMAEModelTester(self )
_a = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ , hidden_size=37 )
def __lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[Any]=False ) -> Tuple:
"""simple docstring"""
_a = copy.deepcopy(lowerCAmelCase_ )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_a = torch.ones((self.model_tester.num_masks,) )
_a = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
_a = mask.expand(self.model_tester.batch_size , -1 ).bool()
_a = bool_masked_pos.to(lowerCAmelCase_ )
if return_labels:
if model_class in [
*get_values(lowerCAmelCase_ ),
]:
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_ )
return inputs_dict
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''VideoMAE does not use inputs_embeds''' )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(lowerCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase_ , nn.Linear ) )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(lowerCAmelCase_ )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def __lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def __lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_ )
@slow
def __lowerCAmelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = VideoMAEModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
def __lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
if not self.has_attentions:
pass
else:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
for model_class in self.all_model_classes:
_a = self.model_tester.seq_length - self.model_tester.num_masks
_a = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
_a = True
_a = False
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
_a = len(lowerCAmelCase_ )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
self.assertEqual(out_len + 1 , len(lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def __lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple ):
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.hidden_states
_a = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCAmelCase_ ) , lowerCAmelCase_ )
_a = self.model_tester.seq_length - self.model_tester.num_masks
_a = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowerCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def snake_case_ ():
'''simple docstring'''
_a = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
_a = np.load(UpperCamelCase )
return list(UpperCamelCase )
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_a = VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics''' ).to(
lowerCAmelCase_ )
_a = self.default_image_processor
_a = prepare_video()
_a = image_processor(lowerCAmelCase_ , return_tensors='''pt''' ).to(lowerCAmelCase_ )
# forward pass
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
# verify the logits
_a = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
_a = torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] ).to(lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' ).to(lowerCAmelCase_ )
_a = self.default_image_processor
_a = prepare_video()
_a = image_processor(lowerCAmelCase_ , return_tensors='''pt''' ).to(lowerCAmelCase_ )
# add boolean mask, indicating which patches to mask
_a = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''' )
_a = torch.load(lowerCAmelCase_ )
# forward pass
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
# verify the logits
_a = torch.Size([1, 14_08, 15_36] )
_a = torch.tensor(
[[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] , device=lowerCAmelCase_ )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , lowerCAmelCase_ , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
_a = torch.tensor([0.5_1_4_2] , device=lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.loss , lowerCAmelCase_ , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
_a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' , norm_pix_loss=lowerCAmelCase_ ).to(
lowerCAmelCase_ )
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
_a = torch.tensor(torch.tensor([0.6_4_6_9] ) , device=lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.loss , lowerCAmelCase_ , atol=1e-4 ) )
| 179 | 1 |
from __future__ import annotations
A__ = tuple[int, int, int]
A__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
A__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
# -------------------------- default selection --------------------------
# rotors --------------------------
A__ = """EGZWVONAHDCLFQMSIPJBYUKXTR"""
A__ = """FOBHMDKEXQNRAULPGSJVTYICZW"""
A__ = """ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
A__ = {
"""A""": """N""",
"""N""": """A""",
"""B""": """O""",
"""O""": """B""",
"""C""": """P""",
"""P""": """C""",
"""D""": """Q""",
"""Q""": """D""",
"""E""": """R""",
"""R""": """E""",
"""F""": """S""",
"""S""": """F""",
"""G""": """T""",
"""T""": """G""",
"""H""": """U""",
"""U""": """H""",
"""I""": """V""",
"""V""": """I""",
"""J""": """W""",
"""W""": """J""",
"""K""": """X""",
"""X""": """K""",
"""L""": """Y""",
"""Y""": """L""",
"""M""": """Z""",
"""Z""": """M""",
}
# -------------------------- extra rotors --------------------------
A__ = """RMDJXFUWGISLHVTCQNKYPBEZOA"""
A__ = """SGLCPQWZHKXAREONTFBVIYJUDM"""
A__ = """HVSICLTYKQUBXDWAJZOMFGPREN"""
A__ = """RZWQHFMVDBKICJLNTUXAGYPSOE"""
A__ = """LFKIJODBEGAMQPXVUHYSTCZRWN"""
A__ = """KOAEGVDHXPQZMLFTYWJNBRCIUS"""
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if (unique_rotsel := len(set(snake_case ) )) < 3:
_lowerCAmelCase = F'Please use 3 unique rotors (not {unique_rotsel})'
raise Exception(snake_case )
# Checks if rotor positions are valid
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = rotpos
if not 0 < rotorposa <= len(snake_case ):
_lowerCAmelCase = F'First rotor position is not within range of 1..26 ({rotorposa}'
raise ValueError(snake_case )
if not 0 < rotorposa <= len(snake_case ):
_lowerCAmelCase = F'Second rotor position is not within range of 1..26 ({rotorposa})'
raise ValueError(snake_case )
if not 0 < rotorposa <= len(snake_case ):
_lowerCAmelCase = F'Third rotor position is not within range of 1..26 ({rotorposa})'
raise ValueError(snake_case )
# Validates string and returns dict
_lowerCAmelCase = _plugboard(snake_case )
return rotpos, rotsel, pbdict
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case , snake_case ):
_lowerCAmelCase = F'Plugboard setting isn\'t type string ({type(snake_case )})'
raise TypeError(snake_case )
elif len(snake_case ) % 2 != 0:
_lowerCAmelCase = F'Odd number of symbols ({len(snake_case )})'
raise Exception(snake_case )
elif pbstring == "":
return {}
pbstring.replace(""" """ , """""" )
# Checks if all characters are unique
_lowerCAmelCase = set()
for i in pbstring:
if i not in abc:
_lowerCAmelCase = F'\'{i}\' not in list of symbols'
raise Exception(snake_case )
elif i in tmppbl:
_lowerCAmelCase = F'Duplicate symbol ({i})'
raise Exception(snake_case )
else:
tmppbl.add(snake_case )
del tmppbl
# Created the dictionary
_lowerCAmelCase = {}
for j in range(0 , len(snake_case ) - 1 , 2 ):
_lowerCAmelCase = pbstring[j + 1]
_lowerCAmelCase = pbstring[j]
return pb
def _UpperCAmelCase ( snake_case , snake_case , snake_case = (rotora, rotora, rotora) , snake_case = "" , ):
"""simple docstring"""
_lowerCAmelCase = text.upper()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = _validator(
snake_case , snake_case , plugb.upper() )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = rotor_position
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
_lowerCAmelCase = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
_lowerCAmelCase = plugboard[symbol]
# rotor ra --------------------------
_lowerCAmelCase = abc.index(snake_case ) + rotorposa
_lowerCAmelCase = rotora[index % len(snake_case )]
# rotor rb --------------------------
_lowerCAmelCase = abc.index(snake_case ) + rotorposa
_lowerCAmelCase = rotora[index % len(snake_case )]
# rotor rc --------------------------
_lowerCAmelCase = abc.index(snake_case ) + rotorposa
_lowerCAmelCase = rotora[index % len(snake_case )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
_lowerCAmelCase = reflector[symbol]
# 2nd rotors
_lowerCAmelCase = abc[rotora.index(snake_case ) - rotorposa]
_lowerCAmelCase = abc[rotora.index(snake_case ) - rotorposa]
_lowerCAmelCase = abc[rotora.index(snake_case ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
_lowerCAmelCase = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(snake_case ):
_lowerCAmelCase = 0
rotorposa += 1
if rotorposa >= len(snake_case ):
_lowerCAmelCase = 0
rotorposa += 1
if rotorposa >= len(snake_case ):
_lowerCAmelCase = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(snake_case )
return "".join(snake_case )
if __name__ == "__main__":
A__ = """This is my Python script that emulates the Enigma machine from WWII."""
A__ = (1, 1, 1)
A__ = """pictures"""
A__ = (rotora, rotora, rotora)
A__ = enigma(message, rotor_pos, rotor_sel, pb)
print("""Encrypted message:""", en)
print("""Decrypted message:""", enigma(en, rotor_pos, rotor_sel, pb))
| 82 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( __lowerCAmelCase )-> float:
'''simple docstring'''
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def lowerCAmelCase_ ( __lowerCAmelCase )-> float:
'''simple docstring'''
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError('''Length must be a positive.''' )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''LlamaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''LlamaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''LlamaForCausalLM''',
'''LlamaModel''',
'''LlamaPreTrainedModel''',
'''LlamaForSequenceClassification''',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 78 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : int = [
['''attention''', '''attn'''],
['''encoder_attention''', '''encoder_attn'''],
['''q_lin''', '''q_proj'''],
['''k_lin''', '''k_proj'''],
['''v_lin''', '''v_proj'''],
['''out_lin''', '''out_proj'''],
['''norm_embeddings''', '''layernorm_embedding'''],
['''position_embeddings''', '''embed_positions'''],
['''embeddings''', '''embed_tokens'''],
['''ffn.lin''', '''fc'''],
]
def snake_case_ ( lowerCAmelCase_ : str ):
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
__lowercase : Tuple = k.replace(lowerCAmelCase_ , lowerCAmelCase_ )
if k.startswith("""encoder""" ):
__lowercase : Union[str, Any] = k.replace(""".attn""" , """.self_attn""" )
__lowercase : Any = k.replace("""norm1""" , """self_attn_layer_norm""" )
__lowercase : Any = k.replace("""norm2""" , """final_layer_norm""" )
elif k.startswith("""decoder""" ):
__lowercase : Optional[int] = k.replace("""norm1""" , """self_attn_layer_norm""" )
__lowercase : Tuple = k.replace("""norm2""" , """encoder_attn_layer_norm""" )
__lowercase : Tuple = k.replace("""norm3""" , """final_layer_norm""" )
return k
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : List[str] = [
"""model.encoder.layernorm_embedding.weight""",
"""model.encoder.layernorm_embedding.bias""",
"""model.decoder.layernorm_embedding.weight""",
"""model.decoder.layernorm_embedding.bias""",
]
for k in keys:
__lowercase : Optional[int] = sd.pop(lowerCAmelCase_ )
__lowercase : List[Any] = k.replace("""layernorm_embedding""" , """layer_norm""" )
assert new_k not in sd
__lowercase : Optional[Any] = v
lowerCamelCase : Any = ['''START''']
@torch.no_grad()
def snake_case_ ( lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str ):
__lowercase : str = torch.load(lowerCAmelCase_ , map_location="""cpu""" )
__lowercase : Optional[int] = model["""model"""]
__lowercase : Optional[Any] = BlenderbotConfig.from_json_file(lowerCAmelCase_ )
__lowercase : str = BlenderbotForConditionalGeneration(lowerCAmelCase_ )
__lowercase : Tuple = m.model.state_dict().keys()
__lowercase : str = []
__lowercase : int = {}
for k, v in sd.items():
if k in IGNORE_KEYS:
continue
__lowercase : Any = rename_state_dict_key(lowerCAmelCase_ )
if new_k not in valid_keys:
failures.append([k, new_k] )
else:
__lowercase : Optional[Any] = v
if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
rename_layernorm_keys(lowerCAmelCase_ )
m.model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
m.half()
m.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
parser.add_argument(
'''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
)
lowerCamelCase : Union[str, Any] = parser.parse_args()
convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json) | 233 |
from __future__ import annotations
import math
lowerCamelCase : List[Any] = '''2020.9.26'''
lowerCamelCase : str = '''xcodz-dot, cclaus, dhruvmanila'''
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float ):
if not all(isinstance(lowerCAmelCase_ , (float, int) ) for val in locals().values() ):
__lowercase : str = F"Input values must either be float or int: {list(locals().values() )}"
raise TypeError(lowerCAmelCase_ )
__lowercase : List[Any] = ((x * distance) / (z + distance)) * scale
__lowercase : Tuple = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : str , lowerCAmelCase_ : float ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""Axis must be a str""" )
__lowercase : Optional[int] = locals()
del input_variables["axis"]
if not all(isinstance(lowerCAmelCase_ , (float, int) ) for val in input_variables.values() ):
__lowercase : List[str] = (
"""Input values except axis must either be float or int: """
F"{list(input_variables.values() )}"
)
raise TypeError(lowerCAmelCase_ )
__lowercase : Tuple = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
__lowercase : int = x * math.cos(lowerCAmelCase_ ) - y * math.sin(lowerCAmelCase_ )
__lowercase : Tuple = y * math.cos(lowerCAmelCase_ ) + x * math.sin(lowerCAmelCase_ )
__lowercase : Union[str, Any] = z
elif axis == "x":
__lowercase : str = y * math.cos(lowerCAmelCase_ ) - z * math.sin(lowerCAmelCase_ )
__lowercase : Dict = z * math.cos(lowerCAmelCase_ ) + y * math.sin(lowerCAmelCase_ )
__lowercase : List[str] = x
elif axis == "y":
__lowercase : List[str] = x * math.cos(lowerCAmelCase_ ) - z * math.sin(lowerCAmelCase_ )
__lowercase : List[str] = z * math.cos(lowerCAmelCase_ ) + x * math.sin(lowerCAmelCase_ )
__lowercase : List[Any] = y
else:
raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'""" )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''') | 233 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = " " ) -> list:
lowercase : str = []
lowercase : int = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE__ ):
if char == separator:
split_words.append(string[last_index:index] )
lowercase : Dict = index + 1
elif index + 1 == len(SCREAMING_SNAKE_CASE__ ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations_with_dp_array(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowercase : Any = sum(
count_of_possible_combinations_with_dp_array(target - item , SCREAMING_SNAKE_CASE__ )
for item in array )
lowercase : Optional[int] = answer
return answer
lowercase : int = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
lowercase : str = [0] * (target + 1)
lowercase : Tuple = 1
for i in range(1 , target + 1 ):
for j in range(SCREAMING_SNAKE_CASE__ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase : Any = 3
lowercase : Optional[Any] = 5
lowercase : Tuple = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 285 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def lowercase ( _SCREAMING_SNAKE_CASE : Iterable[str] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = iter(_UpperCamelCase )
while True:
_UpperCAmelCase = tuple(itertools.islice(_UpperCamelCase , _UpperCamelCase ) )
if not chunk:
return
yield chunk
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_UpperCAmelCase = ''''''
if len(_UpperCamelCase ) < 2:
return dirty
for i in range(len(_UpperCamelCase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_UpperCamelCase ) & 1:
clean += "X"
return clean
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_UpperCAmelCase = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_UpperCamelCase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_UpperCamelCase )
return table
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = generate_table(_UpperCamelCase )
_UpperCAmelCase = prepare_input(_UpperCamelCase )
_UpperCAmelCase = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_UpperCamelCase , 2 ):
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCamelCase ) , 5 )
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCamelCase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = generate_table(_UpperCamelCase )
_UpperCAmelCase = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_UpperCamelCase , 2 ):
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCamelCase ) , 5 )
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCamelCase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 260 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def lowerCAmelCase__ ( _UpperCamelCase : Optional[int] , _UpperCamelCase : List[Any] , _UpperCamelCase : Dict , _UpperCamelCase : List[str] , _UpperCamelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for attribute in key.split('.' ):
snake_case = getattr(_UpperCamelCase , _UpperCamelCase )
if weight_type is not None:
snake_case = getattr(_UpperCamelCase , _UpperCamelCase ).shape
else:
snake_case = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case = value
elif weight_type == "weight_g":
snake_case = value
elif weight_type == "weight_v":
snake_case = value
elif weight_type == "bias":
snake_case = value
else:
snake_case = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def lowerCAmelCase__ ( _UpperCamelCase : List[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
snake_case = []
snake_case = fairseq_model.state_dict()
snake_case = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
snake_case = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , hf_model.config.feat_extract_norm == 'group' , )
snake_case = True
else:
for key, mapped_key in MAPPING.items():
snake_case = 'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or (key.split('w2v_model.' )[-1] == name.split('.' )[0] and not is_finetuned):
snake_case = True
if "*" in mapped_key:
snake_case = name.split(_UpperCamelCase )[0].split('.' )[-2]
snake_case = mapped_key.replace('*' , _UpperCamelCase )
if "weight_g" in name:
snake_case = 'weight_g'
elif "weight_v" in name:
snake_case = 'weight_v'
elif "weight" in name:
snake_case = 'weight'
elif "bias" in name:
snake_case = 'bias'
else:
snake_case = None
set_recursively(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def lowerCAmelCase__ ( _UpperCamelCase : Dict , _UpperCamelCase : Dict , _UpperCamelCase : Tuple , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any ) -> Any:
"""simple docstring"""
snake_case = full_name.split('conv_layers.' )[-1]
snake_case = name.split('.' )
snake_case = int(items[0] )
snake_case = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def lowerCAmelCase__ ( _UpperCamelCase : str , _UpperCamelCase : Any , _UpperCamelCase : List[Any]=None , _UpperCamelCase : Any=None , _UpperCamelCase : Union[str, Any]=True ) -> List[Any]:
"""simple docstring"""
if config_path is not None:
snake_case = HubertConfig.from_pretrained(_UpperCamelCase )
else:
snake_case = HubertConfig()
if is_finetuned:
if dict_path:
snake_case = Dictionary.load(_UpperCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case = target_dict.pad_index
snake_case = target_dict.bos_index
snake_case = target_dict.eos_index
snake_case = len(target_dict.symbols )
snake_case = os.path.join(_UpperCamelCase , 'vocab.json' )
if not os.path.isdir(_UpperCamelCase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCamelCase ) )
return
os.makedirs(_UpperCamelCase , exist_ok=_UpperCamelCase )
with open(_UpperCamelCase , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices , _UpperCamelCase )
snake_case = WavaVecaCTCTokenizer(
_UpperCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=_UpperCamelCase , )
snake_case = True if config.feat_extract_norm == 'layer' else False
snake_case = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=_UpperCamelCase , return_attention_mask=_UpperCamelCase , )
snake_case = WavaVecaProcessor(feature_extractor=_UpperCamelCase , tokenizer=_UpperCamelCase )
processor.save_pretrained(_UpperCamelCase )
snake_case = HubertForCTC(_UpperCamelCase )
else:
snake_case = HubertModel(_UpperCamelCase )
if is_finetuned:
snake_case ,snake_case ,snake_case = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
snake_case ,snake_case ,snake_case = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
snake_case = model[0].eval()
recursively_load_weights(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 150 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__A : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 361 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__A : List[str] = logging.get_logger(__name__)
def UpperCamelCase_ ( A__ : Union[str, Any] , A__ : Tuple=False ):
'''simple docstring'''
lowerCAmelCase_ : Tuple = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """vit.embeddings.cls_token"""),
("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowerCAmelCase_ : List[str] = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def UpperCamelCase_ ( A__ : Any , A__ : Any , A__ : Tuple=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowerCAmelCase_ : Optional[Any] = """"""
else:
lowerCAmelCase_ : Optional[Any] = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCAmelCase_ : List[Any] = state_dict.pop(f'blocks.{i}.attn.qkv.weight' )
lowerCAmelCase_ : Union[str, Any] = state_dict.pop(f'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ : Dict = in_proj_weight[
: config.hidden_size, :
]
lowerCAmelCase_ : List[Any] = in_proj_bias[: config.hidden_size]
lowerCAmelCase_ : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase_ : List[str] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCAmelCase_ : Any = in_proj_weight[
-config.hidden_size :, :
]
lowerCAmelCase_ : Union[str, Any] = in_proj_bias[-config.hidden_size :]
def UpperCamelCase_ ( A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : Dict = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def UpperCamelCase_ ( A__ : List[Any] , A__ : Optional[Any] , A__ : Dict ):
'''simple docstring'''
lowerCAmelCase_ : Tuple = dct.pop(A__ )
lowerCAmelCase_ : Tuple = val
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : str = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ : Optional[int] = Image.open(requests.get(A__ , stream=A__ ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( A__ : Union[str, Any] , A__ : List[Any] ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = ViTConfig()
lowerCAmelCase_ : Any = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
lowerCAmelCase_ : int = True
lowerCAmelCase_ : Tuple = int(vit_name[-12:-10] )
lowerCAmelCase_ : Optional[int] = int(vit_name[-9:-6] )
else:
lowerCAmelCase_ : Optional[int] = 10_00
lowerCAmelCase_ : Tuple = """huggingface/label-files"""
lowerCAmelCase_ : Any = """imagenet-1k-id2label.json"""
lowerCAmelCase_ : Dict = json.load(open(hf_hub_download(A__ , A__ , repo_type="""dataset""" ) , """r""" ) )
lowerCAmelCase_ : Union[str, Any] = {int(A__ ): v for k, v in idalabel.items()}
lowerCAmelCase_ : Union[str, Any] = idalabel
lowerCAmelCase_ : Union[str, Any] = {v: k for k, v in idalabel.items()}
lowerCAmelCase_ : Optional[int] = int(vit_name[-6:-4] )
lowerCAmelCase_ : Dict = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("""tiny""" ):
lowerCAmelCase_ : int = 1_92
lowerCAmelCase_ : List[str] = 7_68
lowerCAmelCase_ : List[str] = 12
lowerCAmelCase_ : int = 3
elif vit_name[9:].startswith("""small""" ):
lowerCAmelCase_ : Optional[Any] = 3_84
lowerCAmelCase_ : Optional[int] = 15_36
lowerCAmelCase_ : Dict = 12
lowerCAmelCase_ : str = 6
else:
pass
else:
if vit_name[4:].startswith("""small""" ):
lowerCAmelCase_ : Tuple = 7_68
lowerCAmelCase_ : Any = 23_04
lowerCAmelCase_ : List[str] = 8
lowerCAmelCase_ : List[str] = 8
elif vit_name[4:].startswith("""base""" ):
pass
elif vit_name[4:].startswith("""large""" ):
lowerCAmelCase_ : Dict = 10_24
lowerCAmelCase_ : List[Any] = 40_96
lowerCAmelCase_ : Any = 24
lowerCAmelCase_ : List[str] = 16
elif vit_name[4:].startswith("""huge""" ):
lowerCAmelCase_ : Optional[int] = 12_80
lowerCAmelCase_ : Dict = 51_20
lowerCAmelCase_ : Union[str, Any] = 32
lowerCAmelCase_ : Optional[int] = 16
# load original model from timm
lowerCAmelCase_ : Union[str, Any] = timm.create_model(A__ , pretrained=A__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowerCAmelCase_ : int = timm_model.state_dict()
if base_model:
remove_classification_head_(A__ )
lowerCAmelCase_ : str = create_rename_keys(A__ , A__ )
for src, dest in rename_keys:
rename_key(A__ , A__ , A__ )
read_in_q_k_v(A__ , A__ , A__ )
# load HuggingFace model
if vit_name[-5:] == "in21k":
lowerCAmelCase_ : int = ViTModel(A__ ).eval()
else:
lowerCAmelCase_ : Optional[int] = ViTForImageClassification(A__ ).eval()
model.load_state_dict(A__ )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
lowerCAmelCase_ : Any = DeiTImageProcessor(size=config.image_size )
else:
lowerCAmelCase_ : Any = ViTImageProcessor(size=config.image_size )
lowerCAmelCase_ : Tuple = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowerCAmelCase_ : int = encoding["""pixel_values"""]
lowerCAmelCase_ : int = model(A__ )
if base_model:
lowerCAmelCase_ : Union[str, Any] = timm_model.forward_features(A__ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(A__ , outputs.pooler_output , atol=1E-3 )
else:
lowerCAmelCase_ : Union[str, Any] = timm_model(A__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(A__ , outputs.logits , atol=1E-3 )
Path(A__ ).mkdir(exist_ok=A__ )
print(f'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(A__ )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(A__ )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_patch16_224",
type=str,
help="Name of the ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
__A : Union[str, Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 89 | 0 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def lowerCAmelCase_ ( __A ) -> Any:
'''simple docstring'''
return EnvironmentCommand()
def lowerCAmelCase_ ( __A ) -> Optional[int]:
'''simple docstring'''
return EnvironmentCommand(args.accelerate_config_file )
class A ( UpperCAmelCase_ ):
@staticmethod
def lowercase_ (__UpperCAmelCase : ArgumentParser ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = parser.add_parser("env" )
download_parser.set_defaults(func=__UpperCAmelCase )
download_parser.add_argument(
"--accelerate-config_file" , default=__UpperCAmelCase , help="The accelerate config file to use for the default values in the launching script." , )
download_parser.set_defaults(func=__UpperCAmelCase )
def __init__(self : Optional[int] , __UpperCAmelCase : str , *__UpperCAmelCase : Tuple ) -> None:
"""simple docstring"""
UpperCAmelCase__ = accelerate_config_file
def lowercase_ (self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = "not installed"
if is_safetensors_available():
import safetensors
UpperCAmelCase__ = safetensors.__version__
elif importlib.util.find_spec("safetensors" ) is not None:
import safetensors
UpperCAmelCase__ = f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""
UpperCAmelCase__ = "not installed"
UpperCAmelCase__ = UpperCAmelCase__ = "not found"
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
UpperCAmelCase__ = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(__UpperCAmelCase ):
UpperCAmelCase__ = load_config_from_file(self._accelerate_config_file ).to_dict()
UpperCAmelCase__ = (
"\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else f"""\t{accelerate_config}"""
)
UpperCAmelCase__ = "not installed"
UpperCAmelCase__ = "NA"
if is_torch_available():
import torch
UpperCAmelCase__ = torch.__version__
UpperCAmelCase__ = torch.cuda.is_available()
UpperCAmelCase__ = "not installed"
UpperCAmelCase__ = "NA"
if is_tf_available():
import tensorflow as tf
UpperCAmelCase__ = tf.__version__
try:
# deprecated in v2.1
UpperCAmelCase__ = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
UpperCAmelCase__ = bool(tf.config.list_physical_devices("GPU" ) )
UpperCAmelCase__ = "not installed"
UpperCAmelCase__ = "not installed"
UpperCAmelCase__ = "not installed"
UpperCAmelCase__ = "NA"
if is_flax_available():
import flax
import jax
import jaxlib
UpperCAmelCase__ = flax.__version__
UpperCAmelCase__ = jax.__version__
UpperCAmelCase__ = jaxlib.__version__
UpperCAmelCase__ = jax.lib.xla_bridge.get_backend().platform
UpperCAmelCase__ = {
"`transformers` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Huggingface_hub version": huggingface_hub.__version__,
"Safetensors version": f"""{safetensors_version}""",
"Accelerate version": f"""{accelerate_version}""",
"Accelerate config": f"""{accelerate_config_str}""",
"PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
"Tensorflow version (GPU?)": f"""{tf_version} ({tf_cuda_available})""",
"Flax version (CPU?/GPU?/TPU?)": f"""{flax_version} ({jax_backend})""",
"Jax version": f"""{jax_version}""",
"JaxLib version": f"""{jaxlib_version}""",
"Using GPU in script?": "<fill in>",
"Using distributed or parallel set-up in script?": "<fill in>",
}
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
print(self.format_dict(__UpperCAmelCase ) )
return info
@staticmethod
def lowercase_ (__UpperCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"
| 65 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE( A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = ConsistencyModelPipeline
SCREAMING_SNAKE_CASE_ : Any = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
SCREAMING_SNAKE_CASE_ : Dict = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
SCREAMING_SNAKE_CASE_ : Optional[Any] = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
@property
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Tuple = UNetaDModel.from_pretrained(
'''diffusers/consistency-models-test''' ,subfolder='''test_unet''' ,)
return unet
@property
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel.from_pretrained(
'''diffusers/consistency-models-test''' ,subfolder='''test_unet_class_cond''' ,)
return unet
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__=False ) -> Union[str, Any]:
"""simple docstring"""
if class_cond:
__SCREAMING_SNAKE_CASE :str = self.dummy_cond_unet
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = self.dummy_uncond_unet
# Default to CM multistep sampler
__SCREAMING_SNAKE_CASE :List[str] = CMStochasticIterativeScheduler(
num_train_timesteps=40 ,sigma_min=0.0_0_2 ,sigma_max=8_0.0 ,)
__SCREAMING_SNAKE_CASE :List[str] = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__=0 ) -> Dict:
"""simple docstring"""
if str(SCREAMING_SNAKE_CASE__ ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE :Tuple = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Dict = {
'''batch_size''': 1,
'''num_inference_steps''': None,
'''timesteps''': [22, 0],
'''generator''': generator,
'''output_type''': '''np''',
}
return inputs
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE :List[str] = self.get_dummy_components()
__SCREAMING_SNAKE_CASE :Optional[Any] = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :str = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE :List[str] = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :Any = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE :List[Any] = self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :str = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Optional[int] = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Optional[int] = 0
__SCREAMING_SNAKE_CASE :Optional[int] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE :Dict = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :List[Any] = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE :Tuple = self.get_dummy_components()
__SCREAMING_SNAKE_CASE :Any = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :List[str] = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Dict = 1
__SCREAMING_SNAKE_CASE :List[str] = None
__SCREAMING_SNAKE_CASE :List[str] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE :List[str] = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :int = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :str = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE :Any = self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :str = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Dict = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = 1
__SCREAMING_SNAKE_CASE :Optional[Any] = None
__SCREAMING_SNAKE_CASE :List[Any] = 0
__SCREAMING_SNAKE_CASE :Any = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE :int = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :Optional[Any] = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__=0 ,SCREAMING_SNAKE_CASE__=False ,SCREAMING_SNAKE_CASE__="cpu" ,SCREAMING_SNAKE_CASE__=torch.floataa ,SCREAMING_SNAKE_CASE__=(1, 3, 64, 64) ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Tuple = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Tuple = {
'''num_inference_steps''': None,
'''timesteps''': [22, 0],
'''class_labels''': 0,
'''generator''': generator,
'''output_type''': '''np''',
}
if get_fixed_latents:
__SCREAMING_SNAKE_CASE :int = self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__ ,device=SCREAMING_SNAKE_CASE__ ,dtype=SCREAMING_SNAKE_CASE__ ,shape=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = latents
return inputs
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__=0 ,SCREAMING_SNAKE_CASE__="cpu" ,SCREAMING_SNAKE_CASE__=torch.floataa ,SCREAMING_SNAKE_CASE__=(1, 3, 64, 64) ) -> int:
"""simple docstring"""
if type(SCREAMING_SNAKE_CASE__ ) == str:
__SCREAMING_SNAKE_CASE :int = torch.device(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Optional[int] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = randn_tensor(SCREAMING_SNAKE_CASE__ ,generator=SCREAMING_SNAKE_CASE__ ,device=SCREAMING_SNAKE_CASE__ ,dtype=SCREAMING_SNAKE_CASE__ )
return latents
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = UNetaDModel.from_pretrained('''diffusers/consistency_models''' ,subfolder='''diffusers_cd_imagenet64_l2''' )
__SCREAMING_SNAKE_CASE :List[str] = CMStochasticIterativeScheduler(
num_train_timesteps=40 ,sigma_min=0.0_0_2 ,sigma_max=8_0.0 ,)
__SCREAMING_SNAKE_CASE :Dict = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ ,scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = self.get_inputs()
__SCREAMING_SNAKE_CASE :List[str] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
__SCREAMING_SNAKE_CASE :Union[str, Any] = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :Dict = np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = UNetaDModel.from_pretrained('''diffusers/consistency_models''' ,subfolder='''diffusers_cd_imagenet64_l2''' )
__SCREAMING_SNAKE_CASE :List[Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 ,sigma_min=0.0_0_2 ,sigma_max=8_0.0 ,)
__SCREAMING_SNAKE_CASE :Any = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ ,scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Optional[int] = self.get_inputs()
__SCREAMING_SNAKE_CASE :int = 1
__SCREAMING_SNAKE_CASE :int = None
__SCREAMING_SNAKE_CASE :Union[str, Any] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
__SCREAMING_SNAKE_CASE :str = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :List[str] = np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
@require_torch_a
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = UNetaDModel.from_pretrained('''diffusers/consistency_models''' ,subfolder='''diffusers_cd_imagenet64_l2''' )
__SCREAMING_SNAKE_CASE :Any = CMStochasticIterativeScheduler(
num_train_timesteps=40 ,sigma_min=0.0_0_2 ,sigma_max=8_0.0 ,)
__SCREAMING_SNAKE_CASE :Any = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ ,scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ ,torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :str = self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__ ,device=SCREAMING_SNAKE_CASE__ )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__ ,enable_math=SCREAMING_SNAKE_CASE__ ,enable_mem_efficient=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE :Optional[Any] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
__SCREAMING_SNAKE_CASE :List[str] = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :List[Any] = np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@require_torch_a
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :str = UNetaDModel.from_pretrained('''diffusers/consistency_models''' ,subfolder='''diffusers_cd_imagenet64_l2''' )
__SCREAMING_SNAKE_CASE :Dict = CMStochasticIterativeScheduler(
num_train_timesteps=40 ,sigma_min=0.0_0_2 ,sigma_max=8_0.0 ,)
__SCREAMING_SNAKE_CASE :int = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ ,scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ ,torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Dict = self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__ ,device=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = 1
__SCREAMING_SNAKE_CASE :int = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__ ,enable_math=SCREAMING_SNAKE_CASE__ ,enable_mem_efficient=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE :str = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
__SCREAMING_SNAKE_CASE :str = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE :Optional[int] = np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 | 191 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
"""feature_extraction_wav2vec2""": ["""Wav2Vec2FeatureExtractor"""],
"""processing_wav2vec2""": ["""Wav2Vec2Processor"""],
"""tokenization_wav2vec2""": ["""Wav2Vec2CTCTokenizer""", """Wav2Vec2Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Wav2Vec2ForAudioFrameClassification""",
"""Wav2Vec2ForCTC""",
"""Wav2Vec2ForMaskedLM""",
"""Wav2Vec2ForPreTraining""",
"""Wav2Vec2ForSequenceClassification""",
"""Wav2Vec2ForXVector""",
"""Wav2Vec2Model""",
"""Wav2Vec2PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFWav2Vec2ForCTC""",
"""TFWav2Vec2Model""",
"""TFWav2Vec2PreTrainedModel""",
"""TFWav2Vec2ForSequenceClassification""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""FlaxWav2Vec2ForCTC""",
"""FlaxWav2Vec2ForPreTraining""",
"""FlaxWav2Vec2Model""",
"""FlaxWav2Vec2PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 166 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None ):
"""simple docstring"""
super().__init__()
UpperCAmelCase__ : Tuple = pad_token_id
UpperCAmelCase__ : Any = max_length
UpperCAmelCase__ : str = vocab
UpperCAmelCase__ : Union[str, Any] = merges
UpperCAmelCase__ : Tuple = BytePairTokenizer(_lowerCamelCase , _lowerCamelCase , sequence_length=_lowerCamelCase )
@classmethod
def _a (cls , _lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = [""" """.join(_lowerCamelCase ) for m in tokenizer.bpe_ranks.keys()]
UpperCAmelCase__ : Tuple = tokenizer.get_vocab()
return cls(_lowerCamelCase , _lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
@classmethod
def _a (cls , _lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = GPTaTokenizer.from_pretrained(_lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
return cls.from_tokenizer(_lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
@classmethod
def _a (cls , _lowerCamelCase ):
"""simple docstring"""
return cls(**_lowerCamelCase )
def _a (self ):
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def _a (self , _lowerCamelCase , _lowerCamelCase = None ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = self.tf_tokenizer(_lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = tf.ones_like(_lowerCamelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
UpperCAmelCase__ : Optional[Any] = max_length if max_length is not None else self.max_length
if max_length is not None:
UpperCAmelCase__ , UpperCAmelCase__ : str = pad_model_inputs(
_lowerCamelCase , max_seq_length=_lowerCamelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 166 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class lowerCAmelCase__ ( a):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = "xlm"
__SCREAMING_SNAKE_CASE = {
"hidden_size": "emb_dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
"n_words": "vocab_size", # For backward compatibility
}
def __init__( self , __lowerCamelCase=3_0_1_4_5 , __lowerCamelCase=2_0_4_8 , __lowerCamelCase=1_2 , __lowerCamelCase=1_6 , __lowerCamelCase=0.1 , __lowerCamelCase=0.1 , __lowerCamelCase=True , __lowerCamelCase=False , __lowerCamelCase=False , __lowerCamelCase=False , __lowerCamelCase=1 , __lowerCamelCase=True , __lowerCamelCase=5_1_2 , __lowerCamelCase=2_0_4_8**-0.5 , __lowerCamelCase=1e-12 , __lowerCamelCase=0.0_2 , __lowerCamelCase=0 , __lowerCamelCase=1 , __lowerCamelCase=2 , __lowerCamelCase=3 , __lowerCamelCase=5 , __lowerCamelCase=True , __lowerCamelCase="first" , __lowerCamelCase=True , __lowerCamelCase=None , __lowerCamelCase=True , __lowerCamelCase=0.1 , __lowerCamelCase=5 , __lowerCamelCase=5 , __lowerCamelCase=0 , __lowerCamelCase=0 , __lowerCamelCase=2 , __lowerCamelCase=0 , **__lowerCamelCase , ) -> Optional[int]:
_A : Optional[Any] = vocab_size
_A : Optional[Any] = emb_dim
_A : Union[str, Any] = n_layers
_A : int = n_heads
_A : Union[str, Any] = dropout
_A : List[str] = attention_dropout
_A : Tuple = gelu_activation
_A : Dict = sinusoidal_embeddings
_A : int = causal
_A : int = asm
_A : int = n_langs
_A : int = use_lang_emb
_A : Union[str, Any] = layer_norm_eps
_A : Union[str, Any] = bos_index
_A : Union[str, Any] = eos_index
_A : Tuple = pad_index
_A : Any = unk_index
_A : Dict = mask_index
_A : str = is_encoder
_A : Union[str, Any] = max_position_embeddings
_A : Optional[int] = embed_init_std
_A : List[str] = init_std
_A : Optional[int] = summary_type
_A : Optional[Any] = summary_use_proj
_A : Dict = summary_activation
_A : Optional[Any] = summary_proj_to_labels
_A : Any = summary_first_dropout
_A : str = start_n_top
_A : Any = end_n_top
_A : int = mask_token_id
_A : List[Any] = lang_id
if "n_words" in kwargs:
_A : Any = kwargs["n_words"]
super().__init__(pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , **__lowerCamelCase)
class lowerCAmelCase__ ( a):
'''simple docstring'''
@property
def _lowerCamelCase ( self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A : List[str] = {0: "batch", 1: "choice", 2: "sequence"}
else:
_A : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
])
| 11 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
'SpeechT5Config',
'SpeechT5HifiGanConfig',
],
'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
'processing_speecht5': ['SpeechT5Processor'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['SpeechT5Tokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'SpeechT5ForSpeechToText',
'SpeechT5ForSpeechToSpeech',
'SpeechT5ForTextToSpeech',
'SpeechT5Model',
'SpeechT5PreTrainedModel',
'SpeechT5HifiGan',
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 11 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCAmelCase_ ( _snake_case : List[str] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Dict = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Optional[Any] = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
__magic_name__ : Optional[Any] = s_dict.pop(_snake_case )
elif "subsample" in key:
__magic_name__ : str = s_dict.pop(_snake_case )
def lowerCAmelCase_ ( _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Dict = emb.weight.shape
__magic_name__ : List[Any] = nn.Linear(_snake_case , _snake_case , bias=_snake_case )
__magic_name__ : str = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Dict ) -> int:
'''simple docstring'''
__magic_name__ : Tuple = torch.load(_snake_case , map_location="cpu" )
__magic_name__ : str = mam_aaa["args"]
__magic_name__ : Union[str, Any] = mam_aaa["model"]
__magic_name__ : List[str] = state_dict["decoder.output_projection.weight"]
remove_ignore_keys_(_snake_case )
rename_keys(_snake_case )
__magic_name__ : Optional[Any] = state_dict["decoder.embed_tokens.weight"].shape[0]
__magic_name__ : int = args.share_decoder_input_output_embed
__magic_name__ : List[str] = [int(_snake_case ) for i in args.conv_kernel_sizes.split("," )]
__magic_name__ : Dict = SpeechaTextConfig(
vocab_size=_snake_case , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , num_conv_layers=len(_snake_case ) , conv_channels=args.conv_channels , conv_kernel_sizes=_snake_case , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=_snake_case , num_beams=5 , max_length=200 , use_cache=_snake_case , decoder_start_token_id=2 , early_stopping=_snake_case , )
__magic_name__ : str = SpeechaTextForConditionalGeneration(_snake_case )
__magic_name__ : Any = model.model.load_state_dict(_snake_case , strict=_snake_case )
if len(_snake_case ) > 0 and not set(_snake_case ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"
F''' but all the following weights are missing {missing}''' )
if tie_embeds:
__magic_name__ : str = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
__magic_name__ : Optional[Any] = lm_head_weights
model.save_pretrained(_snake_case )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
snake_case : Union[str, Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 358 |
from __future__ import annotations
snake_case : Optional[int] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class _snake_case :
def __init__( self , _a , _a ):
__magic_name__ : Any = graph
# mapping node to its parent in resulting breadth first tree
__magic_name__ : dict[str, str | None] = {}
__magic_name__ : List[str] = source_vertex
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = {self.source_vertex}
__magic_name__ : Optional[int] = None
__magic_name__ : int = [self.source_vertex] # first in first out queue
while queue:
__magic_name__ : Optional[Any] = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(_a )
__magic_name__ : Dict = vertex
queue.append(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
if target_vertex == self.source_vertex:
return self.source_vertex
__magic_name__ : str = self.parent.get(_a )
if target_vertex_parent is None:
__magic_name__ : Union[str, Any] = (
f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
)
raise ValueError(_a )
return self.shortest_path(_a ) + f'''->{target_vertex}'''
if __name__ == "__main__":
snake_case : int = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| 41 | 0 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowerCAmelCase_ = logging.getLogger(__name__)
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
if os.path.exists(_UpperCamelCase ):
if os.path.exists(os.path.join(_UpperCamelCase , '''config.json''' ) ) and os.path.isfile(
os.path.join(_UpperCamelCase , '''config.json''' ) ):
os.remove(os.path.join(_UpperCamelCase , '''config.json''' ) )
if os.path.exists(os.path.join(_UpperCamelCase , '''pytorch_model.bin''' ) ) and os.path.isfile(
os.path.join(_UpperCamelCase , '''pytorch_model.bin''' ) ):
os.remove(os.path.join(_UpperCamelCase , '''pytorch_model.bin''' ) )
else:
os.makedirs(_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=False ) -> str:
"""simple docstring"""
snake_case_ : List[Any] = 2
if unlogit:
snake_case_ : List[str] = torch.pow(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Optional[Any] = p * torch.log(_UpperCamelCase )
snake_case_ : List[Any] = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(_UpperCamelCase ) ) ) )
for row in range(len(_UpperCamelCase ) ):
if tensor.dtype != torch.long:
logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:d}''' for x in tensor[row].cpu().data ) )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=None , _UpperCamelCase=False ) -> Tuple:
"""simple docstring"""
snake_case_ , snake_case_ : Optional[int] = model.config.num_hidden_layers, model.config.num_attention_heads
snake_case_ : Optional[int] = torch.zeros(_UpperCamelCase , _UpperCamelCase ).to(args.device )
snake_case_ : Tuple = torch.zeros(_UpperCamelCase , _UpperCamelCase ).to(args.device )
if head_mask is None:
snake_case_ : Optional[Any] = torch.ones(_UpperCamelCase , _UpperCamelCase ).to(args.device )
head_mask.requires_grad_(requires_grad=_UpperCamelCase )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
snake_case_ : Optional[int] = None
snake_case_ : Dict = 0.0
snake_case_ : Tuple = 0.0
for step, inputs in enumerate(tqdm(_UpperCamelCase , desc='''Iteration''' , disable=args.local_rank not in [-1, 0] ) ):
snake_case_ : List[str] = tuple(t.to(args.device ) for t in inputs )
((snake_case_) , ) : Optional[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
snake_case_ : List[str] = model(_UpperCamelCase , labels=_UpperCamelCase , head_mask=_UpperCamelCase )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
snake_case_ , snake_case_ , snake_case_ : str = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(_UpperCamelCase ):
snake_case_ : List[Any] = entropy(attn.detach() , _UpperCamelCase )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(_UpperCamelCase ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
snake_case_ : Optional[int] = 2
snake_case_ : List[Any] = torch.pow(torch.pow(_UpperCamelCase , _UpperCamelCase ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20
if not args.dont_normalize_global_importance:
snake_case_ : Dict = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''' )
print_ad_tensor(_UpperCamelCase )
if compute_importance:
logger.info('''Head importance scores''' )
print_ad_tensor(_UpperCamelCase )
logger.info('''Head ranked by importance scores''' )
snake_case_ : List[str] = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
snake_case_ : Union[str, Any] = torch.arange(
head_importance.numel() , device=args.device )
snake_case_ : Tuple = head_ranks.view_as(_UpperCamelCase )
print_ad_tensor(_UpperCamelCase )
return attn_entropy, head_importance, total_loss
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ : Dict = compute_heads_importance(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase )
snake_case_ : List[str] = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , _UpperCamelCase , original_score * args.masking_threshold )
snake_case_ : Any = torch.ones_like(_UpperCamelCase )
snake_case_ : str = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
snake_case_ : Any = original_score
while current_score >= original_score * args.masking_threshold:
snake_case_ : List[str] = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
snake_case_ : Union[str, Any] = float('''Inf''' )
snake_case_ : Optional[int] = head_importance.view(-1 ).sort()[1]
if len(_UpperCamelCase ) <= num_to_mask:
print('''BREAK BY num_to_mask''' )
break
# mask heads
snake_case_ : Optional[Any] = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist() ) )
snake_case_ : str = new_head_mask.view(-1 )
snake_case_ : Dict = 0.0
snake_case_ : str = new_head_mask.view_as(_UpperCamelCase )
snake_case_ : Any = new_head_mask.clone().detach()
print_ad_tensor(_UpperCamelCase )
# Compute metric and head importance again
snake_case_ , snake_case_ , snake_case_ : List[Any] = compute_heads_importance(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase , head_mask=_UpperCamelCase )
snake_case_ : Dict = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , _UpperCamelCase , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''' )
print_ad_tensor(_UpperCamelCase )
np.save(os.path.join(args.output_dir , '''head_mask.npy''' ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Any = datetime.now()
snake_case_ , snake_case_ , snake_case_ : str = compute_heads_importance(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase , compute_importance=_UpperCamelCase , head_mask=_UpperCamelCase )
snake_case_ : Optional[Any] = 1 / loss
snake_case_ : int = datetime.now() - before_time
snake_case_ : List[Any] = sum(p.numel() for p in model.parameters() )
snake_case_ : List[str] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_UpperCamelCase ) )
}
for k, v in heads_to_prune.items():
if isinstance(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = [
v,
]
assert sum(len(_UpperCamelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(_UpperCamelCase )
snake_case_ : Dict = sum(p.numel() for p in model.parameters() )
snake_case_ : str = datetime.now()
snake_case_ , snake_case_ , snake_case_ : Tuple = compute_heads_importance(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , compute_entropy=_UpperCamelCase , compute_importance=_UpperCamelCase , head_mask=_UpperCamelCase , actually_pruned=_UpperCamelCase , )
snake_case_ : List[Any] = 1 / loss
snake_case_ : Dict = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , _UpperCamelCase , _UpperCamelCase , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , _UpperCamelCase , _UpperCamelCase )
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100 )
save_model(_UpperCamelCase , args.output_dir )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=_UpperCamelCase , type=_UpperCamelCase , required=_UpperCamelCase , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=_UpperCamelCase , type=_UpperCamelCase , required=_UpperCamelCase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=_UpperCamelCase , type=_UpperCamelCase , required=_UpperCamelCase , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=_UpperCamelCase , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=_UpperCamelCase , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=_UpperCamelCase , type=_UpperCamelCase , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=_UpperCamelCase , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''' )
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''' )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''' )
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''' )
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=_UpperCamelCase , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=_UpperCamelCase , help='''Amount to heads to masking at each masking step.''' )
parser.add_argument('''--metric_name''' , default='''acc''' , type=_UpperCamelCase , help='''Metric to use for head masking.''' )
parser.add_argument(
'''--max_seq_length''' , default=128 , type=_UpperCamelCase , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=_UpperCamelCase , help='''Batch size.''' )
parser.add_argument('''--seed''' , type=_UpperCamelCase , default=42 )
parser.add_argument('''--local_rank''' , type=_UpperCamelCase , default=-1 , help='''local_rank for distributed training on gpus''' )
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''' )
parser.add_argument('''--server_ip''' , type=_UpperCamelCase , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=_UpperCamelCase , default='''''' , help='''Can be used for distant debugging.''' )
snake_case_ : Optional[int] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_UpperCamelCase )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
snake_case_ : Optional[int] = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
snake_case_ : List[str] = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
snake_case_ : Union[str, Any] = torch.device('''cuda''' , args.local_rank )
snake_case_ : Tuple = 1
torch.distributed.init_process_group(backend='''nccl''' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
snake_case_ : Optional[int] = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
snake_case_ : int = nn.parallel.DistributedDataParallel(
_UpperCamelCase , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=_UpperCamelCase )
elif args.n_gpu > 1:
snake_case_ : str = nn.DataParallel(_UpperCamelCase )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=_UpperCamelCase )
torch.save(_UpperCamelCase , os.path.join(args.output_dir , '''run_args.bin''' ) )
logger.info('''Training/evaluation parameters %s''' , _UpperCamelCase )
# Prepare dataset
snake_case_ : Any = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
snake_case_ : str = (torch.from_numpy(_UpperCamelCase ),)
snake_case_ : Union[str, Any] = TensorDataset(*_UpperCamelCase )
snake_case_ : Optional[Any] = RandomSampler(_UpperCamelCase )
snake_case_ : Any = DataLoader(_UpperCamelCase , sampler=_UpperCamelCase , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
snake_case_ : Any = mask_heads(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
prune_heads(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
main()
| 279 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if dist[i][j] != float('''inf''' ):
print(int(dist[i][j] ) , end='''\t''' )
else:
print('''INF''' , end='''\t''' )
print()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : int = [[float('''inf''' ) for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
snake_case_ : Dict = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(_UpperCamelCase ):
# looping through rows of graph array
for i in range(_UpperCamelCase ):
# looping through columns of graph array
for j in range(_UpperCamelCase ):
if (
dist[i][k] != float('''inf''' )
and dist[k][j] != float('''inf''' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
snake_case_ : List[Any] = dist[i][k] + dist[k][j]
_print_dist(_UpperCamelCase , _UpperCamelCase )
return dist, v
if __name__ == "__main__":
lowerCAmelCase_ = int(input('''Enter number of vertices: '''))
lowerCAmelCase_ = int(input('''Enter number of edges: '''))
lowerCAmelCase_ = [[float('''inf''') for i in range(v)] for j in range(v)]
for i in range(v):
lowerCAmelCase_ = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print('''\nEdge ''', i + 1)
lowerCAmelCase_ = int(input('''Enter source:'''))
lowerCAmelCase_ = int(input('''Enter destination:'''))
lowerCAmelCase_ = float(input('''Enter weight:'''))
lowerCAmelCase_ = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 279 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class _lowerCamelCase ( _snake_case ):
_lowerCamelCase :Any = "table-transformer"
_lowerCamelCase :List[str] = ["past_key_values"]
_lowerCamelCase :Any = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : List[Any] , UpperCamelCase : Optional[int]=True , UpperCamelCase : List[str]=None , UpperCamelCase : Dict=3 , UpperCamelCase : Dict=1_00 , UpperCamelCase : Optional[int]=6 , UpperCamelCase : List[str]=20_48 , UpperCamelCase : Optional[Any]=8 , UpperCamelCase : Any=6 , UpperCamelCase : Dict=20_48 , UpperCamelCase : List[Any]=8 , UpperCamelCase : int=0.0 , UpperCamelCase : List[Any]=0.0 , UpperCamelCase : str=True , UpperCamelCase : Tuple="relu" , UpperCamelCase : Optional[Any]=2_56 , UpperCamelCase : Any=0.1 , UpperCamelCase : Optional[int]=0.0 , UpperCamelCase : Union[str, Any]=0.0 , UpperCamelCase : Dict=0.02 , UpperCamelCase : Dict=1.0 , UpperCamelCase : Optional[int]=False , UpperCamelCase : Optional[int]="sine" , UpperCamelCase : int="resnet50" , UpperCamelCase : Optional[int]=True , UpperCamelCase : Optional[int]=False , UpperCamelCase : Optional[int]=1 , UpperCamelCase : Dict=5 , UpperCamelCase : Dict=2 , UpperCamelCase : List[str]=1 , UpperCamelCase : Union[str, Any]=1 , UpperCamelCase : Optional[Any]=5 , UpperCamelCase : Tuple=2 , UpperCamelCase : List[str]=0.1 , **UpperCamelCase : Union[str, Any] , ) -> str:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
lowerCAmelCase__ : Tuple = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCAmelCase__ : str = backbone_config.get("""model_type""" )
lowerCAmelCase__ : str = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase__ : str = config_class.from_dict(UpperCamelCase__ )
# set timm attributes to None
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Any = None, None, None
lowerCAmelCase__ : str = use_timm_backbone
lowerCAmelCase__ : Dict = backbone_config
lowerCAmelCase__ : Union[str, Any] = num_channels
lowerCAmelCase__ : int = num_queries
lowerCAmelCase__ : str = d_model
lowerCAmelCase__ : Any = encoder_ffn_dim
lowerCAmelCase__ : Any = encoder_layers
lowerCAmelCase__ : Union[str, Any] = encoder_attention_heads
lowerCAmelCase__ : List[str] = decoder_ffn_dim
lowerCAmelCase__ : int = decoder_layers
lowerCAmelCase__ : int = decoder_attention_heads
lowerCAmelCase__ : int = dropout
lowerCAmelCase__ : Any = attention_dropout
lowerCAmelCase__ : Union[str, Any] = activation_dropout
lowerCAmelCase__ : Union[str, Any] = activation_function
lowerCAmelCase__ : List[str] = init_std
lowerCAmelCase__ : List[str] = init_xavier_std
lowerCAmelCase__ : Optional[Any] = encoder_layerdrop
lowerCAmelCase__ : int = decoder_layerdrop
lowerCAmelCase__ : Tuple = encoder_layers
lowerCAmelCase__ : str = auxiliary_loss
lowerCAmelCase__ : Optional[int] = position_embedding_type
lowerCAmelCase__ : str = backbone
lowerCAmelCase__ : int = use_pretrained_backbone
lowerCAmelCase__ : Union[str, Any] = dilation
# Hungarian matcher
lowerCAmelCase__ : List[Any] = class_cost
lowerCAmelCase__ : Optional[int] = bbox_cost
lowerCAmelCase__ : List[Any] = giou_cost
# Loss coefficients
lowerCAmelCase__ : str = mask_loss_coefficient
lowerCAmelCase__ : int = dice_loss_coefficient
lowerCAmelCase__ : Any = bbox_loss_coefficient
lowerCAmelCase__ : int = giou_loss_coefficient
lowerCAmelCase__ : Tuple = eos_coefficient
super().__init__(is_encoder_decoder=UpperCamelCase__ , **UpperCamelCase__ )
@property
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return self.d_model
class _lowerCamelCase ( _snake_case ):
_lowerCamelCase :Optional[int] = version.parse("1.11" )
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowerCAmelCase ( self : int ) -> float:
"""simple docstring"""
return 1E-5
@property
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
return 12
| 363 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowercase_ ( __UpperCAmelCase ) -> None:
lowerCAmelCase__ , lowerCAmelCase__ : int = analyze_text(__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = list(""" """ + ascii_lowercase )
