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"""simple docstring"""
from __future__ import annotations
from math import gcd
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 3 , ):
'''simple docstring'''
if num < 2:
raise ValueError('The input value cannot be less than 2' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int:
return (pow(_lowerCAmelCase , 2 ) + step) % modulus
for _ in range(_lowerCAmelCase ):
# These track the position within the cycle detection logic.
lowercase__ : Dict = seed
lowercase__ : Tuple = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowercase__ : Union[str, Any] = rand_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase__ : Optional[Any] = rand_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase__ : Union[str, Any] = rand_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowercase__ : Optional[int] = gcd(hare - tortoise , _lowerCAmelCase )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowercase__ : Optional[Any] = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
_UpperCamelCase : str = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
_UpperCamelCase : int = parser.parse_args()
_UpperCamelCase : List[Any] = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(f'''{args.num} is probably prime''')
else:
_UpperCamelCase : int = args.num // divisor
print(f'''{args.num} = {divisor} * {quotient}''')
| 645
|
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=[3_0, 3_0] , a=2 , a=3 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=3 , a=None , a=8 , a=1_0 , ) -> Any:
lowercase__ : List[str] = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Optional[int] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : str = is_training
lowercase__ : Optional[Any] = use_labels
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Dict = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Any = type_sequence_label_size
lowercase__ : Dict = initializer_range
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Tuple = scope
lowercase__ : Tuple = n_targets
lowercase__ : Optional[int] = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowercase__ : Optional[Any] = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowercase__ : Tuple = num_patches + 1 + self.num_detection_tokens
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowercase__ : Tuple = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowercase__ : int = []
for i in range(self.batch_size ):
lowercase__ : Optional[Any] = {}
lowercase__ : Any = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=a )
lowercase__ : List[str] = torch.rand(self.n_targets , 4 , device=a )
labels.append(a )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> List[Any]:
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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=a , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _UpperCAmelCase ( self , a , a , a ) -> int:
lowercase__ : List[str] = YolosModel(config=a )
model.to(a )
model.eval()
lowercase__ : List[Any] = model(a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = YolosForObjectDetection(a )
model.to(a )
model.eval()
lowercase__ : Dict = model(pixel_values=a )
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowercase__ : str = model(pixel_values=a , labels=a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Any = config_and_inputs
lowercase__ : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
lowerCamelCase__ : List[str] = (
{"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
)
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Union[str, Any] = False
def _UpperCAmelCase ( self , a , a , a=False ) -> Dict:
lowercase__ : List[str] = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowercase__ : Optional[Any] = []
for i in range(self.model_tester.batch_size ):
lowercase__ : Dict = {}
lowercase__ : Dict = torch.ones(
size=(self.model_tester.n_targets,) , device=a , dtype=torch.long )
lowercase__ : Optional[Any] = torch.ones(
self.model_tester.n_targets , 4 , device=a , dtype=torch.float )
labels.append(a )
lowercase__ : Union[str, Any] = labels
return inputs_dict
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Dict = YolosModelTester(self )
lowercase__ : Optional[int] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
# YOLOS does not use inputs_embeds
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
lowercase__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Tuple = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Dict = True
# in YOLOS, the seq_len is different
lowercase__ : Tuple = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowercase__ : Optional[int] = True
lowercase__ : str = False
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : str = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Optional[int] = True
lowercase__ : List[Any] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[str] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : Dict = len(a )
# Check attention is always last and order is fine
lowercase__ : Any = True
lowercase__ : int = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = 1
self.assertEqual(out_len + added_hidden_states , len(a ) )
lowercase__ : Tuple = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> List[str]:
def check_hidden_states_output(a , a , a ):
lowercase__ : str = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : int = model(**self._prepare_for_class(a , a ) )
lowercase__ : int = outputs.hidden_states
lowercase__ : Any = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(a ) , a )
# YOLOS has a different seq_length
lowercase__ : Optional[int] = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : List[Any] = True
check_hidden_states_output(a , a , a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*a )
@slow
def _UpperCAmelCase ( self ) -> Union[str, Any]:
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : int = YolosModel.from_pretrained(a )
self.assertIsNotNone(a )
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return AutoImageProcessor.from_pretrained('hustvl/yolos-small' ) if is_vision_available() else None
@slow
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = YolosForObjectDetection.from_pretrained('hustvl/yolos-small' ).to(a )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : Optional[int] = prepare_img()
lowercase__ : int = image_processor(images=a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : int = model(inputs.pixel_values )
# verify outputs
lowercase__ : Tuple = torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : Any = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] , device=a , )
lowercase__ : List[str] = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] , device=a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 ) )
# verify postprocessing
lowercase__ : Optional[Any] = image_processor.post_process_object_detection(
a , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowercase__ : str = torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861] ).to(a )
lowercase__ : Any = [7_5, 7_5, 1_7, 6_3, 1_7]
lowercase__ : Optional[int] = torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495] ).to(a )
self.assertEqual(len(results['scores'] ) , 5 )
self.assertTrue(torch.allclose(results['scores'] , a , atol=1e-4 ) )
self.assertSequenceEqual(results['labels'].tolist() , a )
self.assertTrue(torch.allclose(results['boxes'][0, :] , a ) )
| 645
| 1
|
"""simple docstring"""
from functools import reduce
_UpperCamelCase : Tuple = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def a_ ( _lowerCAmelCase : str = N ):
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _lowerCAmelCase , _lowerCAmelCase : str(int(_lowerCAmelCase ) * int(_lowerCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_lowerCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 645
|
"""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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_UpperCamelCase : int = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ :
def __init__( self , a=False , a=False , a=6.0 , a=None , a=False , a=False , a=None , a="fp4" , a=False , **a , ) -> Tuple:
lowercase__ : str = load_in_abit
lowercase__ : str = load_in_abit
lowercase__ : List[str] = llm_inta_threshold
lowercase__ : Dict = llm_inta_skip_modules
lowercase__ : Tuple = llm_inta_enable_fpaa_cpu_offload
lowercase__ : Any = llm_inta_has_fpaa_weight
lowercase__ : Any = bnb_abit_quant_type
lowercase__ : Dict = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
lowercase__ : Dict = torch.floataa
elif isinstance(a , a ):
lowercase__ : Any = getattr(a , a )
elif isinstance(a , torch.dtype ):
lowercase__ : Any = bnb_abit_compute_dtype
else:
raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype' )
self.post_init()
def _UpperCAmelCase ( self ) -> str:
if not isinstance(self.llm_inta_threshold , a ):
raise ValueError('llm_int8_threshold must be a float' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , a ):
raise ValueError('llm_int8_skip_modules must be a list of strings' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , a ):
raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean' )
if not isinstance(self.llm_inta_has_fpaa_weight , a ):
raise ValueError('llm_int8_has_fp16_weight must be a boolean' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError('bnb_4bit_compute_dtype must be torch.dtype' )
if not isinstance(self.bnb_abit_quant_type , a ):
raise ValueError('bnb_4bit_quant_type must be a string' )
if not isinstance(self.bnb_abit_use_double_quant , a ):
raise ValueError('bnb_4bit_use_double_quant must be a boolean' )
if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes' ) ) >= version.parse(
'0.39.0' ):
raise ValueError(
'4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version' )
def _UpperCAmelCase ( self ) -> Tuple:
return self.load_in_abit or self.load_in_abit
def _UpperCAmelCase ( self ) -> List[str]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> Optional[Any]:
lowercase__ : List[Any] = cls(**a )
lowercase__ : Union[str, Any] = []
for key, value in kwargs.items():
if hasattr(a , a ):
setattr(a , a , a )
to_remove.append(a )
for key in to_remove:
kwargs.pop(a , a )
if return_unused_kwargs:
return config, kwargs
else:
return config
def _UpperCAmelCase ( self , a ) -> Dict:
with open(a , 'w' , encoding='utf-8' ) as writer:
lowercase__ : Any = self.to_dict()
lowercase__ : str = json.dumps(a , indent=2 , sort_keys=a ) + '\n'
writer.write(a )
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : Any = str(output['bnb_4bit_compute_dtype'] ).split('.' )[1]
return output
def __repr__( self ) -> Dict:
return f"""{self.__class__.__name__} {self.to_json_string()}"""
def _UpperCAmelCase ( self , a = True ) -> str:
if use_diff is True:
lowercase__ : List[Any] = self.to_diff_dict()
else:
lowercase__ : List[str] = self.to_dict()
return json.dumps(a , indent=2 , sort_keys=a ) + "\n"
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Tuple = self.to_dict()
# get the default config dict
lowercase__ : Optional[Any] = BitsAndBytesConfig().to_dict()
lowercase__ : int = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
lowercase__ : Optional[int] = value
return serializable_config_dict
| 645
| 1
|
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
_UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = "vision-encoder-decoder"
lowerCamelCase__ : Tuple = True
def __init__( self , **a ) -> Any:
super().__init__(**a )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"""A configuraton of type {self.model_type} cannot be instantiated because """
f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
lowercase__ : int = kwargs.pop('encoder' )
lowercase__ : Optional[Any] = encoder_config.pop('model_type' )
lowercase__ : Dict = kwargs.pop('decoder' )
lowercase__ : Dict = decoder_config.pop('model_type' )
lowercase__ : Optional[Any] = AutoConfig.for_model(a , **a )
lowercase__ : str = AutoConfig.for_model(a , **a )
lowercase__ : List[str] = True
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> PretrainedConfig:
logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
lowercase__ : Optional[Any] = True
lowercase__ : str = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : List[Any] = self.encoder.to_dict()
lowercase__ : Optional[int] = self.decoder.to_dict()
lowercase__ : List[Any] = self.__class__.model_type
return output
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : int = version.parse("1.11")
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def _UpperCAmelCase ( self ) -> float:
return 1e-4
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}} )
class UpperCAmelCase_ ( _a):
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
lowercase__ : Tuple = OrderedDict()
lowercase__ : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
lowercase__ : Union[str, Any] = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
lowercase__ : Optional[int] = {0: 'batch', 1: 'encoder_sequence'}
return common_inputs
def _UpperCAmelCase ( self , a , a = -1 , a = -1 , a = False , a = None , ) -> Mapping[str, Any]:
import torch
lowercase__ : List[Any] = OrderedDict()
lowercase__ : Dict = super().generate_dummy_inputs(
a , batch_size=a , seq_length=a , is_pair=a , framework=a )
lowercase__ , lowercase__ : Dict = dummy_input['input_ids'].shape
lowercase__ : int = (batch, encoder_sequence, self._config.encoder_hidden_size)
lowercase__ : Tuple = dummy_input.pop('input_ids' )
lowercase__ : List[Any] = dummy_input.pop('attention_mask' )
lowercase__ : str = torch.zeros(a )
return common_inputs
class UpperCAmelCase_ ( _a):
@property
def _UpperCAmelCase ( self ) -> None:
pass
def _UpperCAmelCase ( self , a ) -> OnnxConfig:
return VisionEncoderDecoderEncoderOnnxConfig(a )
def _UpperCAmelCase ( self , a , a , a = "default" ) -> OnnxConfig:
lowercase__ : Dict = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(a , a )
| 645
|
"""simple docstring"""
import argparse
import gc
import json
import os
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_UpperCamelCase : int = 16
_UpperCamelCase : Union[str, Any] = 32
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
return int(x / 2**20 )
class UpperCAmelCase_ :
def __enter__( self ) -> Union[str, Any]:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase__ : List[str] = torch.cuda.memory_allocated()
return self
def __exit__( self , *a ) -> Any:
gc.collect()
torch.cuda.empty_cache()
lowercase__ : Optional[Any] = torch.cuda.memory_allocated()
lowercase__ : Union[str, Any] = torch.cuda.max_memory_allocated()
lowercase__ : List[Any] = bamb(self.end - self.begin )
lowercase__ : List[Any] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def a_ ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 , _lowerCAmelCase : str = "bert-base-cased" , _lowerCAmelCase : int = 320 , _lowerCAmelCase : int = 160 , ):
'''simple docstring'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
lowercase__ : Union[str, Any] = load_dataset(
'glue' , 'mrpc' , split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""} )
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase__ : Union[str, Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=_lowerCAmelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_lowerCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(_lowerCAmelCase , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['train'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
lowercase__ : Dict = DataLoader(
tokenized_datasets['validation'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[int] = config['lr']
lowercase__ : Optional[Any] = int(config['num_epochs'] )
lowercase__ : Optional[Any] = int(config['seed'] )
lowercase__ : int = int(config['batch_size'] )
lowercase__ : Union[str, Any] = args.model_name_or_path
set_seed(_lowerCAmelCase )
lowercase__ , lowercase__ : Tuple = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : List[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase , return_dict=_lowerCAmelCase )
# Instantiate optimizer
lowercase__ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase__ : Optional[Any] = optimizer_cls(params=model.parameters() , lr=_lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
lowercase__ : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowercase__ : List[Any] = 1
lowercase__ : List[Any] = (len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=0 , num_training_steps=_lowerCAmelCase , )
else:
lowercase__ : Tuple = DummyScheduler(_lowerCAmelCase , total_num_steps=_lowerCAmelCase , warmup_num_steps=0 )
# 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__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Dict = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
lowercase__ : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase__ : Tuple = 0
# Now we train the model
lowercase__ : Optional[Any] = {}
for epoch in range(_lowerCAmelCase , _lowerCAmelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
lowercase__ : List[Any] = model(**_lowerCAmelCase )
lowercase__ : Dict = outputs.loss
lowercase__ : int = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin ) ) )
accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used ) )
accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked ) )
accelerator.print(
'Total Peak Memory consumed during the train (max): {}'.format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase__ : int = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'peak_memory_utilization.json' ) , 'w' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : int = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=_lowerCAmelCase , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=_lowerCAmelCase , )
parser.add_argument(
'--output_dir' , type=_lowerCAmelCase , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--peak_memory_upper_bound' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.' , )
parser.add_argument(
'--n_train' , type=_lowerCAmelCase , default=320 , help='Number of training examples to use.' , )
parser.add_argument(
'--n_val' , type=_lowerCAmelCase , default=160 , help='Number of validation examples to use.' , )
parser.add_argument(
'--num_epochs' , type=_lowerCAmelCase , default=1 , help='Number of train epochs.' , )
lowercase__ : Any = parser.parse_args()
lowercase__ : Optional[Any] = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 645
| 1
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase__ : Any = f"""Input value of [number={number}] must be an integer"""
raise TypeError(_lowerCAmelCase )
if number < 1:
lowercase__ : List[Any] = f"""Input value of [number={number}] must be > 0"""
raise ValueError(_lowerCAmelCase )
lowercase__ : List[Any] = 1
for i in range(1 , _lowerCAmelCase ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
lowercase__ : List[str] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowercase__ : Dict = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowercase__ : Union[str, Any] = j
return prefix_result
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
return max(prefix_function(_lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : List[str] = KandinskyVaaControlnetPipeline
lowerCamelCase__ : List[str] = ["image_embeds", "negative_image_embeds", "hint"]
lowerCamelCase__ : Tuple = ["image_embeds", "negative_image_embeds", "hint"]
lowerCamelCase__ : List[str] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowerCamelCase__ : Optional[Any] = False
@property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return 3_2
@property
def _UpperCAmelCase ( self ) -> Optional[int]:
return 3_2
@property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return self.time_input_dim
@property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return self.time_input_dim * 4
@property
def _UpperCAmelCase ( self ) -> Any:
return 1_0_0
@property
def _UpperCAmelCase ( self ) -> str:
torch.manual_seed(0 )
lowercase__ : List[str] = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
lowercase__ : List[Any] = UNetaDConditionModel(**a )
return model
@property
def _UpperCAmelCase ( self ) -> List[Any]:
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _UpperCAmelCase ( self ) -> str:
torch.manual_seed(0 )
lowercase__ : Tuple = VQModel(**self.dummy_movq_kwargs )
return model
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Tuple = self.dummy_unet
lowercase__ : Tuple = self.dummy_movq
lowercase__ : Dict = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='linear' , beta_start=0.00_085 , beta_end=0.012 , clip_sample=a , set_alpha_to_one=a , steps_offset=1 , prediction_type='epsilon' , thresholding=a , )
lowercase__ : Any = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def _UpperCAmelCase ( self , a , a=0 ) -> List[str]:
lowercase__ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(a ) ).to(a )
lowercase__ : Any = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
a )
# create hint
lowercase__ : Union[str, Any] = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(a ) ).to(a )
if str(a ).startswith('mps' ):
lowercase__ : str = torch.manual_seed(a )
else:
lowercase__ : str = torch.Generator(device=a ).manual_seed(a )
lowercase__ : List[Any] = {
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 6_4,
'width': 6_4,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : List[str] = 'cpu'
lowercase__ : Any = self.get_dummy_components()
lowercase__ : int = self.pipeline_class(**a )
lowercase__ : str = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowercase__ : Tuple = pipe(**self.get_dummy_inputs(a ) )
lowercase__ : Optional[Any] = output.images
lowercase__ : Dict = pipe(
**self.get_dummy_inputs(a ) , return_dict=a , )[0]
lowercase__ : Dict = image[0, -3:, -3:, -1]
lowercase__ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowercase__ : str = np.array(
[0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> List[str]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy' )
lowercase__ : Tuple = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/hint_image_cat.png' )
lowercase__ : Optional[int] = torch.from_numpy(np.array(a ) ).float() / 255.0
lowercase__ : str = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase__ : str = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(a )
lowercase__ : Optional[Any] = KandinskyVaaControlnetPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-controlnet-depth' , torch_dtype=torch.floataa )
lowercase__ : str = pipeline.to(a )
pipeline.set_progress_bar_config(disable=a )
lowercase__ : Optional[int] = 'A robot, 4k photo'
lowercase__ : int = torch.Generator(device='cuda' ).manual_seed(0 )
lowercase__ , lowercase__ : Optional[int] = pipe_prior(
a , generator=a , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
lowercase__ : Tuple = torch.Generator(device='cuda' ).manual_seed(0 )
lowercase__ : Dict = pipeline(
image_embeds=a , negative_image_embeds=a , hint=a , generator=a , num_inference_steps=1_0_0 , output_type='np' , )
lowercase__ : Dict = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(a , a )
| 645
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=7 , a=3 , a=1_8 , a=3_0 , a=4_0_0 , a=True , a=None , a=True , a=None , a=True , ) -> List[str]:
lowercase__ : Tuple = size if size is not None else {'shortest_edge': 2_0}
lowercase__ : Union[str, Any] = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : str = num_channels
lowercase__ : Any = image_size
lowercase__ : Optional[Any] = min_resolution
lowercase__ : int = max_resolution
lowercase__ : List[Any] = do_resize
lowercase__ : List[str] = size
lowercase__ : str = do_center_crop
lowercase__ : List[Any] = crop_size
lowercase__ : Union[str, Any] = do_flip_channel_order
def _UpperCAmelCase ( self ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Optional[Any] = MobileViTImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Tuple = MobileViTImageProcessingTester(self )
@property
def _UpperCAmelCase ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a , 'do_resize' ) )
self.assertTrue(hasattr(a , 'size' ) )
self.assertTrue(hasattr(a , 'do_center_crop' ) )
self.assertTrue(hasattr(a , 'center_crop' ) )
self.assertTrue(hasattr(a , 'do_flip_channel_order' ) )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 2_0} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
lowercase__ : str = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> str:
# Initialize image_processing
lowercase__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a )
for image in image_inputs:
self.assertIsInstance(a , Image.Image )
# Test not batched input
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : List[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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Tuple:
# Initialize image_processing
lowercase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ : Tuple = 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
lowercase__ : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Dict:
# Initialize image_processing
lowercase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ : Optional[Any] = 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
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : Tuple = 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 645
| 1
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : list ):
'''simple docstring'''
lowercase__ : str = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowercase__ : int = True
for i in range(0 , len(_lowerCAmelCase ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
lowercase__ , lowercase__ : int = input_list[i + 1], input_list[i]
# swapping if elements not in order
lowercase__ : Any = False
for i in range(1 , len(_lowerCAmelCase ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
lowercase__ , lowercase__ : Union[str, Any] = input_list[i + 1], input_list[i]
# swapping if elements not in order
lowercase__ : Union[str, Any] = False
return input_list
if __name__ == "__main__":
print("Enter list to be sorted")
_UpperCamelCase : List[Any] = [int(x) for x in input().split()]
# inputing elements of the list in one line
_UpperCamelCase : Tuple = odd_even_sort(input_list)
print("The sorted list is")
print(sorted_list)
| 645
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=True , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=4 , ) -> Dict:
lowercase__ : Optional[Any] = parent
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : int = is_training
lowercase__ : str = use_attention_mask
lowercase__ : Dict = use_token_type_ids
lowercase__ : Optional[int] = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : List[str] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : int = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[str] = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Tuple = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : int = type_vocab_size
lowercase__ : List[str] = type_sequence_label_size
lowercase__ : Union[str, Any] = initializer_range
lowercase__ : Optional[int] = num_choices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_attention_mask:
lowercase__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : List[str] = None
if self.use_token_type_ids:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ : Any = 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 , is_decoder=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_class_name in self.all_model_classes:
lowercase__ : str = model_class_name.from_pretrained('albert-base-v2' )
lowercase__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : str = FlaxAlbertModel.from_pretrained('albert-base-v2' )
lowercase__ : Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
lowercase__ : Any = model(a , attention_mask=a )[0]
lowercase__ : Tuple = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , a )
lowercase__ : Optional[Any] = np.array(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 1
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=True , a=True , a=9_9 , a=3_2 , a=2 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=3 , a=4 , a=None , ) -> List[str]:
lowercase__ : Optional[Any] = parent
lowercase__ : List[Any] = 1_3
lowercase__ : Union[str, Any] = 7
lowercase__ : List[str] = True
lowercase__ : Dict = True
lowercase__ : Tuple = True
lowercase__ : str = True
lowercase__ : Dict = 9_9
lowercase__ : Any = 3_2
lowercase__ : Dict = 2
lowercase__ : int = 4
lowercase__ : Optional[Any] = 3_7
lowercase__ : Any = 'gelu'
lowercase__ : Optional[int] = 0.1
lowercase__ : Optional[int] = 0.1
lowercase__ : Dict = 5_1_2
lowercase__ : Union[str, Any] = 1_6
lowercase__ : Tuple = 2
lowercase__ : Dict = 0.02
lowercase__ : Any = 3
lowercase__ : Optional[int] = 4
lowercase__ : str = None
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : Union[str, Any] = None
if self.use_input_mask:
lowercase__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Tuple = None
if self.use_token_type_ids:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ : int = None
lowercase__ : Any = None
lowercase__ : Tuple = None
if self.use_labels:
lowercase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ : List[str] = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ : Union[str, Any] = RoFormerConfig(
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 , return_dict=a , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self , a , a , a , a , a , a , a ) -> Optional[int]:
lowercase__ : str = TFRoFormerModel(config=a )
lowercase__ : Union[str, Any] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase__ : Union[str, Any] = [input_ids, input_mask]
lowercase__ : Optional[int] = model(a )
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a , a ) -> Optional[Any]:
lowercase__ : Optional[Any] = True
lowercase__ : Any = TFRoFormerForCausalLM(config=a )
lowercase__ : Dict = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowercase__ : Optional[int] = model(a )['logits']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def _UpperCAmelCase ( self , a , a , a , a , a , a , a ) -> List[str]:
lowercase__ : Tuple = TFRoFormerForMaskedLM(config=a )
lowercase__ : Optional[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowercase__ : Any = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a , a ) -> int:
lowercase__ : Optional[Any] = self.num_labels
lowercase__ : Dict = TFRoFormerForSequenceClassification(config=a )
lowercase__ : str = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowercase__ : int = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a , a ) -> str:
lowercase__ : Optional[int] = self.num_choices
lowercase__ : Tuple = TFRoFormerForMultipleChoice(config=a )
lowercase__ : str = tf.tile(tf.expand_dims(a , 1 ) , (1, self.num_choices, 1) )
lowercase__ : Optional[Any] = tf.tile(tf.expand_dims(a , 1 ) , (1, self.num_choices, 1) )
lowercase__ : Tuple = tf.tile(tf.expand_dims(a , 1 ) , (1, self.num_choices, 1) )
lowercase__ : Union[str, Any] = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
lowercase__ : Union[str, Any] = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self , a , a , a , a , a , a , a ) -> List[str]:
lowercase__ : Any = self.num_labels
lowercase__ : Dict = TFRoFormerForTokenClassification(config=a )
lowercase__ : Tuple = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowercase__ : int = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a , a ) -> Dict:
lowercase__ : Optional[int] = TFRoFormerForQuestionAnswering(config=a )
lowercase__ : List[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowercase__ : Dict = model(a )
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[Any]:
lowercase__ : Tuple = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : List[Any] = config_and_inputs
lowercase__ : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : List[Any] = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
lowerCamelCase__ : List[str] = (
{
"feature-extraction": TFRoFormerModel,
"fill-mask": TFRoFormerForMaskedLM,
"question-answering": TFRoFormerForQuestionAnswering,
"text-classification": TFRoFormerForSequenceClassification,
"text-generation": TFRoFormerForCausalLM,
"token-classification": TFRoFormerForTokenClassification,
"zero-shot": TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : Any = False
def _UpperCAmelCase ( self , a , a , a , a , a ) -> Union[str, Any]:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Optional[int] = TFRoFormerModelTester(self )
lowercase__ : List[Any] = ConfigTester(self , config_class=a , hidden_size=3_7 )
def _UpperCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*a )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*a )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : List[str] = TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base' )
self.assertIsNotNone(a )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : List[Any] = TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
lowercase__ : List[str] = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase__ : Optional[int] = model(a )[0]
# TODO Replace vocab size
lowercase__ : Any = 5_0_0_0_0
lowercase__ : List[Any] = [1, 6, vocab_size]
self.assertEqual(output.shape , a )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
lowercase__ : Optional[Any] = tf.constant(
[
[
[-0.12_053_341, -1.0_264_901, 0.29_221_946],
[-1.5_133_783, 0.197_433, 0.15_190_607],
[-5.0_135_403, -3.900_256, -0.84_038_764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , a , atol=1e-4 )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : List[str] = 1E-4
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Union[str, Any] = tf.constant([[4, 1_0]] )
lowercase__ : int = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
lowercase__ : List[str] = emba(input_ids.shape )
lowercase__ : Optional[Any] = tf.constant(
[[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]] )
tf.debugging.assert_near(a , a , atol=self.tolerance )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[Any] = tf.constant(
[
[0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
[0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
[0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
] )
lowercase__ : Dict = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2 , embedding_dim=5_1_2 )
emba([2, 1_6, 5_1_2] )
lowercase__ : Any = emba.weight[:3, :5]
tf.debugging.assert_near(a , a , atol=self.tolerance )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : Union[str, Any] = 1E-4
def _UpperCAmelCase ( self ) -> Any:
# 2,12,16,64
lowercase__ : List[Any] = tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 , dtype=tf.floataa ) , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
lowercase__ : int = -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 , dtype=tf.floataa ) , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
lowercase__ : List[Any] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2 , embedding_dim=6_4 )
lowercase__ : str = embed_positions([2, 1_6, 7_6_8] )[None, None, :, :]
lowercase__ , lowercase__ : List[str] = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
a , a , a )
lowercase__ : List[Any] = tf.constant(
[
[0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
[-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
[-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
[-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
[0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
[3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
] )
lowercase__ : List[Any] = tf.constant(
[
[0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
[0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
[1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
[2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
[-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
[-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , a , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , a , atol=self.tolerance )
| 645
|
"""simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
lowercase__ : int = 0.0
for coeff in reversed(_lowerCAmelCase ):
lowercase__ : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
_UpperCamelCase : int = (0.0, 0.0, 5.0, 9.3, 7.0)
_UpperCamelCase : Dict = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 645
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : Any = logging.get_logger(__name__)
_UpperCamelCase : Union[str, Any] = {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : int = "roberta"
def __init__( self , a=5_0_2_6_5 , a=7_6_8 , a=1_2 , a=1_2 , a=3_0_7_2 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=2 , a=0.02 , a=1e-12 , a=1 , a=0 , a=2 , a="absolute" , a=True , a=None , **a , ) -> Optional[Any]:
super().__init__(pad_token_id=a , bos_token_id=a , eos_token_id=a , **a )
lowercase__ : str = vocab_size
lowercase__ : str = hidden_size
lowercase__ : str = num_hidden_layers
lowercase__ : Any = num_attention_heads
lowercase__ : Dict = hidden_act
lowercase__ : List[str] = intermediate_size
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Optional[Any] = max_position_embeddings
lowercase__ : Tuple = type_vocab_size
lowercase__ : List[Any] = initializer_range
lowercase__ : Any = layer_norm_eps
lowercase__ : Optional[int] = position_embedding_type
lowercase__ : Union[str, Any] = use_cache
lowercase__ : List[Any] = classifier_dropout
class UpperCAmelCase_ ( _a):
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowercase__ : Optional[int] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowercase__ : Any = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 645
|
"""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
_UpperCamelCase : 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 a_ ( _lowerCAmelCase : Optional[Any]=True ):
'''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=_a))
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = None
lowerCamelCase__ : Optional[Any] = None
def _UpperCAmelCase ( self , a , a ) -> List[Any]:
with TemporaryDirectory() as tmp_dir:
lowercase__ : List[str] = dataset_module_factory(a , cache_dir=a )
lowercase__ : List[Any] = import_main_class(dataset_module.module_path , dataset=a )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=a , config_name=a , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=a ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
lowercase__ : Union[str, Any] = cached_path(a , cache_dir=a )
self.assertTrue(os.path.exists(a ) )
@pytest.mark.integration
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Union[str, Any] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
lowercase__ : int = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : Optional[int] = import_main_class(dataset_module.module_path )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowercase__ : Optional[int] = None
builder_instance.download_and_prepare()
lowercase__ : Optional[int] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Optional[int] = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : List[str] = import_main_class(dataset_module.module_path , dataset=_lowerCAmelCase )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert "train" in ds
assert isinstance(ds['train'] , _lowerCAmelCase )
assert next(iter(ds['train'] ) )
| 645
| 1
|
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any]=False ):
'''simple docstring'''
try:
lowercase__ : Tuple = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
lowercase__ : Tuple = default
else:
# KEY is set, convert it to True or False.
try:
lowercase__ : Dict = strtobool(SCREAMING_SNAKE_CASE_ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"""If set, {key} must be yes or no.""" )
return _value
_UpperCamelCase : Union[str, Any] = parse_flag_from_env("RUN_SLOW", default=False)
_UpperCamelCase : int = parse_flag_from_env("RUN_REMOTE", default=False)
_UpperCamelCase : List[Any] = parse_flag_from_env("RUN_LOCAL", default=True)
_UpperCamelCase : List[Any] = parse_flag_from_env("RUN_PACKAGED", default=True)
# Compression
_UpperCamelCase : List[str] = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="test requires lz4")
_UpperCamelCase : int = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="test requires py7zr")
_UpperCamelCase : List[str] = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="test requires zstandard")
# Audio
_UpperCamelCase : List[Any] = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec("soundfile") is None or version.parse(importlib_metadata.version("soundfile")) < version.parse("0.12.0"),
reason="test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; ",
)
# Beam
_UpperCamelCase : str = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("0.3.2"),
reason="test requires apache-beam and a compatible dill version",
)
# Dill-cloudpickle compatibility
_UpperCamelCase : Optional[int] = pytest.mark.skipif(
config.DILL_VERSION <= version.parse("0.3.2"),
reason="test requires dill>0.3.2 for cloudpickle compatibility",
)
# Windows
_UpperCamelCase : List[str] = pytest.mark.skipif(
sys.platform == "win32",
reason="test should not be run on Windows",
)
def a_ ( _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
try:
import faiss # noqa
except ImportError:
lowercase__ : Optional[Any] = unittest.skip('test requires faiss' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
try:
import regex # noqa
except ImportError:
lowercase__ : Dict = unittest.skip('test requires regex' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
try:
import elasticsearch # noqa
except ImportError:
lowercase__ : Optional[int] = unittest.skip('test requires elasticsearch' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
try:
import sqlalchemy # noqa
except ImportError:
lowercase__ : List[Any] = unittest.skip('test requires sqlalchemy' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
if not config.TORCH_AVAILABLE:
lowercase__ : Tuple = unittest.skip('test requires PyTorch' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Any ):
'''simple docstring'''
if not config.TF_AVAILABLE:
lowercase__ : Dict = unittest.skip('test requires TensorFlow' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
if not config.JAX_AVAILABLE:
lowercase__ : str = unittest.skip('test requires JAX' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if not config.PIL_AVAILABLE:
lowercase__ : Optional[int] = unittest.skip('test requires Pillow' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
try:
import transformers # noqa F401
except ImportError:
return unittest.skip('test requires transformers' )(SCREAMING_SNAKE_CASE_ )
else:
return test_case
def a_ ( _lowerCAmelCase : List[Any] ):
'''simple docstring'''
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip('test requires tiktoken' )(SCREAMING_SNAKE_CASE_ )
else:
return test_case
def a_ ( _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
try:
import spacy # noqa F401
except ImportError:
return unittest.skip('test requires spacy' )(SCREAMING_SNAKE_CASE_ )
else:
return test_case
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
def _require_spacy_model(_lowerCAmelCase : Optional[Any] ):
try:
import spacy # noqa F401
spacy.load(SCREAMING_SNAKE_CASE_ )
except ImportError:
return unittest.skip('test requires spacy' )(SCREAMING_SNAKE_CASE_ )
except OSError:
return unittest.skip('test requires spacy model \'{}\''.format(SCREAMING_SNAKE_CASE_ ) )(SCREAMING_SNAKE_CASE_ )
else:
return test_case
return _require_spacy_model
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip('test requires pyspark' )(SCREAMING_SNAKE_CASE_ )
else:
return test_case
def a_ ( _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip('test requires joblibspark' )(SCREAMING_SNAKE_CASE_ )
else:
return test_case
def a_ ( _lowerCAmelCase : Dict ):
'''simple docstring'''
if not _run_slow_tests or _run_slow_tests == 0:
lowercase__ : List[str] = unittest.skip('test is slow' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
if not _run_local_tests or _run_local_tests == 0:
lowercase__ : List[Any] = unittest.skip('test is local' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
if not _run_packaged_tests or _run_packaged_tests == 0:
lowercase__ : List[str] = unittest.skip('test is packaged' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
if not _run_remote_tests or _run_remote_tests == 0:
lowercase__ : List[str] = unittest.skip('test requires remote' )(SCREAMING_SNAKE_CASE_ )
return test_case
def a_ ( *_lowerCAmelCase : List[Any] ):
'''simple docstring'''
def decorate(cls : Tuple ):
for name, fn in cls.__dict__.items():
if callable(SCREAMING_SNAKE_CASE_ ) and name.startswith('test' ):
for decorator in decorators:
lowercase__ : Union[str, Any] = decorator(SCREAMING_SNAKE_CASE_ )
setattr(cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return cls
return decorate
class UpperCAmelCase_ ( _lowerCamelCase):
pass
class UpperCAmelCase_ ( _lowerCamelCase):
lowerCamelCase__ : Union[str, Any] = 0
lowerCamelCase__ : Optional[int] = 1
lowerCamelCase__ : int = 2
@contextmanager
def a_ ( _lowerCAmelCase : List[Any]=OfflineSimulationMode.CONNECTION_FAILS , _lowerCAmelCase : int=1E-16 ):
'''simple docstring'''
lowercase__ : str = requests.Session().request
def timeout_request(_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] , **_lowerCAmelCase : Tuple ):
# Change the url to an invalid url so that the connection hangs
lowercase__ : Any = 'https://10.255.255.1'
if kwargs.get('timeout' ) is None:
raise RequestWouldHangIndefinitelyError(
f"""Tried a call to {url} in offline mode with no timeout set. Please set a timeout.""" )
lowercase__ : Any = timeout
try:
return online_request(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
lowercase__ : int = url
lowercase__ : Union[str, Any] = e.args[0]
lowercase__ : str = (max_retry_error.args[0].replace('10.255.255.1' , f"""OfflineMock[{url}]""" ),)
lowercase__ : int = (max_retry_error,)
raise
def raise_connection_error(_lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple , **_lowerCAmelCase : int ):
raise requests.ConnectionError('Offline mode is enabled.' , request=SCREAMING_SNAKE_CASE_ )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch('requests.Session.send' , SCREAMING_SNAKE_CASE_ ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch('requests.Session.request' , SCREAMING_SNAKE_CASE_ ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch('datasets.config.HF_DATASETS_OFFLINE' , SCREAMING_SNAKE_CASE_ ):
yield
else:
raise ValueError('Please use a value from the OfflineSimulationMode enum.' )
@contextmanager
def a_ ( *_lowerCAmelCase : Dict , **_lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Optional[Any] = str(Path().resolve() )
with tempfile.TemporaryDirectory(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) as tmp_dir:
try:
os.chdir(SCREAMING_SNAKE_CASE_ )
yield
finally:
os.chdir(SCREAMING_SNAKE_CASE_ )
@contextmanager
def a_ ( ):
'''simple docstring'''
import gc
gc.collect()
lowercase__ : Union[str, Any] = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def a_ ( ):
'''simple docstring'''
import gc
gc.collect()
lowercase__ : Any = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
return deepcopy(SCREAMING_SNAKE_CASE_ ).integers(0 , 100 , 10 ).tolist() == deepcopy(SCREAMING_SNAKE_CASE_ ).integers(0 , 100 , 10 ).tolist()
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
import decorator
from requests.exceptions import HTTPError
def _wrapper(_lowerCAmelCase : Any , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Optional[int] ):
try:
return func(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
except HTTPError as err:
if str(SCREAMING_SNAKE_CASE_ ).startswith('500' ) or str(SCREAMING_SNAKE_CASE_ ).startswith('502' ):
pytest.xfail(str(SCREAMING_SNAKE_CASE_ ) )
raise err
return decorator.decorator(_wrapper , SCREAMING_SNAKE_CASE_ )
class UpperCAmelCase_ :
def __init__( self , a , a , a ) -> Optional[Any]:
lowercase__ : int = returncode
lowercase__ : List[str] = stdout
lowercase__ : Tuple = stderr
async def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ):
'''simple docstring'''
while True:
lowercase__ : Optional[int] = await stream.readline()
if line:
callback(SCREAMING_SNAKE_CASE_ )
else:
break
async def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple=None , _lowerCAmelCase : Any=None , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=False , _lowerCAmelCase : Union[str, Any]=False ):
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(SCREAMING_SNAKE_CASE_ ) )
lowercase__ : List[str] = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=SCREAMING_SNAKE_CASE_ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=SCREAMING_SNAKE_CASE_ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
lowercase__ : Tuple = []
lowercase__ : Union[str, Any] = []
def tee(_lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict="" ):
lowercase__ : List[str] = line.decode('utf-8' ).rstrip()
sink.append(SCREAMING_SNAKE_CASE_ )
if not quiet:
print(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , file=SCREAMING_SNAKE_CASE_ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda _lowerCAmelCase : tee(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , sys.stdout , label='stdout:' ) ),
_read_stream(p.stderr , lambda _lowerCAmelCase : tee(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , sys.stderr , label='stderr:' ) ),
] , timeout=SCREAMING_SNAKE_CASE_ , )
return _RunOutput(await p.wait() , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Union[str, Any]=None , _lowerCAmelCase : Optional[int]=180 , _lowerCAmelCase : Tuple=False , _lowerCAmelCase : Optional[int]=True ):
'''simple docstring'''
lowercase__ : Tuple = asyncio.get_event_loop()
lowercase__ : Any = loop.run_until_complete(
_stream_subprocess(SCREAMING_SNAKE_CASE_ , env=SCREAMING_SNAKE_CASE_ , stdin=SCREAMING_SNAKE_CASE_ , timeout=SCREAMING_SNAKE_CASE_ , quiet=SCREAMING_SNAKE_CASE_ , echo=SCREAMING_SNAKE_CASE_ ) )
lowercase__ : Union[str, Any] = ' '.join(SCREAMING_SNAKE_CASE_ )
if result.returncode > 0:
lowercase__ : str = '\n'.join(result.stderr )
raise RuntimeError(
f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
f"""The combined stderr from workers follows:\n{stderr}""" )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(f"""'{cmd_str}' produced no output.""" )
return result
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = os.environ.get('PYTEST_XDIST_WORKER' , 'gw0' )
lowercase__ : List[str] = re.sub(R'^gw' , '' , SCREAMING_SNAKE_CASE_ , 0 , re.M )
return int(SCREAMING_SNAKE_CASE_ )
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = 2_9500
lowercase__ : List[Any] = pytest_xdist_worker_id()
return port + uniq_delta
| 700
|
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def a_ ( _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray ):
'''simple docstring'''
lowercase__ : Any = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(_lowerCAmelCase , _lowerCAmelCase )
# Predict target for test data
lowercase__ : str = xgb.predict(_lowerCAmelCase )
lowercase__ : Union[str, Any] = predictions.reshape(len(_lowerCAmelCase ) , 1 )
return predictions
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = fetch_california_housing()
lowercase__ , lowercase__ : str = data_handling(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = train_test_split(
_lowerCAmelCase , _lowerCAmelCase , test_size=0.2_5 , random_state=1 )
lowercase__ : Any = xgboost(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Error printing
print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
print(f"""Mean Square Error : {mean_squared_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCamelCase : Optional[Any] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Optional[Any] = [
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Dict = [
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
_UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 701
|
"""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 UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=1_0 , a=3 , a=2 , a=2 , a=2 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=0.9 , a=None , ) -> Optional[Any]:
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Dict = patch_size
lowercase__ : Tuple = tubelet_size
lowercase__ : Optional[int] = num_frames
lowercase__ : Optional[int] = is_training
lowercase__ : int = use_labels
lowercase__ : Optional[int] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = mask_ratio
lowercase__ : Optional[Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase__ : Optional[Any] = (image_size // patch_size) ** 2
lowercase__ : str = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase__ : str = int(mask_ratio * self.seq_length )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Tuple:
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=a , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a ) -> Optional[int]:
lowercase__ : Dict = VideoMAEModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = VideoMAEForPreTraining(a )
model.to(a )
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
lowercase__ : Any = torch.ones((self.num_masks,) )
lowercase__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowercase__ : Optional[int] = mask.expand(self.batch_size , -1 ).bool()
lowercase__ : str = model(a , a )
# model only returns predictions for masked patches
lowercase__ : str = mask.sum().item()
lowercase__ : int = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ : Optional[int] = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Any = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = False
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = VideoMAEModelTester(self )
lowercase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self , a , a , a=False ) -> Optional[int]:
lowercase__ : Union[str, Any] = copy.deepcopy(a )
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
lowercase__ : Optional[Any] = torch.ones((self.model_tester.num_masks,) )
lowercase__ : Any = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowercase__ : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowercase__ : Union[str, Any] = bool_masked_pos.to(a )
if return_labels:
if model_class in [
*get_values(a ),
]:
lowercase__ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = VideoMAEModel.from_pretrained(a )
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = True
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Any = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase__ : Optional[Any] = True
lowercase__ : int = False
lowercase__ : Any = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[int] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Dict = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : List[str] = len(a )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : List[str] = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(a , a ) )
self.assertEqual(out_len + 1 , len(a ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[int] = outputs.hidden_states
lowercase__ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(a ) , a )
lowercase__ : Optional[Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Union[str, Any] = 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] , )
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(a , a , a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def a_ ( ):
'''simple docstring'''
lowercase__ : int = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowercase__ : str = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
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 _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
a )
lowercase__ : str = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**a )
# verify the logits
lowercase__ : str = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : List[Any] = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(a )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(a , return_tensors='pt' ).to(a )
# add boolean mask, indicating which patches to mask
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowercase__ : str = torch.load(a )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**a )
# verify the logits
lowercase__ : Dict = torch.Size([1, 1_4_0_8, 1_5_3_6] )
lowercase__ : List[str] = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=a )
self.assertEqual(outputs.logits.shape , a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase__ : List[Any] = torch.tensor([0.5_142] , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=a ).to(
a )
with torch.no_grad():
lowercase__ : Any = model(**a )
lowercase__ : List[Any] = torch.tensor(torch.tensor([0.6_469] ) , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_UpperCamelCase : Any = False
_UpperCamelCase : Tuple = True
_UpperCamelCase : Optional[Any] = False
if __name__ == "__main__":
_UpperCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--repo_path",
default=None,
type=str,
required=True,
help="The config json file corresponding to the architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
_UpperCamelCase : str = parser.parse_args()
_UpperCamelCase : Union[str, Any] = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
_UpperCamelCase : Optional[int] = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
_UpperCamelCase : Dict = '' if has_file(args.repo_path, "config.json") else 'unet'
with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
_UpperCamelCase : int = reader.read()
_UpperCamelCase : Any = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, "config.json"):
_UpperCamelCase : Tuple = UNetaDModel(**config)
else:
_UpperCamelCase : Tuple = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
_UpperCamelCase : Tuple = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
_UpperCamelCase : str = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
_UpperCamelCase : Union[str, Any] = config[key]
del config[key]
_UpperCamelCase : Optional[Any] = [k.replace("UNetRes", "") for k in config['down_block_types']]
_UpperCamelCase : List[Any] = [k.replace("UNetRes", "") for k in config['up_block_types']]
if do_only_weights:
_UpperCamelCase : Optional[int] = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))
_UpperCamelCase : Any = {}
for param_key, param_value in state_dict.items():
if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
continue
_UpperCamelCase : int = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split(".")[0] == key:
_UpperCamelCase : List[str] = param_value
_UpperCamelCase : List[Any] = True
if not has_changed:
_UpperCamelCase : Tuple = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 702
|
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
"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",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
_UpperCamelCase : List[str] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
for attribute in key.split('.' ):
lowercase__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
lowercase__ : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
lowercase__ : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
lowercase__ : Optional[Any] = value
elif weight_type == "weight_g":
lowercase__ : Dict = value
elif weight_type == "weight_v":
lowercase__ : List[str] = value
elif weight_type == "bias":
lowercase__ : Optional[Any] = value
else:
lowercase__ : List[str] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = []
lowercase__ : List[str] = fairseq_model.state_dict()
lowercase__ : Union[str, Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase__ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
lowercase__ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase__ : List[Any] = 'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase__ : int = True
if "*" in mapped_key:
lowercase__ : Optional[int] = name.split(_lowerCAmelCase )[0].split('.' )[-2]
lowercase__ : List[str] = mapped_key.replace('*' , _lowerCAmelCase )
if "weight_g" in name:
lowercase__ : List[Any] = 'weight_g'
elif "weight_v" in name:
lowercase__ : int = 'weight_v'
elif "bias" in name:
lowercase__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase__ : Union[str, Any] = 'weight'
else:
lowercase__ : int = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : int = full_name.split('conv_layers.' )[-1]
lowercase__ : int = name.split('.' )
lowercase__ : int = int(items[0] )
lowercase__ : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowercase__ : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowercase__ : Optional[int] = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
lowercase__ : List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
lowercase__ : int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Tuple=True ):
'''simple docstring'''
if config_path is not None:
lowercase__ : Any = UniSpeechSatConfig.from_pretrained(_lowerCAmelCase )
else:
lowercase__ : Any = UniSpeechSatConfig()
lowercase__ : Union[str, Any] = ''
if is_finetuned:
lowercase__ : Optional[Any] = UniSpeechSatForCTC(_lowerCAmelCase )
else:
lowercase__ : List[Any] = UniSpeechSatForPreTraining(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
lowercase__ : Union[str, Any] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase : Tuple = 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"
)
_UpperCamelCase : str = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 645
| 0
|
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE):
def __init__( self , a , a = None , a = None , a = True , a = None , a = False , a = None , a = True , a = "arrow" , **a , ) -> int:
super().__init__(
split=A_ , features=A_ , cache_dir=A_ , keep_in_memory=A_ , streaming=A_ , **A_ , )
lowercase__ : str = load_from_cache_file
lowercase__ : List[str] = file_format
lowercase__ : int = Spark(
df=A_ , features=A_ , cache_dir=A_ , working_dir=A_ , **A_ , )
def _UpperCAmelCase ( self ) -> Tuple:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowercase__ : int = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=A_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 703
|
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=3_2 , a=2 , a=3 , a=1_6 , 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=1_0 , a=8 , a=["stage1", "stage2", "stage3"] , a=[1, 2, 3] , ) -> int:
lowercase__ : int = parent
lowercase__ : Union[str, Any] = batch_size
lowercase__ : Dict = image_size
lowercase__ : str = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : List[str] = embed_dim
lowercase__ : Any = depths
lowercase__ : Dict = num_heads
lowercase__ : List[str] = window_size
lowercase__ : int = mlp_ratio
lowercase__ : Tuple = qkv_bias
lowercase__ : Union[str, Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Tuple = drop_path_rate
lowercase__ : List[str] = hidden_act
lowercase__ : Optional[Any] = use_absolute_embeddings
lowercase__ : Optional[Any] = patch_norm
lowercase__ : Any = layer_norm_eps
lowercase__ : List[Any] = initializer_range
lowercase__ : List[str] = is_training
lowercase__ : int = scope
lowercase__ : Optional[int] = use_labels
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : List[str] = encoder_stride
lowercase__ : Optional[Any] = out_features
lowercase__ : Dict = out_indices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return MaskFormerSwinConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , )
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Tuple = MaskFormerSwinModel(config=a )
model.to(a )
model.eval()
lowercase__ : str = model(a )
lowercase__ : str = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ : Dict = 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 ) -> Optional[int]:
lowercase__ : List[Any] = MaskFormerSwinBackbone(config=a )
model.to(a )
model.eval()
lowercase__ : int = model(a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [1_3, 1_6, 1_6, 1_6] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [1_6, 3_2, 6_4] )
# verify ValueError
with self.parent.assertRaises(a ):
lowercase__ : Dict = ['stem']
lowercase__ : List[str] = MaskFormerSwinBackbone(config=a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : List[str] = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
lowerCamelCase__ : str = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : int = False
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = MaskFormerSwinModelTester(self )
lowercase__ : Tuple = ConfigTester(self , config_class=a , embed_dim=3_7 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
def _UpperCAmelCase ( self ) -> Tuple:
self.create_and_test_config_common_properties()
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:
return
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*a )
@unittest.skip('Swin does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
@unittest.skip('Swin does not support feedforward chunking' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
lowercase__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def _UpperCAmelCase ( self ) -> int:
pass
def _UpperCAmelCase ( self , a , a , a , a ) -> Tuple:
lowercase__ : Dict = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : str = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[Any] = outputs.hidden_states
lowercase__ : str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(a ) , a )
# Swin has a different seq_length
lowercase__ : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Tuple = (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] , )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = (
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:
lowercase__ : List[str] = 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"]
lowercase__ : List[str] = True
self.check_hidden_states_output(a , a , a , a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = 3
lowercase__ : str = (
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)
)
lowercase__ : Tuple = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Optional[int] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ : List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase__ : List[str] = 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"]
lowercase__ : int = True
self.check_hidden_states_output(a , a , a , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(a ):
lowercase__ : Union[str, Any] = 0
return t
def check_equivalence(a , a , a , a={} ):
with torch.no_grad():
lowercase__ : Optional[Any] = model(**a , return_dict=a , **a )
lowercase__ : Optional[int] = model(**a , return_dict=a , **a ).to_tuple()
def recursive_check(a , a ):
if isinstance(a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(a , a ):
recursive_check(a , a )
elif isinstance(a , a ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(a , a )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(a ) , set_nan_tensor_to_zero(a ) , atol=1e-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}. Dict has"""
f""" `nan`: {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}."""
