code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def lowercase ( A_ )-> Dict:
'''simple docstring'''
a : Union[str, Any] = model.config
a : List[str] = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
a : str = MBartConfig(
is_decoder=A_ , is_encoder_decoder=A_ , add_cross_attention=A_ , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=A_ , add_final_layer_norm=A_ , )
return encoder_config, decoder_config
def lowercase ( A_ )-> Dict:
'''simple docstring'''
if "encoder.model" in name:
a : List[str] = name.replace("encoder.model" , "encoder" )
if "decoder.model" in name:
a : Union[str, Any] = name.replace("decoder.model" , "decoder" )
if "patch_embed.proj" in name:
a : str = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
a : int = name.replace("patch_embed.norm" , "embeddings.norm" )
if name.startswith("encoder" ):
if "layers" in name:
a : List[str] = "encoder." + name
if "attn.proj" in name:
a : Union[str, Any] = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name and "mask" not in name:
a : Tuple = name.replace("attn" , "attention.self" )
if "norm1" in name:
a : Dict = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
a : str = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
a : List[str] = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
a : List[Any] = name.replace("mlp.fc2" , "output.dense" )
if name == "encoder.norm.weight":
a : Tuple = "encoder.layernorm.weight"
if name == "encoder.norm.bias":
a : Union[str, Any] = "encoder.layernorm.bias"
return name
def lowercase ( A_ , A_ )-> Tuple:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
a : Tuple = orig_state_dict.pop(A_ )
if "qkv" in key:
a : Any = key.split("." )
a : int = int(key_split[3] )
a : Optional[Any] = int(key_split[5] )
a : Dict = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
a : Any = val[:dim, :]
a : Tuple = val[dim : dim * 2, :]
a : Optional[Any] = val[-dim:, :]
else:
a : List[str] = val[:dim]
a : List[str] = val[dim : dim * 2]
a : Tuple = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
a : Union[str, Any] = val
return orig_state_dict
def lowercase ( A_ , A_=None , A_=False )-> List[str]:
'''simple docstring'''
a : Dict = DonutModel.from_pretrained(A_ ).eval()
# load HuggingFace model
a , a : Optional[int] = get_configs(A_ )
a : Union[str, Any] = DonutSwinModel(A_ )
a : Union[str, Any] = MBartForCausalLM(A_ )
a : str = VisionEncoderDecoderModel(encoder=A_ , decoder=A_ )
model.eval()
a : Optional[Any] = original_model.state_dict()
a : Union[str, Any] = convert_state_dict(A_ , A_ )
model.load_state_dict(A_ )
# verify results on scanned document
a : Optional[Any] = load_dataset("hf-internal-testing/example-documents" )
a : Tuple = dataset["test"][0]["image"].convert("RGB" )
a : Any = XLMRobertaTokenizerFast.from_pretrained(A_ , from_slow=A_ )
a : Dict = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
a : Tuple = DonutProcessor(A_ , A_ )
a : Union[str, Any] = processor(A_ , return_tensors="pt" ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
a : List[str] = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
a : int = "When is the coffee break?"
a : int = task_prompt.replace("{user_input}" , A_ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
a : Dict = "<s_rvlcdip>"
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
a : Dict = "<s_cord>"
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
a : List[Any] = "s_cord-v2>"
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
a : int = "<s_zhtrainticket>"
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
a : List[str] = "hello world"
else:
raise ValueError("Model name not supported" )
a : Tuple = original_model.decoder.tokenizer(A_ , add_special_tokens=A_ , return_tensors="pt" )[
"input_ids"
]
a : Optional[int] = original_model.encoder.model.patch_embed(A_ )
a , a : int = model.encoder.embeddings(A_ )
assert torch.allclose(A_ , A_ , atol=1e-3 )
# verify encoder hidden states
a : List[str] = original_model.encoder(A_ )
a : Dict = model.encoder(A_ ).last_hidden_state
assert torch.allclose(A_ , A_ , atol=1e-2 )
# verify decoder hidden states
a : Union[str, Any] = original_model(A_ , A_ , A_ ).logits
a : List[str] = model(A_ , decoder_input_ids=A_ ).logits
assert torch.allclose(A_ , A_ , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(A_ )
processor.save_pretrained(A_ )
if push_to_hub:
model.push_to_hub("nielsr/" + model_name.split("/" )[-1] , commit_message="Update model" )
processor.push_to_hub("nielsr/" + model_name.split("/" )[-1] , commit_message="Update model" )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""naver-clova-ix/donut-base-finetuned-docvqa""",
required=False,
type=str,
help="""Name of the original model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
required=False,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub.""",
)
__lowercase = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 40 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : List[Any] = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Tuple = boundary[0]
_lowerCamelCase : Dict = boundary[1]
_lowerCamelCase : List[Any] = make_points(lowercase__ , lowercase__ , lowercase__ )
_lowerCamelCase : List[Any] = 0.0
y += (h / 2.0) * f(lowercase__ )
for i in x_i:
# print(i)
y += h * f(lowercase__ )
y += (h / 2.0) * f(lowercase__ )
return y
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : str = a + h
while x < (b - h):
yield x
_lowerCamelCase : int = x + h
def _snake_case ( lowercase__ ): # enter your function here
_lowerCamelCase : Optional[Any] = (x - 0) * (x - 0)
return y
def _snake_case ( ):
_lowerCamelCase : int = 0.0 # Lower bound of integration
_lowerCamelCase : Optional[int] = 1.0 # Upper bound of integration
_lowerCamelCase : List[str] = 1_0.0 # define number of steps or resolution
_lowerCamelCase : List[Any] = [a, b] # define boundary of integration
_lowerCamelCase : Optional[Any] = method_a(lowercase__ , lowercase__ )
print(f'''y = {y}''' )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
from collections.abc import Callable
class _lowercase :
def __init__( self: Dict , UpperCamelCase__: Callable | None = None ):