# what is our total sum of probabilities.
lowerCAmelCase__ : List[str] = sum(single_char_strings.values() )
# one length string
lowerCAmelCase__ : List[str] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
lowerCAmelCase__ : List[Any] = single_char_strings[ch]
lowerCAmelCase__ : int = my_str / all_sum
my_fir_sum += prob * math.loga(__UpperCAmelCase ) # entropy formula.
# print entropy
print(f"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
lowerCAmelCase__ : Tuple = sum(two_char_strings.values() )
lowerCAmelCase__ : str = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
lowerCAmelCase__ : Optional[int] = cha + cha
if sequence in two_char_strings:
lowerCAmelCase__ : int = two_char_strings[sequence]
lowerCAmelCase__ : str = int(__UpperCAmelCase ) / all_sum
my_sec_sum += prob * math.loga(__UpperCAmelCase )
# print second entropy
print(f"""{round(-1 * my_sec_sum ):.1f}""" )
# print the difference between them
print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )
def lowercase_ ( __UpperCAmelCase ) -> tuple[dict, dict]:
lowerCAmelCase__ : Any = Counter() # type: ignore
lowerCAmelCase__ : Tuple = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(__UpperCAmelCase ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def lowercase_ ( ) -> Any:
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 212 | 0 |
'''simple docstring'''
class _snake_case :
def __init__( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ = ""
snake_case_ = ""
snake_case_ = []
def lowerCAmelCase__ ( self , a__ , a__ ) -> int:
'''simple docstring'''
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
snake_case_ = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
snake_case_ = self.__min_dist_top_down_dp(a__ , n - 1 )
snake_case_ = self.__min_dist_top_down_dp(m - 1 , a__ )
snake_case_ = self.__min_dist_top_down_dp(m - 1 , n - 1 )
snake_case_ = 1 + min(a__ , a__ , a__ )
return self.dp[m][n]
def lowerCAmelCase__ ( self , a__ , a__ ) -> int:
'''simple docstring'''
snake_case_ = worda
snake_case_ = worda
snake_case_ = [[-1 for _ in range(len(a__ ) )] for _ in range(len(a__ ) )]
return self.__min_dist_top_down_dp(len(a__ ) - 1 , len(a__ ) - 1 )
def lowerCAmelCase__ ( self , a__ , a__ ) -> int:
'''simple docstring'''
snake_case_ = worda
snake_case_ = worda
snake_case_ = len(a__ )
snake_case_ = len(a__ )
snake_case_ = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
snake_case_ = j
elif j == 0: # second string is empty
snake_case_ = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
snake_case_ = self.dp[i - 1][j - 1]
else:
snake_case_ = self.dp[i][j - 1]
snake_case_ = self.dp[i - 1][j]
snake_case_ = self.dp[i - 1][j - 1]
snake_case_ = 1 + min(a__ , a__ , a__ )
return self.dp[m][n]
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : str = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
_SCREAMING_SNAKE_CASE : int = input("Enter the first string: ").strip()
_SCREAMING_SNAKE_CASE : List[str] = input("Enter the second string: ").strip()
print()
print(F"The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}")
print(F"The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}")
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| 85 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
_SCREAMING_SNAKE_CASE : int = {
"gpt-neox-20b": 2048,
}
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : str = VOCAB_FILES_NAMES
lowerCAmelCase_ : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ : str = ["input_ids", "attention_mask"]
def __init__( self , a__=None , a__=None , a__=None , a__="<|endoftext|>" , a__="<|endoftext|>" , a__="<|endoftext|>" , a__=False , **a__ , ) -> Tuple:
'''simple docstring'''
super().__init__(
a__ , a__ , tokenizer_file=a__ , unk_token=a__ , bos_token=a__ , eos_token=a__ , add_prefix_space=a__ , **a__ , )
snake_case_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , a__ ) != add_prefix_space:
snake_case_ = getattr(a__ , pre_tok_state.pop("type" ) )
snake_case_ = add_prefix_space
snake_case_ = pre_tok_class(**a__ )
snake_case_ = add_prefix_space
def lowerCAmelCase__ ( self , a__ , a__ = None ) -> Tuple[str]:
'''simple docstring'''
snake_case_ = self._tokenizer.model.save(a__ , name=a__ )
return tuple(a__ )
def lowerCAmelCase__ ( self , a__ ) -> List[int]:
'''simple docstring'''
snake_case_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(a__ , add_special_tokens=a__ ) + [self.eos_token_id] )
if len(a__ ) > self.model_max_length:
snake_case_ = input_ids[-self.model_max_length :]
return input_ids
| 85 | 1 |
from copy import deepcopy
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Any , __A : list[int] | None = None , __A : int | None = None ):
if arr is None and size is not None:
snake_case__ : Tuple = size
snake_case__ : Tuple = [0] * size
elif arr is not None:
self.init(__A )
else:
raise ValueError("Either arr or size must be specified" )
def _lowercase ( self : Union[str, Any] , __A : list[int] ):
snake_case__ : Tuple = len(__A )
snake_case__ : str = deepcopy(__A )
for i in range(1 , self.size ):
snake_case__ : List[str] = self.next_(__A )
if j < self.size:
self.tree[j] += self.tree[i]
def _lowercase ( self : Dict ):
snake_case__ : List[Any] = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
snake_case__ : Optional[Any] = self.next_(__A )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def _lowercase ( __A : int ):
return index + (index & (-index))
@staticmethod
def _lowercase ( __A : int ):
return index - (index & (-index))
def _lowercase ( self : List[Any] , __A : int , __A : int ):
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
snake_case__ : Dict = self.next_(__A )
def _lowercase ( self : List[Any] , __A : int , __A : int ):
self.add(__A , value - self.get(__A ) )
def _lowercase ( self : List[Any] , __A : int ):
if right == 0:
return 0
snake_case__ : Optional[Any] = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
snake_case__ : str = self.prev(__A )
return result
def _lowercase ( self : Optional[Any] , __A : int , __A : int ):
return self.prefix(__A ) - self.prefix(__A )
def _lowercase ( self : Optional[Any] , __A : int ):
return self.query(__A , index + 1 )
def _lowercase ( self : Tuple , __A : int ):
value -= self.tree[0]
if value < 0:
return -1
snake_case__ : List[str] = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
snake_case__ : List[str] = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 286 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__lowerCamelCase : int = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( snake_case_ : int , snake_case_ : Any ):
snake_case__ : List[str] = b.T
snake_case__ : Union[str, Any] = np.sum(np.square(snake_case_ ) , axis=1 )
snake_case__ : Dict = np.sum(np.square(snake_case_ ) , axis=0 )
snake_case__ : Dict = np.matmul(snake_case_ , snake_case_ )
snake_case__ : Any = aa[:, None] - 2 * ab + ba[None, :]
return d
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[int] , snake_case_ : Tuple ):
snake_case__ : Tuple = x.reshape(-1 , 3 )
snake_case__ : int = squared_euclidean_distance(snake_case_ , snake_case_ )
return np.argmin(snake_case_ , axis=1 )
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = ["pixel_values"]
def __init__( self : str , __A : Optional[Union[List[List[int]], np.ndarray]] = None , __A : bool = True , __A : Dict[str, int] = None , __A : PILImageResampling = PILImageResampling.BILINEAR , __A : bool = True , __A : bool = True , **__A : Union[str, Any] , ):
super().__init__(**__A )
snake_case__ : Optional[int] = size if size is not None else {"height": 2_5_6, "width": 2_5_6}
snake_case__ : List[Any] = get_size_dict(__A )
snake_case__ : Any = np.array(__A ) if clusters is not None else None
snake_case__ : Optional[Any] = do_resize
snake_case__ : Any = size
snake_case__ : List[Any] = resample
snake_case__ : List[Any] = do_normalize
snake_case__ : Dict = do_color_quantize
def _lowercase ( self : List[Any] , __A : np.ndarray , __A : Dict[str, int] , __A : PILImageResampling = PILImageResampling.BILINEAR , __A : Optional[Union[str, ChannelDimension]] = None , **__A : int , ):
snake_case__ : List[Any] = get_size_dict(__A )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''' )
return resize(
__A , size=(size["height"], size["width"]) , resample=__A , data_format=__A , **__A )
def _lowercase ( self : List[Any] , __A : np.ndarray , __A : Optional[Union[str, ChannelDimension]] = None , ):
snake_case__ : List[str] = rescale(image=__A , scale=1 / 1_2_7.5 , data_format=__A )
snake_case__ : List[Any] = image - 1
return image
def _lowercase ( self : Dict , __A : ImageInput , __A : bool = None , __A : Dict[str, int] = None , __A : PILImageResampling = None , __A : bool = None , __A : Optional[bool] = None , __A : Optional[Union[List[List[int]], np.ndarray]] = None , __A : Optional[Union[str, TensorType]] = None , __A : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **__A : Optional[int] , ):
snake_case__ : Any = do_resize if do_resize is not None else self.do_resize
snake_case__ : Union[str, Any] = size if size is not None else self.size
snake_case__ : Union[str, Any] = get_size_dict(__A )
snake_case__ : Optional[Any] = resample if resample is not None else self.resample
snake_case__ : Tuple = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ : Optional[Any] = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
snake_case__ : Union[str, Any] = clusters if clusters is not None else self.clusters
snake_case__ : Union[str, Any] = np.array(__A )
snake_case__ : Any = make_list_of_images(__A )
if not valid_images(__A ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
snake_case__ : Optional[Any] = [to_numpy_array(__A ) for image in images]
if do_resize:
snake_case__ : List[str] = [self.resize(image=__A , size=__A , resample=__A ) for image in images]
if do_normalize:
snake_case__ : Union[str, Any] = [self.normalize(image=__A ) for image in images]
if do_color_quantize:
snake_case__ : int = [to_channel_dimension_format(__A , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
snake_case__ : int = np.array(__A )
snake_case__ : Dict = color_quantize(__A , __A ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
snake_case__ : str = images.shape[0]
snake_case__ : str = images.reshape(__A , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
snake_case__ : Union[str, Any] = list(__A )
else:
snake_case__ : Any = [to_channel_dimension_format(__A , __A ) for image in images]
snake_case__ : Optional[int] = {"input_ids": images}
return BatchFeature(data=__A , tensor_type=__A )
| 286 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a :
"""simple docstring"""
def __init__( self: Optional[int] , UpperCamelCase: Tuple , UpperCamelCase: Optional[int]=13 , UpperCamelCase: List[Any]=32 , UpperCamelCase: str=2 , UpperCamelCase: List[str]=3 , UpperCamelCase: Dict=16 , UpperCamelCase: Dict=[1, 2, 1] , UpperCamelCase: List[Any]=[2, 2, 4] , UpperCamelCase: Optional[int]=2 , UpperCamelCase: str=2.0 , UpperCamelCase: Tuple=True , UpperCamelCase: Union[str, Any]=0.0 , UpperCamelCase: List[str]=0.0 , UpperCamelCase: Optional[int]=0.1 , UpperCamelCase: int="gelu" , UpperCamelCase: Any=False , UpperCamelCase: str=True , UpperCamelCase: List[Any]=0.02 , UpperCamelCase: str=1e-5 , UpperCamelCase: Optional[int]=True , UpperCamelCase: Dict=None , UpperCamelCase: Dict=True , UpperCamelCase: Tuple=10 , UpperCamelCase: Any=8 , ):
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = embed_dim
A__ = depths
A__ = num_heads
A__ = window_size
A__ = mlp_ratio
A__ = qkv_bias
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = drop_path_rate
A__ = hidden_act
A__ = use_absolute_embeddings
A__ = patch_norm
A__ = layer_norm_eps
A__ = initializer_range
A__ = is_training
A__ = scope
A__ = use_labels
A__ = type_sequence_label_size
A__ = encoder_stride
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self: Any ):
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCamelCase ( self: Any , UpperCamelCase: Any , UpperCamelCase: Union[str, Any] , UpperCamelCase: Optional[Any] ):
"""simple docstring"""
A__ = SwinvaModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase )
A__ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
A__ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def UpperCamelCase ( self: str , UpperCamelCase: Optional[Any] , UpperCamelCase: Any , UpperCamelCase: List[Any] ):
"""simple docstring"""
A__ = SwinvaForMaskedImageModeling(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A__ = 1
A__ = SwinvaForMaskedImageModeling(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase ( self: int , UpperCamelCase: Any , UpperCamelCase: List[str] , UpperCamelCase: Tuple ):
"""simple docstring"""
A__ = self.type_sequence_label_size
A__ = SwinvaForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a ( _lowerCamelCase, _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
UpperCAmelCase = (
{"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = SwinvaModelTester(self )
A__ = ConfigTester(self , config_class=UpperCamelCase , embed_dim=37 )
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase )
@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
def UpperCamelCase ( self: int ):
"""simple docstring"""
pass
@unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
pass
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase , nn.Linear ) )
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase )
A__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
A__ = True
A__ = False
A__ = True
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
A__ = outputs.attentions
A__ = len(self.model_tester.depths )
self.assertEqual(len(UpperCamelCase ) , UpperCamelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = config.window_size**2
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
A__ = outputs.attentions
self.assertEqual(len(UpperCamelCase ) , UpperCamelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
A__ = len(UpperCamelCase )
# Check attention is always last and order is fine
A__ = True
A__ = True
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
if hasattr(self.model_tester , """num_hidden_states_types""" ):
A__ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
A__ = 2
self.assertEqual(out_len + added_hidden_states , len(UpperCamelCase ) )
A__ = outputs.attentions
self.assertEqual(len(UpperCamelCase ) , UpperCamelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def UpperCamelCase ( self: Optional[int] , UpperCamelCase: Tuple , UpperCamelCase: Union[str, Any] , UpperCamelCase: int , UpperCamelCase: str ):
"""simple docstring"""
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
A__ = outputs.hidden_states
A__ = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(UpperCamelCase ) , UpperCamelCase )
# Swinv2 has a different seq_length
A__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
A__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
A__ = outputs.reshaped_hidden_states
self.assertEqual(len(UpperCamelCase ) , UpperCamelCase )
A__ , A__ , A__ , A__ = reshaped_hidden_states[0].shape
A__ = (
reshaped_hidden_states[0].view(UpperCamelCase , UpperCamelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
A__ = True
self.check_hidden_states_output(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
self.check_hidden_states_output(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = 3
A__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
A__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
A__ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
A__ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
A__ = True
self.check_hidden_states_output(UpperCamelCase , UpperCamelCase , UpperCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
self.check_hidden_states_output(UpperCamelCase , UpperCamelCase , UpperCamelCase , (padded_height, padded_width) )
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase )
@slow
def UpperCamelCase ( self: Any ):
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = SwinvaModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = _config_zero_init(UpperCamelCase )
for model_class in self.all_model_classes:
A__ = model_class(config=UpperCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
if is_vision_available()
else None
)
@slow
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
UpperCamelCase )
A__ = self.default_image_processor
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
A__ = image_processor(images=UpperCamelCase , return_tensors="""pt""" ).to(UpperCamelCase )
# forward pass
with torch.no_grad():
A__ = model(**UpperCamelCase )
# verify the logits
A__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCamelCase )
A__ = torch.tensor([-0.3_947, -0.4_306, 0.0_026] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 ) )
| 335 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[Any] , UpperCamelCase: Any , UpperCamelCase: Optional[int]=7 , UpperCamelCase: str=3 , UpperCamelCase: int=30 , UpperCamelCase: int=4_00 , UpperCamelCase: Union[str, Any]=True , UpperCamelCase: Tuple=None , UpperCamelCase: Any=True , UpperCamelCase: int=[0.5, 0.5, 0.5] , UpperCamelCase: Any=[0.5, 0.5, 0.5] , UpperCamelCase: Optional[Any]=True , UpperCamelCase: List[Any]=1 / 2_55 , UpperCamelCase: Tuple=True , ):
"""simple docstring"""
A__ = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_rescale
A__ = rescale_factor
A__ = do_pad
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCamelCase ( self: Any , UpperCamelCase: List[str] , UpperCamelCase: int=False ):
"""simple docstring"""
if not batched:
A__ = image_inputs[0]
if isinstance(UpperCamelCase , Image.Image ):
A__ , A__ = image.size
else:
A__ , A__ = image.shape[1], image.shape[2]
if w < h:
A__ = int(self.size["""shortest_edge"""] * h / w )
A__ = self.size["""shortest_edge"""]
elif w > h:
A__ = self.size["""shortest_edge"""]
A__ = int(self.size["""shortest_edge"""] * w / h )
else:
A__ = self.size["""shortest_edge"""]
A__ = self.size["""shortest_edge"""]
else:
A__ = []
for image in image_inputs:
A__ , A__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[0] )[0]
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a ( _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = YolosImageProcessor if is_vision_available() else None
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = YolosImageProcessingTester(self )
@property
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(UpperCamelCase , """image_std""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """size""" ) )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
A__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , numpify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , np.ndarray )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
A__ = self.image_processing_class(do_resize=UpperCamelCase , do_normalize=UpperCamelCase , do_rescale=UpperCamelCase )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
A__ = image_processing_a.pad(UpperCamelCase , return_tensors="""pt""" )
A__ = image_processing_a(UpperCamelCase , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""image_id""": 3_97_69, """annotations""": target}
# encode them
A__ = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
@slow
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}
A__ = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
A__ = YolosImageProcessor(format="""coco_panoptic""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , masks_path=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify masks
A__ = 82_28_73
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , UpperCamelCase )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
| 335 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = tempfile.mkdtemp()
# fmt: off
lowercase_ = ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
lowercase_ = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
lowercase_ = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
lowercase_ = {"""unk_token""": """<unk>"""}
lowercase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowercase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(_lowerCAmelCase ) )
lowercase_ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
lowercase_ = os.path.join(self.tmpdirname , _lowerCAmelCase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def A__ ( self , **UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token="!" , **_lowerCAmelCase )
def A__ ( self , **UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token="!" , **_lowerCAmelCase )
def A__ ( self , **UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def A__ ( self ) -> Any:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A__ ( self ) -> int:
'''simple docstring'''
lowercase_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase_ = [Image.fromarray(np.moveaxis(_lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = self.get_image_processor()
lowercase_ = OwlViTProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase_ = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCAmelCase )
lowercase_ = OwlViTProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase_ = OwlViTProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCAmelCase )
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
lowercase_ = self.get_image_processor(do_normalize=_lowerCAmelCase )
lowercase_ = OwlViTProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=_lowerCAmelCase )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCAmelCase )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = OwlViTProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
lowercase_ = self.prepare_image_inputs()
lowercase_ = image_processor(_lowerCAmelCase , return_tensors="np" )
lowercase_ = processor(images=_lowerCAmelCase , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = OwlViTProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
lowercase_ = """lower newer"""
lowercase_ = processor(text=_lowerCAmelCase , return_tensors="np" )
lowercase_ = tokenizer(_lowerCAmelCase , return_tensors="np" )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() , encoded_processor[key][0].tolist() )
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = OwlViTProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
lowercase_ = """lower newer"""
lowercase_ = self.prepare_image_inputs()
lowercase_ = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = """google/owlvit-base-patch32"""
lowercase_ = OwlViTProcessor.from_pretrained(_lowerCAmelCase )
lowercase_ = ["""cat""", """nasa badge"""]
lowercase_ = processor(text=_lowerCAmelCase )
lowercase_ = 16
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask"] )
self.assertEqual(inputs["input_ids"].shape , (2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = """google/owlvit-base-patch32"""
lowercase_ = OwlViTProcessor.from_pretrained(_lowerCAmelCase )
lowercase_ = [["""cat""", """nasa badge"""], ["""person"""]]
lowercase_ = processor(text=_lowerCAmelCase )
lowercase_ = 16
lowercase_ = len(_lowerCAmelCase )
lowercase_ = max([len(_lowerCAmelCase ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask"] )
self.assertEqual(inputs["input_ids"].shape , (batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ = """google/owlvit-base-patch32"""
lowercase_ = OwlViTProcessor.from_pretrained(_lowerCAmelCase )
lowercase_ = ["""cat""", """nasa badge"""]
lowercase_ = processor(text=_lowerCAmelCase )
lowercase_ = 16
lowercase_ = inputs["""input_ids"""]
lowercase_ = [
[49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask"] )
self.assertEqual(inputs["input_ids"].shape , (2, seq_length) )
self.assertListEqual(list(input_ids[0] ) , predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ) , predicted_ids[1] )
def A__ ( self ) -> str:
'''simple docstring'''
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = OwlViTProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
lowercase_ = self.prepare_image_inputs()
lowercase_ = self.prepare_image_inputs()
lowercase_ = processor(images=_lowerCAmelCase , query_images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["query_pixel_values", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = OwlViTProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
lowercase_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase_ = processor.batch_decode(_lowerCAmelCase )
lowercase_ = tokenizer.batch_decode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
| 370 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
SCREAMING_SNAKE_CASE__ = {
"""vocab_file""": {
"""gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
"""gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
"""gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
"""gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
"""distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
},
"""merges_file""": {
"""gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
"""gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
"""gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
"""gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
"""distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
"""gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
"""gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
"""gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
"""distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
},
}
SCREAMING_SNAKE_CASE__ = {
"""gpt2""": 1_0_2_4,
"""gpt2-medium""": 1_0_2_4,
"""gpt2-large""": 1_0_2_4,
"""gpt2-xl""": 1_0_2_4,
"""distilgpt2""": 1_0_2_4,
}
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ["input_ids", "attention_mask"]
lowerCAmelCase__ = GPTaTokenizer
def __init__( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase="<|endoftext|>" , UpperCAmelCase="<|endoftext|>" , UpperCAmelCase="<|endoftext|>" , UpperCAmelCase=False , **UpperCAmelCase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
UpperCAmelCase , UpperCAmelCase , tokenizer_file=UpperCAmelCase , unk_token=UpperCAmelCase , bos_token=UpperCAmelCase , eos_token=UpperCAmelCase , add_prefix_space=UpperCAmelCase , **UpperCAmelCase , )
lowercase_ = kwargs.pop("add_bos_token" , UpperCAmelCase )
lowercase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , UpperCAmelCase ) != add_prefix_space:
lowercase_ = getattr(UpperCAmelCase , pre_tok_state.pop("type" ) )
lowercase_ = add_prefix_space
lowercase_ = pre_tok_class(**UpperCAmelCase )
lowercase_ = add_prefix_space
def A__ ( self , *UpperCAmelCase , **UpperCAmelCase ) -> BatchEncoding:
'''simple docstring'''
lowercase_ = kwargs.get("is_split_into_words" , UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*UpperCAmelCase , **UpperCAmelCase )
def A__ ( self , *UpperCAmelCase , **UpperCAmelCase ) -> BatchEncoding:
'''simple docstring'''
lowercase_ = kwargs.get("is_split_into_words" , UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*UpperCAmelCase , **UpperCAmelCase )
def A__ ( self , UpperCAmelCase , UpperCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
lowercase_ = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
def A__ ( self , UpperCAmelCase ) -> List[int]:
'''simple docstring'''
lowercase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) + [self.eos_token_id] )
if len(UpperCAmelCase ) > self.model_max_length:
lowercase_ = input_ids[-self.model_max_length :]
return input_ids
| 297 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCAmelCase : str = logging.getLogger(__name__)
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser(
description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).""" )
parser.add_argument("""--file_path""" , type=snake_case__ , default="""data/dump.txt""" , help="""The path to the data.""" )
parser.add_argument("""--tokenizer_type""" , type=snake_case__ , default="""bert""" , choices=["""bert""", """roberta""", """gpt2"""] )
parser.add_argument("""--tokenizer_name""" , type=snake_case__ , default="""bert-base-uncased""" , help="""The tokenizer to use.""" )
parser.add_argument("""--dump_file""" , type=snake_case__ , default="""data/dump""" , help="""The dump file prefix.""" )
__SCREAMING_SNAKE_CASE = parser.parse_args()
logger.info(F'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
__SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained(args.tokenizer_name )
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map["""cls_token"""] # `[CLS]`
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map["""sep_token"""] # `[SEP]`
elif args.tokenizer_type == "roberta":
__SCREAMING_SNAKE_CASE = RobertaTokenizer.from_pretrained(args.tokenizer_name )
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map["""cls_token"""] # `<s>`
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map["""sep_token"""] # `</s>`
elif args.tokenizer_type == "gpt2":
__SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained(args.tokenizer_name )
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map["""bos_token"""] # `<|endoftext|>`
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map["""eos_token"""] # `<|endoftext|>`
logger.info(F'Loading text from {args.file_path}' )
with open(args.file_path , """r""" , encoding="""utf8""" ) as fp:
__SCREAMING_SNAKE_CASE = fp.readlines()
logger.info("""Start encoding""" )
logger.info(F'{len(snake_case__ )} examples to process.' )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1_00_00
__SCREAMING_SNAKE_CASE = time.time()
for text in data:
__SCREAMING_SNAKE_CASE = F'{bos} {text.strip()} {sep}'
__SCREAMING_SNAKE_CASE = tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
rslt.append(snake_case__ )
iter += 1
if iter % interval == 0:
__SCREAMING_SNAKE_CASE = time.time()
logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
__SCREAMING_SNAKE_CASE = time.time()
logger.info("""Finished binarization""" )
logger.info(F'{len(snake_case__ )} examples processed.' )
__SCREAMING_SNAKE_CASE = F'{args.dump_file}.{args.tokenizer_name}.pickle'
__SCREAMING_SNAKE_CASE = tokenizer.vocab_size
if vocab_size < (1 << 16):
__SCREAMING_SNAKE_CASE = [np.uintaa(snake_case__ ) for d in rslt]
else:
__SCREAMING_SNAKE_CASE = [np.intaa(snake_case__ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'Dump to {dp_file}' )
with open(snake_case__ , """wb""" ) as handle:
pickle.dump(rslt_ , snake_case__ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 267 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def _snake_case ( snake_case__ : str , snake_case__ : str ):
A = RobertaPreLayerNormConfig.from_pretrained(
snake_case__ , architectures=['RobertaPreLayerNormForMaskedLM'] )
# convert state_dict
A = torch.load(hf_hub_download(repo_id=snake_case__ , filename='pytorch_model.bin' ) )
A = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith('roberta.' ):
A = 'roberta_prelayernorm.' + tensor_key[len('roberta.' ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith('.self.LayerNorm.weight' ) or tensor_key.endswith('.self.LayerNorm.bias' ):
continue
A = tensor_value
A = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=snake_case__ , config=snake_case__ , state_dict=snake_case__ )
model.save_pretrained(snake_case__ )
# convert tokenizer
A = AutoTokenizer.from_pretrained(snake_case__ )
tokenizer.save_pretrained(snake_case__ )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint-repo''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowercase = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path) | 74 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = LxmertConfig.from_json_file(__UpperCAmelCase )
print(F"Building PyTorch model from configuration: {config}" )
snake_case_ = LxmertForPreTraining(__UpperCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict(), __UpperCAmelCase )
if __name__ == "__main__":
a : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
a : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 72 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class a ( _lowerCamelCase ):
snake_case_ = 42
snake_case_ = None
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase=0.9_9_9, __UpperCAmelCase="cosine", ) -> Dict:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
snake_case_ = []
for i in range(__UpperCAmelCase ):
snake_case_ = i / num_diffusion_timesteps
snake_case_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ), __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase, dtype=torch.floataa )
class a ( _lowerCamelCase , _lowerCamelCase ):
@register_to_config
def __init__( self : List[str] , lowercase_ : int = 1000 , lowercase_ : str = "fixed_small_log" , lowercase_ : bool = True , lowercase_ : Optional[float] = 1.0 , lowercase_ : str = "epsilon" , lowercase_ : str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
snake_case_ = betas_for_alpha_bar(lowercase_ )
snake_case_ = 1.0 - self.betas
snake_case_ = torch.cumprod(self.alphas , dim=0 )
snake_case_ = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
snake_case_ = 1.0
# setable values
snake_case_ = None
snake_case_ = torch.from_numpy(np.arange(0 , lowercase_ )[::-1].copy() )
snake_case_ = variance_type
def A_ ( self : Optional[Any] , lowercase_ : torch.FloatTensor , lowercase_ : Optional[int] = None ):
return sample
def A_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Union[str, torch.device] = None ):
snake_case_ = num_inference_steps
snake_case_ = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
snake_case_ = (np.arange(0 , lowercase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
snake_case_ = torch.from_numpy(lowercase_ ).to(lowercase_ )
def A_ ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : Optional[int]=None , lowercase_ : Tuple=None , lowercase_ : Tuple=None ):
if prev_timestep is None:
snake_case_ = t - 1
snake_case_ = self.alphas_cumprod[t]
snake_case_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
snake_case_ = 1 - alpha_prod_t
snake_case_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
snake_case_ = self.betas[t]
else:
snake_case_ = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
snake_case_ = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
snake_case_ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
snake_case_ = torch.log(torch.clamp(lowercase_ , min=1e-20 ) )
snake_case_ = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
snake_case_ = variance.log()
snake_case_ = beta.log()
snake_case_ = (predicted_variance + 1) / 2
snake_case_ = frac * max_log + (1 - frac) * min_log
return variance
def A_ ( self : List[Any] , lowercase_ : torch.FloatTensor , lowercase_ : int , lowercase_ : torch.FloatTensor , lowercase_ : Optional[int] = None , lowercase_ : int=None , lowercase_ : bool = True , ):
snake_case_ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
snake_case_ ,snake_case_ = torch.split(lowercase_ , sample.shape[1] , dim=1 )
else:
snake_case_ = None
# 1. compute alphas, betas
if prev_timestep is None:
snake_case_ = t - 1
snake_case_ = self.alphas_cumprod[t]
snake_case_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
snake_case_ = 1 - alpha_prod_t
snake_case_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
snake_case_ = self.betas[t]
snake_case_ = self.alphas[t]
else:
snake_case_ = 1 - alpha_prod_t / alpha_prod_t_prev
snake_case_ = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
snake_case_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
snake_case_ = model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
snake_case_ = torch.clamp(
lowercase_ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case_ = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
snake_case_ = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case_ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
snake_case_ = 0
if t > 0:
snake_case_ = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowercase_ , device=model_output.device )
snake_case_ = self._get_variance(
lowercase_ , predicted_variance=lowercase_ , prev_timestep=lowercase_ , )
if self.variance_type == "fixed_small_log":
snake_case_ = variance
elif self.variance_type == "learned_range":
snake_case_ = (0.5 * variance).exp()
else:
raise ValueError(
F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
''' for the UnCLIPScheduler.''' )
snake_case_ = variance * variance_noise
snake_case_ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowercase_ , pred_original_sample=lowercase_ )
def A_ ( self : Any , lowercase_ : torch.FloatTensor , lowercase_ : torch.FloatTensor , lowercase_ : torch.IntTensor , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
snake_case_ = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
snake_case_ = timesteps.to(original_samples.device )
snake_case_ = alphas_cumprod[timesteps] ** 0.5
snake_case_ = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
snake_case_ = sqrt_alpha_prod.unsqueeze(-1 )
snake_case_ = (1 - alphas_cumprod[timesteps]) ** 0.5
snake_case_ = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
snake_case_ = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
snake_case_ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 72 | 1 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class __snake_case :
"""simple docstring"""
def __init__( self , __lowerCamelCase , __lowerCamelCase=13 , __lowerCamelCase=7 , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=False , __lowerCamelCase=True , __lowerCamelCase=99 , __lowerCamelCase=64 , __lowerCamelCase=5 , __lowerCamelCase=4 , __lowerCamelCase=64 , __lowerCamelCase="gelu" , __lowerCamelCase=0.1 , __lowerCamelCase=0.1 , __lowerCamelCase=512 , __lowerCamelCase=16 , __lowerCamelCase=2 , __lowerCamelCase=0.0_2 , __lowerCamelCase=3 , __lowerCamelCase=4 , __lowerCamelCase=None , ):
'''simple docstring'''
__A : Optional[int] = parent
__A : Any = batch_size
__A : Union[str, Any] = seq_length
__A : List[Any] = is_training
__A : Union[str, Any] = use_input_mask
__A : Any = use_token_type_ids
__A : str = use_labels
__A : Tuple = vocab_size
__A : List[str] = hidden_size
__A : Union[str, Any] = num_hidden_layers
__A : List[str] = num_attention_heads
__A : Optional[int] = intermediate_size
__A : int = hidden_act
__A : List[str] = hidden_dropout_prob
__A : Optional[Any] = attention_probs_dropout_prob
__A : Union[str, Any] = max_position_embeddings
__A : int = type_vocab_size
__A : str = type_sequence_label_size
__A : List[Any] = initializer_range
__A : str = num_labels
__A : Optional[int] = num_choices
__A : Dict = scope
def UpperCamelCase__( self ):
'''simple docstring'''
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__A : str = None
if self.use_input_mask:
__A : int = random_attention_mask([self.batch_size, self.seq_length] )
__A : Union[str, Any] = None
__A : str = None
__A : Optional[int] = None
if self.use_labels:
__A : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__A : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__A : int = ids_tensor([self.batch_size] , self.num_choices )
__A : Optional[int] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__( self ):
'''simple docstring'''
return MPNetConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : Tuple = MPNetModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
__A : Any = model(__lowerCamelCase , __lowerCamelCase )
__A : List[Any] = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : Any = MPNetForQuestionAnswering(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
__A : Union[str, Any] = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , start_positions=__lowerCamelCase , end_positions=__lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : Tuple = self.num_labels
__A : Tuple = MPNetForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
__A : Any = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : List[str] = self.num_choices
__A : int = MPNetForMultipleChoice(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
__A : List[str] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__A : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__A : Union[str, Any] = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : Dict = self.num_labels
__A : Union[str, Any] = MPNetForTokenClassification(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
__A : Union[str, Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : str = self.prepare_config_and_inputs()
((__A) , (__A) , (__A) , (__A) , (__A) , (__A)) : Dict = config_and_inputs
__A : int = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __snake_case ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
_lowerCamelCase = (
{
"""feature-extraction""": MPNetModel,
"""fill-mask""": MPNetForMaskedLM,
"""question-answering""": MPNetForQuestionAnswering,
"""text-classification""": MPNetForSequenceClassification,
"""token-classification""": MPNetForTokenClassification,
"""zero-shot""": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = True
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Optional[Any] = MPNetModelTester(self )
__A : int = ConfigTester(self , config_class=__lowerCamelCase , hidden_size=37 )
def UpperCamelCase__( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase__( self ):
'''simple docstring'''
__A : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase )
@require_torch
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCamelCase__( self ):
'''simple docstring'''
__A : List[Any] = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
__A : Union[str, Any] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__A : str = model(__lowerCamelCase )[0]
__A : int = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , __lowerCamelCase )
__A : Tuple = torch.tensor(
[[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowerCamelCase , atol=1e-4 ) )
| 179 |
"""simple docstring"""
def __lowercase ( snake_case_ : dict ) ->set:
'''simple docstring'''
__A : List[str] = set()
# edges = list of graph's edges
__A : Optional[int] = get_edges(snake_case_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__A , __A : str = edges.pop()
chosen_vertices.add(snake_case_ )
chosen_vertices.add(snake_case_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(snake_case_ )
return chosen_vertices
def __lowercase ( snake_case_ : dict ) ->set:
'''simple docstring'''
__A : Tuple = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 179 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ ) -> list:
if len(lowerCAmelCase_ ) == 0:
return []
UpperCAmelCase__ : Optional[Any] = min(lowerCAmelCase_ ), max(lowerCAmelCase_ )
UpperCAmelCase__ : Any = int(max_value - min_value ) + 1
UpperCAmelCase__ : list[list] = [[] for _ in range(lowerCAmelCase_ )]
for i in my_list:
buckets[int(i - min_value )].append(lowerCAmelCase_ )
return [v for bucket in buckets for v in sorted(lowerCAmelCase_ )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 369 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetaImageProcessor
class lowerCamelCase_ ( unittest.TestCase ):
def __init__( self : List[str] , _A : List[Any] , _A : Union[str, Any]=7 , _A : List[str]=3 , _A : str=30 , _A : Tuple=400 , _A : Optional[int]=True , _A : List[str]=None , _A : int=True , _A : int=[0.5, 0.5, 0.5] , _A : Optional[int]=[0.5, 0.5, 0.5] , _A : List[Any]=True , _A : str=1 / 255 , _A : Tuple=True , ):
'''simple docstring'''
UpperCAmelCase__ : str = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : List[Any] = min_resolution
UpperCAmelCase__ : List[str] = max_resolution
UpperCAmelCase__ : Tuple = do_resize
UpperCAmelCase__ : Union[str, Any] = size
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : Union[str, Any] = image_mean
UpperCAmelCase__ : Optional[int] = image_std
UpperCAmelCase__ : Dict = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : int = do_pad
def lowercase_ ( self : Any ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowercase_ ( self : Any , _A : Union[str, Any] , _A : Union[str, Any]=False ):
'''simple docstring'''
if not batched:
UpperCAmelCase__ : Optional[int] = image_inputs[0]
if isinstance(_A , Image.Image ):
UpperCAmelCase__ , UpperCAmelCase__ : str = image.size
else:
UpperCAmelCase__ , UpperCAmelCase__ : int = image.shape[1], image.shape[2]
if w < h:
UpperCAmelCase__ : Optional[Any] = int(self.size['''shortest_edge'''] * h / w )
UpperCAmelCase__ : List[Any] = self.size['''shortest_edge''']
elif w > h:
UpperCAmelCase__ : int = self.size['''shortest_edge''']
UpperCAmelCase__ : Dict = int(self.size['''shortest_edge'''] * w / h )
else:
UpperCAmelCase__ : List[str] = self.size['''shortest_edge''']
UpperCAmelCase__ : Dict = self.size['''shortest_edge''']
else:
UpperCAmelCase__ : int = []
for image in image_inputs:
UpperCAmelCase__ , UpperCAmelCase__ : str = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCAmelCase__ : Union[str, Any] = max(_A , key=lambda _A : item[0] )[0]
UpperCAmelCase__ : Union[str, Any] = max(_A , key=lambda _A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase_ ( __a , unittest.TestCase ):
lowerCAmelCase__ = DetaImageProcessor if is_vision_available() else None
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = DetaImageProcessingTester(self )
@property
def lowercase_ ( self : int ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , '''image_mean''' ) )
self.assertTrue(hasattr(_A , '''image_std''' ) )
self.assertTrue(hasattr(_A , '''do_normalize''' ) )
self.assertTrue(hasattr(_A , '''do_resize''' ) )
self.assertTrue(hasattr(_A , '''do_rescale''' ) )
self.assertTrue(hasattr(_A , '''do_pad''' ) )
self.assertTrue(hasattr(_A , '''size''' ) )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1_333} )
self.assertEqual(image_processor.do_pad , _A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
pass
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
UpperCAmelCase__ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCAmelCase__ , UpperCAmelCase__ : int = self.image_processor_tester.get_expected_values(_A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ , UpperCAmelCase__ : str = self.image_processor_tester.get_expected_values(_A , batched=_A )
UpperCAmelCase__ : Union[str, Any] = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A , np.ndarray )
# Test not batched input
UpperCAmelCase__ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = self.image_processor_tester.get_expected_values(_A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ : List[str] = image_processing(_A , return_tensors='''pt''' ).pixel_values
UpperCAmelCase__ , UpperCAmelCase__ : int = self.image_processor_tester.get_expected_values(_A , batched=_A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test not batched input
UpperCAmelCase__ : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = self.image_processor_tester.get_expected_values(_A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ : List[Any] = image_processing(_A , return_tensors='''pt''' ).pixel_values
UpperCAmelCase__ , UpperCAmelCase__ : Any = self.image_processor_tester.get_expected_values(_A , batched=_A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
UpperCAmelCase__ : str = json.loads(f.read() )
UpperCAmelCase__ : Tuple = {'''image_id''': 39_769, '''annotations''': target}
# encode them
UpperCAmelCase__ : Optional[int] = DetaImageProcessor()
UpperCAmelCase__ : str = image_processing(images=_A , annotations=_A , return_tensors='''pt''' )
# verify pixel values
UpperCAmelCase__ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['''pixel_values'''].shape , _A )
UpperCAmelCase__ : Any = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _A , atol=1e-4 ) )
# verify area
UpperCAmelCase__ : List[Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _A ) )
# verify boxes
UpperCAmelCase__ : int = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _A )
UpperCAmelCase__ : List[Any] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _A , atol=1e-3 ) )
# verify image_id
UpperCAmelCase__ : str = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _A ) )
# verify is_crowd
UpperCAmelCase__ : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _A ) )
# verify class_labels
UpperCAmelCase__ : Union[str, Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _A ) )
# verify orig_size
UpperCAmelCase__ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _A ) )
# verify size
UpperCAmelCase__ : int = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _A ) )
@slow
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
UpperCAmelCase__ : int = json.loads(f.read() )
UpperCAmelCase__ : str = {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}
UpperCAmelCase__ : Dict = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCAmelCase__ : Any = DetaImageProcessor(format='''coco_panoptic''' )
UpperCAmelCase__ : str = image_processing(images=_A , annotations=_A , masks_path=_A , return_tensors='''pt''' )
# verify pixel values
UpperCAmelCase__ : str = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['''pixel_values'''].shape , _A )
UpperCAmelCase__ : Union[str, Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _A , atol=1e-4 ) )
# verify area
UpperCAmelCase__ : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _A ) )
# verify boxes
UpperCAmelCase__ : Dict = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _A )
UpperCAmelCase__ : List[str] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _A , atol=1e-3 ) )
# verify image_id
UpperCAmelCase__ : Optional[int] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _A ) )
# verify is_crowd
UpperCAmelCase__ : Any = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _A ) )
# verify class_labels
UpperCAmelCase__ : Tuple = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _A ) )
# verify masks
UpperCAmelCase__ : Dict = 822_873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _A )
# verify orig_size
UpperCAmelCase__ : str = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _A ) )
# verify size
UpperCAmelCase__ : Optional[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _A ) )
| 299 | 0 |
'''simple docstring'''
import math
def lowerCamelCase ( __lowerCamelCase : int ) ->bool:
_SCREAMING_SNAKE_CASE = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__lowerCamelCase )
def lowerCamelCase ( __lowerCamelCase : float = 1 / 1_2345 ) ->int:
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 3
while True:
_SCREAMING_SNAKE_CASE = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__lowerCamelCase ):
_SCREAMING_SNAKE_CASE = int(__lowerCamelCase )
total_partitions += 1
if check_partition_perfect(__lowerCamelCase ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__lowerCamelCase )
integer += 1
if __name__ == "__main__":
print(f"""{solution() = }""")
| 58 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
snake_case_ = logging.get_logger(__name__) # pylint: disable=invalid-name
snake_case_ = """
Examples:
```py
>>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
>>> from diffusers.utils import load_image
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"A red cartoon frog, 4k\"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
>>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
... )
>>> pipe.to(\"cuda\")
>>> init_image = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/frog.png\"
... )
>>> image = pipe(
... image=init_image,
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... strength=0.2,
... ).images
>>> image[0].save(\"red_frog.png\")
```
"""
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_=8 ):
UpperCAmelCase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
UpperCAmelCase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
def _lowerCAmelCase ( lowercase_ , lowercase_=512 , lowercase_=512 ):
UpperCAmelCase = pil_image.resize((w, h) , resample=Image.BICUBIC , reducing_gap=1 )
UpperCAmelCase = np.array(pil_image.convert('RGB' ) )
UpperCAmelCase = arr.astype(np.floataa ) / 1_2_7.5 - 1
UpperCAmelCase = np.transpose(lowercase_ , [2, 0, 1] )
UpperCAmelCase = torch.from_numpy(lowercase_ ).unsqueeze(0 )
return image
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Dict , lowercase_ :UNetaDConditionModel , lowercase_ :DDPMScheduler , lowercase_ :VQModel , ) -> List[str]:
super().__init__()
self.register_modules(
unet=lowercase_ , scheduler=lowercase_ , movq=lowercase_ , )
UpperCAmelCase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[Any] , lowercase_ :Tuple , lowercase_ :Any ) -> Optional[int]:
# get the original timestep using init_timestep
UpperCAmelCase = min(int(num_inference_steps * strength ) , lowercase_ )
UpperCAmelCase = max(num_inference_steps - init_timestep , 0 )
UpperCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :Dict , lowercase_ :str , lowercase_ :Optional[Any] , lowercase_ :Union[str, Any] , lowercase_ :List[Any] , lowercase_ :Optional[Any] , lowercase_ :Any=None ) -> Any:
if not isinstance(lowercase_ , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_ )}""" )
UpperCAmelCase = image.to(device=lowercase_ , dtype=lowercase_ )
UpperCAmelCase = batch_size * num_images_per_prompt
if image.shape[1] == 4:
UpperCAmelCase = image
else:
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
elif isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [
self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowercase_ )
]
UpperCAmelCase = torch.cat(lowercase_ , dim=0 )
else:
UpperCAmelCase = self.movq.encode(lowercase_ ).latent_dist.sample(lowercase_ )
UpperCAmelCase = self.movq.config.scaling_factor * init_latents
UpperCAmelCase = torch.cat([init_latents] , dim=0 )
UpperCAmelCase = init_latents.shape
UpperCAmelCase = randn_tensor(lowercase_ , generator=lowercase_ , device=lowercase_ , dtype=lowercase_ )
# get latents
UpperCAmelCase = self.scheduler.add_noise(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase = init_latents
return latents
def UpperCAmelCase__ ( self :int , lowercase_ :int=0 ) -> List[str]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
UpperCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
UpperCAmelCase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :str=0 ) -> Dict:
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
UpperCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=lowercase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase = None
for cpu_offloaded_model in [self.unet, self.movq]:
UpperCAmelCase , UpperCAmelCase = cpu_offload_with_hook(lowercase_ , lowercase_ , prev_module_hook=lowercase_ )