) , )
recursive_check(a , a )
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
model.to(a )
model.eval()
lowercase__ : Tuple = self._prepare_for_class(a , a )
lowercase__ : Optional[Any] = self._prepare_for_class(a , a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : List[Any] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a )
lowercase__ : int = self._prepare_for_class(a , a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
lowercase__ : Dict = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : Optional[int] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase , _a):
lowerCamelCase__ : Dict = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowerCamelCase__ : Optional[int] = MaskFormerSwinConfig
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[int] = MaskFormerSwinModelTester(self )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
lowercase__ : Optional[Any] = backbone_class(a )
backbone.to(a )
backbone.eval()
lowercase__ : Union[str, Any] = backbone(**a )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , a )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowercase__ : List[str] = backbone(**a , output_hidden_states=a )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowercase__ , lowercase__ , lowercase__ : int = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowercase__ : List[Any] = backbone(**a , output_attentions=a )
self.assertIsNotNone(outputs.attentions )
| 645
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|
"""simple docstring"""
import operator as op
def a_ ( _lowerCAmelCase : List[Any] ):
'''simple docstring'''
lowercase__ : Optional[Any] = []
lowercase__ : Any = lambda _lowerCAmelCase , _lowerCAmelCase : int(x / y ) # noqa: E731 integer division operation
lowercase__ : str = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(_lowercase )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(_lowercase ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(_lowercase ) , sep=' | ' )
else:
lowercase__ : Optional[int] = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(_lowercase ) , sep=' | ' )
lowercase__ : Tuple = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(_lowercase ) , sep=' | ' )
stack.append(
str(opr[x](int(_lowercase ) , int(_lowercase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(_lowercase ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
_UpperCamelCase : List[str] = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 704
|
"""simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 645
| 0
|
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : list[str] | None = None , _lowerCAmelCase : dict[str, float] | None = None , _lowerCAmelCase : bool = False , ):
'''simple docstring'''
lowercase__ : Tuple = cipher_alphabet or [chr(_A ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
lowercase__ : Dict = {
"a": 0.0_8_4_9_7,
"b": 0.0_1_4_9_2,
"c": 0.0_2_2_0_2,
"d": 0.0_4_2_5_3,
"e": 0.1_1_1_6_2,
"f": 0.0_2_2_2_8,
"g": 0.0_2_0_1_5,
"h": 0.0_6_0_9_4,
"i": 0.0_7_5_4_6,
"j": 0.0_0_1_5_3,
"k": 0.0_1_2_9_2,
"l": 0.0_4_0_2_5,
"m": 0.0_2_4_0_6,
"n": 0.0_6_7_4_9,
"o": 0.0_7_5_0_7,
"p": 0.0_1_9_2_9,
"q": 0.0_0_0_9_5,
"r": 0.0_7_5_8_7,
"s": 0.0_6_3_2_7,
"t": 0.0_9_3_5_6,
"u": 0.0_2_7_5_8,
"v": 0.0_0_9_7_8,
"w": 0.0_2_5_6_0,
"x": 0.0_0_1_5_0,
"y": 0.0_1_9_9_4,
"z": 0.0_0_0_7_7,
}
else:
# Custom frequencies dictionary
lowercase__ : Optional[int] = frequencies_dict
if not case_sensitive:
lowercase__ : str = ciphertext.lower()
# Chi squared statistic values
lowercase__ : dict[int, tuple[float, str]] = {}
# cycle through all of the shifts
for shift in range(len(_A ) ):
lowercase__ : Optional[Any] = ""
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
lowercase__ : Dict = (alphabet_letters.index(letter.lower() ) - shift) % len(
_A )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
lowercase__ : str = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
lowercase__ : List[str] = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
lowercase__ : List[str] = decrypted_with_shift.lower().count(_A )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowercase__ : List[Any] = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowercase__ : str = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
lowercase__ : Any = decrypted_with_shift.count(_A )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowercase__ : str = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowercase__ : int = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
lowercase__ : Optional[int] = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(_lowerCAmelCase : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
lowercase__ : int = min(
_A , key=_A , )
# Get all the data from the most likely cipher (key, decoded message)
(
lowercase__
) : int = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 705
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ , lowercase__ : str = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : List[str] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : List[Any] = controlnet_params
lowercase__ : int = 'bird'
lowercase__ : List[Any] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([canny_image] * num_samples )
lowercase__ : List[Any] = jax.random.PRNGKey(0 )
lowercase__ : Tuple = jax.random.split(a , jax.device_count() )
lowercase__ : str = replicate(a )
lowercase__ : List[str] = shard(a )
lowercase__ : Dict = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : Tuple = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : int = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : Optional[Any] = jnp.array(
[0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ , lowercase__ : int = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : Optional[Any] = controlnet_params
lowercase__ : List[Any] = 'Chef in the kitchen'
lowercase__ : List[str] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
lowercase__ : List[str] = jax.random.PRNGKey(0 )
lowercase__ : str = jax.random.split(a , jax.device_count() )
lowercase__ : Optional[Any] = replicate(a )
lowercase__ : Optional[Any] = shard(a )
lowercase__ : List[Any] = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Union[str, Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : str = jnp.array(
[[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 645
| 0
|
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_UpperCamelCase : Union[str, Any] = data_utils.TransfoXLTokenizer
_UpperCamelCase : List[str] = data_utils.TransfoXLCorpus
_UpperCamelCase : str = data_utils
_UpperCamelCase : Dict = data_utils
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(lowerCamelCase_ , 'rb' ) as fp:
lowercase__ : List[str] = pickle.load(lowerCamelCase_ , encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
lowercase__ : str = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(f"""Save vocabulary to {pytorch_vocab_dump_path}""" )
lowercase__ : Any = corpus.vocab.__dict__
torch.save(lowerCamelCase_ , lowerCamelCase_ )
lowercase__ : Optional[Any] = corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , lowerCamelCase_ )
lowercase__ : Tuple = pytorch_dump_folder_path + '/' + CORPUS_NAME
print(f"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(lowerCamelCase_ , lowerCamelCase_ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
lowercase__ : Optional[int] = os.path.abspath(lowerCamelCase_ )
lowercase__ : Optional[int] = os.path.abspath(lowerCamelCase_ )
print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
lowercase__ : List[Any] = TransfoXLConfig()
else:
lowercase__ : str = TransfoXLConfig.from_json_file(lowerCamelCase_ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowercase__ : Optional[int] = TransfoXLLMHeadModel(lowerCamelCase_ )
lowercase__ : int = load_tf_weights_in_transfo_xl(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Save pytorch-model
lowercase__ : Union[str, Any] = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
lowercase__ : str = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
print(f"""Save PyTorch model to {os.path.abspath(lowerCamelCase_ )}""" )
torch.save(model.state_dict() , lowerCamelCase_ )
print(f"""Save configuration file to {os.path.abspath(lowerCamelCase_ )}""" )
with open(lowerCamelCase_ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--tf_checkpoint_path",
default="",
type=str,
help="An optional path to a TensorFlow checkpoint path to be converted.",
)
parser.add_argument(
"--transfo_xl_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--transfo_xl_dataset_file",
default="",
type=str,
help="An optional dataset file to be converted in a vocabulary.",
)
_UpperCamelCase : List[str] = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 706
|
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 645
| 0
|
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ):
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0' )
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate must be >= 0' )
if principal <= 0:
raise ValueError('principal must be > 0' )
return principal * daily_interest_rate * days_between_payments
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if number_of_compounding_periods <= 0:
raise ValueError('number_of_compounding_periods must be > 0' )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError('nominal_annual_interest_rate_percentage must be >= 0' )
if principal <= 0:
raise ValueError('principal must be > 0' )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if number_of_years <= 0:
raise ValueError('number_of_years must be > 0' )
if nominal_annual_percentage_rate < 0:
raise ValueError('nominal_annual_percentage_rate must be >= 0' )
if principal <= 0:
raise ValueError('principal must be > 0' )
return compound_interest(
_lowerCAmelCase , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
lowercase__ : Union[str, Any] = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(a )
from datasets import load_dataset
lowercase__ : str = load_dataset('nielsr/rvlcdip-demo' )
lowercase__ : Tuple = dataset['train'][0]['image'].convert('RGB' )
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : List[str] = model(**a )
lowercase__ : List[Any] = outputs.logits
lowercase__ : Union[str, Any] = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , a )
lowercase__ : Tuple = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=a , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class UpperCAmelCase_ ( __A):
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[int] = SMALL_MODEL_IDENTIFIER
lowercase__ : int = '''pt'''
lowercase__ : Tuple = '''tf'''
def _UpperCAmelCase ( self , a ) -> int:
lowercase__ : List[Any] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(a )
def _UpperCAmelCase ( self , a ) -> int:
lowercase__ : Tuple = TFAutoModel.from_pretrained(self.test_model , from_pt=a )
model_tf.save_pretrained(a )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[Any] = '''mock_framework'''
# Framework provided - return whatever the user provides
lowercase__ : Optional[int] = FeaturesManager.determine_framework(self.test_model , a )
self.assertEqual(a , a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(a )
lowercase__ : Union[str, Any] = FeaturesManager.determine_framework(a , a )
self.assertEqual(a , a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(a )
lowercase__ : Optional[Any] = FeaturesManager.determine_framework(a , a )
self.assertEqual(a , a )
def _UpperCAmelCase ( self ) -> List[str]:
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(a )
lowercase__ : List[str] = FeaturesManager.determine_framework(a )
self.assertEqual(a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(a )
lowercase__ : Optional[Any] = FeaturesManager.determine_framework(a )
self.assertEqual(a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(a ):
lowercase__ : Union[str, Any] = FeaturesManager.determine_framework(a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[str] = MagicMock(return_value=a )
with patch('transformers.onnx.features.is_tf_available' , a ):
lowercase__ : List[Any] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
lowercase__ : Optional[int] = MagicMock(return_value=a )
with patch('transformers.onnx.features.is_torch_available' , a ):
lowercase__ : Tuple = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(a , self.framework_tf )
# Both in environment -> use PyTorch
lowercase__ : List[str] = MagicMock(return_value=a )
lowercase__ : List[str] = MagicMock(return_value=a )
with patch('transformers.onnx.features.is_tf_available' , a ), patch(
'transformers.onnx.features.is_torch_available' , a ):
lowercase__ : Tuple = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(a , self.framework_pt )
# Both not in environment -> raise error
lowercase__ : str = MagicMock(return_value=a )
lowercase__ : str = MagicMock(return_value=a )
with patch('transformers.onnx.features.is_tf_available' , a ), patch(
'transformers.onnx.features.is_torch_available' , a ):
with self.assertRaises(a ):
lowercase__ : List[str] = FeaturesManager.determine_framework(self.test_model )
| 708
|
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase_ :
@staticmethod
def _UpperCAmelCase ( *a , **a ) -> int:
pass
def a_ ( _lowerCAmelCase : Image ):
'''simple docstring'''
lowercase__ : List[str] = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : Union[str, Any] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Union[str, Any] = DepthEstimationPipeline(model=a , image_processor=a )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self , a , a ) -> Optional[int]:
lowercase__ : Tuple = depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , a )
import datasets
lowercase__ : Tuple = datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
lowercase__ : List[Any] = depth_estimator(
[
Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'http://images.cocodataset.org/val2017/000000039769.jpg',
# RGBA
dataset[0]['file'],
# LA
dataset[1]['file'],
# L
dataset[2]['file'],
] )
self.assertEqual(
[
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
] , a , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@slow
@require_torch
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Tuple = 'Intel/dpt-large'
lowercase__ : Optional[int] = pipeline('depth-estimation' , model=a )
lowercase__ : List[Any] = depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
lowercase__ : Optional[Any] = hashimage(outputs['depth'] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item() ) , 2.662 )
@require_torch
def _UpperCAmelCase ( self ) -> Optional[int]:
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 645
| 0
|
"""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 _UpperCAmelCase ( *a , **a ) -> Tuple:
pass
@is_pipeline_test
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : Any = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def _UpperCAmelCase ( self , a , a , a ) -> Optional[Any]:
lowercase__ : Optional[Any] = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
lowercase__ : Optional[int] = [
{
'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 _UpperCAmelCase ( self , a , a ) -> int:
lowercase__ : Optional[Any] = vqa_pipeline(_lowerCAmelCase , top_k=1 )
self.assertEqual(
_lowerCAmelCase , [
[{'score': ANY(_lowerCAmelCase ), 'answer': ANY(_lowerCAmelCase )}],
[{'score': ANY(_lowerCAmelCase ), 'answer': ANY(_lowerCAmelCase )}],
] , )
@require_torch
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[str] = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
lowercase__ : List[str] = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowercase__ : str = 'How many cats are there?'
lowercase__ : int = vqa_pipeline(image=_lowerCAmelCase , question='How many cats are there?' , top_k=2 )
self.assertEqual(
_lowerCAmelCase , [{'score': ANY(_lowerCAmelCase ), 'answer': ANY(_lowerCAmelCase )}, {'score': ANY(_lowerCAmelCase ), 'answer': ANY(_lowerCAmelCase )}] )
lowercase__ : str = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
_lowerCAmelCase , [{'score': ANY(_lowerCAmelCase ), 'answer': ANY(_lowerCAmelCase )}, {'score': ANY(_lowerCAmelCase ), 'answer': ANY(_lowerCAmelCase )}] )
@slow
@require_torch
def _UpperCAmelCase ( self ) -> int:
lowercase__ : int = pipeline('visual-question-answering' , model='dandelin/vilt-b32-finetuned-vqa' )
lowercase__ : Union[str, Any] = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowercase__ : Any = 'How many cats are there?'
lowercase__ : Optional[int] = vqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
lowercase__ : List[Any] = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
lowercase__ : List[str] = vqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [[{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2 , )
@require_tf
@unittest.skip('Visual question answering not implemented in TF' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
| 709
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class UpperCAmelCase_ ( _a):
def __init__( self ) -> Any:
lowercase__ : Tuple = []
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_init_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[int]:
self.events.append('on_train_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_train_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_epoch_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[Any]:
self.events.append('on_epoch_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_step_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> str:
self.events.append('on_step_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_evaluate' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Tuple:
self.events.append('on_predict' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Union[str, Any]:
self.events.append('on_save' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_log' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_prediction_step' )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> str:
lowercase__ : str = tempfile.mkdtemp()
def _UpperCAmelCase ( self ) -> Dict:
shutil.rmtree(self.output_dir )
def _UpperCAmelCase ( self , a=0 , a=0 , a=6_4 , a=6_4 , a=None , a=False , **a ) -> int:
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
lowercase__ : str = RegressionDataset(length=a )
lowercase__ : Any = RegressionDataset(length=a )
lowercase__ : Optional[Any] = RegressionModelConfig(a=a , b=a )
lowercase__ : Union[str, Any] = RegressionPreTrainedModel(a )
lowercase__ : Tuple = TrainingArguments(self.output_dir , disable_tqdm=a , report_to=[] , **a )
return Trainer(
a , a , train_dataset=a , eval_dataset=a , callbacks=a , )
def _UpperCAmelCase ( self , a , a ) -> Union[str, Any]:
self.assertEqual(len(a ) , len(a ) )
# Order doesn't matter
lowercase__ : Optional[int] = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
lowercase__ : Tuple = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
for cba, cba in zip(a , a ):
if isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(a , a )
elif isinstance(a , a ) and not isinstance(a , a ):
self.assertEqual(a , cba.__class__ )
elif not isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(cba.__class__ , a )
else:
self.assertEqual(a , a )
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
lowercase__ : Dict = ['on_init_end', 'on_train_begin']
lowercase__ : List[Any] = 0
lowercase__ : Optional[int] = len(trainer.get_eval_dataloader() )
lowercase__ : Tuple = ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('on_epoch_begin' )
for _ in range(a ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save' )
expected_events.append('on_epoch_end' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : int = self.get_trainer()
lowercase__ : str = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# Callbacks passed at init are added to the default callbacks
lowercase__ : str = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
lowercase__ : List[Any] = self.get_trainer(disable_tqdm=a )
lowercase__ : Optional[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
lowercase__ : List[str] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Optional[Any] = self.get_trainer()
lowercase__ : List[Any] = trainer.pop_callback(a )
self.assertEqual(cb.__class__ , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# We can also add, pop, or remove by instance
lowercase__ : int = self.get_trainer()
lowercase__ : List[str] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Tuple = self.get_trainer()
lowercase__ : Dict = trainer.callback_handler.callbacks[0]
lowercase__ : Union[str, Any] = trainer.pop_callback(a )
self.assertEqual(a , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Tuple:
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' , category=a )
lowercase__ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# Independent log/save/eval
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
lowercase__ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
lowercase__ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='steps' )
trainer.train()
lowercase__ : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='epoch' )
trainer.train()
lowercase__ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# A bit of everything
lowercase__ : Any = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=1_0 , eval_steps=5 , evaluation_strategy='steps' , )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
lowercase__ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(a ) in warn_mock.call_args[0][0]
| 645
| 0
|
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a_ ( *_lowerCAmelCase : Dict , _lowerCAmelCase : Tuple = None , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=2 ):
'''simple docstring'''
from .. import __version__
lowercase__ : Optional[Any] = take_from
lowercase__ : Any = ()
if not isinstance(args[0] , lowerCamelCase__ ):
lowercase__ : Optional[int] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(lowerCamelCase__ ).base_version ) >= version.parse(lowerCamelCase__ ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
lowercase__ : List[Any] = None
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(lowerCamelCase__ ),)
lowercase__ : List[Any] = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(lowerCamelCase__ , lowerCamelCase__ ):
values += (getattr(lowerCamelCase__ , lowerCamelCase__ ),)
lowercase__ : Any = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
lowercase__ : Optional[int] = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
lowercase__ : Optional[int] = warning + " " if standard_warn else ""
warnings.warn(warning + message , lowerCamelCase__ , stacklevel=lowerCamelCase__ )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) > 0:
lowercase__ : str = inspect.getouterframes(inspect.currentframe() )[1]
lowercase__ : Union[str, Any] = call_frame.filename
lowercase__ : List[str] = call_frame.lineno
lowercase__ : Dict = call_frame.function
lowercase__ : Tuple = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(lowerCamelCase__ ) == 0:
return
elif len(lowerCamelCase__ ) == 1:
return values[0]
return values
| 710
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Tuple = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Dict = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
_UpperCamelCase : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 645
| 0
|
"""simple docstring"""
import math
_UpperCamelCase : Any = 10
_UpperCamelCase : List[str] = 7
_UpperCamelCase : int = BALLS_PER_COLOUR * NUM_COLOURS
def a_ ( _lowerCAmelCase : int = 20 ):
'''simple docstring'''
lowercase__ : Optional[int] = math.comb(_lowerCAmelCase , _lowerCAmelCase )
lowercase__ : Union[str, Any] = math.comb(NUM_BALLS - BALLS_PER_COLOUR , _lowerCAmelCase )
lowercase__ : Any = NUM_COLOURS * (1 - missing_colour / total)
return f"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 711
|
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self , a ) -> str:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
lowercase__ : str = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = 'sshleifer/tiny-gpt2'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = 'sgugger/tiny-distilbert-classification'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , only_pretrain_model=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Optional[int] = 'sshleifer/tiny-gpt2'
lowercase__ : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : List[Any] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : Tuple = TensorFlowBenchmark(a , [config] )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : List[str] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : List[str] = TensorFlowBenchmark(a , [config] )
lowercase__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[int] = AutoConfig.from_pretrained(a )
lowercase__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a , [config] )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = 'patrickvonplaten/t5-tiny-random'
lowercase__ : Any = AutoConfig.from_pretrained(a )
lowercase__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : int = TensorFlowBenchmark(a , configs=[config] )
lowercase__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , use_xla=a , multi_process=a , )
lowercase__ : Any = TensorFlowBenchmark(a )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , save_to_csv=a , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(a , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(a , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(a , 'env.csv' ) , multi_process=a , )
lowercase__ : Union[str, Any] = TensorFlowBenchmark(a )
benchmark.run()
self.assertTrue(Path(os.path.join(a , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'env.csv' ) ).exists() )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Tuple = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(a ):
self.assertTrue(hasattr(a , 'sequential' ) )
self.assertTrue(hasattr(a , 'cumulative' ) )
self.assertTrue(hasattr(a , 'current' ) )
self.assertTrue(hasattr(a , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(a , 'log.txt' ) , log_print=a , trace_memory_line_by_line=a , eager_mode=a , multi_process=a , )
lowercase__ : Optional[int] = TensorFlowBenchmark(a )
lowercase__ : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(a , 'log.txt' ) ).exists() )
| 645
| 0
|
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
_UpperCamelCase : Dict = logging.getLogger(__name__)
_UpperCamelCase : int = {"facebook/bart-base": BartForConditionalGeneration}
_UpperCamelCase : List[str] = {"facebook/bart-base": BartTokenizer}
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.' )
parser.add_argument(
'--validation_file' , type=__lowerCAmelCase , default=__lowerCAmelCase , help='A csv or a json file containing the validation data.' )
parser.add_argument(
'--max_length' , type=__lowerCAmelCase , default=5 , help='The maximum total input sequence length after tokenization.' , )
parser.add_argument(
'--num_beams' , type=__lowerCAmelCase , default=__lowerCAmelCase , help=(
'Number of beams to use for evaluation. This argument will be '
'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
) , )
parser.add_argument(
'--model_name_or_path' , type=__lowerCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=__lowerCAmelCase , )
parser.add_argument(
'--config_name' , type=__lowerCAmelCase , default=__lowerCAmelCase , help='Pretrained config name or path if not the same as model_name' , )
parser.add_argument(
'--device' , type=__lowerCAmelCase , default='cpu' , help='Device where the model will be run' , )
parser.add_argument('--output_file_path' , type=__lowerCAmelCase , default=__lowerCAmelCase , help='Where to store the final ONNX file.' )
lowercase__ : Optional[Any] = parser.parse_args()
return args
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any]="cpu" ):
'''simple docstring'''
lowercase__ : List[Any] = model_dict[model_name].from_pretrained(__lowerCAmelCase ).to(__lowerCAmelCase )
lowercase__ : Optional[Any] = tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase )
if model_name in ["facebook/bart-base"]:
lowercase__ : Optional[Any] = 0
lowercase__ : Optional[Any] = None
lowercase__ : Optional[int] = 0
return huggingface_model, tokenizer
def a_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int ):
'''simple docstring'''
model.eval()
lowercase__ : Dict = None
lowercase__ : str = torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase ) )
with torch.no_grad():
lowercase__ : Optional[int] = """My friends are cool but they eat too many carbs."""
lowercase__ : List[str] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1024 , return_tensors='pt' ).to(model.device )
lowercase__ : int = model.generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , num_beams=__lowerCAmelCase , max_length=__lowerCAmelCase , early_stopping=__lowerCAmelCase , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
__lowerCAmelCase , (
inputs['input_ids'],
inputs['attention_mask'],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , __lowerCAmelCase , opset_version=14 , input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id'] , output_names=['output_ids'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'seq'},
'output_ids': {0: 'batch', 1: 'seq_out'},
} , example_outputs=__lowerCAmelCase , )
logger.info('Model exported to {}'.format(__lowerCAmelCase ) )
lowercase__ : Optional[int] = remove_dup_initializers(os.path.abspath(__lowerCAmelCase ) )
logger.info('Deduplicated and optimized model written to {}'.format(__lowerCAmelCase ) )
lowercase__ : Dict = onnxruntime.InferenceSession(__lowerCAmelCase )
lowercase__ : str = ort_sess.run(
__lowerCAmelCase , {
'input_ids': inputs['input_ids'].cpu().numpy(),
'attention_mask': inputs['attention_mask'].cpu().numpy(),
'num_beams': np.array(__lowerCAmelCase ),
'max_length': np.array(__lowerCAmelCase ),
'decoder_start_token_id': np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1E-3 , atol=1E-3 )
logger.info('Model outputs from torch and ONNX Runtime are similar.' )
logger.info('Success.' )
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = parse_args()
lowercase__ : List[Any] = 5
lowercase__ : Dict = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
lowercase__ : Tuple = torch.device(args.device )
lowercase__ : List[str] = load_model_tokenizer(args.model_name_or_path , __lowerCAmelCase )
if model.config.decoder_start_token_id is None:
raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined' )
model.to(__lowerCAmelCase )
if args.max_length:
lowercase__ : Dict = args.max_length
if args.num_beams:
lowercase__ : Union[str, Any] = args.num_beams
if args.output_file_path:
lowercase__ : List[Any] = args.output_file_path
else:
lowercase__ : Optional[Any] = """BART.onnx"""
logger.info('Exporting model to ONNX' )
export_and_validate_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
main()
| 712
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class UpperCAmelCase_ ( _a):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=False , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=3 , a=4 , a=None , ) -> Any:
lowercase__ : Tuple = parent
lowercase__ : List[Any] = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : List[Any] = is_training
lowercase__ : Optional[Any] = use_input_mask
lowercase__ : Optional[int] = use_token_type_ids
lowercase__ : int = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : int = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : List[str] = num_attention_heads
lowercase__ : Optional[Any] = intermediate_size
lowercase__ : Optional[Any] = hidden_act
lowercase__ : List[str] = hidden_dropout_prob
lowercase__ : List[Any] = attention_probs_dropout_prob
lowercase__ : List[Any] = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : Tuple = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = num_labels
lowercase__ : Tuple = num_choices
lowercase__ : str = scope
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_input_mask:
lowercase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Dict = None
lowercase__ : Optional[Any] = None
lowercase__ : int = None
if self.use_labels:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self ) -> Optional[int]:
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Tuple = DistilBertModel(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , a )
lowercase__ : str = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
lowercase__ : Union[str, Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> int:
lowercase__ : Tuple = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(
a , attention_mask=a , start_positions=a , end_positions=a )
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 , a , a , a , a , a , a ) -> List[str]:
lowercase__ : int = self.num_labels
lowercase__ : Dict = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
lowercase__ : Optional[Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Any:
lowercase__ : Any = self.num_labels
lowercase__ : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Tuple:
lowercase__ : List[Any] = self.num_choices
lowercase__ : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
lowercase__ : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : int = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Union[str, Any] = self.prepare_config_and_inputs()
((lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__)) : List[str] = config_and_inputs
lowercase__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : List[str] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ : str = (
{
"feature-extraction": DistilBertModel,
"fill-mask": DistilBertForMaskedLM,
"question-answering": DistilBertForQuestionAnswering,
"text-classification": DistilBertForSequenceClassification,
"token-classification": DistilBertForTokenClassification,
"zero-shot": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Optional[int] = True
lowerCamelCase__ : Any = True
lowerCamelCase__ : List[Any] = True
lowerCamelCase__ : Optional[Any] = True
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : str = DistilBertModelTester(self )
lowercase__ : int = ConfigTester(self , config_class=a , dim=3_7 )
def _UpperCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : str = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
lowercase__ : Optional[int] = True
lowercase__ : Union[str, Any] = model_class(config=a )
lowercase__ : int = self._prepare_for_class(a , a )
lowercase__ : Tuple = torch.jit.trace(
a , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , 'traced_model.pt' ) )
lowercase__ : Optional[int] = torch.jit.load(os.path.join(a , 'traced_model.pt' ) , map_location=a )
loaded(inputs_dict['input_ids'].to(a ) , inputs_dict['attention_mask'].to(a ) )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : int = DistilBertModel.from_pretrained('distilbert-base-uncased' )
lowercase__ : Union[str, Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase__ : Optional[Any] = model(a , attention_mask=a )[0]
lowercase__ : Tuple = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , a )
lowercase__ : List[Any] = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase : Union[str, Any] = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Optional[int] = [
"NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
"NezhaForNextSentencePrediction",
"NezhaForMaskedLM",
"NezhaForPreTraining",
"NezhaForMultipleChoice",
"NezhaForQuestionAnswering",
"NezhaForSequenceClassification",
"NezhaForTokenClassification",
"NezhaModel",
"NezhaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
_UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 713
|
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif stress < 0:
raise ValueError('Stress cannot be negative' )
elif tangential_force < 0:
raise ValueError('Tangential Force cannot be negative' )
elif area < 0:
raise ValueError('Area cannot be negative' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
import random
def a_ ( _lowerCAmelCase : Any ):
'''simple docstring'''
lowercase__ : Dict = num - 1
lowercase__ : Optional[Any] = 0
while s % 2 == 0:
lowercase__ : List[Any] = s // 2
t += 1
for _ in range(5 ):
lowercase__ : List[str] = random.randrange(2 , num - 1 )
lowercase__ : Union[str, Any] = pow(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if v != 1:
lowercase__ : Tuple = 0
while v != (num - 1):
if i == t - 1:
return False
else:
lowercase__ : str = i + 1
lowercase__ : Any = (v**2) % num
return True
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if num < 2:
return False
lowercase__ : List[str] = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(lowerCAmelCase__ )
def a_ ( _lowerCAmelCase : Union[str, Any] = 1024 ):
'''simple docstring'''
while True:
lowercase__ : Tuple = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(lowerCAmelCase__ ):
return num
if __name__ == "__main__":
_UpperCamelCase : int = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| 714
|
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=[3_0, 3_0] , a=2 , a=3 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=3 , a=None , a=8 , a=1_0 , ) -> Any:
lowercase__ : List[str] = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Optional[int] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : str = is_training
lowercase__ : Optional[Any] = use_labels
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Dict = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Any = type_sequence_label_size
lowercase__ : Dict = initializer_range
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Tuple = scope
lowercase__ : Tuple = n_targets
lowercase__ : Optional[int] = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowercase__ : Optional[Any] = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowercase__ : Tuple = num_patches + 1 + self.num_detection_tokens
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowercase__ : Tuple = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowercase__ : int = []
for i in range(self.batch_size ):
lowercase__ : Optional[Any] = {}
lowercase__ : Any = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=a )
lowercase__ : List[str] = torch.rand(self.n_targets , 4 , device=a )
labels.append(a )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> List[Any]:
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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=a , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _UpperCAmelCase ( self , a , a , a ) -> int:
lowercase__ : List[str] = YolosModel(config=a )
model.to(a )
model.eval()
lowercase__ : List[Any] = model(a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = YolosForObjectDetection(a )
model.to(a )
model.eval()
lowercase__ : Dict = model(pixel_values=a )
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowercase__ : str = model(pixel_values=a , labels=a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Any = config_and_inputs
lowercase__ : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
lowerCamelCase__ : List[str] = (
{"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
)
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Union[str, Any] = False
def _UpperCAmelCase ( self , a , a , a=False ) -> Dict:
lowercase__ : List[str] = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowercase__ : Optional[Any] = []
for i in range(self.model_tester.batch_size ):
lowercase__ : Dict = {}
lowercase__ : Dict = torch.ones(
size=(self.model_tester.n_targets,) , device=a , dtype=torch.long )
lowercase__ : Optional[Any] = torch.ones(
self.model_tester.n_targets , 4 , device=a , dtype=torch.float )
labels.append(a )
lowercase__ : Union[str, Any] = labels
return inputs_dict
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Dict = YolosModelTester(self )
lowercase__ : Optional[int] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
# YOLOS does not use inputs_embeds
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
lowercase__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Tuple = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Dict = True
# in YOLOS, the seq_len is different
lowercase__ : Tuple = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowercase__ : Optional[int] = True
lowercase__ : str = False
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : str = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Optional[int] = True
lowercase__ : List[Any] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[str] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : Dict = len(a )
# Check attention is always last and order is fine
lowercase__ : Any = True
lowercase__ : int = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = 1
self.assertEqual(out_len + added_hidden_states , len(a ) )
lowercase__ : Tuple = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> List[str]:
def check_hidden_states_output(a , a , a ):
lowercase__ : str = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : int = model(**self._prepare_for_class(a , a ) )
lowercase__ : int = outputs.hidden_states
lowercase__ : Any = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(a ) , a )
# YOLOS has a different seq_length
lowercase__ : Optional[int] = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : List[Any] = True
check_hidden_states_output(a , a , a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*a )
@slow
def _UpperCAmelCase ( self ) -> Union[str, Any]:
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : int = YolosModel.from_pretrained(a )
self.assertIsNotNone(a )
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return AutoImageProcessor.from_pretrained('hustvl/yolos-small' ) if is_vision_available() else None
@slow
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = YolosForObjectDetection.from_pretrained('hustvl/yolos-small' ).to(a )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : Optional[int] = prepare_img()
lowercase__ : int = image_processor(images=a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : int = model(inputs.pixel_values )
# verify outputs
lowercase__ : Tuple = torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : Any = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] , device=a , )
lowercase__ : List[str] = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] , device=a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 ) )
# verify postprocessing
lowercase__ : Optional[Any] = image_processor.post_process_object_detection(
a , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowercase__ : str = torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861] ).to(a )
lowercase__ : Any = [7_5, 7_5, 1_7, 6_3, 1_7]
lowercase__ : Optional[int] = torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495] ).to(a )
self.assertEqual(len(results['scores'] ) , 5 )
self.assertTrue(torch.allclose(results['scores'] , a , atol=1e-4 ) )
self.assertSequenceEqual(results['labels'].tolist() , a )
self.assertTrue(torch.allclose(results['boxes'][0, :] , a ) )
| 645
| 0
|
"""simple docstring"""
from math import factorial
def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError('Please enter positive integers for n and k where n >= k' )
return factorial(__A ) // (factorial(__A ) * factorial(n - k ))
if __name__ == "__main__":
print(
"The number of five-card hands possible from a standard",
f'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
"If a class of 40 students must be arranged into groups of",
f'''4 for group projects, there are {combinations(40, 4)} ways''',
"to arrange them.\n",
)
print(
"If 10 teams are competing in a Formula One race, there",
f'''are {combinations(10, 3)} ways that first, second and''',
"third place can be awarded.",
)
| 715
|
"""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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_UpperCamelCase : int = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ :
def __init__( self , a=False , a=False , a=6.0 , a=None , a=False , a=False , a=None , a="fp4" , a=False , **a , ) -> Tuple:
lowercase__ : str = load_in_abit
lowercase__ : str = load_in_abit
lowercase__ : List[str] = llm_inta_threshold
lowercase__ : Dict = llm_inta_skip_modules
lowercase__ : Tuple = llm_inta_enable_fpaa_cpu_offload
lowercase__ : Any = llm_inta_has_fpaa_weight
lowercase__ : Any = bnb_abit_quant_type
lowercase__ : Dict = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
lowercase__ : Dict = torch.floataa
elif isinstance(a , a ):
lowercase__ : Any = getattr(a , a )
elif isinstance(a , torch.dtype ):
lowercase__ : Any = bnb_abit_compute_dtype
else:
raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype' )
self.post_init()
def _UpperCAmelCase ( self ) -> str:
if not isinstance(self.llm_inta_threshold , a ):
raise ValueError('llm_int8_threshold must be a float' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , a ):
raise ValueError('llm_int8_skip_modules must be a list of strings' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , a ):
raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean' )
if not isinstance(self.llm_inta_has_fpaa_weight , a ):
raise ValueError('llm_int8_has_fp16_weight must be a boolean' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError('bnb_4bit_compute_dtype must be torch.dtype' )
if not isinstance(self.bnb_abit_quant_type , a ):
raise ValueError('bnb_4bit_quant_type must be a string' )
if not isinstance(self.bnb_abit_use_double_quant , a ):
raise ValueError('bnb_4bit_use_double_quant must be a boolean' )
if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes' ) ) >= version.parse(
'0.39.0' ):
raise ValueError(
'4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version' )
def _UpperCAmelCase ( self ) -> Tuple:
return self.load_in_abit or self.load_in_abit
def _UpperCAmelCase ( self ) -> List[str]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> Optional[Any]:
lowercase__ : List[Any] = cls(**a )
lowercase__ : Union[str, Any] = []
for key, value in kwargs.items():
if hasattr(a , a ):
setattr(a , a , a )
to_remove.append(a )
for key in to_remove:
kwargs.pop(a , a )
if return_unused_kwargs:
return config, kwargs
else:
return config
def _UpperCAmelCase ( self , a ) -> Dict:
with open(a , 'w' , encoding='utf-8' ) as writer:
lowercase__ : Any = self.to_dict()
lowercase__ : str = json.dumps(a , indent=2 , sort_keys=a ) + '\n'
writer.write(a )
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : Any = str(output['bnb_4bit_compute_dtype'] ).split('.' )[1]
return output
def __repr__( self ) -> Dict:
return f"""{self.__class__.__name__} {self.to_json_string()}"""
def _UpperCAmelCase ( self , a = True ) -> str:
if use_diff is True:
lowercase__ : List[Any] = self.to_diff_dict()
else:
lowercase__ : List[str] = self.to_dict()
return json.dumps(a , indent=2 , sort_keys=a ) + "\n"
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Tuple = self.to_dict()
# get the default config dict
lowercase__ : Optional[Any] = BitsAndBytesConfig().to_dict()
lowercase__ : int = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
lowercase__ : Optional[int] = value
return serializable_config_dict
| 645
| 0
|
"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
_UpperCamelCase : Tuple = logging.get_logger(__name__)
_UpperCamelCase : Optional[int] = {"vocab_file": "spiece.model"}
_UpperCamelCase : int = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
}
}
_UpperCamelCase : List[Any] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
# Segments (not really needed)
_UpperCamelCase : Optional[Any] = 0
_UpperCamelCase : Tuple = 1
_UpperCamelCase : Union[str, Any] = 2
_UpperCamelCase : List[Any] = 3
_UpperCamelCase : Tuple = 4
class UpperCAmelCase_ ( a__):
lowerCamelCase__ : Optional[Any] = VOCAB_FILES_NAMES
lowerCamelCase__ : Dict = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : int = "left"
def __init__( self , a , a=False , a=True , a=False , a="<s>" , a="</s>" , a="<unk>" , a="<sep>" , a="<pad>" , a="<cls>" , a="<mask>" , a=["<eop>", "<eod>"] , a = None , **a , ) -> Any:
lowercase__ : Optional[Any] = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
lowercase__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_A , remove_space=_A , keep_accents=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , additional_special_tokens=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
lowercase__ : int = 3
lowercase__ : Union[str, Any] = do_lower_case
lowercase__ : Dict = remove_space
lowercase__ : int = keep_accents
lowercase__ : List[str] = vocab_file
lowercase__ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_A )
@property
def _UpperCAmelCase ( self ) -> Any:
return len(self.sp_model )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[Any] = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> List[str]:
lowercase__ : List[Any] = self.__dict__.copy()
lowercase__ : List[str] = None
return state
def __setstate__( self , a ) -> str:
lowercase__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
lowercase__ : Union[str, Any] = {}
lowercase__ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _UpperCAmelCase ( self , a ) -> Optional[int]:
if self.remove_space:
lowercase__ : str = ' '.join(inputs.strip().split() )
else:
lowercase__ : Dict = inputs
lowercase__ : List[str] = outputs.replace('``' , '"' ).replace('\'\'' , '"' )
if not self.keep_accents:
lowercase__ : Optional[Any] = unicodedata.normalize('NFKD' , _A )
lowercase__ : Dict = ''.join([c for c in outputs if not unicodedata.combining(_A )] )
if self.do_lower_case:
lowercase__ : Tuple = outputs.lower()
return outputs
def _UpperCAmelCase ( self , a ) -> Optional[int]:
lowercase__ : Union[str, Any] = self.preprocess_text(_A )
lowercase__ : int = self.sp_model.encode(_A , out_type=_A )
lowercase__ : int = []
for piece in pieces:
if len(_A ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit():
lowercase__ : Union[str, Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_A , '' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowercase__ : int = cur_pieces[1:]
else:
lowercase__ : Any = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_A )
else:
new_pieces.append(_A )
return new_pieces
def _UpperCAmelCase ( self , a ) -> str:
return self.sp_model.PieceToId(_A )
def _UpperCAmelCase ( self , a ) -> str:
return self.sp_model.IdToPiece(_A )
def _UpperCAmelCase ( self , a ) -> Dict:
lowercase__ : Optional[Any] = ''.join(_A ).replace(_A , ' ' ).strip()
return out_string
def _UpperCAmelCase ( self , a , a = False , a = None , a = True , **a , ) -> List[str]:
lowercase__ : Dict = kwargs.pop('use_source_tokenizer' , _A )
lowercase__ : Dict = self.convert_ids_to_tokens(_A , skip_special_tokens=_A )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
lowercase__ : Optional[Any] = []
lowercase__ : int = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_A ) )
lowercase__ : Tuple = []
sub_texts.append(_A )
else:
current_sub_text.append(_A )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_A ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
lowercase__ : List[Any] = ''.join(_A )
lowercase__ : Tuple = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
lowercase__ : int = self.clean_up_tokenization(_A )
return clean_text
else:
return text
def _UpperCAmelCase ( self , a , a = None ) -> str:
lowercase__ : str = [self.sep_token_id]
lowercase__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _UpperCAmelCase ( self , a , a = None , a = False ) -> Tuple:
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 not None:
return ([0] * len(_A )) + [1] + ([0] * len(_A )) + [1, 1]
return ([0] * len(_A )) + [1, 1]
def _UpperCAmelCase ( self , a , a = None ) -> Optional[Any]:
lowercase__ : str = [self.sep_token_id]
lowercase__ : Any = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _UpperCAmelCase ( self , a , a = None ) -> List[str]:
if not os.path.isdir(_A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : str = 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:
lowercase__ : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
| 716
|
"""simple docstring"""
import argparse
import gc
import json
import os
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_UpperCamelCase : int = 16
_UpperCamelCase : Union[str, Any] = 32
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
return int(x / 2**20 )
class UpperCAmelCase_ :
def __enter__( self ) -> Union[str, Any]:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase__ : List[str] = torch.cuda.memory_allocated()
return self
def __exit__( self , *a ) -> Any:
gc.collect()
torch.cuda.empty_cache()
lowercase__ : Optional[Any] = torch.cuda.memory_allocated()
lowercase__ : Union[str, Any] = torch.cuda.max_memory_allocated()
lowercase__ : List[Any] = bamb(self.end - self.begin )
lowercase__ : List[Any] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def a_ ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 , _lowerCAmelCase : str = "bert-base-cased" , _lowerCAmelCase : int = 320 , _lowerCAmelCase : int = 160 , ):
'''simple docstring'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
lowercase__ : Union[str, Any] = load_dataset(
'glue' , 'mrpc' , split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""} )
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase__ : Union[str, Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=_lowerCAmelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_lowerCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(_lowerCAmelCase , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['train'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
lowercase__ : Dict = DataLoader(
tokenized_datasets['validation'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[int] = config['lr']
lowercase__ : Optional[Any] = int(config['num_epochs'] )
lowercase__ : Optional[Any] = int(config['seed'] )
lowercase__ : int = int(config['batch_size'] )
lowercase__ : Union[str, Any] = args.model_name_or_path
set_seed(_lowerCAmelCase )
lowercase__ , lowercase__ : Tuple = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : List[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase , return_dict=_lowerCAmelCase )
# Instantiate optimizer
lowercase__ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase__ : Optional[Any] = optimizer_cls(params=model.parameters() , lr=_lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
lowercase__ : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowercase__ : List[Any] = 1
lowercase__ : List[Any] = (len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=0 , num_training_steps=_lowerCAmelCase , )
else:
lowercase__ : Tuple = DummyScheduler(_lowerCAmelCase , total_num_steps=_lowerCAmelCase , warmup_num_steps=0 )
# 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__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Dict = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
lowercase__ : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase__ : Tuple = 0
# Now we train the model
lowercase__ : Optional[Any] = {}
for epoch in range(_lowerCAmelCase , _lowerCAmelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
lowercase__ : List[Any] = model(**_lowerCAmelCase )
lowercase__ : Dict = outputs.loss
lowercase__ : int = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin ) ) )
accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used ) )
accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked ) )
accelerator.print(
'Total Peak Memory consumed during the train (max): {}'.format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase__ : int = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'peak_memory_utilization.json' ) , 'w' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : int = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=_lowerCAmelCase , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=_lowerCAmelCase , )
parser.add_argument(
'--output_dir' , type=_lowerCAmelCase , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--peak_memory_upper_bound' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.' , )
parser.add_argument(
'--n_train' , type=_lowerCAmelCase , default=320 , help='Number of training examples to use.' , )
parser.add_argument(
'--n_val' , type=_lowerCAmelCase , default=160 , help='Number of validation examples to use.' , )
parser.add_argument(
'--num_epochs' , type=_lowerCAmelCase , default=1 , help='Number of train epochs.' , )
lowercase__ : Any = parser.parse_args()
lowercase__ : Optional[Any] = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 645
| 0
|
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
lowercase__ : Optional[Any] = b * b - 4 * a * c
lowercase__ : Optional[int] = (-b + sqrt(_lowerCamelCase )) / (2 * a)
lowercase__ : int = (-b - sqrt(_lowerCamelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def a_ ( ):
'''simple docstring'''
lowercase__ : List[str] = quadratic_roots(a=5 , b=6 , c=1 )
print(f"""The solutions are: {solutiona} and {solutiona}""" )
if __name__ == "__main__":
main()
| 717
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
lowercase__ : List[str] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowercase__ : Dict = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowercase__ : Union[str, Any] = j
return prefix_result
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
return max(prefix_function(_lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
import os
import sys
_UpperCamelCase : List[Any] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
_UpperCamelCase : str = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def a_ ( *_lowerCAmelCase : Any , **_lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
return AutoConfig.from_pretrained(*a_ , **a_ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def a_ ( *_lowerCAmelCase : str , **_lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(*a_ , **a_ )
@add_start_docstrings(AutoModel.__doc__ )
def a_ ( *_lowerCAmelCase : List[str] , **_lowerCAmelCase : int ):
'''simple docstring'''
return AutoModel.from_pretrained(*a_ , **a_ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def a_ ( *_lowerCAmelCase : Any , **_lowerCAmelCase : Tuple ):
'''simple docstring'''
return AutoModelForCausalLM.from_pretrained(*a_ , **a_ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def a_ ( *_lowerCAmelCase : Dict , **_lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
return AutoModelForMaskedLM.from_pretrained(*a_ , **a_ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def a_ ( *_lowerCAmelCase : Optional[int] , **_lowerCAmelCase : str ):
'''simple docstring'''
return AutoModelForSequenceClassification.from_pretrained(*a_ , **a_ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def a_ ( *_lowerCAmelCase : List[str] , **_lowerCAmelCase : int ):
'''simple docstring'''
return AutoModelForQuestionAnswering.from_pretrained(*a_ , **a_ )
| 718
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=7 , a=3 , a=1_8 , a=3_0 , a=4_0_0 , a=True , a=None , a=True , a=None , a=True , ) -> List[str]:
lowercase__ : Tuple = size if size is not None else {'shortest_edge': 2_0}
lowercase__ : Union[str, Any] = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : str = num_channels
lowercase__ : Any = image_size
lowercase__ : Optional[Any] = min_resolution
lowercase__ : int = max_resolution
lowercase__ : List[Any] = do_resize
lowercase__ : List[str] = size
lowercase__ : str = do_center_crop
lowercase__ : List[Any] = crop_size
lowercase__ : Union[str, Any] = do_flip_channel_order
def _UpperCAmelCase ( self ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Optional[Any] = MobileViTImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Tuple = MobileViTImageProcessingTester(self )
@property
def _UpperCAmelCase ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a , 'do_resize' ) )
self.assertTrue(hasattr(a , 'size' ) )
self.assertTrue(hasattr(a , 'do_center_crop' ) )
self.assertTrue(hasattr(a , 'center_crop' ) )
self.assertTrue(hasattr(a , 'do_flip_channel_order' ) )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 2_0} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
lowercase__ : str = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> str:
# Initialize image_processing
lowercase__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a )
for image in image_inputs:
self.assertIsInstance(a , Image.Image )
# Test not batched input
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : List[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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Tuple:
# Initialize image_processing
lowercase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ : Tuple = 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
lowercase__ : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Dict:
# Initialize image_processing
lowercase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ : Optional[Any] = 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
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : Tuple = 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 645
| 0
|
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
lowercase__ : Tuple = original_name.split('.' )[0]
lowercase__ : Tuple = key.split('.' )
lowercase__ : Optional[Any] = int(key_list[key_list.index(_A ) - 2] )
lowercase__ : int = int(key_list[key_list.index(_A ) - 1] )
lowercase__ : Any = orig_block_num - offset
lowercase__ : List[Any] = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def a_ ( _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
lowercase__ : List[Any] = OrderedDict()
lowercase__ , lowercase__ : List[str] = 0, 0
for key, value in state_dict.items():
if key.startswith('network' ):
lowercase__ : Optional[int] = key.replace('network' , 'poolformer.encoder' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('bias' ) and "patch_embed" not in key:
patch_emb_offset += 1
lowercase__ : str = key[: key.find('proj' )]
lowercase__ : Union[str, Any] = key.replace(_A , f"""patch_embeddings.{total_embed_found}.""" )
lowercase__ : str = key.replace('proj' , 'projection' )
if key.endswith('bias' ):
total_embed_found += 1
if "patch_embeddings" in key:
lowercase__ : Tuple = 'poolformer.encoder.' + key
if "mlp.fc1" in key:
lowercase__ : Dict = replace_key_with_offset(_A , _A , 'mlp.fc1' , 'output.conv1' )
if "mlp.fc2" in key:
lowercase__ : Any = replace_key_with_offset(_A , _A , 'mlp.fc2' , 'output.conv2' )
if "norm1" in key:
lowercase__ : int = replace_key_with_offset(_A , _A , 'norm1' , 'before_norm' )
if "norm2" in key:
lowercase__ : Dict = replace_key_with_offset(_A , _A , 'norm2' , 'after_norm' )
if "layer_scale_1" in key:
lowercase__ : Optional[int] = replace_key_with_offset(_A , _A , 'layer_scale_1' , 'layer_scale_1' )
if "layer_scale_2" in key:
lowercase__ : Union[str, Any] = replace_key_with_offset(_A , _A , 'layer_scale_2' , 'layer_scale_2' )
if "head" in key:
lowercase__ : Any = key.replace('head' , 'classifier' )
lowercase__ : Tuple = value
return new_state_dict
def a_ ( ):
'''simple docstring'''
lowercase__ : List[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase__ : int = Image.open(requests.get(_A , stream=_A ).raw )
return image
@torch.no_grad()
def a_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
lowercase__ : Any = PoolFormerConfig()
# set attributes based on model_name
lowercase__ : Any = 'huggingface/label-files'
lowercase__ : Optional[int] = model_name[-3:]
lowercase__ : Optional[Any] = 1000
lowercase__ : Tuple = 'imagenet-1k-id2label.json'
lowercase__ : Optional[Any] = (1, 1000)
# set config attributes
lowercase__ : Any = json.load(open(hf_hub_download(_A , _A , repo_type='dataset' ) , 'r' ) )
lowercase__ : Dict = {int(_A ): v for k, v in idalabel.items()}
lowercase__ : Union[str, Any] = idalabel
lowercase__ : List[str] = {v: k for k, v in idalabel.items()}
if size == "s12":
lowercase__ : List[str] = [2, 2, 6, 2]
lowercase__ : Dict = [64, 128, 320, 512]
lowercase__ : Any = 4.0
lowercase__ : Optional[Any] = 0.9
elif size == "s24":
lowercase__ : Optional[Any] = [4, 4, 12, 4]
lowercase__ : List[Any] = [64, 128, 320, 512]
lowercase__ : Optional[int] = 4.0
lowercase__ : int = 0.9
elif size == "s36":
lowercase__ : Dict = [6, 6, 18, 6]
lowercase__ : Optional[Any] = [64, 128, 320, 512]
lowercase__ : int = 4.0
lowercase__ : str = 1E-6
lowercase__ : Any = 0.9
elif size == "m36":
lowercase__ : List[Any] = [6, 6, 18, 6]
lowercase__ : List[str] = [96, 192, 384, 768]
lowercase__ : Union[str, Any] = 4.0
lowercase__ : int = 1E-6
lowercase__ : Union[str, Any] = 0.9_5
elif size == "m48":
lowercase__ : str = [8, 8, 24, 8]
lowercase__ : Union[str, Any] = [96, 192, 384, 768]
lowercase__ : Optional[Any] = 4.0
lowercase__ : Any = 1E-6
lowercase__ : Dict = 0.9_5
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
lowercase__ : Optional[int] = PoolFormerImageProcessor(crop_pct=_A )
# Prepare image
lowercase__ : Any = prepare_img()
lowercase__ : Union[str, Any] = image_processor(images=_A , return_tensors='pt' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
lowercase__ : Dict = torch.load(_A , map_location=torch.device('cpu' ) )
# rename keys
lowercase__ : Dict = rename_keys(_A )
# create HuggingFace model and load state dict
lowercase__ : Dict = PoolFormerForImageClassification(_A )
model.load_state_dict(_A )
model.eval()
# Define image processor
lowercase__ : Union[str, Any] = PoolFormerImageProcessor(crop_pct=_A )
lowercase__ : Any = image_processor(images=prepare_img() , return_tensors='pt' ).pixel_values
# forward pass
lowercase__ : Tuple = model(_A )
lowercase__ : Any = outputs.logits
# define expected logit slices for different models
if size == "s12":
lowercase__ : Any = torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9] )
elif size == "s24":
lowercase__ : Tuple = torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5] )
elif size == "s36":
lowercase__ : List[str] = torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8] )
elif size == "m36":
lowercase__ : Tuple = torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8] )
elif size == "m48":
lowercase__ : Tuple = torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , _A , atol=1E-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(_A ).mkdir(exist_ok=_A )
model.save_pretrained(_A )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_A )
if __name__ == "__main__":
_UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="poolformer_s12",
type=str,
help="Name of the model you\'d like to convert.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
_UpperCamelCase : Union[str, Any] = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 719
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=True , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=4 , ) -> Dict:
lowercase__ : Optional[Any] = parent
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : int = is_training
lowercase__ : str = use_attention_mask
lowercase__ : Dict = use_token_type_ids
lowercase__ : Optional[int] = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : List[str] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : int = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[str] = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Tuple = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : int = type_vocab_size
lowercase__ : List[str] = type_sequence_label_size
lowercase__ : Union[str, Any] = initializer_range
lowercase__ : Optional[int] = num_choices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_attention_mask:
lowercase__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : List[str] = None
if self.use_token_type_ids:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ : Any = 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 , is_decoder=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_class_name in self.all_model_classes:
lowercase__ : str = model_class_name.from_pretrained('albert-base-v2' )
lowercase__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : str = FlaxAlbertModel.from_pretrained('albert-base-v2' )
lowercase__ : Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
lowercase__ : Any = model(a , attention_mask=a )[0]
lowercase__ : Tuple = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , a )
lowercase__ : Optional[Any] = np.array(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Tuple ):
'''simple docstring'''
lowercase__ : Optional[Any] = (boundary[1] - boundary[0]) / steps
lowercase__ : Optional[int] = boundary[0]
lowercase__ : int = boundary[1]
lowercase__ : List[str] = make_points(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase__ : Optional[int] = 0.0
y += (h / 2.0) * f(_lowerCAmelCase )
for i in x_i:
# print(i)
y += h * f(_lowerCAmelCase )
y += (h / 2.0) * f(_lowerCAmelCase )
return y
def a_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
'''simple docstring'''
lowercase__ : List[Any] = a + h
while x < (b - h):
yield x
lowercase__ : Tuple = x + h
def a_ ( _lowerCAmelCase : Union[str, Any] ): # enter your function here
'''simple docstring'''
lowercase__ : Any = (x - 0) * (x - 0)
return y
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = 0.0 # Lower bound of integration
lowercase__ : List[Any] = 1.0 # Upper bound of integration
lowercase__ : str = 1_0.0 # define number of steps or resolution
lowercase__ : Dict = [a, b] # define boundary of integration
lowercase__ : int = method_a(_lowerCAmelCase , _lowerCAmelCase )
print(f"""y = {y}""" )
if __name__ == "__main__":
main()
| 720
|
"""simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
lowercase__ : int = 0.0
for coeff in reversed(_lowerCAmelCase ):
lowercase__ : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
_UpperCamelCase : int = (0.0, 0.0, 5.0, 9.3, 7.0)
_UpperCamelCase : Dict = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Optional[int] = ['''ViTFeatureExtractor''']
_UpperCamelCase : str = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : int = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Any = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[str] = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
_UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 721
|
"""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
_UpperCamelCase : 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 a_ ( _lowerCAmelCase : Optional[Any]=True ):
'''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=_a))
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = None
lowerCamelCase__ : Optional[Any] = None
def _UpperCAmelCase ( self , a , a ) -> List[Any]:
with TemporaryDirectory() as tmp_dir:
lowercase__ : List[str] = dataset_module_factory(a , cache_dir=a )
lowercase__ : List[Any] = import_main_class(dataset_module.module_path , dataset=a )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=a , config_name=a , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=a ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
lowercase__ : Union[str, Any] = cached_path(a , cache_dir=a )
self.assertTrue(os.path.exists(a ) )
@pytest.mark.integration
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Union[str, Any] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
lowercase__ : int = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : Optional[int] = import_main_class(dataset_module.module_path )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowercase__ : Optional[int] = None
builder_instance.download_and_prepare()
lowercase__ : Optional[int] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Optional[int] = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : List[str] = import_main_class(dataset_module.module_path , dataset=_lowerCAmelCase )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert "train" in ds
assert isinstance(ds['train'] , _lowerCAmelCase )
assert next(iter(ds['train'] ) )
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Any = abs(snake_case__ )
lowercase__ : Optional[Any] = 0
while n > 0:
res += n % 10
n //= 10
return res
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : List[str] = abs(snake_case__ )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
return sum(int(snake_case__ ) for c in str(abs(snake_case__ ) ) )
def a_ ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_lowerCAmelCase : Callable , _lowerCAmelCase : int ) -> None:
lowercase__ : str = f"""{func.__name__}({value})"""
lowercase__ : Any = timeit(f"""__main__.{call}""" , setup='import __main__' )
print(f"""{call:56} = {func(snake_case__ )} -- {timing:.4f} seconds""" )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(snake_case__ , snake_case__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 700
|
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def a_ ( _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray ):
'''simple docstring'''
lowercase__ : Any = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(_lowerCAmelCase , _lowerCAmelCase )
# Predict target for test data
lowercase__ : str = xgb.predict(_lowerCAmelCase )
lowercase__ : Union[str, Any] = predictions.reshape(len(_lowerCAmelCase ) , 1 )
return predictions
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = fetch_california_housing()
lowercase__ , lowercase__ : str = data_handling(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = train_test_split(
_lowerCAmelCase , _lowerCAmelCase , test_size=0.2_5 , random_state=1 )
lowercase__ : Any = xgboost(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Error printing
print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
print(f"""Mean Square Error : {mean_squared_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 645
| 0
|
"""simple docstring"""
import os
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = os.path.dirname(os.path.realpath(__SCREAMING_SNAKE_CASE ) )
lowercase__ : Optional[int] = os.path.join(__SCREAMING_SNAKE_CASE , 'triangle.txt' )
with open(__SCREAMING_SNAKE_CASE ) as f:
lowercase__ : List[str] = f.readlines()
lowercase__ : List[str] = []
for line in triangle:
lowercase__ : int = []
for number in line.strip().split(' ' ):
numbers_from_line.append(int(__SCREAMING_SNAKE_CASE ) )
a.append(__SCREAMING_SNAKE_CASE )
for i in range(1 , len(__SCREAMING_SNAKE_CASE ) ):
for j in range(len(a[i] ) ):
lowercase__ : Optional[int] = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowercase__ : Optional[int] = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 701
|
"""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 UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=1_0 , a=3 , a=2 , a=2 , a=2 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=0.9 , a=None , ) -> Optional[Any]:
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Dict = patch_size
lowercase__ : Tuple = tubelet_size
lowercase__ : Optional[int] = num_frames
lowercase__ : Optional[int] = is_training
lowercase__ : int = use_labels
lowercase__ : Optional[int] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = mask_ratio
lowercase__ : Optional[Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase__ : Optional[Any] = (image_size // patch_size) ** 2
lowercase__ : str = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase__ : str = int(mask_ratio * self.seq_length )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Tuple:
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=a , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a ) -> Optional[int]:
lowercase__ : Dict = VideoMAEModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = VideoMAEForPreTraining(a )
model.to(a )
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
lowercase__ : Any = torch.ones((self.num_masks,) )
lowercase__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowercase__ : Optional[int] = mask.expand(self.batch_size , -1 ).bool()
lowercase__ : str = model(a , a )
# model only returns predictions for masked patches
lowercase__ : str = mask.sum().item()
lowercase__ : int = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ : Optional[int] = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Any = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = False
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = VideoMAEModelTester(self )
lowercase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self , a , a , a=False ) -> Optional[int]:
lowercase__ : Union[str, Any] = copy.deepcopy(a )
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
lowercase__ : Optional[Any] = torch.ones((self.model_tester.num_masks,) )
lowercase__ : Any = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowercase__ : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowercase__ : Union[str, Any] = bool_masked_pos.to(a )
if return_labels:
if model_class in [
*get_values(a ),
]:
lowercase__ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = VideoMAEModel.from_pretrained(a )
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = True
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Any = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase__ : Optional[Any] = True
lowercase__ : int = False
lowercase__ : Any = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[int] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Dict = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : List[str] = len(a )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : List[str] = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(a , a ) )
self.assertEqual(out_len + 1 , len(a ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[int] = outputs.hidden_states
lowercase__ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(a ) , a )
lowercase__ : Optional[Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Union[str, Any] = 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] , )
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(a , a , a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def a_ ( ):
'''simple docstring'''
lowercase__ : int = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowercase__ : str = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
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 _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
a )
lowercase__ : str = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**a )
# verify the logits
lowercase__ : str = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : List[Any] = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(a )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(a , return_tensors='pt' ).to(a )
# add boolean mask, indicating which patches to mask
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowercase__ : str = torch.load(a )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**a )
# verify the logits
lowercase__ : Dict = torch.Size([1, 1_4_0_8, 1_5_3_6] )
lowercase__ : List[str] = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=a )
self.assertEqual(outputs.logits.shape , a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase__ : List[Any] = torch.tensor([0.5_142] , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=a ).to(
a )
with torch.no_grad():
lowercase__ : Any = model(**a )
lowercase__ : List[Any] = torch.tensor(torch.tensor([0.6_469] ) , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def a_ ( _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
return 1.0 / (1.0 + np.exp(-_outputs ))
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
lowercase__ : int = np.max(_outputs , axis=-1 , keepdims=_lowerCAmelCase )
lowercase__ : List[Any] = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_lowerCAmelCase )
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Tuple = "sigmoid"
lowerCamelCase__ : int = "softmax"
lowerCamelCase__ : Union[str, Any] = "none"
@add_end_docstrings(
_a , R"\n return_all_scores (`bool`, *optional*, defaults to `False`):\n Whether to return all prediction scores or just the one of the predicted class.\n function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n has several labels, will apply the softmax function on the output.\n - `\"sigmoid\"`: Applies the sigmoid function on the output.\n - `\"softmax\"`: Applies the softmax function on the output.\n - `\"none\"`: Does not apply any function on the output.\n " , )
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = False
lowerCamelCase__ : Optional[Any] = ClassificationFunction.NONE
def __init__( self , **a ) -> List[str]:
super().__init__(**a )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def _UpperCAmelCase ( self , a=None , a=None , a="" , **a ) -> Optional[Any]:
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
lowercase__ : Optional[int] = tokenizer_kwargs
lowercase__ : Any = {}
if hasattr(self.model.config , 'return_all_scores' ) and return_all_scores is None:
lowercase__ : Union[str, Any] = self.model.config.return_all_scores
if isinstance(a , a ) or top_k is None:
lowercase__ : str = top_k
lowercase__ : str = False
elif return_all_scores is not None:
warnings.warn(
'`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'
' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.' , a , )
if return_all_scores:
lowercase__ : int = None
else:
lowercase__ : List[str] = 1
if isinstance(a , a ):
lowercase__ : List[Any] = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
lowercase__ : Any = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *a , **a ) -> Dict:
lowercase__ : Tuple = super().__call__(*a , **a )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
lowercase__ : Optional[int] = 'top_k' not in kwargs
if isinstance(args[0] , a ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def _UpperCAmelCase ( self , a , **a ) -> Dict[str, GenericTensor]:
lowercase__ : str = self.framework
if isinstance(a , a ):
return self.tokenizer(**a , return_tensors=a , **a )
elif isinstance(a , a ) and len(a ) == 1 and isinstance(inputs[0] , a ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=a , **a )
elif isinstance(a , a ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.' )
return self.tokenizer(a , return_tensors=a , **a )
def _UpperCAmelCase ( self , a ) -> List[Any]:
return self.model(**a )
def _UpperCAmelCase ( self , a , a=None , a=1 , a=True ) -> List[Any]:
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
lowercase__ : List[Any] = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
lowercase__ : Optional[int] = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , 'function_to_apply' ) and function_to_apply is None:
lowercase__ : Tuple = self.model.config.function_to_apply
else:
lowercase__ : List[str] = ClassificationFunction.NONE
lowercase__ : Any = model_outputs['logits'][0]
lowercase__ : List[str] = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
lowercase__ : Tuple = sigmoid(a )
elif function_to_apply == ClassificationFunction.SOFTMAX:
lowercase__ : Optional[Any] = softmax(a )
elif function_to_apply == ClassificationFunction.NONE:
lowercase__ : Any = outputs
else:
raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""" )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
lowercase__ : Optional[int] = [
{'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(a )
]
if not _legacy:
dict_scores.sort(key=lambda a : x["score"] , reverse=a )
if top_k is not None:
lowercase__ : List[Any] = dict_scores[:top_k]
return dict_scores
| 702
|
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
"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",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
_UpperCamelCase : List[str] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
for attribute in key.split('.' ):
lowercase__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
lowercase__ : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
lowercase__ : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
lowercase__ : Optional[Any] = value
elif weight_type == "weight_g":
lowercase__ : Dict = value
elif weight_type == "weight_v":
lowercase__ : List[str] = value
elif weight_type == "bias":
lowercase__ : Optional[Any] = value
else:
lowercase__ : List[str] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = []
lowercase__ : List[str] = fairseq_model.state_dict()
lowercase__ : Union[str, Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase__ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
lowercase__ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase__ : List[Any] = 'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase__ : int = True
if "*" in mapped_key:
lowercase__ : Optional[int] = name.split(_lowerCAmelCase )[0].split('.' )[-2]
lowercase__ : List[str] = mapped_key.replace('*' , _lowerCAmelCase )
if "weight_g" in name:
lowercase__ : List[Any] = 'weight_g'
elif "weight_v" in name:
lowercase__ : int = 'weight_v'
elif "bias" in name:
lowercase__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase__ : Union[str, Any] = 'weight'
else:
lowercase__ : int = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : int = full_name.split('conv_layers.' )[-1]
lowercase__ : int = name.split('.' )
lowercase__ : int = int(items[0] )
lowercase__ : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowercase__ : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowercase__ : Optional[int] = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
lowercase__ : List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
lowercase__ : int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Tuple=True ):
'''simple docstring'''
if config_path is not None:
lowercase__ : Any = UniSpeechSatConfig.from_pretrained(_lowerCAmelCase )
else:
lowercase__ : Any = UniSpeechSatConfig()
lowercase__ : Union[str, Any] = ''
if is_finetuned:
lowercase__ : Optional[Any] = UniSpeechSatForCTC(_lowerCAmelCase )
else:
lowercase__ : List[Any] = UniSpeechSatForPreTraining(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
lowercase__ : Union[str, Any] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase : Tuple = 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"
)
_UpperCamelCase : str = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 645
| 0
|
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCAmelCase_ ( UpperCamelCase_):
@slow
@require_torch
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : List[str] = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' , 'prajjwal1/bert-tiny' )
lowercase__ : Optional[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
lowercase__ : Any = bertabert.config.encoder.vocab_size
lowercase__ : List[str] = tokenizer.sep_token_id
lowercase__ : Any = tokenizer.cls_token_id
lowercase__ : Tuple = 1_2_8
lowercase__ : str = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='train[:1%]' )
lowercase__ : List[str] = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='validation[:1%]' )
lowercase__ : Optional[int] = train_dataset.select(range(3_2 ) )
lowercase__ : Tuple = val_dataset.select(range(1_6 ) )
lowercase__ : List[str] = 4
def _map_to_encoder_decoder_inputs(a ):
# Tokenizer will automatically set [BOS] <text> [EOS]
lowercase__ : Dict = tokenizer(batch['article'] , padding='max_length' , truncation=__A , max_length=5_1_2 )
lowercase__ : List[str] = tokenizer(batch['highlights'] , padding='max_length' , truncation=__A , max_length=1_2_8 )
lowercase__ : str = inputs.input_ids
lowercase__ : int = inputs.attention_mask
lowercase__ : Tuple = outputs.input_ids
lowercase__ : Dict = outputs.input_ids.copy()
lowercase__ : Optional[Any] = [
[-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
lowercase__ : List[Any] = outputs.attention_mask
assert all(len(__A ) == 5_1_2 for x in inputs.input_ids )
assert all(len(__A ) == 1_2_8 for x in outputs.input_ids )
return batch
def _compute_metrics(a ):
lowercase__ : Dict = pred.label_ids
lowercase__ : Any = pred.predictions
# all unnecessary tokens are removed
lowercase__ : str = tokenizer.batch_decode(__A , skip_special_tokens=__A )
lowercase__ : Any = tokenizer.batch_decode(__A , skip_special_tokens=__A )
lowercase__ : str = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__A ) )] ) / len(__A )
return {"accuracy": accuracy}
# map train dataset
lowercase__ : Tuple = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__A , batch_size=__A , remove_columns=['article', 'highlights'] , )
train_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
# same for validation dataset
lowercase__ : int = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__A , batch_size=__A , remove_columns=['article', 'highlights'] , )
val_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
lowercase__ : Dict = self.get_auto_remove_tmp_dir()
lowercase__ : List[str] = SeqaSeqTrainingArguments(
output_dir=__A , per_device_train_batch_size=__A , per_device_eval_batch_size=__A , predict_with_generate=__A , evaluation_strategy='steps' , do_train=__A , do_eval=__A , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
lowercase__ : Dict = SeqaSeqTrainer(
model=__A , args=__A , compute_metrics=_compute_metrics , train_dataset=__A , eval_dataset=__A , tokenizer=__A , )
# start training
trainer.train()
| 703
|
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=3_2 , a=2 , a=3 , a=1_6 , 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=1_0 , a=8 , a=["stage1", "stage2", "stage3"] , a=[1, 2, 3] , ) -> int:
lowercase__ : int = parent
lowercase__ : Union[str, Any] = batch_size
lowercase__ : Dict = image_size
lowercase__ : str = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : List[str] = embed_dim
lowercase__ : Any = depths
lowercase__ : Dict = num_heads
lowercase__ : List[str] = window_size
lowercase__ : int = mlp_ratio
lowercase__ : Tuple = qkv_bias
lowercase__ : Union[str, Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Tuple = drop_path_rate
lowercase__ : List[str] = hidden_act
lowercase__ : Optional[Any] = use_absolute_embeddings
lowercase__ : Optional[Any] = patch_norm
lowercase__ : Any = layer_norm_eps
lowercase__ : List[Any] = initializer_range
lowercase__ : List[str] = is_training
lowercase__ : int = scope
lowercase__ : Optional[int] = use_labels
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : List[str] = encoder_stride
lowercase__ : Optional[Any] = out_features
lowercase__ : Dict = out_indices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return MaskFormerSwinConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , )
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Tuple = MaskFormerSwinModel(config=a )
model.to(a )
model.eval()
lowercase__ : str = model(a )
lowercase__ : str = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ : Dict = 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 ) -> Optional[int]:
lowercase__ : List[Any] = MaskFormerSwinBackbone(config=a )
model.to(a )
model.eval()
lowercase__ : int = model(a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [1_3, 1_6, 1_6, 1_6] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [1_6, 3_2, 6_4] )
# verify ValueError
with self.parent.assertRaises(a ):
lowercase__ : Dict = ['stem']
lowercase__ : List[str] = MaskFormerSwinBackbone(config=a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : List[str] = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
lowerCamelCase__ : str = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : int = False
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = MaskFormerSwinModelTester(self )
lowercase__ : Tuple = ConfigTester(self , config_class=a , embed_dim=3_7 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
def _UpperCAmelCase ( self ) -> Tuple:
self.create_and_test_config_common_properties()
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:
return
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*a )
@unittest.skip('Swin does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
@unittest.skip('Swin does not support feedforward chunking' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
lowercase__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def _UpperCAmelCase ( self ) -> int:
pass
def _UpperCAmelCase ( self , a , a , a , a ) -> Tuple:
lowercase__ : Dict = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : str = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[Any] = outputs.hidden_states
lowercase__ : str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(a ) , a )
# Swin has a different seq_length
lowercase__ : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Tuple = (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] , )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = (
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:
lowercase__ : List[str] = 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"]
lowercase__ : List[str] = True
self.check_hidden_states_output(a , a , a , a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = 3
lowercase__ : str = (
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)
)
lowercase__ : Tuple = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Optional[int] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ : List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase__ : List[str] = 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"]
lowercase__ : int = True
self.check_hidden_states_output(a , a , a , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(a ):
lowercase__ : Union[str, Any] = 0
return t
def check_equivalence(a , a , a , a={} ):
with torch.no_grad():
lowercase__ : Optional[Any] = model(**a , return_dict=a , **a )
lowercase__ : Optional[int] = model(**a , return_dict=a , **a ).to_tuple()
def recursive_check(a , a ):
if isinstance(a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(a , a ):
recursive_check(a , a )
elif isinstance(a , a ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(a , a )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(a ) , set_nan_tensor_to_zero(a ) , atol=1e-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}. Dict has"""
f""" `nan`: {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}."""
) , )
recursive_check(a , a )
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
model.to(a )
model.eval()
lowercase__ : Tuple = self._prepare_for_class(a , a )
lowercase__ : Optional[Any] = self._prepare_for_class(a , a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : List[Any] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a )
lowercase__ : int = self._prepare_for_class(a , a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
lowercase__ : Dict = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : Optional[int] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase , _a):
lowerCamelCase__ : Dict = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowerCamelCase__ : Optional[int] = MaskFormerSwinConfig
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[int] = MaskFormerSwinModelTester(self )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
lowercase__ : Optional[Any] = backbone_class(a )
backbone.to(a )
backbone.eval()
lowercase__ : Union[str, Any] = backbone(**a )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , a )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowercase__ : List[str] = backbone(**a , output_hidden_states=a )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowercase__ , lowercase__ , lowercase__ : int = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowercase__ : List[Any] = backbone(**a , output_attentions=a )
self.assertIsNotNone(outputs.attentions )
| 645
| 0
|
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
_UpperCamelCase : Any = datasets.utils.logging.get_logger(__name__)
_UpperCamelCase : Tuple = ["names", "prefix"]
_UpperCamelCase : str = ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
_UpperCamelCase : List[Any] = ["encoding_errors", "on_bad_lines"]
_UpperCamelCase : List[Any] = ["date_format"]
@dataclass
class UpperCAmelCase_ ( datasets.BuilderConfig):
lowerCamelCase__ : Tuple = ","
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : Union[str, Any] = "infer"
lowerCamelCase__ : Tuple = None
lowerCamelCase__ : Tuple = None
lowerCamelCase__ : Any = None
lowerCamelCase__ : int = None
lowerCamelCase__ : Any = None
lowerCamelCase__ : int = True
lowerCamelCase__ : int = None
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : List[Any] = None
lowerCamelCase__ : Optional[Any] = None
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : List[str] = None
lowerCamelCase__ : str = None
lowerCamelCase__ : int = None
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : Optional[int] = True
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : List[Any] = True
lowerCamelCase__ : Any = None
lowerCamelCase__ : Dict = "."
lowerCamelCase__ : str = None
lowerCamelCase__ : Optional[Any] = "\""
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Union[str, Any] = None
lowerCamelCase__ : Union[str, Any] = None
lowerCamelCase__ : Any = True
lowerCamelCase__ : Any = True
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Optional[Any] = True
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = None
lowerCamelCase__ : List[str] = 1_0_0_0_0
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Optional[Any] = "strict"
lowerCamelCase__ : Optional[int] = "error"
lowerCamelCase__ : Optional[Any] = None
def _UpperCAmelCase ( self ) -> int:
if self.delimiter is not None:
lowercase__ : Optional[Any] = self.delimiter
if self.column_names is not None:
lowercase__ : int = self.column_names
@property
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Union[str, Any] = {
"sep": self.sep,
"header": self.header,
"names": self.names,
"index_col": self.index_col,
"usecols": self.usecols,
"prefix": self.prefix,
"mangle_dupe_cols": self.mangle_dupe_cols,
"engine": self.engine,
"converters": self.converters,
"true_values": self.true_values,
"false_values": self.false_values,
"skipinitialspace": self.skipinitialspace,
"skiprows": self.skiprows,
"nrows": self.nrows,
"na_values": self.na_values,
"keep_default_na": self.keep_default_na,
"na_filter": self.na_filter,
"verbose": self.verbose,
"skip_blank_lines": self.skip_blank_lines,
"thousands": self.thousands,
"decimal": self.decimal,
"lineterminator": self.lineterminator,
"quotechar": self.quotechar,
"quoting": self.quoting,
"escapechar": self.escapechar,
"comment": self.comment,
"encoding": self.encoding,
"dialect": self.dialect,
"error_bad_lines": self.error_bad_lines,
"warn_bad_lines": self.warn_bad_lines,
"skipfooter": self.skipfooter,
"doublequote": self.doublequote,
"memory_map": self.memory_map,
"float_precision": self.float_precision,
"chunksize": self.chunksize,
"encoding_errors": self.encoding_errors,
"on_bad_lines": self.on_bad_lines,
"date_format": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , a ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class UpperCAmelCase_ ( datasets.ArrowBasedBuilder):
lowerCamelCase__ : List[Any] = CsvConfig
def _UpperCAmelCase ( self ) -> int:
return datasets.DatasetInfo(features=self.config.features )
def _UpperCAmelCase ( self , a ) -> int:
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}""" )
lowercase__ : Optional[Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(a , (str, list, tuple) ):
lowercase__ : List[str] = data_files
if isinstance(a , a ):
lowercase__ : str = [files]
lowercase__ : Union[str, Any] = [dl_manager.iter_files(a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
lowercase__ : str = []
for split_name, files in data_files.items():
if isinstance(a , a ):
lowercase__ : Any = [files]
lowercase__ : int = [dl_manager.iter_files(a ) for file in files]
splits.append(datasets.SplitGenerator(name=a , gen_kwargs={'files': files} ) )
return splits
def _UpperCAmelCase ( self , a ) -> pa.Table:
if self.config.features is not None:
lowercase__ : Tuple = self.config.features.arrow_schema
if all(not require_storage_cast(a ) for feature in self.config.features.values() ):
# cheaper cast
lowercase__ : int = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=a )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
lowercase__ : int = table_cast(a , a )
return pa_table
def _UpperCAmelCase ( self , a ) -> Union[str, Any]:
lowercase__ : List[str] = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
lowercase__ : Optional[int] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(a ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(a ) ):
lowercase__ : List[Any] = pd.read_csv(a , iterator=a , dtype=a , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(a ):
lowercase__ : List[str] = pa.Table.from_pandas(a )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(a )
except ValueError as e:
logger.error(f"""Failed to read file '{file}' with error {type(a )}: {e}""" )
raise
| 704
|
"""simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 645
| 0
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : str = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_UpperCamelCase : Optional[int] = {
"vocab_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli": (
"https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
),
},
}
_UpperCamelCase : List[Any] = {
"squeezebert/squeezebert-uncased": 5_12,
"squeezebert/squeezebert-mnli": 5_12,
"squeezebert/squeezebert-mnli-headless": 5_12,
}
_UpperCamelCase : List[str] = {
"squeezebert/squeezebert-uncased": {"do_lower_case": True},
"squeezebert/squeezebert-mnli": {"do_lower_case": True},
"squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE):
lowerCamelCase__ : Dict = VOCAB_FILES_NAMES
lowerCamelCase__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Dict = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : Optional[Any] = SqueezeBertTokenizer
def __init__( self , a=None , a=None , a=True , a="[UNK]" , a="[SEP]" , a="[PAD]" , a="[CLS]" , a="[MASK]" , a=True , a=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 , )
lowercase__ : Union[str, Any] = 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
):
lowercase__ : List[Any] = getattr(_a , normalizer_state.pop('type' ) )
lowercase__ : Union[str, Any] = do_lower_case
lowercase__ : List[str] = strip_accents
lowercase__ : Dict = tokenize_chinese_chars
lowercase__ : List[str] = normalizer_class(**_a )
lowercase__ : Any = do_lower_case
def _UpperCAmelCase ( self , a , a=None ) -> Dict:
lowercase__ : Any = [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 _UpperCAmelCase ( self , a , a = None ) -> List[Any]:
lowercase__ : Dict = [self.sep_token_id]
lowercase__ : Union[str, Any] = [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 _UpperCAmelCase ( self , a , a = None ) -> List[str]:
lowercase__ : Union[str, Any] = self._tokenizer.model.save(_a , name=_a )
return tuple(_a )
| 705
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ , lowercase__ : str = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : List[str] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : List[Any] = controlnet_params
lowercase__ : int = 'bird'
lowercase__ : List[Any] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([canny_image] * num_samples )
lowercase__ : List[Any] = jax.random.PRNGKey(0 )
lowercase__ : Tuple = jax.random.split(a , jax.device_count() )
lowercase__ : str = replicate(a )
lowercase__ : List[str] = shard(a )
lowercase__ : Dict = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : Tuple = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : int = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : Optional[Any] = jnp.array(
[0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ , lowercase__ : int = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : Optional[Any] = controlnet_params
lowercase__ : List[Any] = 'Chef in the kitchen'
lowercase__ : List[str] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
lowercase__ : List[str] = jax.random.PRNGKey(0 )
lowercase__ : str = jax.random.split(a , jax.device_count() )
lowercase__ : Optional[Any] = replicate(a )
lowercase__ : Optional[Any] = shard(a )
lowercase__ : List[Any] = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Union[str, Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : str = jnp.array(
[[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 645
| 0
|
"""simple docstring"""
import os
import sys
import unittest
_UpperCamelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCamelCase : Union[str, Any] = os.path.join(git_repo_path, "src", "diffusers")
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = find_backend(' if not is_torch_available():' )
self.assertEqual(a_ , 'torch' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
lowercase__ : Any = find_backend(' if not (is_torch_available() and is_transformers_available()):' )
self.assertEqual(a_ , 'torch_and_transformers' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
lowercase__ : Union[str, Any] = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):' )
self.assertEqual(a_ , 'torch_and_transformers_and_onnx' )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Dict = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , a_ )
self.assertIn('torch_and_transformers' , a_ )
self.assertIn('flax_and_transformers' , a_ )
self.assertIn('torch_and_transformers_and_onnx' , a_ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'] )
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'] )
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'] )
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'] )
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'] )
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'] )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(a_ , '\nCONSTANT = None\n' )
lowercase__ : Optional[int] = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
a_ , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
lowercase__ : List[Any] = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
lowercase__ : Union[str, Any] = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(a_ , a_ )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Union[str, Any] = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
lowercase__ : List[Any] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , a_ )
| 706
|
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 645
| 0
|
"""simple docstring"""
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 UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Union[str, Any] = tempfile.mkdtemp()
lowercase__ : Tuple = BlipImageProcessor()
lowercase__ : Optional[int] = GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )
lowercase__ : str = BlipaProcessor(__lowerCAmelCase , __lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def _UpperCAmelCase ( self , **a ) -> List[Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase ).tokenizer
def _UpperCAmelCase ( self , **a ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase ).image_processor
def _UpperCAmelCase ( self ) -> Dict:
shutil.rmtree(self.tmpdirname )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[int] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
lowercase__ : List[Any] = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _UpperCAmelCase ( self ) -> int:
lowercase__ : str = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ : int = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowercase__ : Any = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
lowercase__ : Optional[Any] = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__lowerCAmelCase , padding_value=1.0 )
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 _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[int] = self.get_image_processor()
lowercase__ : Any = self.get_tokenizer()
lowercase__ : Dict = BlipaProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
lowercase__ : Optional[Any] = self.prepare_image_inputs()
lowercase__ : Tuple = image_processor(__lowerCAmelCase , return_tensors='np' )
lowercase__ : List[str] = processor(images=__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 _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Optional[Any] = self.get_image_processor()
lowercase__ : Dict = self.get_tokenizer()
lowercase__ : Any = BlipaProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
lowercase__ : Optional[Any] = 'lower newer'
lowercase__ : Optional[int] = processor(text=__lowerCAmelCase )
lowercase__ : Union[str, Any] = tokenizer(__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = self.get_image_processor()
lowercase__ : Optional[int] = self.get_tokenizer()
lowercase__ : List[str] = BlipaProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
lowercase__ : Any = 'lower newer'
lowercase__ : Optional[Any] = self.prepare_image_inputs()
lowercase__ : Tuple = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'input_ids', 'attention_mask'] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : int = self.get_image_processor()
lowercase__ : Tuple = self.get_tokenizer()
lowercase__ : str = BlipaProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
lowercase__ : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ : int = processor.batch_decode(__lowerCAmelCase )
lowercase__ : Optional[Any] = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : int = self.get_image_processor()
lowercase__ : Union[str, Any] = self.get_tokenizer()
lowercase__ : Tuple = BlipaProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
lowercase__ : Dict = 'lower newer'
lowercase__ : Optional[int] = self.prepare_image_inputs()
lowercase__ : Dict = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'input_ids', 'attention_mask'] )
| 707
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
lowercase__ : Union[str, Any] = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(a )
from datasets import load_dataset
lowercase__ : str = load_dataset('nielsr/rvlcdip-demo' )
lowercase__ : Tuple = dataset['train'][0]['image'].convert('RGB' )
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : List[str] = model(**a )
lowercase__ : List[Any] = outputs.logits
lowercase__ : Union[str, Any] = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , a )