# We'll offload the last model manually.
UpperCAmelCase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase__ ( self :List[Any] ) -> Dict:
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowercase_ , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowercase_ )
def __call__( self :str , lowercase_ :Union[torch.FloatTensor, List[torch.FloatTensor]] , lowercase_ :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] , lowercase_ :Union[torch.FloatTensor, List[torch.FloatTensor]] , lowercase_ :int = 5_12 , lowercase_ :int = 5_12 , lowercase_ :int = 1_00 , lowercase_ :float = 4.0 , lowercase_ :float = 0.3 , lowercase_ :int = 1 , lowercase_ :Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ :Optional[str] = "pil" , lowercase_ :bool = True , ) -> List[str]:
UpperCAmelCase = self._execution_device
UpperCAmelCase = guidance_scale > 1.0
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = torch.cat(lowercase_ , dim=0 )
UpperCAmelCase = image_embeds.shape[0]
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = torch.cat(lowercase_ , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase = image_embeds.repeat_interleave(lowercase_ , dim=0 )
UpperCAmelCase = negative_image_embeds.repeat_interleave(lowercase_ , dim=0 )
UpperCAmelCase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowercase_ )
if not isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [image]
if not all(isinstance(lowercase_ , (PIL.Image.Image, torch.Tensor) ) for i in image ):
raise ValueError(
f"""Input is in incorrect format: {[type(lowercase_ ) for i in image]}. Currently, we only support PIL image and pytorch tensor""" )
UpperCAmelCase = torch.cat([prepare_image(lowercase_ , lowercase_ , lowercase_ ) for i in image] , dim=0 )
UpperCAmelCase = image.to(dtype=image_embeds.dtype , device=lowercase_ )
UpperCAmelCase = self.movq.encode(lowercase_ )['latents']
UpperCAmelCase = latents.repeat_interleave(lowercase_ , dim=0 )
self.scheduler.set_timesteps(lowercase_ , device=lowercase_ )
UpperCAmelCase , UpperCAmelCase = self.get_timesteps(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase = timesteps[:1].repeat(batch_size * num_images_per_prompt )
UpperCAmelCase , UpperCAmelCase = downscale_height_and_width(lowercase_ , lowercase_ , self.movq_scale_factor )
UpperCAmelCase = self.prepare_latents(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , image_embeds.dtype , lowercase_ , lowercase_ )
for i, t in enumerate(self.progress_bar(lowercase_ ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase = {'image_embeds': image_embeds}
UpperCAmelCase = self.unet(
sample=lowercase_ , timestep=lowercase_ , encoder_hidden_states=lowercase_ , added_cond_kwargs=lowercase_ , return_dict=lowercase_ , )[0]
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
UpperCAmelCase , UpperCAmelCase = noise_pred.chunk(2 )
UpperCAmelCase , UpperCAmelCase = variance_pred.chunk(2 )
UpperCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
UpperCAmelCase , UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase = self.scheduler.step(
lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ , )[0]
# post-processing
UpperCAmelCase = self.movq.decode(lowercase_ , force_not_quantize=lowercase_ )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
UpperCAmelCase = image * 0.5 + 0.5
UpperCAmelCase = image.clamp(0 , 1 )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase_ )
| 78 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowerCAmelCase , lowerCAmelCase ) ) )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if dataset.ndim != value_array.ndim:
_lowerCAmelCase = (
"""Wrong input data's dimensions... """
f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
)
raise ValueError(lowerCAmelCase )
try:
if dataset.shape[1] != value_array.shape[1]:
_lowerCAmelCase = (
"""Wrong input data's shape... """
f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
)
raise ValueError(lowerCAmelCase )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError("""Wrong shape""" )
if dataset.dtype != value_array.dtype:
_lowerCAmelCase = (
"""Input data have different datatype... """
f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
)
raise TypeError(lowerCAmelCase )
_lowerCAmelCase = []
for value in value_array:
_lowerCAmelCase = euclidean(lowerCAmelCase , dataset[0] )
_lowerCAmelCase = dataset[0].tolist()
for dataset_value in dataset[1:]:
_lowerCAmelCase = euclidean(lowerCAmelCase , lowerCAmelCase )
if dist > temp_dist:
_lowerCAmelCase = temp_dist
_lowerCAmelCase = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
return np.dot(lowerCAmelCase , lowerCAmelCase ) / (norm(lowerCAmelCase ) * norm(lowerCAmelCase ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 220 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
A__ : str =5_00_00
A__ : Optional[int] =50_00
A__ , A__ : Optional[int] =os.path.split(__file__)
A__ : Tuple =os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
for i in range(lowerCAmelCase ):
_lowerCAmelCase = dataset[i]
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
for i in range(0 , len(lowerCAmelCase ) , lowerCAmelCase ):
_lowerCAmelCase = dataset[i : i + batch_size]
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with dataset.formatted_as(type=lowerCAmelCase ):
for i in range(lowerCAmelCase ):
_lowerCAmelCase = dataset[i]
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with dataset.formatted_as(type=lowerCAmelCase ):
for i in range(0 , lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = dataset[i : i + batch_size]
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
_lowerCAmelCase = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
]
_lowerCAmelCase = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("""generating dataset""" )
_lowerCAmelCase = datasets.Features(
{"""list""": datasets.Sequence(datasets.Value("""float32""" ) ), """numbers""": datasets.Value("""float32""" )} )
_lowerCAmelCase = generate_example_dataset(
os.path.join(lowerCAmelCase , """dataset.arrow""" ) , lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes={"""list""": (1_00,)} , )
print("""first set of iterations""" )
for func, kwargs in functions:
print(func.__name__ , str(lowerCAmelCase ) )
_lowerCAmelCase = func(lowerCAmelCase , **lowerCAmelCase )
print("""shuffling dataset""" )
_lowerCAmelCase = dataset.shuffle()
print("""Second set of iterations (after shuffling""" )
for func, kwargs in functions_shuffled:
print("""shuffled """ , func.__name__ , str(lowerCAmelCase ) )
_lowerCAmelCase = func(
lowerCAmelCase , **lowerCAmelCase )
with open(lowerCAmelCase , """wb""" ) as f:
f.write(json.dumps(lowerCAmelCase ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 220 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ):
'''simple docstring'''
raise RuntimeError('CUDA out of memory.' )
class lowercase ( nn.Module ):
def __init__( self ):
super().__init__()
snake_case_ = nn.Linear(3 , 4 )
snake_case_ = nn.BatchNormad(4 )
snake_case_ = nn.Linear(4 , 5 )
def a ( self , snake_case ):
return self.lineara(self.batchnorm(self.lineara(snake_case ) ) )
class lowercase ( unittest.TestCase ):
def a ( self ):
snake_case_ = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(snake_case ):
nonlocal batch_sizes
batch_sizes.append(snake_case )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(snake_case , [128, 64, 32, 16, 8] )
def a ( self ):
snake_case_ = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(snake_case , snake_case ):
nonlocal batch_sizes
batch_sizes.append(snake_case )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
snake_case_ , snake_case_ = mock_training_loop_function('hello' )
self.assertListEqual(snake_case , [128, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, 'hello'] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(snake_case ):
pass
with self.assertRaises(snake_case ) as cm:
mock_training_loop_function()
self.assertIn('No executable batch size found, reached zero.' , cm.exception.args[0] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(snake_case ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(snake_case ) as cm:
mock_training_loop_function()
self.assertIn('No executable batch size found, reached zero.' , cm.exception.args[0] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(snake_case , snake_case , snake_case ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(snake_case ) as cm:
mock_training_loop_function(128 , 'hello' , 'world' )
self.assertIn('Batch size was passed into `f`' , cm.exception.args[0] )
self.assertIn('`f(arg1=\'hello\', arg2=\'world\')' , cm.exception.args[0] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(snake_case ):
raise ValueError('Oops, we had an error!' )
with self.assertRaises(snake_case ) as cm:
mock_training_loop_function()
self.assertIn('Oops, we had an error!' , cm.exception.args[0] )
@require_cuda
def a ( self ):
snake_case_ = torch.cuda.memory_allocated()
snake_case_ = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , snake_case )
snake_case_ = release_memory(snake_case )
self.assertEqual(torch.cuda.memory_allocated() , snake_case )
| 285 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : List[Any] = logging.get_logger()
@dataclass
class lowercase :
__SCREAMING_SNAKE_CASE : nn.Module
__SCREAMING_SNAKE_CASE : List[nn.Module] = field(default_factory=lowercase_ )
__SCREAMING_SNAKE_CASE : list = field(default_factory=lowercase_ )
def a ( self , snake_case , snake_case , snake_case ):
snake_case_ = len(list(m.modules() ) ) == 1 or isinstance(snake_case , nn.Convad ) or isinstance(snake_case , nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self , snake_case ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def a ( self ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class lowercase :
__SCREAMING_SNAKE_CASE : nn.Module
__SCREAMING_SNAKE_CASE : nn.Module
__SCREAMING_SNAKE_CASE : int = 0
__SCREAMING_SNAKE_CASE : List = field(default_factory=lowercase_ )
__SCREAMING_SNAKE_CASE : List = field(default_factory=lowercase_ )
def __call__( self , snake_case ):
snake_case_ = Tracker(self.dest )(snake_case ).parametrized
snake_case_ = Tracker(self.src )(snake_case ).parametrized
snake_case_ = list(filter(lambda snake_case : type(snake_case ) not in self.src_skip , snake_case ) )
snake_case_ = list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip , snake_case ) )
if len(snake_case ) != len(snake_case ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(snake_case )} operations while'''
F''' destination module has {len(snake_case )}.''' )
for dest_m, src_m in zip(snake_case , snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = True ):
'''simple docstring'''
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ ).eval()
snake_case_ = ResNetForImageClassification(UpperCamelCase__ ).eval()
snake_case_ = ModuleTransfer(src=UpperCamelCase__ , dest=UpperCamelCase__ )
snake_case_ = torch.randn((1, 3, 224, 224) )
module_transfer(UpperCamelCase__ )
assert torch.allclose(from_model(UpperCamelCase__ ) , our_model(UpperCamelCase__ ).logits ), "The model logits don't match the original one."
snake_case_ = F'''resnet{"-".join(name.split("resnet" ) )}'''
print(UpperCamelCase__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='Add model' , use_temp_dir=UpperCamelCase__ , )
# we can use the convnext one
snake_case_ = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='Add image processor' , use_temp_dir=UpperCamelCase__ , )
print(F'''Pushed {checkpoint_name}''' )
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = True ):
'''simple docstring'''
snake_case_ = 'imagenet-1k-id2label.json'
snake_case_ = 1000
snake_case_ = (1, num_labels)
snake_case_ = 'huggingface/label-files'
snake_case_ = num_labels
snake_case_ = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
snake_case_ = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = partial(UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
snake_case_ = {
'resnet18': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type='basic' ),
'resnet26': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
'resnet34': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type='basic' ),
'resnet50': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
'resnet101': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
'resnet152': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(UpperCamelCase__ , names_to_config[model_name] , UpperCamelCase__ , UpperCamelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, expected_shape
if __name__ == "__main__":
_UpperCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
_UpperCAmelCase : Optional[Any] = parser.parse_args()
_UpperCAmelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 285 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Dict =DebertaVaTokenizer
lowerCamelCase : Optional[Any] =DebertaVaTokenizerFast
lowerCamelCase : List[str] =True
lowerCamelCase : Any =True
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase : List[Any] = DebertaVaTokenizer(lowerCAmelCase , unk_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = """this is a test"""
__lowerCAmelCase : Tuple = """this is a test"""
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = """<pad>"""
__lowerCAmelCase : List[Any] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ) , lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ) , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """<unk>""" )
self.assertEqual(vocab_keys[-1] , """[PAD]""" )
self.assertEqual(len(lowerCAmelCase ) , 3_00_01 )
def SCREAMING_SNAKE_CASE ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 3_00_00 )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = """ \tHeLLo!how \n Are yoU? """
__lowerCAmelCase : Tuple = ["""▁hello""", """!""", """how""", """▁are""", """▁you""", """?"""]
# fmt: on
__lowerCAmelCase : Optional[Any] = DebertaVaTokenizer(lowerCAmelCase , do_lower_case=lowerCAmelCase )
__lowerCAmelCase : List[str] = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : str = DebertaVaTokenizerFast(lowerCAmelCase , do_lower_case=lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
@unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.""" )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.""" )
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : int = """I was born in 92000, and this is falsé."""
__lowerCAmelCase : int = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
__lowerCAmelCase : Union[str, Any] = DebertaVaTokenizer(lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : int = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = DebertaVaTokenizerFast(lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : Any = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ) -> int:
"""simple docstring"""
__lowerCAmelCase : Any = """I was born in 92000, and this is falsé."""
__lowerCAmelCase : Optional[int] = ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
__lowerCAmelCase : Union[str, Any] = DebertaVaTokenizer(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : List[Any] = DebertaVaTokenizerFast(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : Tuple = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Any = """I was born in 92000, and this is falsé."""
__lowerCAmelCase : Union[str, Any] = ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
# fmt: on
__lowerCAmelCase : Optional[Any] = DebertaVaTokenizer(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : str = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Optional[int] = DebertaVaTokenizerFast(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Tuple = """I was born in 92000, and this is falsé."""
__lowerCAmelCase : int = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
__lowerCAmelCase : Union[str, Any] = DebertaVaTokenizer(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : int = DebertaVaTokenizerFast(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : int = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = """ \tHeLLo!how \n Are yoU? """
__lowerCAmelCase : Any = ["""▁""", """<unk>""", """e""", """<unk>""", """o""", """!""", """how""", """▁""", """<unk>""", """re""", """▁yo""", """<unk>""", """?"""]
# fmt: on
__lowerCAmelCase : Tuple = DebertaVaTokenizer(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Any = DebertaVaTokenizerFast(lowerCAmelCase , do_lower_case=lowerCAmelCase , split_by_punct=lowerCAmelCase )
__lowerCAmelCase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : int = self.get_tokenizer()
__lowerCAmelCase : Tuple = self.get_rust_tokenizer()
__lowerCAmelCase : int = """I was born in 92000, and this is falsé."""
__lowerCAmelCase : int = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
__lowerCAmelCase : int = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Tuple = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : int = rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : List[Any] = self.get_rust_tokenizer()
__lowerCAmelCase : Any = tokenizer.encode(lowerCAmelCase )
__lowerCAmelCase : str = rust_tokenizer.encode(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any ) -> int:
"""simple docstring"""
__lowerCAmelCase : int = """This is a test"""
__lowerCAmelCase : List[Any] = [13, 1, 43_98, 25, 21, 12_89]
__lowerCAmelCase : Dict = ["""▁""", """T""", """his""", """▁is""", """▁a""", """▁test"""]
__lowerCAmelCase : Dict = ["""▁""", """<unk>""", """his""", """▁is""", """▁a""", """▁test"""]
__lowerCAmelCase : Dict = DebertaVaTokenizer(lowerCAmelCase , keep_accents=lowerCAmelCase )
__lowerCAmelCase : Any = DebertaVaTokenizerFast(lowerCAmelCase , keep_accents=lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : List[str] = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : List[Any] = tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Dict = rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : List[Any] = rust_tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : List[Any] = rust_tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
# fmt: off
__lowerCAmelCase : List[Any] = """I was born in 92000, and this is falsé."""
__lowerCAmelCase : List[Any] = [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
__lowerCAmelCase : Union[str, Any] = ["""▁""", """I""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """.""", ]
__lowerCAmelCase : str = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
# fmt: on
__lowerCAmelCase : Any = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : int = tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : str = rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = rust_tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Dict = rust_tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
"""simple docstring"""
__lowerCAmelCase : List[Any] = DebertaVaTokenizer(lowerCAmelCase )
__lowerCAmelCase : Tuple = tokenizer.encode("""sequence builders""" )
__lowerCAmelCase : int = tokenizer.encode("""multi-sequence build""" )
__lowerCAmelCase : str = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , lowerCAmelCase )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , lowerCAmelCase , )
@slow
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = {"""input_ids""": [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase , model_name="""microsoft/deberta-v2-xlarge""" , revision="""ad6e42c1532ddf3a15c39246b63f5559d558b670""" , )
| 139 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : Optional[Any] , *lowerCAmelCase : int , **lowerCAmelCase : Optional[int] ) -> None:
"""simple docstring"""
warnings.warn(
"""The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use GLPNImageProcessor instead.""" , lowerCAmelCase , )
super().__init__(*lowerCAmelCase , **lowerCAmelCase )
| 139 | 1 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
{"""dataset""": """snli""", """config_name""": """plain_text"""},
{"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
{"""dataset""": """wiki40b""", """config_name""": """en"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
{"""dataset""": """natural_questions""", """config_name""": """default"""},
]
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int]=True ) -> Tuple:
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case__ ) )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = None
__UpperCamelCase: int = None
def _A ( self : str , A : str , A : List[Any] ):
with TemporaryDirectory() as tmp_dir:
_UpperCAmelCase : int = dataset_module_factory(A , cache_dir=A )
_UpperCAmelCase : List[Any] = import_main_class(dataset_module.module_path , dataset=A )
_UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=A , config_name=A , hash=dataset_module.hash , )
_UpperCAmelCase : Tuple = "/".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A ).replace(os.sep , "/" ),
config.DATASET_INFO_FILENAME,
] )
_UpperCAmelCase : Optional[Any] = cached_path(A , cache_dir=A )
self.assertTrue(os.path.exists(A ) )
@pytest.mark.integration
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : int = tmp_path_factory.mktemp("test_hf_gcp" ) / "test_wikipedia_simple"
_UpperCAmelCase : Union[str, Any] = dataset_module_factory("wikipedia" , cache_dir=_UpperCAmelCase )
_UpperCAmelCase : str = import_main_class(dataset_module.module_path )
_UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=_UpperCAmelCase , config_name="20220301.frr" , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
_UpperCAmelCase : Dict = None
builder_instance.download_and_prepare()
_UpperCAmelCase : List[str] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[str] = dataset_module_factory("wikipedia" , cache_dir=_UpperCAmelCase )
_UpperCAmelCase : List[Any] = import_main_class(dataset_module.module_path , dataset=_UpperCAmelCase )
_UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=_UpperCAmelCase , config_name="20220301.frr" , hash=dataset_module.hash , )
_UpperCAmelCase : Dict = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
assert "train" in ds
assert isinstance(ds["train"] , _UpperCAmelCase )
assert next(iter(ds["train"] ) )
| 31 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __magic_name__ ( _UpperCamelCase ):
def __init__( self : Optional[int] ,_UpperCAmelCase : Union[str, "sqlalchemy.sql.Selectable"] ,_UpperCAmelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] ,_UpperCAmelCase : Optional[Features] = None ,_UpperCAmelCase : str = None ,_UpperCAmelCase : bool = False ,**_UpperCAmelCase : Dict ,):
super().__init__(features=_UpperCAmelCase ,cache_dir=_UpperCAmelCase ,keep_in_memory=_UpperCAmelCase ,**_UpperCAmelCase )
_a : Tuple = Sql(
cache_dir=_UpperCAmelCase ,features=_UpperCAmelCase ,sql=_UpperCAmelCase ,con=_UpperCAmelCase ,**_UpperCAmelCase ,)
def __lowercase ( self : Dict ):
_a : Optional[Any] = None
_a : Dict = None
_a : Dict = None
_a : Optional[int] = None
self.builder.download_and_prepare(
download_config=_UpperCAmelCase ,download_mode=_UpperCAmelCase ,verification_mode=_UpperCAmelCase ,base_path=_UpperCAmelCase ,)
# Build dataset for splits
_a : List[str] = self.builder.as_dataset(
split='train' ,verification_mode=_UpperCAmelCase ,in_memory=self.keep_in_memory )
return dataset
class __magic_name__ :
def __init__( self : Optional[int] ,_UpperCAmelCase : Dataset ,_UpperCAmelCase : str ,_UpperCAmelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] ,_UpperCAmelCase : Optional[int] = None ,_UpperCAmelCase : Optional[int] = None ,**_UpperCAmelCase : Dict ,):
if num_proc is not None and num_proc <= 0:
raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""" )
_a : Dict = dataset
_a : List[Any] = name
_a : Tuple = con
_a : Union[str, Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_a : List[Any] = num_proc
_a : Tuple = to_sql_kwargs
def __lowercase ( self : List[Any] ):
_a : Tuple = self.to_sql_kwargs.pop('sql' ,_UpperCAmelCase )
_a : str = self.to_sql_kwargs.pop('con' ,_UpperCAmelCase )
_a : Optional[Any] = self.to_sql_kwargs.pop('index' ,_UpperCAmelCase )
_a : Any = self._write(index=_UpperCAmelCase ,**self.to_sql_kwargs )
return written
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : Dict ):
_a , _a , _a : Any = args
_a : Tuple = {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
_a : Dict = query_table(
table=self.dataset.data ,key=slice(_UpperCAmelCase ,offset + self.batch_size ) ,indices=self.dataset._indices ,)
_a : Tuple = batch.to_pandas()
_a : Dict = df.to_sql(self.name ,self.con ,index=_UpperCAmelCase ,**_UpperCAmelCase )
return num_rows or len(_UpperCAmelCase )
def __lowercase ( self : int ,_UpperCAmelCase : Optional[int] ,**_UpperCAmelCase : List[Any] ):
_a : Union[str, Any] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 ,len(self.dataset ) ,self.batch_size ) ,unit='ba' ,disable=not logging.is_progress_bar_enabled() ,desc='Creating SQL from Arrow format' ,):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
_a , _a : List[Any] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql ,[(offset, index, to_sql_kwargs) for offset in range(0 ,_UpperCAmelCase ,_UpperCAmelCase )] ,) ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,unit='ba' ,disable=not logging.is_progress_bar_enabled() ,desc='Creating SQL from Arrow format' ,):
written += num_rows
return written
| 89 | 0 |
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) -> float:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('''All input parameters must be positive''' )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError('''Relative densities cannot be greater than one''' )
else:
lowerCamelCase__ : Dict = 1 - (matter_density + radiation_density + dark_energy)
lowerCamelCase__ : Any = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
lowerCamelCase__ : int = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
_A : int = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 362 |
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCamelCase__ : Any = str(abs(UpperCAmelCase ) )
lowerCamelCase__ : Union[str, Any] = [list(UpperCAmelCase ) for char in range(len(UpperCAmelCase ) )]
for index in range(len(UpperCAmelCase ) ):
num_transpositions[index].pop(UpperCAmelCase )
return max(
int(''''''.join(list(UpperCAmelCase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__('doctest').testmod()
| 265 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : int):
'''simple docstring'''
__lowercase =parent
def __lowerCamelCase ( self : int):
'''simple docstring'''
return {}
def _A ( ):
"""simple docstring"""
__lowercase ='<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR="FFFFFF">\n <HR>\n <a href="http://google.com">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style="color:#0000FF">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>'
__lowercase ='\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n '
return [html_string_a, html_string_a]
@require_bsa
class _UpperCamelCase ( A , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = MarkupLMFeatureExtractor if is_bsa_available() else None
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =MarkupLMFeatureExtractionTester(self)
@property
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
return self.feature_extract_tester.prepare_feat_extract_dict()
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__lowercase =self.feature_extraction_class()
# Test not batched input
__lowercase =get_html_strings()[0]
__lowercase =feature_extractor(_lowerCAmelCase)
# fmt: off
__lowercase =[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
__lowercase =[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
# fmt: on
self.assertEqual(encoding.nodes , _lowerCAmelCase)
self.assertEqual(encoding.xpaths , _lowerCAmelCase)
# Test batched
__lowercase =get_html_strings()
__lowercase =feature_extractor(_lowerCAmelCase)
# fmt: off
__lowercase =expected_nodes + [['My First Heading', 'My first paragraph.']]
__lowercase =expected_xpaths + [['/html/body/h1', '/html/body/p']]
self.assertEqual(len(encoding.nodes) , 2)
self.assertEqual(len(encoding.xpaths) , 2)
self.assertEqual(encoding.nodes , _lowerCAmelCase)
self.assertEqual(encoding.xpaths , _lowerCAmelCase)
| 166 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = """umt5"""
lowerCAmelCase__ = ["""past_key_values"""]
def __init__( self : Optional[int] , _lowerCAmelCase : int=2_5_0_1_1_2 , _lowerCAmelCase : Union[str, Any]=5_1_2 , _lowerCAmelCase : List[Any]=6_4 , _lowerCAmelCase : Optional[Any]=1_0_2_4 , _lowerCAmelCase : Union[str, Any]=8 , _lowerCAmelCase : Any=None , _lowerCAmelCase : Tuple=6 , _lowerCAmelCase : str=3_2 , _lowerCAmelCase : List[str]=1_2_8 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Tuple=1e-6 , _lowerCAmelCase : List[Any]=1.0 , _lowerCAmelCase : Union[str, Any]="gated-gelu" , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : Tuple="T5Tokenizer" , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : List[str]=0 , _lowerCAmelCase : Union[str, Any]=1 , _lowerCAmelCase : Any=0 , **_lowerCAmelCase : int , ):
'''simple docstring'''
super().__init__(
is_encoder_decoder=_lowerCAmelCase , tokenizer_class=_lowerCAmelCase , tie_word_embeddings=_lowerCAmelCase , pad_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , **_lowerCAmelCase , )
__lowercase =vocab_size
__lowercase =d_model
__lowercase =d_kv
__lowercase =d_ff
__lowercase =num_layers
__lowercase =(
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__lowercase =num_heads
__lowercase =relative_attention_num_buckets
__lowercase =relative_attention_max_distance
__lowercase =dropout_rate
__lowercase =layer_norm_epsilon
__lowercase =initializer_factor
__lowercase =feed_forward_proj
__lowercase =use_cache
__lowercase =self.feed_forward_proj.split('-')
__lowercase =act_info[-1]
__lowercase =act_info[0] == 'gated'
if len(_lowerCAmelCase) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase) > 2:
raise ValueError(
f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'')
if feed_forward_proj == "gated-gelu":
__lowercase ='gelu_new'
@property
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
return self.d_model
@property
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
return self.num_heads
@property
def __lowerCamelCase ( self : int):
'''simple docstring'''
return self.num_layers
class _UpperCamelCase ( A ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase ={
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__lowercase ='past_encoder_sequence + sequence'
__lowercase ={0: 'batch'}
__lowercase ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__lowercase ={0: 'batch', 1: 'decoder_sequence'}
__lowercase ={0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase , direction='inputs')
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
return 1_3
@property
def __lowerCamelCase ( self : int):
'''simple docstring'''
return 5e-4
| 166 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=False ) -> Optional[int]:
_a : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""transformer.blocks.{i}.norm1.weight""", f"""vilt.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.norm1.bias""", f"""vilt.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""transformer.blocks.{i}.attn.proj.weight""", f"""vilt.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(f"""transformer.blocks.{i}.attn.proj.bias""", f"""vilt.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""transformer.blocks.{i}.norm2.weight""", f"""vilt.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.norm2.bias""", f"""vilt.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(f"""transformer.blocks.{i}.mlp.fc1.weight""", f"""vilt.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc1.bias""", f"""vilt.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.weight""", f"""vilt.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.bias""", f"""vilt.encoder.layer.{i}.output.dense.bias""") )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> Tuple:
for i in range(config.num_hidden_layers ):
_a : List[str] = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.weight""" )
_a : List[str] = state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Tuple = in_proj_weight[
: config.hidden_size, :
]
_a : Tuple = in_proj_bias[: config.hidden_size]
_a : List[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_a : str = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_a : Optional[int] = in_proj_weight[
-config.hidden_size :, :
]
_a : List[Any] = in_proj_bias[-config.hidden_size :]
def __lowerCamelCase ( lowerCAmelCase_ ) -> List[str]:
_a : int = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Optional[int]:
_a : Tuple = dct.pop(lowerCAmelCase_ )
_a : int = val
@torch.no_grad()
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> Tuple:
_a : int = ViltConfig(image_size=384 , patch_size=32 , tie_word_embeddings=lowerCAmelCase_ )
_a : Tuple = False
_a : Any = False
_a : Optional[Any] = False
_a : int = False
if "vqa" in checkpoint_url:
_a : List[Any] = True
_a : Any = 3129
_a : List[str] = 'huggingface/label-files'
_a : str = 'vqa2-id2label.json'
_a : int = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type='dataset' ) , 'r' ) )
_a : Dict = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
_a : Tuple = idalabel
_a : Tuple = {v: k for k, v in idalabel.items()}
_a : Tuple = ViltForQuestionAnswering(lowerCAmelCase_ )
elif "nlvr" in checkpoint_url:
_a : Any = True
_a : int = 2
_a : Optional[Any] = {0: 'False', 1: 'True'}
_a : Dict = {v: k for k, v in config.idalabel.items()}
_a : Union[str, Any] = 3
_a : List[Any] = ViltForImagesAndTextClassification(lowerCAmelCase_ )
elif "irtr" in checkpoint_url:
_a : Tuple = True
_a : Any = ViltForImageAndTextRetrieval(lowerCAmelCase_ )
elif "mlm_itm" in checkpoint_url:
_a : Optional[int] = True
_a : Tuple = ViltForMaskedLM(lowerCAmelCase_ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
_a : Dict = torch.hub.load_state_dict_from_url(lowerCAmelCase_ , map_location='cpu' )['state_dict']
_a : List[Any] = create_rename_keys(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
read_in_q_k_v(lowerCAmelCase_ , lowerCAmelCase_ )
if mlm_model or irtr_model:
_a : List[Any] = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
_a , _a : Tuple = model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowerCAmelCase_ )
# Define processor
_a : Union[str, Any] = ViltImageProcessor(size=384 )
_a : Optional[int] = BertTokenizer.from_pretrained('bert-base-uncased' )
_a : Optional[Any] = ViltProcessor(lowerCAmelCase_ , lowerCAmelCase_ )
# Forward pass on example inputs (image + text)
if nlvr_model:
_a : List[str] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowerCAmelCase_ ).raw )
_a : str = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowerCAmelCase_ ).raw )
_a : Union[str, Any] = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
_a : Optional[Any] = processor(lowerCAmelCase_ , lowerCAmelCase_ , return_tensors='pt' )
_a : List[str] = processor(lowerCAmelCase_ , lowerCAmelCase_ , return_tensors='pt' )
_a : Dict = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
_a : Union[str, Any] = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=lowerCAmelCase_ ).raw )
if mlm_model:
_a : List[Any] = 'a bunch of [MASK] laying on a [MASK].'
else:
_a : Tuple = 'How many cats are there?'
_a : Union[str, Any] = processor(lowerCAmelCase_ , lowerCAmelCase_ , return_tensors='pt' )
_a : Tuple = model(**lowerCAmelCase_ )
# Verify outputs
if mlm_model:
_a : str = torch.Size([1, 11, 30522] )
_a : Optional[Any] = torch.tensor([-12.5_061, -12.5_123, -12.5_174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowerCAmelCase_ , atol=1E-4 )
# verify masked token prediction equals "cats"
_a : Any = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
_a : Optional[Any] = torch.Size([1, 3129] )
_a : Dict = torch.tensor([-15.9_495, -18.1_472, -10.3_041] )
assert torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowerCAmelCase_ , atol=1E-4 )
# verify vqa prediction equals "2"
_a : str = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
_a : Union[str, Any] = torch.Size([1, 2] )
_a : List[Any] = torch.tensor([-2.8_721, 2.1_291] )
assert torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowerCAmelCase = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 107 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowerCAmelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCAmelCase = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCAmelCase = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> tuple[str, float]:
_a : List[Any] = len([g for position, g in enumerate(lowerCAmelCase_ ) if g == main_target[position]] )
return (item, float(lowerCAmelCase_ ))
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> tuple[str, str]:
_a : Dict = random.randint(0 , len(lowerCAmelCase_ ) - 1 )
_a : Optional[int] = parent_a[:random_slice] + parent_a[random_slice:]
_a : Optional[int] = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
_a : Optional[Any] = list(lowerCAmelCase_ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
_a : Optional[int] = random.choice(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , ) -> list[str]:
_a : List[str] = []
# Generate more children proportionally to the fitness score.
_a : Tuple = int(parent_a[1] * 100 ) + 1
_a : Tuple = 10 if child_n >= 10 else child_n
for _ in range(lowerCAmelCase_ ):
_a : Any = population_score[random.randint(0 , lowerCAmelCase_ )][0]
_a , _a : Tuple = crossover(parent_a[0] , lowerCAmelCase_ )
# Append new string to the population list.
pop.append(mutate(lowerCAmelCase_ , lowerCAmelCase_ ) )
pop.append(mutate(lowerCAmelCase_ , lowerCAmelCase_ ) )
return pop
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
_a : Dict = f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(lowerCAmelCase_ )
# Verify that the target contains no genes besides the ones inside genes variable.
_a : Optional[int] = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
_a : List[Any] = f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(lowerCAmelCase_ )
# Generate random starting population.
_a : Union[str, Any] = []
for _ in range(lowerCAmelCase_ ):
population.append(''.join([random.choice(lowerCAmelCase_ ) for i in range(len(lowerCAmelCase_ ) )] ) )
# Just some logs to know what the algorithms is doing.
_a , _a : Union[str, Any] = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(lowerCAmelCase_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
_a : Optional[Any] = [evaluate(lowerCAmelCase_ , lowerCAmelCase_ ) for item in population]
# Check if there is a matching evolution.
_a : Tuple = sorted(lowerCAmelCase_ , key=lambda lowerCAmelCase_ : x[1] , reverse=lowerCAmelCase_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"""\nGeneration: {generation}"""
f"""\nTotal Population:{total_population}"""
f"""\nBest score: {population_score[0][1]}"""
f"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
_a : Dict = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(lowerCAmelCase_ )
# Normalize population score to be between 0 and 1.
_a : Tuple = [
(item, score / len(lowerCAmelCase_ )) for item, score in population_score
]
# This is selection
for i in range(lowerCAmelCase_ ):
population.extend(select(population_score[int(lowerCAmelCase_ )] , lowerCAmelCase_ , lowerCAmelCase_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(lowerCAmelCase_ ) > N_POPULATION:
break
if __name__ == "__main__":
__lowerCAmelCase = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
__lowerCAmelCase = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = basic(target_str, genes_list)
print(
f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
)
| 107 | 1 |
class A_ :
def __init__( self : Dict , UpperCAmelCase : int , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] ) -> Optional[int]:
__lowerCAmelCase: Dict = name
__lowerCAmelCase: Union[str, Any] = value
__lowerCAmelCase: str = weight
def __repr__( self : Dict ) -> Any:
return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def UpperCAmelCase ( self : int ) -> int:
return self.value
def UpperCAmelCase ( self : Tuple ) -> Optional[Any]:
return self.name
def UpperCAmelCase ( self : str ) -> Optional[Any]:
return self.weight
def UpperCAmelCase ( self : Any ) -> List[str]:
return self.value / self.weight
def _a ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase: List[str] = []
for i in range(len(SCREAMING_SNAKE_CASE ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = sorted(SCREAMING_SNAKE_CASE , key=SCREAMING_SNAKE_CASE , reverse=SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = []
__lowerCAmelCase: Dict = 0.0, 0.0
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _a ( ) -> Tuple:
"""simple docstring"""
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
_A : Dict ='''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
_A : List[str] =[
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
_A : str =dict(zip(vocab, range(len(vocab))))
_A : List[str] =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
with tempfile.TemporaryDirectory() as tmpdirname:
_A : Union[str, Any] =Path(tmpdirname)
_A : str =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
_A : int =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
_A : List[Any] =build_dir / VOCAB_FILES_NAMES['''merges_file''']
with open(src_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, '''w''') as fp:
fp.write('''\n'''.join(merges))
_A : int =FSMTTokenizer(
langs=['''en''', '''ru'''],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
_A : List[str] =FSMTConfig(
langs=['''ru''', '''en'''],
src_vocab_size=1_000,
tgt_vocab_size=1_000,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
_A : Union[str, Any] =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')
# Test
_A : List[str] =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A : Tuple =tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'Generated {mname_tiny}')
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 41 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
A_ = get_tests_dir('''fixtures''')
class lowercase( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self: Optional[Any] ):
'''simple docstring'''
_snake_case : Dict = mock.Mock()
_snake_case : List[str] = 500
_snake_case : Dict = {}
_snake_case : Optional[Any] = HTTPError
_snake_case : List[Any] = {}
# Download this model to make sure it's in the cache.
_snake_case : int = ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("""requests.Session.request""", return_value=a_ ) as mock_head:
_snake_case : Dict = ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
_snake_case : List[Any] = ViTImageProcessor.from_pretrained(
"""https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json""" )
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
with self.assertRaises(a_ ):
# config is in subfolder, the following should not work without specifying the subfolder
_snake_case : Union[str, Any] = AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants""" )
_snake_case : Optional[Any] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/stable-diffusion-all-variants""", subfolder="""feature_extractor""" )
self.assertIsNotNone(a_ )
@is_staging_test
class lowercase( unittest.TestCase ):
'''simple docstring'''
@classmethod
def UpperCamelCase_ ( cls: int ):
'''simple docstring'''
_snake_case : List[Any] = TOKEN
HfFolder.save_token(a_ )
@classmethod
def UpperCamelCase_ ( cls: Tuple ):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id="""test-image-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="""valid_org/test-image-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="""test-dynamic-image-processor""" )
except HTTPError:
pass
def UpperCamelCase_ ( self: int ):
'''simple docstring'''
_snake_case : Any = ViTImageProcessor.from_pretrained(a_ )
image_processor.push_to_hub("""test-image-processor""", use_auth_token=self._token )
_snake_case : int = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(a_, getattr(a_, a_ ) )
# Reset repo
delete_repo(token=self._token, repo_id="""test-image-processor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
a_, repo_id="""test-image-processor""", push_to_hub=a_, use_auth_token=self._token )
_snake_case : Any = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(a_, getattr(a_, a_ ) )
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
_snake_case : int = ViTImageProcessor.from_pretrained(a_ )
image_processor.push_to_hub("""valid_org/test-image-processor""", use_auth_token=self._token )
_snake_case : Tuple = ViTImageProcessor.from_pretrained("""valid_org/test-image-processor""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(a_, getattr(a_, a_ ) )
# Reset repo
delete_repo(token=self._token, repo_id="""valid_org/test-image-processor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
a_, repo_id="""valid_org/test-image-processor-org""", push_to_hub=a_, use_auth_token=self._token )
_snake_case : Tuple = ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(a_, getattr(a_, a_ ) )
def UpperCamelCase_ ( self: int ):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_snake_case : int = CustomImageProcessor.from_pretrained(a_ )
image_processor.push_to_hub("""test-dynamic-image-processor""", use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}, )
_snake_case : Optional[Any] = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=a_ )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, """CustomImageProcessor""" )
| 132 |
"""simple docstring"""
from typing import Any
class lowercase:
'''simple docstring'''
def __init__( self: Dict, a_: Any ):
'''simple docstring'''
_snake_case : Dict = data
_snake_case : Optional[Any] = None
class lowercase:
'''simple docstring'''
def __init__( self: str ):
'''simple docstring'''
_snake_case : Any = None
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
_snake_case : List[Any] = self.head
while temp is not None:
print(temp.data, end=""" """ )
_snake_case : int = temp.next
print()
def UpperCamelCase_ ( self: Union[str, Any], a_: Any ):
'''simple docstring'''
_snake_case : Optional[Any] = Node(a_ )
_snake_case : Union[str, Any] = self.head
_snake_case : List[Any] = new_node
def UpperCamelCase_ ( self: Tuple, a_: List[str], a_: Union[str, Any] ):
'''simple docstring'''
if node_data_a == node_data_a:
return
else:
_snake_case : int = self.head
while node_a is not None and node_a.data != node_data_a:
_snake_case : List[Any] = node_a.next
_snake_case : List[Any] = self.head
while node_a is not None and node_a.data != node_data_a:
_snake_case : List[Any] = node_a.next
if node_a is None or node_a is None:
return
_snake_case , _snake_case : int = node_a.data, node_a.data
if __name__ == "__main__":
A_ = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('''After swapping''')
ll.print_list()
| 132 | 1 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : List[Any] = ''
_lowerCamelCase : Dict = ''
_lowerCamelCase : Any = []
_lowerCamelCase : Tuple = 0
_lowerCamelCase : Optional[int] = 256
_lowerCamelCase : Dict = 0
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : Tuple = 0
_lowerCamelCase : List[str] = 0
def A_ ( self , lowercase ):
_lowerCamelCase : int = cva.imread(lowercase , 0 )
_lowerCamelCase : int = copy.deepcopy(self.img )
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Any = plt.hist(self.img.ravel() , 256 , [0, 256] , label='x' )
_lowerCamelCase : Tuple = np.sum(lowercase )
for i in range(len(lowercase ) ):
_lowerCamelCase : Optional[int] = x[i] / self.k
self.sk += prk
_lowerCamelCase : List[str] = (self.L - 1) * self.sk
if self.rem != 0:
_lowerCamelCase : int = int(last % last )
_lowerCamelCase : Optional[int] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowercase )
_lowerCamelCase : int = int(np.ma.count(self.img ) / self.img[1].size )
_lowerCamelCase : Tuple = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
_lowerCamelCase : Optional[int] = self.img[j][i]
if num != self.last_list[num]:
_lowerCamelCase : int = self.last_list[num]
cva.imwrite('output_data/output.jpg' , self.img )
def A_ ( self ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A_ ( self ):
cva.imshow('Output-Image' , self.img )
cva.imshow('Input-Image' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowercase__ = os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
lowercase__ = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image() | 96 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
@add_end_docstrings(__magic_name__ )
class A__ ( __magic_name__ ):
def __init__( self : int , *a : Dict , **a : Union[str, Any] ):
'''simple docstring'''
super().__init__(*a , **a )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def _lowerCamelCase ( self : Dict , a : List[str]=None ):
'''simple docstring'''
lowerCAmelCase__ : Any = {}
if top_k is not None:
lowerCAmelCase__ : Tuple = top_k
return {}, {}, postprocess_params
def __call__( self : Any , a : Union[str, List[str], "Image.Image", List["Image.Image"]] , **a : List[Any] ):
'''simple docstring'''
return super().__call__(a , **a )
def _lowerCamelCase ( self : Any , a : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = load_image(a )
lowerCAmelCase__ : Optional[int] = self.image_processor(images=a , return_tensors=self.framework )
return model_inputs
def _lowerCamelCase ( self : Optional[int] , a : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = self.model(**a )
return model_outputs
def _lowerCamelCase ( self : Optional[Any] , a : List[Any] , a : List[Any]=5 ):
'''simple docstring'''
if top_k > self.model.config.num_labels:
lowerCAmelCase__ : Optional[int] = self.model.config.num_labels
if self.framework == "pt":
lowerCAmelCase__ : List[Any] = model_outputs.logits.softmax(-1 )[0]
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = probs.topk(a )
elif self.framework == "tf":
lowerCAmelCase__ : Any = stable_softmax(model_outputs.logits , axis=-1 )[0]
lowerCAmelCase__ : Any = tf.math.top_k(a , k=a )
lowerCAmelCase__ , lowerCAmelCase__ : int = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowerCAmelCase__ : List[Any] = scores.tolist()
lowerCAmelCase__ : List[str] = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a , a )] | 212 | 0 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def a_ ( _lowercase , _lowercase , _lowercase = None ):
if version.parse(hfh.__version__ ).release < version.parse('''0.11.0''' ).release:
# old versions of hfh don't url-encode the file path
_UpperCamelCase : Optional[int] = quote(_lowercase )
return hfh.hf_hub_url(_lowercase , _lowercase , repo_type='''dataset''' , revision=_lowercase )
| 128 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
UpperCamelCase_ ="""examples/"""
UpperCamelCase_ ={
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), r"""\1version=\"VERSION\","""),
"""doc""": (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase_ ={
"""init""": """src/transformers/__init__.py""",
"""setup""": """setup.py""",
}
UpperCamelCase_ ="""README.md"""
def a_ ( _lowercase , _lowercase , _lowercase ):
with open(_lowercase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_UpperCamelCase : Tuple = f.read()
_UpperCamelCase , _UpperCamelCase : List[Any] = REPLACE_PATTERNS[pattern]
_UpperCamelCase : Optional[Any] = replace.replace('''VERSION''' , _lowercase )
_UpperCamelCase : List[Any] = re_pattern.sub(_lowercase , _lowercase )
with open(_lowercase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(_lowercase )
def a_ ( _lowercase ):
for folder, directories, fnames in os.walk(_lowercase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('''research_projects''' )
if "legacy" in directories:
directories.remove('''legacy''' )
for fname in fnames:
if fname.endswith('''.py''' ):
update_version_in_file(os.path.join(_lowercase , _lowercase ) , _lowercase , pattern='''examples''' )
def a_ ( _lowercase , _lowercase=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_lowercase , _lowercase , _lowercase )
if not patch:
update_version_in_examples(_lowercase )
def a_ ( ):
_UpperCamelCase : Any = '''🤗 Transformers currently provides the following architectures'''
_UpperCamelCase : List[str] = '''1. Want to contribute a new model?'''