lowercase__ : Tuple = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=a , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a_ ( _lowerCAmelCase : Any ):
for param in module.parameters():
lowercase__ : Optional[Any] = False
def a_ ( ):
lowercase__ : Optional[int] = """cuda""" if torch.cuda.is_available() else """cpu"""
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
lowercase__ : Union[str, Any] = """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 a_ ( _lowerCAmelCase : Union[str, Any] ):
lowercase__ : Union[str, Any] = plt.imshow(UpperCAmelCase__ )
fig.axes.get_xaxis().set_visible(UpperCAmelCase__ )
fig.axes.get_yaxis().set_visible(UpperCAmelCase__ )
plt.show()
def a_ ( ):
lowercase__ : Dict = datetime.now()
lowercase__ : Optional[Any] = current_time.strftime('%H:%M:%S' )
return timestamp
| 708
|
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase_ :
@staticmethod
def _UpperCAmelCase ( *a , **a ) -> int:
pass
def a_ ( _lowerCAmelCase : Image ):
'''simple docstring'''
lowercase__ : List[str] = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : Union[str, Any] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Union[str, Any] = DepthEstimationPipeline(model=a , image_processor=a )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self , a , a ) -> Optional[int]:
lowercase__ : Tuple = depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , a )
import datasets
lowercase__ : Tuple = datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
lowercase__ : List[Any] = depth_estimator(
[
Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'http://images.cocodataset.org/val2017/000000039769.jpg',
# RGBA
dataset[0]['file'],
# LA
dataset[1]['file'],
# L
dataset[2]['file'],
] )
self.assertEqual(
[
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
] , a , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@slow
@require_torch
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Tuple = 'Intel/dpt-large'
lowercase__ : Optional[int] = pipeline('depth-estimation' , model=a )
lowercase__ : List[Any] = depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
lowercase__ : Optional[Any] = hashimage(outputs['depth'] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item() ) , 2.662 )
@require_torch
def _UpperCAmelCase ( self ) -> Optional[int]:
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase : Optional[int] = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[str] = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Any = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : int = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
_UpperCamelCase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 709
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class UpperCAmelCase_ ( _a):
def __init__( self ) -> Any:
lowercase__ : Tuple = []
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_init_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[int]:
self.events.append('on_train_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_train_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_epoch_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[Any]:
self.events.append('on_epoch_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_step_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> str:
self.events.append('on_step_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_evaluate' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Tuple:
self.events.append('on_predict' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Union[str, Any]:
self.events.append('on_save' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_log' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_prediction_step' )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> str:
lowercase__ : str = tempfile.mkdtemp()
def _UpperCAmelCase ( self ) -> Dict:
shutil.rmtree(self.output_dir )
def _UpperCAmelCase ( self , a=0 , a=0 , a=6_4 , a=6_4 , a=None , a=False , **a ) -> int:
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
lowercase__ : str = RegressionDataset(length=a )
lowercase__ : Any = RegressionDataset(length=a )
lowercase__ : Optional[Any] = RegressionModelConfig(a=a , b=a )
lowercase__ : Union[str, Any] = RegressionPreTrainedModel(a )
lowercase__ : Tuple = TrainingArguments(self.output_dir , disable_tqdm=a , report_to=[] , **a )
return Trainer(
a , a , train_dataset=a , eval_dataset=a , callbacks=a , )
def _UpperCAmelCase ( self , a , a ) -> Union[str, Any]:
self.assertEqual(len(a ) , len(a ) )
# Order doesn't matter
lowercase__ : Optional[int] = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
lowercase__ : Tuple = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
for cba, cba in zip(a , a ):
if isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(a , a )
elif isinstance(a , a ) and not isinstance(a , a ):
self.assertEqual(a , cba.__class__ )
elif not isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(cba.__class__ , a )
else:
self.assertEqual(a , a )
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
lowercase__ : Dict = ['on_init_end', 'on_train_begin']
lowercase__ : List[Any] = 0
lowercase__ : Optional[int] = len(trainer.get_eval_dataloader() )
lowercase__ : Tuple = ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('on_epoch_begin' )
for _ in range(a ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save' )
expected_events.append('on_epoch_end' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : int = self.get_trainer()
lowercase__ : str = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# Callbacks passed at init are added to the default callbacks
lowercase__ : str = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
lowercase__ : List[Any] = self.get_trainer(disable_tqdm=a )
lowercase__ : Optional[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
lowercase__ : List[str] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Optional[Any] = self.get_trainer()
lowercase__ : List[Any] = trainer.pop_callback(a )
self.assertEqual(cb.__class__ , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# We can also add, pop, or remove by instance
lowercase__ : int = self.get_trainer()
lowercase__ : List[str] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Tuple = self.get_trainer()
lowercase__ : Dict = trainer.callback_handler.callbacks[0]
lowercase__ : Union[str, Any] = trainer.pop_callback(a )
self.assertEqual(a , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Tuple:
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' , category=a )
lowercase__ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# Independent log/save/eval
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
lowercase__ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
lowercase__ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='steps' )
trainer.train()
lowercase__ : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='epoch' )
trainer.train()
lowercase__ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# A bit of everything
lowercase__ : Any = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=1_0 , eval_steps=5 , evaluation_strategy='steps' , )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
lowercase__ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(a ) in warn_mock.call_args[0][0]
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : Optional[int] ={
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[Any] =[
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
_UpperCamelCase : Tuple =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 710
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Tuple = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Dict = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
_UpperCamelCase : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 645
| 0
|
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCamelCase : List[Any] = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
_UpperCamelCase : Any = direct_transformers_import(PATH_TO_TRANSFORMERS)
_UpperCamelCase : Any = transformers.models.auto.configuration_auto.CONFIG_MAPPING
_UpperCamelCase : Union[str, Any] = {
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used as `self.bert_model = BertModel(config, ...)`
"DPRConfig": True,
# not used in modeling files, but it's an important information
"FSMTConfig": ["langs"],
# used internally in the configuration class file
"GPTNeoConfig": ["attention_types"],
# used internally in the configuration class file
"EsmConfig": ["is_folding_model"],
# used during training (despite we don't have training script for these models yet)
"Mask2FormerConfig": ["ignore_value"],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"OneFormerConfig": ["ignore_value", "norm"],
# used during preprocessing and collation, see `collating_graphormer.py`
"GraphormerConfig": ["spatial_pos_max"],
# used internally in the configuration class file
"T5Config": ["feed_forward_proj"],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"MT5Config": ["feed_forward_proj", "tokenizer_class"],
"UMT5Config": ["feed_forward_proj", "tokenizer_class"],
# used internally in the configuration class file
"LongT5Config": ["feed_forward_proj"],
# used internally in the configuration class file
"SwitchTransformersConfig": ["feed_forward_proj"],
# having default values other than `1e-5` - we can't fix them without breaking
"BioGptConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"GLPNConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"SegformerConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"CvtConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"PerceiverConfig": ["layer_norm_eps"],
# used internally to calculate the feature size
"InformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"AutoformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate `mlp_dim`
"SamVisionConfig": ["mlp_ratio"],
# For (head) training, but so far not implemented
"ClapAudioConfig": ["num_classes"],
# Not used, but providing useful information to users
"SpeechT5HifiGanConfig": ["sampling_rate"],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"CLIPSegConfig": True,
"DeformableDetrConfig": True,
"DetaConfig": True,
"DinatConfig": True,
"DonutSwinConfig": True,
"EfficientFormerConfig": True,
"FSMTConfig": True,
"JukeboxConfig": True,
"LayoutLMv2Config": True,
"MaskFormerSwinConfig": True,
"MT5Config": True,
"NatConfig": True,
"OneFormerConfig": True,
"PerceiverConfig": True,
"RagConfig": True,
"SpeechT5Config": True,
"SwinConfig": True,
"Swin2SRConfig": True,
"Swinv2Config": True,
"SwitchTransformersConfig": True,
"TableTransformerConfig": True,
"TapasConfig": True,
"TransfoXLConfig": True,
"UniSpeechConfig": True,
"UniSpeechSatConfig": True,
"WavLMConfig": True,
"WhisperConfig": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"JukeboxPriorConfig": True,
# TODO: @Younes (for `is_decoder`)
"Pix2StructTextConfig": True,
}
)
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : int = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f"""config.{attribute}""" in modeling_source
or f"""getattr(config, \"{attribute}\"""" in modeling_source
or f"""getattr(self.config, \"{attribute}\"""" in modeling_source
):
lowercase__ : Optional[Any] = True
# Deal with multi-line cases
elif (
re.search(
Rf"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"""" , _snake_case , )
is not None
):
lowercase__ : Any = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
lowercase__ : List[str] = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
lowercase__ : str = [
'bos_index',
'eos_index',
'pad_index',
'unk_index',
'mask_index',
'image_size',
'use_cache',
'out_features',
'out_indices',
]
lowercase__ : Dict = ['encoder_no_repeat_ngram_size']
# Special cases to be allowed
lowercase__ : Any = True
if not attribute_used:
lowercase__ : str = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
lowercase__ : Dict = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
lowercase__ : Dict = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
lowercase__ : List[str] = True
elif attribute.endswith('_token_id' ):
lowercase__ : int = True
# configuration class specific cases
if not case_allowed:
lowercase__ : Optional[int] = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
lowercase__ : Tuple = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def a_ ( _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : Optional[int] = dict(inspect.signature(config_class.__init__ ).parameters )
lowercase__ : Dict = [x for x in list(signature.keys() ) if x not in ['self', 'kwargs']]
lowercase__ : Any = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
lowercase__ : Dict = {}
if len(config_class.attribute_map ) > 0:
lowercase__ : Optional[Any] = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
lowercase__ : str = inspect.getsourcefile(_snake_case )
lowercase__ : Tuple = os.path.dirname(_snake_case )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
lowercase__ : str = [os.path.join(_snake_case , _snake_case ) for fn in os.listdir(_snake_case ) if fn.startswith('modeling_' )]
# Get the source code strings
lowercase__ : int = []
for path in modeling_paths:
if os.path.isfile(_snake_case ):
with open(_snake_case ) as fp:
modeling_sources.append(fp.read() )
lowercase__ : List[Any] = []
for config_param, default_value in zip(_snake_case , _snake_case ):
# `attributes` here is all the variant names for `config_param`
lowercase__ : List[str] = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(_snake_case , _snake_case , _snake_case , _snake_case ):
unused_attributes.append(attributes[0] )
return sorted(_snake_case )
def a_ ( ):
'''simple docstring'''
lowercase__ : List[Any] = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
lowercase__ : List[Any] = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda _lowerCAmelCase : inspect.isclass(_snake_case )
and issubclass(_snake_case , _snake_case )
and inspect.getmodule(_snake_case ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
lowercase__ : Any = check_config_attributes_being_used(_snake_case )
if len(_snake_case ) > 0:
lowercase__ : List[Any] = unused_attributes
if len(_snake_case ) > 0:
lowercase__ : Optional[int] = 'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
for name, attributes in configs_with_unused_attributes.items():
error += f"""{name}: {attributes}\n"""
raise ValueError(_snake_case )
if __name__ == "__main__":
check_config_attributes()
| 711
|
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self , a ) -> str:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
lowercase__ : str = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = 'sshleifer/tiny-gpt2'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = 'sgugger/tiny-distilbert-classification'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , only_pretrain_model=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Optional[int] = 'sshleifer/tiny-gpt2'
lowercase__ : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : List[Any] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : Tuple = TensorFlowBenchmark(a , [config] )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : List[str] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : List[str] = TensorFlowBenchmark(a , [config] )
lowercase__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[int] = AutoConfig.from_pretrained(a )
lowercase__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a , [config] )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = 'patrickvonplaten/t5-tiny-random'
lowercase__ : Any = AutoConfig.from_pretrained(a )
lowercase__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : int = TensorFlowBenchmark(a , configs=[config] )
lowercase__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , use_xla=a , multi_process=a , )
lowercase__ : Any = TensorFlowBenchmark(a )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , save_to_csv=a , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(a , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(a , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(a , 'env.csv' ) , multi_process=a , )
lowercase__ : Union[str, Any] = TensorFlowBenchmark(a )
benchmark.run()
self.assertTrue(Path(os.path.join(a , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'env.csv' ) ).exists() )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Tuple = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(a ):
self.assertTrue(hasattr(a , 'sequential' ) )
self.assertTrue(hasattr(a , 'cumulative' ) )
self.assertTrue(hasattr(a , 'current' ) )
self.assertTrue(hasattr(a , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(a , 'log.txt' ) , log_print=a , trace_memory_line_by_line=a , eager_mode=a , multi_process=a , )
lowercase__ : Optional[int] = TensorFlowBenchmark(a )
lowercase__ : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(a , 'log.txt' ) ).exists() )
| 645
| 0
|
"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if "resnet-50" in model_name:
lowercase__ : str = ResNetConfig.from_pretrained('microsoft/resnet-50' )
elif "resnet-101" in model_name:
lowercase__ : str = ResNetConfig.from_pretrained('microsoft/resnet-101' )
else:
raise ValueError('Model name should include either resnet50 or resnet101' )
lowercase__ : int = DetrConfig(use_timm_backbone=_lowercase , backbone_config=_lowercase )
# set label attributes
lowercase__ : Dict = '''panoptic''' in model_name
if is_panoptic:
lowercase__ : str = 250
else:
lowercase__ : Any = 91
lowercase__ : Optional[int] = '''huggingface/label-files'''
lowercase__ : Dict = '''coco-detection-id2label.json'''
lowercase__ : Tuple = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type='dataset' ) , 'r' ) )
lowercase__ : int = {int(_lowercase ): v for k, v in idalabel.items()}
lowercase__ : str = idalabel
lowercase__ : Optional[int] = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : List[Any] = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight') )
rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight') )
rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias') )
rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean') )
rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
f"""encoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
f"""decoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
) )
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
) )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
] )
return rename_keys
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : int = state_dict.pop(_lowercase )
lowercase__ : List[str] = val
def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : str=False ):
'''simple docstring'''
lowercase__ : Tuple = ''''''
if is_panoptic:
lowercase__ : List[Any] = '''detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ : int = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
lowercase__ : List[str] = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase__ : Any = in_proj_weight[:256, :]
lowercase__ : int = in_proj_bias[:256]
lowercase__ : Any = in_proj_weight[256:512, :]
lowercase__ : Union[str, Any] = in_proj_bias[256:512]
lowercase__ : int = in_proj_weight[-256:, :]
lowercase__ : int = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase__ : Optional[int] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
lowercase__ : str = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase__ : Optional[int] = in_proj_weight[:256, :]
lowercase__ : Tuple = in_proj_bias[:256]
lowercase__ : Union[str, Any] = in_proj_weight[256:512, :]
lowercase__ : int = in_proj_bias[256:512]
lowercase__ : List[str] = in_proj_weight[-256:, :]
lowercase__ : Optional[Any] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase__ : Dict = state_dict.pop(
f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
lowercase__ : Any = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase__ : Union[str, Any] = in_proj_weight_cross_attn[:256, :]
lowercase__ : int = in_proj_bias_cross_attn[:256]
lowercase__ : Tuple = in_proj_weight_cross_attn[256:512, :]
lowercase__ : List[Any] = in_proj_bias_cross_attn[256:512]
lowercase__ : Union[str, Any] = in_proj_weight_cross_attn[-256:, :]
lowercase__ : Any = in_proj_bias_cross_attn[-256:]
def a_ ( ):
'''simple docstring'''
lowercase__ : Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : Dict = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : Optional[int]=False ):
'''simple docstring'''
lowercase__ : str = get_detr_config(_lowercase )
# load original model from torch hub
lowercase__ : Optional[int] = {
'''detr-resnet-50''': '''detr_resnet50''',
'''detr-resnet-101''': '''detr_resnet101''',
}
logger.info(f"""Converting model {model_name}...""" )
lowercase__ : str = torch.hub.load('facebookresearch/detr' , model_name_to_original_name[model_name] , pretrained=_lowercase ).eval()
lowercase__ : int = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(_lowercase ):
if is_panoptic:
lowercase__ : List[str] = '''detr.''' + src
rename_key(_lowercase , _lowercase , _lowercase )
# query, key and value matrices need special treatment
read_in_q_k_v(_lowercase , is_panoptic=_lowercase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ : Optional[Any] = '''detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
lowercase__ : str = state_dict.pop(_lowercase )
lowercase__ : Any = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
lowercase__ : Optional[Any] = state_dict.pop(_lowercase )
lowercase__ : Optional[int] = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
lowercase__ : Optional[Any] = state_dict.pop(_lowercase )
lowercase__ : Union[str, Any] = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
lowercase__ : Optional[Any] = state_dict.pop(_lowercase )
lowercase__ : List[str] = val
# finally, create HuggingFace model and load state dict
lowercase__ : List[str] = DetrForSegmentation(_lowercase ) if is_panoptic else DetrForObjectDetection(_lowercase )
model.load_state_dict(_lowercase )
model.eval()
# verify our conversion on an image
lowercase__ : List[Any] = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
lowercase__ : List[str] = DetrImageProcessor(format=_lowercase )
lowercase__ : List[str] = processor(images=prepare_img() , return_tensors='pt' )
lowercase__ : Optional[int] = encoding['''pixel_values''']
lowercase__ : List[str] = detr(_lowercase )
lowercase__ : List[Any] = model(_lowercase )
assert torch.allclose(outputs.logits , original_outputs['pred_logits'] , atol=1E-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs['pred_boxes'] , atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['pred_masks'] , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
processor.save_pretrained(_lowercase )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('Uploading PyTorch model and image processor to the hub...' )
model.push_to_hub(f"""nielsr/{model_name}""" )
processor.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
_UpperCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
_UpperCamelCase : List[str] = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 712
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class UpperCAmelCase_ ( _a):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=False , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=3 , a=4 , a=None , ) -> Any:
lowercase__ : Tuple = parent
lowercase__ : List[Any] = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : List[Any] = is_training
lowercase__ : Optional[Any] = use_input_mask
lowercase__ : Optional[int] = use_token_type_ids
lowercase__ : int = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : int = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : List[str] = num_attention_heads
lowercase__ : Optional[Any] = intermediate_size
lowercase__ : Optional[Any] = hidden_act
lowercase__ : List[str] = hidden_dropout_prob
lowercase__ : List[Any] = attention_probs_dropout_prob
lowercase__ : List[Any] = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : Tuple = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = num_labels
lowercase__ : Tuple = num_choices
lowercase__ : str = scope
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_input_mask:
lowercase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Dict = None
lowercase__ : Optional[Any] = None
lowercase__ : int = None
if self.use_labels:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self ) -> Optional[int]:
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Tuple = DistilBertModel(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , a )
lowercase__ : str = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
lowercase__ : Union[str, Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> int:
lowercase__ : Tuple = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(
a , attention_mask=a , start_positions=a , end_positions=a )
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 , a , a , a , a , a , a ) -> List[str]:
lowercase__ : int = self.num_labels
lowercase__ : Dict = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
lowercase__ : Optional[Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Any:
lowercase__ : Any = self.num_labels
lowercase__ : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Tuple:
lowercase__ : List[Any] = self.num_choices
lowercase__ : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
lowercase__ : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : int = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Union[str, Any] = self.prepare_config_and_inputs()
((lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__)) : List[str] = config_and_inputs
lowercase__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : List[str] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ : str = (
{
"feature-extraction": DistilBertModel,
"fill-mask": DistilBertForMaskedLM,
"question-answering": DistilBertForQuestionAnswering,
"text-classification": DistilBertForSequenceClassification,
"token-classification": DistilBertForTokenClassification,
"zero-shot": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Optional[int] = True
lowerCamelCase__ : Any = True
lowerCamelCase__ : List[Any] = True
lowerCamelCase__ : Optional[Any] = True
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : str = DistilBertModelTester(self )
lowercase__ : int = ConfigTester(self , config_class=a , dim=3_7 )
def _UpperCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : str = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
lowercase__ : Optional[int] = True
lowercase__ : Union[str, Any] = model_class(config=a )
lowercase__ : int = self._prepare_for_class(a , a )
lowercase__ : Tuple = torch.jit.trace(
a , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , 'traced_model.pt' ) )
lowercase__ : Optional[int] = torch.jit.load(os.path.join(a , 'traced_model.pt' ) , map_location=a )
loaded(inputs_dict['input_ids'].to(a ) , inputs_dict['attention_mask'].to(a ) )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : int = DistilBertModel.from_pretrained('distilbert-base-uncased' )
lowercase__ : Union[str, Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase__ : Optional[Any] = model(a , attention_mask=a )[0]
lowercase__ : Tuple = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , a )
lowercase__ : List[Any] = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import argparse
import os
import re
import packaging.version
_UpperCamelCase : Union[str, Any] = "examples/"
_UpperCamelCase : Any = {
"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 : List[Any] = {
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
_UpperCamelCase : Tuple = "README.md"
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Dict ):
'''simple docstring'''
with open(__UpperCamelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowercase__ : List[Any] = f.read()
lowercase__ , lowercase__ : int = REPLACE_PATTERNS[pattern]
lowercase__ : Optional[int] = replace.replace('VERSION' , __UpperCamelCase )
lowercase__ : Tuple = re_pattern.sub(__UpperCamelCase , __UpperCamelCase )
with open(__UpperCamelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(__UpperCamelCase )
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
for folder, directories, fnames in os.walk(__UpperCamelCase ):
# 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(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase , pattern='examples' )
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict=False ):
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if not patch:
update_version_in_examples(__UpperCamelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : int = '🤗 Transformers currently provides the following architectures'
lowercase__ : Tuple = '1. Want to contribute a new model?'
with open(__UpperCamelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowercase__ : List[Any] = f.readlines()
# Find the start of the list.
lowercase__ : Optional[int] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowercase__ : Optional[Any] = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
lowercase__ : Optional[int] = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(__UpperCamelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(__UpperCamelCase )
def a_ ( ):
'''simple docstring'''
with open(REPLACE_FILES['init'] , 'r' ) as f:
lowercase__ : Tuple = f.read()
lowercase__ : Optional[int] = REPLACE_PATTERNS['init'][0].search(__UpperCamelCase ).groups()[0]
return packaging.version.parse(__UpperCamelCase )
def a_ ( _lowerCAmelCase : Union[str, Any]=False ):
'''simple docstring'''
lowercase__ : int = 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:
lowercase__ : Union[str, Any] = default_version.base_version
elif patch:
lowercase__ : List[str] = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
lowercase__ : Dict = f"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
lowercase__ : Tuple = input(f"""Which version are you releasing? [{default_version}]""" )
if len(__UpperCamelCase ) == 0:
lowercase__ : int = default_version
print(f"""Updating version to {version}.""" )
global_version_update(__UpperCamelCase , patch=__UpperCamelCase )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = get_version()
lowercase__ : Union[str, Any] = f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
lowercase__ : Tuple = current_version.base_version
# Check with the user we got that right.
lowercase__ : Optional[int] = input(f"""Which version are we developing now? [{dev_version}]""" )
if len(__UpperCamelCase ) == 0:
lowercase__ : List[Any] = dev_version
print(f"""Updating version to {version}.""" )
global_version_update(__UpperCamelCase )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
_UpperCamelCase : Dict = 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 : Union[str, Any] = 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()
| 713
|
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif stress < 0:
raise ValueError('Stress cannot be negative' )
elif tangential_force < 0:
raise ValueError('Tangential Force cannot be negative' )
elif area < 0:
raise ValueError('Area cannot be negative' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCamelCase : Any = logging.get_logger(__name__)
_UpperCamelCase : Any = {
"facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}
class UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__):
lowerCamelCase__ : Optional[int] = 'convnextv2'
def __init__( self , a=3 , a=4 , a=4 , a=None , a=None , a="gelu" , a=0.02 , a=1e-12 , a=0.0 , a=2_2_4 , a=None , a=None , **a , ) -> Optional[int]:
super().__init__(**a )
lowercase__ : Dict = num_channels
lowercase__ : Optional[Any] = patch_size
lowercase__ : str = num_stages
lowercase__ : List[Any] = [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
lowercase__ : str = [3, 3, 9, 3] if depths is None else depths
lowercase__ : int = hidden_act
lowercase__ : int = initializer_range
lowercase__ : str = layer_norm_eps
lowercase__ : Optional[int] = drop_path_rate
lowercase__ : Dict = image_size
lowercase__ : List[str] = ["""stem"""] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
lowercase__ : str = get_aligned_output_features_output_indices(
out_features=a , out_indices=a , stage_names=self.stage_names )
| 714
|
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=[3_0, 3_0] , a=2 , a=3 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=3 , a=None , a=8 , a=1_0 , ) -> Any:
lowercase__ : List[str] = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Optional[int] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : str = is_training
lowercase__ : Optional[Any] = use_labels
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Dict = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Any = type_sequence_label_size
lowercase__ : Dict = initializer_range
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Tuple = scope
lowercase__ : Tuple = n_targets
lowercase__ : Optional[int] = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowercase__ : Optional[Any] = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowercase__ : Tuple = num_patches + 1 + self.num_detection_tokens
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowercase__ : Tuple = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowercase__ : int = []
for i in range(self.batch_size ):
lowercase__ : Optional[Any] = {}
lowercase__ : Any = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=a )
lowercase__ : List[str] = torch.rand(self.n_targets , 4 , device=a )
labels.append(a )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> List[Any]:
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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=a , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _UpperCAmelCase ( self , a , a , a ) -> int:
lowercase__ : List[str] = YolosModel(config=a )
model.to(a )
model.eval()
lowercase__ : List[Any] = model(a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = YolosForObjectDetection(a )
model.to(a )
model.eval()
lowercase__ : Dict = model(pixel_values=a )
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowercase__ : str = model(pixel_values=a , labels=a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Any = config_and_inputs
lowercase__ : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
lowerCamelCase__ : List[str] = (
{"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
)
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Union[str, Any] = False
def _UpperCAmelCase ( self , a , a , a=False ) -> Dict:
lowercase__ : List[str] = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowercase__ : Optional[Any] = []
for i in range(self.model_tester.batch_size ):
lowercase__ : Dict = {}
lowercase__ : Dict = torch.ones(
size=(self.model_tester.n_targets,) , device=a , dtype=torch.long )
lowercase__ : Optional[Any] = torch.ones(
self.model_tester.n_targets , 4 , device=a , dtype=torch.float )
labels.append(a )
lowercase__ : Union[str, Any] = labels
return inputs_dict
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Dict = YolosModelTester(self )
lowercase__ : Optional[int] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
# YOLOS does not use inputs_embeds
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
lowercase__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Tuple = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Dict = True
# in YOLOS, the seq_len is different
lowercase__ : Tuple = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowercase__ : Optional[int] = True
lowercase__ : str = False
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : str = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Optional[int] = True
lowercase__ : List[Any] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[str] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : Dict = len(a )
# Check attention is always last and order is fine
lowercase__ : Any = True
lowercase__ : int = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = 1
self.assertEqual(out_len + added_hidden_states , len(a ) )
lowercase__ : Tuple = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> List[str]:
def check_hidden_states_output(a , a , a ):
lowercase__ : str = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : int = model(**self._prepare_for_class(a , a ) )
lowercase__ : int = outputs.hidden_states
lowercase__ : Any = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(a ) , a )
# YOLOS has a different seq_length
lowercase__ : Optional[int] = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : List[Any] = True
check_hidden_states_output(a , a , a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*a )
@slow
def _UpperCAmelCase ( self ) -> Union[str, Any]:
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : int = YolosModel.from_pretrained(a )
self.assertIsNotNone(a )
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return AutoImageProcessor.from_pretrained('hustvl/yolos-small' ) if is_vision_available() else None
@slow
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = YolosForObjectDetection.from_pretrained('hustvl/yolos-small' ).to(a )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : Optional[int] = prepare_img()
lowercase__ : int = image_processor(images=a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : int = model(inputs.pixel_values )
# verify outputs
lowercase__ : Tuple = torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : Any = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] , device=a , )
lowercase__ : List[str] = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] , device=a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 ) )
# verify postprocessing
lowercase__ : Optional[Any] = image_processor.post_process_object_detection(
a , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowercase__ : str = torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861] ).to(a )
lowercase__ : Any = [7_5, 7_5, 1_7, 6_3, 1_7]
lowercase__ : Optional[int] = torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495] ).to(a )
self.assertEqual(len(results['scores'] ) , 5 )
self.assertTrue(torch.allclose(results['scores'] , a , atol=1e-4 ) )
self.assertSequenceEqual(results['labels'].tolist() , a )
self.assertTrue(torch.allclose(results['boxes'][0, :] , a ) )
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase : Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeTowerTextConfig",
"BridgeTowerVisionConfig",
],
"processing_bridgetower": ["BridgeTowerProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : int = ["BridgeTowerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[str] = [
"BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
"BridgeTowerForContrastiveLearning",
"BridgeTowerForImageAndTextRetrieval",
"BridgeTowerForMaskedLM",
"BridgeTowerModel",
"BridgeTowerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
_UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 715
|
"""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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_UpperCamelCase : int = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ :
def __init__( self , a=False , a=False , a=6.0 , a=None , a=False , a=False , a=None , a="fp4" , a=False , **a , ) -> Tuple:
lowercase__ : str = load_in_abit
lowercase__ : str = load_in_abit
lowercase__ : List[str] = llm_inta_threshold
lowercase__ : Dict = llm_inta_skip_modules
lowercase__ : Tuple = llm_inta_enable_fpaa_cpu_offload
lowercase__ : Any = llm_inta_has_fpaa_weight
lowercase__ : Any = bnb_abit_quant_type
lowercase__ : Dict = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
lowercase__ : Dict = torch.floataa
elif isinstance(a , a ):
lowercase__ : Any = getattr(a , a )
elif isinstance(a , torch.dtype ):
lowercase__ : Any = bnb_abit_compute_dtype
else:
raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype' )
self.post_init()
def _UpperCAmelCase ( self ) -> str:
if not isinstance(self.llm_inta_threshold , a ):
raise ValueError('llm_int8_threshold must be a float' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , a ):
raise ValueError('llm_int8_skip_modules must be a list of strings' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , a ):
raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean' )
if not isinstance(self.llm_inta_has_fpaa_weight , a ):
raise ValueError('llm_int8_has_fp16_weight must be a boolean' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError('bnb_4bit_compute_dtype must be torch.dtype' )
if not isinstance(self.bnb_abit_quant_type , a ):
raise ValueError('bnb_4bit_quant_type must be a string' )
if not isinstance(self.bnb_abit_use_double_quant , a ):
raise ValueError('bnb_4bit_use_double_quant must be a boolean' )
if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes' ) ) >= version.parse(
'0.39.0' ):
raise ValueError(
'4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version' )
def _UpperCAmelCase ( self ) -> Tuple:
return self.load_in_abit or self.load_in_abit
def _UpperCAmelCase ( self ) -> List[str]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> Optional[Any]:
lowercase__ : List[Any] = cls(**a )
lowercase__ : Union[str, Any] = []
for key, value in kwargs.items():
if hasattr(a , a ):
setattr(a , a , a )
to_remove.append(a )
for key in to_remove:
kwargs.pop(a , a )
if return_unused_kwargs:
return config, kwargs
else:
return config
def _UpperCAmelCase ( self , a ) -> Dict:
with open(a , 'w' , encoding='utf-8' ) as writer:
lowercase__ : Any = self.to_dict()
lowercase__ : str = json.dumps(a , indent=2 , sort_keys=a ) + '\n'
writer.write(a )
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : Any = str(output['bnb_4bit_compute_dtype'] ).split('.' )[1]
return output
def __repr__( self ) -> Dict:
return f"""{self.__class__.__name__} {self.to_json_string()}"""
def _UpperCAmelCase ( self , a = True ) -> str:
if use_diff is True:
lowercase__ : List[Any] = self.to_diff_dict()
else:
lowercase__ : List[str] = self.to_dict()
return json.dumps(a , indent=2 , sort_keys=a ) + "\n"
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Tuple = self.to_dict()
# get the default config dict
lowercase__ : Optional[Any] = BitsAndBytesConfig().to_dict()
lowercase__ : int = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
lowercase__ : Optional[int] = value
return serializable_config_dict
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : List[str] = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
lowercase__ : Optional[int] = set()
# Replace all the whitespace in our sentence
lowercase__ : Union[str, Any] = input_str.replace(' ' , '' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(_lowerCAmelCase ) == 26
def a_ ( _lowerCAmelCase : Dict = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
lowercase__ : int = [False] * 26
for char in input_str:
if char.islower():
lowercase__ : Tuple = True
elif char.isupper():
lowercase__ : str = True
return all(_lowerCAmelCase )
def a_ ( _lowerCAmelCase : Tuple = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def a_ ( ):
'''simple docstring'''
from timeit import timeit
lowercase__ : Any = "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
print(timeit('is_pangram()' , setup=_lowerCAmelCase ) )
print(timeit('is_pangram_faster()' , setup=_lowerCAmelCase ) )
print(timeit('is_pangram_fastest()' , setup=_lowerCAmelCase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 716
|
"""simple docstring"""
import argparse
import gc
import json
import os
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_UpperCamelCase : int = 16
_UpperCamelCase : Union[str, Any] = 32
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
return int(x / 2**20 )
class UpperCAmelCase_ :
def __enter__( self ) -> Union[str, Any]:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase__ : List[str] = torch.cuda.memory_allocated()
return self
def __exit__( self , *a ) -> Any:
gc.collect()
torch.cuda.empty_cache()
lowercase__ : Optional[Any] = torch.cuda.memory_allocated()
lowercase__ : Union[str, Any] = torch.cuda.max_memory_allocated()
lowercase__ : List[Any] = bamb(self.end - self.begin )
lowercase__ : List[Any] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def a_ ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 , _lowerCAmelCase : str = "bert-base-cased" , _lowerCAmelCase : int = 320 , _lowerCAmelCase : int = 160 , ):
'''simple docstring'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
lowercase__ : Union[str, Any] = load_dataset(
'glue' , 'mrpc' , split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""} )
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase__ : Union[str, Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=_lowerCAmelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_lowerCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(_lowerCAmelCase , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['train'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
lowercase__ : Dict = DataLoader(
tokenized_datasets['validation'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[int] = config['lr']
lowercase__ : Optional[Any] = int(config['num_epochs'] )
lowercase__ : Optional[Any] = int(config['seed'] )
lowercase__ : int = int(config['batch_size'] )
lowercase__ : Union[str, Any] = args.model_name_or_path
set_seed(_lowerCAmelCase )
lowercase__ , lowercase__ : Tuple = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : List[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase , return_dict=_lowerCAmelCase )
# Instantiate optimizer
lowercase__ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase__ : Optional[Any] = optimizer_cls(params=model.parameters() , lr=_lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
lowercase__ : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowercase__ : List[Any] = 1
lowercase__ : List[Any] = (len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=0 , num_training_steps=_lowerCAmelCase , )
else:
lowercase__ : Tuple = DummyScheduler(_lowerCAmelCase , total_num_steps=_lowerCAmelCase , warmup_num_steps=0 )
# 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__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Dict = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
lowercase__ : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase__ : Tuple = 0
# Now we train the model
lowercase__ : Optional[Any] = {}
for epoch in range(_lowerCAmelCase , _lowerCAmelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
lowercase__ : List[Any] = model(**_lowerCAmelCase )
lowercase__ : Dict = outputs.loss
lowercase__ : int = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin ) ) )
accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used ) )
accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked ) )
accelerator.print(
'Total Peak Memory consumed during the train (max): {}'.format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase__ : int = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'peak_memory_utilization.json' ) , 'w' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : int = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=_lowerCAmelCase , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=_lowerCAmelCase , )
parser.add_argument(
'--output_dir' , type=_lowerCAmelCase , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--peak_memory_upper_bound' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.' , )
parser.add_argument(
'--n_train' , type=_lowerCAmelCase , default=320 , help='Number of training examples to use.' , )
parser.add_argument(
'--n_val' , type=_lowerCAmelCase , default=160 , help='Number of validation examples to use.' , )
parser.add_argument(
'--num_epochs' , type=_lowerCAmelCase , default=1 , help='Number of train epochs.' , )
lowercase__ : Any = parser.parse_args()
lowercase__ : Optional[Any] = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 645
| 0
|
"""simple docstring"""
from __future__ import annotations
_UpperCamelCase : List[Any] = list[tuple[int, int]]
_UpperCamelCase : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_UpperCamelCase : Optional[Any] = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class UpperCAmelCase_ :
def __init__( self , a , a , a , a , a , a , ) -> int:
lowercase__ : Union[str, Any] = pos_x
lowercase__ : Tuple = pos_y
lowercase__ : str = (pos_y, pos_x)
lowercase__ : Dict = goal_x
lowercase__ : Any = goal_y
lowercase__ : int = g_cost
lowercase__ : Dict = parent
lowercase__ : int = self.calculate_heuristic()
def _UpperCAmelCase ( self ) -> float:
lowercase__ : Tuple = abs(self.pos_x - self.goal_x )
lowercase__ : Any = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self , a ) -> bool:
return self.f_cost < other.f_cost
class UpperCAmelCase_ :
def __init__( self , a , a ) -> Optional[int]:
lowercase__ : Optional[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , a )
lowercase__ : Any = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , a )
lowercase__ : Dict = [self.start]
lowercase__ : int = []
lowercase__ : List[Any] = False
def _UpperCAmelCase ( self ) -> Path | None:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
lowercase__ : Tuple = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
lowercase__ : List[str] = True
return self.retrace_path(a )
self.closed_nodes.append(a )
lowercase__ : str = self.get_successors(a )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(a )
else:
# retrieve the best current path
lowercase__ : Any = self.open_nodes.pop(self.open_nodes.index(a ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(a )
else:
self.open_nodes.append(a )
if not self.reached:
return [self.start.pos]
return None
def _UpperCAmelCase ( self , a ) -> list[Node]:
lowercase__ : Any = []
for action in delta:
lowercase__ : Optional[int] = parent.pos_x + action[1]
lowercase__ : Any = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
a , a , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , a , ) )
return successors
def _UpperCAmelCase ( self , a ) -> Path:
lowercase__ : Union[str, Any] = node
lowercase__ : Dict = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowercase__ : Optional[int] = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
_UpperCamelCase : Dict = (0, 0)
_UpperCamelCase : Optional[int] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
_UpperCamelCase : List[str] = GreedyBestFirst(init, goal)
_UpperCamelCase : Any = greedy_bf.search()
if path:
for pos_x, pos_y in path:
_UpperCamelCase : str = 2
for elem in grid:
print(elem)
| 717
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
lowercase__ : List[str] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowercase__ : Dict = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowercase__ : Union[str, Any] = j
return prefix_result
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
return max(prefix_function(_lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
_UpperCamelCase : Union[str, Any] = {
"169M": 12,
"430M": 24,
"1B5": 24,
"3B": 32,
"7B": 32,
"14B": 40,
}
_UpperCamelCase : List[Any] = {
"169M": 7_68,
"430M": 10_24,
"1B5": 20_48,
"3B": 25_60,
"7B": 40_96,
"14B": 51_20,
}
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : List[str] = list(state_dict.keys() )
for name in state_dict_keys:
lowercase__ : Optional[int] = state_dict.pop(__lowerCAmelCase )
# emb -> embedding
if name.startswith('emb.' ):
lowercase__ : Tuple = name.replace('emb.' , 'embeddings.' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('blocks.0.ln0' ):
lowercase__ : Optional[int] = name.replace('blocks.0.ln0' , 'blocks.0.pre_ln' )
# att -> attention
lowercase__ : Union[str, Any] = re.sub(R'blocks\.(\d+)\.att' , R'blocks.\1.attention' , __lowerCAmelCase )
# ffn -> feed_forward
lowercase__ : Dict = re.sub(R'blocks\.(\d+)\.ffn' , R'blocks.\1.feed_forward' , __lowerCAmelCase )
# time_mix_k -> time_mix_key and reshape
if name.endswith('.time_mix_k' ):
lowercase__ : int = name.replace('.time_mix_k' , '.time_mix_key' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('.time_mix_v' ):
lowercase__ : Union[str, Any] = name.replace('.time_mix_v' , '.time_mix_value' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('.time_mix_r' ):
lowercase__ : int = name.replace('.time_mix_r' , '.time_mix_receptance' )
if name != "head.weight":
lowercase__ : List[str] = "rwkv." + name
lowercase__ : Optional[Any] = weight
return state_dict
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : int=False , _lowerCAmelCase : Dict=None ):
'''simple docstring'''
if tokenizer_file is None:
print('No `--tokenizer_file` provided, we will use the default tokenizer.' )
lowercase__ : str = 5_0277
lowercase__ : Optional[Any] = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b' )
else:
lowercase__ : Tuple = PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase )
lowercase__ : List[Any] = len(__lowerCAmelCase )
tokenizer.save_pretrained(__lowerCAmelCase )
# 2. Build the config
lowercase__ : Optional[int] = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
lowercase__ : Optional[Any] = candidate
break
if size is None:
raise ValueError('Could not infer the size, please provide it with the `--size` argument.' )
if size not in possible_sizes:
raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}.""" )
lowercase__ : Any = RwkvConfig(
vocab_size=__lowerCAmelCase , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(__lowerCAmelCase )
# 3. Download model file then convert state_dict
lowercase__ : str = hf_hub_download(__lowerCAmelCase , __lowerCAmelCase )
lowercase__ : Optional[int] = torch.load(__lowerCAmelCase , map_location='cpu' )
lowercase__ : Any = convert_state_dict(__lowerCAmelCase )
# 4. Split in shards and save
lowercase__ : List[str] = shard_checkpoint(__lowerCAmelCase )
for shard_file, shard in shards.items():
torch.save(__lowerCAmelCase , os.path.join(__lowerCAmelCase , __lowerCAmelCase ) )
if index is not None:
lowercase__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
# Save the index as well
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
lowercase__ : int = json.dumps(__lowerCAmelCase , indent=2 , sort_keys=__lowerCAmelCase ) + "\n"
f.write(__lowerCAmelCase )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.' )
lowercase__ : Union[str, Any] = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
lowercase__ : Union[str, Any] = torch.load(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(__lowerCAmelCase , __lowerCAmelCase ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('Please provide a `model_name` to push the model to the Hub.' )
lowercase__ : Optional[Any] = AutoModelForCausalLM.from_pretrained(__lowerCAmelCase )
model.push_to_hub(__lowerCAmelCase , max_shard_size='2GB' )
tokenizer.push_to_hub(__lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint."
)
parser.add_argument(
"--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo."
)
parser.add_argument(
"--output_dir", default=None, type=str, required=True, help="Where to save the converted model."