with open(_lowercase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_UpperCamelCase : List[Any] = f.readlines()
# Find the start of the list.
_UpperCamelCase : Optional[Any] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_UpperCamelCase : Any = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
_UpperCamelCase : Tuple = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(_lowercase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(_lowercase )
def a_ ( ):
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
_UpperCamelCase : List[Any] = f.read()
_UpperCamelCase : List[Any] = REPLACE_PATTERNS['''init'''][0].search(_lowercase ).groups()[0]
return packaging.version.parse(_lowercase )
def a_ ( _lowercase=False ):
_UpperCamelCase : Union[str, Any] = get_version()
if patch and default_version.is_devrelease:
raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
if default_version.is_devrelease:
_UpperCamelCase : List[str] = default_version.base_version
elif patch:
_UpperCamelCase : Union[str, Any] = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
_UpperCamelCase : str = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
_UpperCamelCase : Optional[int] = input(F"""Which version are you releasing? [{default_version}]""" )
if len(_lowercase ) == 0:
_UpperCamelCase : str = default_version
print(F"""Updating version to {version}.""" )
global_version_update(_lowercase , patch=_lowercase )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def a_ ( ):
_UpperCamelCase : Any = get_version()
_UpperCamelCase : Dict = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
_UpperCamelCase : Union[str, Any] = current_version.base_version
# Check with the user we got that right.
_UpperCamelCase : int = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(_lowercase ) == 0:
_UpperCamelCase : List[str] = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(_lowercase )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase_ =argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
UpperCamelCase_ =parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work()
| 128 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Any = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : str = [
'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
'SEWForCTC',
'SEWForSequenceClassification',
'SEWModel',
'SEWPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
lowerCamelCase_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 286 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowerCamelCase_ : Any = re.compile(r'\s+')
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(_UpperCAmelCase , '' , example['content'] ).encode('utf-8' ) ).hexdigest()}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = [len(_UpperCAmelCase ) for line in example['content'].splitlines()]
return {"line_mean": np.mean(_UpperCAmelCase ), "line_max": max(_UpperCAmelCase )}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = np.mean([c.isalnum() for c in example['content']] )
return {"alpha_frac": alpha_frac}
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example['hash'] )
return True
else:
return False
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=5 ):
"""simple docstring"""
A_ : Optional[int] = ['auto-generated', 'autogenerated', 'automatically generated']
A_ : List[str] = example['content'].splitlines()
for _, line in zip(range(_UpperCAmelCase ) , _UpperCAmelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=5 , _UpperCAmelCase=0.05 ):
"""simple docstring"""
A_ : Any = ['unit tests', 'test file', 'configuration file']
A_ : Dict = example['content'].splitlines()
A_ : List[Any] = 0
A_ : str = 0
# first test
for _, line in zip(range(_UpperCAmelCase ) , _UpperCAmelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
A_ : Tuple = example['content'].count('\n' )
A_ : Tuple = int(coeff * nlines )
for line in lines:
count_config += line.lower().count('config' )
count_test += line.lower().count('test' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = ['def ', 'class ', 'for ', 'while ']
A_ : Tuple = example['content'].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=4 ):
"""simple docstring"""
A_ : Union[str, Any] = example['content'].splitlines()
A_ : Any = 0
for line in lines:
counter += line.lower().count('=' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[Any] = tokenizer(example['content'] , truncation=_UpperCAmelCase )['input_ids']
A_ : Dict = len(example['content'] ) / len(_UpperCAmelCase )
return {"ratio": ratio}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = {}
results.update(get_hash(_UpperCAmelCase ) )
results.update(line_stats(_UpperCAmelCase ) )
results.update(alpha_stats(_UpperCAmelCase ) )
results.update(char_token_ratio(_UpperCAmelCase ) )
results.update(is_autogenerated(_UpperCAmelCase ) )
results.update(is_config_or_test(_UpperCAmelCase ) )
results.update(has_no_keywords(_UpperCAmelCase ) )
results.update(has_few_assignments(_UpperCAmelCase ) )
return results
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if not check_uniques(_UpperCAmelCase , _UpperCAmelCase ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
with open(_UpperCAmelCase , 'rb' ) as f_in:
with gzip.open(str(_UpperCAmelCase ) + '.gz' , 'wb' , compresslevel=6 ) as f_out:
shutil.copyfileobj(_UpperCAmelCase , _UpperCAmelCase )
os.unlink(_UpperCAmelCase )
# Settings
lowerCamelCase_ : Optional[int] = HfArgumentParser(PreprocessingArguments)
lowerCamelCase_ : Optional[Any] = parser.parse_args()
if args.num_workers is None:
lowerCamelCase_ : int = multiprocessing.cpu_count()
lowerCamelCase_ : Tuple = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowerCamelCase_ : Tuple = time.time()
lowerCamelCase_ : Tuple = load_dataset(args.dataset_name, split='train')
print(F"Time to load dataset: {time.time()-t_start:.2f}")
# Run preprocessing
lowerCamelCase_ : List[str] = time.time()
lowerCamelCase_ : Optional[int] = ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")
# Deduplicate hashes
lowerCamelCase_ : int = set(ds.unique('hash'))
lowerCamelCase_ : Union[str, Any] = len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")
# Deduplicate data and apply heuristics
lowerCamelCase_ : Optional[int] = time.time()
lowerCamelCase_ : Tuple = ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(F"Time to filter dataset: {time.time()-t_start:.2f}")
print(F"Size of filtered dataset: {len(ds_filter)}")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowerCamelCase_ : Union[str, Any] = time.time()
lowerCamelCase_ , lowerCamelCase_ : str = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"Time to deduplicate dataset: {time.time()-t_start:.2f}")
print(F"Size of deduplicate dataset: {len(ds_filter)}")
# Save data in batches of samples_per_file
lowerCamelCase_ : Tuple = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / 'duplicate_clusters.json', 'w') as f:
json.dump(duplicate_clusters, f)
lowerCamelCase_ : Optional[Any] = output_dir / 'data'
data_dir.mkdir(exist_ok=True)
lowerCamelCase_ : List[str] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowerCamelCase_ : Optional[int] = str(data_dir / F"file-{file_number+1:012}.json")
lowerCamelCase_ : List[str] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"Time to save dataset: {time.time()-t_start:.2f}") | 286 | 1 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Dict:
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str ) -> Dict:
'''simple docstring'''
A__ = tmp_path / 'cache'
A__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
A__ = SqlDatasetReader(
'dataset' , 'sqlite:///' + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ , keep_in_memory=SCREAMING_SNAKE_CASE__ ).read()
_check_sql_dataset(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@require_sqlalchemy
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def _snake_case( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any ) -> Union[str, Any]:
'''simple docstring'''
A__ = tmp_path / 'cache'
A__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
A__ = features.copy() if features else default_expected_features
A__ = (
Features({feature: Value(SCREAMING_SNAKE_CASE__ ) for feature, dtype in features.items()} ) if features is not None else None
)
A__ = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , features=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
_check_sql_dataset(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__ ) ) as con:
A__ = con.cursor()
cur.execute('SELECT * FROM dataset' )
for row in cur:
yield row
@require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Dict:
'''simple docstring'''
A__ = tmp_path / 'cache'
A__ = os.path.join(SCREAMING_SNAKE_CASE__ , 'tmp.sql' )
A__ = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
SqlDatasetWriter(SCREAMING_SNAKE_CASE__ , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=1 ).write()
A__ = iter_sql_file(SCREAMING_SNAKE_CASE__ )
A__ = iter_sql_file(SCREAMING_SNAKE_CASE__ )
for rowa, rowa in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
assert rowa == rowa
@require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict ) -> List[str]:
'''simple docstring'''
A__ = tmp_path / 'cache'
A__ = os.path.join(SCREAMING_SNAKE_CASE__ , 'tmp.sql' )
A__ = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
SqlDatasetWriter(SCREAMING_SNAKE_CASE__ , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=2 ).write()
A__ = iter_sql_file(SCREAMING_SNAKE_CASE__ )
A__ = iter_sql_file(SCREAMING_SNAKE_CASE__ )
for rowa, rowa in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
assert rowa == rowa
@require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Tuple:
'''simple docstring'''
A__ = tmp_path / 'cache'
A__ = os.path.join(SCREAMING_SNAKE_CASE__ , 'tmp.sql' )
A__ = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
SqlDatasetWriter(SCREAMING_SNAKE_CASE__ , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=0 ).write()
| 355 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : List[Any],lowercase_ : str )-> List[Any]:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'],model_result['ss'] ):
A__ = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(lowercase_ )
def snake_case__ ( self : Dict )-> List[str]:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def snake_case__ ( self : Dict )-> List[str]:
'''simple docstring'''
A__ = 'sgugger/tiny-distilbert-classification'
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,only_pretrain_model=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def snake_case__ ( self : List[Any] )-> Any:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == 'cpu','Cant do half precision' )
def snake_case__ ( self : Any )-> Dict:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,fpaa=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def snake_case__ ( self : Any )-> Optional[Any]:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = AutoConfig.from_pretrained(lowercase_ )
# set architectures equal to `None`
A__ = None
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_,configs=[config] )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def snake_case__ ( self : Union[str, Any] )-> int:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == 'cpu','Can\'t do half precision' )
def snake_case__ ( self : List[Any] )-> Dict:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],fpaa=lowercase_,multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def snake_case__ ( self : int )-> Optional[int]:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = AutoConfig.from_pretrained(lowercase_ )
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_,configs=[config] )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def snake_case__ ( self : List[Any] )-> Any:
'''simple docstring'''
A__ = 'sshleifer/tinier_bart'
A__ = AutoConfig.from_pretrained(lowercase_ )
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_,configs=[config] )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def snake_case__ ( self : List[str] )-> List[str]:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
A__ = AutoConfig.from_pretrained(lowercase_ )
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_,configs=[config] )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def snake_case__ ( self : int )-> Union[str, Any]:
'''simple docstring'''
A__ = 'sshleifer/tinier_bart'
A__ = AutoConfig.from_pretrained(lowercase_ )
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_,configs=[config] )
A__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def snake_case__ ( self : Optional[Any] )-> Tuple:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,save_to_csv=lowercase_,sequence_lengths=[8],batch_sizes=[1],inference_time_csv_file=os.path.join(lowercase_,'inf_time.csv' ),train_memory_csv_file=os.path.join(lowercase_,'train_mem.csv' ),inference_memory_csv_file=os.path.join(lowercase_,'inf_mem.csv' ),train_time_csv_file=os.path.join(lowercase_,'train_time.csv' ),env_info_csv_file=os.path.join(lowercase_,'env.csv' ),multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
benchmark.run()
self.assertTrue(Path(os.path.join(lowercase_,'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_,'train_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_,'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_,'train_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_,'env.csv' ) ).exists() )
def snake_case__ ( self : Tuple )-> str:
'''simple docstring'''
A__ = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(lowercase_ : Optional[Any] ):
self.assertTrue(hasattr(lowercase_,'sequential' ) )
self.assertTrue(hasattr(lowercase_,'cumulative' ) )
self.assertTrue(hasattr(lowercase_,'current' ) )
self.assertTrue(hasattr(lowercase_,'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
A__ = PyTorchBenchmarkArguments(
models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],log_filename=os.path.join(lowercase_,'log.txt' ),log_print=lowercase_,trace_memory_line_by_line=lowercase_,multi_process=lowercase_,)
A__ = PyTorchBenchmark(lowercase_ )
A__ = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(lowercase_,'log.txt' ) ).exists() )
| 282 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
def __init__( self : Optional[Any] , lowercase : int , lowercase : str=13 , lowercase : Any=7 , lowercase : str=True , lowercase : int=True , lowercase : int=True , lowercase : Any=True , lowercase : Any=99 , lowercase : Any=32 , lowercase : Dict=5 , lowercase : Optional[int]=4 , lowercase : Dict=37 , lowercase : int="gelu" , lowercase : Union[str, Any]=0.1 , lowercase : Union[str, Any]=0.1 , lowercase : str=512 , lowercase : Tuple=16 , lowercase : List[str]=2 , lowercase : str=0.02 , lowercase : str=3 , lowercase : Dict=4 , lowercase : int=None , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : Tuple ):
'''simple docstring'''
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
def A ( self : Optional[Any] , lowercase : Union[str, Any] , lowercase : Optional[Any] , lowercase : Tuple , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : List[Any] , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = NystromformerModel(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase )
UpperCAmelCase = model(lowercase , token_type_ids=lowercase )
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Union[str, Any] , lowercase : Optional[int] , lowercase : Any , lowercase : str , lowercase : int , lowercase : int , lowercase : Dict , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = NystromformerForMaskedLM(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Union[str, Any] , lowercase : Optional[Any] , lowercase : Dict , lowercase : Tuple , lowercase : int , lowercase : Optional[Any] , lowercase : List[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = NystromformerForQuestionAnswering(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , start_positions=lowercase , end_positions=lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[str] , lowercase : Optional[Any] , lowercase : List[str] , lowercase : Optional[Any] , lowercase : str , lowercase : Optional[int] , lowercase : Tuple , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForSequenceClassification(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Any , lowercase : str , lowercase : List[Any] , lowercase : str , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : Tuple , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForTokenClassification(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Optional[int] , lowercase : int , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : Any , lowercase : Optional[Any] , lowercase : List[str] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = self.num_choices
UpperCAmelCase = NystromformerForMultipleChoice(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _a ( __a , __a , unittest.TestCase ):
__a : Optional[int] = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
__a : List[Any] = (
{
"""feature-extraction""": NystromformerModel,
"""fill-mask""": NystromformerForMaskedLM,
"""question-answering""": NystromformerForQuestionAnswering,
"""text-classification""": NystromformerForSequenceClassification,
"""token-classification""": NystromformerForTokenClassification,
"""zero-shot""": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : Optional[int] = False
__a : int = False
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = NystromformerModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def A ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase = type
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase )
@slow
def A ( self : List[Any] ):
'''simple docstring'''
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = NystromformerModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_torch
class _a ( unittest.TestCase ):
@slow
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = NystromformerModel.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
UpperCAmelCase = model(lowercase )[0]
UpperCAmelCase = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , lowercase )
UpperCAmelCase = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=1E-4 ) )
@slow
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = '''the [MASK] of Belgium is Brussels'''
UpperCAmelCase = AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = tokenizer(lowercase , return_tensors='''pt''' )
with torch.no_grad():
UpperCAmelCase = model(encoding.input_ids ).logits
UpperCAmelCase = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(lowercase ) , '''capital''' )
| 34 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class a__:
def __init__( self : str , __snake_case : Union[str, Any] , __snake_case : List[str]=13 , __snake_case : Tuple=7 , __snake_case : Optional[Any]=False , __snake_case : Dict=True , __snake_case : List[Any]=False , __snake_case : Optional[int]=False , __snake_case : Optional[Any]=19 , __snake_case : Any=32 , __snake_case : Union[str, Any]=5 , __snake_case : Union[str, Any]=4 , __snake_case : int=37 , __snake_case : Union[str, Any]="gelu" , __snake_case : Optional[Any]=0.1 , __snake_case : List[str]=0.1 , __snake_case : int=5_12 , __snake_case : int=16 , __snake_case : Tuple=2 , __snake_case : str=0.02 , __snake_case : str=3 , __snake_case : Dict=4 , __snake_case : List[Any]=None , ):
a : Tuple = parent
a : List[str] = batch_size
a : Optional[Any] = seq_length
a : Tuple = is_training
a : Optional[Any] = use_input_mask
a : List[Any] = use_token_type_ids
a : List[Any] = use_labels
a : int = vocab_size
a : Union[str, Any] = hidden_size
a : Any = num_hidden_layers
a : List[str] = num_attention_heads
a : int = intermediate_size
a : str = hidden_act
a : Tuple = hidden_dropout_prob
a : Union[str, Any] = attention_probs_dropout_prob
a : List[str] = max_position_embeddings
a : Any = type_vocab_size
a : List[str] = type_sequence_label_size
a : Union[str, Any] = initializer_range
a : Optional[int] = num_labels
a : Optional[Any] = num_choices
a : Optional[int] = scope
def lowercase_ ( self : List[Any] ):
a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a : Dict = None
if self.use_input_mask:
a : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
a : Optional[Any] = None
a : Optional[int] = None
a : Dict = None
if self.use_labels:
a : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
a : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ ( self : List[Any] ):
a : Any = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , is_folding_model=__snake_case , esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False} , )
return config
def lowercase_ ( self : Optional[Any] , __snake_case : int , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : str , __snake_case : Any ):
a : Tuple = EsmForProteinFolding(config=__snake_case ).float()
model.to(__snake_case )
model.eval()
a : Dict = model(__snake_case , attention_mask=__snake_case )
a : Union[str, Any] = model(__snake_case )
a : List[Any] = model(__snake_case )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def lowercase_ ( self : Optional[Any] ):
a : Tuple = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) : Optional[Any] = config_and_inputs
a : Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a__( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase__ = False
lowercase__ = (EsmForProteinFolding,) if is_torch_available() else ()
lowercase__ = ()
lowercase__ = {} if is_torch_available() else {}
lowercase__ = False
def lowercase_ ( self : int ):
a : Tuple = EsmFoldModelTester(self )
a : Any = ConfigTester(self , config_class=__snake_case , hidden_size=37 )
def lowercase_ ( self : List[str] ):
self.config_tester.run_common_tests()
def lowercase_ ( self : Union[str, Any] ):
a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
@unittest.skip('Does not support attention outputs' )
def lowercase_ ( self : str ):
pass
@unittest.skip
def lowercase_ ( self : Optional[int] ):
pass
@unittest.skip('Esm does not support embedding resizing' )
def lowercase_ ( self : Optional[int] ):
pass
@unittest.skip('Esm does not support embedding resizing' )
def lowercase_ ( self : Any ):
pass
@unittest.skip('ESMFold does not support passing input embeds!' )
def lowercase_ ( self : Any ):
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowercase_ ( self : Union[str, Any] ):
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowercase_ ( self : List[Any] ):
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowercase_ ( self : List[Any] ):
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowercase_ ( self : int ):
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowercase_ ( self : List[Any] ):
pass
@unittest.skip('ESMFold does not output hidden states in the normal way.' )
def lowercase_ ( self : int ):
pass
@unittest.skip('ESMfold does not output hidden states in the normal way.' )
def lowercase_ ( self : int ):
pass
@unittest.skip('ESMFold only has one output format.' )
def lowercase_ ( self : Dict ):
pass
@unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality' )
def lowercase_ ( self : Tuple ):
pass
@unittest.skip('ESMFold does not support input chunking.' )
def lowercase_ ( self : List[str] ):
pass
@unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.' )
def lowercase_ ( self : List[Any] ):
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def lowercase_ ( self : Union[str, Any] ):
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def lowercase_ ( self : Any ):
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def lowercase_ ( self : List[str] ):
pass
@unittest.skip('ESMFold doesn\'t support data parallel.' )
def lowercase_ ( self : Dict ):
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase_ ( self : Union[str, Any] ):
pass
@require_torch
class a__( lowerCamelCase__ ):
@slow
def lowercase_ ( self : Optional[int] ):
a : Optional[Any] = EsmForProteinFolding.from_pretrained('facebook/esmfold_v1' ).float()
model.eval()
a : int = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
a : Any = model(__snake_case )['positions']
a : Dict = torch.tensor([2.5828, 0.7993, -10.9334] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , __snake_case , atol=1e-4 ) ) | 297 | 0 |
import heapq
import sys
import numpy as np
__lowerCAmelCase = tuple[int, int]
class __a :
def __init__( self ) -> Optional[int]:
'''simple docstring'''
lowercase__: str = []
lowercase__: List[Any] = set()
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
if not self.empty():
return self.elements[0][0]
else:
return float('inf' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
return len(self.elements ) == 0
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(lowerCAmelCase__ )
else:
# update
# print("update", item)
lowercase__: int = []
(lowercase__): Union[str, Any] = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
(lowercase__): Union[str, Any] = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if item in self.set:
self.set.remove(lowerCAmelCase__ )
lowercase__: Optional[Any] = []
(lowercase__): Dict = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
(lowercase__): Union[str, Any] = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
return self.elements[0][1]
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
(lowercase__): str = heapq.heappop(self.elements )
self.set.remove(lowerCAmelCase__ )
return (priority, item)
def snake_case_ ( snake_case , snake_case ) -> Optional[int]:
# euclidean distance
lowercase__: Optional[int] = np.array(snake_case )
lowercase__: List[str] = np.array(snake_case )
return np.linalg.norm(a - b )
def snake_case_ ( snake_case , snake_case ) -> Optional[int]:
# integer division by time variable
return consistent_heuristic(snake_case , snake_case ) // t
def snake_case_ ( snake_case , snake_case ) -> str:
# manhattan distance
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> Dict:
lowercase__: Dict = g_function[start] + Wa * heuristics[i](snake_case , snake_case )
return ans
def snake_case_ ( snake_case , snake_case , snake_case ) -> Optional[Any]:
lowercase__: Tuple = np.chararray((n, n) )
for i in range(snake_case ):
for j in range(snake_case ):
lowercase__: List[str] = '*'
for i in range(snake_case ):
for j in range(snake_case ):
if (j, (n - 1) - i) in blocks:
lowercase__: List[Any] = '#'
lowercase__: Tuple = '-'
lowercase__: int = back_pointer[goal]
while x != start:
(lowercase__): Any = x
# print(x)
lowercase__: Optional[Any] = '-'
lowercase__: Any = back_pointer[x]
lowercase__: str = '-'
for i in range(snake_case ):
for j in range(snake_case ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=' ' )
print('<-- End position' , end=' ' )
else:
print(grid[i][j] , end=' ' )
print()
print('^' )
print('Start position' )
print()
print('# is an obstacle' )
print('- is the path taken by algorithm' )
print('PATH TAKEN BY THE ALGORITHM IS:-' )
lowercase__: int = back_pointer[goal]
while x != start:
print(snake_case , end=' ' )
lowercase__: List[Any] = back_pointer[x]
print(snake_case )
sys.exit()
def snake_case_ ( snake_case ) -> Tuple:
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ) -> Optional[int]:
for itera in range(snake_case ):
open_list[itera].remove_element(snake_case )
# print("s", s)
# print("j", j)
(lowercase__): str = s
lowercase__: Union[str, Any] = (x - 1, y)
lowercase__: Union[str, Any] = (x + 1, y)
lowercase__: Union[str, Any] = (x, y + 1)
lowercase__: List[Any] = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(snake_case ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(snake_case )
lowercase__: List[Any] = -1
lowercase__: str = float('inf' )
if valid(snake_case ) and g_function[neighbours] > g_function[s] + 1:
lowercase__: int = g_function[s] + 1
lowercase__: str = s
if neighbours not in close_list_anchor:
open_list[0].put(snake_case , key(snake_case , 0 , snake_case , snake_case ) )
if neighbours not in close_list_inad:
for var in range(1 , snake_case ):
if key(snake_case , snake_case , snake_case , snake_case ) <= Wa * key(
snake_case , 0 , snake_case , snake_case ):
open_list[j].put(
snake_case , key(snake_case , snake_case , snake_case , snake_case ) )
def snake_case_ ( ) -> Optional[int]:
lowercase__: List[Any] = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
__lowerCAmelCase = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
__lowerCAmelCase = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
__lowerCAmelCase = make_common_ground()
__lowerCAmelCase = blocks_blk
# hyper parameters
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = 20
__lowerCAmelCase = 3 # one consistent and two other inconsistent
# start and end destination
__lowerCAmelCase = (0, 0)
__lowerCAmelCase = (n - 1, n - 1)
__lowerCAmelCase = 1
def snake_case_ ( snake_case , snake_case , snake_case ) -> Optional[int]:
lowercase__: Optional[Any] = {start: 0, goal: float('inf' )}
lowercase__: List[str] = {start: -1, goal: -1}
lowercase__: Optional[int] = []
lowercase__: Tuple = set()
for i in range(snake_case ):
open_list.append(PriorityQueue() )
open_list[i].put(snake_case , key(snake_case , snake_case , snake_case , snake_case ) )
lowercase__: list[int] = []
lowercase__: list[int] = []
while open_list[0].minkey() < float('inf' ):
for i in range(1 , snake_case ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float('inf' ):
do_something(snake_case , snake_case , snake_case )
else:
lowercase__: Dict = open_list[i].top_show()
visited.add(snake_case )
expand_state(
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , )
close_list_inad.append(snake_case )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float('inf' ):
do_something(snake_case , snake_case , snake_case )
else:
lowercase__: str = open_list[0].top_show()
visited.add(snake_case )
expand_state(
snake_case , 0 , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , )
close_list_anchor.append(snake_case )
print('No path found to goal' )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(snake_case ):
if (j, i) in blocks:
print('#' , end=' ' )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print('*' , end=' ' )
else:
print('-' , end=' ' )
else:
print('*' , end=' ' )
if (j, i) == (n - 1, n - 1):
print('<-- End position' , end=' ' )
print()
print('^' )
print('Start position' )
print()
print('# is an obstacle' )
print('- is the path taken by algorithm' )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 356 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerCAmelCase = logging.getLogger(__name__)
def snake_case_ ( snake_case , snake_case ) -> Optional[int]:
lowercase__: Optional[int] = np.argmax(snake_case , axis=1 )
return np.sum(outputs == labels )
def snake_case_ ( snake_case ) -> Dict:
with open(snake_case , encoding='utf_8' ) as f:
lowercase__: str = csv.reader(snake_case )
lowercase__: int = []
next(snake_case ) # skip the first line
for line in tqdm(snake_case ):
output.append((' '.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Tuple:
lowercase__: List[Any] = []
for dataset in encoded_datasets:
lowercase__: Dict = len(snake_case )
lowercase__: int = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowercase__: int = np.zeros((n_batch, 2) , dtype=np.intaa )
lowercase__: Optional[int] = np.full((n_batch, 2, input_len) , fill_value=-1_00 , dtype=np.intaa )
lowercase__: Optional[Any] = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(snake_case ):
lowercase__: List[Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowercase__: List[Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowercase__: Union[str, Any] = with_conta
lowercase__: List[Any] = with_conta
lowercase__: Any = len(snake_case ) - 1
lowercase__: Dict = len(snake_case ) - 1
lowercase__: Optional[Any] = with_conta
lowercase__: Tuple = with_conta
lowercase__: int = mc_label
lowercase__: Any = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(snake_case ) for t in all_inputs ) )
return tensor_datasets
def snake_case_ ( ) -> Union[str, Any]:
lowercase__: Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=snake_case , default='openai-gpt' , help='pretrained model name' )
parser.add_argument('--do_train' , action='store_true' , help='Whether to run training.' )
parser.add_argument('--do_eval' , action='store_true' , help='Whether to run eval on the dev set.' )
parser.add_argument(
'--output_dir' , default=snake_case , type=snake_case , required=snake_case , help='The output directory where the model predictions and checkpoints will be written.' , )
parser.add_argument('--train_dataset' , type=snake_case , default='' )
parser.add_argument('--eval_dataset' , type=snake_case , default='' )
parser.add_argument('--seed' , type=snake_case , default=42 )
parser.add_argument('--num_train_epochs' , type=snake_case , default=3 )
parser.add_argument('--train_batch_size' , type=snake_case , default=8 )
parser.add_argument('--eval_batch_size' , type=snake_case , default=16 )
parser.add_argument('--adam_epsilon' , default=1e-8 , type=snake_case , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , type=snake_case , default=1 )
parser.add_argument(
'--max_steps' , default=-1 , type=snake_case , help=(
'If > 0: set total number of training steps to perform. Override num_train_epochs.'
) , )
parser.add_argument(
'--gradient_accumulation_steps' , type=snake_case , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--learning_rate' , type=snake_case , default=6.25e-5 )
parser.add_argument('--warmup_steps' , default=0 , type=snake_case , help='Linear warmup over warmup_steps.' )
parser.add_argument('--lr_schedule' , type=snake_case , default='warmup_linear' )
parser.add_argument('--weight_decay' , type=snake_case , default=0.0_1 )
parser.add_argument('--lm_coef' , type=snake_case , default=0.9 )
parser.add_argument('--n_valid' , type=snake_case , default=3_74 )
parser.add_argument('--server_ip' , type=snake_case , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=snake_case , default='' , help='Can be used for distant debugging.' )
lowercase__: List[str] = parser.parse_args()
print(snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowercase__: Any = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
lowercase__: Tuple = torch.cuda.device_count()
logger.info('device: {}, n_gpu {}'.format(snake_case , snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError('At least one of `do_train` or `do_eval` must be True.' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowercase__: Any = ['_start_', '_delimiter_', '_classify_']
lowercase__: Any = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(snake_case )
lowercase__: int = tokenizer.convert_tokens_to_ids(snake_case )
lowercase__: int = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(snake_case ) )
model.to(snake_case )
# Load and encode the datasets
def tokenize_and_encode(snake_case ):
if isinstance(snake_case , snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(snake_case ) )
elif isinstance(snake_case , snake_case ):
return obj
return [tokenize_and_encode(snake_case ) for o in obj]
logger.info('Encoding dataset...' )
lowercase__: Dict = load_rocstories_dataset(args.train_dataset )
lowercase__: Dict = load_rocstories_dataset(args.eval_dataset )
lowercase__: str = (train_dataset, eval_dataset)
lowercase__: Any = tokenize_and_encode(snake_case )
# Compute the max input length for the Transformer
lowercase__: Optional[Any] = model.config.n_positions // 2 - 2
lowercase__: Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowercase__: List[str] = min(snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowercase__: str = pre_process_datasets(snake_case , snake_case , snake_case , *snake_case )
lowercase__ , lowercase__: Optional[Any] = tensor_datasets[0], tensor_datasets[1]
lowercase__: List[str] = TensorDataset(*snake_case )
lowercase__: Dict = RandomSampler(snake_case )
lowercase__: Optional[int] = DataLoader(snake_case , sampler=snake_case , batch_size=args.train_batch_size )
lowercase__: str = TensorDataset(*snake_case )
lowercase__: str = SequentialSampler(snake_case )
lowercase__: Optional[Any] = DataLoader(snake_case , sampler=snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowercase__: Union[str, Any] = args.max_steps
lowercase__: Tuple = args.max_steps // (len(snake_case ) // args.gradient_accumulation_steps) + 1
else:
lowercase__: Optional[Any] = len(snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
lowercase__: str = list(model.named_parameters() )
lowercase__: Any = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
lowercase__: str = [
{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'weight_decay': args.weight_decay,
},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], 'weight_decay': 0.0},
]
lowercase__: Tuple = AdamW(snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
lowercase__: Tuple = get_linear_schedule_with_warmup(
snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=snake_case )
if args.do_train:
lowercase__ , lowercase__ , lowercase__: int = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='Epoch' ):
lowercase__: str = 0
lowercase__: Optional[Any] = 0
lowercase__: List[Any] = tqdm(snake_case , desc='Training' )
for step, batch in enumerate(snake_case ):
lowercase__: Union[str, Any] = tuple(t.to(snake_case ) for t in batch )
lowercase__ , lowercase__ , lowercase__ , lowercase__: List[Any] = batch
lowercase__: List[str] = model(snake_case , mc_token_ids=snake_case , lm_labels=snake_case , mc_labels=snake_case )
lowercase__: Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowercase__: Union[str, Any] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowercase__: Tuple = 'Training loss: {:.2e} lr: {:.2e}'.format(snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowercase__: Any = model.module if hasattr(snake_case , 'module' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowercase__: Tuple = os.path.join(args.output_dir , snake_case )
lowercase__: List[str] = os.path.join(args.output_dir , snake_case )
torch.save(model_to_save.state_dict() , snake_case )
model_to_save.config.to_json_file(snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowercase__: Optional[Any] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowercase__: Any = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(snake_case )
if args.do_eval:
model.eval()
lowercase__ , lowercase__: Optional[Any] = 0, 0
lowercase__ , lowercase__: List[Any] = 0, 0
for batch in tqdm(snake_case , desc='Evaluating' ):
lowercase__: str = tuple(t.to(snake_case ) for t in batch )
lowercase__ , lowercase__ , lowercase__ , lowercase__: Union[str, Any] = batch
with torch.no_grad():
lowercase__ , lowercase__ , lowercase__ , lowercase__: Any = model(
snake_case , mc_token_ids=snake_case , lm_labels=snake_case , mc_labels=snake_case )
lowercase__: Dict = mc_logits.detach().cpu().numpy()
lowercase__: Tuple = mc_labels.to('cpu' ).numpy()
lowercase__: Dict = accuracy(snake_case , snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowercase__: Optional[int] = eval_loss / nb_eval_steps
lowercase__: Optional[int] = eval_accuracy / nb_eval_examples
lowercase__: int = tr_loss / nb_tr_steps if args.do_train else None
lowercase__: Optional[Any] = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}
lowercase__: Dict = os.path.join(args.output_dir , 'eval_results.txt' )
with open(snake_case , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s' , snake_case , str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 288 | 0 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
lowerCAmelCase__ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class __snake_case ( datasets.BuilderConfig):
snake_case__ : Optional[datasets.Features] = None
def snake_case_ ( A_ : "pyspark.sql.DataFrame", A_ : List[int], ):
'''simple docstring'''
import pyspark
def generate_fn():
_lowerCamelCase : Any = df.select('''*''', pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
_lowerCamelCase : List[str] = df_with_partition_id.select('''*''' ).where(F'''part_id = {partition_id}''' ).drop('''part_id''' )
_lowerCamelCase : Optional[Any] = partition_df.collect()
_lowerCamelCase : int = 0
for row in rows:
yield F'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class __snake_case ( _BaseExamplesIterable):
def __init__( self : List[str] , __lowerCAmelCase : "pyspark.sql.DataFrame" , __lowerCAmelCase : List[str]=None , ):
"""simple docstring"""
_lowerCamelCase : int = df
_lowerCamelCase : Union[str, Any] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowerCamelCase : str = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[int] ):
"""simple docstring"""
yield from self.generate_examples_fn()
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : np.random.Generator ):
"""simple docstring"""
_lowerCamelCase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(__lowerCAmelCase )
return SparkExamplesIterable(self.df , partition_order=__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : List[Any] = self.split_shard_indices_by_worker(__lowerCAmelCase , __lowerCAmelCase )
return SparkExamplesIterable(self.df , partition_order=__lowerCAmelCase )
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
return len(self.partition_order )
class __snake_case ( datasets.DatasetBuilder):
snake_case__ : Dict = SparkConfig
def __init__( self : str , __lowerCAmelCase : "pyspark.sql.DataFrame" , __lowerCAmelCase : str = None , __lowerCAmelCase : str = None , **__lowerCAmelCase : str , ):
"""simple docstring"""
import pyspark
_lowerCamelCase : Union[str, Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowerCamelCase : Tuple = df
_lowerCamelCase : Tuple = working_dir
super().__init__(
cache_dir=__lowerCAmelCase , config_name=str(self.df.semanticHash() ) , **__lowerCAmelCase , )
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
def create_cache_and_write_probe(__lowerCAmelCase : Optional[int] ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=__lowerCAmelCase )
_lowerCamelCase : str = os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(__lowerCAmelCase , '''a''' )
return [probe_file]
if self._spark.conf.get('''spark.master''' , '''''' ).startswith('''local''' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowerCamelCase : Union[str, Any] = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(__lowerCAmelCase ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : datasets.download.download_manager.DownloadManager ):
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : str ):
"""simple docstring"""
import pyspark
def get_arrow_batch_size(__lowerCAmelCase : Union[str, Any] ):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )
_lowerCamelCase : Optional[int] = self.df.count()
_lowerCamelCase : Optional[Any] = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowerCamelCase : Union[str, Any] = (
self.df.limit(__lowerCAmelCase )
.repartition(1 )
.mapInArrow(__lowerCAmelCase , '''batch_bytes: long''' )
.agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowerCamelCase : Any = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowerCamelCase : Union[str, Any] = min(__lowerCAmelCase , int(approx_total_size / max_shard_size ) )
_lowerCamelCase : List[str] = self.df.repartition(__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : int , ):
"""simple docstring"""
import pyspark
_lowerCamelCase : Dict = ParquetWriter if file_format == '''parquet''' else ArrowWriter
_lowerCamelCase : Optional[Any] = os.path.join(self._working_dir , os.path.basename(__lowerCAmelCase ) ) if self._working_dir else fpath
_lowerCamelCase : Tuple = file_format == '''parquet'''
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowerCamelCase : int = self.config.features
_lowerCamelCase : Union[str, Any] = self._writer_batch_size
_lowerCamelCase : Optional[int] = self._fs.storage_options
def write_arrow(__lowerCAmelCase : Optional[int] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowerCamelCase : int = pyspark.TaskContext().taskAttemptId()
_lowerCamelCase : Optional[int] = next(__lowerCAmelCase , __lowerCAmelCase )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
_lowerCamelCase : Tuple = 0
_lowerCamelCase : Optional[Any] = writer_class(
features=__lowerCAmelCase , path=working_fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , writer_batch_size=__lowerCAmelCase , storage_options=__lowerCAmelCase , embed_local_files=__lowerCAmelCase , )
_lowerCamelCase : Dict = pa.Table.from_batches([first_batch] )
writer.write_table(__lowerCAmelCase )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
shard_id += 1
_lowerCamelCase : Dict = writer_class(
features=writer._features , path=working_fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , writer_batch_size=__lowerCAmelCase , storage_options=__lowerCAmelCase , embed_local_files=__lowerCAmelCase , )
_lowerCamelCase : int = pa.Table.from_batches([batch] )
writer.write_table(__lowerCAmelCase )
if writer._num_bytes > 0:
_lowerCamelCase , _lowerCamelCase : List[str] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(__lowerCAmelCase ) ):
_lowerCamelCase : List[Any] = os.path.join(os.path.dirname(__lowerCAmelCase ) , os.path.basename(__lowerCAmelCase ) )
shutil.move(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Any = (
self.df.mapInArrow(__lowerCAmelCase , '''task_id: long, num_examples: long, num_bytes: long''' )
.groupBy('''task_id''' )
.agg(
pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ) , pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ) , pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ) , pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : "datasets.SplitGenerator" , __lowerCAmelCase : str = "arrow" , __lowerCAmelCase : Optional[Union[str, int]] = None , __lowerCAmelCase : Optional[int] = None , **__lowerCAmelCase : List[Any] , ):
"""simple docstring"""
self._validate_cache_dir()
_lowerCamelCase : Union[str, Any] = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(__lowerCAmelCase )
_lowerCamelCase : str = not is_remote_filesystem(self._fs )
_lowerCamelCase : List[Any] = os.path.join if is_local else posixpath.join
_lowerCamelCase : Optional[int] = '''-TTTTT-SSSSS-of-NNNNN'''
_lowerCamelCase : List[Any] = f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
_lowerCamelCase : List[Any] = path_join(self._output_dir , __lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : List[str] = 0
_lowerCamelCase : Any = 0
_lowerCamelCase : List[str] = []
_lowerCamelCase : Union[str, Any] = []
for task_id, content in self._prepare_split_single(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : Optional[int] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(__lowerCAmelCase )
_lowerCamelCase : Tuple = total_num_examples
_lowerCamelCase : Dict = total_num_bytes
# should rename everything at the end
logger.debug(f'''Renaming {total_shards} shards.''' )
if total_shards > 1:
_lowerCamelCase : Any = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowerCamelCase : Optional[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
__lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int , ):
rename(
__lowerCAmelCase , fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , fpath.replace('''TTTTT-SSSSS''' , f'''{global_shard_id:05d}''' ).replace('''NNNNN''' , f'''{total_shards:05d}''' ) , )
_lowerCamelCase : Tuple = []
_lowerCamelCase : Optional[Any] = 0
for i in range(len(__lowerCAmelCase ) ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = task_id_and_num_shards[i]
for shard_id in range(__lowerCAmelCase ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(__lowerCAmelCase , len(__lowerCAmelCase ) ).map(lambda __lowerCAmelCase : _rename_shard(*__lowerCAmelCase ) ).collect()
else:
# don't use any pattern
_lowerCamelCase : List[Any] = 0
_lowerCamelCase : List[str] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , fpath.replace(__lowerCAmelCase , '''''' ) , )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : "datasets.SplitGenerator" , ):
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 72 |
"""simple docstring"""
from __future__ import annotations
def snake_case_ ( A_ : str ):
'''simple docstring'''
return [ord(A_ ) - 96 for elem in plain]
def snake_case_ ( A_ : list[int] ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Dict = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''', A_ )
print('''Decoded:''', decode(A_ ) )
if __name__ == "__main__":
main()
| 72 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def __lowerCamelCase ( ) -> Generator[int, None, None]:
"""simple docstring"""
A__ : dict[int, int] ={}
A__ : int =2
while True:
A__ : int =factor_map.pop(__snake_case, __snake_case )
if factor:
A__ : List[Any] =factor + prime
while x in factor_map:
x += factor
A__ : str =factor
else:
A__ : Union[str, Any] =prime
yield prime
prime += 1
def __lowerCamelCase ( __snake_case : float = 1E10 ) -> int:
"""simple docstring"""
A__ : Optional[Any] =sieve()
A__ : int =1
while True:
A__ : str =next(__snake_case )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__snake_case )
n += 2
if __name__ == "__main__":
print(solution())
| 136 |
'''simple docstring'''
import math
__snake_case : List[Any] = 10
__snake_case : Dict = 7
__snake_case : str = BALLS_PER_COLOUR * NUM_COLOURS
def __lowerCamelCase ( __snake_case : int = 20 ) -> str:
"""simple docstring"""
A__ : Union[str, Any] =math.comb(__snake_case, __snake_case )
A__ : str =math.comb(NUM_BALLS - BALLS_PER_COLOUR, __snake_case )
A__ : Optional[int] =NUM_COLOURS * (1 - missing_colour / total)
return f"{result:.9f}"
if __name__ == "__main__":
print(solution(20))
| 136 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class SCREAMING_SNAKE_CASE__ ( datasets.BuilderConfig ):
"""simple docstring"""
a : Optional[datasets.Features] =None
class SCREAMING_SNAKE_CASE__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
a : Tuple =PandasConfig
def lowercase__ ( self ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
lowerCAmelCase : Any = dl_manager.download_and_extract(self.config.data_files )
if isinstance(snake_case__ , (str, list, tuple) ):
lowerCAmelCase : List[Any] = data_files
if isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase : Dict = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase : List[str] = [dl_manager.iter_files(snake_case__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
lowerCAmelCase : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase : List[Any] = [dl_manager.iter_files(snake_case__ ) for file in files]
splits.append(datasets.SplitGenerator(name=snake_case__ , gen_kwargs={"files": files} ) )
return splits
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
lowerCAmelCase : Union[str, Any] = table_cast(snake_case__ , self.config.features.arrow_schema )
return pa_table
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