)
parser.add_argument(
"--tokenizer_file",
default=None,
type=str,
help="Path to the tokenizer file to use (if not provided, only the model is converted).",
)
parser.add_argument(
"--size",
default=None,
type=str,
help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Push to the Hub the converted model.",
)
parser.add_argument(
"--model_name",
default=None,
type=str,
help="Name of the pushed model on the Hub, including the username / organization.",
)
_UpperCamelCase : Optional[Any] = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 718
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=7 , a=3 , a=1_8 , a=3_0 , a=4_0_0 , a=True , a=None , a=True , a=None , a=True , ) -> List[str]:
lowercase__ : Tuple = size if size is not None else {'shortest_edge': 2_0}
lowercase__ : Union[str, Any] = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : str = num_channels
lowercase__ : Any = image_size
lowercase__ : Optional[Any] = min_resolution
lowercase__ : int = max_resolution
lowercase__ : List[Any] = do_resize
lowercase__ : List[str] = size
lowercase__ : str = do_center_crop
lowercase__ : List[Any] = crop_size
lowercase__ : Union[str, Any] = do_flip_channel_order
def _UpperCAmelCase ( self ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Optional[Any] = MobileViTImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Tuple = MobileViTImageProcessingTester(self )
@property
def _UpperCAmelCase ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a , 'do_resize' ) )
self.assertTrue(hasattr(a , 'size' ) )
self.assertTrue(hasattr(a , 'do_center_crop' ) )
self.assertTrue(hasattr(a , 'center_crop' ) )
self.assertTrue(hasattr(a , 'do_flip_channel_order' ) )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 2_0} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
lowercase__ : str = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> str:
# Initialize image_processing
lowercase__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a )
for image in image_inputs:
self.assertIsInstance(a , Image.Image )
# Test not batched input
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : List[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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Tuple:
# Initialize image_processing
lowercase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ : Tuple = 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
lowercase__ : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Dict:
# Initialize image_processing
lowercase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ : Optional[Any] = 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
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : Tuple = 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 645
| 0
|
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCamelCase : int = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
_UpperCamelCase : Union[str, Any] = direct_transformers_import(PATH_TO_TRANSFORMERS)
_UpperCamelCase : int = transformers.models.auto.configuration_auto.CONFIG_MAPPING
_UpperCamelCase : List[Any] = {
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used as `self.bert_model = BertModel(config, ...)`
"DPRConfig": True,
# not used in modeling files, but it's an important information
"FSMTConfig": ["langs"],
# used internally in the configuration class file
"GPTNeoConfig": ["attention_types"],
# used internally in the configuration class file
"EsmConfig": ["is_folding_model"],
# used during training (despite we don't have training script for these models yet)
"Mask2FormerConfig": ["ignore_value"],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"OneFormerConfig": ["ignore_value", "norm"],
# used during preprocessing and collation, see `collating_graphormer.py`
"GraphormerConfig": ["spatial_pos_max"],
# used internally in the configuration class file
"T5Config": ["feed_forward_proj"],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"MT5Config": ["feed_forward_proj", "tokenizer_class"],
"UMT5Config": ["feed_forward_proj", "tokenizer_class"],
# used internally in the configuration class file
"LongT5Config": ["feed_forward_proj"],
# used internally in the configuration class file
"SwitchTransformersConfig": ["feed_forward_proj"],
# having default values other than `1e-5` - we can't fix them without breaking
"BioGptConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"GLPNConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"SegformerConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"CvtConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"PerceiverConfig": ["layer_norm_eps"],
# used internally to calculate the feature size
"InformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"AutoformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate `mlp_dim`
"SamVisionConfig": ["mlp_ratio"],
# For (head) training, but so far not implemented
"ClapAudioConfig": ["num_classes"],
# Not used, but providing useful information to users
"SpeechT5HifiGanConfig": ["sampling_rate"],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"CLIPSegConfig": True,
"DeformableDetrConfig": True,
"DetaConfig": True,
"DinatConfig": True,
"DonutSwinConfig": True,
"EfficientFormerConfig": True,
"FSMTConfig": True,
"JukeboxConfig": True,
"LayoutLMv2Config": True,
"MaskFormerSwinConfig": True,
"MT5Config": True,
"NatConfig": True,
"OneFormerConfig": True,
"PerceiverConfig": True,
"RagConfig": True,
"SpeechT5Config": True,
"SwinConfig": True,
"Swin2SRConfig": True,
"Swinv2Config": True,
"SwitchTransformersConfig": True,
"TableTransformerConfig": True,
"TapasConfig": True,
"TransfoXLConfig": True,
"UniSpeechConfig": True,
"UniSpeechSatConfig": True,
"WavLMConfig": True,
"WhisperConfig": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"JukeboxPriorConfig": True,
# TODO: @Younes (for `is_decoder`)
"Pix2StructTextConfig": True,
}
)
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : List[Any] = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f"""config.{attribute}""" in modeling_source
or f"""getattr(config, \"{attribute}\"""" in modeling_source
or f"""getattr(self.config, \"{attribute}\"""" in modeling_source
):
lowercase__ : Optional[int] = True
# Deal with multi-line cases
elif (
re.search(
Rf"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"""" , lowercase__ , )
is not None
):
lowercase__ : List[str] = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
lowercase__ : Union[str, Any] = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
lowercase__ : str = [
'bos_index',
'eos_index',
'pad_index',
'unk_index',
'mask_index',
'image_size',
'use_cache',
'out_features',
'out_indices',
]
lowercase__ : Any = ['encoder_no_repeat_ngram_size']
# Special cases to be allowed
lowercase__ : List[str] = True
if not attribute_used:
lowercase__ : List[str] = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
lowercase__ : Union[str, Any] = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
lowercase__ : Dict = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
lowercase__ : Union[str, Any] = True
elif attribute.endswith('_token_id' ):
lowercase__ : Optional[int] = True
# configuration class specific cases
if not case_allowed:
lowercase__ : int = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
lowercase__ : Any = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
lowercase__ : Any = dict(inspect.signature(config_class.__init__ ).parameters )
lowercase__ : Optional[Any] = [x for x in list(signature.keys() ) if x not in ['self', 'kwargs']]
lowercase__ : str = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
lowercase__ : Optional[Any] = {}
if len(config_class.attribute_map ) > 0:
lowercase__ : List[str] = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
lowercase__ : List[str] = inspect.getsourcefile(lowercase__ )
lowercase__ : Dict = os.path.dirname(lowercase__ )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
lowercase__ : str = [os.path.join(lowercase__ , lowercase__ ) for fn in os.listdir(lowercase__ ) if fn.startswith('modeling_' )]
# Get the source code strings
lowercase__ : Optional[Any] = []
for path in modeling_paths:
if os.path.isfile(lowercase__ ):
with open(lowercase__ ) as fp:
modeling_sources.append(fp.read() )
lowercase__ : Dict = []
for config_param, default_value in zip(lowercase__ , lowercase__ ):
# `attributes` here is all the variant names for `config_param`
lowercase__ : str = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
unused_attributes.append(attributes[0] )
return sorted(lowercase__ )
def a_ ( ):
'''simple docstring'''
lowercase__ : Any = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
lowercase__ : Union[str, Any] = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda _lowerCAmelCase : inspect.isclass(lowercase__ )
and issubclass(lowercase__ , lowercase__ )
and inspect.getmodule(lowercase__ ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
lowercase__ : List[Any] = check_config_attributes_being_used(lowercase__ )
if len(lowercase__ ) > 0:
lowercase__ : int = unused_attributes
if len(lowercase__ ) > 0:
lowercase__ : Union[str, Any] = 'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
for name, attributes in configs_with_unused_attributes.items():
error += f"""{name}: {attributes}\n"""
raise ValueError(lowercase__ )
if __name__ == "__main__":
check_config_attributes()
| 719
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=True , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=4 , ) -> Dict:
lowercase__ : Optional[Any] = parent
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : int = is_training
lowercase__ : str = use_attention_mask
lowercase__ : Dict = use_token_type_ids
lowercase__ : Optional[int] = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : List[str] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : int = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[str] = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Tuple = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : int = type_vocab_size
lowercase__ : List[str] = type_sequence_label_size
lowercase__ : Union[str, Any] = initializer_range
lowercase__ : Optional[int] = num_choices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_attention_mask:
lowercase__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : List[str] = None
if self.use_token_type_ids:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ : Any = 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 , is_decoder=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_class_name in self.all_model_classes:
lowercase__ : str = model_class_name.from_pretrained('albert-base-v2' )
lowercase__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : str = FlaxAlbertModel.from_pretrained('albert-base-v2' )
lowercase__ : Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
lowercase__ : Any = model(a , attention_mask=a )[0]
lowercase__ : Tuple = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , a )
lowercase__ : Optional[Any] = np.array(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_UpperCamelCase : str = 0
_UpperCamelCase : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_UpperCamelCase : Tuple = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
_UpperCamelCase : Tuple = tuple[int, int]
class UpperCAmelCase_ :
def __init__( self , a , a , a , a , a , a , ) -> None:
lowercase__ : Optional[int] = pos_x
lowercase__ : List[Any] = pos_y
lowercase__ : Union[str, Any] = (pos_y, pos_x)
lowercase__ : List[Any] = goal_x
lowercase__ : Optional[Any] = goal_y
lowercase__ : str = g_cost
lowercase__ : str = parent
lowercase__ : Optional[Any] = self.calculate_heuristic()
lowercase__ : List[str] = self.g_cost + self.h_cost
def _UpperCAmelCase ( self ) -> float:
lowercase__ : List[str] = self.pos_x - self.goal_x
lowercase__ : Optional[Any] = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(__a ) + abs(__a )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self , a ) -> bool:
return self.f_cost < other.f_cost
class UpperCAmelCase_ :
def __init__( self , a , a ) -> Optional[Any]:
lowercase__ : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , __a )
lowercase__ : Tuple = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , __a )
lowercase__ : Optional[Any] = [self.start]
lowercase__ : list[Node] = []
lowercase__ : Tuple = False
def _UpperCAmelCase ( self ) -> list[TPosition]:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
lowercase__ : Tuple = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(__a )
self.closed_nodes.append(__a )
lowercase__ : Dict = self.get_successors(__a )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(__a )
else:
# retrieve the best current path
lowercase__ : Any = self.open_nodes.pop(self.open_nodes.index(__a ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(__a )
else:
self.open_nodes.append(__a )
return [self.start.pos]
def _UpperCAmelCase ( self , a ) -> list[Node]:
lowercase__ : str = []
for action in delta:
lowercase__ : Union[str, Any] = parent.pos_x + action[1]
lowercase__ : Optional[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
__a , __a , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , __a , ) )
return successors
def _UpperCAmelCase ( self , a ) -> list[TPosition]:
lowercase__ : Dict = node
lowercase__ : Tuple = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowercase__ : str = current_node.parent
path.reverse()
return path
class UpperCAmelCase_ :
def __init__( self , a , a ) -> None:
lowercase__ : Dict = AStar(__a , __a )
lowercase__ : str = AStar(__a , __a )
lowercase__ : int = False
def _UpperCAmelCase ( self ) -> list[TPosition]:
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
lowercase__ : Tuple = self.fwd_astar.open_nodes.pop(0 )
lowercase__ : List[Any] = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
__a , __a )
self.fwd_astar.closed_nodes.append(__a )
self.bwd_astar.closed_nodes.append(__a )
lowercase__ : int = current_bwd_node
lowercase__ : List[Any] = current_fwd_node
lowercase__ : Optional[int] = {
self.fwd_astar: self.fwd_astar.get_successors(__a ),
self.bwd_astar: self.bwd_astar.get_successors(__a ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(__a )
else:
# retrieve the best current path
lowercase__ : str = astar.open_nodes.pop(
astar.open_nodes.index(__a ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(__a )
else:
astar.open_nodes.append(__a )
return [self.fwd_astar.start.pos]
def _UpperCAmelCase ( self , a , a ) -> list[TPosition]:
lowercase__ : str = self.fwd_astar.retrace_path(__a )
lowercase__ : Any = self.bwd_astar.retrace_path(__a )
bwd_path.pop()
bwd_path.reverse()
lowercase__ : Any = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
_UpperCamelCase : Optional[int] = (0, 0)
_UpperCamelCase : Optional[int] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_UpperCamelCase : Optional[int] = time.time()
_UpperCamelCase : Optional[int] = AStar(init, goal)
_UpperCamelCase : int = a_star.search()
_UpperCamelCase : Optional[Any] = time.time() - start_time
print(f'''AStar execution time = {end_time:f} seconds''')
_UpperCamelCase : List[Any] = time.time()
_UpperCamelCase : List[str] = BidirectionalAStar(init, goal)
_UpperCamelCase : List[str] = time.time() - bd_start_time
print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')
| 720
|
"""simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
lowercase__ : int = 0.0
for coeff in reversed(_lowerCAmelCase ):
lowercase__ : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
_UpperCamelCase : int = (0.0, 0.0, 5.0, 9.3, 7.0)
_UpperCamelCase : Dict = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
'''simple docstring'''
if not len(_UpperCamelCase ) == len(_UpperCamelCase ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
lowercase__ , lowercase__ , lowercase__ : Optional[int] = equationa
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = equationa
# Calculate the determinants of the matrices
lowercase__ : int = aa * ba - aa * ba
lowercase__ : str = ca * ba - ca * ba
lowercase__ : Optional[int] = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
lowercase__ : Dict = determinant_x / determinant
lowercase__ : Tuple = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 721
|
"""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
_UpperCamelCase : 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 a_ ( _lowerCAmelCase : Optional[Any]=True ):
'''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=_a))
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = None
lowerCamelCase__ : Optional[Any] = None
def _UpperCAmelCase ( self , a , a ) -> List[Any]:
with TemporaryDirectory() as tmp_dir:
lowercase__ : List[str] = dataset_module_factory(a , cache_dir=a )
lowercase__ : List[Any] = import_main_class(dataset_module.module_path , dataset=a )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=a , config_name=a , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=a ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
lowercase__ : Union[str, Any] = cached_path(a , cache_dir=a )
self.assertTrue(os.path.exists(a ) )
@pytest.mark.integration
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Union[str, Any] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
lowercase__ : int = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : Optional[int] = import_main_class(dataset_module.module_path )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowercase__ : Optional[int] = None
builder_instance.download_and_prepare()
lowercase__ : Optional[int] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Optional[int] = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : List[str] = import_main_class(dataset_module.module_path , dataset=_lowerCAmelCase )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert "train" in ds
assert isinstance(ds['train'] , _lowerCAmelCase )
assert next(iter(ds['train'] ) )
| 645
| 0
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase_ ( __a):
def __init__( self , a , a , a , **a ) -> Union[str, Any]:
lowercase__ : Optional[Any] = feature_size
lowercase__ : List[Any] = sampling_rate
lowercase__ : Optional[int] = padding_value
lowercase__ : Optional[Any] = kwargs.pop('padding_side' , 'right' )
lowercase__ : str = kwargs.pop('return_attention_mask' , A__ )
super().__init__(**A__ )
def _UpperCAmelCase ( self , a , a = True , a = None , a = False , a = None , a = None , a = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(A__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
lowercase__ : Any = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
f""" to this method that includes {self.model_input_names[0]}, but you provided"""
f""" {list(processed_features.keys() )}""" )
lowercase__ : List[str] = processed_features[self.model_input_names[0]]
lowercase__ : str = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A__ ) == 0:
if return_attention_mask:
lowercase__ : Dict = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
lowercase__ : Tuple = required_input[0]
if isinstance(A__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
lowercase__ : List[str] = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A__ ):
lowercase__ : Tuple = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A__ ):
lowercase__ : Optional[Any] = 'tf'
elif is_torch_tensor(A__ ):
lowercase__ : Any = 'pt'
elif isinstance(A__ , (int, float, list, tuple, np.ndarray) ):
lowercase__ : List[str] = 'np'
else:
raise ValueError(
f"""type of {first_element} unknown: {type(A__ )}. """
'Should be one of a python, numpy, pytorch or tensorflow object.' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
lowercase__ : Optional[int] = to_numpy(A__ )
else:
lowercase__ : int = [to_numpy(A__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
lowercase__ : int = self._get_padding_strategies(padding=A__ , max_length=A__ )
lowercase__ : int = processed_features[self.model_input_names[0]]
lowercase__ : Any = len(A__ )
if not all(len(A__ ) == batch_size for v in processed_features.values() ):
raise ValueError('Some items in the output dictionary have a different batch size than others.' )
lowercase__ : int = []
for i in range(A__ ):
lowercase__ : Any = {k: v[i] for k, v in processed_features.items()}
# truncation
lowercase__ : Dict = self._truncate(
A__ , max_length=A__ , pad_to_multiple_of=A__ , truncation=A__ , )
truncated_inputs.append(A__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
lowercase__ : Any = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
lowercase__ : Any = PaddingStrategy.MAX_LENGTH
lowercase__ : Optional[int] = {}
for i in range(A__ ):
# padding
lowercase__ : Union[str, Any] = self._pad(
truncated_inputs[i] , max_length=A__ , padding_strategy=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , )
for key, value in outputs.items():
if key not in batch_outputs:
lowercase__ : Tuple = []
if value.dtype is np.dtype(np.floataa ):
lowercase__ : Optional[int] = value.astype(np.floataa )
batch_outputs[key].append(A__ )
return BatchFeature(A__ , tensor_type=A__ )
def _UpperCAmelCase ( self , a , a = None , a = PaddingStrategy.DO_NOT_PAD , a = None , a = None , ) -> dict:
lowercase__ : List[str] = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
lowercase__ : Any = len(A__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowercase__ : Tuple = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowercase__ : Dict = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
lowercase__ : int = np.ones(len(A__ ) , dtype=np.intaa )
if needs_to_be_padded:
lowercase__ : Optional[Any] = max_length - len(A__ )
if self.padding_side == "right":
if return_attention_mask:
lowercase__ : List[str] = np.pad(
processed_features['attention_mask'] , (0, difference) )
lowercase__ : Dict = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
lowercase__ : Optional[Any] = np.pad(
A__ , A__ , 'constant' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
lowercase__ : Optional[int] = np.pad(
processed_features['attention_mask'] , (difference, 0) )
lowercase__ : Any = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
lowercase__ : Dict = np.pad(
A__ , A__ , 'constant' , constant_values=self.padding_value )
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )
return processed_features
def _UpperCAmelCase ( self , a , a = None , a = None , a = None , ) -> Union[str, Any]:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.' )
lowercase__ : str = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowercase__ : Any = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowercase__ : Union[str, Any] = len(A__ ) > max_length
if needs_to_be_truncated:
lowercase__ : List[str] = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
lowercase__ : List[Any] = processed_features['attention_mask'][:max_length]
return processed_features
def _UpperCAmelCase ( self , a=False , a=None ) -> Union[str, Any]:
# Get padding strategy
if padding is not False:
if padding is True:
lowercase__ : List[Any] = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A__ , A__ ):
lowercase__ : Optional[Any] = PaddingStrategy(A__ )
elif isinstance(A__ , A__ ):
lowercase__ : List[Any] = padding
else:
lowercase__ : List[Any] = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.' )
return padding_strategy
| 700
|
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def a_ ( _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray ):
'''simple docstring'''
lowercase__ : Any = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(_lowerCAmelCase , _lowerCAmelCase )
# Predict target for test data
lowercase__ : str = xgb.predict(_lowerCAmelCase )
lowercase__ : Union[str, Any] = predictions.reshape(len(_lowerCAmelCase ) , 1 )
return predictions
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = fetch_california_housing()
lowercase__ , lowercase__ : str = data_handling(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = train_test_split(
_lowerCAmelCase , _lowerCAmelCase , test_size=0.2_5 , random_state=1 )
lowercase__ : Any = xgboost(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Error printing
print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
print(f"""Mean Square Error : {mean_squared_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 645
| 0
|
"""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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCamelCase : str = logging.get_logger(__name__)
_UpperCamelCase : Any = {
"microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}
class UpperCAmelCase_ ( _a , _a):
lowerCamelCase__ : List[Any] = 'resnet'
lowerCamelCase__ : List[str] = ['basic', 'bottleneck']
def __init__( self , a=3 , a=6_4 , a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , a=[3, 4, 6, 3] , a="bottleneck" , a="relu" , a=False , a=None , a=None , **a , ) -> str:
super().__init__(**a )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types )}""" )
lowercase__ : Any = num_channels
lowercase__ : Optional[Any] = embedding_size
lowercase__ : Optional[Any] = hidden_sizes
lowercase__ : List[str] = depths
lowercase__ : str = layer_type
lowercase__ : Optional[Any] = hidden_act
lowercase__ : int = downsample_in_first_stage
lowercase__ : str = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(a ) + 1 )]
lowercase__ , lowercase__ : Dict = get_aligned_output_features_output_indices(
out_features=a , out_indices=a , stage_names=self.stage_names )
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Optional[int] = version.parse("1.11")
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def _UpperCAmelCase ( self ) -> float:
return 1e-3
| 701
|
"""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 UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=1_0 , a=3 , a=2 , a=2 , a=2 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=0.9 , a=None , ) -> Optional[Any]:
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Dict = patch_size
lowercase__ : Tuple = tubelet_size
lowercase__ : Optional[int] = num_frames
lowercase__ : Optional[int] = is_training
lowercase__ : int = use_labels
lowercase__ : Optional[int] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = mask_ratio
lowercase__ : Optional[Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase__ : Optional[Any] = (image_size // patch_size) ** 2
lowercase__ : str = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase__ : str = int(mask_ratio * self.seq_length )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Tuple:
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=a , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a ) -> Optional[int]:
lowercase__ : Dict = VideoMAEModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = VideoMAEForPreTraining(a )
model.to(a )
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
lowercase__ : Any = torch.ones((self.num_masks,) )
lowercase__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowercase__ : Optional[int] = mask.expand(self.batch_size , -1 ).bool()
lowercase__ : str = model(a , a )
# model only returns predictions for masked patches
lowercase__ : str = mask.sum().item()
lowercase__ : int = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ : Optional[int] = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Any = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = False
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = VideoMAEModelTester(self )
lowercase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self , a , a , a=False ) -> Optional[int]:
lowercase__ : Union[str, Any] = copy.deepcopy(a )
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
lowercase__ : Optional[Any] = torch.ones((self.model_tester.num_masks,) )
lowercase__ : Any = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowercase__ : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowercase__ : Union[str, Any] = bool_masked_pos.to(a )
if return_labels:
if model_class in [
*get_values(a ),
]:
lowercase__ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = VideoMAEModel.from_pretrained(a )
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = True
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Any = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase__ : Optional[Any] = True
lowercase__ : int = False
lowercase__ : Any = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[int] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Dict = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : List[str] = len(a )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : List[str] = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(a , a ) )
self.assertEqual(out_len + 1 , len(a ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[int] = outputs.hidden_states
lowercase__ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(a ) , a )
lowercase__ : Optional[Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Union[str, Any] = 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] , )
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(a , a , a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def a_ ( ):
'''simple docstring'''
lowercase__ : int = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowercase__ : str = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
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 _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
a )
lowercase__ : str = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**a )
# verify the logits
lowercase__ : str = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : List[Any] = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(a )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(a , return_tensors='pt' ).to(a )
# add boolean mask, indicating which patches to mask
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowercase__ : str = torch.load(a )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**a )
# verify the logits
lowercase__ : Dict = torch.Size([1, 1_4_0_8, 1_5_3_6] )
lowercase__ : List[str] = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=a )
self.assertEqual(outputs.logits.shape , a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase__ : List[Any] = torch.tensor([0.5_142] , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=a ).to(
a )
with torch.no_grad():
lowercase__ : Any = model(**a )
lowercase__ : List[Any] = torch.tensor(torch.tensor([0.6_469] ) , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
| 645
| 0
|
"""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
# and perform gradient accumulation
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_UpperCamelCase : Tuple = 16
_UpperCamelCase : List[Any] = 32
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple = 16 ):
'''simple docstring'''
lowercase__ : Tuple = AutoTokenizer.from_pretrained('bert-base-cased' )
lowercase__ : List[Any] = load_dataset('glue' , 'mrpc' )
def tokenize_function(_lowerCAmelCase : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : Tuple = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=lowercase__ , max_length=lowercase__ )
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__ : List[Any] = datasets.map(
lowercase__ , batched=lowercase__ , 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(_lowerCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : Dict = 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__ : List[Any] = 16
elif accelerator.mixed_precision != "no":
lowercase__ : Union[str, Any] = 8
else:
lowercase__ : Optional[Any] = None
return tokenizer.pad(
lowercase__ , padding='longest' , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors='pt' , )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['train'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowercase__ : int = DataLoader(
tokenized_datasets['validation'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
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
_UpperCamelCase : int = mocked_dataloaders # noqa: F811
def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
if os.environ.get('TESTING_MOCKED_DATALOADERS' , lowercase__ ) == "1":
lowercase__ : Dict = 2
# New Code #
lowercase__ : Any = int(args.gradient_accumulation_steps )
# Initialize accelerator
lowercase__ : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase__ )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[int] = config['lr']
lowercase__ : Optional[Any] = int(config['num_epochs'] )
lowercase__ : List[str] = int(config['seed'] )
lowercase__ : Union[str, Any] = int(config['batch_size'] )
lowercase__ : List[str] = evaluate.load('glue' , 'mrpc' )
set_seed(lowercase__ )
lowercase__ , lowercase__ : Union[str, Any] = get_dataloaders(lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Optional[int] = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=lowercase__ )
# 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__ : Any = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : Union[str, Any] = AdamW(params=model.parameters() , lr=lowercase__ )
# Instantiate scheduler
lowercase__ : Dict = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=100 , num_training_steps=(len(lowercase__ ) * num_epochs) , )
# 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__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase__ ):
lowercase__ : Optional[Any] = model(**lowercase__ )
lowercase__ : List[str] = output.loss
accelerator.backward(lowercase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Optional[Any] = model(**lowercase__ )
lowercase__ : List[Any] = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : List[str] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowercase__ : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
def a_ ( ):
'''simple docstring'''
lowercase__ : Dict = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=lowercase__ , default=lowercase__ , 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.' , )
# New Code #
parser.add_argument(
'--gradient_accumulation_steps' , type=lowercase__ , default=1 , help='The number of minibatches to be ran before gradients are accumulated.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
lowercase__ : Any = parser.parse_args()
lowercase__ : int = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 702
|
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
"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",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
_UpperCamelCase : List[str] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
for attribute in key.split('.' ):
lowercase__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
lowercase__ : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
lowercase__ : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
lowercase__ : Optional[Any] = value
elif weight_type == "weight_g":
lowercase__ : Dict = value
elif weight_type == "weight_v":
lowercase__ : List[str] = value
elif weight_type == "bias":
lowercase__ : Optional[Any] = value
else:
lowercase__ : List[str] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = []
lowercase__ : List[str] = fairseq_model.state_dict()
lowercase__ : Union[str, Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase__ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
lowercase__ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase__ : List[Any] = 'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase__ : int = True
if "*" in mapped_key:
lowercase__ : Optional[int] = name.split(_lowerCAmelCase )[0].split('.' )[-2]
lowercase__ : List[str] = mapped_key.replace('*' , _lowerCAmelCase )
if "weight_g" in name:
lowercase__ : List[Any] = 'weight_g'
elif "weight_v" in name:
lowercase__ : int = 'weight_v'
elif "bias" in name:
lowercase__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase__ : Union[str, Any] = 'weight'
else:
lowercase__ : int = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : int = full_name.split('conv_layers.' )[-1]
lowercase__ : int = name.split('.' )
lowercase__ : int = int(items[0] )
lowercase__ : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowercase__ : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowercase__ : Optional[int] = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
lowercase__ : List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
lowercase__ : int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Tuple=True ):
'''simple docstring'''
if config_path is not None:
lowercase__ : Any = UniSpeechSatConfig.from_pretrained(_lowerCAmelCase )
else:
lowercase__ : Any = UniSpeechSatConfig()
lowercase__ : Union[str, Any] = ''
if is_finetuned:
lowercase__ : Optional[Any] = UniSpeechSatForCTC(_lowerCAmelCase )
else:
lowercase__ : List[Any] = UniSpeechSatForPreTraining(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
lowercase__ : Union[str, Any] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase : Tuple = 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"
)
_UpperCamelCase : str = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 645
| 0
|
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def a_ ( ):
'''simple docstring'''
lowercase__ : List[Any] = ArgumentParser('Transformers CLI tool' , usage='transformers-cli <command> [<args>]' )
lowercase__ : List[str] = parser.add_subparsers(help='transformers-cli command helpers' )
# Register commands
ConvertCommand.register_subcommand(lowerCAmelCase_ )
DownloadCommand.register_subcommand(lowerCAmelCase_ )
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
RunCommand.register_subcommand(lowerCAmelCase_ )
ServeCommand.register_subcommand(lowerCAmelCase_ )
UserCommands.register_subcommand(lowerCAmelCase_ )
AddNewModelCommand.register_subcommand(lowerCAmelCase_ )
AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_ )
LfsCommands.register_subcommand(lowerCAmelCase_ )
PTtoTFCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
lowercase__ : str = parser.parse_args()
if not hasattr(lowerCAmelCase_ , 'func' ):
parser.print_help()
exit(1 )
# Run
lowercase__ : Union[str, Any] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 703
|
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=3_2 , a=2 , a=3 , a=1_6 , 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=1_0 , a=8 , a=["stage1", "stage2", "stage3"] , a=[1, 2, 3] , ) -> int:
lowercase__ : int = parent
lowercase__ : Union[str, Any] = batch_size
lowercase__ : Dict = image_size
lowercase__ : str = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : List[str] = embed_dim
lowercase__ : Any = depths
lowercase__ : Dict = num_heads
lowercase__ : List[str] = window_size
lowercase__ : int = mlp_ratio
lowercase__ : Tuple = qkv_bias
lowercase__ : Union[str, Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Tuple = drop_path_rate
lowercase__ : List[str] = hidden_act
lowercase__ : Optional[Any] = use_absolute_embeddings
lowercase__ : Optional[Any] = patch_norm
lowercase__ : Any = layer_norm_eps
lowercase__ : List[Any] = initializer_range
lowercase__ : List[str] = is_training
lowercase__ : int = scope
lowercase__ : Optional[int] = use_labels
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : List[str] = encoder_stride
lowercase__ : Optional[Any] = out_features
lowercase__ : Dict = out_indices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return MaskFormerSwinConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , )
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Tuple = MaskFormerSwinModel(config=a )
model.to(a )
model.eval()
lowercase__ : str = model(a )
lowercase__ : str = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ : Dict = 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 ) -> Optional[int]:
lowercase__ : List[Any] = MaskFormerSwinBackbone(config=a )
model.to(a )
model.eval()
lowercase__ : int = model(a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [1_3, 1_6, 1_6, 1_6] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [1_6, 3_2, 6_4] )
# verify ValueError
with self.parent.assertRaises(a ):
lowercase__ : Dict = ['stem']
lowercase__ : List[str] = MaskFormerSwinBackbone(config=a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : List[str] = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
lowerCamelCase__ : str = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : int = False
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = MaskFormerSwinModelTester(self )
lowercase__ : Tuple = ConfigTester(self , config_class=a , embed_dim=3_7 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
def _UpperCAmelCase ( self ) -> Tuple:
self.create_and_test_config_common_properties()
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:
return
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*a )
@unittest.skip('Swin does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
@unittest.skip('Swin does not support feedforward chunking' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
lowercase__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def _UpperCAmelCase ( self ) -> int:
pass
def _UpperCAmelCase ( self , a , a , a , a ) -> Tuple:
lowercase__ : Dict = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : str = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[Any] = outputs.hidden_states
lowercase__ : str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(a ) , a )
# Swin has a different seq_length
lowercase__ : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Tuple = (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] , )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = (
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:
lowercase__ : List[str] = 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"]
lowercase__ : List[str] = True
self.check_hidden_states_output(a , a , a , a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = 3
lowercase__ : str = (
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)
)
lowercase__ : Tuple = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Optional[int] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ : List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase__ : List[str] = 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"]
lowercase__ : int = True
self.check_hidden_states_output(a , a , a , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(a ):
lowercase__ : Union[str, Any] = 0
return t
def check_equivalence(a , a , a , a={} ):
with torch.no_grad():
lowercase__ : Optional[Any] = model(**a , return_dict=a , **a )
lowercase__ : Optional[int] = model(**a , return_dict=a , **a ).to_tuple()
def recursive_check(a , a ):
if isinstance(a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(a , a ):
recursive_check(a , a )
elif isinstance(a , a ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(a , a )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(a ) , set_nan_tensor_to_zero(a ) , atol=1e-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}. Dict has"""
f""" `nan`: {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}."""
) , )
recursive_check(a , a )
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
model.to(a )
model.eval()
lowercase__ : Tuple = self._prepare_for_class(a , a )
lowercase__ : Optional[Any] = self._prepare_for_class(a , a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : List[Any] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a )
lowercase__ : int = self._prepare_for_class(a , a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
lowercase__ : Dict = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : Optional[int] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase , _a):
lowerCamelCase__ : Dict = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowerCamelCase__ : Optional[int] = MaskFormerSwinConfig
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[int] = MaskFormerSwinModelTester(self )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
lowercase__ : Optional[Any] = backbone_class(a )
backbone.to(a )
backbone.eval()
lowercase__ : Union[str, Any] = backbone(**a )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , a )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowercase__ : List[str] = backbone(**a , output_hidden_states=a )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowercase__ , lowercase__ , lowercase__ : int = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowercase__ : List[Any] = backbone(**a , output_attentions=a )
self.assertIsNotNone(outputs.attentions )
| 645
| 0
|
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
_UpperCamelCase : Dict = get_logger(__name__)
_UpperCamelCase : int = Path(__file__).parent / "model_card_template.md"
_UpperCamelCase : Optional[Any] = uuida().hex
_UpperCamelCase : int = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
_UpperCamelCase : Optional[int] = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
_UpperCamelCase : Tuple = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def a_ ( _lowerCAmelCase : Union[Dict, str, None] = None ):
'''simple docstring'''
lowercase__ : List[Any] = f"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f"""; torch/{_torch_version}"""
if is_flax_available():
ua += f"""; jax/{_jax_version}"""
ua += f"""; flax/{_flax_version}"""
if is_onnx_available():
ua += f"""; onnxruntime/{_onnxruntime_version}"""
# CI will set this value to True
if os.environ.get('DIFFUSERS_IS_CI' , '' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items() )
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
ua += "; " + user_agent
return ua
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None , _lowerCAmelCase : Optional[str] = None ):
'''simple docstring'''
if token is None:
lowercase__ : Optional[Any] = HfFolder.get_token()
if organization is None:
lowercase__ : Tuple = whoami(SCREAMING_SNAKE_CASE__ )["""name"""]
return f"""{username}/{model_id}"""
else:
return f"""{organization}/{model_id}"""
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : str ):
'''simple docstring'''
if not is_jinja_available():
raise ValueError(
'Modelcard rendering is based on Jinja templates.'
' Please make sure to have `jinja` installed before using `create_model_card`.'
' To install it, please run `pip install Jinja2`.' )
if hasattr(SCREAMING_SNAKE_CASE__ , 'local_rank' ) and args.local_rank not in [-1, 0]:
return
lowercase__ : Optional[int] = args.hub_token if hasattr(SCREAMING_SNAKE_CASE__ , 'hub_token' ) else None
lowercase__ : Union[str, Any] = get_full_repo_name(SCREAMING_SNAKE_CASE__ , token=SCREAMING_SNAKE_CASE__ )
lowercase__ : Optional[Any] = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='en' , license='apache-2.0' , library_name='diffusers' , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=SCREAMING_SNAKE_CASE__ , model_name=SCREAMING_SNAKE_CASE__ , repo_name=SCREAMING_SNAKE_CASE__ , dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE__ , 'dataset_name' ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE__ , 'gradient_accumulation_steps' ) else None
) , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ , 'adam_beta1' ) else None , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ , 'adam_beta2' ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE__ , 'adam_weight_decay' ) else None , adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE__ , 'adam_epsilon' ) else None , lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE__ , 'lr_scheduler' ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE__ , 'lr_warmup_steps' ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE__ , 'ema_inv_gamma' ) else None , ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE__ , 'ema_power' ) else None , ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE__ , 'ema_max_decay' ) else None , mixed_precision=args.mixed_precision , )
lowercase__ : Tuple = os.path.join(args.output_dir , 'README.md' )
model_card.save(SCREAMING_SNAKE_CASE__ )
def a_ ( _lowerCAmelCase : Optional[str] , _lowerCAmelCase : Optional[str] = None ):
'''simple docstring'''
if resolved_file is None or commit_hash is not None:
return commit_hash
lowercase__ : Optional[int] = str(Path(SCREAMING_SNAKE_CASE__ ).as_posix() )
lowercase__ : List[str] = re.search(R'snapshots/([^/]+)/' , SCREAMING_SNAKE_CASE__ )
if search is None:
return None
lowercase__ : Union[str, Any] = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE__ ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_UpperCamelCase : Optional[Any] = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_UpperCamelCase : str = os.path.join(hf_cache_home, "diffusers")
def a_ ( _lowerCAmelCase : Optional[str] = None , _lowerCAmelCase : Optional[str] = None ):
'''simple docstring'''
if new_cache_dir is None:
lowercase__ : Tuple = DIFFUSERS_CACHE
if old_cache_dir is None:
lowercase__ : List[Any] = old_diffusers_cache
lowercase__ : List[str] = Path(SCREAMING_SNAKE_CASE__ ).expanduser()
lowercase__ : Union[str, Any] = Path(SCREAMING_SNAKE_CASE__ ).expanduser()
for old_blob_path in old_cache_dir.glob('**/blobs/*' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
lowercase__ : Union[str, Any] = new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE__ )
new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
os.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
try:
os.symlink(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
except OSError:
logger.warning(
'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
_UpperCamelCase : Optional[int] = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
_UpperCamelCase : Union[str, Any] = 0
else:
with open(cache_version_file) as f:
try:
_UpperCamelCase : Optional[Any] = int(f.read())
except ValueError:
_UpperCamelCase : Any = 0
if cache_version < 1:
_UpperCamelCase : int = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
_UpperCamelCase : Any = "\n".join(traceback.format_tb(e.__traceback__))
logger.error(
f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
"the directory exists and can be written to."
)
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ):
'''simple docstring'''
if variant is not None:
lowercase__ : int = weights_name.split('.' )
lowercase__ : Dict = splits[:-1] + [variant] + splits[-1:]
lowercase__ : int = """.""".join(SCREAMING_SNAKE_CASE__ )
return weights_name
def a_ ( _lowerCAmelCase : Union[str, Any] , *,
_lowerCAmelCase : Any , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : int=None , ):
'''simple docstring'''
lowercase__ : int = str(SCREAMING_SNAKE_CASE__ )
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
return pretrained_model_name_or_path
elif os.path.isdir(SCREAMING_SNAKE_CASE__ ):
if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ):
# Load from a PyTorch checkpoint
lowercase__ : str = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ):
lowercase__ : List[str] = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return model_file
else:
raise EnvironmentError(
f"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.""" )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(SCREAMING_SNAKE_CASE__ ).base_version ) >= version.parse('0.20.0' )
):
try:
lowercase__ : Tuple = hf_hub_download(
SCREAMING_SNAKE_CASE__ , filename=_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , proxies=SCREAMING_SNAKE_CASE__ , resume_download=SCREAMING_SNAKE_CASE__ , local_files_only=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , user_agent=SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ , revision=revision or commit_hash , )
warnings.warn(
f"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.""" , SCREAMING_SNAKE_CASE__ , )
return model_file
except: # noqa: E722
warnings.warn(
f"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )}\' so that the correct variant file can be added.""" , SCREAMING_SNAKE_CASE__ , )
try:
# 2. Load model file as usual
lowercase__ : List[str] = hf_hub_download(
SCREAMING_SNAKE_CASE__ , filename=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , proxies=SCREAMING_SNAKE_CASE__ , resume_download=SCREAMING_SNAKE_CASE__ , local_files_only=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , user_agent=SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
'login`.' )
except RevisionNotFoundError:
raise EnvironmentError(
f"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
'this model name. Check the model page at '
f"""\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.""" )
except EntryNotFoundError:
raise EnvironmentError(
f"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.""" )
except HTTPError as err:
raise EnvironmentError(
f"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}""" )
except ValueError:
raise EnvironmentError(
f"""We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it"""
f""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
f""" directory containing a file named {weights_name} or"""
' \nCheckout your internet connection or see how to run the library in'
' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.' )
except EnvironmentError:
raise EnvironmentError(
f"""Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from """
'\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
f"""Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory """
f"""containing a file named {weights_name}""" )
| 704
|
"""simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 645
| 0
|
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). " , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE):
lowerCamelCase__ : int = RobertaConfig
lowerCamelCase__ : int = "roberta"
def __init__( self , a ) -> Optional[Any]:
super().__init__(__snake_case )
lowercase__ : Optional[int] = RobertaEmbeddings(__snake_case )
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. " , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE):
lowerCamelCase__ : Any = RobertaConfig
lowerCamelCase__ : int = "roberta"
def __init__( self , a ) -> Optional[Any]:
super().__init__(__snake_case )
lowercase__ : Any = config.num_labels
lowercase__ : Union[str, Any] = config.num_hidden_layers
lowercase__ : Optional[int] = DeeRobertaModel(__snake_case )
lowercase__ : Dict = nn.Dropout(config.hidden_dropout_prob )
lowercase__ : Optional[Any] = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(__snake_case )
def _UpperCAmelCase ( self , a=None , a=None , a=None , a=None , a=None , a=None , a=None , a=-1 , a=False , ) -> Tuple:
lowercase__ : List[str] = self.num_layers
try:
lowercase__ : Tuple = self.roberta(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , position_ids=__snake_case , head_mask=__snake_case , inputs_embeds=__snake_case , )
lowercase__ : int = outputs[1]
lowercase__ : str = self.dropout(__snake_case )
lowercase__ : List[Any] = self.classifier(__snake_case )
lowercase__ : Optional[int] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
lowercase__ : Union[str, Any] = e.message
lowercase__ : List[Any] = e.exit_layer
lowercase__ : Union[str, Any] = outputs[0]
if not self.training:
lowercase__ : List[str] = entropy(__snake_case )
lowercase__ : Union[str, Any] = []
lowercase__ : Dict = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
lowercase__ : List[str] = MSELoss()
lowercase__ : Tuple = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
lowercase__ : List[str] = CrossEntropyLoss()
lowercase__ : Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
lowercase__ : List[Any] = []
for highway_exit in outputs[-1]:
lowercase__ : Optional[int] = highway_exit[0]
if not self.training:
highway_logits_all.append(__snake_case )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
lowercase__ : Dict = MSELoss()
lowercase__ : int = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
lowercase__ : int = CrossEntropyLoss()
lowercase__ : List[Any] = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(__snake_case )
if train_highway:
lowercase__ : Dict = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
lowercase__ : Union[str, Any] = (loss,) + outputs
if not self.training:
lowercase__ : List[str] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
lowercase__ : Union[str, Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 705
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ , lowercase__ : str = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : List[str] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : List[Any] = controlnet_params
lowercase__ : int = 'bird'
lowercase__ : List[Any] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([canny_image] * num_samples )
lowercase__ : List[Any] = jax.random.PRNGKey(0 )
lowercase__ : Tuple = jax.random.split(a , jax.device_count() )
lowercase__ : str = replicate(a )
lowercase__ : List[str] = shard(a )
lowercase__ : Dict = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : Tuple = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : int = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : Optional[Any] = jnp.array(
[0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ , lowercase__ : int = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : Optional[Any] = controlnet_params
lowercase__ : List[Any] = 'Chef in the kitchen'
lowercase__ : List[str] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
lowercase__ : List[str] = jax.random.PRNGKey(0 )
lowercase__ : str = jax.random.split(a , jax.device_count() )
lowercase__ : Optional[Any] = replicate(a )
lowercase__ : Optional[Any] = shard(a )
lowercase__ : List[Any] = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Union[str, Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : str = jnp.array(
[[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 645
| 0
|
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[str] = 'ZinengTang/tvlt-base'
lowercase__ : Optional[int] = tempfile.mkdtemp()
def _UpperCAmelCase ( self , **a ) -> List[str]:
return TvltImageProcessor.from_pretrained(self.checkpoint , **a )
def _UpperCAmelCase ( self , **a ) -> Optional[int]:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **a )
def _UpperCAmelCase ( self ) -> str:
shutil.rmtree(self.tmpdirname )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : int = self.get_image_processor()
lowercase__ : int = self.get_feature_extractor()
lowercase__ : Any = TvltProcessor(image_processor=a , feature_extractor=a )
processor.save_pretrained(self.tmpdirname )
lowercase__ : Optional[Any] = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , a )
self.assertIsInstance(processor.image_processor , a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : str = self.get_image_processor()
lowercase__ : Tuple = self.get_feature_extractor()
lowercase__ : Any = TvltProcessor(image_processor=a , feature_extractor=a )
lowercase__ : Tuple = np.ones([1_2_0_0_0] )
lowercase__ : int = feature_extractor(a , return_tensors='np' )
lowercase__ : str = processor(audio=a , return_tensors='np' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = self.get_image_processor()
lowercase__ : Dict = self.get_feature_extractor()
lowercase__ : int = TvltProcessor(image_processor=a , feature_extractor=a )
lowercase__ : str = np.ones([3, 2_2_4, 2_2_4] )
lowercase__ : str = image_processor(a , return_tensors='np' )
lowercase__ : List[Any] = processor(images=a , return_tensors='np' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : int = self.get_image_processor()
lowercase__ : Dict = self.get_feature_extractor()
lowercase__ : Tuple = TvltProcessor(image_processor=a , feature_extractor=a )
lowercase__ : Optional[Any] = np.ones([1_2_0_0_0] )
lowercase__ : Dict = np.ones([3, 2_2_4, 2_2_4] )
lowercase__ : Optional[int] = processor(audio=a , images=a )
self.assertListEqual(list(inputs.keys() ) , ['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask'] )
# test if it raises when no input is passed
with pytest.raises(a ):
processor()
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.get_image_processor()
lowercase__ : Optional[int] = self.get_feature_extractor()
lowercase__ : Optional[int] = TvltProcessor(image_processor=a , feature_extractor=a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='`processor` and `image_processor`+`feature_extractor` model input names do not match' , )
| 706
|
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_UpperCamelCase : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Any = ["MLukeTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
_UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 707
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
lowercase__ : Union[str, Any] = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(a )
from datasets import load_dataset
lowercase__ : str = load_dataset('nielsr/rvlcdip-demo' )
lowercase__ : Tuple = dataset['train'][0]['image'].convert('RGB' )
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : List[str] = model(**a )
lowercase__ : List[Any] = outputs.logits
lowercase__ : Union[str, Any] = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , a )
lowercase__ : Tuple = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=a , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : Any = logging.get_logger(__name__)
_UpperCamelCase : int = "▁"
_UpperCamelCase : List[Any] = {"vocab_file": "sentencepiece.bpe.model"}
_UpperCamelCase : str = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
),
}
}
_UpperCamelCase : Optional[int] = {
"facebook/nllb-200-distilled-600M": 10_24,
}
# fmt: off
_UpperCamelCase : Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class UpperCAmelCase_ ( UpperCAmelCase_):
lowerCamelCase__ : Optional[Any] = VOCAB_FILES_NAMES
lowerCamelCase__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Optional[Any] = ["input_ids", "attention_mask"]
lowerCamelCase__ : List[int] = []
lowerCamelCase__ : List[int] = []
def __init__( self , a , a="<s>" , a="</s>" , a="</s>" , a="<s>" , a="<unk>" , a="<pad>" , a="<mask>" , a=None , a=None , a=None , a = None , a=None , a=False , **a , ) -> Any:
lowercase__ : Dict = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else mask_token
lowercase__ : Any = {} if sp_model_kwargs is None else sp_model_kwargs
lowercase__ : List[str] = legacy_behaviour
super().__init__(
bos_token=_snake_case , eos_token=_snake_case , unk_token=_snake_case , sep_token=_snake_case , cls_token=_snake_case , pad_token=_snake_case , mask_token=_snake_case , tokenizer_file=_snake_case , src_lang=_snake_case , tgt_lang=_snake_case , additional_special_tokens=_snake_case , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=_snake_case , **_snake_case , )
lowercase__ : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_snake_case ) )
lowercase__ : List[str] = 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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase__ : Optional[Any] = {'<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
lowercase__ : int = 1
lowercase__ : List[str] = len(self.sp_model )
lowercase__ : List[Any] = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_snake_case )
}
lowercase__ : Union[str, Any] = {v: k for k, v in self.lang_code_to_id.items()}
lowercase__ : int = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
lowercase__ : List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
lowercase__ : Dict = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
lowercase__ : int = src_lang if src_lang is not None else 'eng_Latn'
lowercase__ : Tuple = self.lang_code_to_id[self._src_lang]
lowercase__ : Union[str, Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ) -> str:
lowercase__ : str = self.__dict__.copy()
lowercase__ : int = None
lowercase__ : Optional[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , a ) -> Tuple:
lowercase__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
lowercase__ : str = {}
lowercase__ : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def _UpperCAmelCase ( self ) -> int:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def _UpperCAmelCase ( self ) -> Optional[int]:
return self._src_lang
@src_lang.setter
def _UpperCAmelCase ( self , a ) -> Dict:
lowercase__ : Optional[int] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _UpperCAmelCase ( self , a , a = None , a = False ) -> Optional[Any]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_snake_case , token_ids_a=_snake_case , already_has_special_tokens=_snake_case )
lowercase__ : List[str] = [1] * len(self.prefix_tokens )
lowercase__ : Dict = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(_snake_case )) + suffix_ones
return prefix_ones + ([0] * len(_snake_case )) + ([0] * len(_snake_case )) + suffix_ones
def _UpperCAmelCase ( self , a , a = None ) -> int:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _UpperCAmelCase ( self , a , a = None ) -> Dict:
lowercase__ : List[Any] = [self.sep_token_id]
lowercase__ : 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]
def _UpperCAmelCase ( self , a , a , a , a , **a ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
lowercase__ : Optional[Any] = src_lang
lowercase__ : Union[str, Any] = self(_snake_case , add_special_tokens=_snake_case , return_tensors=_snake_case , **_snake_case )
lowercase__ : Any = self.convert_tokens_to_ids(_snake_case )
lowercase__ : str = tgt_lang_id
return inputs
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Tuple = {self.convert_ids_to_tokens(_snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _UpperCAmelCase ( self , a ) -> Optional[int]:
return self.sp_model.encode(_snake_case , out_type=_snake_case )
def _UpperCAmelCase ( self , a ) -> Optional[int]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase__ : str = self.sp_model.PieceToId(_snake_case )
# 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 _UpperCAmelCase ( self , a ) -> int:
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 _UpperCAmelCase ( self , a ) -> List[str]:
lowercase__ : str = ''.join(_snake_case ).replace(_snake_case , ' ' ).strip()
return out_string
def _UpperCAmelCase ( self , a , a = None ) -> Any:
if not os.path.isdir(_snake_case ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : Optional[int] = os.path.join(
_snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(_snake_case , 'wb' ) as fi:
lowercase__ : List[Any] = self.sp_model.serialized_model_proto()
fi.write(_snake_case )
return (out_vocab_file,)
def _UpperCAmelCase ( self , a , a = "eng_Latn" , a = None , a = "fra_Latn" , **a , ) -> Optional[int]:
lowercase__ : str = src_lang
lowercase__ : List[Any] = tgt_lang
return super().prepare_seqaseq_batch(_snake_case , _snake_case , **_snake_case )
def _UpperCAmelCase ( self ) -> Optional[int]:
return self.set_src_lang_special_tokens(self.src_lang )
def _UpperCAmelCase ( self ) -> List[str]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
lowercase__ : int = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
lowercase__ : List[str] = []
lowercase__ : int = [self.eos_token_id, self.cur_lang_code]
else:
lowercase__ : List[str] = [self.cur_lang_code]
lowercase__ : str = [self.eos_token_id]
def _UpperCAmelCase ( self , a ) -> List[str]:
lowercase__ : Tuple = self.lang_code_to_id[lang]
if self.legacy_behaviour:
lowercase__ : Dict = []
lowercase__ : List[Any] = [self.eos_token_id, self.cur_lang_code]
else:
lowercase__ : Dict = [self.cur_lang_code]
lowercase__ : Any = [self.eos_token_id]
| 708
|
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase_ :
@staticmethod
def _UpperCAmelCase ( *a , **a ) -> int:
pass
def a_ ( _lowerCAmelCase : Image ):
'''simple docstring'''
lowercase__ : List[str] = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : Union[str, Any] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Union[str, Any] = DepthEstimationPipeline(model=a , image_processor=a )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self , a , a ) -> Optional[int]:
lowercase__ : Tuple = depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , a )
import datasets
lowercase__ : Tuple = datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
lowercase__ : List[Any] = depth_estimator(
[
Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'http://images.cocodataset.org/val2017/000000039769.jpg',
# RGBA
dataset[0]['file'],
# LA
dataset[1]['file'],
# L
dataset[2]['file'],
] )
self.assertEqual(
[
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
] , a , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@slow
@require_torch
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Tuple = 'Intel/dpt-large'
lowercase__ : Optional[int] = pipeline('depth-estimation' , model=a )
lowercase__ : List[Any] = depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
lowercase__ : Optional[Any] = hashimage(outputs['depth'] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item() ) , 2.662 )
@require_torch
def _UpperCAmelCase ( self ) -> Optional[int]:
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 645
| 0
|
"""simple docstring"""
from functools import lru_cache
def a_ ( _lowerCAmelCase : Any ) -> Optional[Any]:
'''simple docstring'''
lowercase__ : Optional[Any] = 2
lowercase__ : List[str] = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_lowerCAmelCase )
if n > 1:
factors.add(_lowerCAmelCase )
return factors
@lru_cache
def a_ ( _lowerCAmelCase : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return len(unique_prime_factors(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Tuple ) -> List[str]:
'''simple docstring'''
return len(set(_lowerCAmelCase ) ) in (0, 1)
def a_ ( _lowerCAmelCase : Optional[Any] ) -> int:
'''simple docstring'''
lowercase__ : str = 2
while True:
# Increment each value of a generated range
lowercase__ : Union[str, Any] = [base + i for i in range(_lowerCAmelCase )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
lowercase__ : Optional[int] = [upf_len(_lowerCAmelCase ) for x in group]
checker.append(_lowerCAmelCase )