for i, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
with open(snake_case__ , "rb" ) as f:
lowerCAmelCase : int = pa.Table.from_pandas(pd.read_pickle(snake_case__ ) )
yield i, self._cast_table(snake_case__ )
| 108 |
import math
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__lowerCamelCase )
def A__ ( __lowerCamelCase = 1 / 1_23_45 ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 3
while True:
SCREAMING_SNAKE_CASE_ = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = int(__lowerCamelCase )
total_partitions += 1
if check_partition_perfect(__lowerCamelCase ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__lowerCamelCase )
integer += 1
if __name__ == "__main__":
print(F"""{solution() = }""")
| 299 | 0 |
'''simple docstring'''
__UpperCAmelCase =range(2, 2_0 + 1)
__UpperCAmelCase =[1_0**k for k in range(ks[-1] + 1)]
__UpperCAmelCase ={}
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
__lowerCamelCase = sum(a_i[j] for j in range(_snake_case , len(_snake_case ) ) )
__lowerCamelCase = sum(a_i[j] * base[j] for j in range(min(len(_snake_case ) , _snake_case ) ) )
__lowerCamelCase = 0, 0
__lowerCamelCase = n - i
__lowerCamelCase = memo.get(_snake_case )
if sub_memo is not None:
__lowerCamelCase = sub_memo.get(_snake_case )
if jumps is not None and len(_snake_case ) > 0:
# find and make the largest jump without going over
__lowerCamelCase = -1
for _k in range(len(_snake_case ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
__lowerCamelCase = _k
break
if max_jump >= 0:
__lowerCamelCase = jumps[max_jump]
# since the difference between jumps is cached, add c
__lowerCamelCase = diff + c
for j in range(min(_snake_case , len(_snake_case ) ) ):
__lowerCamelCase = divmod(_snake_case , 10 )
if new_c > 0:
add(_snake_case , _snake_case , _snake_case )
else:
__lowerCamelCase = []
else:
__lowerCamelCase = {c: []}
__lowerCamelCase = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
__lowerCamelCase = next_term(_snake_case , k - 1 , i + dn , _snake_case )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
__lowerCamelCase = compute(_snake_case , _snake_case , i + dn , _snake_case )
diff += _diff
dn += terms_jumped
__lowerCamelCase = sub_memo[c]
# keep jumps sorted by # of terms skipped
__lowerCamelCase = 0
while j < len(_snake_case ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_snake_case , (diff, dn, k) )
return (diff, dn)
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
if i >= n:
return 0, i
if k > len(_snake_case ):
a_i.extend([0 for _ in range(k - len(_snake_case ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
__lowerCamelCase = i
__lowerCamelCase = 0, 0, 0
for j in range(len(_snake_case ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
__lowerCamelCase = ds_c + ds_b
diff += addend
__lowerCamelCase = 0
for j in range(_snake_case ):
__lowerCamelCase = a_i[j] + addend
__lowerCamelCase = divmod(_snake_case , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_snake_case , _snake_case , _snake_case )
return diff, i - start_i
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
for j in range(_snake_case , len(_snake_case ) ):
__lowerCamelCase = digits[j] + addend
if s >= 10:
__lowerCamelCase = divmod(_snake_case , 10 )
__lowerCamelCase = addend // 10 + quotient
else:
__lowerCamelCase = s
__lowerCamelCase = addend // 10
if addend == 0:
break
while addend > 0:
__lowerCamelCase = divmod(_snake_case , 10 )
digits.append(_snake_case )
def __lowerCAmelCase ( UpperCamelCase__ = 10**15 ) -> int:
__lowerCamelCase = [1]
__lowerCamelCase = 1
__lowerCamelCase = 0
while True:
__lowerCamelCase = next_term(_snake_case , 20 , i + dn , _snake_case )
dn += terms_jumped
if dn == n - i:
break
__lowerCamelCase = 0
for j in range(len(_snake_case ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'{solution() = }')
| 369 | '''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ="▁"
__UpperCAmelCase ={"vocab_file": "prophetnet.tokenizer"}
__UpperCAmelCase ={
"vocab_file": {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
),
}
}
__UpperCAmelCase ={
"microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}
__UpperCAmelCase ={
"microsoft/xprophetnet-large-wiki100-cased": 5_1_2,
}
def __lowerCAmelCase ( UpperCamelCase__ ) -> List[str]:
__lowerCamelCase = collections.OrderedDict()
with open(UpperCamelCase__ , '''r''' , encoding='''utf-8''' ) as reader:
__lowerCamelCase = reader.readlines()
for index, token in enumerate(UpperCamelCase__ ):
__lowerCamelCase = token.rstrip('''\n''' )
__lowerCamelCase = index
return vocab
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Optional[Any] =VOCAB_FILES_NAMES
lowerCamelCase : Any =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Optional[int] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Union[str, Any] =["input_ids", "attention_mask"]
def __init__( self : int , a : List[str] , a : Optional[int]="[SEP]" , a : int="[SEP]" , a : str="[SEP]" , a : List[Any]="[UNK]" , a : List[Any]="[PAD]" , a : str="[CLS]" , a : List[str]="[MASK]" , a : Optional[Dict[str, Any]] = None , **a : str , ):
"""simple docstring"""
__lowerCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=a , eos_token=a , sep_token=a , unk_token=a , pad_token=a , cls_token=a , mask_token=a , sp_model_kwargs=self.sp_model_kwargs , **a , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
''' pip install sentencepiece''' )
raise
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(a ) )
__lowerCamelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
__lowerCamelCase = {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}
for i in range(10 ):
__lowerCamelCase = f"""[unused{i}]"""
__lowerCamelCase = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
__lowerCamelCase = 12
__lowerCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(a )
def __getstate__( self : List[str] ):
"""simple docstring"""
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = None
return state
def __setstate__( self : int , a : List[Any] ):
"""simple docstring"""
__lowerCamelCase = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
''' pip install sentencepiece''' )
raise
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCamelCase = {}
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : str , a : List[int] , a : Optional[List[int]] = None , a : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a , token_ids_a=a , already_has_special_tokens=a )
if token_ids_a is None:
return ([0] * len(a )) + [1]
return ([0] * len(a )) + [1] + ([0] * len(a )) + [1]
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : List[int] , a : Optional[List[int]] = None ):
"""simple docstring"""
__lowerCamelCase = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase = {self.convert_ids_to_tokens(a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : str ):
"""simple docstring"""
return self.sp_model.encode(a , out_type=a )
def SCREAMING_SNAKE_CASE__ ( self : Dict , a : int ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__lowerCamelCase = self.sp_model.PieceToId(a )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , a : Union[str, Any] ):
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : Tuple ):
"""simple docstring"""
__lowerCamelCase = ''''''.join(a ).replace(a , ''' ''' ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self : int , a : str , a : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = os.path.join(
a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , a )
elif not os.path.isfile(self.vocab_file ):
with open(a , '''wb''' ) as fi:
__lowerCamelCase = self.sp_model.serialized_model_proto()
fi.write(a )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE__ ( self : Any , a : List[int] , a : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
__lowerCamelCase = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 237 | 0 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a ( a_ ):
# to overwrite at feature extractactor specific tests
UpperCAmelCase_ : Optional[int] =None
UpperCAmelCase_ : List[Any] =None
@property
def UpperCamelCase_ ( self ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def UpperCamelCase_ ( self ):
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_lowerCamelCase , 'feature_size' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'sampling_rate' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'padding_value' ) )
def UpperCamelCase_ ( self ):
lowercase = self.feat_extract_tester.prepare_inputs_for_common()
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_lowerCamelCase ) == len(_lowerCamelCase ) for x, y in zip(_lowerCamelCase , processed_features[input_name] ) ) )
lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
lowercase = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def UpperCamelCase_ ( self ):
lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def UpperCamelCase_ ( self ):
lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} , tensor_type='tf' )
lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def UpperCamelCase_ ( self , _lowerCamelCase=False ):
def _inputs_have_equal_length(_lowerCamelCase ):
lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCamelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCamelCase , _lowerCamelCase ):
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCamelCase , _lowerCamelCase ):
if not np.allclose(np.asarray(_lowerCamelCase ) , np.asarray(_lowerCamelCase ) , atol=1e-3 ):
return False
return True
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase )
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} )
lowercase = self.feat_extract_tester.seq_length_diff
lowercase = self.feat_extract_tester.max_seq_length + pad_diff
lowercase = self.feat_extract_tester.min_seq_length
lowercase = self.feat_extract_tester.batch_size
lowercase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
lowercase = feat_extract.pad(_lowerCamelCase , padding=_lowerCamelCase )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[-1] ) )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' , return_tensors='np' )
lowercase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='max_length' )[input_name]
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=_lowerCamelCase , return_tensors='np' )
lowercase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCamelCase , _lowerCamelCase ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
lowercase = feat_extract.pad(_lowerCamelCase , pad_to_multiple_of=1_0 )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' , pad_to_multiple_of=1_0 )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , pad_to_multiple_of=1_0 , max_length=_lowerCamelCase )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , pad_to_multiple_of=1_0 , max_length=_lowerCamelCase , return_tensors='np' , )
lowercase = input_a[input_name]
self.assertTrue(all(len(_lowerCamelCase ) % 1_0 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_lowerCamelCase , _lowerCamelCase ) )
lowercase = pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
self.assertTrue(all(len(_lowerCamelCase ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
lowercase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def UpperCamelCase_ ( self , _lowerCamelCase=False ):
def _inputs_have_equal_length(_lowerCamelCase ):
lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCamelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCamelCase , _lowerCamelCase ):
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCamelCase , _lowerCamelCase ):
if not np.allclose(np.asarray(_lowerCamelCase ) , np.asarray(_lowerCamelCase ) , atol=1e-3 ):
return False
return True
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase )
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[0] ) , truncation=_lowerCamelCase )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[0] ) )
lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
# truncate to smallest with np
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[0] ) , return_tensors='np' , truncation=_lowerCamelCase , )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[0] ) , return_tensors='np' )
lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
# truncate to middle
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[1] ) , truncation=_lowerCamelCase , return_tensors='np' , )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[1] ) , truncation=_lowerCamelCase )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[1] ) , return_tensors='np' )
lowercase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCamelCase , _lowerCamelCase ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , truncation=_lowerCamelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='longest' , truncation=_lowerCamelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='longest' , truncation=_lowerCamelCase )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='max_length' , truncation=_lowerCamelCase )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
lowercase = 1_2
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCamelCase , truncation=_lowerCamelCase , )
lowercase = input_a[input_name]
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCamelCase , )
lowercase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
lowercase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
lowercase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
def UpperCamelCase_ ( self ):
self._check_padding(numpify=_lowerCamelCase )
def UpperCamelCase_ ( self ):
self._check_padding(numpify=_lowerCamelCase )
def UpperCamelCase_ ( self ):
self._check_truncation(numpify=_lowerCamelCase )
def UpperCamelCase_ ( self ):
self._check_truncation(numpify=_lowerCamelCase )
@require_torch
def UpperCamelCase_ ( self ):
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
lowercase = self.feat_extract_tester.prepare_inputs_for_common()
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} )
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' , return_tensors='np' )[input_name]
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def UpperCamelCase_ ( self ):
lowercase = self.feature_extraction_class(**self.feat_extract_dict )
lowercase = self.feat_extract_tester.prepare_inputs_for_common()
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} )
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' , return_tensors='np' )[input_name]
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' , return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def UpperCamelCase_ ( self ):
lowercase = self.feat_extract_dict
lowercase = True
lowercase = self.feature_extraction_class(**_lowerCamelCase )
lowercase = self.feat_extract_tester.prepare_inputs_for_common()
lowercase = [len(_lowerCamelCase ) for x in speech_inputs]
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} )
lowercase = feat_extract.pad(_lowerCamelCase , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , _lowerCamelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _lowerCamelCase )
def UpperCamelCase_ ( self ):
lowercase = self.feat_extract_dict
lowercase = True
lowercase = self.feature_extraction_class(**_lowerCamelCase )
lowercase = self.feat_extract_tester.prepare_inputs_for_common()
lowercase = [len(_lowerCamelCase ) for x in speech_inputs]
lowercase = feat_extract.model_input_names[0]
lowercase = BatchFeature({input_name: speech_inputs} )
lowercase = min(_lowerCamelCase )
lowercase = feat_extract.pad(
_lowerCamelCase , padding='max_length' , max_length=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='np' )
self.assertIn('attention_mask' , _lowerCamelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 220 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _SCREAMING_SNAKE_CASE ( __snake_case : str = "AAPL" ):
'''simple docstring'''
lowercase = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
lowercase = BeautifulSoup(requests.get(__snake_case ).text , 'html.parser' )
lowercase = 'My(6px) Pos(r) smartphone_Mt(6px)'
return soup.find('div' , class_=class_ ).find('span' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')
| 220 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class lowerCAmelCase_ ( a__ , a__ , unittest.TestCase ):
UpperCAmelCase__ : List[Any] = IFPipeline
UpperCAmelCase__ : List[Any] = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
UpperCAmelCase__ : Optional[Any] = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase__ : Union[str, Any] = PipelineTesterMixin.required_optional_params - {"latents"}
def snake_case_ ( self ) -> str:
return self._get_dummy_components()
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=0 ) -> Any:
if str(SCREAMING_SNAKE_CASE_ ).startswith('mps' ):
UpperCamelCase : str = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Union[str, Any] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def snake_case_ ( self ) -> Dict:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda', reason='float16 requires CUDA' )
def snake_case_ ( self ) -> List[str]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def snake_case_ ( self ) -> str:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def snake_case_ ( self ) -> Optional[int]:
self._test_save_load_local()
def snake_case_ ( self ) -> Any:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2, )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available(), reason='XFormers attention is only available with CUDA and `xformers` installed', )
def snake_case_ ( self ) -> Dict:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ) -> int:
# if
UpperCamelCase : Optional[Any] = IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0', variant='fp16', torch_dtype=torch.floataa )
UpperCamelCase : List[Any] = IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0', variant='fp16', torch_dtype=torch.floataa, text_encoder=SCREAMING_SNAKE_CASE_, tokenizer=SCREAMING_SNAKE_CASE_ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
UpperCamelCase , UpperCamelCase : List[Any] = pipe_a.encode_prompt('anime turtle', device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : List[Any] = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
UpperCamelCase : Optional[int] = IFImgaImgPipeline(**pipe_a.components )
UpperCamelCase : str = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
UpperCamelCase : str = IFInpaintingPipeline(**pipe_a.components )
UpperCamelCase : Optional[Any] = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
UpperCamelCase : Union[str, Any] = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCamelCase : Tuple = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE_, negative_prompt_embeds=SCREAMING_SNAKE_CASE_, num_inference_steps=2, generator=SCREAMING_SNAKE_CASE_, output_type='np', )
UpperCamelCase : str = output.images[0]
assert image.shape == (64, 64, 3)
UpperCamelCase : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
UpperCamelCase : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# pipeline 2
_start_torch_memory_measurement()
UpperCamelCase : Optional[Any] = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCamelCase : List[Any] = floats_tensor((1, 3, 64, 64), rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE_, negative_prompt_embeds=SCREAMING_SNAKE_CASE_, image=SCREAMING_SNAKE_CASE_, generator=SCREAMING_SNAKE_CASE_, num_inference_steps=2, output_type='np', )
UpperCamelCase : Optional[Any] = output.images[0]
assert image.shape == (256, 256, 3)
UpperCamelCase : List[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
UpperCamelCase : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
# pipeline 1
_start_torch_memory_measurement()
UpperCamelCase : Optional[Any] = floats_tensor((1, 3, 64, 64), rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCamelCase : List[str] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE_, negative_prompt_embeds=SCREAMING_SNAKE_CASE_, image=SCREAMING_SNAKE_CASE_, num_inference_steps=2, generator=SCREAMING_SNAKE_CASE_, output_type='np', )
UpperCamelCase : Union[str, Any] = output.images[0]
assert image.shape == (64, 64, 3)
UpperCamelCase : str = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
UpperCamelCase : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# pipeline 2
_start_torch_memory_measurement()
UpperCamelCase : int = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCamelCase : Union[str, Any] = floats_tensor((1, 3, 256, 256), rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = floats_tensor((1, 3, 64, 64), rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE_, negative_prompt_embeds=SCREAMING_SNAKE_CASE_, image=SCREAMING_SNAKE_CASE_, original_image=SCREAMING_SNAKE_CASE_, generator=SCREAMING_SNAKE_CASE_, num_inference_steps=2, output_type='np', )
UpperCamelCase : Any = output.images[0]
assert image.shape == (256, 256, 3)
UpperCamelCase : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
UpperCamelCase : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
UpperCamelCase : List[str] = floats_tensor((1, 3, 64, 64), rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = floats_tensor((1, 3, 64, 64), rng=random.Random(1 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCamelCase : Dict = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE_, negative_prompt_embeds=SCREAMING_SNAKE_CASE_, image=SCREAMING_SNAKE_CASE_, mask_image=SCREAMING_SNAKE_CASE_, num_inference_steps=2, generator=SCREAMING_SNAKE_CASE_, output_type='np', )
UpperCamelCase : Any = output.images[0]
assert image.shape == (64, 64, 3)
UpperCamelCase : List[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
UpperCamelCase : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# pipeline 2
_start_torch_memory_measurement()
UpperCamelCase : int = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCamelCase : int = floats_tensor((1, 3, 64, 64), rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = floats_tensor((1, 3, 256, 256), rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = floats_tensor((1, 3, 256, 256), rng=random.Random(1 ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE_, negative_prompt_embeds=SCREAMING_SNAKE_CASE_, image=SCREAMING_SNAKE_CASE_, mask_image=SCREAMING_SNAKE_CASE_, original_image=SCREAMING_SNAKE_CASE_, generator=SCREAMING_SNAKE_CASE_, num_inference_steps=2, output_type='np', )
UpperCamelCase : Tuple = output.images[0]
assert image.shape == (256, 256, 3)
UpperCamelCase : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
UpperCamelCase : Tuple = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def UpperCamelCase ( ) -> Union[str, Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 103 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def UpperCamelCase ( ) -> Tuple:
UpperCamelCase : List[str] = 9
UpperCamelCase : Optional[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
UpperCamelCase : int = kruskal(snake_case__ , snake_case__ )
UpperCamelCase : List[str] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(snake_case__ ) == sorted(snake_case__ )
| 103 | 1 |
'''simple docstring'''
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _snake_case :
def __init__( self : Dict ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : str=13 ,SCREAMING_SNAKE_CASE__ : List[str]=7 ,SCREAMING_SNAKE_CASE__ : Optional[int]=True ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=True ,SCREAMING_SNAKE_CASE__ : Tuple=True ,SCREAMING_SNAKE_CASE__ : List[str]=True ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=99 ,SCREAMING_SNAKE_CASE__ : Any=64 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=32 ,SCREAMING_SNAKE_CASE__ : Dict=5 ,SCREAMING_SNAKE_CASE__ : int=4 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=37 ,SCREAMING_SNAKE_CASE__ : Tuple="gelu" ,SCREAMING_SNAKE_CASE__ : str=0.1 ,SCREAMING_SNAKE_CASE__ : List[Any]=0.1 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=512 ,SCREAMING_SNAKE_CASE__ : List[Any]=16 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=2 ,SCREAMING_SNAKE_CASE__ : int=0.02 ,SCREAMING_SNAKE_CASE__ : str=3 ,SCREAMING_SNAKE_CASE__ : Dict=4 ,SCREAMING_SNAKE_CASE__ : Dict=None ,):
SCREAMING_SNAKE_CASE:Dict = parent
SCREAMING_SNAKE_CASE:List[str] = batch_size
SCREAMING_SNAKE_CASE:str = seq_length
SCREAMING_SNAKE_CASE:List[str] = is_training
SCREAMING_SNAKE_CASE:List[str] = use_input_mask
SCREAMING_SNAKE_CASE:Optional[int] = use_token_type_ids
SCREAMING_SNAKE_CASE:List[str] = use_labels
SCREAMING_SNAKE_CASE:Tuple = vocab_size
SCREAMING_SNAKE_CASE:Any = hidden_size
SCREAMING_SNAKE_CASE:List[Any] = embedding_size
SCREAMING_SNAKE_CASE:Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE:List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE:List[Any] = intermediate_size
SCREAMING_SNAKE_CASE:Dict = hidden_act
SCREAMING_SNAKE_CASE:Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE:int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE:List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE:Dict = type_vocab_size
SCREAMING_SNAKE_CASE:List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE:str = initializer_range
SCREAMING_SNAKE_CASE:Union[str, Any] = num_labels
SCREAMING_SNAKE_CASE:Dict = num_choices
SCREAMING_SNAKE_CASE:str = scope
def __UpperCamelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE:List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE:Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE:Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE:str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE:Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
SCREAMING_SNAKE_CASE:Union[str, Any] = None
SCREAMING_SNAKE_CASE:List[str] = None
SCREAMING_SNAKE_CASE:str = None
if self.use_labels:
SCREAMING_SNAKE_CASE:str = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE:Any = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE:Optional[Any] = ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE:int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Tuple ):
return MobileBertConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,embedding_size=self.embedding_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=SCREAMING_SNAKE_CASE__ ,initializer_range=self.initializer_range ,)
def __UpperCamelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Optional[int] ):
SCREAMING_SNAKE_CASE:Union[str, Any] = MobileBertModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:List[str] = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[int] = model(SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Any = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.hidden_size) )
def __UpperCamelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : Optional[int] ):
SCREAMING_SNAKE_CASE:Optional[Any] = MobileBertForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:Optional[Any] = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Dict ):
SCREAMING_SNAKE_CASE:str = MobileBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:Optional[Any] = model(
SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, 2) )
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : int ):
SCREAMING_SNAKE_CASE:str = MobileBertForPreTraining(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:int = model(
SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ ,next_sentence_label=SCREAMING_SNAKE_CASE__ ,)
self.parent.assertEqual(result.prediction_logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape ,(self.batch_size, 2) )
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : str ):
SCREAMING_SNAKE_CASE:Optional[Any] = MobileBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:str = model(
SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,start_positions=SCREAMING_SNAKE_CASE__ ,end_positions=SCREAMING_SNAKE_CASE__ ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ):
SCREAMING_SNAKE_CASE:Dict = self.num_labels
SCREAMING_SNAKE_CASE:List[Any] = MobileBertForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:Optional[int] = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : str ):
SCREAMING_SNAKE_CASE:Optional[Any] = self.num_labels
SCREAMING_SNAKE_CASE:str = MobileBertForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:int = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Tuple ):
SCREAMING_SNAKE_CASE:List[str] = self.num_choices
SCREAMING_SNAKE_CASE:List[str] = MobileBertForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE:Optional[int] = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE:Dict = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE:str = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE:Union[str, Any] = model(
SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE:Any = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) ,
):List[str] = config_and_inputs
SCREAMING_SNAKE_CASE:Dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _snake_case ( _a , _a , unittest.TestCase ):
_A : Optional[Any] = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_A : int = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_A : int = True
def __UpperCamelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Dict=False ):
SCREAMING_SNAKE_CASE:Any = super()._prepare_for_class(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,return_labels=SCREAMING_SNAKE_CASE__ )
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE:Tuple = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) ,dtype=torch.long ,device=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:List[str] = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=SCREAMING_SNAKE_CASE__ )
return inputs_dict
def __UpperCamelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE:Tuple = MobileBertModelTester(self )
SCREAMING_SNAKE_CASE:int = ConfigTester(self ,config_class=SCREAMING_SNAKE_CASE__ ,hidden_size=37 )
def __UpperCamelCase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE:List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE:str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Any ):
SCREAMING_SNAKE_CASE:List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : int ):
SCREAMING_SNAKE_CASE:Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : str ):
SCREAMING_SNAKE_CASE:Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Dict ):
SCREAMING_SNAKE_CASE:int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE:str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE:Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*SCREAMING_SNAKE_CASE__ )
def A_ ( snake_case ):
return torch.tensor(
snake_case , dtype=torch.long , device=snake_case , )
A_ = 1e-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _snake_case ( unittest.TestCase ):
@slow
def __UpperCamelCase ( self : Any ):
SCREAMING_SNAKE_CASE:Union[str, Any] = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Any = _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE:Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )[0]
SCREAMING_SNAKE_CASE:Dict = torch.Size((1, 9, 512) )
self.assertEqual(output.shape ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Dict = torch.tensor(
[
[
[-2.4_736_526e07, 8.2_691_656e04, 1.6_521_838e05],
[-5.7_541_704e-01, 3.9_056_022e00, 4.4_011_507e00],
[2.6_047_359e00, 1.5_677_652e00, -1.7_324_188e-01],
]
] ,device=SCREAMING_SNAKE_CASE__ ,)
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
SCREAMING_SNAKE_CASE:Optional[int] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
SCREAMING_SNAKE_CASE:List[Any] = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 139 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
A_ = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 4_80_00,
"sample_size": 6_55_36,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 4_80_00,
"sample_size": 6_55_36,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 4_80_00,
"sample_size": 13_10_72,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 1_60_00,
"sample_size": 6_55_36,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 1_60_00,
"sample_size": 6_55_36,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 1_60_00,
"sample_size": 6_55_36,
},
}
def A_ ( snake_case , snake_case ):
return torch.atana(snake_case , snake_case ) / math.pi * 2
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:List[Any] = torch.sin(t * math.pi / 2 ) ** 2
SCREAMING_SNAKE_CASE:Any = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(snake_case , snake_case )
class _snake_case ( _a ):
pass
class _snake_case ( nn.Module ):
def __init__( self : int ,SCREAMING_SNAKE_CASE__ : str ):
super().__init__()
SCREAMING_SNAKE_CASE:List[Any] = DiffusionAttnUnetaD(SCREAMING_SNAKE_CASE__ ,n_attn_layers=4 )
SCREAMING_SNAKE_CASE:List[str] = deepcopy(self.diffusion )
SCREAMING_SNAKE_CASE:Dict = torch.quasirandom.SobolEngine(1 ,scramble=SCREAMING_SNAKE_CASE__ )
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:List[Any] = MODELS_MAP[model_name]["url"]
os.system(F'''wget {url} ./''' )
return F'''./{model_name}.ckpt'''
A_ = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
A_ = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
A_ = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
A_ = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
A_ = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
A_ = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def A_ ( snake_case ):
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(F'''ResConvBlock error with {name}''' )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def A_ ( snake_case ):
for key, value in ATTN_MAP.items():
if name.startswith(snake_case ) and not isinstance(snake_case , snake_case ):
return name.replace(snake_case , snake_case )
elif name.startswith(snake_case ):
return [name.replace(snake_case , snake_case ) for v in value]
raise ValueError(F'''Attn error with {name}''' )
def A_ ( snake_case , snake_case=13 ):
SCREAMING_SNAKE_CASE:Optional[Any] = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
SCREAMING_SNAKE_CASE:List[str] = 0
if string.startswith("net.3." ):
depth += 1
SCREAMING_SNAKE_CASE:Union[str, Any] = string[6:]
elif string.startswith("net." ):
SCREAMING_SNAKE_CASE:int = string[4:]
while string.startswith("main.7." ):
depth += 1
SCREAMING_SNAKE_CASE:Union[str, Any] = string[7:]
if string.startswith("main." ):
SCREAMING_SNAKE_CASE:str = string[5:]
# mid block
if string[:2].isdigit():
SCREAMING_SNAKE_CASE:Tuple = string[:2]
SCREAMING_SNAKE_CASE:Optional[Any] = string[2:]
else:
SCREAMING_SNAKE_CASE:Optional[Any] = string[0]
SCREAMING_SNAKE_CASE:Optional[Any] = string[1:]
if depth == max_depth:
SCREAMING_SNAKE_CASE:Any = MID_NUM_TO_LAYER[layer_num]
SCREAMING_SNAKE_CASE:List[str] = "mid_block"
elif depth > 0 and int(snake_case ) < 7:
SCREAMING_SNAKE_CASE:Union[str, Any] = DOWN_NUM_TO_LAYER[layer_num]
SCREAMING_SNAKE_CASE:Dict = F'''down_blocks.{depth}'''
elif depth > 0 and int(snake_case ) > 7:
SCREAMING_SNAKE_CASE:Any = UP_NUM_TO_LAYER[layer_num]
SCREAMING_SNAKE_CASE:Union[str, Any] = F'''up_blocks.{max_depth - depth - 1}'''
elif depth == 0:
SCREAMING_SNAKE_CASE:Optional[int] = DEPTH_0_TO_LAYER[layer_num]
SCREAMING_SNAKE_CASE:Any = F'''up_blocks.{max_depth - 1}''' if int(snake_case ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.''' )
SCREAMING_SNAKE_CASE:List[Any] = string_left[1:]
if "resnets" in new_layer:
SCREAMING_SNAKE_CASE:List[str] = convert_resconv_naming(snake_case )
elif "attentions" in new_layer:
SCREAMING_SNAKE_CASE:List[Any] = convert_attn_naming(snake_case )
SCREAMING_SNAKE_CASE:List[Any] = new_string_left
if not isinstance(snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Tuple = prefix + "." + new_layer + "." + string_left
else:
SCREAMING_SNAKE_CASE:int = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:int = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
SCREAMING_SNAKE_CASE:str = rename(snake_case )
# check if we need to transform from Conv => Linear for attention
if isinstance(snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Optional[int] = transform_conv_attns(snake_case , snake_case , snake_case )
else:
SCREAMING_SNAKE_CASE:Optional[int] = v
return new_state_dict
def A_ ( snake_case , snake_case , snake_case ):
if len(snake_case ) == 1:
if len(v.shape ) == 3:
# weight
SCREAMING_SNAKE_CASE:List[str] = v[:, :, 0]
else:
# bias
SCREAMING_SNAKE_CASE:Optional[Any] = v
else:
# qkv matrices
SCREAMING_SNAKE_CASE:Optional[int] = v.shape[0]
SCREAMING_SNAKE_CASE:Optional[Any] = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
SCREAMING_SNAKE_CASE:Union[str, Any] = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
SCREAMING_SNAKE_CASE:List[Any] = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Union[str, Any] = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
SCREAMING_SNAKE_CASE:List[str] = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
SCREAMING_SNAKE_CASE:List[str] = download(snake_case )
SCREAMING_SNAKE_CASE:List[str] = MODELS_MAP[model_name]["sample_rate"]
SCREAMING_SNAKE_CASE:Tuple = MODELS_MAP[model_name]["sample_size"]
SCREAMING_SNAKE_CASE:Union[str, Any] = Object()
SCREAMING_SNAKE_CASE:int = sample_size
SCREAMING_SNAKE_CASE:Any = sample_rate
SCREAMING_SNAKE_CASE:List[str] = 0
SCREAMING_SNAKE_CASE:Optional[Any] = UNetaDModel(sample_size=snake_case , sample_rate=snake_case )
SCREAMING_SNAKE_CASE:Optional[Any] = diffusers_model.state_dict()
SCREAMING_SNAKE_CASE:Optional[Any] = DiffusionUncond(snake_case )
orig_model.load_state_dict(torch.load(args.model_path , map_location=snake_case )["state_dict"] )
SCREAMING_SNAKE_CASE:Union[str, Any] = orig_model.diffusion_ema.eval()
SCREAMING_SNAKE_CASE:Dict = orig_model.state_dict()
SCREAMING_SNAKE_CASE:Union[str, Any] = rename_orig_weights(snake_case )
SCREAMING_SNAKE_CASE:Dict = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
SCREAMING_SNAKE_CASE:Dict = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(snake_case ) == 0, F'''Problem with {renamed_minus_diffusers}'''
assert all(k.endswith("kernel" ) for k in list(snake_case ) ), F'''Problem with {diffusers_minus_renamed}'''
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
if key == "time_proj.weight":
SCREAMING_SNAKE_CASE:Dict = value.squeeze()
SCREAMING_SNAKE_CASE:Union[str, Any] = value
diffusers_model.load_state_dict(snake_case )
SCREAMING_SNAKE_CASE:int = 100
SCREAMING_SNAKE_CASE:int = 33
SCREAMING_SNAKE_CASE:Any = IPNDMScheduler(num_train_timesteps=snake_case )
SCREAMING_SNAKE_CASE:str = torch.manual_seed(snake_case )
SCREAMING_SNAKE_CASE:Union[str, Any] = torch.randn([1, 2, config.sample_size] , generator=snake_case ).to(snake_case )
SCREAMING_SNAKE_CASE:int = torch.linspace(1 , 0 , steps + 1 , device=snake_case )[:-1]
SCREAMING_SNAKE_CASE:List[Any] = get_crash_schedule(snake_case )
SCREAMING_SNAKE_CASE:Union[str, Any] = DanceDiffusionPipeline(unet=snake_case , scheduler=snake_case )
SCREAMING_SNAKE_CASE:Union[str, Any] = torch.manual_seed(33 )
SCREAMING_SNAKE_CASE:Union[str, Any] = pipe(num_inference_steps=snake_case , generator=snake_case ).audios
SCREAMING_SNAKE_CASE:Tuple = sampling.iplms_sample(snake_case , snake_case , snake_case , {} )
SCREAMING_SNAKE_CASE:Union[str, Any] = generated.clamp(-1 , 1 )
SCREAMING_SNAKE_CASE:Union[str, Any] = (generated - audio).abs().sum()
SCREAMING_SNAKE_CASE:str = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , snake_case )
print("Diff max" , snake_case )
assert diff_max < 1e-3, F'''Diff max: {diff_max} is too much :-/'''
print(F'''Conversion for {model_name} successful!''' )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
A_ = parser.parse_args()
main(args)
| 139 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any]=13 , _lowerCAmelCase : Optional[Any]=7 , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Any=99 , _lowerCAmelCase : Union[str, Any]=16 , _lowerCAmelCase : Optional[Any]=36 , _lowerCAmelCase : Dict=6 , _lowerCAmelCase : Dict=6 , _lowerCAmelCase : Tuple=6 , _lowerCAmelCase : Optional[Any]=37 , _lowerCAmelCase : Optional[int]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Dict=5_12 , _lowerCAmelCase : Tuple=16 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : Dict=0.02 , _lowerCAmelCase : Optional[int]=3 , _lowerCAmelCase : Union[str, Any]=4 , _lowerCAmelCase : str=None , ):
__snake_case : Any = parent
__snake_case : str = batch_size
__snake_case : List[str] = seq_length
__snake_case : str = is_training
__snake_case : Optional[int] = use_input_mask
__snake_case : Optional[Any] = use_token_type_ids
__snake_case : Optional[Any] = use_labels
__snake_case : str = vocab_size
__snake_case : int = embedding_size
__snake_case : Any = hidden_size
__snake_case : List[Any] = num_hidden_layers
__snake_case : Tuple = num_hidden_groups
__snake_case : List[str] = num_attention_heads
__snake_case : List[str] = intermediate_size
__snake_case : Dict = hidden_act
__snake_case : Optional[int] = hidden_dropout_prob
__snake_case : int = attention_probs_dropout_prob
__snake_case : Any = max_position_embeddings
__snake_case : List[str] = type_vocab_size
__snake_case : List[Any] = type_sequence_label_size
__snake_case : List[Any] = initializer_range
__snake_case : Tuple = num_labels
__snake_case : Tuple = num_choices
__snake_case : Dict = scope
def lowerCAmelCase__ ( self : int ):
__snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case : List[str] = None
if self.use_input_mask:
__snake_case : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case : List[Any] = None
if self.use_token_type_ids:
__snake_case : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case : List[Any] = None
__snake_case : Optional[Any] = None
__snake_case : Optional[Any] = None
if self.use_labels:
__snake_case : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__snake_case : str = ids_tensor([self.batch_size] , self.num_choices )
__snake_case : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self : Dict ):
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def lowerCAmelCase__ ( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
__snake_case : List[Any] = AlbertModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__snake_case : Any = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase )
__snake_case : List[Any] = model(_lowerCAmelCase , token_type_ids=_lowerCAmelCase )
__snake_case : Tuple = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCAmelCase__ ( self : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : int ):
__snake_case : Union[str, Any] = AlbertForPreTraining(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__snake_case : Optional[int] = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , labels=_lowerCAmelCase , sentence_order_label=_lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def lowerCAmelCase__ ( self : Union[str, Any] , _lowerCAmelCase : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] ):
__snake_case : Union[str, Any] = AlbertForMaskedLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__snake_case : Union[str, Any] = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str ):
__snake_case : Tuple = AlbertForQuestionAnswering(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__snake_case : Dict = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , start_positions=_lowerCAmelCase , end_positions=_lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase__ ( self : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Dict , _lowerCAmelCase : int , _lowerCAmelCase : List[str] ):
__snake_case : Dict = self.num_labels
__snake_case : Optional[Any] = AlbertForSequenceClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__snake_case : Any = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : str ):
__snake_case : List[str] = self.num_labels
__snake_case : Dict = AlbertForTokenClassification(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__snake_case : List[str] = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase__ ( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
__snake_case : Optional[Any] = self.num_choices
__snake_case : Union[str, Any] = AlbertForMultipleChoice(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__snake_case : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case : List[Any] = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , labels=_lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase__ ( self : Optional[Any] ):
__snake_case : int = self.prepare_config_and_inputs()
(
__snake_case
) : Any = config_and_inputs
__snake_case : Any = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
A : List[str] = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
A : Any = (
{
"feature-extraction": AlbertModel,
"fill-mask": AlbertForMaskedLM,
"question-answering": AlbertForQuestionAnswering,
"text-classification": AlbertForSequenceClassification,
"token-classification": AlbertForTokenClassification,
"zero-shot": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
A : Tuple = True
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int]=False ):
__snake_case : Union[str, Any] = super()._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
if return_labels:
if model_class in get_values(_lowerCAmelCase ):
__snake_case : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_lowerCAmelCase )
__snake_case : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowerCAmelCase )
return inputs_dict
def lowerCAmelCase__ ( self : Optional[Any] ):
__snake_case : Dict = AlbertModelTester(self )
__snake_case : Union[str, Any] = ConfigTester(self , config_class=_lowerCAmelCase , hidden_size=37 )
def lowerCAmelCase__ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self : Any ):
__snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : Any ):
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : Optional[Any] ):
__snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : str ):
__snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : Dict ):
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : Union[str, Any] ):
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : Union[str, Any] ):
__snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__snake_case : List[str] = type
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@slow
def lowerCAmelCase__ ( self : Tuple ):
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Optional[Any] = AlbertModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def lowerCAmelCase__ ( self : int ):
__snake_case : str = AlbertModel.from_pretrained("""albert-base-v2""" )
__snake_case : Optional[Any] = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
__snake_case : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__snake_case : Any = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__snake_case : Tuple = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , _lowerCAmelCase )
__snake_case : List[str] = torch.tensor(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 363 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def snake_case__ ( self : Any ):
__snake_case : Dict = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
__snake_case : str = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__snake_case : List[str] = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
__snake_case : str = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 1_60_00,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
__snake_case : Optional[Any] = tempfile.mkdtemp()
__snake_case : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__snake_case : Any = os.path.join(self.tmpdirname , _lowerCAmelCase )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
with open(self.feature_extraction_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
# load decoder from hub
__snake_case : Optional[int] = """hf-internal-testing/ngram-beam-search-decoder"""
def snake_case__ ( self : Optional[Any] , **_lowerCAmelCase : Tuple ):
__snake_case : int = self.add_kwargs_tokens_map.copy()
kwargs.update(_lowerCAmelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def snake_case__ ( self : Union[str, Any] , **_lowerCAmelCase : Optional[int] ):
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def snake_case__ ( self : Dict , **_lowerCAmelCase : Tuple ):
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **_lowerCAmelCase )
def snake_case__ ( self : List[str] ):
shutil.rmtree(self.tmpdirname )
def snake_case__ ( self : Union[str, Any] ):
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Tuple = self.get_feature_extractor()
__snake_case : Dict = self.get_decoder()
__snake_case : List[str] = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
__snake_case : Dict = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCAmelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowerCAmelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , _lowerCAmelCase )
def snake_case__ ( self : Tuple ):
__snake_case : Tuple = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
__snake_case : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def snake_case__ ( self : int ):
__snake_case : Tuple = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(_lowerCAmelCase , """include""" ):
WavaVecaProcessorWithLM(
tokenizer=_lowerCAmelCase , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def snake_case__ ( self : Dict ):
__snake_case : int = self.get_feature_extractor()
__snake_case : str = self.get_tokenizer()
__snake_case : Dict = self.get_decoder()
__snake_case : Any = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__snake_case : List[Any] = floats_list((3, 10_00) )
__snake_case : Optional[Any] = feature_extractor(_lowerCAmelCase , return_tensors="""np""" )
__snake_case : Tuple = processor(_lowerCAmelCase , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def snake_case__ ( self : Optional[int] ):
__snake_case : Any = self.get_feature_extractor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = self.get_decoder()
__snake_case : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__snake_case : Optional[int] = """This is a test string"""
__snake_case : Union[str, Any] = processor(text=_lowerCAmelCase )
__snake_case : Dict = tokenizer(_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case__ ( self : List[str] , _lowerCAmelCase : List[Any]=(2, 10, 16) , _lowerCAmelCase : str=77 ):
np.random.seed(_lowerCAmelCase )
return np.random.rand(*_lowerCAmelCase )
def snake_case__ ( self : Tuple ):
__snake_case : List[str] = self.get_feature_extractor()
__snake_case : List[str] = self.get_tokenizer()
__snake_case : List[str] = self.get_decoder()
__snake_case : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__snake_case : Optional[int] = self._get_dummy_logits(shape=(10, 16) , seed=13 )
__snake_case : int = processor.decode(_lowerCAmelCase )
__snake_case : Optional[int] = decoder.decode_beams(_lowerCAmelCase )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def snake_case__ ( self : List[str] , _lowerCAmelCase : List[str] ):
__snake_case : int = self.get_feature_extractor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = self.get_decoder()
__snake_case : Dict = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__snake_case : int = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
__snake_case : Tuple = processor.batch_decode(_lowerCAmelCase )
else:
with get_context(_lowerCAmelCase ).Pool() as pool:
__snake_case : int = processor.batch_decode(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : int = list(_lowerCAmelCase )
with get_context("""fork""" ).Pool() as p:
__snake_case : Tuple = decoder.decode_beams_batch(_lowerCAmelCase , _lowerCAmelCase )
__snake_case , __snake_case , __snake_case : List[Any] = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(_lowerCAmelCase , decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] , decoded_processor.text )
self.assertListEqual(_lowerCAmelCase , decoded_processor.logit_score )
self.assertListEqual(_lowerCAmelCase , decoded_processor.lm_score )
def snake_case__ ( self : Optional[int] ):
__snake_case : Optional[Any] = self.get_feature_extractor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = self.get_decoder()
__snake_case : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__snake_case : int = self._get_dummy_logits()
__snake_case : List[str] = 15
__snake_case : Optional[Any] = -20.0
__snake_case : Tuple = -4.0
__snake_case : List[Any] = processor.batch_decode(
_lowerCAmelCase , beam_width=_lowerCAmelCase , beam_prune_logp=_lowerCAmelCase , token_min_logp=_lowerCAmelCase , )
__snake_case : List[str] = decoded_processor_out.text
__snake_case : str = list(_lowerCAmelCase )
with get_context("""fork""" ).Pool() as pool:
__snake_case : Dict = decoder.decode_beams_batch(
_lowerCAmelCase , _lowerCAmelCase , beam_width=_lowerCAmelCase , beam_prune_logp=_lowerCAmelCase , token_min_logp=_lowerCAmelCase , )
__snake_case : int = [d[0][0] for d in decoded_decoder_out]
__snake_case : List[Any] = [d[0][2] for d in decoded_decoder_out]
__snake_case : List[Any] = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] , _lowerCAmelCase )
self.assertTrue(np.array_equal(_lowerCAmelCase , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , _lowerCAmelCase , atol=1e-3 ) )
self.assertTrue(np.array_equal(_lowerCAmelCase , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , _lowerCAmelCase , atol=1e-3 ) )
def snake_case__ ( self : Any ):
__snake_case : List[Any] = self.get_feature_extractor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Union[str, Any] = self.get_decoder()
__snake_case : Dict = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__snake_case : Any = self._get_dummy_logits()
__snake_case : Any = 2.0
__snake_case : int = 5.0
__snake_case : Optional[int] = -20.0
__snake_case : Optional[int] = True
__snake_case : Any = processor.batch_decode(
_lowerCAmelCase , alpha=_lowerCAmelCase , beta=_lowerCAmelCase , unk_score_offset=_lowerCAmelCase , lm_score_boundary=_lowerCAmelCase , )
__snake_case : str = decoded_processor_out.text
__snake_case : int = list(_lowerCAmelCase )
decoder.reset_params(
alpha=_lowerCAmelCase , beta=_lowerCAmelCase , unk_score_offset=_lowerCAmelCase , lm_score_boundary=_lowerCAmelCase , )
with get_context("""fork""" ).Pool() as pool:
__snake_case : Tuple = decoder.decode_beams_batch(
_lowerCAmelCase , _lowerCAmelCase , )
__snake_case : int = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] , _lowerCAmelCase )
__snake_case : List[str] = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , _lowerCAmelCase )
def snake_case__ ( self : Dict ):
__snake_case : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__snake_case : Union[str, Any] = processor.decoder.model_container[processor.decoder._model_key]
__snake_case : Optional[int] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
__snake_case : Union[str, Any] = os.listdir(_lowerCAmelCase )
__snake_case : List[str] = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Optional[Any] ):
__snake_case : Union[str, Any] = snapshot_download("""hf-internal-testing/processor_with_lm""" )
__snake_case : Dict = WavaVecaProcessorWithLM.from_pretrained(_lowerCAmelCase )
__snake_case : Optional[Any] = processor.decoder.model_container[processor.decoder._model_key]
__snake_case : Optional[int] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
__snake_case : List[str] = os.listdir(_lowerCAmelCase )
__snake_case : List[Any] = os.listdir(_lowerCAmelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Optional[Any] ):
__snake_case : Optional[int] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__snake_case : str = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__snake_case : Optional[int] = floats_list((3, 10_00) )
__snake_case : Union[str, Any] = processor_wavaveca(_lowerCAmelCase , return_tensors="""np""" )
__snake_case : Union[str, Any] = processor_auto(_lowerCAmelCase , return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1e-2 )
__snake_case : Dict = self._get_dummy_logits()
__snake_case : List[Any] = processor_wavaveca.batch_decode(_lowerCAmelCase )
__snake_case : List[Any] = processor_auto.batch_decode(_lowerCAmelCase )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def snake_case__ ( self : str ):
__snake_case : int = self.get_feature_extractor()
__snake_case : List[str] = self.get_tokenizer()
__snake_case : Optional[Any] = self.get_decoder()
__snake_case : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
@staticmethod
def snake_case__ ( _lowerCAmelCase : Any , _lowerCAmelCase : Tuple ):
__snake_case : Union[str, Any] = [d[key] for d in offsets]
return retrieved_list
def snake_case__ ( self : Dict ):
__snake_case : int = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__snake_case : List[str] = self._get_dummy_logits()[0]
__snake_case : str = processor.decode(_lowerCAmelCase , output_word_offsets=_lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """end_offset""" ) , [1, 3, 5] )
def snake_case__ ( self : List[str] ):
__snake_case : Any = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__snake_case : Optional[int] = self._get_dummy_logits()
__snake_case : int = processor.batch_decode(_lowerCAmelCase , output_word_offsets=_lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) for o in outputs["""word_offsets"""]] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """end_offset""" ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def snake_case__ ( self : Optional[Any] ):
import torch
__snake_case : Optional[Any] = load_dataset("""common_voice""" , """en""" , split="""train""" , streaming=_lowerCAmelCase )
__snake_case : Any = ds.cast_column("""audio""" , datasets.Audio(sampling_rate=1_60_00 ) )
__snake_case : List[Any] = iter(_lowerCAmelCase )
__snake_case : Optional[int] = next(_lowerCAmelCase )
__snake_case : str = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
__snake_case : str = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
__snake_case : List[str] = processor(sample["""audio"""]["""array"""] , return_tensors="""pt""" ).input_values
with torch.no_grad():
__snake_case : Dict = model(_lowerCAmelCase ).logits.cpu().numpy()
__snake_case : Any = processor.decode(logits[0] , output_word_offsets=_lowerCAmelCase )
__snake_case : Optional[Any] = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
__snake_case : Dict = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
__snake_case : Dict = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) , _lowerCAmelCase )
self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) , output.text )
# output times
__snake_case : Dict = torch.tensor(self.get_from_offsets(_lowerCAmelCase , """start_time""" ) )
__snake_case : Optional[Any] = torch.tensor(self.get_from_offsets(_lowerCAmelCase , """end_time""" ) )
# fmt: off
__snake_case : Optional[Any] = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
__snake_case : Optional[int] = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=0.01 ) )
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=0.01 ) )
| 20 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCamelCase ( self : Dict ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _UpperCamelCase ( self : str ) -> List[str]:
_UpperCamelCase = 1
_UpperCamelCase = 3
_UpperCamelCase = (32, 32)
_UpperCamelCase = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(__UpperCamelCase )
return image
@property
def _UpperCamelCase ( self : List[Any] ) -> Dict:
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
return model
@property
def _UpperCamelCase ( self : str ) -> Union[str, Any]:
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
return model
@property
def _UpperCamelCase ( self : Optional[int] ) -> Any:
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(__UpperCamelCase )
@property
def _UpperCamelCase ( self : str ) -> Dict:
def extract(*__UpperCamelCase : str , **__UpperCamelCase : Dict ):
class UpperCAmelCase_ :
def __init__( self : List[str] ) -> Optional[Any]:
_UpperCamelCase = torch.ones([0] )
def _UpperCamelCase ( self : List[Any] , __UpperCamelCase : Tuple ) -> int:
self.pixel_values.to(__UpperCamelCase )
return self
return Out()
return extract
def _UpperCamelCase ( self : Optional[int] ) -> str:
_UpperCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase = self.dummy_cond_unet
_UpperCamelCase = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=__UpperCamelCase , set_alpha_to_one=__UpperCamelCase , )
_UpperCamelCase = self.dummy_vae
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# make sure here that pndm scheduler skips prk
_UpperCamelCase = StableDiffusionPipeline(
unet=__UpperCamelCase , scheduler=__UpperCamelCase , vae=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=self.dummy_extractor , )
_UpperCamelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = """A painting of a squirrel eating a burger"""
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(0 )
_UpperCamelCase = sd_pipe([prompt] , generator=__UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' )
_UpperCamelCase = output.images
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(0 )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , return_dict=__UpperCamelCase , )[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCamelCase = np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _UpperCamelCase ( self : Any ) -> Dict:
_UpperCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase = self.dummy_cond_unet
_UpperCamelCase = PNDMScheduler(skip_prk_steps=__UpperCamelCase )
_UpperCamelCase = self.dummy_vae
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# make sure here that pndm scheduler skips prk
_UpperCamelCase = StableDiffusionPipeline(
unet=__UpperCamelCase , scheduler=__UpperCamelCase , vae=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=self.dummy_extractor , )
_UpperCamelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = """A painting of a squirrel eating a burger"""
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(0 )
_UpperCamelCase = sd_pipe([prompt] , generator=__UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' )
_UpperCamelCase = output.images
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(0 )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , return_dict=__UpperCamelCase , )[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCamelCase = np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _UpperCamelCase ( self : int ) -> Optional[int]:
_UpperCamelCase = StableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-lms-pipe''' , safety_checker=__UpperCamelCase )
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert isinstance(pipe.scheduler , __UpperCamelCase )
assert pipe.safety_checker is None
_UpperCamelCase = pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCamelCase )
_UpperCamelCase = StableDiffusionPipeline.from_pretrained(__UpperCamelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCamelCase = pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def _UpperCamelCase ( self : List[str] ) -> List[Any]:
_UpperCamelCase = self.dummy_cond_unet
_UpperCamelCase = PNDMScheduler(skip_prk_steps=__UpperCamelCase )
_UpperCamelCase = self.dummy_vae
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# put models in fp16
_UpperCamelCase = unet.half()
_UpperCamelCase = vae.half()
_UpperCamelCase = bert.half()
# make sure here that pndm scheduler skips prk
_UpperCamelCase = StableDiffusionPipeline(
unet=__UpperCamelCase , scheduler=__UpperCamelCase , vae=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=self.dummy_extractor , )
_UpperCamelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = """A painting of a squirrel eating a burger"""
_UpperCamelCase = sd_pipe([prompt] , num_inference_steps=2 , output_type='''np''' ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self : List[Any] ) -> int:
_UpperCamelCase = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' , safety_checker=__UpperCamelCase )
_UpperCamelCase = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCamelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_UpperCamelCase = 40_0366_0346
_UpperCamelCase = 7
# without safety guidance (sld_guidance_scale = 0)
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=__UpperCamelCase , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=__UpperCamelCase , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
_UpperCamelCase = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' , safety_checker=__UpperCamelCase )
_UpperCamelCase = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCamelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = """padme amidala taking a bath artwork, safe for work, no nudity"""
_UpperCamelCase = 27_3497_1755
_UpperCamelCase = 7
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=__UpperCamelCase , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=__UpperCamelCase , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
_UpperCamelCase = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' )
_UpperCamelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_UpperCamelCase = 10_4435_5234
_UpperCamelCase = 12
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=__UpperCamelCase , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
_UpperCamelCase = sd_pipe(
[prompt] , generator=__UpperCamelCase , guidance_scale=__UpperCamelCase , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 256 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ) -> Any:
raise RuntimeError("""CUDA out of memory.""" )
class UpperCamelCase_ ( nn.Module ):
def __init__( self ) -> Any:
super().__init__()
UpperCAmelCase : Tuple = nn.Linear(3 , 4 )
UpperCAmelCase : Tuple = nn.BatchNormad(4 )
UpperCAmelCase : int = nn.Linear(4 , 5 )
def _lowercase( self , A ) -> Any:
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(A ):
nonlocal batch_sizes
batch_sizes.append(A )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(A , [128, 64, 32, 16, 8] )
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[Any] = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(A , A ):
nonlocal batch_sizes
batch_sizes.append(A )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
UpperCAmelCase , UpperCAmelCase : Optional[int] = mock_training_loop_function("""hello""" )
self.assertListEqual(A , [128, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, """hello"""] )
def _lowercase( self ) -> Any:
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(A ):
pass
with self.assertRaises(A ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _lowercase( self ) -> Optional[int]:
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(A ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(A ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _lowercase( self ) -> Optional[Any]:
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(A , A , A ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(A ) as cm:
mock_training_loop_function(128 , """hello""" , """world""" )
self.assertIn("""Batch size was passed into `f`""" , cm.exception.args[0] )
self.assertIn("""`f(arg1='hello', arg2='world')""" , cm.exception.args[0] )
def _lowercase( self ) -> int:
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(A ):
raise ValueError("""Oops, we had an error!""" )
with self.assertRaises(A ) as cm:
mock_training_loop_function()
self.assertIn("""Oops, we had an error!""" , cm.exception.args[0] )
@require_cuda
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[Any] = torch.cuda.memory_allocated()
UpperCAmelCase : List[str] = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , A )
UpperCAmelCase : Tuple = release_memory(A )
self.assertEqual(torch.cuda.memory_allocated() , A )
| 265 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ['''YolosFeatureExtractor''']
lowerCamelCase__ = ['''YolosImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''YolosForObjectDetection''',
'''YolosModel''',
'''YolosPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class __magic_name__ (__lowercase ):
lowerCamelCase__ = '''mobilenet_v2'''
def __init__( self , _a=3 , _a=224 , _a=1.0 , _a=8 , _a=8 , _a=6 , _a=32 , _a=True , _a=True , _a="relu6" , _a=True , _a=0.8 , _a=0.0_2 , _a=0.0_0_1 , _a=255 , **_a , ) -> Dict:
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = image_size
lowerCAmelCase_ = depth_multiplier
lowerCAmelCase_ = depth_divisible_by
lowerCAmelCase_ = min_depth
lowerCAmelCase_ = expand_ratio
lowerCAmelCase_ = output_stride
lowerCAmelCase_ = first_layer_is_expansion
lowerCAmelCase_ = finegrained_output
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = tf_padding
lowerCAmelCase_ = classifier_dropout_prob
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = semantic_loss_ignore_index
class __magic_name__ (__lowercase ):
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __a ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def __a ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})] )
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )
@property
def __a ( self ) -> float:
return 1E-4
| 22 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : Optional[int]=13 , __lowerCamelCase : List[str]=7 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Dict=True , __lowerCamelCase : Optional[int]=False , __lowerCamelCase : str=True , __lowerCamelCase : List[str]=99 , __lowerCamelCase : List[Any]=32 , __lowerCamelCase : Any=5 , __lowerCamelCase : str=4 , __lowerCamelCase : List[Any]=64 , __lowerCamelCase : Dict="gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : List[str]=0.1 , __lowerCamelCase : Union[str, Any]=5_12 , __lowerCamelCase : List[Any]=16 , __lowerCamelCase : str=2 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : List[Any]=4 , __lowerCamelCase : Tuple=None , __lowerCamelCase : List[Any]=2 , __lowerCamelCase : List[Any]=2 , __lowerCamelCase : Optional[Any]=2 , __lowerCamelCase : str=2 , __lowerCamelCase : Dict=4 , __lowerCamelCase : Optional[int]=1 , ) -> Any:
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
a = q_groups
a = k_groups
a = v_groups
a = post_attention_groups
a = intermediate_groups
a = output_groups
def __UpperCAmelCase ( self : int ) -> Optional[int]:
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCAmelCase ( self : Dict ) -> Dict:
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def __UpperCAmelCase ( self : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[Any] ) -> str:
a = SqueezeBertModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , __lowerCamelCase )
a = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Any , __lowerCamelCase : int , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any] , __lowerCamelCase : List[Any] ) -> Dict:
a = SqueezeBertForMaskedLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[int] , __lowerCamelCase : str ) -> Union[str, Any]:
a = SqueezeBertForQuestionAnswering(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , start_positions=__lowerCamelCase , end_positions=__lowerCamelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Any , __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : str ) -> Any:
a = self.num_labels
a = SqueezeBertForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : int , __lowerCamelCase : str , __lowerCamelCase : str , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Tuple ) -> Any:
a = self.num_labels
a = SqueezeBertForTokenClassification(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Dict , __lowerCamelCase : Tuple ) -> Optional[int]:
a = self.num_choices
a = SqueezeBertForMultipleChoice(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCAmelCase ( self : Any ) -> Optional[int]:
a = self.prepare_config_and_inputs()
((a) , (a) , (a) , (a) , (a) , (a)) = config_and_inputs
a = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class snake_case__ (_UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
SCREAMING_SNAKE_CASE_ : Tuple = (
{
"""feature-extraction""": SqueezeBertModel,
"""fill-mask""": SqueezeBertForMaskedLM,
"""question-answering""": SqueezeBertForQuestionAnswering,
"""text-classification""": SqueezeBertForSequenceClassification,
"""token-classification""": SqueezeBertForTokenClassification,
"""zero-shot""": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : str = True
SCREAMING_SNAKE_CASE_ : Optional[int] = False
def __UpperCAmelCase ( self : Tuple ) -> Any:
a = SqueezeBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCamelCase , dim=37 )
def __UpperCAmelCase ( self : int ) -> int:
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self : Tuple ) -> Dict:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[int] ) -> Dict:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*__lowerCamelCase )
def __UpperCAmelCase ( self : Dict ) -> Any:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__lowerCamelCase )
def __UpperCAmelCase ( self : Union[str, Any] ) -> str:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__lowerCamelCase )
@slow
def __UpperCAmelCase ( self : Any ) -> Any:
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = SqueezeBertModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
@require_sentencepiece
@require_tokenizers
@require_torch
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
def __UpperCAmelCase ( self : str ) -> Union[str, Any]:
a = SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli" )
a = torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]] )
a = model(__lowerCamelCase )[0]
a = torch.Size((1, 3) )
self.assertEqual(output.shape , __lowerCamelCase )
a = torch.tensor([[0.6_401, -0.0_349, -0.6_041]] )
self.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-4 ) )
| 107 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class snake_case__ :
"""simple docstring"""
def __init__( self : Any , __lowerCamelCase : list[tuple[float, float]] ) -> Tuple:
a = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
a = len(__lowerCamelCase ) - 1
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : float ) -> list[float]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
a = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , __lowerCamelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__lowerCamelCase ) , 5 ) == 1
return output_values
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : float ) -> tuple[float, float]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
a = self.basis_function(__lowerCamelCase )
a = 0.0
a = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : float = 0.01 ) -> List[str]:
from matplotlib import pyplot as plt # type: ignore
a = [] # x coordinates of points to plot
a = [] # y coordinates of points to plot
a = 0.0
while t <= 1:
a = self.bezier_curve_function(__lowerCamelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
a = [i[0] for i in self.list_of_points]
a = [i[1] for i in self.list_of_points]
plt.plot(
__lowerCamelCase , __lowerCamelCase , color="blue" , label="Curve of Degree " + str(self.degree ) , )
plt.scatter(__lowerCamelCase , __lowerCamelCase , color="red" , label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 107 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ : int = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : List[str] = ["""BloomTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Optional[int] = [
"""BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BloomForCausalLM""",
"""BloomModel""",
"""BloomPreTrainedModel""",
"""BloomForSequenceClassification""",
"""BloomForTokenClassification""",
"""BloomForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
lowerCamelCase_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 215 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
lowerCamelCase_ : List[Any] = TypeVar("""T""")
class __A ( Generic[T] ):
"""simple docstring"""
def __init__( self , __A ) -> None:
a =data
a =self
a =0
class __A ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> None:
# map from node name to the node object
a ={}
def SCREAMING_SNAKE_CASE ( self , __A ) -> None:
# create a new set with x as its member
a =DisjointSetTreeNode(__A )
def SCREAMING_SNAKE_CASE ( self , __A ) -> DisjointSetTreeNode[T]:
# find the set x belongs to (with path-compression)
a =self.map[data]
if elem_ref != elem_ref.parent:
a =self.find_set(elem_ref.parent.data )
return elem_ref.parent
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> None:
# helper function for union operation
if nodea.rank > nodea.rank:
a =nodea
else:
a =nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> None:
# merge 2 disjoint sets
self.link(self.find_set(__A ) , self.find_set(__A ) )
class __A ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> None:
# connections: map from the node to the neighbouring nodes (with weights)
a ={}
def SCREAMING_SNAKE_CASE ( self , __A ) -> None:
# add a node ONLY if its not present in the graph
if node not in self.connections:
a ={}
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A ) -> None:
# add an edge with the given weight
self.add_node(__A )
self.add_node(__A )
a =weight
a =weight
def SCREAMING_SNAKE_CASE ( self ) -> GraphUndirectedWeighted[T]:
a =[]
a =set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __A : x[2] )
# creating the disjoint set
a =DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__A )
# MST generation
a =0
a =0
a =GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
a , a , a =edges[index]
index += 1
a =disjoint_set.find_set(__A )
a =disjoint_set.find_set(__A )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__A , __A , __A )
disjoint_set.union(__A , __A )
return graph | 215 | 1 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
a :List[str] = float("nan")
class __a :
'''simple docstring'''
def __init__( self , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = sys.stdout
SCREAMING_SNAKE_CASE__ : Union[str, Any] = open(_a , """a""" )
def __getattr__( self , _a ) -> Optional[int]:
"""simple docstring"""
return getattr(self.stdout , _a )
def _a ( self , _a ) -> Any:
"""simple docstring"""
self.stdout.write(_a )
# strip tqdm codes
self.file.write(re.sub(r"""^.*\r""" , """""" , _a , 0 , re.M ) )
def _lowercase ( __lowerCAmelCase=80 , __lowerCAmelCase=False ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any = []
# deal with critical env vars
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""CUDA_VISIBLE_DEVICES"""]
for key in env_keys:
SCREAMING_SNAKE_CASE__ : List[Any] = os.environ.get(__lowerCAmelCase , __lowerCAmelCase )
if val is not None:
cmd.append(F'''{key}={val}''' )
# python executable (not always needed if the script is executable)
SCREAMING_SNAKE_CASE__ : Optional[int] = sys.executable if full_python_path else sys.executable.split("""/""" )[-1]
cmd.append(__lowerCAmelCase )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
while len(__lowerCAmelCase ) > 0:
current_line += F'''{cmd.pop(0 )} '''
if len(__lowerCAmelCase ) == 0 or len(__lowerCAmelCase ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = """"""
return "\\\n".join(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# unwrap multi-line input
SCREAMING_SNAKE_CASE__ : int = re.sub(r"""[\\\n]+""" , """ """ , args.base_cmd )
# remove --output_dir if any and set our own
SCREAMING_SNAKE_CASE__ : Any = re.sub("""--output_dir\s+[^\s]+""" , """""" , args.base_cmd )
args.base_cmd += F''' --output_dir {output_dir}'''
# ensure we have --overwrite_output_dir
SCREAMING_SNAKE_CASE__ : Any = re.sub("""--overwrite_output_dir\s+""" , """""" , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# Enable to debug everything but the run itself, to do it fast and see the progress.
# This is useful for debugging the output formatting quickly - we can remove it later once
# everybody is happy with the output
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6_666, 222.22_222_222] )} , )
SCREAMING_SNAKE_CASE__ : Optional[int] = subprocess.run(__lowerCAmelCase , capture_output=__lowerCAmelCase , text=__lowerCAmelCase )
if verbose:
print("""STDOUT""" , result.stdout )
print("""STDERR""" , result.stderr )
# save the streams
SCREAMING_SNAKE_CASE__ : Optional[int] = variation.replace(""" """ , """-""" )
with open(Path(__lowerCAmelCase ) / F'''log.{prefix}.stdout.txt''' , """w""" ) as f:
f.write(result.stdout )
with open(Path(__lowerCAmelCase ) / F'''log.{prefix}.stderr.txt''' , """w""" ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print("""failed""" )
return {target_metric_key: nan}
with io.open(F'''{output_dir}/all_results.json''' , """r""" , encoding="""utf-8""" ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] = json.load(__lowerCAmelCase )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : Tuple = []
SCREAMING_SNAKE_CASE__ : str = F'''{id}: {variation:<{longest_variation_len}}'''
SCREAMING_SNAKE_CASE__ : Tuple = F'''{preamble}: '''
SCREAMING_SNAKE_CASE__ : List[Any] = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(__lowerCAmelCase ) , desc=__lowerCAmelCase , leave=__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Tuple = process_run_single(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = single_run_metrics[target_metric_key]
if not math.isnan(__lowerCAmelCase ):
metrics.append(__lowerCAmelCase )
results.append(__lowerCAmelCase )
outcome += "✓"
else:
outcome += "✘"
SCREAMING_SNAKE_CASE__ : int = F'''\33[2K\r{outcome}'''
if len(__lowerCAmelCase ) > 0:
SCREAMING_SNAKE_CASE__ : int = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
SCREAMING_SNAKE_CASE__ : Optional[int] = round(mean_metrics[target_metric_key] , 2 )
SCREAMING_SNAKE_CASE__ : Dict = F'''{outcome} {mean_target}'''
if len(__lowerCAmelCase ) > 1:
results_str += F''' {tuple(round(__lowerCAmelCase , 2 ) for x in results )}'''
print(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = variation
return mean_metrics
else:
print(__lowerCAmelCase )
return {variation_key: variation, target_metric_key: nan}
def _lowercase ( ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict = torch.cuda.get_device_properties(torch.device("""cuda""" ) )
return F'''
Datetime : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )}
Software:
transformers: {transformers.__version__}
torch : {torch.__version__}
cuda : {torch.version.cuda}
python : {platform.python_version()}
Hardware:
{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple = pd.DataFrame(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """variation"""
SCREAMING_SNAKE_CASE__ : Optional[int] = """diff_%"""
SCREAMING_SNAKE_CASE__ : Any = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
SCREAMING_SNAKE_CASE__ : Dict = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(__lowerCAmelCase ):
# as a fallback, use the minimal value as the sentinel
SCREAMING_SNAKE_CASE__ : Any = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[Any] = df.apply(
lambda __lowerCAmelCase : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis="""columns""" , )
# re-order columns
SCREAMING_SNAKE_CASE__ : Any = [variation_key, target_metric_key, diff_key, *report_metric_keys]
SCREAMING_SNAKE_CASE__ : Optional[Any] = df.reindex(__lowerCAmelCase , axis="""columns""" ) # reorder cols
# capitalize
SCREAMING_SNAKE_CASE__ : List[Any] = df.rename(str.capitalize , axis="""columns""" )
# make the cols as narrow as possible
SCREAMING_SNAKE_CASE__ : Any = df.rename(lambda __lowerCAmelCase : c.replace("""_""" , """<br>""" ) , axis="""columns""" )
SCREAMING_SNAKE_CASE__ : int = df.rename(lambda __lowerCAmelCase : c.replace("""_""" , """\n""" ) , axis="""columns""" )
SCREAMING_SNAKE_CASE__ : str = ["""""", """Copy between the cut-here-lines and paste as is to github or a forum"""]
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=__lowerCAmelCase , floatfmt=""".2f""" )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=__lowerCAmelCase , floatfmt=""".2f""" )]
print("""\n\n""".join(__lowerCAmelCase ) )
def _lowercase ( ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--base-cmd""" , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""Base cmd""" , )
parser.add_argument(
"""--variations""" , default=__lowerCAmelCase , type=__lowerCAmelCase , nargs="""+""" , required=__lowerCAmelCase , help="""Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'""" , )
parser.add_argument(
"""--base-variation""" , default=__lowerCAmelCase , type=__lowerCAmelCase , help="""Baseline variation to compare to. if None the minimal target value will be used to compare against""" , )
parser.add_argument(
"""--target-metric-key""" , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""Target metric key in output_dir/all_results.json, e.g., train_samples_per_second""" , )
parser.add_argument(
"""--report-metric-keys""" , default="""""" , type=__lowerCAmelCase , help="""Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples""" , )
parser.add_argument(
"""--repeat-times""" , default=1 , type=__lowerCAmelCase , help="""How many times to re-run each variation - an average will be reported""" , )
parser.add_argument(
"""--output_dir""" , default="""output_benchmark""" , type=__lowerCAmelCase , help="""The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked""" , )
parser.add_argument(
"""--verbose""" , default=__lowerCAmelCase , action="""store_true""" , help="""Whether to show the outputs of each run or just the benchmark progress""" , )
SCREAMING_SNAKE_CASE__ : Tuple = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Any = args.output_dir
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = get_base_command(__lowerCAmelCase , __lowerCAmelCase )
# split each dimension into its --foo variations
SCREAMING_SNAKE_CASE__ : Dict = [list(map(str.strip , re.split(r"""\|""" , __lowerCAmelCase ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
SCREAMING_SNAKE_CASE__ : str = list(map(str.strip , map(""" """.join , itertools.product(*__lowerCAmelCase ) ) ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = max(len(__lowerCAmelCase ) for x in variations )
# split wanted keys
SCREAMING_SNAKE_CASE__ : int = args.report_metric_keys.split()
# capture prints into a log file for convenience
SCREAMING_SNAKE_CASE__ : Optional[Any] = F'''benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S' )}.txt'''
print(F'''\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt''' )
print(F'''and this script\'s output is also piped into {report_fn}''' )
SCREAMING_SNAKE_CASE__ : Any = Tee(__lowerCAmelCase )
print(F'''\n*** Running {len(__lowerCAmelCase )} benchmarks:''' )
print(F'''Base command: {' '.join(__lowerCAmelCase )}''' )
SCREAMING_SNAKE_CASE__ : str = """variation"""
SCREAMING_SNAKE_CASE__ : int = []
for id, variation in enumerate(tqdm(__lowerCAmelCase , desc="""Total completion: """ , leave=__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : Tuple = base_cmd + variation.split()
results.append(
process_run(
id + 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , args.target_metric_key , __lowerCAmelCase , args.repeat_times , __lowerCAmelCase , args.verbose , ) )
process_results(__lowerCAmelCase , args.target_metric_key , __lowerCAmelCase , args.base_variation , __lowerCAmelCase )
if __name__ == "__main__":
main()
| 132 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a :str = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n"
a :List[Any] = "\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"
a :int = r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n >>> metric = datasets.load_metric(\"competition_math\")\n >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n >>> print(results)\n {'accuracy': 1.0}\n"
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __a (datasets.Metric):
'''simple docstring'''
def _a ( self ) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ),
"""references""": datasets.Value("""string""" ),
} ) , homepage="""https://github.com/hendrycks/math""" , codebase_urls=["""https://github.com/hendrycks/math"""] , )
def _a ( self , _a , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0.0
for i, j in zip(_a , _a ):
n_correct += 1.0 if math_equivalence.is_equiv(_a , _a ) else 0.0
SCREAMING_SNAKE_CASE__ : List[str] = n_correct / len(_a )
return {
"accuracy": accuracy,
}
| 132 | 1 |
"""simple docstring"""
lowercase__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
lowercase__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[int]:
a__: List[Any] = True
a__: Dict = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
order.append(_SCREAMING_SNAKE_CASE )
return order
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[int]:
a__: int = True
a__: str = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return component
def __a ( _SCREAMING_SNAKE_CASE ) ->list[list[int]]:
a__: Optional[int] = len(_SCREAMING_SNAKE_CASE ) * [False]
a__: dict[int, list[int]] = {vert: [] for vert in range(len(_SCREAMING_SNAKE_CASE ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_SCREAMING_SNAKE_CASE )
a__: int = []
for i, was_visited in enumerate(_SCREAMING_SNAKE_CASE ):
if not was_visited:
order += topology_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: int = []
a__: Optional[int] = len(_SCREAMING_SNAKE_CASE ) * [False]
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
a__: Union[str, Any] = order[len(_SCREAMING_SNAKE_CASE ) - i - 1]
if not visited[vert]:
a__: Dict = find_components(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
components_list.append(_SCREAMING_SNAKE_CASE )
return components_list
| 364 | """simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowercase__ = logging.get_logger(__name__)
lowercase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowercase__ = {
'vocab_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowercase__ = {
'vocab_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowercase__ = {
'vocab_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowercase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': 512,
'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowercase__ = {
'facebook/dpr-question_encoder-single-nq-base': 512,
'facebook/dpr-question_encoder-multiset-base': 512,
}
lowercase__ = {
'facebook/dpr-reader-single-nq-base': 512,
'facebook/dpr-reader-multiset-base': 512,
}
lowercase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowercase__ = {
'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowercase__ = {
'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class __snake_case ( __lowerCAmelCase ):
a__ = VOCAB_FILES_NAMES
a__ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
a__ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class __snake_case ( __lowerCAmelCase ):
a__ = VOCAB_FILES_NAMES
a__ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
a__ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowercase__ = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
lowercase__ = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
lowercase__ = r'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n '
@add_start_docstrings(__lowerCAmelCase )
class __snake_case :
def __call__( self , lowercase , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = None , lowercase = None , lowercase = None , **lowercase , ) -> BatchEncoding:
'''simple docstring'''
if titles is None and texts is None:
return super().__call__(
lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , return_tensors=lowercase , return_attention_mask=lowercase , **lowercase , )
elif titles is None or texts is None:
a__: str = titles if texts is None else texts
return super().__call__(
lowercase , lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , return_tensors=lowercase , return_attention_mask=lowercase , **lowercase , )
a__: Tuple = titles if not isinstance(lowercase , lowercase) else [titles]
a__: Optional[int] = texts if not isinstance(lowercase , lowercase) else [texts]
a__: Dict = len(lowercase)
a__: Dict = questions if not isinstance(lowercase , lowercase) else [questions] * n_passages
if len(lowercase) != len(lowercase):
raise ValueError(
f'There should be as many titles than texts but got {len(lowercase)} titles and {len(lowercase)} texts.')
a__: List[str] = super().__call__(lowercase , lowercase , padding=lowercase , truncation=lowercase)['input_ids']
a__: List[Any] = super().__call__(lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase)['input_ids']
a__: Optional[Any] = {
'input_ids': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(lowercase , lowercase)
]
}
if return_attention_mask is not False:
a__: Dict = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
a__: int = attention_mask
return self.pad(lowercase , padding=lowercase , max_length=lowercase , return_tensors=lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase = 16 , lowercase = 64 , lowercase = 4 , ) -> List[DPRSpanPrediction]:
'''simple docstring'''
a__: Dict = reader_input['input_ids']
a__ , a__ , a__: Tuple = reader_output[:3]
a__: Tuple = len(lowercase)
a__: Optional[Any] = sorted(range(lowercase) , reverse=lowercase , key=relevance_logits.__getitem__)
a__: List[DPRReaderOutput] = []
for doc_id in sorted_docs:
a__: Tuple = list(input_ids[doc_id])
# assuming question & title information is at the beginning of the sequence
a__: Dict = sequence_ids.index(self.sep_token_id , 2) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
a__: Any = sequence_ids.index(self.pad_token_id)
else:
a__: Optional[Any] = len(lowercase)
a__: Optional[int] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=lowercase , top_spans=lowercase , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=lowercase , start_index=lowercase , end_index=lowercase , text=self.decode(sequence_ids[start_index : end_index + 1]) , ))
if len(lowercase) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , ) -> List[DPRSpanPrediction]:
'''simple docstring'''
a__: Optional[Any] = []
for start_index, start_score in enumerate(lowercase):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
scores.append(((start_index, start_index + answer_length), start_score + end_score))
a__: str = sorted(lowercase , key=lambda lowercase: x[1] , reverse=lowercase)
a__: Optional[Any] = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]')
a__: str = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f'Span is too long: {length} > {max_answer_length}')
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals):
continue
chosen_span_intervals.append((start_index, end_index))
if len(lowercase) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__lowerCAmelCase )
class __snake_case ( __lowerCAmelCase , __lowerCAmelCase ):
a__ = VOCAB_FILES_NAMES
a__ = READER_PRETRAINED_VOCAB_FILES_MAP
a__ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = READER_PRETRAINED_INIT_CONFIGURATION
a__ = ["""input_ids""", """attention_mask"""]
| 203 | 0 |
from ...configuration_utils import PretrainedConfig
class _lowercase (a_ ):
'''simple docstring'''
lowercase__ = """bert-generation"""
def __init__( self , snake_case__=5_0358 , snake_case__=1024 , snake_case__=24 , snake_case__=16 , snake_case__=4096 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__=2 , snake_case__=1 , snake_case__="absolute" , snake_case__=True , **snake_case__ , ):
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = hidden_act
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = initializer_range
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = position_embedding_type
UpperCamelCase_ = use_cache
| 128 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
UpperCAmelCase : List[Any] =logging.get_logger(__name__)
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
return [
int(10_00 * (box[0] / width)),
int(10_00 * (box[1] / height)),
int(10_00 * (box[2] / width)),
int(10_00 * (box[3] / height)),
]
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None):
UpperCamelCase_ = tesseract_config if tesseract_config is not None else ""
# apply OCR
UpperCamelCase_ = to_pil_image(_lowerCAmelCase)
UpperCamelCase_ , UpperCamelCase_ = pil_image.size
UpperCamelCase_ = pytesseract.image_to_data(_lowerCAmelCase , lang=_lowerCAmelCase , output_type="dict" , config=_lowerCAmelCase)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
UpperCamelCase_ = [idx for idx, word in enumerate(_lowerCAmelCase) if not word.strip()]
UpperCamelCase_ = [word for idx, word in enumerate(_lowerCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase_ = [coord for idx, coord in enumerate(_lowerCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase_ = [coord for idx, coord in enumerate(_lowerCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase_ = [coord for idx, coord in enumerate(_lowerCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase_ = [coord for idx, coord in enumerate(_lowerCAmelCase) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
UpperCamelCase_ = []
for x, y, w, h in zip(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
UpperCamelCase_ = [x, y, x + w, y + h]
actual_boxes.append(_lowerCAmelCase)
# finally, normalize the bounding boxes
UpperCamelCase_ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase))
assert len(_lowerCAmelCase) == len(_lowerCAmelCase), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class _lowercase (a_ ):
'''simple docstring'''
lowercase__ = ["""pixel_values"""]
def __init__( self , snake_case__ = True , snake_case__ = None , snake_case__ = PILImageResampling.BILINEAR , snake_case__ = True , snake_case__ = None , snake_case__ = "" , **snake_case__ , ):
'''simple docstring'''
super().__init__(**snake_case__ )
UpperCamelCase_ = size if size is not None else {"height": 224, "width": 224}
UpperCamelCase_ = get_size_dict(snake_case__ )
UpperCamelCase_ = do_resize
UpperCamelCase_ = size
UpperCamelCase_ = resample
UpperCamelCase_ = apply_ocr
UpperCamelCase_ = ocr_lang
UpperCamelCase_ = tesseract_config
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ = PILImageResampling.BILINEAR , snake_case__ = None , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = get_size_dict(snake_case__ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
UpperCamelCase_ = (size["height"], size["width"])
return resize(snake_case__ , size=snake_case__ , resample=snake_case__ , data_format=snake_case__ , **snake_case__ )
def _lowerCamelCase ( self , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = ChannelDimension.FIRST , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCamelCase_ = size if size is not None else self.size
UpperCamelCase_ = get_size_dict(snake_case__ )
UpperCamelCase_ = resample if resample is not None else self.resample
UpperCamelCase_ = apply_ocr if apply_ocr is not None else self.apply_ocr
UpperCamelCase_ = ocr_lang if ocr_lang is not None else self.ocr_lang
UpperCamelCase_ = tesseract_config if tesseract_config is not None else self.tesseract_config
UpperCamelCase_ = make_list_of_images(snake_case__ )
if not valid_images(snake_case__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(snake_case__ ) for image in images]
if apply_ocr:
requires_backends(self , "pytesseract" )
UpperCamelCase_ = []
UpperCamelCase_ = []
for image in images:
UpperCamelCase_ , UpperCamelCase_ = apply_tesseract(snake_case__ , snake_case__ , snake_case__ )
words_batch.append(snake_case__ )
boxes_batch.append(snake_case__ )
if do_resize:
UpperCamelCase_ = [self.resize(image=snake_case__ , size=snake_case__ , resample=snake_case__ ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
UpperCamelCase_ = [flip_channel_order(snake_case__ ) for image in images]
UpperCamelCase_ = [to_channel_dimension_format(snake_case__ , snake_case__ ) for image in images]
UpperCamelCase_ = BatchFeature(data={"pixel_values": images} , tensor_type=snake_case__ )
if apply_ocr:
UpperCamelCase_ = words_batch
UpperCamelCase_ = boxes_batch
return data
| 128 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def __init__( self : Optional[Any] , lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[Any]=13 , lowerCamelCase_ : Optional[Any]=7 , lowerCamelCase_ : Dict=True , lowerCamelCase_ : Any=True , lowerCamelCase_ : List[str]=True , lowerCamelCase_ : int=True , lowerCamelCase_ : Tuple=99 , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : int=5 , lowerCamelCase_ : int=4 , lowerCamelCase_ : List[str]=37 , lowerCamelCase_ : str="gelu" , lowerCamelCase_ : Optional[int]=0.1 , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Tuple=512 , lowerCamelCase_ : Any=16 , lowerCamelCase_ : Tuple=2 , lowerCamelCase_ : int=0.0_2 , lowerCamelCase_ : Any=4 , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_attention_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_choices
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_attention_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = config_and_inputs
UpperCamelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = config_and_inputs
UpperCamelCase = True
UpperCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase , unittest.TestCase ):
__lowerCAmelCase = True
__lowerCAmelCase = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
UpperCamelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
UpperCamelCase = model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=lowerCamelCase_ )
UpperCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase_ )
@require_flax
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=lowerCamelCase_ )
UpperCamelCase = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
UpperCamelCase = model(lowerCamelCase_ )[0]
UpperCamelCase = [1, 11, 5_0265]
self.assertEqual(list(output.shape ) , lowerCamelCase_ )
# compare the actual values for a slice.
UpperCamelCase = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase_ , atol=1E-4 ) )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=lowerCamelCase_ )
UpperCamelCase = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
UpperCamelCase = model(lowerCamelCase_ )[0]