# If all numbers in the list are equal, return the group variable.
if equality(_lowerCAmelCase ):
return group
# Increment our base variable by 1
base += 1
def a_ ( _lowerCAmelCase : Dict = 4 ) -> str:
'''simple docstring'''
lowercase__ : Union[str, Any] = run(_lowerCAmelCase )
return results[0] if len(_lowerCAmelCase ) else None
if __name__ == "__main__":
print(solution())
| 709
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class UpperCAmelCase_ ( _a):
def __init__( self ) -> Any:
lowercase__ : Tuple = []
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_init_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[int]:
self.events.append('on_train_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_train_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_epoch_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[Any]:
self.events.append('on_epoch_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_step_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> str:
self.events.append('on_step_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_evaluate' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Tuple:
self.events.append('on_predict' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Union[str, Any]:
self.events.append('on_save' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_log' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_prediction_step' )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> str:
lowercase__ : str = tempfile.mkdtemp()
def _UpperCAmelCase ( self ) -> Dict:
shutil.rmtree(self.output_dir )
def _UpperCAmelCase ( self , a=0 , a=0 , a=6_4 , a=6_4 , a=None , a=False , **a ) -> int:
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
lowercase__ : str = RegressionDataset(length=a )
lowercase__ : Any = RegressionDataset(length=a )
lowercase__ : Optional[Any] = RegressionModelConfig(a=a , b=a )
lowercase__ : Union[str, Any] = RegressionPreTrainedModel(a )
lowercase__ : Tuple = TrainingArguments(self.output_dir , disable_tqdm=a , report_to=[] , **a )
return Trainer(
a , a , train_dataset=a , eval_dataset=a , callbacks=a , )
def _UpperCAmelCase ( self , a , a ) -> Union[str, Any]:
self.assertEqual(len(a ) , len(a ) )
# Order doesn't matter
lowercase__ : Optional[int] = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
lowercase__ : Tuple = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
for cba, cba in zip(a , a ):
if isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(a , a )
elif isinstance(a , a ) and not isinstance(a , a ):
self.assertEqual(a , cba.__class__ )
elif not isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(cba.__class__ , a )
else:
self.assertEqual(a , a )
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
lowercase__ : Dict = ['on_init_end', 'on_train_begin']
lowercase__ : List[Any] = 0
lowercase__ : Optional[int] = len(trainer.get_eval_dataloader() )
lowercase__ : Tuple = ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('on_epoch_begin' )
for _ in range(a ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save' )
expected_events.append('on_epoch_end' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : int = self.get_trainer()
lowercase__ : str = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# Callbacks passed at init are added to the default callbacks
lowercase__ : str = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
lowercase__ : List[Any] = self.get_trainer(disable_tqdm=a )
lowercase__ : Optional[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
lowercase__ : List[str] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Optional[Any] = self.get_trainer()
lowercase__ : List[Any] = trainer.pop_callback(a )
self.assertEqual(cb.__class__ , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# We can also add, pop, or remove by instance
lowercase__ : int = self.get_trainer()
lowercase__ : List[str] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Tuple = self.get_trainer()
lowercase__ : Dict = trainer.callback_handler.callbacks[0]
lowercase__ : Union[str, Any] = trainer.pop_callback(a )
self.assertEqual(a , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Tuple:
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' , category=a )
lowercase__ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# Independent log/save/eval
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
lowercase__ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
lowercase__ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='steps' )
trainer.train()
lowercase__ : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='epoch' )
trainer.train()
lowercase__ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# A bit of everything
lowercase__ : Any = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=1_0 , eval_steps=5 , evaluation_strategy='steps' , )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
lowercase__ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(a ) in warn_mock.call_args[0][0]
| 645
| 0
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
_UpperCamelCase : Tuple =logging.get_logger(__name__)
_UpperCamelCase : Any ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
_UpperCamelCase : Any ={
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
_UpperCamelCase : List[str] ={
"facebook/bart-base": 10_24,
"facebook/bart-large": 10_24,
"facebook/bart-large-mnli": 10_24,
"facebook/bart-large-cnn": 10_24,
"facebook/bart-large-xsum": 10_24,
"yjernite/bart_eli5": 10_24,
}
class UpperCAmelCase_ ( UpperCamelCase__):
lowerCamelCase__ : int = VOCAB_FILES_NAMES
lowerCamelCase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : Optional[int] = ["input_ids", "attention_mask"]
lowerCamelCase__ : Optional[Any] = BartTokenizer
def __init__( self , a=None , a=None , a=None , a="replace" , a="<s>" , a="</s>" , a="</s>" , a="<s>" , a="<unk>" , a="<pad>" , a="<mask>" , a=False , a=True , **a , ) -> Tuple:
super().__init__(
_a , _a , tokenizer_file=_a , errors=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , add_prefix_space=_a , trim_offsets=_a , **_a , )
lowercase__ : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , _a ) != add_prefix_space:
lowercase__ : Tuple = getattr(_a , pre_tok_state.pop('type' ) )
lowercase__ : Union[str, Any] = add_prefix_space
lowercase__ : List[str] = pre_tok_class(**_a )
lowercase__ : List[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase__ : int = """post_processor"""
lowercase__ : Optional[Any] = getattr(self.backend_tokenizer , _a , _a )
if tokenizer_component_instance:
lowercase__ : Dict = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowercase__ : str = tuple(state['sep'] )
if "cls" in state:
lowercase__ : List[str] = tuple(state['cls'] )
lowercase__ : str = False
if state.get('add_prefix_space' , _a ) != add_prefix_space:
lowercase__ : Any = add_prefix_space
lowercase__ : Dict = True
if state.get('trim_offsets' , _a ) != trim_offsets:
lowercase__ : List[Any] = trim_offsets
lowercase__ : Union[str, Any] = True
if changes_to_apply:
lowercase__ : List[str] = getattr(_a , state.pop('type' ) )
lowercase__ : int = component_class(**_a )
setattr(self.backend_tokenizer , _a , _a )
@property
def _UpperCAmelCase ( self ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error('Using mask_token, but it is not set yet.' )
return None
return str(self._mask_token )
@mask_token.setter
def _UpperCAmelCase ( self , a ) -> Tuple:
lowercase__ : Union[str, Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else value
lowercase__ : Any = value
def _UpperCAmelCase ( self , *a , **a ) -> BatchEncoding:
lowercase__ : Optional[int] = kwargs.get('is_split_into_words' , _a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'to use it with pretokenized inputs.' )
return super()._batch_encode_plus(*_a , **_a )
def _UpperCAmelCase ( self , *a , **a ) -> BatchEncoding:
lowercase__ : str = kwargs.get('is_split_into_words' , _a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'to use it with pretokenized inputs.' )
return super()._encode_plus(*_a , **_a )
def _UpperCAmelCase ( self , a , a = None ) -> Tuple[str]:
lowercase__ : Union[str, Any] = self._tokenizer.model.save(_a , name=_a )
return tuple(_a )
def _UpperCAmelCase ( self , a , a=None ) -> Tuple:
lowercase__ : Any = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _UpperCAmelCase ( self , a , a = None ) -> List[int]:
lowercase__ : List[Any] = [self.sep_token_id]
lowercase__ : Tuple = [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]
| 710
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Tuple = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Dict = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
_UpperCamelCase : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 645
| 0
|
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
_UpperCamelCase : int = ['text', 'image', 'audio']
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : Union[str, Any] = []
for input_type in input_types:
if input_type == "text":
inputs.append('Text input' )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png' ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
inputs.append(create_inputs(__UpperCamelCase ) )
else:
raise ValueError(f"""Invalid type requested: {input_type}""" )
return inputs
def a_ ( _lowerCAmelCase : List ):
'''simple docstring'''
lowercase__ : Any = []
for output in outputs:
if isinstance(__UpperCamelCase , (str, AgentText) ):
output_types.append('text' )
elif isinstance(__UpperCamelCase , (Image.Image, AgentImage) ):
output_types.append('image' )
elif isinstance(__UpperCamelCase , (torch.Tensor, AgentAudio) ):
output_types.append('audio' )
else:
raise ValueError(f"""Invalid output: {output}""" )
return output_types
@is_tool_test
class UpperCAmelCase_ :
def _UpperCAmelCase ( self ) -> Optional[Any]:
self.assertTrue(hasattr(self.tool , 'inputs' ) )
self.assertTrue(hasattr(self.tool , 'outputs' ) )
lowercase__ : Dict = self.tool.inputs
for _input in inputs:
if isinstance(_input , UpperCamelCase__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowercase__ : int = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[Any] = create_inputs(self.tool.inputs )
lowercase__ : Tuple = self.tool(*UpperCamelCase__ )
# There is a single output
if len(self.tool.outputs ) == 1:
lowercase__ : Union[str, Any] = [outputs]
self.assertListEqual(output_types(UpperCamelCase__ ) , self.tool.outputs )
def _UpperCAmelCase ( self ) -> Optional[Any]:
self.assertTrue(hasattr(self.tool , 'description' ) )
self.assertTrue(hasattr(self.tool , 'default_checkpoint' ) )
self.assertTrue(self.tool.description.startswith('This is a tool that' ) )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Any = create_inputs(self.tool.inputs )
lowercase__ : Optional[Any] = self.tool(*UpperCamelCase__ )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowercase__ : List[Any] = [outputs]
self.assertEqual(len(UpperCamelCase__ ) , len(self.tool.outputs ) )
for output, output_type in zip(UpperCamelCase__ , self.tool.outputs ):
lowercase__ : List[Any] = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(UpperCamelCase__ , UpperCamelCase__ ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : Dict = create_inputs(self.tool.inputs )
lowercase__ : str = []
for _input, input_type in zip(UpperCamelCase__ , self.tool.inputs ):
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowercase__ : Union[str, Any] = self.tool(*UpperCamelCase__ )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowercase__ : List[Any] = [outputs]
self.assertEqual(len(UpperCamelCase__ ) , len(self.tool.outputs ) )
| 711
|
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self , a ) -> str:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
lowercase__ : str = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = 'sshleifer/tiny-gpt2'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = 'sgugger/tiny-distilbert-classification'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , only_pretrain_model=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Optional[int] = 'sshleifer/tiny-gpt2'
lowercase__ : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : List[Any] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : Tuple = TensorFlowBenchmark(a , [config] )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : List[str] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : List[str] = TensorFlowBenchmark(a , [config] )
lowercase__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[int] = AutoConfig.from_pretrained(a )
lowercase__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a , [config] )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = 'patrickvonplaten/t5-tiny-random'
lowercase__ : Any = AutoConfig.from_pretrained(a )
lowercase__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : int = TensorFlowBenchmark(a , configs=[config] )
lowercase__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , use_xla=a , multi_process=a , )
lowercase__ : Any = TensorFlowBenchmark(a )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , save_to_csv=a , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(a , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(a , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(a , 'env.csv' ) , multi_process=a , )
lowercase__ : Union[str, Any] = TensorFlowBenchmark(a )
benchmark.run()
self.assertTrue(Path(os.path.join(a , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'env.csv' ) ).exists() )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Tuple = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(a ):
self.assertTrue(hasattr(a , 'sequential' ) )
self.assertTrue(hasattr(a , 'cumulative' ) )
self.assertTrue(hasattr(a , 'current' ) )
self.assertTrue(hasattr(a , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(a , 'log.txt' ) , log_print=a , trace_memory_line_by_line=a , eager_mode=a , multi_process=a , )
lowercase__ : Optional[int] = TensorFlowBenchmark(a )
lowercase__ : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(a , 'log.txt' ) ).exists() )
| 645
| 0
|
"""simple docstring"""
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class UpperCAmelCase_ ( nn.Module):
def __init__( self , a , a ) -> int:
super().__init__()
lowercase__ : Any = module
lowercase__ : int = nn.Sequential(
nn.Linear(module.in_features , _lowerCamelCase , bias=_lowerCamelCase ) , nn.Linear(_lowerCamelCase , module.out_features , bias=_lowerCamelCase ) , )
lowercase__ : Dict = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=_lowerCamelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def _UpperCAmelCase ( self , a , *a , **a ) -> Dict:
return self.module(_lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase ) + self.adapter(_lowerCamelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : Any = "bigscience/bloom-1b7"
# Constant values
lowerCamelCase__ : str = 2.109659552692574
lowerCamelCase__ : Tuple = "Hello my name is"
lowerCamelCase__ : Any = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I")
EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n")
EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University")
lowerCamelCase__ : List[str] = 1_0
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : List[str] = AutoTokenizer.from_pretrained(self.model_name )
class UpperCAmelCase_ ( lowerCAmelCase__):
def _UpperCAmelCase ( self ) -> str:
super().setUp()
# Models and tokenizer
lowercase__ : Dict = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='auto' )
lowercase__ : List[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
def _UpperCAmelCase ( self ) -> int:
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> str:
lowercase__ : List[Any] = self.model_abit.config
self.assertTrue(hasattr(_lowerCamelCase , 'quantization_config' ) )
lowercase__ : Optional[Any] = config.to_dict()
lowercase__ : Optional[Any] = config.to_diff_dict()
lowercase__ : Any = config.to_json_string()
def _UpperCAmelCase ( self ) -> Union[str, Any]:
from bitsandbytes.nn import Paramsabit
lowercase__ : Tuple = self.model_fpaa.get_memory_footprint()
lowercase__ : Any = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
lowercase__ : Any = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def _UpperCAmelCase ( self ) -> Tuple:
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(_lowerCamelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[str] = self.tokenizer(self.input_text , return_tensors='pt' )
lowercase__ : Dict = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=1_0 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_lowerCamelCase ) , self.EXPECTED_OUTPUTS )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = BitsAndBytesConfig()
lowercase__ : Any = True
lowercase__ : str = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_lowerCamelCase , device_map='auto' )
lowercase__ : Dict = self.tokenizer(self.input_text , return_tensors='pt' )
lowercase__ : Optional[int] = model_abit_from_config.generate(
input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=1_0 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_lowerCamelCase ) , self.EXPECTED_OUTPUTS )
def _UpperCAmelCase ( self ) -> Any:
with self.assertRaises(_lowerCamelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(_lowerCamelCase )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[Any] = BitsAndBytesConfig()
with self.assertRaises(_lowerCamelCase ):
lowercase__ : Optional[Any] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_lowerCamelCase , load_in_abit=_lowerCamelCase , device_map='auto' , bnb_abit_quant_type='nf4' , )
def _UpperCAmelCase ( self ) -> Optional[int]:
with self.assertRaises(_lowerCamelCase ):
# Tries with `str`
self.model_abit.to('cpu' )
with self.assertRaises(_lowerCamelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(_lowerCamelCase ):
# Tries with a `device`
self.model_abit.to(torch.device('cuda:0' ) )
with self.assertRaises(_lowerCamelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(_lowerCamelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
lowercase__ : List[str] = self.tokenizer(self.input_text , return_tensors='pt' )
lowercase__ : Union[str, Any] = self.model_fpaa.to(torch.floataa )
lowercase__ : Tuple = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=1_0 )
# Check this does not throw an error
lowercase__ : Union[str, Any] = self.model_fpaa.to('cpu' )
# Check this does not throw an error
lowercase__ : Optional[Any] = self.model_fpaa.half()
# Check this does not throw an error
lowercase__ : Dict = self.model_fpaa.float()
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=_lowerCamelCase , device_map='auto' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class UpperCAmelCase_ ( unittest.TestCase):
@classmethod
def _UpperCAmelCase ( cls ) -> List[Any]:
lowercase__ : List[str] = 't5-small'
lowercase__ : Union[str, Any] = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense
lowercase__ : Any = AutoTokenizer.from_pretrained(cls.model_name )
lowercase__ : Tuple = 'Translate in German: Hello, my dog is cute'
def _UpperCAmelCase ( self ) -> str:
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> List[Any]:
from transformers import TaForConditionalGeneration
lowercase__ : Dict = TaForConditionalGeneration._keep_in_fpaa_modules
lowercase__ : Tuple = None
# test with `t5-small`
lowercase__ : Tuple = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
lowercase__ : List[Any] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
lowercase__ : Any = model.generate(**_lowerCamelCase )
# test with `flan-t5-small`
lowercase__ : Dict = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
lowercase__ : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
lowercase__ : Tuple = model.generate(**_lowerCamelCase )
lowercase__ : str = modules
def _UpperCAmelCase ( self ) -> List[Any]:
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
lowercase__ : List[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
lowercase__ : Tuple = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
lowercase__ : str = model.generate(**_lowerCamelCase )
# test with `flan-t5-small`
lowercase__ : Optional[int] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
lowercase__ : int = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
lowercase__ : Union[str, Any] = model.generate(**_lowerCamelCase )
class UpperCAmelCase_ ( lowerCAmelCase__):
def _UpperCAmelCase ( self ) -> Union[str, Any]:
super().setUp()
# model_name
lowercase__ : Optional[Any] = 'bigscience/bloom-560m'
lowercase__ : Optional[int] = 't5-small'
# Different types of model
lowercase__ : Optional[Any] = AutoModel.from_pretrained(self.model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
# Sequence classification model
lowercase__ : List[Any] = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
# CausalLM model
lowercase__ : List[str] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_lowerCamelCase , device_map='auto' )
# Seq2seq model
lowercase__ : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=_lowerCamelCase , device_map='auto' )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> str:
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class UpperCAmelCase_ ( lowerCAmelCase__):
def _UpperCAmelCase ( self ) -> Tuple:
super().setUp()
def _UpperCAmelCase ( self ) -> Dict:
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Any = pipeline(
'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
lowercase__ : Optional[Any] = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class UpperCAmelCase_ ( lowerCAmelCase__):
def _UpperCAmelCase ( self ) -> Optional[Any]:
super().setUp()
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Union[str, Any] = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=_lowerCamelCase , device_map='balanced' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
lowercase__ : Any = self.tokenizer(self.input_text , return_tensors='pt' )
# Second real batch
lowercase__ : Dict = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=1_0 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=_lowerCamelCase ) , self.EXPECTED_OUTPUTS )
class UpperCAmelCase_ ( lowerCAmelCase__):
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Tuple = 'facebook/opt-350m'
super().setUp()
def _UpperCAmelCase ( self ) -> str:
if version.parse(importlib.metadata.version('bitsandbytes' ) ) < version.parse('0.37.0' ):
return
# Step 1: freeze all parameters
lowercase__ : Optional[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_lowerCamelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
lowercase__ : Optional[Any] = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
lowercase__ : List[str] = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(_lowerCamelCase ) ):
lowercase__ : List[Any] = LoRALayer(module.q_proj , rank=1_6 )
lowercase__ : str = LoRALayer(module.k_proj , rank=1_6 )
lowercase__ : Optional[int] = LoRALayer(module.v_proj , rank=1_6 )
# Step 3: dummy batch
lowercase__ : Optional[int] = self.tokenizer('Test batch ' , return_tensors='pt' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
lowercase__ : List[str] = model.forward(**_lowerCamelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(_lowerCamelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class UpperCAmelCase_ ( lowerCAmelCase__):
lowerCamelCase__ : Dict = "gpt2-xl"
lowerCamelCase__ : Union[str, Any] = 3.3191854854152187
| 712
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class UpperCAmelCase_ ( _a):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=False , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=3 , a=4 , a=None , ) -> Any:
lowercase__ : Tuple = parent
lowercase__ : List[Any] = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : List[Any] = is_training
lowercase__ : Optional[Any] = use_input_mask
lowercase__ : Optional[int] = use_token_type_ids
lowercase__ : int = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : int = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : List[str] = num_attention_heads
lowercase__ : Optional[Any] = intermediate_size
lowercase__ : Optional[Any] = hidden_act
lowercase__ : List[str] = hidden_dropout_prob
lowercase__ : List[Any] = attention_probs_dropout_prob
lowercase__ : List[Any] = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : Tuple = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = num_labels
lowercase__ : Tuple = num_choices
lowercase__ : str = scope
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_input_mask:
lowercase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Dict = None
lowercase__ : Optional[Any] = None
lowercase__ : int = None
if self.use_labels:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self ) -> Optional[int]:
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Tuple = DistilBertModel(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , a )
lowercase__ : str = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
lowercase__ : Union[str, Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> int:
lowercase__ : Tuple = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(
a , attention_mask=a , start_positions=a , end_positions=a )
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 , a , a , a , a , a , a ) -> List[str]:
lowercase__ : int = self.num_labels
lowercase__ : Dict = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
lowercase__ : Optional[Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Any:
lowercase__ : Any = self.num_labels
lowercase__ : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Tuple:
lowercase__ : List[Any] = self.num_choices
lowercase__ : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
lowercase__ : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : int = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Union[str, Any] = self.prepare_config_and_inputs()
((lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__)) : List[str] = config_and_inputs
lowercase__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : List[str] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ : str = (
{
"feature-extraction": DistilBertModel,
"fill-mask": DistilBertForMaskedLM,
"question-answering": DistilBertForQuestionAnswering,
"text-classification": DistilBertForSequenceClassification,
"token-classification": DistilBertForTokenClassification,
"zero-shot": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Optional[int] = True
lowerCamelCase__ : Any = True
lowerCamelCase__ : List[Any] = True
lowerCamelCase__ : Optional[Any] = True
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : str = DistilBertModelTester(self )
lowercase__ : int = ConfigTester(self , config_class=a , dim=3_7 )
def _UpperCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : str = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
lowercase__ : Optional[int] = True
lowercase__ : Union[str, Any] = model_class(config=a )
lowercase__ : int = self._prepare_for_class(a , a )
lowercase__ : Tuple = torch.jit.trace(
a , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , 'traced_model.pt' ) )
lowercase__ : Optional[int] = torch.jit.load(os.path.join(a , 'traced_model.pt' ) , map_location=a )
loaded(inputs_dict['input_ids'].to(a ) , inputs_dict['attention_mask'].to(a ) )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : int = DistilBertModel.from_pretrained('distilbert-base-uncased' )
lowercase__ : Union[str, Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase__ : Optional[Any] = model(a , attention_mask=a )[0]
lowercase__ : Tuple = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , a )
lowercase__ : List[Any] = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : list ):
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(__snake_case ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 713
|
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif stress < 0:
raise ValueError('Stress cannot be negative' )
elif tangential_force < 0:
raise ValueError('Tangential Force cannot be negative' )
elif area < 0:
raise ValueError('Area cannot be negative' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : Any ):
'''simple docstring'''
lowercase__ : Optional[Any] = len(a__ )
lowercase__ : int = []
for i in range(len(a__ ) - pat_len + 1 ):
lowercase__ : str = True
for j in range(a__ ):
if s[i + j] != pattern[j]:
lowercase__ : List[str] = False
break
if match_found:
position.append(a__ )
return position
if __name__ == "__main__":
assert naive_pattern_search("ABCDEFG", "DE") == [3]
print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))
| 714
|
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=[3_0, 3_0] , a=2 , a=3 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=3 , a=None , a=8 , a=1_0 , ) -> Any:
lowercase__ : List[str] = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Optional[int] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : str = is_training
lowercase__ : Optional[Any] = use_labels
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Dict = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Any = type_sequence_label_size
lowercase__ : Dict = initializer_range
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Tuple = scope
lowercase__ : Tuple = n_targets
lowercase__ : Optional[int] = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowercase__ : Optional[Any] = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowercase__ : Tuple = num_patches + 1 + self.num_detection_tokens
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowercase__ : Tuple = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowercase__ : int = []
for i in range(self.batch_size ):
lowercase__ : Optional[Any] = {}
lowercase__ : Any = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=a )
lowercase__ : List[str] = torch.rand(self.n_targets , 4 , device=a )
labels.append(a )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> List[Any]:
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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=a , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _UpperCAmelCase ( self , a , a , a ) -> int:
lowercase__ : List[str] = YolosModel(config=a )
model.to(a )
model.eval()
lowercase__ : List[Any] = model(a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = YolosForObjectDetection(a )
model.to(a )
model.eval()
lowercase__ : Dict = model(pixel_values=a )
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowercase__ : str = model(pixel_values=a , labels=a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Any = config_and_inputs
lowercase__ : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
lowerCamelCase__ : List[str] = (
{"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
)
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Union[str, Any] = False
def _UpperCAmelCase ( self , a , a , a=False ) -> Dict:
lowercase__ : List[str] = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowercase__ : Optional[Any] = []
for i in range(self.model_tester.batch_size ):
lowercase__ : Dict = {}
lowercase__ : Dict = torch.ones(
size=(self.model_tester.n_targets,) , device=a , dtype=torch.long )
lowercase__ : Optional[Any] = torch.ones(
self.model_tester.n_targets , 4 , device=a , dtype=torch.float )
labels.append(a )
lowercase__ : Union[str, Any] = labels
return inputs_dict
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Dict = YolosModelTester(self )
lowercase__ : Optional[int] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
# YOLOS does not use inputs_embeds
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
lowercase__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Tuple = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Dict = True
# in YOLOS, the seq_len is different
lowercase__ : Tuple = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowercase__ : Optional[int] = True
lowercase__ : str = False
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : str = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Optional[int] = True
lowercase__ : List[Any] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[str] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : Dict = len(a )
# Check attention is always last and order is fine
lowercase__ : Any = True
lowercase__ : int = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = 1
self.assertEqual(out_len + added_hidden_states , len(a ) )
lowercase__ : Tuple = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> List[str]:
def check_hidden_states_output(a , a , a ):
lowercase__ : str = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : int = model(**self._prepare_for_class(a , a ) )
lowercase__ : int = outputs.hidden_states
lowercase__ : Any = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(a ) , a )
# YOLOS has a different seq_length
lowercase__ : Optional[int] = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : List[Any] = True
check_hidden_states_output(a , a , a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*a )
@slow
def _UpperCAmelCase ( self ) -> Union[str, Any]:
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : int = YolosModel.from_pretrained(a )
self.assertIsNotNone(a )
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return AutoImageProcessor.from_pretrained('hustvl/yolos-small' ) if is_vision_available() else None
@slow
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = YolosForObjectDetection.from_pretrained('hustvl/yolos-small' ).to(a )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : Optional[int] = prepare_img()
lowercase__ : int = image_processor(images=a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : int = model(inputs.pixel_values )
# verify outputs
lowercase__ : Tuple = torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : Any = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] , device=a , )
lowercase__ : List[str] = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] , device=a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 ) )
# verify postprocessing
lowercase__ : Optional[Any] = image_processor.post_process_object_detection(
a , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowercase__ : str = torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861] ).to(a )
lowercase__ : Any = [7_5, 7_5, 1_7, 6_3, 1_7]
lowercase__ : Optional[int] = torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495] ).to(a )
self.assertEqual(len(results['scores'] ) , 5 )
self.assertTrue(torch.allclose(results['scores'] , a , atol=1e-4 ) )
self.assertSequenceEqual(results['labels'].tolist() , a )
self.assertTrue(torch.allclose(results['boxes'][0, :] , a ) )
| 645
| 0
|
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_UpperCamelCase : List[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def a_ ( ):
'''simple docstring'''
lowercase__ : List[str] = os.path.dirname(os.path.realpath(UpperCamelCase__ ) )
lowercase__ : str = os.path.join(UpperCamelCase__ , 'words.txt' )
lowercase__ : str = ''
with open(UpperCamelCase__ ) as f:
lowercase__ : Dict = f.readline()
lowercase__ : List[str] = [word.strip('\"' ) for word in words.strip('\r\n' ).split(',' )]
lowercase__ : Union[str, Any] = [
word
for word in [sum(ord(UpperCamelCase__ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(UpperCamelCase__ )
if __name__ == "__main__":
print(solution())
| 715
|
"""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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_UpperCamelCase : int = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ :
def __init__( self , a=False , a=False , a=6.0 , a=None , a=False , a=False , a=None , a="fp4" , a=False , **a , ) -> Tuple:
lowercase__ : str = load_in_abit
lowercase__ : str = load_in_abit
lowercase__ : List[str] = llm_inta_threshold
lowercase__ : Dict = llm_inta_skip_modules
lowercase__ : Tuple = llm_inta_enable_fpaa_cpu_offload
lowercase__ : Any = llm_inta_has_fpaa_weight
lowercase__ : Any = bnb_abit_quant_type
lowercase__ : Dict = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
lowercase__ : Dict = torch.floataa
elif isinstance(a , a ):
lowercase__ : Any = getattr(a , a )
elif isinstance(a , torch.dtype ):
lowercase__ : Any = bnb_abit_compute_dtype
else:
raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype' )
self.post_init()
def _UpperCAmelCase ( self ) -> str:
if not isinstance(self.llm_inta_threshold , a ):
raise ValueError('llm_int8_threshold must be a float' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , a ):
raise ValueError('llm_int8_skip_modules must be a list of strings' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , a ):
raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean' )
if not isinstance(self.llm_inta_has_fpaa_weight , a ):
raise ValueError('llm_int8_has_fp16_weight must be a boolean' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError('bnb_4bit_compute_dtype must be torch.dtype' )
if not isinstance(self.bnb_abit_quant_type , a ):
raise ValueError('bnb_4bit_quant_type must be a string' )
if not isinstance(self.bnb_abit_use_double_quant , a ):
raise ValueError('bnb_4bit_use_double_quant must be a boolean' )
if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes' ) ) >= version.parse(
'0.39.0' ):
raise ValueError(
'4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version' )
def _UpperCAmelCase ( self ) -> Tuple:
return self.load_in_abit or self.load_in_abit
def _UpperCAmelCase ( self ) -> List[str]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> Optional[Any]:
lowercase__ : List[Any] = cls(**a )
lowercase__ : Union[str, Any] = []
for key, value in kwargs.items():
if hasattr(a , a ):
setattr(a , a , a )
to_remove.append(a )
for key in to_remove:
kwargs.pop(a , a )
if return_unused_kwargs:
return config, kwargs
else:
return config
def _UpperCAmelCase ( self , a ) -> Dict:
with open(a , 'w' , encoding='utf-8' ) as writer:
lowercase__ : Any = self.to_dict()
lowercase__ : str = json.dumps(a , indent=2 , sort_keys=a ) + '\n'
writer.write(a )
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : Any = str(output['bnb_4bit_compute_dtype'] ).split('.' )[1]
return output
def __repr__( self ) -> Dict:
return f"""{self.__class__.__name__} {self.to_json_string()}"""
def _UpperCAmelCase ( self , a = True ) -> str:
if use_diff is True:
lowercase__ : List[Any] = self.to_diff_dict()
else:
lowercase__ : List[str] = self.to_dict()
return json.dumps(a , indent=2 , sort_keys=a ) + "\n"
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Tuple = self.to_dict()
# get the default config dict
lowercase__ : Optional[Any] = BitsAndBytesConfig().to_dict()
lowercase__ : int = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
lowercase__ : Optional[int] = value
return serializable_config_dict
| 645
| 0
|
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class UpperCAmelCase_ ( __UpperCAmelCase):
def __init__( self , a = "▁" , a = True , a = "<unk>" , a = "</s>" , a = "<pad>" , ) -> Optional[Any]:
lowercase__ : List[str] = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
lowercase__ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowercase__ : Tuple = token_dict['token']
lowercase__ : List[Any] = Tokenizer(Unigram() )
lowercase__ : str = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) , ' ' ),
normalizers.Lowercase(),
] )
lowercase__ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE ),
pre_tokenizers.Digits(individual_digits=__SCREAMING_SNAKE_CASE ),
pre_tokenizers.Punctuation(),
] )
lowercase__ : Any = decoders.Metaspace(replacement=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = TemplateProcessing(
single=f"""$A {self.special_tokens["eos"]["token"]}""" , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
lowercase__ : str = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self , a , a = 8_0_0_0 , a = True , ) -> Tuple:
lowercase__ : List[str] = trainers.UnigramTrainer(
vocab_size=__SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=__SCREAMING_SNAKE_CASE , )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase__ : Union[str, Any] = [files]
self._tokenizer.train(__SCREAMING_SNAKE_CASE , trainer=__SCREAMING_SNAKE_CASE )
self.add_unk_id()
def _UpperCAmelCase ( self , a , a = 8_0_0_0 , a = True , ) -> Tuple:
lowercase__ : List[str] = trainers.UnigramTrainer(
vocab_size=__SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=__SCREAMING_SNAKE_CASE , )
self._tokenizer.train_from_iterator(__SCREAMING_SNAKE_CASE , trainer=__SCREAMING_SNAKE_CASE )
self.add_unk_id()
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : int = json.loads(self._tokenizer.to_str() )
lowercase__ : int = self.special_tokens['unk']['id']
lowercase__ : List[str] = Tokenizer.from_str(json.dumps(__SCREAMING_SNAKE_CASE ) )
| 716
|
"""simple docstring"""
import argparse
import gc
import json
import os
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_UpperCamelCase : int = 16
_UpperCamelCase : Union[str, Any] = 32
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
return int(x / 2**20 )
class UpperCAmelCase_ :
def __enter__( self ) -> Union[str, Any]:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase__ : List[str] = torch.cuda.memory_allocated()
return self
def __exit__( self , *a ) -> Any:
gc.collect()
torch.cuda.empty_cache()
lowercase__ : Optional[Any] = torch.cuda.memory_allocated()
lowercase__ : Union[str, Any] = torch.cuda.max_memory_allocated()
lowercase__ : List[Any] = bamb(self.end - self.begin )
lowercase__ : List[Any] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def a_ ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 , _lowerCAmelCase : str = "bert-base-cased" , _lowerCAmelCase : int = 320 , _lowerCAmelCase : int = 160 , ):
'''simple docstring'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
lowercase__ : Union[str, Any] = load_dataset(
'glue' , 'mrpc' , split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""} )
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase__ : Union[str, Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=_lowerCAmelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_lowerCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(_lowerCAmelCase , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['train'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
lowercase__ : Dict = DataLoader(
tokenized_datasets['validation'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[int] = config['lr']
lowercase__ : Optional[Any] = int(config['num_epochs'] )
lowercase__ : Optional[Any] = int(config['seed'] )
lowercase__ : int = int(config['batch_size'] )
lowercase__ : Union[str, Any] = args.model_name_or_path
set_seed(_lowerCAmelCase )
lowercase__ , lowercase__ : Tuple = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : List[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase , return_dict=_lowerCAmelCase )
# Instantiate optimizer
lowercase__ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase__ : Optional[Any] = optimizer_cls(params=model.parameters() , lr=_lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
lowercase__ : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowercase__ : List[Any] = 1
lowercase__ : List[Any] = (len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=0 , num_training_steps=_lowerCAmelCase , )
else:
lowercase__ : Tuple = DummyScheduler(_lowerCAmelCase , total_num_steps=_lowerCAmelCase , warmup_num_steps=0 )
# 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__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Dict = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
lowercase__ : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase__ : Tuple = 0
# Now we train the model
lowercase__ : Optional[Any] = {}
for epoch in range(_lowerCAmelCase , _lowerCAmelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
lowercase__ : List[Any] = model(**_lowerCAmelCase )
lowercase__ : Dict = outputs.loss
lowercase__ : int = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin ) ) )
accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used ) )
accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked ) )
accelerator.print(
'Total Peak Memory consumed during the train (max): {}'.format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase__ : int = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'peak_memory_utilization.json' ) , 'w' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : int = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=_lowerCAmelCase , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=_lowerCAmelCase , )
parser.add_argument(
'--output_dir' , type=_lowerCAmelCase , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--peak_memory_upper_bound' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.' , )
parser.add_argument(
'--n_train' , type=_lowerCAmelCase , default=320 , help='Number of training examples to use.' , )
parser.add_argument(
'--n_val' , type=_lowerCAmelCase , default=160 , help='Number of validation examples to use.' , )
parser.add_argument(
'--num_epochs' , type=_lowerCAmelCase , default=1 , help='Number of train epochs.' , )
lowercase__ : Any = parser.parse_args()
lowercase__ : Optional[Any] = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 645
| 0
|
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class UpperCAmelCase_ :
def __init__( self ) -> None:
lowercase__ : list[Any] = []
lowercase__ : int = 0
lowercase__ : int = 0
def _UpperCAmelCase ( self ) -> bool:
return self.head == self.tail
def _UpperCAmelCase ( self , a ) -> None:
self.data.append(__a )
lowercase__ : Optional[Any] = self.tail + 1
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = self.data[self.head]
lowercase__ : Union[str, Any] = self.head + 1
return ret
def _UpperCAmelCase ( self ) -> int:
return self.tail - self.head
def _UpperCAmelCase ( self ) -> None:
print(self.data )
print('**************' )
print(self.data[self.head : self.tail] )
class UpperCAmelCase_ :
def __init__( self , a ) -> None:
lowercase__ : Tuple = data
lowercase__ : MyNode | None = None
lowercase__ : MyNode | None = None
lowercase__ : int = 1
def _UpperCAmelCase ( self ) -> Any:
return self.data
def _UpperCAmelCase ( self ) -> MyNode | None:
return self.left
def _UpperCAmelCase ( self ) -> MyNode | None:
return self.right
def _UpperCAmelCase ( self ) -> int:
return self.height
def _UpperCAmelCase ( self , a ) -> None:
lowercase__ : Optional[int] = data
def _UpperCAmelCase ( self , a ) -> None:
lowercase__ : List[str] = node
def _UpperCAmelCase ( self , a ) -> None:
lowercase__ : Tuple = node
def _UpperCAmelCase ( self , a ) -> None:
lowercase__ : List[str] = height
def a_ ( _lowerCAmelCase : MyNode | None ):
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
'''simple docstring'''
if a > b:
return a
return b
def a_ ( _lowerCAmelCase : MyNode ):
'''simple docstring'''
print('left rotation node:' , node.get_data() )
lowercase__ : List[str] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(_lowerCAmelCase )
lowercase__ : Union[str, Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_lowerCAmelCase )
lowercase__ : Any = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(_lowerCAmelCase )
return ret
def a_ ( _lowerCAmelCase : MyNode ):
'''simple docstring'''
print('right rotation node:' , node.get_data() )
lowercase__ : List[str] = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(_lowerCAmelCase )
lowercase__ : Optional[Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_lowerCAmelCase )
lowercase__ : Optional[int] = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(_lowerCAmelCase )
return ret
def a_ ( _lowerCAmelCase : MyNode ):
'''simple docstring'''
lowercase__ : Dict = node.get_left()
assert left_child is not None
node.set_left(left_rotation(_lowerCAmelCase ) )
return right_rotation(_lowerCAmelCase )
def a_ ( _lowerCAmelCase : MyNode ):
'''simple docstring'''
lowercase__ : List[str] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(_lowerCAmelCase ) )
return left_rotation(_lowerCAmelCase )
def a_ ( _lowerCAmelCase : MyNode | None , _lowerCAmelCase : Any ):
'''simple docstring'''
if node is None:
return MyNode(_lowerCAmelCase )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , _lowerCAmelCase ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
lowercase__ : Optional[Any] = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
lowercase__ : Union[str, Any] = right_rotation(_lowerCAmelCase )
else:
lowercase__ : Any = lr_rotation(_lowerCAmelCase )
else:
node.set_right(insert_node(node.get_right() , _lowerCAmelCase ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
lowercase__ : Any = node.get_right()
assert right_child is not None
if data < right_child.get_data():
lowercase__ : List[str] = rl_rotation(_lowerCAmelCase )
else:
lowercase__ : Dict = left_rotation(_lowerCAmelCase )
lowercase__ : Optional[int] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_lowerCAmelCase )
return node
def a_ ( _lowerCAmelCase : MyNode ):
'''simple docstring'''
while True:
lowercase__ : Optional[int] = root.get_right()
if right_child is None:
break
lowercase__ : Any = right_child
return root.get_data()
def a_ ( _lowerCAmelCase : MyNode ):
'''simple docstring'''
while True:
lowercase__ : Optional[Any] = root.get_left()
if left_child is None:
break
lowercase__ : Tuple = left_child
return root.get_data()
def a_ ( _lowerCAmelCase : MyNode , _lowerCAmelCase : Any ):
'''simple docstring'''
lowercase__ : Tuple = root.get_left()
lowercase__ : Union[str, Any] = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
lowercase__ : Tuple = get_left_most(_lowerCAmelCase )
root.set_data(_lowerCAmelCase )
root.set_right(del_node(_lowerCAmelCase , _lowerCAmelCase ) )
elif left_child is not None:
lowercase__ : List[str] = left_child
elif right_child is not None:
lowercase__ : Dict = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('No such data' )
return root
else:
root.set_left(del_node(_lowerCAmelCase , _lowerCAmelCase ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(_lowerCAmelCase , _lowerCAmelCase ) )
if get_height(_lowerCAmelCase ) - get_height(_lowerCAmelCase ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
lowercase__ : Optional[Any] = left_rotation(_lowerCAmelCase )
else:
lowercase__ : Optional[Any] = rl_rotation(_lowerCAmelCase )
elif get_height(_lowerCAmelCase ) - get_height(_lowerCAmelCase ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
lowercase__ : List[Any] = right_rotation(_lowerCAmelCase )
else:
lowercase__ : Union[str, Any] = lr_rotation(_lowerCAmelCase )
lowercase__ : str = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(_lowerCAmelCase )
return root
class UpperCAmelCase_ :
def __init__( self ) -> None:
lowercase__ : MyNode | None = None
def _UpperCAmelCase ( self ) -> int:
return get_height(self.root )
def _UpperCAmelCase ( self , a ) -> None:
print('insert:' + str(__a ) )
lowercase__ : Any = insert_node(self.root , __a )
def _UpperCAmelCase ( self , a ) -> None:
print('delete:' + str(__a ) )
if self.root is None:
print('Tree is empty!' )
return
lowercase__ : Union[str, Any] = del_node(self.root , __a )
def __str__( self , ) -> str: # a level traversale, gives a more intuitive look on the tree
lowercase__ : List[str] = ''
lowercase__ : Optional[int] = MyQueue()
q.push(self.root )
lowercase__ : Union[str, Any] = self.get_height()
if layer == 0:
return output
lowercase__ : Union[str, Any] = 0
while not q.is_empty():
lowercase__ : Dict = q.pop()
lowercase__ : Tuple = ' ' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(__a )
q.push(__a )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
lowercase__ : Optional[Any] = cnt + 1
for i in range(1_0_0 ):
if cnt == math.pow(2 , __a ) - 1:
lowercase__ : Dict = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def a_ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
_UpperCamelCase : Optional[int] = AVLtree()
_UpperCamelCase : Optional[Any] = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 717
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
lowercase__ : List[str] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowercase__ : Dict = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowercase__ : Union[str, Any] = j
return prefix_result
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
return max(prefix_function(_lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_UpperCamelCase : str = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.proj": "output.dense",
"attention.self_mask": "attn_mask",
"mlp.fc1": "intermediate.dense",
"mlp.fc2": "output.dense",
"norm1": "layernorm_before",
"norm2": "layernorm_after",
"bn0": "batch_norm",
}
_UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")
def a_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any]=False ):
'''simple docstring'''
lowercase__ , lowercase__ : List[Any] = create_model(
'HTSAT-tiny' , 'roberta' , _lowercase , precision='fp32' , device='cuda:0' if torch.cuda.is_available() else 'cpu' , enable_fusion=_lowercase , fusion_type='aff_2d' if enable_fusion else None , )
return model, model_cfg
def a_ ( _lowerCAmelCase : List[Any] ):
'''simple docstring'''
lowercase__ : Optional[Any] = {}
lowercase__ : Any = R'.*sequential.(\d+).*'
lowercase__ : Tuple = R'.*_projection.(\d+).*'
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowercase__ : Tuple = key.replace(_lowercase , _lowercase )
if re.match(_lowercase , _lowercase ):
# replace sequential layers with list
lowercase__ : Tuple = re.match(_lowercase , _lowercase ).group(1 )
lowercase__ : int = key.replace(f"""sequential.{sequential_layer}.""" , f"""layers.{int(_lowercase )//3}.linear.""" )
elif re.match(_lowercase , _lowercase ):
lowercase__ : str = int(re.match(_lowercase , _lowercase ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
lowercase__ : List[Any] = 1 if projecton_layer == 0 else 2
lowercase__ : List[str] = key.replace(f"""_projection.{projecton_layer}.""" , f"""_projection.linear{transformers_projection_layer}.""" )
if "audio" and "qkv" in key:
# split qkv into query key and value
lowercase__ : Optional[Any] = value
lowercase__ : str = mixed_qkv.size(0 ) // 3
lowercase__ : Optional[Any] = mixed_qkv[:qkv_dim]
lowercase__ : Dict = mixed_qkv[qkv_dim : qkv_dim * 2]
lowercase__ : List[Any] = mixed_qkv[qkv_dim * 2 :]
lowercase__ : Optional[Any] = query_layer
lowercase__ : Union[str, Any] = key_layer
lowercase__ : Optional[int] = value_layer
else:
lowercase__ : str = value
return model_state_dict
def a_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Tuple=False ):
'''simple docstring'''
lowercase__ , lowercase__ : Optional[Any] = init_clap(_lowercase , enable_fusion=_lowercase )
clap_model.eval()
lowercase__ : Tuple = clap_model.state_dict()
lowercase__ : List[Any] = rename_state_dict(_lowercase )
lowercase__ : Optional[Any] = ClapConfig()
lowercase__ : Dict = enable_fusion
lowercase__ : List[str] = ClapModel(_lowercase )
# ignore the spectrogram embedding layer
model.load_state_dict(_lowercase , strict=_lowercase )
model.save_pretrained(_lowercase )
transformers_config.save_pretrained(_lowercase )
if __name__ == "__main__":
_UpperCamelCase : Union[str, Any] = 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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
_UpperCamelCase : str = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 718
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=7 , a=3 , a=1_8 , a=3_0 , a=4_0_0 , a=True , a=None , a=True , a=None , a=True , ) -> List[str]:
lowercase__ : Tuple = size if size is not None else {'shortest_edge': 2_0}
lowercase__ : Union[str, Any] = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : str = num_channels
lowercase__ : Any = image_size
lowercase__ : Optional[Any] = min_resolution
lowercase__ : int = max_resolution
lowercase__ : List[Any] = do_resize
lowercase__ : List[str] = size
lowercase__ : str = do_center_crop
lowercase__ : List[Any] = crop_size
lowercase__ : Union[str, Any] = do_flip_channel_order
def _UpperCAmelCase ( self ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Optional[Any] = MobileViTImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Tuple = MobileViTImageProcessingTester(self )
@property
def _UpperCAmelCase ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a , 'do_resize' ) )
self.assertTrue(hasattr(a , 'size' ) )
self.assertTrue(hasattr(a , 'do_center_crop' ) )
self.assertTrue(hasattr(a , 'center_crop' ) )
self.assertTrue(hasattr(a , 'do_flip_channel_order' ) )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 2_0} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
lowercase__ : str = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> str:
# Initialize image_processing
lowercase__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a )
for image in image_inputs:
self.assertIsInstance(a , Image.Image )
# Test not batched input
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : List[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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Tuple:
# Initialize image_processing
lowercase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ : Tuple = 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
lowercase__ : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Dict:
# Initialize image_processing
lowercase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ : Optional[Any] = 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
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : Tuple = 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase : Optional[int] = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotSmallConfig",
"BlenderbotSmallOnnxConfig",
],
"tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Union[str, Any] = ["BlenderbotSmallTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[Any] = [
"BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotSmallForCausalLM",
"BlenderbotSmallForConditionalGeneration",
"BlenderbotSmallModel",
"BlenderbotSmallPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Optional[int] = [
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[Any] = [
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBlenderbotSmallPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
_UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 719
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=True , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=4 , ) -> Dict:
lowercase__ : Optional[Any] = parent
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : int = is_training
lowercase__ : str = use_attention_mask
lowercase__ : Dict = use_token_type_ids
lowercase__ : Optional[int] = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : List[str] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : int = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[str] = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Tuple = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : int = type_vocab_size
lowercase__ : List[str] = type_sequence_label_size
lowercase__ : Union[str, Any] = initializer_range
lowercase__ : Optional[int] = num_choices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_attention_mask:
lowercase__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : List[str] = None
if self.use_token_type_ids:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ : Any = 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 , is_decoder=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_class_name in self.all_model_classes:
lowercase__ : str = model_class_name.from_pretrained('albert-base-v2' )
lowercase__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : str = FlaxAlbertModel.from_pretrained('albert-base-v2' )
lowercase__ : Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
lowercase__ : Any = model(a , attention_mask=a )[0]
lowercase__ : Tuple = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , a )
lowercase__ : Optional[Any] = np.array(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
from itertools import permutations
def a_ ( _lowerCAmelCase : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowercase__ : Tuple = [7, 11, 13, 17]
for i, test in enumerate(lowercase_ ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def a_ ( _lowerCAmelCase : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(lowercase_ , lowercase_ ) ) )
for num in permutations(range(lowercase_ ) )
if is_substring_divisible(lowercase_ ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 720
|
"""simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
lowercase__ : int = 0.0
for coeff in reversed(_lowerCAmelCase ):
lowercase__ : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
_UpperCamelCase : int = (0.0, 0.0, 5.0, 9.3, 7.0)
_UpperCamelCase : Dict = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 645
| 0
|
"""simple docstring"""
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE):
pass
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE):
pass
class UpperCAmelCase_ :
def __init__( self ) -> Tuple:
lowercase__ : List[Any] = [
[],
[],
[],
]
def _UpperCAmelCase ( self , a , a ) -> Dict:
try:
if len(self.queues[priority] ) >= 1_0_0:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(_a )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def _UpperCAmelCase ( self ) -> str:
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self ) -> str:
return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )
class UpperCAmelCase_ :
def __init__( self ) -> Tuple:
lowercase__ : Union[str, Any] = []
def _UpperCAmelCase ( self , a ) -> Union[str, Any]:
if len(self.queue ) == 1_0_0:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(_a )
def _UpperCAmelCase ( self ) -> Any:
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
lowercase__ : List[Any] = min(self.queue )
self.queue.remove(_a )
return data
def __str__( self ) -> Dict:
return str(self.queue )
def a_ ( ):
'''simple docstring'''
lowercase__ : Any = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 100 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 128 )
print(lowerCAmelCase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(lowerCAmelCase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def a_ ( ):
'''simple docstring'''
lowercase__ : str = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(100 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(128 )
print(lowerCAmelCase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(lowerCAmelCase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 721
|
"""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
_UpperCamelCase : 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 a_ ( _lowerCAmelCase : Optional[Any]=True ):
'''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=_a))
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = None
lowerCamelCase__ : Optional[Any] = None
def _UpperCAmelCase ( self , a , a ) -> List[Any]:
with TemporaryDirectory() as tmp_dir:
lowercase__ : List[str] = dataset_module_factory(a , cache_dir=a )
lowercase__ : List[Any] = import_main_class(dataset_module.module_path , dataset=a )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=a , config_name=a , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=a ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
lowercase__ : Union[str, Any] = cached_path(a , cache_dir=a )
self.assertTrue(os.path.exists(a ) )
@pytest.mark.integration
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Union[str, Any] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
lowercase__ : int = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : Optional[int] = import_main_class(dataset_module.module_path )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowercase__ : Optional[int] = None
builder_instance.download_and_prepare()
lowercase__ : Optional[int] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Optional[int] = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : List[str] = import_main_class(dataset_module.module_path , dataset=_lowerCAmelCase )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert "train" in ds
assert isinstance(ds['train'] , _lowerCAmelCase )
assert next(iter(ds['train'] ) )
| 645
| 0
|
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Union[str, Any] = LayoutLMTokenizer
lowerCamelCase__ : Tuple = LayoutLMTokenizerFast
lowerCamelCase__ : Union[str, Any] = True
lowerCamelCase__ : Optional[Any] = True
def _UpperCAmelCase ( self ) -> Any:
super().setUp()
lowercase__ : List[str] = [
'[UNK]',
'[CLS]',
'[SEP]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
lowercase__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def _UpperCAmelCase ( self , **a ) -> Optional[int]:
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def _UpperCAmelCase ( self , a ) -> Union[str, Any]:
lowercase__ : Optional[Any] = 'UNwant\u00E9d,running'
lowercase__ : List[Any] = 'unwanted, running'
return input_text, output_text
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Tuple = self.tokenizer_class(self.vocab_file )
lowercase__ : List[str] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(UpperCamelCase__ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [7, 4, 5, 1_0, 8, 9] )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
| 700
|
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def a_ ( _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray ):
'''simple docstring'''
lowercase__ : Any = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(_lowerCAmelCase , _lowerCAmelCase )
# Predict target for test data
lowercase__ : str = xgb.predict(_lowerCAmelCase )
lowercase__ : Union[str, Any] = predictions.reshape(len(_lowerCAmelCase ) , 1 )
return predictions
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = fetch_california_housing()
lowercase__ , lowercase__ : str = data_handling(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = train_test_split(
_lowerCAmelCase , _lowerCAmelCase , test_size=0.2_5 , random_state=1 )
lowercase__ : Any = xgboost(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Error printing
print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
print(f"""Mean Square Error : {mean_squared_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 645
| 0
|
"""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
_UpperCamelCase : Any = {
"""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
}
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case_):
lowerCamelCase__ : Dict = """maskformer"""
lowerCamelCase__ : int = {"""hidden_size""": """mask_feature_size"""}
lowerCamelCase__ : str = ["""resnet""", """swin"""]
lowerCamelCase__ : List[str] = ["""detr"""]
def __init__( self , a = 2_5_6 , a = 2_5_6 , a = 0.1 , a = False , a = None , a = None , a = 0.02 , a = 1.0 , a = 1.0 , a = 1.0 , a = 20.0 , a = None , **a , ) -> Optional[int]:
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Optional[int] = SwinConfig(
image_size=3_8_4 , in_channels=3 , patch_size=4 , embed_dim=1_2_8 , depths=[2, 2, 1_8, 2] , num_heads=[4, 8, 1_6, 3_2] , window_size=1_2 , drop_path_rate=0.3 , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(a , a ):
lowercase__ : str = backbone_config.pop('model_type' )
lowercase__ : int = CONFIG_MAPPING[backbone_model_type]
lowercase__ : 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
lowercase__ : Optional[int] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Optional[int] = (
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 ):
lowercase__ : str = CONFIG_MAPPING[decoder_type]
lowercase__ : List[Any] = config_class.from_dict(a )
lowercase__ : Tuple = backbone_config
lowercase__ : int = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : int = init_std
lowercase__ : Dict = init_xavier_std
# Hungarian matcher && loss
lowercase__ : int = cross_entropy_weight
lowercase__ : Tuple = dice_weight
lowercase__ : Dict = mask_weight
lowercase__ : int = use_auxiliary_loss
lowercase__ : Tuple = no_object_weight
lowercase__ : List[Any] = output_auxiliary_logits
lowercase__ : Optional[int] = self.decoder_config.encoder_attention_heads
lowercase__ : Tuple = self.decoder_config.num_hidden_layers
super().__init__(**a )
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> List[Any]:
return cls(
backbone_config=a , decoder_config=a , **a , )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = copy.deepcopy(self.__dict__ )
lowercase__ : Optional[int] = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : str = self.__class__.model_type
return output
| 701
|
"""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 UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=1_0 , a=3 , a=2 , a=2 , a=2 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=0.9 , a=None , ) -> Optional[Any]:
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Dict = patch_size
lowercase__ : Tuple = tubelet_size
lowercase__ : Optional[int] = num_frames
lowercase__ : Optional[int] = is_training
lowercase__ : int = use_labels
lowercase__ : Optional[int] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = mask_ratio
lowercase__ : Optional[Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase__ : Optional[Any] = (image_size // patch_size) ** 2
lowercase__ : str = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase__ : str = int(mask_ratio * self.seq_length )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Tuple:
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=a , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a ) -> Optional[int]:
lowercase__ : Dict = VideoMAEModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = VideoMAEForPreTraining(a )
model.to(a )
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
lowercase__ : Any = torch.ones((self.num_masks,) )
lowercase__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowercase__ : Optional[int] = mask.expand(self.batch_size , -1 ).bool()
lowercase__ : str = model(a , a )
# model only returns predictions for masked patches
lowercase__ : str = mask.sum().item()
lowercase__ : int = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ : Optional[int] = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Any = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = False
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = VideoMAEModelTester(self )
lowercase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self , a , a , a=False ) -> Optional[int]:
lowercase__ : Union[str, Any] = copy.deepcopy(a )
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
lowercase__ : Optional[Any] = torch.ones((self.model_tester.num_masks,) )
lowercase__ : Any = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowercase__ : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowercase__ : Union[str, Any] = bool_masked_pos.to(a )
if return_labels:
if model_class in [
*get_values(a ),
]:
lowercase__ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = VideoMAEModel.from_pretrained(a )
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = True
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Any = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase__ : Optional[Any] = True
lowercase__ : int = False
lowercase__ : Any = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[int] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Dict = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : List[str] = len(a )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : List[str] = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(a , a ) )
self.assertEqual(out_len + 1 , len(a ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[int] = outputs.hidden_states
lowercase__ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(a ) , a )
lowercase__ : Optional[Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Union[str, Any] = 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] , )
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(a , a , a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def a_ ( ):
'''simple docstring'''
lowercase__ : int = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowercase__ : str = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
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 _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
a )
lowercase__ : str = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**a )
# verify the logits
lowercase__ : str = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : List[Any] = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(a )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(a , return_tensors='pt' ).to(a )
# add boolean mask, indicating which patches to mask
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowercase__ : str = torch.load(a )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**a )
# verify the logits
lowercase__ : Dict = torch.Size([1, 1_4_0_8, 1_5_3_6] )
lowercase__ : List[str] = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=a )
self.assertEqual(outputs.logits.shape , a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase__ : List[Any] = torch.tensor([0.5_142] , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=a ).to(
a )
with torch.no_grad():
lowercase__ : Any = model(**a )
lowercase__ : List[Any] = torch.tensor(torch.tensor([0.6_469] ) , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
| 645
| 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 a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : int = MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowercase__ : List[str] = MobileBertForPreTraining(SCREAMING_SNAKE_CASE_ )
# Load weights from tf checkpoint
lowercase__ : Dict = load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
_UpperCamelCase : Union[str, 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(
"--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."