# compare the actual values for a slice.
UpperCamelCase = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase_ , atol=1E-4 ) )
| 165 | def lowercase( UpperCamelCase_ = 1000000 ) -> int:
'''simple docstring'''
UpperCamelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , UpperCamelCase_ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 165 | 1 |
"""simple docstring"""
import sys
from collections import defaultdict
class A__ :
def __init__( self ):
__lowerCAmelCase : Optional[Any] = []
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
return self.node_position[vertex]
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = pos
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__lowerCAmelCase : Union[str, Any] = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__lowerCAmelCase : str = 2 * start + 1
else:
__lowerCAmelCase : Dict = 2 * start + 2
if heap[smallest_child] < heap[start]:
__lowerCAmelCase , __lowerCAmelCase : str = heap[smallest_child], positions[smallest_child]
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = (
heap[start],
positions[start],
)
__lowerCAmelCase , __lowerCAmelCase : Any = temp, tempa
__lowerCAmelCase : Dict = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _SCREAMING_SNAKE_CASE )
self.top_to_bottom(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = position[index]
while index != 0:
__lowerCAmelCase : str = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__lowerCAmelCase : Any = heap[parent]
__lowerCAmelCase : Tuple = position[parent]
self.set_position(position[parent] , _SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase : str = val
__lowerCAmelCase : Dict = temp
self.set_position(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
break
__lowerCAmelCase : str = parent
else:
__lowerCAmelCase : List[Any] = val
__lowerCAmelCase : int = temp
self.set_position(_SCREAMING_SNAKE_CASE , 0 )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) // 2 - 1
for i in range(_SCREAMING_SNAKE_CASE , -1 , -1 ):
self.top_to_bottom(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Tuple = positions[0]
__lowerCAmelCase : Optional[int] = sys.maxsize
self.top_to_bottom(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
return temp
def __lowerCAmelCase (_UpperCamelCase ):
__lowerCAmelCase : int = Heap()
__lowerCAmelCase : str = [0] * len(_UpperCamelCase )
__lowerCAmelCase : int = [-1] * len(_UpperCamelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__lowerCAmelCase : List[str] = [] # Heap of Distance of vertices from their neighboring vertex
__lowerCAmelCase : Tuple = []
for vertex in range(len(_UpperCamelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCamelCase )
heap.node_position.append(_UpperCamelCase )
__lowerCAmelCase : Any = []
__lowerCAmelCase : List[str] = 1
__lowerCAmelCase : Optional[int] = sys.maxsize
for neighbor, distance in adjacency_list[0]:
__lowerCAmelCase : int = 0
__lowerCAmelCase : List[Any] = distance
heap.heapify(_UpperCamelCase , _UpperCamelCase )
for _ in range(1 , len(_UpperCamelCase ) ):
__lowerCAmelCase : str = heap.delete_minimum(_UpperCamelCase , _UpperCamelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__lowerCAmelCase : int = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCamelCase )]
):
__lowerCAmelCase : Tuple = distance
heap.bottom_to_top(
_UpperCamelCase , heap.get_position(_UpperCamelCase ) , _UpperCamelCase , _UpperCamelCase )
__lowerCAmelCase : str = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
lowerCamelCase__ = int(input("""Enter number of edges: """).strip())
lowerCamelCase__ = defaultdict(list)
for _ in range(edges_number):
lowerCamelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list)) | 86 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A ( self : int ):
'''simple docstring'''
_snake_case = FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base' )
_snake_case = AutoTokenizer.from_pretrained('xlm-roberta-base' )
_snake_case = 'The dog is cute and lives in the garden house'
_snake_case = jnp.array([tokenizer.encode(lowercase )] )
_snake_case = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
_snake_case = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
_snake_case = model(lowercase )['last_hidden_state']
self.assertEqual(output.shape , lowercase )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , lowercase , atol=1E-3 ) ) | 282 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
# Initialise PyTorch model
lowercase :Optional[int] = LxmertConfig.from_json_file(lowerCamelCase )
print(F"Building PyTorch model from configuration: {config}" )
lowercase :Any = LxmertForPreTraining(lowerCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(lowerCamelCase, lowerCamelCase, lowerCamelCase )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict(), lowerCamelCase )
if __name__ == "__main__":
_UpperCAmelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_UpperCAmelCase : Any = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 158 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
_UpperCAmelCase : Optional[Any] = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
from run_translation import main # noqa
set_seed(42)
_UpperCAmelCase : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
_UpperCAmelCase : Any = "sshleifer/tiny-mbart"
@require_torch
class __lowerCAmelCase ( lowerCAmelCase):
def SCREAMING_SNAKE_CASE ( self: int , _lowerCAmelCase: int=False , _lowerCAmelCase: str=None , _lowerCAmelCase: Dict=True , _lowerCAmelCase: Dict=True , _lowerCAmelCase: Optional[int]=True , _lowerCAmelCase: Union[str, Any]=True , ):
lowercase :Any = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=_lowerCAmelCase , num_train_epochs=1 , distributed=_lowerCAmelCase , extra_args_str=_lowerCAmelCase , predict_with_generate=_lowerCAmelCase , do_train=_lowerCAmelCase , do_eval=_lowerCAmelCase , do_predict=_lowerCAmelCase , )
lowercase :List[Any] = TrainerState.load_from_json(os.path.join(_lowerCAmelCase , "trainer_state.json" ) ).log_history
if not do_eval:
return
lowercase :Union[str, Any] = [log for log in logs if "eval_loss" in log.keys()]
lowercase :Any = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
lowercase :Optional[Any] = eval_metrics[-1]
assert isinstance(last_step_stats["eval_bleu"] , _lowerCAmelCase )
assert not math.isnan(float(last_step_stats["eval_loss"] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def SCREAMING_SNAKE_CASE ( self: str ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase )
@require_torch_multi_gpu
def SCREAMING_SNAKE_CASE ( self: Tuple ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp simple" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp simple --fp16" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Dict ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp zero_dp_2" , predict_with_generate=_lowerCAmelCase )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
self.run_seqaseq_quick(
distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp zero_dp_2 --fp16" , predict_with_generate=_lowerCAmelCase )
@require_apex
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--fp16 --fp16_backend=apex" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--fp16 --fp16_backend=apex" )
@parameterized.expand(["base", "low", "high", "mixed"] )
@require_torch_multi_gpu
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _lowerCAmelCase: Any ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
lowercase :List[Any] = {
# test with the default log_level - should be info and thus log info once
"base": {"extra_args_str": "", "n_matches": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
}
lowercase :str = experiments[experiment_id]
lowercase :Dict = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
lowercase :List[str] = "Running training"
with CaptureStderr() as cl:
self.run_seqaseq_quick(**_lowerCAmelCase , extra_args_str=data["extra_args_str"] )
lowercase :Dict = len(re.findall(_lowerCAmelCase , cl.err ) )
self.assertEqual(_lowerCAmelCase , data["n_matches"] )
@slow
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Dict = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=_lowerCAmelCase , learning_rate=3e-4 , num_train_epochs=10 , distributed=_lowerCAmelCase , )
# Check metrics
lowercase :List[str] = TrainerState.load_from_json(os.path.join(_lowerCAmelCase , "trainer_state.json" ) ).log_history
lowercase :Dict = [log for log in logs if "eval_loss" in log.keys()]
lowercase :str = eval_metrics[0]
lowercase :Optional[int] = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["eval_bleu"] , _lowerCAmelCase )
# test if do_predict saves generations and metrics
lowercase :Optional[Any] = os.listdir(_lowerCAmelCase )
lowercase :List[str] = {os.path.basename(_lowerCAmelCase ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def SCREAMING_SNAKE_CASE ( self: Tuple ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(_lowerCAmelCase: str ) -> Tuple[int, float]:
lowercase :Tuple = "--skip_memory_metrics 0"
lowercase :List[str] = self.run_trainer(
max_len=1_28 , model_name=_lowerCAmelCase , learning_rate=3e-4 , num_train_epochs=1 , optim=_lowerCAmelCase , distributed=_lowerCAmelCase , extra_args_str=_lowerCAmelCase , do_eval=_lowerCAmelCase , do_predict=_lowerCAmelCase , n_gpus_to_use=1 , )
# Check metrics
lowercase :List[str] = TrainerState.load_from_json(Path(_lowerCAmelCase , "trainer_state.json" ) ).log_history
lowercase :Dict = int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
lowercase :Any = int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )
lowercase :List[str] = logs[0]["train_loss"]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
lowercase , lowercase , lowercase :Optional[Any] = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
lowercase , lowercase , lowercase :List[str] = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
lowercase :List[Any] = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
lowercase :List[str] = gpu_peak_mem_orig + gpu_alloc_mem_orig
lowercase :List[str] = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
lowercase :Tuple = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
lowercase :Union[str, Any] = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
_lowerCAmelCase , _lowerCAmelCase , "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
_lowerCAmelCase , _lowerCAmelCase , "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
_lowerCAmelCase , _lowerCAmelCase , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def SCREAMING_SNAKE_CASE ( self: Dict , _lowerCAmelCase: int , _lowerCAmelCase: str , _lowerCAmelCase: int , _lowerCAmelCase: float = 3e-3 , _lowerCAmelCase: str = "adafactor" , _lowerCAmelCase: bool = False , _lowerCAmelCase: str = None , _lowerCAmelCase: int = 0 , _lowerCAmelCase: bool = True , _lowerCAmelCase: bool = True , _lowerCAmelCase: bool = True , _lowerCAmelCase: bool = True , _lowerCAmelCase: int = None , ):
lowercase :Optional[int] = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
lowercase :Optional[Any] = self.get_auto_remove_tmp_dir()
lowercase :Tuple = F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(_lowerCAmelCase )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(_lowerCAmelCase )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
lowercase :Union[str, Any] = F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(_lowerCAmelCase )}\n ".split()
lowercase :str = "\n --do_predict\n ".split()
lowercase :Union[str, Any] = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
lowercase :Optional[int] = get_gpu_count()
lowercase :str = get_torch_dist_unique_port()
lowercase :Union[str, Any] = F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
lowercase :Optional[int] = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_lowerCAmelCase , env=self.get_env() )
else:
lowercase :Tuple = ["run_translation.py"] + args
with patch.object(_lowerCAmelCase , "argv" , _lowerCAmelCase ):
main()
return output_dir
| 158 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __A ( A_ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __A ( unittest.TestCase ):
@property
def _lowercase (self : int ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _lowercase (self : str ):
UpperCAmelCase_ = ort.SessionOptions()
UpperCAmelCase_ = False
return options
def _lowercase (self : List[Any] ):
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase_ = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
UpperCAmelCase_ = "A red cat sitting on a park bench"
UpperCAmelCase_ = np.random.RandomState(0 )
UpperCAmelCase_ = pipe(
prompt=_lowerCamelCase , image=_lowerCamelCase , mask_image=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCamelCase , output_type="np" , )
UpperCAmelCase_ = output.images
UpperCAmelCase_ = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase_ = np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _lowercase (self : int ):
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase_ = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase_ = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=_lowerCamelCase , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
UpperCAmelCase_ = "A red cat sitting on a park bench"
UpperCAmelCase_ = np.random.RandomState(0 )
UpperCAmelCase_ = pipe(
prompt=_lowerCamelCase , image=_lowerCamelCase , mask_image=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=20 , generator=_lowerCamelCase , output_type="np" , )
UpperCAmelCase_ = output.images
UpperCAmelCase_ = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase_ = np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 1 |
"""simple docstring"""
UpperCAmelCase__ = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 288 | 0 |
__A = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__A = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__A = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 277 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowercase ( unittest.TestCase):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[Any]:
UpperCAmelCase_= tempfile.mkdtemp()
UpperCAmelCase_= BlipImageProcessor()
UpperCAmelCase_= GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
UpperCAmelCase_= BlipaProcessor(__UpperCAmelCase , __UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **__UpperCAmelCase : Union[str, Any] ) -> int:
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).tokenizer
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , **__UpperCAmelCase : str ) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).image_processor
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
shutil.rmtree(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
UpperCAmelCase_= [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCAmelCase_= [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
UpperCAmelCase_= BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase_= self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCAmelCase_= self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
UpperCAmelCase_= BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[int]:
UpperCAmelCase_= self.get_image_processor()
UpperCAmelCase_= self.get_tokenizer()
UpperCAmelCase_= BlipaProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
UpperCAmelCase_= self.prepare_image_inputs()
UpperCAmelCase_= image_processor(__UpperCAmelCase , return_tensors="""np""" )
UpperCAmelCase_= processor(images=__UpperCAmelCase , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
UpperCAmelCase_= self.get_image_processor()
UpperCAmelCase_= self.get_tokenizer()
UpperCAmelCase_= BlipaProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
UpperCAmelCase_= """lower newer"""
UpperCAmelCase_= processor(text=__UpperCAmelCase )
UpperCAmelCase_= tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
UpperCAmelCase_= self.get_image_processor()
UpperCAmelCase_= self.get_tokenizer()
UpperCAmelCase_= BlipaProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
UpperCAmelCase_= """lower newer"""
UpperCAmelCase_= self.prepare_image_inputs()
UpperCAmelCase_= processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def _SCREAMING_SNAKE_CASE ( self : str ) -> Any:
UpperCAmelCase_= self.get_image_processor()
UpperCAmelCase_= self.get_tokenizer()
UpperCAmelCase_= BlipaProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
UpperCAmelCase_= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase_= processor.batch_decode(__UpperCAmelCase )
UpperCAmelCase_= tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
UpperCAmelCase_= self.get_image_processor()
UpperCAmelCase_= self.get_tokenizer()
UpperCAmelCase_= BlipaProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
UpperCAmelCase_= """lower newer"""
UpperCAmelCase_= self.prepare_image_inputs()
UpperCAmelCase_= processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
| 277 | 1 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
return np.dot(__lowerCAmelCase , __lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str , *,
lowerCAmelCase_ : float = np.inf , lowerCAmelCase_ : str = "linear" , lowerCAmelCase_ : float = 0.0 , ):
"""simple docstring"""
lowercase_ = regularization
lowercase_ = gamma
if kernel == "linear":
lowercase_ = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError("""rbf kernel requires gamma""")
if not isinstance(self.gamma , (float, int)):
raise ValueError("""gamma must be float or int""")
if not self.gamma > 0:
raise ValueError("""gamma must be > 0""")
lowercase_ = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
lowercase_ = F'''Unknown kernel: {kernel}'''
raise ValueError(lowerCAmelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : ndarray , lowerCAmelCase_ : ndarray):
"""simple docstring"""
return np.dot(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : ndarray , lowerCAmelCase_ : ndarray):
"""simple docstring"""
return np.exp(-(self.gamma * norm_squared(vectora - vectora)))
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : list[ndarray] , lowerCAmelCase_ : ndarray):
"""simple docstring"""
lowercase_ = observations
lowercase_ = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((lowercase_) , ) = np.shape(lowerCAmelCase_)
def to_minimize(lowerCAmelCase_ : ndarray) -> float:
lowercase_ = 0
((lowercase_) , ) = np.shape(lowerCAmelCase_)
for i in range(lowerCAmelCase_):
for j in range(lowerCAmelCase_):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j])
)
return 1 / 2 * s - sum(lowerCAmelCase_)
lowercase_ = LinearConstraint(lowerCAmelCase_ , 0 , 0)
lowercase_ = Bounds(0 , self.regularization)
lowercase_ = minimize(
lowerCAmelCase_ , np.ones(lowerCAmelCase_) , bounds=lowerCAmelCase_ , constraints=[ly_contraint]).x
lowercase_ = l_star
# calculating mean offset of separation plane to points
lowercase_ = 0
for i in range(lowerCAmelCase_):
for j in range(lowerCAmelCase_):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j])
lowercase_ = s / n
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : ndarray):
"""simple docstring"""
lowercase_ = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , lowerCAmelCase_)
for n in range(len(self.classes)))
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
UpperCAmelCase : Tuple = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"
UpperCAmelCase : Optional[int] = "\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), 'per_category_accuracy': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n"
UpperCAmelCase : List[str] = "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = False , ) -> Dict:
'''simple docstring'''
if label_map is not None:
for old_id, new_id in label_map.items():
lowercase_ = new_id
# turn into Numpy arrays
lowercase_ = np.array(__lowerCAmelCase )
lowercase_ = np.array(__lowerCAmelCase )
if reduce_labels:
lowercase_ = 2_55
lowercase_ = label - 1
lowercase_ = 2_55
lowercase_ = label != ignore_index
lowercase_ = np.not_equal(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = pred_label[mask]
lowercase_ = np.array(__lowerCAmelCase )[mask]
lowercase_ = pred_label[pred_label == label]
lowercase_ = np.histogram(__lowerCAmelCase , bins=__lowerCAmelCase , range=(0, num_labels - 1) )[0]
lowercase_ = np.histogram(__lowerCAmelCase , bins=__lowerCAmelCase , range=(0, num_labels - 1) )[0]
lowercase_ = np.histogram(__lowerCAmelCase , bins=__lowerCAmelCase , range=(0, num_labels - 1) )[0]
lowercase_ = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = False , ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = np.zeros((num_labels,) , dtype=np.floataa )
lowercase_ = np.zeros((num_labels,) , dtype=np.floataa )
lowercase_ = np.zeros((num_labels,) , dtype=np.floataa )
lowercase_ = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ , lowercase_ , lowercase_ , lowercase_ = intersect_and_union(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = False , ) -> Any:
'''simple docstring'''
lowercase_ , lowercase_ , lowercase_ , lowercase_ = total_intersect_and_union(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# compute metrics
lowercase_ = {}
lowercase_ = total_area_intersect.sum() / total_area_label.sum()
lowercase_ = total_area_intersect / total_area_union
lowercase_ = total_area_intersect / total_area_label
lowercase_ = np.nanmean(__lowerCAmelCase )
lowercase_ = np.nanmean(__lowerCAmelCase )
lowercase_ = all_acc
lowercase_ = iou
lowercase_ = acc
if nan_to_num is not None:
lowercase_ = {metric: np.nan_to_num(__lowerCAmelCase , nan=__lowerCAmelCase ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"""predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""))),
"""references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""))),
}) , reference_urls=[
"""https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
] , )
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase_ : Any , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : bool , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[Dict[int, int]] = None , lowerCAmelCase_ : bool = False , ):
"""simple docstring"""
lowercase_ = mean_iou(
results=lowerCAmelCase_ , gt_seg_maps=lowerCAmelCase_ , num_labels=lowerCAmelCase_ , ignore_index=lowerCAmelCase_ , nan_to_num=lowerCAmelCase_ , label_map=lowerCAmelCase_ , reduce_labels=lowerCAmelCase_ , )
return iou_result
| 136 | 1 |
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
_A = logging.getLogger(__name__)
class _lowercase ( __UpperCAmelCase ):
def _UpperCamelCase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None , UpperCAmelCase_=None ) -> Optional[int]:
lowerCamelCase : Any = self.layer[current_layer](UpperCAmelCase_ , UpperCAmelCase_ , head_mask[current_layer] )
lowerCamelCase : int = layer_outputs[0]
return hidden_states
@add_start_docstrings(
'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.' , __UpperCAmelCase , )
class _lowercase ( __UpperCAmelCase ):
def __init__( self , UpperCAmelCase_ ) -> List[Any]:
super().__init__(UpperCAmelCase_ )
lowerCamelCase : Tuple = BertEncoderWithPabee(UpperCAmelCase_ )
self.init_weights()
lowerCamelCase : List[Any] = 0
lowerCamelCase : Dict = 0
lowerCamelCase : Any = 0
lowerCamelCase : List[str] = 0
def _UpperCamelCase ( self , UpperCAmelCase_ ) -> Tuple:
lowerCamelCase : List[Any] = threshold
def _UpperCamelCase ( self , UpperCAmelCase_ ) -> List[str]:
lowerCamelCase : Optional[Any] = patience
def _UpperCamelCase ( self ) -> Any:
lowerCamelCase : str = 0
lowerCamelCase : List[str] = 0
def _UpperCamelCase ( self ) -> List[str]:
lowerCamelCase : Tuple = self.inference_layers_num / self.inference_instances_num
lowerCamelCase : str = (
F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
)
print(UpperCAmelCase_ )
@add_start_docstrings_to_model_forward(UpperCAmelCase_ )
def _UpperCamelCase ( self , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=False , ) -> Tuple:
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
lowerCamelCase : Union[str, Any] = input_ids.size()
elif inputs_embeds is not None:
lowerCamelCase : Optional[Any] = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
lowerCamelCase : Dict = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowerCamelCase : Any = torch.ones(UpperCAmelCase_ , device=UpperCAmelCase_ )
if token_type_ids is None:
lowerCamelCase : Union[str, Any] = torch.zeros(UpperCAmelCase_ , dtype=torch.long , device=UpperCAmelCase_ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowerCamelCase : torch.Tensor = self.get_extended_attention_mask(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowerCamelCase , lowerCamelCase , lowerCamelCase : Union[str, Any] = encoder_hidden_states.size()
lowerCamelCase : int = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowerCamelCase : str = torch.ones(UpperCAmelCase_ , device=UpperCAmelCase_ )
lowerCamelCase : Union[str, Any] = self.invert_attention_mask(UpperCAmelCase_ )
else:
lowerCamelCase : Optional[Any] = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowerCamelCase : Tuple = self.get_head_mask(UpperCAmelCase_ , self.config.num_hidden_layers )
lowerCamelCase : Tuple = self.embeddings(
input_ids=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , inputs_embeds=UpperCAmelCase_ )
lowerCamelCase : Optional[int] = embedding_output
if self.training:
lowerCamelCase : Tuple = []
for i in range(self.config.num_hidden_layers ):
lowerCamelCase : Dict = self.encoder.adaptive_forward(
UpperCAmelCase_ , current_layer=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , head_mask=UpperCAmelCase_ )
lowerCamelCase : List[str] = self.pooler(UpperCAmelCase_ )
lowerCamelCase : int = output_layers[i](output_dropout(UpperCAmelCase_ ) )
res.append(UpperCAmelCase_ )
elif self.patience == 0: # Use all layers for inference
lowerCamelCase : Optional[Any] = self.encoder(
UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , head_mask=UpperCAmelCase_ , encoder_hidden_states=UpperCAmelCase_ , encoder_attention_mask=UpperCAmelCase_ , )
lowerCamelCase : Any = self.pooler(encoder_outputs[0] )
lowerCamelCase : str = [output_layers[self.config.num_hidden_layers - 1](UpperCAmelCase_ )]
else:
lowerCamelCase : Union[str, Any] = 0
lowerCamelCase : Union[str, Any] = None
lowerCamelCase : Any = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
lowerCamelCase : int = self.encoder.adaptive_forward(
UpperCAmelCase_ , current_layer=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , head_mask=UpperCAmelCase_ )
lowerCamelCase : Optional[Any] = self.pooler(UpperCAmelCase_ )
lowerCamelCase : Tuple = output_layers[i](UpperCAmelCase_ )
if regression:
lowerCamelCase : List[Any] = logits.detach()
if patient_result is not None:
lowerCamelCase : str = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
lowerCamelCase : Tuple = 0
else:
lowerCamelCase : str = logits.detach().argmax(dim=1 )
if patient_result is not None:
lowerCamelCase : List[Any] = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(UpperCAmelCase_ ) ):
patient_counter += 1
else:
lowerCamelCase : Dict = 0
lowerCamelCase : Optional[int] = logits
if patient_counter == self.patience:
break
lowerCamelCase : List[str] = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. ' , __UpperCAmelCase , )
class _lowercase ( __UpperCAmelCase ):
def __init__( self , UpperCAmelCase_ ) -> Any:
super().__init__(UpperCAmelCase_ )
lowerCamelCase : Dict = config.num_labels
lowerCamelCase : Optional[Any] = BertModelWithPabee(UpperCAmelCase_ )
lowerCamelCase : Any = nn.Dropout(config.hidden_dropout_prob )
lowerCamelCase : Dict = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(UpperCAmelCase_ )
def _UpperCamelCase ( self , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , ) -> Any:
lowerCamelCase : int = self.bert(
input_ids=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , head_mask=UpperCAmelCase_ , inputs_embeds=UpperCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowerCamelCase : Any = (logits[-1],)
if labels is not None:
lowerCamelCase : str = None
lowerCamelCase : Optional[Any] = 0
for ix, logits_item in enumerate(UpperCAmelCase_ ):
if self.num_labels == 1:
# We are doing regression
lowerCamelCase : str = MSELoss()
lowerCamelCase : Optional[int] = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
lowerCamelCase : Union[str, Any] = CrossEntropyLoss()
lowerCamelCase : Any = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
lowerCamelCase : Optional[Any] = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowerCamelCase : List[Any] = (total_loss / total_weights,) + outputs
return outputs
| 205 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_A = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : str = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(a_, a_ )
_A = {
'blocks': 'layers',
'mlp.0': 'fc1',
'mlp.2': 'fc2',
'mlp_ln': 'final_layer_norm',
'.attn.query': '.self_attn.q_proj',
'.attn.key': '.self_attn.k_proj',
'.attn.value': '.self_attn.v_proj',
'.attn_ln': '.self_attn_layer_norm',
'.attn.out': '.self_attn.out_proj',
'.cross_attn.query': '.encoder_attn.q_proj',
'.cross_attn.key': '.encoder_attn.k_proj',
'.cross_attn.value': '.encoder_attn.v_proj',
'.cross_attn_ln': '.encoder_attn_layer_norm',
'.cross_attn.out': '.encoder_attn.out_proj',
'decoder.ln.': 'decoder.layer_norm.',
'encoder.ln.': 'encoder.layer_norm.',
'token_embedding': 'embed_tokens',
'encoder.positional_embedding': 'encoder.embed_positions.weight',
'decoder.positional_embedding': 'decoder.embed_positions.weight',
'ln_post': 'layer_norm',
}
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : Tuple = list(s_dict.keys() )
for key in keys:
lowerCamelCase : List[Any] = key
for k, v in WHISPER_MAPPING.items():
if k in key:
lowerCamelCase : Optional[int] = new_key.replace(a_, a_ )
print(F"""{key} -> {new_key}""" )
lowerCamelCase : Any = s_dict.pop(a_ )
return s_dict
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase : int = emb.weight.shape
lowerCamelCase : Dict = nn.Linear(a_, a_, bias=a_ )
lowerCamelCase : Union[str, Any] = emb.weight.data
return lin_layer
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
os.makedirs(a_, exist_ok=a_ )
lowerCamelCase : Union[str, Any] = os.path.basename(a_ )
lowerCamelCase : Any = url.split('/' )[-2]
lowerCamelCase : Tuple = os.path.join(a_, a_ )
if os.path.exists(a_ ) and not os.path.isfile(a_ ):
raise RuntimeError(F"""{download_target} exists and is not a regular file""" )
if os.path.isfile(a_ ):
lowerCamelCase : Union[str, Any] = open(a_, 'rb' ).read()
if hashlib.shaaaa(a_ ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file""" )
with urllib.request.urlopen(a_ ) as source, open(a_, 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ), ncols=80, unit='iB', unit_scale=a_, unit_divisor=1024 ) as loop:
while True:
lowerCamelCase : Union[str, Any] = source.read(8192 )
if not buffer:
break
output.write(a_ )
loop.update(len(a_ ) )
lowerCamelCase : int = open(a_, 'rb' ).read()
if hashlib.shaaaa(a_ ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
if ".pt" not in checkpoint_path:
lowerCamelCase : str = _download(_MODELS[checkpoint_path] )
else:
lowerCamelCase : Any = torch.load(a_, map_location='cpu' )
lowerCamelCase : List[str] = original_checkpoint['dims']
lowerCamelCase : Any = original_checkpoint['model_state_dict']
lowerCamelCase : Tuple = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(a_ )
rename_keys(a_ )
lowerCamelCase : List[Any] = True
lowerCamelCase : str = state_dict['decoder.layers.0.fc1.weight'].shape[0]
lowerCamelCase : Optional[int] = WhisperConfig(
vocab_size=dimensions['n_vocab'], encoder_ffn_dim=a_, decoder_ffn_dim=a_, num_mel_bins=dimensions['n_mels'], d_model=dimensions['n_audio_state'], max_target_positions=dimensions['n_text_ctx'], encoder_layers=dimensions['n_audio_layer'], encoder_attention_heads=dimensions['n_audio_head'], decoder_layers=dimensions['n_text_layer'], decoder_attention_heads=dimensions['n_text_state'], max_source_positions=dimensions['n_audio_ctx'], )
lowerCamelCase : Union[str, Any] = WhisperForConditionalGeneration(a_ )
lowerCamelCase , lowerCamelCase : Optional[int] = model.model.load_state_dict(a_, strict=a_ )
if len(a_ ) > 0 and not set(a_ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F""" but all the following weights are missing {missing}""" )
if tie_embeds:
lowerCamelCase : List[Any] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
lowerCamelCase : Tuple = proj_out_weights
model.save_pretrained(a_ )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# # Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_A = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 205 | 1 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = "sshleifer/bart-tiny-random"
__a = "patrickvonplaten/t5-tiny-random"
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCamelCase ( self : List[Any] ):
return AutoConfig.from_pretrained(snake_case_ )
def lowerCamelCase ( self : Dict ):
snake_case__ , *snake_case__ : List[str] = create_student_by_copying_alternating_layers(snake_case_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def lowerCamelCase ( self : Tuple ):
snake_case__ , *snake_case__ : Tuple = create_student_by_copying_alternating_layers(snake_case_ , tempfile.mkdtemp() , e=1 , d=snake_case_ )
def lowerCamelCase ( self : Any ):
snake_case__ , *snake_case__ : Tuple = create_student_by_copying_alternating_layers(snake_case_ , tempfile.mkdtemp() , e=1 , d=snake_case_ )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def lowerCamelCase ( self : Dict ):
snake_case__ , *snake_case__ : Optional[int] = create_student_by_copying_alternating_layers(snake_case_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def lowerCamelCase ( self : Tuple ):
with self.assertRaises(snake_case_ ):
create_student_by_copying_alternating_layers(snake_case_ , tempfile.mkdtemp() , e=snake_case_ , d=snake_case_ )
| 35 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
def UpperCAmelCase_ ( self :Optional[Any] )-> Tuple:
A__ = tempfile.mkdtemp()
# fmt: off
A__ = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
A__ = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
A__ = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
A__ = {"unk_token": "<unk>"}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowercase_ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowercase_ ) )
A__ = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
"image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
A__ = os.path.join(self.tmpdirname , lowercase_ )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self :Any , **lowercase_ :Union[str, Any] )-> Tuple:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase_ ( self :Any , **lowercase_ :Tuple )-> Dict:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase_ ( self :Dict , **lowercase_ :Union[str, Any] )-> Any:
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase_ ( self :List[str] )-> int:
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase_ ( self :Optional[int] )-> Optional[int]:
A__ = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
A__ = [Image.fromarray(np.moveaxis(lowercase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase_ ( self :int )-> List[Any]:
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer()
A__ = self.get_image_processor()
A__ = CLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
processor_slow.save_pretrained(self.tmpdirname )
A__ = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowercase_ )
A__ = CLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
processor_fast.save_pretrained(self.tmpdirname )
A__ = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowercase_ )
self.assertIsInstance(processor_fast.tokenizer , lowercase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowercase_ )
self.assertIsInstance(processor_fast.image_processor , lowercase_ )
def UpperCAmelCase_ ( self :Optional[Any] )-> Optional[int]:
A__ = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A__ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
A__ = self.get_image_processor(do_normalize=lowercase_ , padding_value=1.0 )
A__ = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowercase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowercase_ )
def UpperCAmelCase_ ( self :List[Any] )-> Tuple:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = CLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
A__ = self.prepare_image_inputs()
A__ = image_processor(lowercase_ , return_tensors="np" )
A__ = processor(images=lowercase_ , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase_ ( self :Optional[int] )-> Dict:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = CLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
A__ = "lower newer"
A__ = processor(text=lowercase_ )
A__ = tokenizer(lowercase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase_ ( self :str )-> Any:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = CLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
A__ = "lower newer"
A__ = self.prepare_image_inputs()
A__ = processor(text=lowercase_ , images=lowercase_ )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(lowercase_ ):
processor()
def UpperCAmelCase_ ( self :Tuple )-> Tuple:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = CLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
A__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A__ = processor.batch_decode(lowercase_ )
A__ = tokenizer.batch_decode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self :List[Any] )-> Dict:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = CLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
A__ = "lower newer"
A__ = self.prepare_image_inputs()
A__ = processor(text=lowercase_ , images=lowercase_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 237 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : str , snake_case : bool = False ):
'''simple docstring'''
if not isinstance(snake_case , snake_case ):
snake_case_ = f'Expected string as input, found {type(snake_case )}'
raise ValueError(snake_case )
if not isinstance(snake_case , snake_case ):
snake_case_ = f'Expected boolean as use_pascal parameter, found {type(snake_case )}'
raise ValueError(snake_case )
snake_case_ = input_str.split("_" )
snake_case_ = 0 if use_pascal else 1
snake_case_ = words[start_index:]
snake_case_ = [word[0].upper() + word[1:] for word in words_to_capitalize]
snake_case_ = "" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 92 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
_SCREAMING_SNAKE_CASE : Tuple = get_logger(__name__)
def UpperCamelCase_( snake_case : Optional[Any] , snake_case : Optional[Any] , snake_case : Optional[Any] , snake_case : int , snake_case : List[Any]=0 ):
'''simple docstring'''
os.makedirs(snake_case , exist_ok=snake_case )
with FSDP.state_dict_type(
snake_case , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
snake_case_ = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
snake_case_ = f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
snake_case_ = os.path.join(snake_case , snake_case )
if accelerator.process_index == 0:
logger.info(f'Saving model to {output_model_file}' )
torch.save(snake_case , snake_case )
logger.info(f'Model saved to {output_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
snake_case_ = (
f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
if model_index == 0
else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
)
snake_case_ = os.path.join(snake_case , snake_case )
logger.info(f'Saving model to {output_model_file}' )
torch.save(snake_case , snake_case )
logger.info(f'Model saved to {output_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
snake_case_ = os.path.join(snake_case , f'{MODEL_NAME}_{model_index}' )
os.makedirs(snake_case , exist_ok=snake_case )
logger.info(f'Saving model to {ckpt_dir}' )
snake_case_ = {"model": state_dict}
dist_cp.save_state_dict(
state_dict=snake_case , storage_writer=dist_cp.FileSystemWriter(snake_case ) , planner=DefaultSavePlanner() , )
logger.info(f'Model saved to {ckpt_dir}' )
def UpperCamelCase_( snake_case : Optional[int] , snake_case : Optional[Any] , snake_case : int , snake_case : Union[str, Any] , snake_case : Any=0 ):
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
snake_case , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(snake_case ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
"initializing FSDP object" )
return
snake_case_ = f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
snake_case_ = os.path.join(snake_case , snake_case )
logger.info(f'Loading model from {input_model_file}' )
snake_case_ = torch.load(snake_case )
logger.info(f'Model loaded from {input_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
snake_case_ = (
f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
if model_index == 0
else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
)
snake_case_ = os.path.join(snake_case , snake_case )
logger.info(f'Loading model from {input_model_file}' )
snake_case_ = torch.load(snake_case )
logger.info(f'Model loaded from {input_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
snake_case_ = (
os.path.join(snake_case , f'{MODEL_NAME}_{model_index}' )
if f'{MODEL_NAME}' not in input_dir
else input_dir
)
logger.info(f'Loading model from {ckpt_dir}' )
snake_case_ = {"model": model.state_dict()}
dist_cp.load_state_dict(
state_dict=snake_case , storage_reader=dist_cp.FileSystemReader(snake_case ) , planner=DefaultLoadPlanner() , )
snake_case_ = state_dict["model"]
logger.info(f'Model loaded from {ckpt_dir}' )
model.load_state_dict(snake_case )
def UpperCamelCase_( snake_case : str , snake_case : List[str] , snake_case : Any , snake_case : Tuple , snake_case : Optional[Any] , snake_case : Tuple=0 ):
'''simple docstring'''
os.makedirs(snake_case , exist_ok=snake_case )
with FSDP.state_dict_type(
snake_case , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
snake_case_ = FSDP.optim_state_dict(snake_case , snake_case )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
snake_case_ = (
f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
)
snake_case_ = os.path.join(snake_case , snake_case )
logger.info(f'Saving Optimizer state to {output_optimizer_file}' )
torch.save(snake_case , snake_case )
logger.info(f'Optimizer state saved in {output_optimizer_file}' )
else:
snake_case_ = os.path.join(snake_case , f'{OPTIMIZER_NAME}_{optimizer_index}' )
os.makedirs(snake_case , exist_ok=snake_case )
logger.info(f'Saving Optimizer state to {ckpt_dir}' )
dist_cp.save_state_dict(
state_dict={"optimizer": optim_state} , storage_writer=dist_cp.FileSystemWriter(snake_case ) , planner=DefaultSavePlanner() , )
logger.info(f'Optimizer state saved in {ckpt_dir}' )
def UpperCamelCase_( snake_case : Optional[Any] , snake_case : List[str] , snake_case : Union[str, Any] , snake_case : int , snake_case : Optional[int] , snake_case : Union[str, Any]=0 ):
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
snake_case , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
snake_case_ = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
snake_case_ = (
f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
)
snake_case_ = os.path.join(snake_case , snake_case )
logger.info(f'Loading Optimizer state from {input_optimizer_file}' )
snake_case_ = torch.load(snake_case )
logger.info(f'Optimizer state loaded from {input_optimizer_file}' )
else:
snake_case_ = (
os.path.join(snake_case , f'{OPTIMIZER_NAME}_{optimizer_index}' )
if f'{OPTIMIZER_NAME}' not in input_dir
else input_dir
)
logger.info(f'Loading Optimizer from {ckpt_dir}' )
snake_case_ = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="optimizer" , storage_reader=dist_cp.FileSystemReader(snake_case ) , )
snake_case_ = optim_state["optimizer"]
logger.info(f'Optimizer loaded from {ckpt_dir}' )
snake_case_ = FSDP.optim_state_dict_to_load(snake_case , snake_case , snake_case )
optimizer.load_state_dict(snake_case )
| 92 | 1 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase( __UpperCamelCase : Optional[int] ):
lowerCAmelCase_ : Union[str, Any] = int(__UpperCamelCase )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = t // 3600, (t // 60) % 60, t % 60
return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""
def UpperCamelCase( __UpperCamelCase : Dict ,__UpperCamelCase : Tuple ,__UpperCamelCase : List[str] ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : int=300 ):
# docstyle-ignore
return f"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def UpperCamelCase( __UpperCamelCase : int ):
lowerCAmelCase_ : str = '''<table border="1" class="dataframe">\n'''
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f""" <th>{i}</th>\n"""
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
lowerCAmelCase_ : Optional[Any] = f"""{elt:.6f}""" if isinstance(__UpperCamelCase ,__UpperCamelCase ) else str(__UpperCamelCase )
html_code += f""" <td>{elt}</td>\n"""
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __snake_case :
_a = 5
_a = 0.2
def __init__( self : Optional[Any] , A_ : int , A_ : Optional[str] = None , A_ : bool = True , A_ : Optional["NotebookTrainingTracker"] = None , A_ : int = 3_0_0 , ):
lowerCAmelCase_ : Union[str, Any] = total
lowerCAmelCase_ : int = '''''' if prefix is None else prefix
lowerCAmelCase_ : Optional[Any] = leave
lowerCAmelCase_ : Union[str, Any] = parent
lowerCAmelCase_ : Any = width
lowerCAmelCase_ : Optional[Any] = None
lowerCAmelCase_ : Tuple = None
lowerCAmelCase_ : str = None
def UpperCAmelCase__ ( self : Union[str, Any] , A_ : int , A_ : bool = False , A_ : str = None):
lowerCAmelCase_ : Dict = value
if comment is not None:
lowerCAmelCase_ : List[str] = comment
if self.last_value is None:
lowerCAmelCase_ : List[str] = time.time()
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Union[str, Any] = None
lowerCAmelCase_ : str = self.warmup
lowerCAmelCase_ : List[Any] = 1
self.update_bar(A_)
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total):
if self.first_calls > 0:
self.first_calls -= 1
lowerCAmelCase_ : Tuple = time.time()
lowerCAmelCase_ : List[str] = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
lowerCAmelCase_ : int = self.elapsed_time / (value - self.start_value)
else:
lowerCAmelCase_ : str = None
if value >= self.total:
lowerCAmelCase_ : int = self.total
lowerCAmelCase_ : List[str] = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
lowerCAmelCase_ : List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(A_)
lowerCAmelCase_ : Tuple = value
lowerCAmelCase_ : Optional[int] = current_time
if self.average_time_per_item is None:
lowerCAmelCase_ : List[Any] = 1
else:
lowerCAmelCase_ : Any = max(int(self.update_every / self.average_time_per_item) , 1)
def UpperCAmelCase__ ( self : int , A_ : Optional[int] , A_ : List[str]=None):
lowerCAmelCase_ : str = ''' ''' * (len(str(self.total)) - len(str(A_))) + str(A_)
if self.elapsed_time is None:
lowerCAmelCase_ : str = F"""[{spaced_value}/{self.total} : < :"""
elif self.predicted_remaining is None:
lowerCAmelCase_ : Tuple = F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)}"""
else:
lowerCAmelCase_ : Any = (
F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <"""
F""" {format_time(self.predicted_remaining)}"""
)
self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
self.label += "]" if self.comment is None or len(self.comment) == 0 else F""", {self.comment}]"""
self.display()
def UpperCAmelCase__ ( self : Tuple):
lowerCAmelCase_ : Union[str, Any] = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width)
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
lowerCAmelCase_ : int = disp.display(disp.HTML(self.html_code) , display_id=A_)
else:
self.output.update(disp.HTML(self.html_code))
def UpperCAmelCase__ ( self : Optional[int]):
if self.parent is None and self.output is not None:
self.output.update(disp.HTML(''''''))
class __snake_case ( UpperCamelCase_ ):
def __init__( self : Union[str, Any] , A_ : Optional[int] , A_ : Dict=None):
super().__init__(A_)
lowerCAmelCase_ : int = None if column_names is None else [column_names]
lowerCAmelCase_ : Optional[int] = None
def UpperCAmelCase__ ( self : List[str]):
lowerCAmelCase_ : Optional[Any] = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width)
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table)
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
lowerCAmelCase_ : Optional[Any] = disp.display(disp.HTML(self.html_code) , display_id=A_)
else:
self.output.update(disp.HTML(self.html_code))
def UpperCAmelCase__ ( self : List[str] , A_ : Optional[int]):
if self.inner_table is None:
lowerCAmelCase_ : List[Any] = [list(values.keys()), list(values.values())]
else:
lowerCAmelCase_ : List[Any] = self.inner_table[0]
if len(self.inner_table) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(A_)
lowerCAmelCase_ : str = columns
self.inner_table.append([values[c] for c in columns])
def UpperCAmelCase__ ( self : Optional[Any] , A_ : Optional[Any] , A_ : Any=None , A_ : Optional[int]=3_0_0):
lowerCAmelCase_ : Any = NotebookProgressBar(A_ , prefix=A_ , parent=self , width=A_)
return self.child_bar
def UpperCAmelCase__ ( self : List[str]):
lowerCAmelCase_ : Dict = None
self.display()
class __snake_case ( UpperCamelCase_ ):
def __init__( self : Dict):
lowerCAmelCase_ : str = None
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : Tuple = False
def UpperCAmelCase__ ( self : int , A_ : Optional[Any] , A_ : List[Any] , A_ : Union[str, Any] , **A_ : str):
lowerCAmelCase_ : str = '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : List[str] = [self.first_column] + ['''Training Loss''']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('''Validation Loss''')
lowerCAmelCase_ : str = NotebookTrainingTracker(state.max_steps , A_)
def UpperCAmelCase__ ( self : Optional[int] , A_ : Optional[Any] , A_ : Optional[int] , A_ : List[str] , **A_ : List[Any]):
lowerCAmelCase_ : int = int(state.epoch) if int(state.epoch) == state.epoch else F"""{state.epoch:.2f}"""
self.training_tracker.update(
state.global_step + 1 , comment=F"""Epoch {epoch}/{state.num_train_epochs}""" , force_update=self._force_next_update , )
lowerCAmelCase_ : Dict = False
def UpperCAmelCase__ ( self : Optional[Any] , A_ : List[str] , A_ : Optional[Any] , A_ : int , A_ : List[str]=None , **A_ : List[str]):
if not has_length(A_):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
lowerCAmelCase_ : int = self.training_tracker.add_child(len(A_))
else:
lowerCAmelCase_ : List[str] = NotebookProgressBar(len(A_))
self.prediction_bar.update(1)
else:
self.prediction_bar.update(self.prediction_bar.value + 1)
def UpperCAmelCase__ ( self : List[Any] , A_ : Optional[Any] , A_ : List[str] , A_ : Dict , **A_ : Tuple):
if self.prediction_bar is not None:
self.prediction_bar.close()
lowerCAmelCase_ : List[Any] = None
def UpperCAmelCase__ ( self : List[Any] , A_ : Optional[Any] , A_ : str , A_ : Any , A_ : Tuple=None , **A_ : str):
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
lowerCAmelCase_ : List[str] = {'''Training Loss''': logs['''loss''']}
# First column is necessarily Step sine we're not in epoch eval strategy
lowerCAmelCase_ : int = state.global_step
self.training_tracker.write_line(A_)
def UpperCAmelCase__ ( self : int , A_ : Union[str, Any] , A_ : Tuple , A_ : Tuple , A_ : Optional[Any]=None , **A_ : Optional[Any]):
if self.training_tracker is not None:
lowerCAmelCase_ : Any = {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
for log in reversed(state.log_history):
if "loss" in log:
lowerCAmelCase_ : Dict = log['''loss''']
break
if self.first_column == "Epoch":
lowerCAmelCase_ : str = int(state.epoch)
else:
lowerCAmelCase_ : Optional[int] = state.global_step
lowerCAmelCase_ : Optional[Any] = '''eval'''
for k in metrics:
if k.endswith('''_loss'''):
lowerCAmelCase_ : str = re.sub(r'''\_loss$''' , '''''' , A_)
lowerCAmelCase_ : Any = metrics.pop('''total_flos''' , A_)
lowerCAmelCase_ : List[Any] = metrics.pop('''epoch''' , A_)
lowerCAmelCase_ : Optional[Any] = metrics.pop(F"""{metric_key_prefix}_runtime""" , A_)
lowerCAmelCase_ : List[Any] = metrics.pop(F"""{metric_key_prefix}_samples_per_second""" , A_)
lowerCAmelCase_ : List[Any] = metrics.pop(F"""{metric_key_prefix}_steps_per_second""" , A_)
lowerCAmelCase_ : Tuple = metrics.pop(F"""{metric_key_prefix}_jit_compilation_time""" , A_)
for k, v in metrics.items():
if k == F"""{metric_key_prefix}_loss""":
lowerCAmelCase_ : Optional[int] = v
else:
lowerCAmelCase_ : str = k.split('''_''')
lowerCAmelCase_ : List[str] = ''' '''.join([part.capitalize() for part in splits[1:]])
lowerCAmelCase_ : List[Any] = v
self.training_tracker.write_line(A_)
self.training_tracker.remove_child()
lowerCAmelCase_ : Any = None
# Evaluation takes a long time so we should force the next update.
lowerCAmelCase_ : str = True
def UpperCAmelCase__ ( self : Union[str, Any] , A_ : Optional[int] , A_ : Union[str, Any] , A_ : Any , **A_ : int):
self.training_tracker.update(
state.global_step , comment=F"""Epoch {int(state.epoch)}/{state.num_train_epochs}""" , force_update=A_)
lowerCAmelCase_ : str = None
| 103 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCamelCase( ):
lowerCAmelCase_ : List[str] = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch '''
'''helper utility that will spawn up '''
'''multiple distributed processes'''
) )
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' ,type=__UpperCamelCase ,default=1 ,help='''Number of TPU cores to use (1 or 8).''' )
# positional
parser.add_argument(
'''training_script''' ,type=__UpperCamelCase ,help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) ,)
# rest from the training program
parser.add_argument('''training_script_args''' ,nargs=__UpperCamelCase )
return parser.parse_args()
def UpperCamelCase( ):
lowerCAmelCase_ : str = parse_args()
# Import training_script as a module.
lowerCAmelCase_ : str = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowerCAmelCase_ : Tuple = script_fpath.stem
lowerCAmelCase_ : Union[str, Any] = importlib.import_module(__UpperCamelCase )
# Patch sys.argv
lowerCAmelCase_ : Optional[int] = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn ,args=() ,nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 103 | 1 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE = object()
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> List[Any]:
A__ = tuple((re.compile(x + "$" ) for x in qs) )
for i in range(len(lowercase_ ) - len(lowercase_ ) + 1 ):
A__ = [x.match(lowercase_ ) for x, y in zip(lowercase_ , ks[i:] )]
if matches and all(lowercase_ ):
return True
return False
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> Optional[int]:
def replace(lowercase_ , lowercase_ ):
for rule, replacement in rules:
if _match(lowercase_ , lowercase_ ):
return replacement
return val
return replace
def _SCREAMING_SNAKE_CASE ( ) -> List[str]:
return [
# embeddings
(("transformer", "wpe", "embedding"), P("mp" , lowercase_ )),
(("transformer", "wte", "embedding"), P("mp" , lowercase_ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowercase_ , "mp" )),
(("attention", "out_proj", "kernel"), P("mp" , lowercase_ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(lowercase_ , "mp" )),
(("mlp", "c_fc", "bias"), P("mp" )),
(("mlp", "c_proj", "kernel"), P("mp" , lowercase_ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
A__ = _get_partition_rules()
A__ = _replacement_rules(lowercase_ )
A__ = {k: _unmatched for k in flatten_dict(lowercase_ )}
A__ = {k: replace(lowercase_ , lowercase_ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(lowercase_ ) )
| 230 |
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=10_00 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
A__ = n - 1
A__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
A__ = 0
while count < prec:
A__ = random.randint(2 , n - 1 )
A__ = bin_exp_mod(lowercase_ , lowercase_ , lowercase_ )
if b != 1:
A__ = True
for _ in range(lowercase_ ):
if b == n - 1:
A__ = False
break
A__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = abs(int(input("Enter bound : ").strip()))
print("Here's the list of primes:")
print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 230 | 1 |
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = """"""
for word_or_phrase in separated:
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise Exception('join() accepts only strings to be joined' )
joined += word_or_phrase + separator
return joined.strip(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 339 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowercase : Any = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase )
class __snake_case ( lowerCAmelCase ):
def __init__( self ,*snake_case ,**snake_case ):
'''simple docstring'''
super().__init__(*snake_case ,**snake_case )
requires_backends(self ,"""vision""" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def _SCREAMING_SNAKE_CASE ( self ,snake_case=None ):
'''simple docstring'''
lowercase : List[Any] = {}
if top_k is not None:
lowercase : int = top_k
return {}, {}, postprocess_params
def __call__( self ,snake_case ,**snake_case ):
'''simple docstring'''
return super().__call__(snake_case ,**snake_case )
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
lowercase : Any = load_image(snake_case )
lowercase : List[Any] = self.image_processor(images=snake_case ,return_tensors=self.framework )
return model_inputs
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
lowercase : int = self.model(**snake_case )
return model_outputs
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case=5 ):
'''simple docstring'''
if top_k > self.model.config.num_labels:
lowercase : Tuple = self.model.config.num_labels
if self.framework == "pt":
lowercase : str = model_outputs.logits.softmax(-1 )[0]
lowercase , lowercase : Dict = probs.topk(snake_case )
elif self.framework == "tf":
lowercase : Optional[int] = stable_softmax(model_outputs.logits ,axis=-1 )[0]
lowercase : Union[str, Any] = tf.math.top_k(snake_case ,k=snake_case )
lowercase , lowercase : List[str] = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f"Unsupported framework: {self.framework}" )
lowercase : Tuple = scores.tolist()
lowercase : Dict = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case ,snake_case )]
| 20 | 0 |
'''simple docstring'''
import math
import random
def _a ( _lowercase : List[str] , _lowercase : Union[str, Any] = False ):
'''simple docstring'''
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase :Optional[Any] = 0.02
def _a ( _lowercase : int , _lowercase : Dict ):
'''simple docstring'''
__UpperCAmelCase : Tuple = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__lowerCAmelCase ):
# Forward propagation
__UpperCAmelCase : Any = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
__UpperCAmelCase : Tuple = (expected / 100) - layer_a
# Error delta
__UpperCAmelCase : List[str] = layer_1_error * sigmoid_function(__lowerCAmelCase , __lowerCAmelCase )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase :Tuple = int(input("Expected value: "))
__UpperCAmelCase :Union[str, Any] = int(input("Number of propagations: "))
print(forward_propagation(expected, number_propagations)) | 362 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class a ( _a ):
"""simple docstring"""
def __init__( self : Optional[Any] ) -> int:
# test for the above condition
self.test()
def lowerCamelCase__ ( self : Dict ) -> List[str]:
__UpperCAmelCase : Optional[int] = 0
__UpperCAmelCase : Any = False
while not completed:
if counter == 1:
self.reset()
__UpperCAmelCase : Optional[int] = self.advance()
if not self.does_advance(snake_case ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : int = self.update(snake_case )
counter += 1
if counter > 1_0000:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[str]:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase__ ( self : Optional[int] , snake_case : int ) -> Optional[int]:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase__ ( self : List[Any] , snake_case : int ) -> int:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase__ ( self : int ) -> Optional[int]:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase__ ( self : int ) -> Tuple:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : List[Any]=False ) -> Any:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class a ( _a ):
"""simple docstring"""
def __init__( self : int , snake_case : List[int] ) -> Tuple:
super(snake_case , self ).__init__()
if not isinstance(snake_case , snake_case ) or len(snake_case ) == 0:
raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(snake_case , snake_case ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
__UpperCAmelCase : Dict = token_ids
__UpperCAmelCase : Tuple = len(self.token_ids )
__UpperCAmelCase : List[str] = -1 # the index of the currently fulfilled step
__UpperCAmelCase : int = False
def lowerCamelCase__ ( self : List[str] ) -> str:
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def lowerCamelCase__ ( self : Any , snake_case : int ) -> Optional[int]:
if not isinstance(snake_case , snake_case ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : int ) -> Optional[int]:
if not isinstance(snake_case , snake_case ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case )}' )
__UpperCAmelCase : Any = False
__UpperCAmelCase : Tuple = False
__UpperCAmelCase : Tuple = False
if self.does_advance(snake_case ):
self.fulfilled_idx += 1
__UpperCAmelCase : Union[str, Any] = True
if self.fulfilled_idx == (self.seqlen - 1):
__UpperCAmelCase : List[Any] = True
__UpperCAmelCase : Union[str, Any] = completed
else:
# failed to make progress.
__UpperCAmelCase : List[str] = True
self.reset()
return stepped, completed, reset
def lowerCamelCase__ ( self : int ) -> List[Any]:
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : Union[str, Any] = 0
def lowerCamelCase__ ( self : str ) -> Optional[int]:
return self.seqlen - (self.fulfilled_idx + 1)
def lowerCamelCase__ ( self : int , snake_case : Dict=False ) -> List[str]:
__UpperCAmelCase : List[str] = PhrasalConstraint(self.token_ids )
if stateful:
__UpperCAmelCase : int = self.seqlen
__UpperCAmelCase : Optional[Any] = self.fulfilled_idx
__UpperCAmelCase : List[Any] = self.completed
return new_constraint
class a :
"""simple docstring"""
def __init__( self : List[str] , snake_case : List[List[int]] , snake_case : Dict=True ) -> Any:
__UpperCAmelCase : List[Any] = max([len(snake_case ) for one in nested_token_ids] )
__UpperCAmelCase : Union[str, Any] = {}
for token_ids in nested_token_ids:
__UpperCAmelCase : List[str] = root
for tidx, token_id in enumerate(snake_case ):
if token_id not in level:
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : Tuple = level[token_id]
if no_subsets and self.has_subsets(snake_case , snake_case ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
f' {nested_token_ids}.' )
__UpperCAmelCase : Tuple = root
def lowerCamelCase__ ( self : Dict , snake_case : List[str] ) -> List[Any]:
__UpperCAmelCase : Dict = self.trie
for current_token in current_seq:
__UpperCAmelCase : List[str] = start[current_token]
__UpperCAmelCase : str = list(start.keys() )
return next_tokens
def lowerCamelCase__ ( self : Optional[Any] , snake_case : List[str] ) -> Any:
__UpperCAmelCase : Optional[Any] = self.next_tokens(snake_case )
return len(snake_case ) == 0
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : int ) -> Optional[int]:
__UpperCAmelCase : str = list(root.values() )
if len(snake_case ) == 0:
return 1
else:
return sum([self.count_leaves(snake_case ) for nn in next_nodes] )
def lowerCamelCase__ ( self : Optional[int] , snake_case : int , snake_case : Dict ) -> str:
__UpperCAmelCase : Dict = self.count_leaves(snake_case )
return len(snake_case ) != leaf_count
class a ( _a ):
"""simple docstring"""
def __init__( self : Union[str, Any] , snake_case : List[List[int]] ) -> str:
super(snake_case , self ).__init__()
if not isinstance(snake_case , snake_case ) or len(snake_case ) == 0:
raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(snake_case , snake_case ) for token_ids in nested_token_ids ):
raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(snake_case , snake_case ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
__UpperCAmelCase : Optional[int] = DisjunctiveTrie(snake_case )
__UpperCAmelCase : Tuple = nested_token_ids
__UpperCAmelCase : List[Any] = self.trie.max_height
__UpperCAmelCase : List[Any] = []
__UpperCAmelCase : Union[str, Any] = False
def lowerCamelCase__ ( self : int ) -> List[Any]:
__UpperCAmelCase : Optional[int] = self.trie.next_tokens(self.current_seq )
if len(snake_case ) == 0:
return None
else:
return token_list
def lowerCamelCase__ ( self : Tuple , snake_case : int ) -> Dict:
if not isinstance(snake_case , snake_case ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case )}' )
__UpperCAmelCase : List[str] = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def lowerCamelCase__ ( self : Any , snake_case : int ) -> Tuple:
if not isinstance(snake_case , snake_case ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case )}' )
__UpperCAmelCase : Dict = False
__UpperCAmelCase : Optional[Any] = False
__UpperCAmelCase : str = False
if self.does_advance(snake_case ):
self.current_seq.append(snake_case )
__UpperCAmelCase : int = True
else:
__UpperCAmelCase : Optional[Any] = True
self.reset()
__UpperCAmelCase : Optional[Any] = self.trie.reached_leaf(self.current_seq )
__UpperCAmelCase : Tuple = completed
return stepped, completed, reset
def lowerCamelCase__ ( self : Dict ) -> Optional[Any]:
__UpperCAmelCase : str = False
__UpperCAmelCase : Tuple = []
def lowerCamelCase__ ( self : Tuple ) -> Any:
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def lowerCamelCase__ ( self : Any , snake_case : Optional[int]=False ) -> Tuple:
__UpperCAmelCase : str = DisjunctiveConstraint(self.token_ids )
if stateful:
__UpperCAmelCase : Tuple = self.seqlen
__UpperCAmelCase : Dict = self.current_seq
__UpperCAmelCase : str = self.completed
return new_constraint
class a :
"""simple docstring"""
def __init__( self : Union[str, Any] , snake_case : List[Constraint] ) -> Union[str, Any]:
__UpperCAmelCase : Optional[Any] = constraints
# max # of steps required to fulfill a given constraint
__UpperCAmelCase : int = max([c.seqlen for c in constraints] )
__UpperCAmelCase : int = len(snake_case )
__UpperCAmelCase : Optional[int] = False
self.init_state()
def lowerCamelCase__ ( self : List[str] ) -> List[str]:
__UpperCAmelCase : List[Any] = []
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : str = [constraint.copy(stateful=snake_case ) for constraint in self.constraints]
def lowerCamelCase__ ( self : Optional[int] ) -> Optional[Any]:
__UpperCAmelCase : Dict = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def lowerCamelCase__ ( self : Tuple ) -> int:
__UpperCAmelCase : int = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
__UpperCAmelCase : Optional[int] = constraint.advance()
if isinstance(snake_case , snake_case ):
token_list.append(snake_case )
elif isinstance(snake_case , snake_case ):
token_list.extend(snake_case )
else:
__UpperCAmelCase : Optional[Any] = self.inprogress_constraint.advance()
if isinstance(snake_case , snake_case ):
token_list.append(snake_case )
elif isinstance(snake_case , snake_case ):
token_list.extend(snake_case )
if len(snake_case ) == 0:
return None
else:
return token_list
def lowerCamelCase__ ( self : List[str] , snake_case : Optional[List[int]] ) -> Optional[int]:
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
__UpperCAmelCase , __UpperCAmelCase : Dict = self.add(snake_case )
# the entire list of constraints are fulfilled
if self.completed:
break
def lowerCamelCase__ ( self : List[str] , snake_case : int ) -> List[str]:
if not isinstance(snake_case , snake_case ):
raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.' )
__UpperCAmelCase , __UpperCAmelCase : str = False, False
if self.completed:
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : str = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Any = self.inprogress_constraint.update(snake_case )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case ) )
__UpperCAmelCase : Optional[Any] = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
__UpperCAmelCase : str = None
if len(self.pending_constraints ) == 0:
# we're done!
__UpperCAmelCase : Optional[int] = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(snake_case ):
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = pending_constraint.update(snake_case )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(snake_case )
__UpperCAmelCase : Tuple = None
if not complete and stepped:
__UpperCAmelCase : List[Any] = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
__UpperCAmelCase : Optional[int] = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
__UpperCAmelCase : Any = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def lowerCamelCase__ ( self : int , snake_case : Optional[int]=True ) -> Optional[int]:
__UpperCAmelCase : Union[str, Any] = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
__UpperCAmelCase : str = [
constraint.copy(stateful=snake_case ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
__UpperCAmelCase : Union[str, Any] = self.inprogress_constraint.copy(stateful=snake_case )
__UpperCAmelCase : Tuple = [constraint.copy() for constraint in self.pending_constraints]
return new_state | 240 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : float ) -> float:
'''simple docstring'''
if edge <= 0 or not isinstance(__lowercase , __lowercase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def UpperCAmelCase_ ( __lowercase : float ) -> float:
'''simple docstring'''
if edge <= 0 or not isinstance(__lowercase , __lowercase ):
raise ValueError("Length must be a positive." )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class A_ :
def __init__( self : str , snake_case_ : int , snake_case_ : Union[str, Any]=2 , snake_case_ : List[Any]=True , snake_case_ : str=False , snake_case_ : str=1_0 , snake_case_ : str=3 , snake_case_ : Dict=3_2 * 4 , snake_case_ : Any=3_2 * 6 , snake_case_ : Optional[Any]=4 , snake_case_ : Optional[int]=3_2 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = is_training
_UpperCAmelCase = use_auxiliary_loss
_UpperCAmelCase = num_queries
_UpperCAmelCase = num_channels
_UpperCAmelCase = min_size
_UpperCAmelCase = max_size
_UpperCAmelCase = num_labels
_UpperCAmelCase = mask_feature_size
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case_ )
_UpperCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case_ )
_UpperCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case_ ) > 0.5
).float()
_UpperCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=snake_case_ ) > 0.5).long()
_UpperCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase ( self : List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def lowercase ( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] ):
_UpperCAmelCase = output.encoder_hidden_states
_UpperCAmelCase = output.pixel_decoder_hidden_states
_UpperCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , config.decoder_config.decoder_layers )
def lowercase ( self : Tuple , snake_case_ : str , snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Optional[Any]=False ):
with torch.no_grad():
_UpperCAmelCase = MaskFormerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ , output_hidden_states=snake_case_ )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case_ , snake_case_ )
def lowercase ( self : Any , snake_case_ : List[str] , snake_case_ : List[Any] , snake_case_ : int , snake_case_ : str , snake_case_ : List[Any] ):
_UpperCAmelCase = MaskFormerForInstanceSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
def comm_check_on_output(snake_case_ : int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
comm_check_on_output(snake_case_ )
_UpperCAmelCase = model(
pixel_values=snake_case_ , pixel_mask=snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
comm_check_on_output(snake_case_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Dict = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_lowerCamelCase : Tuple = (
{"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Dict = False
_lowerCamelCase : Any = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = MaskFormerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : int ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_ )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def lowercase ( self : Any ):
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def lowercase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def lowercase ( self : Any ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase ( self : Union[str, Any] ):
pass
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
@slow
def lowercase ( self : Optional[int] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
_UpperCAmelCase = MaskFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = (self.model_tester.min_size,) * 2
_UpperCAmelCase = {
"pixel_values": torch.randn((2, 3, *size) , device=snake_case_ ),
"mask_labels": torch.randn((2, 1_0, *size) , device=snake_case_ ),
"class_labels": torch.zeros(2 , 1_0 , device=snake_case_ ).long(),
}
_UpperCAmelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
def lowercase ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ , output_attentions=snake_case_ )
self.assertTrue(outputs.attentions is not None )
def lowercase ( self : int ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ ).loss
loss.backward()
def lowercase ( self : int ):
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
_UpperCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_UpperCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__SCREAMING_SNAKE_CASE :Dict = 1e-4
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class A_ ( unittest.TestCase ):
@cached_property
def lowercase ( self : Dict ):
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def lowercase ( self : List[Any] ):
_UpperCAmelCase = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(snake_case_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[
[1.6_512e00, -5.2_572e00, -3.3_519e00],
[3.6_169e-02, -5.9_025e00, -2.9_313e00],
[1.0_766e-04, -7.7_630e00, -5.1_263e00],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : int ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors="pt" , )
_UpperCAmelCase = inputs["pixel_values"].to(snake_case_ )
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["mask_labels"]]
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["class_labels"]]
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
| 22 | 1 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
A__ : Dict ='''true'''
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase=82 , lowerCAmelCase=16 ):
"""simple docstring"""
set_seed(42 )
_lowerCAmelCase = RegressionModel()
_lowerCAmelCase = deepcopy(_a )
_lowerCAmelCase = RegressionDataset(length=_a )
_lowerCAmelCase = DataLoader(_a , batch_size=_a )
model.to(accelerator.device )
_lowerCAmelCase = accelerator.prepare(_a , _a )
return model, ddp_model, dataloader
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase=False ):
"""simple docstring"""
_lowerCAmelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
_lowerCAmelCase = load_dataset("""glue""" , """mrpc""" , split="""validation""" )
def tokenize_function(lowerCAmelCase ):
_lowerCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=_a , max_length=_a )
return outputs
with accelerator.main_process_first():
_lowerCAmelCase = dataset.map(
_a , batched=_a , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
_lowerCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase ):
if use_longest:
return tokenizer.pad(_a , padding="""longest""" , return_tensors="""pt""" )
return tokenizer.pad(_a , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return DataLoader(_a , shuffle=_a , collate_fn=_a , batch_size=16 )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = Accelerator(dispatch_batches=_a , split_batches=_a )
_lowerCAmelCase = get_dataloader(_a , not dispatch_batches )
_lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(
"""hf-internal-testing/mrpc-bert-base-cased""" , return_dict=_a )
_lowerCAmelCase = accelerator.prepare(_a , _a )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = []
for batch in dataloader:
_lowerCAmelCase = batch.values()
with torch.no_grad():
_lowerCAmelCase = model(_a )
_lowerCAmelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
_lowerCAmelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_a )
targs.append(_a )
_lowerCAmelCase = torch.cat(_a ), torch.cat(_a )
return logits, targs
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase=82 , lowerCAmelCase=False , lowerCAmelCase=False , lowerCAmelCase=16 ):
"""simple docstring"""
_lowerCAmelCase = get_basic_setup(_a , _a , _a )
_lowerCAmelCase = generate_predictions(_a , _a , _a )
assert (
len(_a ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_a )}"
def UpperCamelCase__ ( lowerCAmelCase = False , lowerCAmelCase = False ):
"""simple docstring"""
_lowerCAmelCase = evaluate.load("""glue""" , """mrpc""" )
_lowerCAmelCase = get_mrpc_setup(_a , _a )
# First do baseline
_lowerCAmelCase = setup["""no"""]
model.to(_a )
model.eval()
for batch in dataloader:
batch.to(_a )
with torch.inference_mode():
_lowerCAmelCase = model(**_a )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_a , references=batch["""labels"""] )
_lowerCAmelCase = metric.compute()
# Then do distributed
_lowerCAmelCase = setup["""ddp"""]
model.eval()
for batch in dataloader:
with torch.inference_mode():
_lowerCAmelCase = model(**_a )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
_lowerCAmelCase = batch["""labels"""]
_lowerCAmelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_a , references=_a )
_lowerCAmelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = Accelerator(split_batches=_a , dispatch_batches=_a )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("""**Testing gather_for_metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(_a , _a )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test torch metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
_lowerCAmelCase = Accelerator(split_batches=_a , dispatch_batches=_a )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(_a , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test last batch is not dropped when perfectly divisible**""" )
_lowerCAmelCase = Accelerator()
test_torch_metrics(_a , 5_12 )
accelerator.state._reset_state()
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 365 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase :
@staticmethod
def lowercase__ ( *__snake_case : Optional[Any] , **__snake_case : Any ) -> Tuple:
pass
@is_pipeline_test
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
_lowercase: Union[str, Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def lowercase__ ( self : List[str] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : List[str] ) -> int:
_lowerCAmelCase = pipeline("""visual-question-answering""" , model="""hf-internal-testing/tiny-vilt-random-vqa""" )
_lowerCAmelCase = [
{
"""image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""question""": """How many cats are there?""",
},
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""question""": """How many cats are there?""",
},
]
return vqa_pipeline, examples
def lowercase__ ( self : Any , __snake_case : List[Any] , __snake_case : List[Any] ) -> Union[str, Any]:
_lowerCAmelCase = vqa_pipeline(__snake_case , top_k=1 )
self.assertEqual(
__snake_case , [
[{"""score""": ANY(__snake_case ), """answer""": ANY(__snake_case )}],
[{"""score""": ANY(__snake_case ), """answer""": ANY(__snake_case )}],
] , )
@require_torch
def lowercase__ ( self : str ) -> int:
_lowerCAmelCase = pipeline("""visual-question-answering""" , model="""hf-internal-testing/tiny-vilt-random-vqa""" )
_lowerCAmelCase = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
_lowerCAmelCase = """How many cats are there?"""
_lowerCAmelCase = vqa_pipeline(image=__snake_case , question="""How many cats are there?""" , top_k=2 )
self.assertEqual(
__snake_case , [{"""score""": ANY(__snake_case ), """answer""": ANY(__snake_case )}, {"""score""": ANY(__snake_case ), """answer""": ANY(__snake_case )}] )
_lowerCAmelCase = vqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
__snake_case , [{"""score""": ANY(__snake_case ), """answer""": ANY(__snake_case )}, {"""score""": ANY(__snake_case ), """answer""": ANY(__snake_case )}] )
@slow
@require_torch
def lowercase__ ( self : List[Any] ) -> List[str]:
_lowerCAmelCase = pipeline("""visual-question-answering""" , model="""dandelin/vilt-b32-finetuned-vqa""" )
_lowerCAmelCase = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
_lowerCAmelCase = """How many cats are there?"""
_lowerCAmelCase = vqa_pipeline(image=__snake_case , question=__snake_case , top_k=2 )
self.assertEqual(
nested_simplify(__snake_case , decimals=4 ) , [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}] )
_lowerCAmelCase = vqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(__snake_case , decimals=4 ) , [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}] )
_lowerCAmelCase = vqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__snake_case , decimals=4 ) , [[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]] * 2 , )
@require_tf
@unittest.skip("""Visual question answering not implemented in TF""" )
def lowercase__ ( self : List[str] ) -> Union[str, Any]:
pass
| 220 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class lowercase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = StableDiffusionPanoramaPipeline
UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def _snake_case ( self ) -> List[str]:
torch.manual_seed(0 )
_UpperCAmelCase : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=1 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
_UpperCAmelCase : Tuple = DDIMScheduler()
torch.manual_seed(0 )
_UpperCAmelCase : Tuple = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
torch.manual_seed(0 )
_UpperCAmelCase : Dict = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
_UpperCAmelCase : Any = CLIPTextModel(a_ )
_UpperCAmelCase : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_UpperCAmelCase : int = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _snake_case ( self ,a_ ,a_=0 ) -> List[str]:
_UpperCAmelCase : List[str] = torch.manual_seed(a_ )
_UpperCAmelCase : Optional[int] = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
# Setting height and width to None to prevent OOMs on CPU.
"""height""": None,
"""width""": None,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : List[str] = self.get_dummy_components()
_UpperCAmelCase : List[str] = StableDiffusionPanoramaPipeline(**a_ )
_UpperCAmelCase : Optional[int] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(a_ )
_UpperCAmelCase : Tuple = sd_pipe(**a_ ).images
_UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Optional[int] = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Tuple:
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def _snake_case ( self ) -> Tuple:
super().test_inference_batch_single_identical(batch_size=2 ,expected_max_diff=3.2_5E-3 )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : List[str] = self.get_dummy_components()
_UpperCAmelCase : int = StableDiffusionPanoramaPipeline(**a_ )
_UpperCAmelCase : Optional[int] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Any = self.get_dummy_inputs(a_ )
_UpperCAmelCase : Dict = """french fries"""
_UpperCAmelCase : Any = sd_pipe(**a_ ,negative_prompt=a_ )
_UpperCAmelCase : Optional[int] = output.images
_UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Dict = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Optional[int] = self.get_dummy_components()
_UpperCAmelCase : List[Any] = StableDiffusionPanoramaPipeline(**a_ )
_UpperCAmelCase : List[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Optional[int] = self.get_dummy_inputs(a_ )
_UpperCAmelCase : int = sd_pipe(**a_ ,view_batch_size=2 )
_UpperCAmelCase : Optional[Any] = output.images
_UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : int = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : int = self.get_dummy_components()
_UpperCAmelCase : str = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" )
_UpperCAmelCase : Dict = StableDiffusionPanoramaPipeline(**a_ )
_UpperCAmelCase : Optional[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(a_ )
_UpperCAmelCase : List[str] = sd_pipe(**a_ ).images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Tuple = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Any = self.get_dummy_components()
_UpperCAmelCase : List[str] = PNDMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,skip_prk_steps=a_ )
_UpperCAmelCase : Tuple = StableDiffusionPanoramaPipeline(**a_ )
_UpperCAmelCase : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : str = self.get_dummy_inputs(a_ )
_UpperCAmelCase : Any = sd_pipe(**a_ ).images
_UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Optional[Any] = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ,a_=0 ) -> Any:
_UpperCAmelCase : Optional[Any] = torch.manual_seed(a_ )
_UpperCAmelCase : str = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Optional[Any] = """stabilityai/stable-diffusion-2-base"""
_UpperCAmelCase : List[str] = DDIMScheduler.from_pretrained(a_ ,subfolder="""scheduler""" )
_UpperCAmelCase : List[Any] = StableDiffusionPanoramaPipeline.from_pretrained(a_ ,scheduler=a_ ,safety_checker=a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
_UpperCAmelCase : Tuple = self.get_inputs()
_UpperCAmelCase : Union[str, Any] = pipe(**a_ ).images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2_048, 3)
_UpperCAmelCase : Dict = np.array(
[
0.3696_8392,
0.2702_5372,
0.3244_6766,
0.2837_9387,
0.3636_3274,
0.3073_3347,
0.2710_0027,
0.2705_4125,
0.2553_6096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : List[str] = StableDiffusionPanoramaPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-base""" ,safety_checker=a_ )
_UpperCAmelCase : Union[str, Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
_UpperCAmelCase : Optional[int] = self.get_inputs()
_UpperCAmelCase : Union[str, Any] = pipe(**a_ ).images
_UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2_048, 3)
_UpperCAmelCase : Dict = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : List[Any] = 0
def callback_fn(a_ ,a_ ,a_ ) -> None:
_UpperCAmelCase : Optional[int] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
_UpperCAmelCase : List[Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
_UpperCAmelCase : Any = latents[0, -3:, -3:, -1]
_UpperCAmelCase : Optional[Any] = np.array(
[
0.1868_1869,
0.3390_7816,
0.536_1276,
0.1443_2865,
-0.0285_6611,
-0.7394_1123,
0.2339_7987,
0.4732_2682,
-0.3782_3164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
_UpperCAmelCase : Any = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
_UpperCAmelCase : Tuple = latents[0, -3:, -3:, -1]
_UpperCAmelCase : Union[str, Any] = np.array(
[
0.1853_9645,
0.3398_7248,
0.537_8559,
0.1443_7142,
-0.0245_5261,
-0.733_8317,
0.2399_0755,
0.4735_6272,
-0.378_6505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
_UpperCAmelCase : Dict = False
_UpperCAmelCase : Tuple = """stabilityai/stable-diffusion-2-base"""
_UpperCAmelCase : List[str] = DDIMScheduler.from_pretrained(a_ ,subfolder="""scheduler""" )
_UpperCAmelCase : Tuple = StableDiffusionPanoramaPipeline.from_pretrained(a_ ,scheduler=a_ ,safety_checker=a_ )
_UpperCAmelCase : Optional[Any] = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
_UpperCAmelCase : int = self.get_inputs()
pipe(**a_ ,callback=a_ ,callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _snake_case ( self ) -> Any:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCAmelCase : Tuple = """stabilityai/stable-diffusion-2-base"""
_UpperCAmelCase : Tuple = DDIMScheduler.from_pretrained(a_ ,subfolder="""scheduler""" )
_UpperCAmelCase : Optional[Any] = StableDiffusionPanoramaPipeline.from_pretrained(a_ ,scheduler=a_ ,safety_checker=a_ )
_UpperCAmelCase : Dict = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_UpperCAmelCase : int = self.get_inputs()
_UpperCAmelCase : List[str] = pipe(**a_ )
_UpperCAmelCase : List[Any] = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 215 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None )-> List[str]:
'''simple docstring'''
_UpperCAmelCase : Any = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
_UpperCAmelCase ,_UpperCAmelCase : Tuple = True, True
_UpperCAmelCase : List[Any] = dfs(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return path
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = 0
_UpperCAmelCase : Optional[int] = -1
for i in range(lowerCAmelCase_ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
_UpperCAmelCase : Optional[int] = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : str = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
_UpperCAmelCase ,_UpperCAmelCase : int = check_circuit_or_path(lowerCAmelCase_ , lowerCAmelCase_ )
if check == 3:
print("""graph is not Eulerian""" )
print("""no path""" )
return
_UpperCAmelCase : Dict = 1
if check == 2:
_UpperCAmelCase : Dict = odd_node
print("""graph has a Euler path""" )
if check == 1:
print("""graph has a Euler cycle""" )
_UpperCAmelCase : Dict = dfs(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
print(lowerCAmelCase_ )
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Any = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
_UpperCAmelCase : int = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
_UpperCAmelCase : Tuple = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
_UpperCAmelCase : List[Any] = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
_UpperCAmelCase : List[str] = {
1: [],
2: []
# all degree is zero
}
_UpperCAmelCase : Union[str, Any] = 10
check_euler(lowerCAmelCase_ , lowerCAmelCase_ )
check_euler(lowerCAmelCase_ , lowerCAmelCase_ )
check_euler(lowerCAmelCase_ , lowerCAmelCase_ )
check_euler(lowerCAmelCase_ , lowerCAmelCase_ )
check_euler(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 215 | 1 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _lowerCAmelCase ( _UpperCamelCase : dict ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def _lowerCAmelCase ( _UpperCamelCase : np.ndarray , _UpperCamelCase : np.ndarray ) -> XGBClassifier:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =XGBClassifier()
classifier.fit(_UpperCamelCase , _UpperCamelCase )
return classifier
def _lowerCAmelCase ( ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =load_iris()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =data_handling(_UpperCamelCase )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =train_test_split(
_UpperCamelCase , _UpperCamelCase , test_size=0.25 )
_SCREAMING_SNAKE_CASE =iris['target_names']
# Create an XGBoost Classifier from the training data
_SCREAMING_SNAKE_CASE =xgboost(_UpperCamelCase , _UpperCamelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , display_labels=_UpperCamelCase , cmap='Blues' , normalize='true' , )
plt.title('Normalized Confusion Matrix - IRIS Dataset' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 114 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : List[str] = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowerCamelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 114 | 1 |
import pytest
_UpperCAmelCase : Dict ="""__dummy_dataset1__"""
_UpperCAmelCase : Dict ="""
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
\"tokens\": datasets.Sequence(datasets.Value(\"string\")),
\"ner_tags\": datasets.Sequence(
datasets.features.ClassLabel(
names=[
\"O\",
\"B-PER\",
\"I-PER\",
\"B-ORG\",
\"I-ORG\",
\"B-LOC\",
\"I-LOC\",
]
)
),
\"langs\": datasets.Sequence(datasets.Value(\"string\")),
\"spans\": datasets.Sequence(datasets.Value(\"string\")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
]
def _generate_examples(self, filepath):
with open(filepath, \"r\", encoding=\"utf-8\") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
"""
@pytest.fixture
def lowerCAmelCase ( )-> Dict:
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowerCAmelCase ( )-> int:
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
lowerCAmelCase_ : Optional[int] = dataset_loading_script_name
lowerCAmelCase_ : Union[str, Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = script_dir / f"""{script_name}.py"""
with open(lowerCAmelCase_ , '''w''' ) as f:
f.write(lowerCAmelCase_ )
return str(lowerCAmelCase_ ) | 262 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCAmelCase ( unittest.TestCase ):
__UpperCAmelCase : Union[str, Any] = JukeboxTokenizer
__UpperCAmelCase : Union[str, Any] = {
'''artist''': '''Zac Brown Band''',
'''genres''': '''Country''',
'''lyrics''': '''I met a traveller from an antique land,
Who said "Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
''',
}
@require_torch
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
import torch
snake_case : Any = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics" )
snake_case : Optional[Any] = tokenizer(**self.metas )["input_ids"]
# fmt: off
snake_case : Optional[int] = [
torch.tensor([[
0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
import torch
snake_case : Tuple = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics" )
snake_case : Optional[Any] = tokenizer(**self.metas )["input_ids"]
# fmt: off
snake_case : List[Any] = [
torch.tensor([[
0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 203 | 0 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCAmelCase : Optional[int] = namedtuple("covid_data", "cases deaths recovered")
def __snake_case ( SCREAMING_SNAKE_CASE__ : str = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE__ ).content ).xpath(SCREAMING_SNAKE_CASE__ ) )
_lowerCAmelCase : List[Any] = "Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats()))
| 202 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_lowerCAmelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_lowerCAmelCase : int = {
"vocab_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
),
"google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
),
"google/electra-base-generator": (
"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
),
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
),
},
}
_lowerCAmelCase : List[Any] = {
"google/electra-small-generator": 5_12,
"google/electra-base-generator": 5_12,
"google/electra-large-generator": 5_12,
"google/electra-small-discriminator": 5_12,
"google/electra-base-discriminator": 5_12,
"google/electra-large-discriminator": 5_12,
}
_lowerCAmelCase : Optional[Any] = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class UpperCAmelCase_ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : List[str] = PRETRAINED_INIT_CONFIGURATION
__SCREAMING_SNAKE_CASE : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : Tuple = ElectraTokenizer
def __init__( self : Dict , A : Dict=None , A : Optional[int]=None , A : Dict=True , A : Optional[Any]="[UNK]" , A : Any="[SEP]" , A : str="[PAD]" , A : Tuple="[CLS]" , A : Optional[Any]="[MASK]" , A : Any=True , A : Tuple=None , **A : Any , ):
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase : List[str] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , A ) != do_lower_case
or normalizer_state.get("strip_accents" , A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , A ) != tokenize_chinese_chars
):
_UpperCAmelCase : Union[str, Any] = getattr(A , normalizer_state.pop("type" ) )
_UpperCAmelCase : Dict = do_lower_case
_UpperCAmelCase : Optional[int] = strip_accents
_UpperCAmelCase : Any = tokenize_chinese_chars
_UpperCAmelCase : Optional[Any] = normalizer_class(**A )
_UpperCAmelCase : int = do_lower_case
def snake_case_ ( self : Tuple , A : str , A : int=None ):
_UpperCAmelCase : Dict = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case_ ( self : Any , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case_ ( self : Any , A : str , A : Optional[str] = None ):
_UpperCAmelCase : List[Any] = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 202 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : List[str] = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = ["YolosFeatureExtractor"]
A_ : Optional[int] = ["YolosImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
"YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
"YolosForObjectDetection",
"YolosModel",
"YolosPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
A_ : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 165 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def A ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = namedtuple("""result""" , """name value""" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("""Only one argument must be 0""" )
elif power < 0:
raise ValueError(
"""Power cannot be negative in any electrical/electronics system""" )
elif voltage == 0:
return result("""voltage""" , power / current )
elif current == 0:
return result("""current""" , power / voltage )
elif power == 0:
return result("""power""" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 165 | 1 |
import warnings
from functools import wraps
from typing import Callable
def a( A : Callable ) -> Callable:
"""simple docstring"""
@wraps(A )
def _inner_fn(*A : Any , **A : Union[str, Any] ):
warnings.warn(
(f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''') , A , )
return fn(*A , **A )
return _inner_fn
| 352 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__(self , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ ):
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowerCamelCase_ , )
a = kwargs.pop("feature_extractor" )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(lowerCamelCase_ , lowerCamelCase_ )
def __call__(self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ ):
"""simple docstring"""
if text is None and visual_prompt is None and images is None:
raise ValueError("You have to specify either text, visual prompt or images." )
if text is not None and visual_prompt is not None:
raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )
if text is not None:
a = self.tokenizer(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if visual_prompt is not None:
a = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if images is not None:
a = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if visual_prompt is not None and images is not None:
a = {
"pixel_values": image_features.pixel_values,
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
a = {
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase_ ) , tensor_type=lowerCamelCase_ )
def UpperCamelCase_ (self , *lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase_ (self , *lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCamelCase_ , **lowerCamelCase_ )
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowerCamelCase_ , )
return self.image_processor_class
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowerCamelCase_ , )
return self.image_processor
| 71 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : Union[str, Any] = ["pixel_values"]
def __init__( self , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = PILImageResampling.BICUBIC , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = True , _lowerCAmelCase = 1 / 255 , _lowerCAmelCase = True , _lowerCAmelCase = IMAGENET_DEFAULT_MEAN , _lowerCAmelCase = IMAGENET_DEFAULT_STD , **_lowerCAmelCase , ) -> None:
super().__init__(**_lowerCAmelCase )
_lowerCAmelCase = size if size is not None else {"shortest_edge": 224}
_lowerCAmelCase = get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
_lowerCAmelCase = crop_size if crop_size is not None else {"height": 224, "width": 224}
_lowerCAmelCase = get_size_dict(_lowerCAmelCase , param_name="crop_size" )
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = resample
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = do_rescale
_lowerCAmelCase = rescale_factor
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_lowerCAmelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = PILImageResampling.BICUBIC , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
_lowerCAmelCase = int((256 / 224) * size["shortest_edge"] )
_lowerCAmelCase = get_resize_output_image_size(_lowerCAmelCase , size=_lowerCAmelCase , default_to_square=_lowerCAmelCase )
_lowerCAmelCase = {"height": output_size[0], "width": output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
return resize(
_lowerCAmelCase , size=(size_dict["height"], size_dict["width"]) , resample=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(_lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
return center_crop(_lowerCAmelCase , size=(size["height"], size["width"]) , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
return rescale(_lowerCAmelCase , scale=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
return normalize(_lowerCAmelCase , mean=_lowerCAmelCase , std=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = ChannelDimension.FIRST , **_lowerCAmelCase , ) -> BatchFeature:
_lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase = resample if resample is not None else self.resample
_lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase = image_std if image_std is not None else self.image_std
_lowerCAmelCase = size if size is not None else self.size
_lowerCAmelCase = get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
_lowerCAmelCase = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase = get_size_dict(_lowerCAmelCase , param_name="crop_size" )
_lowerCAmelCase = make_list_of_images(_lowerCAmelCase )
if not valid_images(_lowerCAmelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
_lowerCAmelCase = [to_numpy_array(_lowerCAmelCase ) for image in images]
if do_resize:
_lowerCAmelCase = [self.resize(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for image in images]
if do_center_crop:
_lowerCAmelCase = [self.center_crop(_lowerCAmelCase , _lowerCAmelCase ) for image in images]
if do_rescale:
_lowerCAmelCase = [self.rescale(_lowerCAmelCase , _lowerCAmelCase ) for image in images]
if do_normalize:
_lowerCAmelCase = [self.normalize(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for image in images]
_lowerCAmelCase = [to_channel_dimension_format(_lowerCAmelCase , _lowerCAmelCase ) for image in images]
_lowerCAmelCase = {"pixel_values": images}
return BatchFeature(data=_lowerCAmelCase , tensor_type=_lowerCAmelCase )
| 158 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
while a != 0:
_lowerCAmelCase , _lowerCAmelCase = b % a, a
return b
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) != 1:
_lowerCAmelCase = F'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1, 0, a
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0, 1, m
while va != 0:
_lowerCAmelCase = ua // va
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 158 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_A : Any =logging.get_logger(__name__)
if is_vision_available():
import PIL
class _lowercase ( _UpperCAmelCase ):
a = ["pixel_values"]
def __init__( self: Union[str, Any] , UpperCamelCase__: List[Any] = True , UpperCamelCase__: str = None , UpperCamelCase__: Any = PILImageResampling.BICUBIC , UpperCamelCase__: Tuple = True , UpperCamelCase__: List[str] = None , UpperCamelCase__: int = True , UpperCamelCase__: Any = 1 / 255 , UpperCamelCase__: Dict = True , UpperCamelCase__: Dict = None , UpperCamelCase__: List[Any] = None , UpperCamelCase__: List[str] = True , **UpperCamelCase__: Tuple , ):
super().__init__(**_UpperCAmelCase )
lowerCamelCase__ : str = size if size is not None else {'''shortest_edge''': 224}
lowerCamelCase__ : List[Any] = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
lowerCamelCase__ : List[str] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCamelCase__ : Dict = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase , param_name="""crop_size""" )
lowerCamelCase__ : Optional[Any] = do_resize
lowerCamelCase__ : Any = size
lowerCamelCase__ : Optional[Any] = resample
lowerCamelCase__ : List[Any] = do_center_crop
lowerCamelCase__ : Optional[Any] = crop_size
lowerCamelCase__ : Optional[Any] = do_rescale
lowerCamelCase__ : Any = rescale_factor
lowerCamelCase__ : List[Any] = do_normalize
lowerCamelCase__ : str = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase__ : Dict = image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase__ : str = do_convert_rgb
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase__: List[str] , UpperCamelCase__: Tuple , UpperCamelCase__: Union[str, Any] = PILImageResampling.BICUBIC , UpperCamelCase__: Dict = None , **UpperCamelCase__: Optional[Any] , ):
lowerCamelCase__ : Tuple = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowerCamelCase__ : Dict = get_resize_output_image_size(_UpperCAmelCase , size=size["""shortest_edge"""] , default_to_square=_UpperCAmelCase )
return resize(_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase_ ( self: str , UpperCamelCase__: Optional[Any] , UpperCamelCase__: Tuple , UpperCamelCase__: Optional[Any] = None , **UpperCamelCase__: Tuple , ):
lowerCamelCase__ : Dict = get_size_dict(_UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(_UpperCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase_ ( self: Any , UpperCamelCase__: Union[str, Any] , UpperCamelCase__: Union[str, Any] , UpperCamelCase__: int = None , **UpperCamelCase__: Optional[int] , ):
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase__: Dict , UpperCamelCase__: Tuple , UpperCamelCase__: Dict , UpperCamelCase__: Any = None , **UpperCamelCase__: Union[str, Any] , ):
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase__: Dict , UpperCamelCase__: Tuple = None , UpperCamelCase__: Tuple = None , UpperCamelCase__: Union[str, Any] = None , UpperCamelCase__: Any = None , UpperCamelCase__: Any = None , UpperCamelCase__: Union[str, Any] = None , UpperCamelCase__: List[str] = None , UpperCamelCase__: str = None , UpperCamelCase__: Any = None , UpperCamelCase__: Optional[Any] = None , UpperCamelCase__: List[Any] = None , UpperCamelCase__: Optional[Any] = None , UpperCamelCase__: int = ChannelDimension.FIRST , **UpperCamelCase__: List[Any] , ):
lowerCamelCase__ : List[Any] = do_resize if do_resize is not None else self.do_resize
lowerCamelCase__ : Optional[int] = size if size is not None else self.size
lowerCamelCase__ : Optional[int] = get_size_dict(_UpperCAmelCase , param_name="""size""" , default_to_square=_UpperCAmelCase )
lowerCamelCase__ : str = resample if resample is not None else self.resample
lowerCamelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase__ : List[str] = crop_size if crop_size is not None else self.crop_size
lowerCamelCase__ : Dict = get_size_dict(_UpperCAmelCase , param_name="""crop_size""" , default_to_square=_UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase__ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase__ : str = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase__ : List[Any] = image_mean if image_mean is not None else self.image_mean
lowerCamelCase__ : int = image_std if image_std is not None else self.image_std
lowerCamelCase__ : int = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase__ : Tuple = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase__ : Union[str, Any] = [convert_to_rgb(_UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase__ : Optional[int] = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
lowerCamelCase__ : str = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_center_crop:
lowerCamelCase__ : Optional[Any] = [self.center_crop(image=_UpperCAmelCase , size=_UpperCAmelCase ) for image in images]
if do_rescale:
lowerCamelCase__ : Any = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
lowerCamelCase__ : Optional[Any] = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
lowerCamelCase__ : Tuple = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
lowerCamelCase__ : Dict = {'''pixel_values''': images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
| 352 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def SCREAMING_SNAKE_CASE_ (UpperCamelCase=None , UpperCamelCase=None ) -> Any:
return field(default_factory=lambda: default , metadata=UpperCamelCase )
@dataclass
class _lowercase :
a = field(
metadata={"""help""": """The csv file to plot."""} , )
a = field(
default=_lowercase , metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""} , )
a = field(
default=_lowercase , metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""} , )
a = field(
default=_lowercase , metadata={"""help""": """Disable logarithmic scale when plotting"""} , )
a = field(
default=_lowercase , metadata={
"""help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
} , )
a = field(
default=_lowercase , metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""} , )
a = list_field(
default=_lowercase , metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""} )
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Dict:
try:
int(UpperCamelCase )
return True
except ValueError:
return False
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> int:
try:
float(UpperCamelCase )
return True
except ValueError:
return False
class _lowercase :
def __init__( self: Tuple , UpperCamelCase__: str ):
lowerCamelCase__ : int = args
lowerCamelCase__ : Optional[int] = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline="""""" ) as csv_file:
lowerCamelCase__ : str = csv.DictReader(UpperCamelCase__ )
for row in reader:
lowerCamelCase__ : Optional[int] = row["""model"""]
self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""] ) )
self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""] ) )
if can_convert_to_int(row["""result"""] ):
# value is not None
lowerCamelCase__ : Tuple = int(row["""result"""] )
elif can_convert_to_float(row["""result"""] ):
# value is not None
lowerCamelCase__ : Any = float(row["""result"""] )
def lowerCamelCase_ ( self: str ):
lowerCamelCase__ , lowerCamelCase__ : Tuple = plt.subplots()
lowerCamelCase__ : Any = """Time usage""" if self.args.is_time else """Memory usage"""
lowerCamelCase__ : List[str] = title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale("""log""" )
ax.set_yscale("""log""" )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
lowerCamelCase__ : Any = sorted(set(self.result_dict[model_name]["""bsz"""] ) )
lowerCamelCase__ : int = sorted(set(self.result_dict[model_name]["""seq_len"""] ) )
lowerCamelCase__ : Any = self.result_dict[model_name]["""result"""]
((lowerCamelCase__) , (lowerCamelCase__)) : Dict = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
lowerCamelCase__ : Any = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
lowerCamelCase__ : int = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=UpperCamelCase__ , )
else:
lowerCamelCase__ : List[Any] = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((lowerCamelCase__) , (lowerCamelCase__)) : List[str] = (
("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
)
lowerCamelCase__ : int = np.asarray(UpperCamelCase__ , UpperCamelCase__ )[: len(UpperCamelCase__ )]
plt.scatter(
UpperCamelCase__ , UpperCamelCase__ , label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
plt.plot(UpperCamelCase__ , UpperCamelCase__ , """--""" )
title_str += F''' {label_model_name} vs.'''
lowerCamelCase__ : Any = title_str[:-4]
lowerCamelCase__ : Optional[int] = """Time in s""" if self.args.is_time else """Memory in MB"""
# plot
plt.title(UpperCamelCase__ )
plt.xlabel(UpperCamelCase__ )
plt.ylabel(UpperCamelCase__ )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def SCREAMING_SNAKE_CASE_ () -> str:
lowerCamelCase__ : str = HfArgumentParser(UpperCamelCase )
lowerCamelCase__ : str = parser.parse_args_into_dataclasses()[0]
lowerCamelCase__ : Any = Plot(args=UpperCamelCase )
plot.plot()
if __name__ == "__main__":
main()
| 129 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : int, *_snake_case : int, _snake_case : Dict=None, _snake_case : Optional[Any]=None, **_snake_case : Dict ) ->Optional[Any]:
super().__init__(*_snake_case, **_snake_case )
snake_case__ : Tuple = eval_examples
snake_case__ : Optional[Any] = post_process_function
def lowercase_ ( self : Dict, _snake_case : Optional[Dataset] = None, _snake_case : Optional[int]=None, _snake_case : Optional[List[str]] = None, _snake_case : str = "eval", **_snake_case : List[str], ) ->Dict[str, float]:
snake_case__ : Optional[int] = gen_kwargs.copy()
snake_case__ : List[Any] = (
gen_kwargs['max_length'] if gen_kwargs.get('max_length' ) is not None else self.args.generation_max_length
)
snake_case__ : Tuple = (
gen_kwargs['num_beams'] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
)
snake_case__ : Dict = gen_kwargs
snake_case__ : int = self.eval_dataset if eval_dataset is None else eval_dataset
snake_case__ : Union[str, Any] = self.get_eval_dataloader(_snake_case )
snake_case__ : Tuple = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
snake_case__ : str = self.compute_metrics
snake_case__ : Optional[int] = None
snake_case__ : Optional[Any] = time.time()
snake_case__ : List[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
snake_case__ : Optional[Any] = eval_loop(
_snake_case, description='Evaluation', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=_snake_case, metric_key_prefix=_snake_case, )
finally:
snake_case__ : Any = compute_metrics
snake_case__ : List[str] = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_snake_case, _snake_case, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size ), ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
snake_case__ : List[str] = self.post_process_function(_snake_case, _snake_case, _snake_case )
snake_case__ : List[Any] = self.compute_metrics(_snake_case )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
snake_case__ : Union[str, Any] = metrics.pop(_snake_case )
metrics.update(output.metrics )
else:
snake_case__ : List[str] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_snake_case )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
snake_case__ : str = self.callback_handler.on_evaluate(self.args, self.state, self.control, _snake_case )
return metrics
def lowercase_ ( self : int, _snake_case : List[Any], _snake_case : Optional[Any], _snake_case : List[Any]=None, _snake_case : str = "test", **_snake_case : List[str] ) ->Any:
snake_case__ : int = gen_kwargs.copy()
snake_case__ : Any = self.get_test_dataloader(_snake_case )
# Temporarily disable metric computation, we will do it in the loop here.
snake_case__ : Optional[Any] = self.compute_metrics
snake_case__ : Optional[int] = None
snake_case__ : Any = time.time()
snake_case__ : int = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
snake_case__ : str = eval_loop(
_snake_case, description='Prediction', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=_snake_case, metric_key_prefix=_snake_case, )
finally:
snake_case__ : Optional[Any] = compute_metrics
snake_case__ : str = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_snake_case, _snake_case, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size ), ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
snake_case__ : List[Any] = self.post_process_function(_snake_case, _snake_case, _snake_case, 'predict' )
snake_case__ : Optional[Any] = self.compute_metrics(_snake_case )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
snake_case__ : List[Any] = metrics.pop(_snake_case )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=_snake_case )
| 277 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ :int = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :List[str] = [
"LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"LongT5EncoderModel",
"LongT5ForConditionalGeneration",
"LongT5Model",
"LongT5PreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :int = [
"FlaxLongT5ForConditionalGeneration",
"FlaxLongT5Model",
"FlaxLongT5PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
a_ :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 277 | 1 |
"""simple docstring"""
def _A ( SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
a__ : List[Any] =[0] * len(lowerCamelCase__ )
a__ : List[Any] =[]
a__ : List[Any] =[]
a__ : List[Any] =0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCamelCase__ ) ):
if indegree[i] == 0:
queue.append(lowerCamelCase__ )
while queue:
a__ : List[Any] =queue.pop(0 )
cnt += 1
topo.append(lowerCamelCase__ )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(lowerCamelCase__ )
if cnt != len(lowerCamelCase__ ):
print("Cycle exists" )
else:
print(lowerCamelCase__ )
# Adjacency List of Graph
UpperCAmelCase : Tuple = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 350 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Any = (PNDMScheduler,)
_lowercase : str = (("""num_inference_steps""", 50),)
def _lowercase ( self , **lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Dict ={
"num_train_timesteps": 1_0_0_0,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowercase ( self , lowerCAmelCase__=0 , **lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
a__ : Optional[int] =dict(self.forward_default_kwargs )
a__ : Tuple =kwargs.pop("num_inference_steps" , lowerCAmelCase__ )
a__ : List[str] =self.dummy_sample
a__ : List[str] =0.1 * sample
a__ : str =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a__ : int =self.get_scheduler_config(**lowerCAmelCase__ )
a__ : Union[str, Any] =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
a__ : Any =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
a__ : List[Any] =scheduler_class.from_pretrained(lowerCAmelCase__ )
new_scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
a__ : str =dummy_past_residuals[:]
a__ : Any =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Dict =new_scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a__ : Optional[int] =scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Union[str, Any] =new_scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _lowercase ( self ) -> int:
'''simple docstring'''
pass
def _lowercase ( self , lowerCAmelCase__=0 , **lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ : Optional[int] =dict(self.forward_default_kwargs )
a__ : List[str] =kwargs.pop("num_inference_steps" , lowerCAmelCase__ )
a__ : List[str] =self.dummy_sample
a__ : int =0.1 * sample
a__ : Tuple =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a__ : Dict =self.get_scheduler_config()
a__ : List[str] =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals (must be after setting timesteps)
a__ : Dict =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
a__ : Dict =scheduler_class.from_pretrained(lowerCAmelCase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residual (must be after setting timesteps)
a__ : Optional[int] =dummy_past_residuals[:]
a__ : Optional[Any] =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : List[Any] =new_scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a__ : List[str] =scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Any =new_scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _lowercase ( self , **lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Union[str, Any] =self.scheduler_classes[0]
a__ : Optional[Any] =self.get_scheduler_config(**lowerCAmelCase__ )
a__ : Any =scheduler_class(**lowerCAmelCase__ )
a__ : int =1_0
a__ : Union[str, Any] =self.dummy_model()
a__ : Optional[int] =self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase__ )
for i, t in enumerate(scheduler.prk_timesteps ):
a__ : List[Any] =model(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : Optional[Any] =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
a__ : int =model(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : int =scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
return sample
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : str =dict(self.forward_default_kwargs )
a__ : Tuple =kwargs.pop("num_inference_steps" , lowerCAmelCase__ )
for scheduler_class in self.scheduler_classes:
a__ : Union[str, Any] =self.get_scheduler_config()
a__ : List[str] =scheduler_class(**lowerCAmelCase__ )
a__ : List[Any] =self.dummy_sample
a__ : Dict =0.1 * sample
if num_inference_steps is not None and hasattr(lowerCAmelCase__ , "set_timesteps" ):
scheduler.set_timesteps(lowerCAmelCase__ )
elif num_inference_steps is not None and not hasattr(lowerCAmelCase__ , "set_timesteps" ):
a__ : int =num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a__ : Tuple =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
a__ : str =dummy_past_residuals[:]
a__ : List[Any] =scheduler.step_prk(lowerCAmelCase__ , 0 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : int =scheduler.step_prk(lowerCAmelCase__ , 1 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
a__ : List[str] =scheduler.step_plms(lowerCAmelCase__ , 0 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Dict =scheduler.step_plms(lowerCAmelCase__ , 1 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
for timesteps in [1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCAmelCase__ )
a__ : Optional[Any] =self.scheduler_classes[0]
a__ : Tuple =self.get_scheduler_config(steps_offset=1 )
a__ : Optional[Any] =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(1_0 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] ) , )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
for beta_start, beta_end in zip([0.00_01, 0.0_01] , [0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowercase ( self ) -> Dict:
'''simple docstring'''
for t in [1, 5, 1_0]:
self.check_over_forward(time_step=lowerCAmelCase__ )
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 1_0] , [1_0, 5_0, 1_0_0] ):
self.check_over_forward(num_inference_steps=lowerCAmelCase__ )
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Dict =2_7
for scheduler_class in self.scheduler_classes:
a__ : Tuple =self.dummy_sample
a__ : Dict =0.1 * sample
a__ : Dict =self.get_scheduler_config()
a__ : int =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
a__ : Any =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaises(lowerCAmelCase__ ):
a__ : List[Any] =self.scheduler_classes[0]
a__ : Dict =self.get_scheduler_config()
a__ : Tuple =scheduler_class(**lowerCAmelCase__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] =self.full_loop()
a__ : str =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Optional[Any] =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 1_98.13_18 ) < 1E-2
assert abs(result_mean.item() - 0.25_80 ) < 1E-3
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : str =self.full_loop(prediction_type="v_prediction" )
a__ : int =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Optional[int] =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 67.39_86 ) < 1E-2
assert abs(result_mean.item() - 0.08_78 ) < 1E-3
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
a__ : Tuple =self.full_loop(set_alpha_to_one=lowerCAmelCase__ , beta_start=0.01 )
a__ : str =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Dict =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 2_30.03_99 ) < 1E-2
assert abs(result_mean.item() - 0.29_95 ) < 1E-3
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : Dict =self.full_loop(set_alpha_to_one=lowerCAmelCase__ , beta_start=0.01 )
a__ : Union[str, Any] =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Union[str, Any] =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 1_86.94_82 ) < 1E-2
assert abs(result_mean.item() - 0.24_34 ) < 1E-3
| 148 | 0 |
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