)
_UpperCamelCase : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 702
|
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
"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",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
_UpperCamelCase : List[str] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
for attribute in key.split('.' ):
lowercase__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
lowercase__ : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
lowercase__ : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
lowercase__ : Optional[Any] = value
elif weight_type == "weight_g":
lowercase__ : Dict = value
elif weight_type == "weight_v":
lowercase__ : List[str] = value
elif weight_type == "bias":
lowercase__ : Optional[Any] = value
else:
lowercase__ : List[str] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = []
lowercase__ : List[str] = fairseq_model.state_dict()
lowercase__ : Union[str, Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase__ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
lowercase__ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase__ : List[Any] = 'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase__ : int = True
if "*" in mapped_key:
lowercase__ : Optional[int] = name.split(_lowerCAmelCase )[0].split('.' )[-2]
lowercase__ : List[str] = mapped_key.replace('*' , _lowerCAmelCase )
if "weight_g" in name:
lowercase__ : List[Any] = 'weight_g'
elif "weight_v" in name:
lowercase__ : int = 'weight_v'
elif "bias" in name:
lowercase__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase__ : Union[str, Any] = 'weight'
else:
lowercase__ : int = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : int = full_name.split('conv_layers.' )[-1]
lowercase__ : int = name.split('.' )
lowercase__ : int = int(items[0] )
lowercase__ : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowercase__ : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowercase__ : Optional[int] = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
lowercase__ : List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
lowercase__ : int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Tuple=True ):
'''simple docstring'''
if config_path is not None:
lowercase__ : Any = UniSpeechSatConfig.from_pretrained(_lowerCAmelCase )
else:
lowercase__ : Any = UniSpeechSatConfig()
lowercase__ : Union[str, Any] = ''
if is_finetuned:
lowercase__ : Optional[Any] = UniSpeechSatForCTC(_lowerCAmelCase )
else:
lowercase__ : List[Any] = UniSpeechSatForPreTraining(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
lowercase__ : Union[str, Any] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase : Tuple = 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"
)
_UpperCamelCase : str = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 645
| 0
|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Optional[Any] = ["image_processor", "tokenizer"]
lowerCamelCase__ : str = "LayoutLMv3ImageProcessor"
lowerCamelCase__ : List[str] = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
def __init__( self , a=None , a=None , **a ) -> Optional[int]:
lowercase__ : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a , )
lowercase__ : int = kwargs.pop('feature_extractor' )
lowercase__ : Dict = 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__(a , a )
def __call__( self , a , a = None , a = None , a = None , a = None , a = True , a = False , a = None , a = None , a = 0 , a = None , a = None , a = None , a = False , a = False , a = False , a = False , a = True , a = None , **a , ) -> List[Any]:
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
lowercase__ : List[str] = self.image_processor(images=a , return_tensors=a )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(a , a ):
lowercase__ : Optional[Any] = [text] # add batch dimension (as the image processor always adds a batch dimension)
lowercase__ : List[str] = features['words']
lowercase__ : List[Any] = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=a , add_special_tokens=a , padding=a , truncation=a , max_length=a , stride=a , pad_to_multiple_of=a , return_token_type_ids=a , return_attention_mask=a , return_overflowing_tokens=a , return_special_tokens_mask=a , return_offsets_mapping=a , return_length=a , verbose=a , return_tensors=a , **a , )
# add pixel values
lowercase__ : Tuple = features.pop('pixel_values' )
if return_overflowing_tokens is True:
lowercase__ : List[str] = self.get_overflowing_images(a , encoded_inputs['overflow_to_sample_mapping'] )
lowercase__ : List[str] = images
return encoded_inputs
def _UpperCAmelCase ( self , a , a ) -> Optional[Any]:
lowercase__ : int = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(a ) != len(a ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
f""" {len(a )} and {len(a )}""" )
return images_with_overflow
def _UpperCAmelCase ( self , *a , **a ) -> Dict:
return self.tokenizer.batch_decode(*a , **a )
def _UpperCAmelCase ( self , *a , **a ) -> Union[str, Any]:
return self.tokenizer.decode(*a , **a )
@property
def _UpperCAmelCase ( self ) -> int:
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def _UpperCAmelCase ( self ) -> List[str]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a , )
return self.image_processor_class
@property
def _UpperCAmelCase ( self ) -> Dict:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a , )
return self.image_processor
| 703
|
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=3_2 , a=2 , a=3 , a=1_6 , 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=1_0 , a=8 , a=["stage1", "stage2", "stage3"] , a=[1, 2, 3] , ) -> int:
lowercase__ : int = parent
lowercase__ : Union[str, Any] = batch_size
lowercase__ : Dict = image_size
lowercase__ : str = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : List[str] = embed_dim
lowercase__ : Any = depths
lowercase__ : Dict = num_heads
lowercase__ : List[str] = window_size
lowercase__ : int = mlp_ratio
lowercase__ : Tuple = qkv_bias
lowercase__ : Union[str, Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Tuple = drop_path_rate
lowercase__ : List[str] = hidden_act
lowercase__ : Optional[Any] = use_absolute_embeddings
lowercase__ : Optional[Any] = patch_norm
lowercase__ : Any = layer_norm_eps
lowercase__ : List[Any] = initializer_range
lowercase__ : List[str] = is_training
lowercase__ : int = scope
lowercase__ : Optional[int] = use_labels
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : List[str] = encoder_stride
lowercase__ : Optional[Any] = out_features
lowercase__ : Dict = out_indices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return MaskFormerSwinConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , )
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Tuple = MaskFormerSwinModel(config=a )
model.to(a )
model.eval()
lowercase__ : str = model(a )
lowercase__ : str = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ : Dict = 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 ) -> Optional[int]:
lowercase__ : List[Any] = MaskFormerSwinBackbone(config=a )
model.to(a )
model.eval()
lowercase__ : int = model(a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [1_3, 1_6, 1_6, 1_6] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [1_6, 3_2, 6_4] )
# verify ValueError
with self.parent.assertRaises(a ):
lowercase__ : Dict = ['stem']
lowercase__ : List[str] = MaskFormerSwinBackbone(config=a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : List[str] = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
lowerCamelCase__ : str = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : int = False
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = MaskFormerSwinModelTester(self )
lowercase__ : Tuple = ConfigTester(self , config_class=a , embed_dim=3_7 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
def _UpperCAmelCase ( self ) -> Tuple:
self.create_and_test_config_common_properties()
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:
return
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*a )
@unittest.skip('Swin does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
@unittest.skip('Swin does not support feedforward chunking' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
lowercase__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def _UpperCAmelCase ( self ) -> int:
pass
def _UpperCAmelCase ( self , a , a , a , a ) -> Tuple:
lowercase__ : Dict = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : str = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[Any] = outputs.hidden_states
lowercase__ : str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(a ) , a )
# Swin has a different seq_length
lowercase__ : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Tuple = (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] , )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = (
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:
lowercase__ : List[str] = 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"]
lowercase__ : List[str] = True
self.check_hidden_states_output(a , a , a , a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = 3
lowercase__ : str = (
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)
)
lowercase__ : Tuple = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Optional[int] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ : List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase__ : List[str] = 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"]
lowercase__ : int = True
self.check_hidden_states_output(a , a , a , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(a ):
lowercase__ : Union[str, Any] = 0
return t
def check_equivalence(a , a , a , a={} ):
with torch.no_grad():
lowercase__ : Optional[Any] = model(**a , return_dict=a , **a )
lowercase__ : Optional[int] = model(**a , return_dict=a , **a ).to_tuple()
def recursive_check(a , a ):
if isinstance(a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(a , a ):
recursive_check(a , a )
elif isinstance(a , a ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(a , a )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(a ) , set_nan_tensor_to_zero(a ) , atol=1e-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}. Dict has"""
f""" `nan`: {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}."""
) , )
recursive_check(a , a )
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
model.to(a )
model.eval()
lowercase__ : Tuple = self._prepare_for_class(a , a )
lowercase__ : Optional[Any] = self._prepare_for_class(a , a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : List[Any] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a )
lowercase__ : int = self._prepare_for_class(a , a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
lowercase__ : Dict = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : Optional[int] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase , _a):
lowerCamelCase__ : Dict = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowerCamelCase__ : Optional[int] = MaskFormerSwinConfig
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[int] = MaskFormerSwinModelTester(self )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
lowercase__ : Optional[Any] = backbone_class(a )
backbone.to(a )
backbone.eval()
lowercase__ : Union[str, Any] = backbone(**a )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , a )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowercase__ : List[str] = backbone(**a , output_hidden_states=a )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowercase__ , lowercase__ , lowercase__ : int = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowercase__ : List[Any] = backbone(**a , output_attentions=a )
self.assertIsNotNone(outputs.attentions )
| 645
| 0
|
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_UpperCamelCase : List[str] = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def a_ ( ):
'''simple docstring'''
lowercase__ : str = os.path.dirname(os.path.realpath(__lowercase ) )
lowercase__ : Tuple = os.path.join(__lowercase , 'words.txt' )
lowercase__ : str = ''
with open(__lowercase ) as f:
lowercase__ : Optional[Any] = f.readline()
lowercase__ : Tuple = [word.strip('"' ) for word in words.strip('\r\n' ).split(',' )]
lowercase__ : Optional[Any] = [
word
for word in [sum(ord(__lowercase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__lowercase )
if __name__ == "__main__":
print(solution())
| 704
|
"""simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 645
| 0
|
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
lowercase__ , lowercase__ : str = image.size
lowercase__ , lowercase__ : Any = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
lowercase__ : str = image.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] )
lowercase__ : List[Any] = np.array(__snake_case ).astype(np.floataa ) / 2_5_5.0
lowercase__ : Any = image[None].transpose(0 , 3 , 1 , 2 )
lowercase__ : Dict = torch.from_numpy(__snake_case )
return 2.0 * image - 1.0
class UpperCAmelCase_ ( __lowercase):
def __init__( self , a , a , a , ) -> int:
super().__init__()
self.register_modules(vqvae=_A , unet=_A , scheduler=_A )
@torch.no_grad()
def __call__( self , a = None , a = 1 , a = 1_0_0 , a = 0.0 , a = None , a = "pil" , a = True , ) -> Dict:
if isinstance(_A , PIL.Image.Image ):
lowercase__ : Tuple = 1
elif isinstance(_A , torch.Tensor ):
lowercase__ : Optional[Any] = image.shape[0]
else:
raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_A )}""" )
if isinstance(_A , PIL.Image.Image ):
lowercase__ : Union[str, Any] = preprocess(_A )
lowercase__ , lowercase__ : List[Any] = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
lowercase__ : Optional[int] = (batch_size, self.unet.config.in_channels // 2, height, width)
lowercase__ : Optional[int] = next(self.unet.parameters() ).dtype
lowercase__ : Optional[int] = randn_tensor(_A , generator=_A , device=self.device , dtype=_A )
lowercase__ : Any = image.to(device=self.device , dtype=_A )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(_A , device=self.device )
lowercase__ : str = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
lowercase__ : Union[str, Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowercase__ : int = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowercase__ : List[str] = {}
if accepts_eta:
lowercase__ : Optional[int] = eta
for t in self.progress_bar(_A ):
# concat latents and low resolution image in the channel dimension.
lowercase__ : Optional[int] = torch.cat([latents, image] , dim=1 )
lowercase__ : str = self.scheduler.scale_model_input(_A , _A )
# predict the noise residual
lowercase__ : Union[str, Any] = self.unet(_A , _A ).sample
# compute the previous noisy sample x_t -> x_t-1
lowercase__ : Optional[int] = self.scheduler.step(_A , _A , _A , **_A ).prev_sample
# decode the image latents with the VQVAE
lowercase__ : int = self.vqvae.decode(_A ).sample
lowercase__ : Optional[Any] = torch.clamp(_A , -1.0 , 1.0 )
lowercase__ : Optional[int] = image / 2 + 0.5
lowercase__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase__ : Any = self.numpy_to_pil(_A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_A )
| 705
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ , lowercase__ : str = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : List[str] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : List[Any] = controlnet_params
lowercase__ : int = 'bird'
lowercase__ : List[Any] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([canny_image] * num_samples )
lowercase__ : List[Any] = jax.random.PRNGKey(0 )
lowercase__ : Tuple = jax.random.split(a , jax.device_count() )
lowercase__ : str = replicate(a )
lowercase__ : List[str] = shard(a )
lowercase__ : Dict = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : Tuple = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : int = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : Optional[Any] = jnp.array(
[0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ , lowercase__ : int = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : Optional[Any] = controlnet_params
lowercase__ : List[Any] = 'Chef in the kitchen'
lowercase__ : List[str] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
lowercase__ : List[str] = jax.random.PRNGKey(0 )
lowercase__ : str = jax.random.split(a , jax.device_count() )
lowercase__ : Optional[Any] = replicate(a )
lowercase__ : Optional[Any] = shard(a )
lowercase__ : List[Any] = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Union[str, Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : str = jnp.array(
[[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 645
| 0
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class UpperCAmelCase_ :
lowerCamelCase__ : str = LEDConfig
lowerCamelCase__ : Dict = {}
lowerCamelCase__ : Dict = '''gelu'''
def __init__( self , a , a=1_3 , a=7 , a=True , a=False , a=9_9 , a=3_2 , a=2 , a=4 , a=3_7 , a=0.1 , a=0.1 , a=2_0 , a=2 , a=1 , a=0 , a=4 , ) -> Optional[Any]:
lowercase__ : Dict = parent
lowercase__ : List[str] = batch_size
lowercase__ : Dict = seq_length
lowercase__ : Tuple = is_training
lowercase__ : Optional[int] = use_labels
lowercase__ : Union[str, Any] = vocab_size
lowercase__ : int = hidden_size
lowercase__ : str = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : str = intermediate_size
lowercase__ : Tuple = hidden_dropout_prob
lowercase__ : int = attention_probs_dropout_prob
lowercase__ : Any = max_position_embeddings
lowercase__ : int = eos_token_id
lowercase__ : Any = pad_token_id
lowercase__ : str = bos_token_id
lowercase__ : Dict = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
lowercase__ : List[str] = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
lowercase__ : Optional[Any] = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowercase__ : List[Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowercase__ : List[Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
lowercase__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
lowercase__ : Dict = prepare_led_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
lowercase__ : List[str] = tf.concat(
[tf.zeros_like(snake_case__ )[:, :-1], tf.ones_like(snake_case__ )[:, -1:]] , axis=-1 , )
lowercase__ : int = global_attention_mask
return config, inputs_dict
def _UpperCAmelCase ( self , a , a ) -> Optional[int]:
lowercase__ : List[str] = TFLEDModel(config=snake_case__ ).get_decoder()
lowercase__ : Tuple = inputs_dict['input_ids']
lowercase__ : List[Any] = input_ids[:1, :]
lowercase__ : List[Any] = inputs_dict['attention_mask'][:1, :]
lowercase__ : Union[str, Any] = 1
# first forward pass
lowercase__ : str = model(snake_case__ , attention_mask=snake_case__ , use_cache=snake_case__ )
lowercase__ , lowercase__ : str = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowercase__ : Union[str, Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase__ : List[str] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowercase__ : Tuple = tf.concat([input_ids, next_tokens] , axis=-1 )
lowercase__ : int = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowercase__ : Union[str, Any] = model(snake_case__ , attention_mask=snake_case__ )[0]
lowercase__ : Any = model(snake_case__ , attention_mask=snake_case__ , past_key_values=snake_case__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowercase__ : Any = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowercase__ : Optional[Any] = output_from_no_past[:, -3:, random_slice_idx]
lowercase__ : int = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case__ , snake_case__ , rtol=1e-3 )
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Any=None , _lowerCAmelCase : Union[str, Any]=None , _lowerCAmelCase : int=None , ):
'''simple docstring'''
if attention_mask is None:
lowercase__ : Tuple = tf.cast(tf.math.not_equal(lowerCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowercase__ : str = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowercase__ : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase__ : Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class UpperCAmelCase_ ( __a , __a , unittest.TestCase):
lowerCamelCase__ : str = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
lowerCamelCase__ : int = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
lowerCamelCase__ : List[Any] = (
{
'''conversational''': TFLEDForConditionalGeneration,
'''feature-extraction''': TFLEDModel,
'''summarization''': TFLEDForConditionalGeneration,
'''text2text-generation''': TFLEDForConditionalGeneration,
'''translation''': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCamelCase__ : Any = True
lowerCamelCase__ : Dict = False
lowerCamelCase__ : int = False
lowerCamelCase__ : Optional[int] = False
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Optional[Any] = TFLEDModelTester(self )
lowercase__ : Any = ConfigTester(self , config_class=snake_case__ )
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> str:
lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = tf.zeros_like(inputs_dict['attention_mask'] )
lowercase__ : List[str] = 2
lowercase__ : str = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['global_attention_mask'] , )
lowercase__ : Union[str, Any] = True
lowercase__ : Union[str, Any] = self.model_tester.seq_length
lowercase__ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(a ):
lowercase__ : int = outputs.decoder_attentions
self.assertEqual(len(snake_case__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(a ):
lowercase__ : Dict = [t.numpy() for t in outputs.encoder_attentions]
lowercase__ : List[Any] = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(snake_case__ ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(snake_case__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
lowercase__ : Optional[Any] = True
lowercase__ : Union[str, Any] = False
lowercase__ : Optional[Any] = False
lowercase__ : int = model_class(snake_case__ )
lowercase__ : Union[str, Any] = model(self._prepare_for_class(snake_case__ , snake_case__ ) )
lowercase__ : Any = len(snake_case__ )
self.assertEqual(config.output_hidden_states , snake_case__ )
check_encoder_attentions_output(snake_case__ )
if self.is_encoder_decoder:
lowercase__ : int = model_class(snake_case__ )
lowercase__ : List[str] = model(self._prepare_for_class(snake_case__ , snake_case__ ) )
self.assertEqual(config.output_hidden_states , snake_case__ )
check_decoder_attentions_output(snake_case__ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
lowercase__ : Dict = True
lowercase__ : Tuple = model_class(snake_case__ )
lowercase__ : Any = model(self._prepare_for_class(snake_case__ , snake_case__ ) )
self.assertEqual(config.output_hidden_states , snake_case__ )
check_encoder_attentions_output(snake_case__ )
# Check attention is always last and order is fine
lowercase__ : List[Any] = True
lowercase__ : Union[str, Any] = True
lowercase__ : Optional[Any] = model_class(snake_case__ )
lowercase__ : int = model(self._prepare_for_class(snake_case__ , snake_case__ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(snake_case__ ) )
self.assertEqual(model.config.output_hidden_states , snake_case__ )
check_encoder_attentions_output(snake_case__ )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def _UpperCAmelCase ( self ) -> List[str]:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
# TODO: Head-masking not yet implement
pass
def a_ ( _lowerCAmelCase : Dict ):
'''simple docstring'''
return tf.constant(lowerCAmelCase__ , dtype=tf.intaa )
_UpperCamelCase : Union[str, Any] = 1e-4
@slow
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : List[Any] = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
lowercase__ : str = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowercase__ : str = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowercase__ : List[Any] = prepare_led_inputs_dict(model.config , snake_case__ , snake_case__ )
lowercase__ : Any = model(**snake_case__ )[0]
lowercase__ : List[Any] = (1, 1_0_2_4, 7_6_8)
self.assertEqual(output.shape , snake_case__ )
# change to expected output here
lowercase__ : Dict = tf.convert_to_tensor(
[[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]] , )
tf.debugging.assert_near(output[:, :3, :3] , snake_case__ , atol=1e-3 )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : List[Any] = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
lowercase__ : Dict = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowercase__ : str = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowercase__ : Dict = prepare_led_inputs_dict(model.config , snake_case__ , snake_case__ )
lowercase__ : Dict = model(**snake_case__ )[0]
lowercase__ : Union[str, Any] = (1, 1_0_2_4, model.config.vocab_size)
self.assertEqual(output.shape , snake_case__ )
# change to expected output here
lowercase__ : str = tf.convert_to_tensor(
[[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]] , )
tf.debugging.assert_near(output[:, :3, :3] , snake_case__ , atol=1e-3 , rtol=1e-3 )
| 706
|
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 645
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : Any = logging.get_logger(__name__)
_UpperCamelCase : Optional[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 UpperCAmelCase_ ( __lowerCamelCase):
lowerCamelCase__ : Dict = '''canine'''
def __init__( self , a=7_6_8 , a=1_2 , a=1_2 , a=3_0_7_2 , a="gelu" , a=0.1 , a=0.1 , a=1_6_3_8_4 , a=1_6 , a=0.02 , a=1e-12 , a=0 , a=0XE_000 , a=0XE_001 , a=4 , a=4 , a=8 , a=1_6_3_8_4 , a=1_2_8 , **a , ) -> Optional[int]:
super().__init__(pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ )
lowercase__ : Dict = max_position_embeddings
lowercase__ : List[Any] = hidden_size
lowercase__ : str = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : List[str] = intermediate_size
lowercase__ : Tuple = hidden_act
lowercase__ : Any = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : List[Any] = initializer_range
lowercase__ : Union[str, Any] = type_vocab_size
lowercase__ : List[str] = layer_norm_eps
# Character config:
lowercase__ : List[Any] = downsampling_rate
lowercase__ : Union[str, Any] = upsampling_kernel_size
lowercase__ : Optional[Any] = num_hash_functions
lowercase__ : str = num_hash_buckets
lowercase__ : int = local_transformer_stride
| 707
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
lowercase__ : Union[str, Any] = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(a )
from datasets import load_dataset
lowercase__ : str = load_dataset('nielsr/rvlcdip-demo' )
lowercase__ : Tuple = dataset['train'][0]['image'].convert('RGB' )
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : List[str] = model(**a )
lowercase__ : List[Any] = outputs.logits
lowercase__ : Union[str, Any] = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , a )
lowercase__ : Tuple = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=a , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[Any] = IFInpaintingSuperResolutionPipeline
lowerCamelCase__ : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
lowerCamelCase__ : List[str] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"})
lowerCamelCase__ : Tuple = PipelineTesterMixin.required_optional_params - {"latents"}
def _UpperCAmelCase ( self ) -> List[Any]:
return self._get_superresolution_dummy_components()
def _UpperCAmelCase ( self , a , a=0 ) -> Optional[Any]:
if str(a ).startswith('mps' ):
lowercase__ : Any = torch.manual_seed(a )
else:
lowercase__ : Union[str, Any] = torch.Generator(device=a ).manual_seed(a )
lowercase__ : str = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(a ) ).to(a )
lowercase__ : int = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(a ) ).to(a )
lowercase__ : Optional[Any] = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(a ) ).to(a )
lowercase__ : Dict = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'original_image': original_image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _UpperCAmelCase ( self ) -> List[str]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def _UpperCAmelCase ( self ) -> Dict:
# 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 _UpperCAmelCase ( self ) -> int:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _UpperCAmelCase ( self ) -> List[str]:
self._test_save_load_local()
def _UpperCAmelCase ( self ) -> int:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 708
|
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase_ :
@staticmethod
def _UpperCAmelCase ( *a , **a ) -> int:
pass
def a_ ( _lowerCAmelCase : Image ):
'''simple docstring'''
lowercase__ : List[str] = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ : Union[str, Any] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Union[str, Any] = DepthEstimationPipeline(model=a , image_processor=a )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self , a , a ) -> Optional[int]:
lowercase__ : Tuple = depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , a )
import datasets
lowercase__ : Tuple = datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
lowercase__ : List[Any] = depth_estimator(
[
Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'http://images.cocodataset.org/val2017/000000039769.jpg',
# RGBA
dataset[0]['file'],
# LA
dataset[1]['file'],
# L
dataset[2]['file'],
] )
self.assertEqual(
[
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
] , a , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@slow
@require_torch
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Tuple = 'Intel/dpt-large'
lowercase__ : Optional[int] = pipeline('depth-estimation' , model=a )
lowercase__ : List[Any] = depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
lowercase__ : Optional[Any] = hashimage(outputs['depth'] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item() ) , 2.662 )
@require_torch
def _UpperCAmelCase ( self ) -> Optional[int]:
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : list ) -> list:
'''simple docstring'''
if len(_A ) <= 1:
return [tuple(_A )]
lowercase__ : Any = []
def generate(_lowerCAmelCase : int , _lowerCAmelCase : list ):
lowercase__ : List[str] = [0] * n
res.append(tuple(_A ) )
lowercase__ : List[Any] = 0
while i < n:
if c[i] < i:
if i % 2 == 0:
lowercase__ , lowercase__ : List[str] = arr[i], arr[0]
else:
lowercase__ , lowercase__ : List[str] = arr[i], arr[c[i]]
res.append(tuple(_A ) )
c[i] += 1
lowercase__ : Any = 0
else:
lowercase__ : Union[str, Any] = 0
i += 1
generate(len(_A ) , _A )
return res
if __name__ == "__main__":
_UpperCamelCase : str = input("Enter numbers separated by a comma:\n").strip()
_UpperCamelCase : Optional[Any] = [int(item) for item in user_input.split(",")]
print(heaps(arr))
| 709
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class UpperCAmelCase_ ( _a):
def __init__( self ) -> Any:
lowercase__ : Tuple = []
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_init_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[int]:
self.events.append('on_train_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_train_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_epoch_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Optional[Any]:
self.events.append('on_epoch_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_step_begin' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> str:
self.events.append('on_step_end' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> int:
self.events.append('on_evaluate' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Tuple:
self.events.append('on_predict' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Union[str, Any]:
self.events.append('on_save' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> List[str]:
self.events.append('on_log' )
def _UpperCAmelCase ( self , a , a , a , **a ) -> Any:
self.events.append('on_prediction_step' )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> str:
lowercase__ : str = tempfile.mkdtemp()
def _UpperCAmelCase ( self ) -> Dict:
shutil.rmtree(self.output_dir )
def _UpperCAmelCase ( self , a=0 , a=0 , a=6_4 , a=6_4 , a=None , a=False , **a ) -> int:
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
lowercase__ : str = RegressionDataset(length=a )
lowercase__ : Any = RegressionDataset(length=a )
lowercase__ : Optional[Any] = RegressionModelConfig(a=a , b=a )
lowercase__ : Union[str, Any] = RegressionPreTrainedModel(a )
lowercase__ : Tuple = TrainingArguments(self.output_dir , disable_tqdm=a , report_to=[] , **a )
return Trainer(
a , a , train_dataset=a , eval_dataset=a , callbacks=a , )
def _UpperCAmelCase ( self , a , a ) -> Union[str, Any]:
self.assertEqual(len(a ) , len(a ) )
# Order doesn't matter
lowercase__ : Optional[int] = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
lowercase__ : Tuple = sorted(a , key=lambda a : cb.__name__ if isinstance(a , a ) else cb.__class__.__name__ )
for cba, cba in zip(a , a ):
if isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(a , a )
elif isinstance(a , a ) and not isinstance(a , a ):
self.assertEqual(a , cba.__class__ )
elif not isinstance(a , a ) and isinstance(a , a ):
self.assertEqual(cba.__class__ , a )
else:
self.assertEqual(a , a )
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
lowercase__ : Dict = ['on_init_end', 'on_train_begin']
lowercase__ : List[Any] = 0
lowercase__ : Optional[int] = len(trainer.get_eval_dataloader() )
lowercase__ : Tuple = ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('on_epoch_begin' )
for _ in range(a ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save' )
expected_events.append('on_epoch_end' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : int = self.get_trainer()
lowercase__ : str = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# Callbacks passed at init are added to the default callbacks
lowercase__ : str = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
lowercase__ : List[Any] = self.get_trainer(disable_tqdm=a )
lowercase__ : Optional[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
lowercase__ : List[str] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Optional[Any] = self.get_trainer()
lowercase__ : List[Any] = trainer.pop_callback(a )
self.assertEqual(cb.__class__ , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
# We can also add, pop, or remove by instance
lowercase__ : int = self.get_trainer()
lowercase__ : List[str] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(a )
expected_callbacks.remove(a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
lowercase__ : Tuple = self.get_trainer()
lowercase__ : Dict = trainer.callback_handler.callbacks[0]
lowercase__ : Union[str, Any] = trainer.pop_callback(a )
self.assertEqual(a , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
trainer.add_callback(a )
expected_callbacks.insert(0 , a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , a )
def _UpperCAmelCase ( self ) -> Tuple:
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' , category=a )
lowercase__ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# Independent log/save/eval
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
lowercase__ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
lowercase__ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='steps' )
trainer.train()
lowercase__ : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
lowercase__ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='epoch' )
trainer.train()
lowercase__ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# A bit of everything
lowercase__ : Any = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=1_0 , eval_steps=5 , evaluation_strategy='steps' , )
trainer.train()
lowercase__ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(a , self.get_expected_events(a ) )
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
lowercase__ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(a ) in warn_mock.call_args[0][0]
| 645
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|
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase : str =logging.get_logger(__name__)
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
lowercase__ : str = WavaVecaForSequenceClassification.from_pretrained(A_ , config=A_ )
lowercase__ : int = downstream_dict['projector.weight']
lowercase__ : List[Any] = downstream_dict['projector.bias']
lowercase__ : Tuple = downstream_dict['model.post_net.linear.weight']
lowercase__ : Dict = downstream_dict['model.post_net.linear.bias']
return model
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : Dict = WavaVecaForAudioFrameClassification.from_pretrained(A_ , config=A_ )
lowercase__ : List[Any] = downstream_dict['model.linear.weight']
lowercase__ : List[Any] = downstream_dict['model.linear.bias']
return model
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : str = WavaVecaForXVector.from_pretrained(A_ , config=A_ )
lowercase__ : Tuple = downstream_dict['connector.weight']
lowercase__ : str = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowercase__ : Union[str, Any] = downstream_dict[
f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
]
lowercase__ : int = downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
lowercase__ : Dict = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
lowercase__ : Optional[Any] = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
lowercase__ : Tuple = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
lowercase__ : Dict = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
lowercase__ : Optional[Any] = downstream_dict['objective.W']
return model
@torch.no_grad()
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ):
'''simple docstring'''
lowercase__ : Optional[Any] = torch.load(A_ , map_location='cpu' )
lowercase__ : List[Any] = checkpoint['Downstream']
lowercase__ : Dict = WavaVecaConfig.from_pretrained(A_ )
lowercase__ : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
A_ , return_attention_mask=A_ , do_normalize=A_ )
lowercase__ : str = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
lowercase__ : Optional[int] = convert_classification(A_ , A_ , A_ )
elif arch.endswith('ForAudioFrameClassification' ):
lowercase__ : List[Any] = convert_diarization(A_ , A_ , A_ )
elif arch.endswith('ForXVector' ):
lowercase__ : List[Any] = convert_xvector(A_ , A_ , A_ )
else:
raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}""" )
if hf_config.use_weighted_layer_sum:
lowercase__ : Tuple = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(A_ )
hf_model.save_pretrained(A_ )
if __name__ == "__main__":
_UpperCamelCase : Optional[Any] =argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
_UpperCamelCase : str =parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 710
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Tuple = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Dict = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
_UpperCamelCase : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 645
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|
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
_UpperCamelCase : str = datasets.logging.get_logger(__name__)
_UpperCamelCase : Any = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = "{COMET}: A Neural Framework for {MT} Evaluation",
author = "Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
pages = "2685--2702",
}
'''
_UpperCamelCase : int = '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''
_UpperCamelCase : Any = '''
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric(\'comet\')
>>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use
>>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
>>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
>>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results["scores"]])
[0.19, 0.92]
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class UpperCAmelCase_ ( datasets.Metric):
def _UpperCAmelCase ( self ) -> Any:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://unbabel.github.io/COMET/html/index.html' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'sources': datasets.Value('string' , id='sequence' ),
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/Unbabel/COMET'] , reference_urls=[
'https://github.com/Unbabel/COMET',
'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
] , )
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
if self.config_name == "default":
lowercase__ : Optional[Any] = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da' ) )
else:
lowercase__ : List[str] = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def _UpperCAmelCase ( self , a , a , a , a=None , a=False ) -> Dict:
if gpus is None:
lowercase__ : Optional[int] = 1 if torch.cuda.is_available() else 0
lowercase__ : Any = {'src': sources, 'mt': predictions, 'ref': references}
lowercase__ : List[str] = [dict(zip(_A , _A ) ) for t in zip(*data.values() )]
lowercase__ : int = self.scorer.predict(_A , gpus=_A , progress_bar=_A )
return {"mean_score": mean_score, "scores": scores}
| 711
|
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self , a ) -> str:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
lowercase__ : str = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = 'sshleifer/tiny-gpt2'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = 'sgugger/tiny-distilbert-classification'
lowercase__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , only_pretrain_model=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Optional[int] = 'sshleifer/tiny-gpt2'
lowercase__ : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : List[Any] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=a , multi_process=a , )
lowercase__ : Tuple = TensorFlowBenchmark(a , [config] )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : List[str] = AutoConfig.from_pretrained(a )
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : List[str] = TensorFlowBenchmark(a , [config] )
lowercase__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : Optional[Any] = TensorFlowBenchmark(a )
lowercase__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[int] = AutoConfig.from_pretrained(a )
lowercase__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : str = TensorFlowBenchmark(a , [config] )
lowercase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = 'patrickvonplaten/t5-tiny-random'
lowercase__ : Any = AutoConfig.from_pretrained(a )
lowercase__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=a , )
lowercase__ : int = TensorFlowBenchmark(a , configs=[config] )
lowercase__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=a , inference=a , sequence_lengths=[8] , batch_sizes=[1] , use_xla=a , multi_process=a , )
lowercase__ : Any = TensorFlowBenchmark(a )
lowercase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Any = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , save_to_csv=a , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(a , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(a , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(a , 'env.csv' ) , multi_process=a , )
lowercase__ : Union[str, Any] = TensorFlowBenchmark(a )
benchmark.run()
self.assertTrue(Path(os.path.join(a , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(a , 'env.csv' ) ).exists() )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Tuple = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(a ):
self.assertTrue(hasattr(a , 'sequential' ) )
self.assertTrue(hasattr(a , 'cumulative' ) )
self.assertTrue(hasattr(a , 'current' ) )
self.assertTrue(hasattr(a , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=a , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(a , 'log.txt' ) , log_print=a , trace_memory_line_by_line=a , eager_mode=a , multi_process=a , )
lowercase__ : Optional[int] = TensorFlowBenchmark(a )
lowercase__ : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(a , 'log.txt' ) ).exists() )
| 645
| 0
|
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_UpperCamelCase : List[Any] = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_UpperCamelCase : str = "main"
# Default branch name
_UpperCamelCase : Any = "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
_UpperCamelCase : Optional[int] = "aaaaaaa"
# This commit does not exist, so we should 404.
_UpperCamelCase : Optional[Any] = "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
_UpperCamelCase : str = "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"
@contextlib.contextmanager
def a_ ( ):
'''simple docstring'''
print('Welcome!' )
yield
print('Bye!' )
@contextlib.contextmanager
def a_ ( ):
'''simple docstring'''
print('Bonjour!' )
yield
print('Au revoir!' )
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> Optional[int]:
assert transformers.__spec__ is not None
assert importlib.util.find_spec('transformers' ) is not None
class UpperCAmelCase_ ( unittest.TestCase):
@unittest.mock.patch('sys.stdout' , new_callable=io.StringIO )
def _UpperCAmelCase ( self , a ) -> Dict:
with ContextManagers([] ):
print('Transformers are awesome!' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , 'Transformers are awesome!\n' )
@unittest.mock.patch('sys.stdout' , new_callable=io.StringIO )
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
with ContextManagers([context_en()] ):
print('Transformers are awesome!' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , 'Welcome!\nTransformers are awesome!\nBye!\n' )
@unittest.mock.patch('sys.stdout' , new_callable=io.StringIO )
def _UpperCAmelCase ( self , a ) -> List[Any]:
with ContextManagers([context_fr(), context_en()] ):
print('Transformers are awesome!' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , 'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n' )
@require_torch
def _UpperCAmelCase ( self ) -> Optional[int]:
self.assertEqual(find_labels(__a ) , ['labels'] )
self.assertEqual(find_labels(__a ) , ['labels', 'next_sentence_label'] )
self.assertEqual(find_labels(__a ) , ['start_positions', 'end_positions'] )
class UpperCAmelCase_ ( _a):
pass
self.assertEqual(find_labels(__a ) , ['labels'] )
@require_tf
def _UpperCAmelCase ( self ) -> List[Any]:
self.assertEqual(find_labels(__a ) , ['labels'] )
self.assertEqual(find_labels(__a ) , ['labels', 'next_sentence_label'] )
self.assertEqual(find_labels(__a ) , ['start_positions', 'end_positions'] )
class UpperCAmelCase_ ( _a):
pass
self.assertEqual(find_labels(__a ) , ['labels'] )
@require_flax
def _UpperCAmelCase ( self ) -> str:
self.assertEqual(find_labels(__a ) , [] )
self.assertEqual(find_labels(__a ) , [] )
self.assertEqual(find_labels(__a ) , [] )
class UpperCAmelCase_ ( _a):
pass
self.assertEqual(find_labels(__a ) , [] )
| 712
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class UpperCAmelCase_ ( _a):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=False , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=3 , a=4 , a=None , ) -> Any:
lowercase__ : Tuple = parent
lowercase__ : List[Any] = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : List[Any] = is_training
lowercase__ : Optional[Any] = use_input_mask
lowercase__ : Optional[int] = use_token_type_ids
lowercase__ : int = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : int = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : List[str] = num_attention_heads
lowercase__ : Optional[Any] = intermediate_size
lowercase__ : Optional[Any] = hidden_act
lowercase__ : List[str] = hidden_dropout_prob
lowercase__ : List[Any] = attention_probs_dropout_prob
lowercase__ : List[Any] = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : Tuple = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = num_labels
lowercase__ : Tuple = num_choices
lowercase__ : str = scope
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_input_mask:
lowercase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Dict = None
lowercase__ : Optional[Any] = None
lowercase__ : int = None
if self.use_labels:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self ) -> Optional[int]:
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Tuple = DistilBertModel(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , a )
lowercase__ : str = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Dict:
lowercase__ : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
lowercase__ : Union[str, Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> int:
lowercase__ : Tuple = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(
a , attention_mask=a , start_positions=a , end_positions=a )
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 , a , a , a , a , a , a ) -> List[str]:
lowercase__ : int = self.num_labels
lowercase__ : Dict = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
lowercase__ : Optional[Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Any:
lowercase__ : Any = self.num_labels
lowercase__ : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
lowercase__ : Any = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Tuple:
lowercase__ : List[Any] = self.num_choices
lowercase__ : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
lowercase__ : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : int = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Union[str, Any] = self.prepare_config_and_inputs()
((lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__)) : List[str] = config_and_inputs
lowercase__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : List[str] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ : str = (
{
"feature-extraction": DistilBertModel,
"fill-mask": DistilBertForMaskedLM,
"question-answering": DistilBertForQuestionAnswering,
"text-classification": DistilBertForSequenceClassification,
"token-classification": DistilBertForTokenClassification,
"zero-shot": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Optional[int] = True
lowerCamelCase__ : Any = True
lowerCamelCase__ : List[Any] = True
lowerCamelCase__ : Optional[Any] = True
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : str = DistilBertModelTester(self )
lowercase__ : int = ConfigTester(self , config_class=a , dim=3_7 )
def _UpperCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : str = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
lowercase__ : Optional[int] = True
lowercase__ : Union[str, Any] = model_class(config=a )
lowercase__ : int = self._prepare_for_class(a , a )
lowercase__ : Tuple = torch.jit.trace(
a , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , 'traced_model.pt' ) )
lowercase__ : Optional[int] = torch.jit.load(os.path.join(a , 'traced_model.pt' ) , map_location=a )
loaded(inputs_dict['input_ids'].to(a ) , inputs_dict['attention_mask'].to(a ) )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : int = DistilBertModel.from_pretrained('distilbert-base-uncased' )
lowercase__ : Union[str, Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase__ : Optional[Any] = model(a , attention_mask=a )[0]
lowercase__ : Tuple = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , a )
lowercase__ : List[Any] = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import math
class UpperCAmelCase_ :
def __init__( self , a=0 ) -> List[Any]: # a graph with Node 0,1,...,N-1
lowercase__ : Any = n
lowercase__ : Optional[int] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # adjacency matrix for weight
lowercase__ : Union[str, Any] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # dp[i][j] stores minimum distance from i to j
def _UpperCAmelCase ( self , a , a , a ) -> str:
lowercase__ : Union[str, Any] = w
def _UpperCAmelCase ( self ) -> Any:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase__ : Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def _UpperCAmelCase ( self , a , a ) -> Optional[int]:
return self.dp[u][v]
if __name__ == "__main__":
_UpperCamelCase : Union[str, Any] = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 713
|
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif stress < 0:
raise ValueError('Stress cannot be negative' )
elif tangential_force < 0:
raise ValueError('Tangential Force cannot be negative' )
elif area < 0:
raise ValueError('Area cannot be negative' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : int ):
'''simple docstring'''
def count_of_possible_combinations(_lowerCAmelCase : int ) -> 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(A__ )
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : int ):
'''simple docstring'''
def count_of_possible_combinations_with_dp_array(
_lowerCAmelCase : int , _lowerCAmelCase : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowercase__ : Dict = sum(
count_of_possible_combinations_with_dp_array(target - item , A__ )
for item in array )
lowercase__ : Tuple = answer
return answer
lowercase__ : Optional[Any] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(A__ , A__ )
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Any = [0] * (target + 1)
lowercase__ : Union[str, Any] = 1
for i in range(1 , target + 1 ):
for j in range(A__ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCamelCase : Tuple = 3
_UpperCamelCase : int = 5
_UpperCamelCase : Optional[Any] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 714
|
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=[3_0, 3_0] , a=2 , a=3 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=3 , a=None , a=8 , a=1_0 , ) -> Any:
lowercase__ : List[str] = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Optional[int] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : str = is_training
lowercase__ : Optional[Any] = use_labels
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Dict = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Any = type_sequence_label_size
lowercase__ : Dict = initializer_range
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Tuple = scope
lowercase__ : Tuple = n_targets
lowercase__ : Optional[int] = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowercase__ : Optional[Any] = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowercase__ : Tuple = num_patches + 1 + self.num_detection_tokens
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowercase__ : Tuple = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowercase__ : int = []
for i in range(self.batch_size ):
lowercase__ : Optional[Any] = {}
lowercase__ : Any = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=a )
lowercase__ : List[str] = torch.rand(self.n_targets , 4 , device=a )
labels.append(a )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> List[Any]:
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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=a , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _UpperCAmelCase ( self , a , a , a ) -> int:
lowercase__ : List[str] = YolosModel(config=a )
model.to(a )
model.eval()
lowercase__ : List[Any] = model(a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = YolosForObjectDetection(a )
model.to(a )
model.eval()
lowercase__ : Dict = model(pixel_values=a )
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowercase__ : str = model(pixel_values=a , labels=a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Any = config_and_inputs
lowercase__ : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
lowerCamelCase__ : List[str] = (
{"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
)
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Union[str, Any] = False
def _UpperCAmelCase ( self , a , a , a=False ) -> Dict:
lowercase__ : List[str] = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowercase__ : Optional[Any] = []
for i in range(self.model_tester.batch_size ):
lowercase__ : Dict = {}
lowercase__ : Dict = torch.ones(
size=(self.model_tester.n_targets,) , device=a , dtype=torch.long )
lowercase__ : Optional[Any] = torch.ones(
self.model_tester.n_targets , 4 , device=a , dtype=torch.float )
labels.append(a )
lowercase__ : Union[str, Any] = labels
return inputs_dict
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Dict = YolosModelTester(self )
lowercase__ : Optional[int] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
# YOLOS does not use inputs_embeds
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
lowercase__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Tuple = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Dict = True
# in YOLOS, the seq_len is different
lowercase__ : Tuple = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowercase__ : Optional[int] = True
lowercase__ : str = False
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : str = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Optional[int] = True
lowercase__ : List[Any] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[str] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : Dict = len(a )
# Check attention is always last and order is fine
lowercase__ : Any = True
lowercase__ : int = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = 1
self.assertEqual(out_len + added_hidden_states , len(a ) )
lowercase__ : Tuple = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> List[str]:
def check_hidden_states_output(a , a , a ):
lowercase__ : str = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : int = model(**self._prepare_for_class(a , a ) )
lowercase__ : int = outputs.hidden_states
lowercase__ : Any = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(a ) , a )
# YOLOS has a different seq_length
lowercase__ : Optional[int] = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : List[Any] = True
check_hidden_states_output(a , a , a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*a )
@slow
def _UpperCAmelCase ( self ) -> Union[str, Any]:
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : int = YolosModel.from_pretrained(a )
self.assertIsNotNone(a )
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return AutoImageProcessor.from_pretrained('hustvl/yolos-small' ) if is_vision_available() else None
@slow
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = YolosForObjectDetection.from_pretrained('hustvl/yolos-small' ).to(a )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : Optional[int] = prepare_img()
lowercase__ : int = image_processor(images=a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : int = model(inputs.pixel_values )
# verify outputs
lowercase__ : Tuple = torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : Any = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] , device=a , )
lowercase__ : List[str] = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] , device=a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 ) )
# verify postprocessing
lowercase__ : Optional[Any] = image_processor.post_process_object_detection(
a , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowercase__ : str = torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861] ).to(a )
lowercase__ : Any = [7_5, 7_5, 1_7, 6_3, 1_7]
lowercase__ : Optional[int] = torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495] ).to(a )
self.assertEqual(len(results['scores'] ) , 5 )
self.assertTrue(torch.allclose(results['scores'] , a , atol=1e-4 ) )
self.assertSequenceEqual(results['labels'].tolist() , a )
self.assertTrue(torch.allclose(results['boxes'][0, :] , a ) )
| 645
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|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCamelCase : Dict = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_UpperCamelCase : List[str] = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
_UpperCamelCase : Tuple = {
"distilbert-base-uncased": 5_12,
"distilbert-base-uncased-distilled-squad": 5_12,
"distilbert-base-cased": 5_12,
"distilbert-base-cased-distilled-squad": 5_12,
"distilbert-base-german-cased": 5_12,
"distilbert-base-multilingual-cased": 5_12,
}
_UpperCamelCase : List[Any] = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class UpperCAmelCase_ ( _UpperCamelCase):
lowerCamelCase__ : Tuple = VOCAB_FILES_NAMES
lowerCamelCase__ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : Dict = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__ : Optional[Any] = ["input_ids", "attention_mask"]
lowerCamelCase__ : Union[str, Any] = DistilBertTokenizer
def __init__( self , a=None , a=None , a=True , a="[UNK]" , a="[SEP]" , a="[PAD]" , a="[CLS]" , a="[MASK]" , a=True , a=None , **a , ) -> Optional[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 , )
lowercase__ : Dict = 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
):
lowercase__ : List[Any] = getattr(a , normalizer_state.pop('type' ) )
lowercase__ : Optional[int] = do_lower_case
lowercase__ : Any = strip_accents
lowercase__ : int = tokenize_chinese_chars
lowercase__ : Dict = normalizer_class(**a )
lowercase__ : List[Any] = do_lower_case
def _UpperCAmelCase ( self , a , a=None ) -> Union[str, Any]:
lowercase__ : Optional[int] = [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 _UpperCAmelCase ( self , a , a = None ) -> Optional[Any]:
lowercase__ : Optional[Any] = [self.sep_token_id]
lowercase__ : str = [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 _UpperCAmelCase ( self , a , a = None ) -> List[Any]:
lowercase__ : List[str] = self._tokenizer.model.save(a , name=a )
return tuple(a )
| 715
|
"""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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_UpperCamelCase : int = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ :
def __init__( self , a=False , a=False , a=6.0 , a=None , a=False , a=False , a=None , a="fp4" , a=False , **a , ) -> Tuple:
lowercase__ : str = load_in_abit
lowercase__ : str = load_in_abit
lowercase__ : List[str] = llm_inta_threshold
lowercase__ : Dict = llm_inta_skip_modules
lowercase__ : Tuple = llm_inta_enable_fpaa_cpu_offload
lowercase__ : Any = llm_inta_has_fpaa_weight
lowercase__ : Any = bnb_abit_quant_type
lowercase__ : Dict = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
lowercase__ : Dict = torch.floataa
elif isinstance(a , a ):
lowercase__ : Any = getattr(a , a )
elif isinstance(a , torch.dtype ):
lowercase__ : Any = bnb_abit_compute_dtype
else:
raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype' )
self.post_init()
def _UpperCAmelCase ( self ) -> str:
if not isinstance(self.llm_inta_threshold , a ):
raise ValueError('llm_int8_threshold must be a float' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , a ):
raise ValueError('llm_int8_skip_modules must be a list of strings' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , a ):
raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean' )
if not isinstance(self.llm_inta_has_fpaa_weight , a ):
raise ValueError('llm_int8_has_fp16_weight must be a boolean' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError('bnb_4bit_compute_dtype must be torch.dtype' )
if not isinstance(self.bnb_abit_quant_type , a ):
raise ValueError('bnb_4bit_quant_type must be a string' )
if not isinstance(self.bnb_abit_use_double_quant , a ):
raise ValueError('bnb_4bit_use_double_quant must be a boolean' )
if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes' ) ) >= version.parse(
'0.39.0' ):
raise ValueError(
'4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version' )
def _UpperCAmelCase ( self ) -> Tuple:
return self.load_in_abit or self.load_in_abit
def _UpperCAmelCase ( self ) -> List[str]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> Optional[Any]:
lowercase__ : List[Any] = cls(**a )
lowercase__ : Union[str, Any] = []
for key, value in kwargs.items():
if hasattr(a , a ):
setattr(a , a , a )
to_remove.append(a )
for key in to_remove:
kwargs.pop(a , a )
if return_unused_kwargs:
return config, kwargs
else:
return config
def _UpperCAmelCase ( self , a ) -> Dict:
with open(a , 'w' , encoding='utf-8' ) as writer:
lowercase__ : Any = self.to_dict()
lowercase__ : str = json.dumps(a , indent=2 , sort_keys=a ) + '\n'
writer.write(a )
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : Any = str(output['bnb_4bit_compute_dtype'] ).split('.' )[1]
return output
def __repr__( self ) -> Dict:
return f"""{self.__class__.__name__} {self.to_json_string()}"""
def _UpperCAmelCase ( self , a = True ) -> str:
if use_diff is True:
lowercase__ : List[Any] = self.to_diff_dict()
else:
lowercase__ : List[str] = self.to_dict()
return json.dumps(a , indent=2 , sort_keys=a ) + "\n"
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
lowercase__ : Tuple = self.to_dict()
# get the default config dict
lowercase__ : Optional[Any] = BitsAndBytesConfig().to_dict()
lowercase__ : int = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
lowercase__ : Optional[int] = value
return serializable_config_dict
| 645
| 0
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
lowercase__ : int = len(a__ )
lowercase__ : Dict = sum(a__ )
lowercase__ : Optional[int] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
lowercase__ : Any = True
for i in range(1 , s + 1 ):
lowercase__ : Union[str, Any] = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
lowercase__ : Tuple = dp[i][j - 1]
if arr[i - 1] <= j:
lowercase__ : Optional[Any] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
lowercase__ : Dict = s - 2 * j
break
return diff
| 716
|
"""simple docstring"""
import argparse
import gc
import json
import os
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_UpperCamelCase : int = 16
_UpperCamelCase : Union[str, Any] = 32
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
return int(x / 2**20 )
class UpperCAmelCase_ :
def __enter__( self ) -> Union[str, Any]:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase__ : List[str] = torch.cuda.memory_allocated()
return self
def __exit__( self , *a ) -> Any:
gc.collect()
torch.cuda.empty_cache()
lowercase__ : Optional[Any] = torch.cuda.memory_allocated()
lowercase__ : Union[str, Any] = torch.cuda.max_memory_allocated()
lowercase__ : List[Any] = bamb(self.end - self.begin )
lowercase__ : List[Any] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def a_ ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 , _lowerCAmelCase : str = "bert-base-cased" , _lowerCAmelCase : int = 320 , _lowerCAmelCase : int = 160 , ):
'''simple docstring'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
lowercase__ : Union[str, Any] = load_dataset(
'glue' , 'mrpc' , split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""} )
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase__ : Union[str, Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=_lowerCAmelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_lowerCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(_lowerCAmelCase , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['train'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
lowercase__ : Dict = DataLoader(
tokenized_datasets['validation'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[int] = config['lr']
lowercase__ : Optional[Any] = int(config['num_epochs'] )
lowercase__ : Optional[Any] = int(config['seed'] )
lowercase__ : int = int(config['batch_size'] )
lowercase__ : Union[str, Any] = args.model_name_or_path
set_seed(_lowerCAmelCase )
lowercase__ , lowercase__ : Tuple = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : List[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase , return_dict=_lowerCAmelCase )
# Instantiate optimizer
lowercase__ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase__ : Optional[Any] = optimizer_cls(params=model.parameters() , lr=_lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
lowercase__ : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowercase__ : List[Any] = 1
lowercase__ : List[Any] = (len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=0 , num_training_steps=_lowerCAmelCase , )
else:
lowercase__ : Tuple = DummyScheduler(_lowerCAmelCase , total_num_steps=_lowerCAmelCase , warmup_num_steps=0 )
# 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__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Dict = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
lowercase__ : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase__ : Tuple = 0
# Now we train the model
lowercase__ : Optional[Any] = {}
for epoch in range(_lowerCAmelCase , _lowerCAmelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
lowercase__ : List[Any] = model(**_lowerCAmelCase )
lowercase__ : Dict = outputs.loss
lowercase__ : int = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin ) ) )
accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used ) )
accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked ) )
accelerator.print(
'Total Peak Memory consumed during the train (max): {}'.format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase__ : int = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'peak_memory_utilization.json' ) , 'w' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : int = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=_lowerCAmelCase , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=_lowerCAmelCase , )
parser.add_argument(
'--output_dir' , type=_lowerCAmelCase , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--peak_memory_upper_bound' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.' , )
parser.add_argument(
'--n_train' , type=_lowerCAmelCase , default=320 , help='Number of training examples to use.' , )
parser.add_argument(
'--n_val' , type=_lowerCAmelCase , default=160 , help='Number of validation examples to use.' , )
parser.add_argument(
'--num_epochs' , type=_lowerCAmelCase , default=1 , help='Number of train epochs.' , )
lowercase__ : Any = parser.parse_args()
lowercase__ : Optional[Any] = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 645
| 0
|
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
return {key.lstrip('-' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def a_ ( ):
'''simple docstring'''
lowercase__ : str = ArgumentParser(
'HuggingFace Datasets CLI tool' , usage='datasets-cli <command> [<args>]' , allow_abbrev=snake_case__ )
lowercase__ : List[Any] = parser.add_subparsers(help='datasets-cli command helpers' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(snake_case__ )
EnvironmentCommand.register_subcommand(snake_case__ )
TestCommand.register_subcommand(snake_case__ )
RunBeamCommand.register_subcommand(snake_case__ )
DummyDataCommand.register_subcommand(snake_case__ )
# Parse args
lowercase__ , lowercase__ : int = parser.parse_known_args()
if not hasattr(snake_case__ , 'func' ):
parser.print_help()
exit(1 )
lowercase__ : Any = parse_unknown_args(snake_case__ )
# Run
lowercase__ : str = args.func(snake_case__ , **snake_case__ )
service.run()
if __name__ == "__main__":
main()
| 717
|
"""simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
lowercase__ : List[str] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowercase__ : Dict = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowercase__ : Union[str, Any] = j
return prefix_result
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
return max(prefix_function(_lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645
| 0
|
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
lowercase__ : Dict = f"""{sampling_rate}"""
lowercase__ : Optional[Any] = '''1'''
lowercase__ : Union[str, Any] = '''f32le'''
lowercase__ : Any = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(_lowerCAmelCase , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
lowercase__ : Optional[int] = ffmpeg_process.communicate(_lowerCAmelCase )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
lowercase__ : str = output_stream[0]
lowercase__ : str = np.frombuffer(_lowerCAmelCase , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Any , _lowerCAmelCase : str = "f32le" , ):
'''simple docstring'''
lowercase__ : List[Any] = f"""{sampling_rate}"""
lowercase__ : List[str] = '''1'''
if format_for_conversion == "s16le":
lowercase__ : Dict = 2
elif format_for_conversion == "f32le":
lowercase__ : int = 4
else:
raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
lowercase__ : Optional[Any] = platform.system()
if system == "Linux":
lowercase__ : Union[str, Any] = '''alsa'''
lowercase__ : Tuple = '''default'''
elif system == "Darwin":
lowercase__ : List[Any] = '''avfoundation'''
lowercase__ : Optional[int] = ''':0'''
elif system == "Windows":
lowercase__ : str = '''dshow'''
lowercase__ : str = '''default'''
lowercase__ : Any = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
lowercase__ : Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
lowercase__ : Any = _ffmpeg_stream(_lowerCAmelCase , _lowerCAmelCase )
for item in iterator:
yield item
def a_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Any = None , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Optional[int] = "f32le" , ):
'''simple docstring'''
if stream_chunk_s is not None:
lowercase__ : Dict = stream_chunk_s
else:
lowercase__ : Dict = chunk_length_s
lowercase__ : int = ffmpeg_microphone(_lowerCAmelCase , _lowerCAmelCase , format_for_conversion=_lowerCAmelCase )
if format_for_conversion == "s16le":
lowercase__ : Dict = np.intaa
lowercase__ : Optional[Any] = 2
elif format_for_conversion == "f32le":
lowercase__ : Optional[Any] = np.floataa
lowercase__ : Dict = 4
else:
raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
lowercase__ : List[Any] = chunk_length_s / 6
lowercase__ : str = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(_lowerCAmelCase , (int, float) ):
lowercase__ : Optional[int] = [stride_length_s, stride_length_s]
lowercase__ : Tuple = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
lowercase__ : str = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
lowercase__ : Union[str, Any] = datetime.datetime.now()
lowercase__ : int = datetime.timedelta(seconds=_lowerCAmelCase )
for item in chunk_bytes_iter(_lowerCAmelCase , _lowerCAmelCase , stride=(stride_left, stride_right) , stream=_lowerCAmelCase ):
# Put everything back in numpy scale
lowercase__ : Dict = np.frombuffer(item['raw'] , dtype=_lowerCAmelCase )
lowercase__ : List[str] = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
lowercase__ : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : int = False ):
'''simple docstring'''
lowercase__ : str = b''''''
lowercase__ : List[Any] = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
lowercase__ : List[str] = 0
for raw in iterator:
acc += raw
if stream and len(_lowerCAmelCase ) < chunk_len:
lowercase__ : Optional[Any] = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(_lowerCAmelCase ) >= chunk_len:
# We are flushing the accumulator
lowercase__ : Dict = (_stride_left, stride_right)
lowercase__ : Optional[Any] = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
lowercase__ : Tuple = False
yield item
lowercase__ : Union[str, Any] = stride_left
lowercase__ : Tuple = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(_lowerCAmelCase ) > stride_left:
lowercase__ : Tuple = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
lowercase__ : Dict = False
yield item
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : Tuple = 2**24 # 16Mo
try:
with subprocess.Popen(_lowerCAmelCase , stdout=subprocess.PIPE , bufsize=_lowerCAmelCase ) as ffmpeg_process:
while True:
lowercase__ : Optional[int] = ffmpeg_process.stdout.read(_lowerCAmelCase )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to stream audio files from filename' ) from error
| 718
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=7 , a=3 , a=1_8 , a=3_0 , a=4_0_0 , a=True , a=None , a=True , a=None , a=True , ) -> List[str]:
lowercase__ : Tuple = size if size is not None else {'shortest_edge': 2_0}
lowercase__ : Union[str, Any] = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : str = num_channels
lowercase__ : Any = image_size
lowercase__ : Optional[Any] = min_resolution
lowercase__ : int = max_resolution
lowercase__ : List[Any] = do_resize
lowercase__ : List[str] = size
lowercase__ : str = do_center_crop
lowercase__ : List[Any] = crop_size
lowercase__ : Union[str, Any] = do_flip_channel_order
def _UpperCAmelCase ( self ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Optional[Any] = MobileViTImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Tuple = MobileViTImageProcessingTester(self )
@property
def _UpperCAmelCase ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a , 'do_resize' ) )
self.assertTrue(hasattr(a , 'size' ) )
self.assertTrue(hasattr(a , 'do_center_crop' ) )
self.assertTrue(hasattr(a , 'center_crop' ) )
self.assertTrue(hasattr(a , 'do_flip_channel_order' ) )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 2_0} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
lowercase__ : str = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> str:
# Initialize image_processing
lowercase__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a )
for image in image_inputs:
self.assertIsInstance(a , Image.Image )
# Test not batched input
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : List[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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Tuple:
# Initialize image_processing
lowercase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ : Tuple = 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
lowercase__ : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCAmelCase ( self ) -> Dict:
# Initialize image_processing
lowercase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ : Optional[Any] = 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
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowercase__ : Tuple = 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,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 645
| 0
|
"""simple docstring"""
_UpperCamelCase : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_UpperCamelCase : str = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_UpperCamelCase : Union[str, Any] = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 719
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=True , a=True , a=9_9 , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=4 , ) -> Dict:
lowercase__ : Optional[Any] = parent
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = seq_length
lowercase__ : int = is_training
lowercase__ : str = use_attention_mask
lowercase__ : Dict = use_token_type_ids
lowercase__ : Optional[int] = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : List[str] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : int = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[str] = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Tuple = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : int = type_vocab_size
lowercase__ : List[str] = type_sequence_label_size
lowercase__ : Union[str, Any] = initializer_range
lowercase__ : Optional[int] = num_choices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : str = None
if self.use_attention_mask:
lowercase__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : List[str] = None
if self.use_token_type_ids:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ : Any = 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 , is_decoder=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : Optional[int] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_class_name in self.all_model_classes:
lowercase__ : str = model_class_name.from_pretrained('albert-base-v2' )
lowercase__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : str = FlaxAlbertModel.from_pretrained('albert-base-v2' )
lowercase__ : Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : Optional[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
lowercase__ : Any = model(a , attention_mask=a )[0]
lowercase__ : Tuple = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , a )
lowercase__ : Optional[Any] = np.array(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
_UpperCamelCase : List[str] = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
_UpperCamelCase : Optional[Any] = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : List[Any] = (images / 2 + 0.5).clamp(0 , 1 )
lowercase__ : List[Any] = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowercase__ : Optional[int] = numpy_to_pil(__a )
return images
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if images.ndim == 3:
lowercase__ : str = images[None, ...]
lowercase__ : List[Any] = (images * 255).round().astype('uint8' )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
lowercase__ : Union[str, Any] = [Image.fromarray(image.squeeze() , mode='L' ) for image in images]
else:
lowercase__ : List[str] = [Image.fromarray(__a ) for image in images]
return pil_images
| 720
|
"""simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
lowercase__ : int = 0.0
for coeff in reversed(_lowerCAmelCase ):
lowercase__ : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
_UpperCamelCase : int = (0.0, 0.0, 5.0, 9.3, 7.0)
_UpperCamelCase : Dict = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 645
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_UpperCamelCase : List[Any] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _snake_case):
def __init__( self , *a , **a ) -> List[str]:
warnings.warn(
'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use SegformerImageProcessor instead.' , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 721
|
"""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
_UpperCamelCase : 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 a_ ( _lowerCAmelCase : Optional[Any]=True ):
'''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=_a))
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = None
lowerCamelCase__ : Optional[Any] = None
def _UpperCAmelCase ( self , a , a ) -> List[Any]:
with TemporaryDirectory() as tmp_dir:
lowercase__ : List[str] = dataset_module_factory(a , cache_dir=a )
lowercase__ : List[Any] = import_main_class(dataset_module.module_path , dataset=a )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=a , config_name=a , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=a ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
lowercase__ : Union[str, Any] = cached_path(a , cache_dir=a )
self.assertTrue(os.path.exists(a ) )
@pytest.mark.integration
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Union[str, Any] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
lowercase__ : int = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : Optional[int] = import_main_class(dataset_module.module_path )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowercase__ : Optional[int] = None
builder_instance.download_and_prepare()
lowercase__ : Optional[int] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Optional[int] = dataset_module_factory('wikipedia' , cache_dir=_lowerCAmelCase )
lowercase__ : List[str] = import_main_class(dataset_module.module_path , dataset=_lowerCAmelCase )
lowercase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
lowercase__ : Union[str, Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert "train" in ds
assert isinstance(ds['train'] , _lowerCAmelCase )
assert next(iter(ds['train'] ) )
| 645
| 0
|
"""simple docstring"""
import argparse
import copy
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : int = {}
with open(_lowerCAmelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
lowercase__ : List[str] = []
_list.append([line.split()[1], line.split()[2]] )
lowercase__ : Tuple = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
lowercase__ : List[Any] = []
_list.append([line.split()[0], line.split()[2]] )
lowercase__ : Union[str, Any] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ):
'''simple docstring'''
with open(_lowerCAmelCase ) as f:
lowercase__ : Optional[int] = f.read(1 )
lowercase__ : List[Any] = start_node
lowercase__ : List[Any] = []
lowercase__ : str = start_node
lowercase__ : str = 0
while visiting not in first_solution:
lowercase__ : Optional[int] = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowerCAmelCase ) and k[0] not in first_solution:
lowercase__ : List[Any] = k[1]
lowercase__ : str = k[0]
first_solution.append(_lowerCAmelCase )
lowercase__ : Any = distance_of_first_solution + int(_lowerCAmelCase )
lowercase__ : Optional[int] = best_node
first_solution.append(_lowerCAmelCase )
lowercase__ : str = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
lowercase__ : str = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Any ):
'''simple docstring'''
lowercase__ : Tuple = []
for n in solution[1:-1]:
lowercase__ : Dict = solution.index(_lowerCAmelCase )
for kn in solution[1:-1]:
lowercase__ : Tuple = solution.index(_lowerCAmelCase )
if n == kn:
continue
lowercase__ : Union[str, Any] = copy.deepcopy(_lowerCAmelCase )
lowercase__ : Optional[int] = kn
lowercase__ : List[Any] = n
lowercase__ : List[Any] = 0
for k in _tmp[:-1]:
lowercase__ : Optional[int] = _tmp[_tmp.index(_lowerCAmelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
lowercase__ : Optional[int] = distance + int(i[1] )
_tmp.append(_lowerCAmelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
lowercase__ : Union[str, Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowerCAmelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : str = 1
lowercase__ : List[Any] = first_solution
lowercase__ : Any = []
lowercase__ : str = distance_of_first_solution
lowercase__ : str = solution
while count <= iters:
lowercase__ : Union[str, Any] = find_neighborhood(_lowerCAmelCase , _lowerCAmelCase )
lowercase__ : Dict = 0
lowercase__ : int = neighborhood[index_of_best_solution]
lowercase__ : Optional[int] = len(_lowerCAmelCase ) - 1
lowercase__ : List[Any] = False
while not found:
lowercase__ : List[Any] = 0
while i < len(_lowerCAmelCase ):
if best_solution[i] != solution[i]:
lowercase__ : List[str] = best_solution[i]
lowercase__ : Dict = solution[i]
break
lowercase__ : Any = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
lowercase__ : str = True
lowercase__ : int = best_solution[:-1]
lowercase__ : Any = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
lowercase__ : Optional[int] = cost
lowercase__ : str = solution
else:
lowercase__ : Optional[int] = index_of_best_solution + 1
lowercase__ : List[Any] = neighborhood[index_of_best_solution]
if len(_lowerCAmelCase ) >= size:
tabu_list.pop(0 )
lowercase__ : Optional[int] = count + 1
return best_solution_ever, best_cost
def a_ ( _lowerCAmelCase : str=None ):
'''simple docstring'''
lowercase__ : List[str] = generate_neighbours(args.File )
lowercase__ : Optional[Any] = generate_first_solution(
args.File , _lowerCAmelCase )
lowercase__ : int = tabu_search(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.Iterations , args.Size , )
print(f"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
_UpperCamelCase : List[str] = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 700
|
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def a_ ( _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray ):
'''simple docstring'''
lowercase__ : Any = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(_lowerCAmelCase , _lowerCAmelCase )
# Predict target for test data
lowercase__ : str = xgb.predict(_lowerCAmelCase )
lowercase__ : Union[str, Any] = predictions.reshape(len(_lowerCAmelCase ) , 1 )
return predictions
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = fetch_california_housing()
lowercase__ , lowercase__ : str = data_handling(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = train_test_split(
_lowerCAmelCase , _lowerCAmelCase , test_size=0.2_5 , random_state=1 )
lowercase__ : Any = xgboost(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Error printing
print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
print(f"""Mean Square Error : {mean_squared_error(_lowerCAmelCase , _lowerCAmelCase )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 645
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class UpperCAmelCase_ ( __UpperCAmelCase):
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_lowerCamelCase , 'tf_padding' ) )
self.parent.assertTrue(hasattr(_lowerCamelCase , 'depth_multiplier' ) )
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=3 , a=3_2 , a=0.25 , a=8 , a=8 , a=6 , a=3_2 , a=True , a=True , a=True , a="relu6" , a=1_2_8_0 , a=0.1 , a=0.02 , a=True , a=True , a=1_0 , a=None , ) -> Any:
lowercase__ : Tuple = parent
lowercase__ : int = batch_size
lowercase__ : int = num_channels
lowercase__ : Dict = image_size
lowercase__ : Any = depth_multiplier
lowercase__ : Any = depth_divisible_by
lowercase__ : Optional[Any] = min_depth
lowercase__ : Optional[Any] = expand_ratio
lowercase__ : Optional[Any] = tf_padding
lowercase__ : Any = output_stride
lowercase__ : List[Any] = first_layer_is_expansion
lowercase__ : Optional[Any] = finegrained_output
lowercase__ : List[str] = hidden_act
lowercase__ : int = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
lowercase__ : Any = classifier_dropout_prob
lowercase__ : Dict = use_labels
lowercase__ : Any = is_training
lowercase__ : Any = num_labels
lowercase__ : List[Any] = initializer_range
lowercase__ : Optional[int] = scope
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = None
lowercase__ : List[str] = None
if self.use_labels:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase__ : Any = self.get_config()
return config, pixel_values, labels, pixel_labels
def _UpperCAmelCase ( self ) -> Optional[int]:
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a , a ) -> Optional[int]:
lowercase__ : Tuple = MobileNetVaModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
lowercase__ : Any = model(_lowerCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def _UpperCAmelCase ( self , a , a , a , a ) -> int:
lowercase__ : List[str] = self.num_labels
lowercase__ : Optional[int] = MobileNetVaForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
lowercase__ : List[str] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a ) -> Union[str, Any]:
lowercase__ : List[str] = self.num_labels
lowercase__ : str = MobileNetVaForSemanticSegmentation(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
lowercase__ : Union[str, Any] = model(_lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowercase__ : Optional[Any] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[Any] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = config_and_inputs
lowercase__ : Optional[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase):
lowerCamelCase__ : Union[str, Any] = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase__ : Tuple = (
{
"feature-extraction": MobileNetVaModel,
"image-classification": MobileNetVaForImageClassification,
"image-segmentation": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : int = False
lowerCamelCase__ : List[str] = False
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : int = MobileNetVaModelTester(self )
lowercase__ : List[str] = MobileNetVaConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileNetV2 does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Optional[Any]:
pass
@unittest.skip(reason='MobileNetV2 does not support input and output embeddings' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@unittest.skip(reason='MobileNetV2 does not output attentions' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> str:
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Dict = model_class(_lowerCamelCase )
lowercase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Union[str, Any] = [*signature.parameters.keys()]
lowercase__ : Optional[int] = ['pixel_values']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
lowercase__ : Tuple = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
lowercase__ : Union[str, Any] = outputs.hidden_states
lowercase__ : Dict = 1_6
self.assertEqual(len(_lowerCamelCase ) , _lowerCamelCase )
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : str = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase )
@slow
def _UpperCAmelCase ( self ) -> Dict:
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = MobileNetVaModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return (
MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224' ) if is_vision_available() else None
)
@slow
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : int = MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224' ).to(_lowerCamelCase )
lowercase__ : List[Any] = self.default_image_processor
lowercase__ : Union[str, Any] = prepare_img()
lowercase__ : Tuple = image_processor(images=_lowerCamelCase , return_tensors='pt' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**_lowerCamelCase )
# verify the logits
lowercase__ : Optional[Any] = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
lowercase__ : str = torch.tensor([0.2_445, -1.1_993, 0.1_905] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Optional[Any] = MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513' )
lowercase__ : Optional[Any] = model.to(_lowerCamelCase )
lowercase__ : Any = MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513' )
lowercase__ : List[Any] = prepare_img()
lowercase__ : List[Any] = image_processor(images=_lowerCamelCase , return_tensors='pt' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
lowercase__ : Optional[int] = model(**_lowerCamelCase )
lowercase__ : Optional[int] = outputs.logits
# verify the logits
lowercase__ : int = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape , _lowerCamelCase )
lowercase__ : Tuple = torch.tensor(
[
[[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
[[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
[[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
] , device=_lowerCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _lowerCamelCase , atol=1e-4 ) )
| 701
|
"""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 UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=1_0 , a=3 , a=2 , a=2 , a=2 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=0.9 , a=None , ) -> Optional[Any]:
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Dict = patch_size
lowercase__ : Tuple = tubelet_size
lowercase__ : Optional[int] = num_frames
lowercase__ : Optional[int] = is_training
lowercase__ : int = use_labels
lowercase__ : Optional[int] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = mask_ratio
lowercase__ : Optional[Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase__ : Optional[Any] = (image_size // patch_size) ** 2
lowercase__ : str = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase__ : str = int(mask_ratio * self.seq_length )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Tuple:
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=a , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a ) -> Optional[int]:
lowercase__ : Dict = VideoMAEModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = VideoMAEForPreTraining(a )
model.to(a )
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
lowercase__ : Any = torch.ones((self.num_masks,) )
lowercase__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowercase__ : Optional[int] = mask.expand(self.batch_size , -1 ).bool()
lowercase__ : str = model(a , a )
# model only returns predictions for masked patches
lowercase__ : str = mask.sum().item()
lowercase__ : int = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ : Optional[int] = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Any = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = False
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = VideoMAEModelTester(self )
lowercase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self , a , a , a=False ) -> Optional[int]:
lowercase__ : Union[str, Any] = copy.deepcopy(a )
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
lowercase__ : Optional[Any] = torch.ones((self.model_tester.num_masks,) )
lowercase__ : Any = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowercase__ : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowercase__ : Union[str, Any] = bool_masked_pos.to(a )
if return_labels:
if model_class in [
*get_values(a ),
]:
lowercase__ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = VideoMAEModel.from_pretrained(a )
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = True
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Any = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase__ : Optional[Any] = True
lowercase__ : int = False
lowercase__ : Any = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[int] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Dict = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : List[str] = len(a )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : List[str] = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(a , a ) )
self.assertEqual(out_len + 1 , len(a ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(a ) , 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 _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[int] = outputs.hidden_states
lowercase__ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(a ) , a )
lowercase__ : Optional[Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Union[str, Any] = 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] , )
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(a , a , a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def a_ ( ):
'''simple docstring'''
lowercase__ : int = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowercase__ : str = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
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 _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
a )
lowercase__ : str = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**a )
# verify the logits
lowercase__ : str = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : List[Any] = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(a )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(a , return_tensors='pt' ).to(a )
# add boolean mask, indicating which patches to mask
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowercase__ : str = torch.load(a )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**a )
# verify the logits
lowercase__ : Dict = torch.Size([1, 1_4_0_8, 1_5_3_6] )
lowercase__ : List[str] = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=a )
self.assertEqual(outputs.logits.shape , a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase__ : List[Any] = torch.tensor([0.5_142] , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=a ).to(
a )
with torch.no_grad():
lowercase__ : Any = model(**a )
lowercase__ : List[Any] = torch.tensor(torch.tensor([0.6_469] ) , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
| 645
| 0
|
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_UpperCamelCase : int = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n'
_UpperCamelCase : List[Any] = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n'
_UpperCamelCase : str = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n'
_UpperCamelCase : Tuple = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n'
_UpperCamelCase : Dict = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class UpperCAmelCase_ ( datasets.Metric):
def _UpperCAmelCase ( self ) -> Optional[int]:
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' ) ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/openai/human-eval' , codebase_urls=['https://github.com/openai/human-eval'] , reference_urls=['https://github.com/openai/human-eval'] , license=_LICENSE , )
def _UpperCAmelCase ( self , a , a , a=[1, 1_0, 1_0_0] , a=4 , a=3.0 ) -> Optional[int]:
if os.getenv('HF_ALLOW_CODE_EVAL' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('This metric is currently not supported on Windows.' )
with ThreadPoolExecutor(max_workers=__lowerCamelCase ) as executor:
lowercase__ : Optional[Any] = []
lowercase__ : Any = Counter()
lowercase__ : List[str] = 0
lowercase__ : str = defaultdict(__lowerCamelCase )
for task_id, (candidates, test_case) in enumerate(zip(__lowerCamelCase , __lowerCamelCase ) ):
for candidate in candidates:
lowercase__ : Union[str, Any] = candidate + '''\n''' + test_case
lowercase__ : List[str] = (test_program, timeout, task_id, completion_id[task_id])
lowercase__ : List[Any] = executor.submit(__lowerCamelCase , *__lowerCamelCase )
futures.append(__lowerCamelCase )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(__lowerCamelCase ):
lowercase__ : int = future.result()
results[result["task_id"]].append((result['completion_id'], result) )
lowercase__ : List[str] = [], []
for result in results.values():
result.sort()
lowercase__ : List[str] = [r[1]['''passed'''] for r in result]
total.append(len(__lowerCamelCase ) )
correct.append(sum(__lowerCamelCase ) )
lowercase__ : List[str] = np.array(__lowerCamelCase )
lowercase__ : int = np.array(__lowerCamelCase )
lowercase__ : List[Any] = k
lowercase__ : List[str] = {f"""pass@{k}""": estimate_pass_at_k(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ):
'''simple docstring'''
def estimator(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
lowercase__ : List[Any] = itertools.repeat(lowerCamelCase_ , len(lowerCamelCase_ ) )
else:
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ )
lowercase__ : Optional[Any] = iter(lowerCamelCase_ )
return np.array([estimator(int(lowerCamelCase_ ) , int(lowerCamelCase_ ) , lowerCamelCase_ ) for n, c in zip(lowerCamelCase_ , lowerCamelCase_ )] )
| 702
|
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
"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",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
_UpperCamelCase : List[str] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
for attribute in key.split('.' ):
lowercase__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
lowercase__ : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
lowercase__ : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
lowercase__ : Optional[Any] = value
elif weight_type == "weight_g":
lowercase__ : Dict = value
elif weight_type == "weight_v":
lowercase__ : List[str] = value
elif weight_type == "bias":
lowercase__ : Optional[Any] = value
else:
lowercase__ : List[str] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = []
lowercase__ : List[str] = fairseq_model.state_dict()
lowercase__ : Union[str, Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase__ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
lowercase__ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase__ : List[Any] = 'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase__ : int = True
if "*" in mapped_key:
lowercase__ : Optional[int] = name.split(_lowerCAmelCase )[0].split('.' )[-2]
lowercase__ : List[str] = mapped_key.replace('*' , _lowerCAmelCase )
if "weight_g" in name:
lowercase__ : List[Any] = 'weight_g'
elif "weight_v" in name:
lowercase__ : int = 'weight_v'
elif "bias" in name:
lowercase__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase__ : Union[str, Any] = 'weight'
else:
lowercase__ : int = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : int = full_name.split('conv_layers.' )[-1]
lowercase__ : int = name.split('.' )
lowercase__ : int = int(items[0] )
lowercase__ : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowercase__ : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowercase__ : Optional[int] = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
lowercase__ : List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
lowercase__ : int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Tuple=True ):
'''simple docstring'''
if config_path is not None:
lowercase__ : Any = UniSpeechSatConfig.from_pretrained(_lowerCAmelCase )
else:
lowercase__ : Any = UniSpeechSatConfig()
lowercase__ : Union[str, Any] = ''
if is_finetuned:
lowercase__ : Optional[Any] = UniSpeechSatForCTC(_lowerCAmelCase )
else:
lowercase__ : List[Any] = UniSpeechSatForPreTraining(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
lowercase__ : Union[str, Any] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase : Tuple = 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"
)
_UpperCamelCase : str = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 645
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase : str = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Optional[int] = ["LlamaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : int = ["LlamaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Any = [
"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
_UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 703
|
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=3_2 , a=2 , a=3 , a=1_6 , 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=1_0 , a=8 , a=["stage1", "stage2", "stage3"] , a=[1, 2, 3] , ) -> int:
lowercase__ : int = parent
lowercase__ : Union[str, Any] = batch_size
lowercase__ : Dict = image_size
lowercase__ : str = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : List[str] = embed_dim
lowercase__ : Any = depths
lowercase__ : Dict = num_heads
lowercase__ : List[str] = window_size
lowercase__ : int = mlp_ratio
lowercase__ : Tuple = qkv_bias
lowercase__ : Union[str, Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Tuple = drop_path_rate
lowercase__ : List[str] = hidden_act
lowercase__ : Optional[Any] = use_absolute_embeddings
lowercase__ : Optional[Any] = patch_norm
lowercase__ : Any = layer_norm_eps
lowercase__ : List[Any] = initializer_range
lowercase__ : List[str] = is_training
lowercase__ : int = scope
lowercase__ : Optional[int] = use_labels
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : List[str] = encoder_stride
lowercase__ : Optional[Any] = out_features
lowercase__ : Dict = out_indices
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return MaskFormerSwinConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , )
def _UpperCAmelCase ( self , a , a , a ) -> Dict:
lowercase__ : Tuple = MaskFormerSwinModel(config=a )
model.to(a )
model.eval()
lowercase__ : str = model(a )
lowercase__ : str = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ : Dict = 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 ) -> Optional[int]:
lowercase__ : List[Any] = MaskFormerSwinBackbone(config=a )
model.to(a )
model.eval()
lowercase__ : int = model(a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [1_3, 1_6, 1_6, 1_6] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [1_6, 3_2, 6_4] )
# verify ValueError
with self.parent.assertRaises(a ):
lowercase__ : Dict = ['stem']
lowercase__ : List[str] = MaskFormerSwinBackbone(config=a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : int = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Tuple = config_and_inputs
lowercase__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Optional[int] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : List[str] = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
lowerCamelCase__ : str = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : int = False
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = MaskFormerSwinModelTester(self )
lowercase__ : Tuple = ConfigTester(self , config_class=a , embed_dim=3_7 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
def _UpperCAmelCase ( self ) -> Tuple:
self.create_and_test_config_common_properties()
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:
return
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*a )
@unittest.skip('Swin does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
@unittest.skip('Swin does not support feedforward chunking' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
lowercase__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def _UpperCAmelCase ( self ) -> int:
pass
def _UpperCAmelCase ( self , a , a , a , a ) -> Tuple:
lowercase__ : Dict = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : str = model(**self._prepare_for_class(a , a ) )
lowercase__ : List[Any] = outputs.hidden_states
lowercase__ : str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(a ) , a )
# Swin has a different seq_length
lowercase__ : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Tuple = (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] , )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = (
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:
lowercase__ : List[str] = 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"]
lowercase__ : List[str] = True
self.check_hidden_states_output(a , a , a , a )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = 3
lowercase__ : str = (
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)
)
lowercase__ : Tuple = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Optional[int] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ : List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase__ : List[str] = 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"]
lowercase__ : int = True
self.check_hidden_states_output(a , a , a , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def _UpperCAmelCase ( self ) -> Optional[int]:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> Any:
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(a ):
lowercase__ : Union[str, Any] = 0
return t
def check_equivalence(a , a , a , a={} ):
with torch.no_grad():
lowercase__ : Optional[Any] = model(**a , return_dict=a , **a )
lowercase__ : Optional[int] = model(**a , return_dict=a , **a ).to_tuple()
def recursive_check(a , a ):
if isinstance(a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(a , a ):
recursive_check(a , a )
elif isinstance(a , a ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(a , a )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(a ) , set_nan_tensor_to_zero(a ) , atol=1e-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}. Dict has"""
f""" `nan`: {torch.isnan(a ).any()} and `inf`: {torch.isinf(a )}."""
) , )
recursive_check(a , a )
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(a )
model.to(a )
model.eval()
lowercase__ : Tuple = self._prepare_for_class(a , a )
lowercase__ : Optional[Any] = self._prepare_for_class(a , a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : List[Any] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a )
lowercase__ : Any = self._prepare_for_class(a , a )
lowercase__ : int = self._prepare_for_class(a , a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
lowercase__ : Dict = self._prepare_for_class(a , a , return_labels=a )
lowercase__ : Optional[int] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a , {'output_hidden_states': True} )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase , _a):
lowerCamelCase__ : Dict = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowerCamelCase__ : Optional[int] = MaskFormerSwinConfig
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[int] = MaskFormerSwinModelTester(self )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
lowercase__ : Optional[Any] = backbone_class(a )
backbone.to(a )
backbone.eval()
lowercase__ : Union[str, Any] = backbone(**a )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , a )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowercase__ : List[str] = backbone(**a , output_hidden_states=a )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowercase__ , lowercase__ , lowercase__ : int = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowercase__ : List[Any] = backbone(**a , output_attentions=a )
self.assertIsNotNone(outputs.attentions )
| 645
| 0
|
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