# Stores actual heap items.
lowerCamelCase__ : list = []
# Stores indexes of each item for supporting updates and deletion.
lowerCamelCase__ : dict = {}
# Stores current size of heap.
lowerCamelCase__ : Optional[Any] = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowerCamelCase__ : Optional[Any] = key or (lambda UpperCamelCase__ : x)
def lowerCamelCase_ ( self: Any , UpperCamelCase__: int ):
return int((i - 1) / 2 ) if i > 0 else None
def lowerCamelCase_ ( self: Tuple , UpperCamelCase__: int ):
lowerCamelCase__ : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def lowerCamelCase_ ( self: str , UpperCamelCase__: int ):
lowerCamelCase__ : Union[str, Any] = int(2 * i + 2 )
return right if 0 < right < self.size else None
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase__: int , UpperCamelCase__: int ):
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowerCamelCase__ , lowerCamelCase__ : int = self.arr[j], self.arr[i]
def lowerCamelCase_ ( self: List[str] , UpperCamelCase__: int , UpperCamelCase__: int ):
return self.arr[i][1] < self.arr[j][1]
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase__: int ):
lowerCamelCase__ : str = self._left(UpperCamelCase__ )
lowerCamelCase__ : Optional[int] = self._right(UpperCamelCase__ )
lowerCamelCase__ : List[Any] = i
if left is not None and not self._cmp(UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase__ : Optional[Any] = left
if right is not None and not self._cmp(UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase__ : Union[str, Any] = right
return valid_parent
def lowerCamelCase_ ( self: Dict , UpperCamelCase__: int ):
lowerCamelCase__ : Any = self._parent(UpperCamelCase__ )
while parent is not None and not self._cmp(UpperCamelCase__ , UpperCamelCase__ ):
self._swap(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase__ , lowerCamelCase__ : Dict = parent, self._parent(UpperCamelCase__ )
def lowerCamelCase_ ( self: List[str] , UpperCamelCase__: int ):
lowerCamelCase__ : Tuple = self._get_valid_parent(UpperCamelCase__ )
while valid_parent != index:
self._swap(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = valid_parent, self._get_valid_parent(UpperCamelCase__ )
def lowerCamelCase_ ( self: Dict , UpperCamelCase__: int , UpperCamelCase__: int ):
if item not in self.pos_map:
return
lowerCamelCase__ : Optional[Any] = self.pos_map[item]
lowerCamelCase__ : List[str] = [item, self.key(UpperCamelCase__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(UpperCamelCase__ )
self._heapify_down(UpperCamelCase__ )
def lowerCamelCase_ ( self: Dict , UpperCamelCase__: int ):
if item not in self.pos_map:
return
lowerCamelCase__ : int = self.pos_map[item]
del self.pos_map[item]
lowerCamelCase__ : Any = self.arr[self.size - 1]
lowerCamelCase__ : int = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(UpperCamelCase__ )
self._heapify_down(UpperCamelCase__ )
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase__: int , UpperCamelCase__: int ):
lowerCamelCase__ : List[str] = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(UpperCamelCase__ )] )
else:
lowerCamelCase__ : Any = [item, self.key(UpperCamelCase__ )]
lowerCamelCase__ : Optional[int] = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def lowerCamelCase_ ( self: Any ):
return self.arr[0] if self.size else None
def lowerCamelCase_ ( self: Tuple ):
lowerCamelCase__ : List[str] = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def SCREAMING_SNAKE_CASE_ () -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 41 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ ):
return math.sqrt(lowercase__ ) * math.sqrt(lowercase__ ) == num
def _snake_case ( lowercase__ ):
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : List[Any] = n
while left <= right:
_lowerCamelCase : str = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_lowerCamelCase : str = mid - 1
else:
_lowerCamelCase : Optional[int] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class __UpperCAmelCase ( unittest.TestCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=7 , lowerCAmelCase_=3 , lowerCAmelCase_=18 , lowerCAmelCase_=30 , lowerCAmelCase_=4_00 , lowerCAmelCase_=True , lowerCAmelCase_=None , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=[0.5, 0.5, 0.5] , lowerCAmelCase_=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
_snake_case = parent
_snake_case = batch_size
_snake_case = num_channels
_snake_case = image_size
_snake_case = min_resolution
_snake_case = max_resolution
_snake_case = do_resize
_snake_case = size if size is not None else {'height': 18, 'width': 20}
_snake_case = do_thumbnail
_snake_case = do_align_axis
_snake_case = do_pad
_snake_case = do_normalize
_snake_case = image_mean
_snake_case = image_std
def lowerCamelCase ( self ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __UpperCAmelCase ( _lowerCamelCase , unittest.TestCase ):
__lowercase = DonutImageProcessor if is_vision_available() else None
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = DonutImageProcessingTester(self )
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_resize' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'size' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_thumbnail' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_align_long_axis' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_pad' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'image_mean' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'image_std' ) )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
_snake_case = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
_snake_case = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
@is_flaky()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase_ , Image.Image )
# Test not batched input
_snake_case = 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.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_snake_case = image_processing(lowerCAmelCase_ , 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.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ , numpify=lowerCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase_ , np.ndarray )
# Test not batched input
_snake_case = 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.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_snake_case = image_processing(lowerCAmelCase_ , 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.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ , torchify=lowerCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase_ , torch.Tensor )
# Test not batched input
_snake_case = 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.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_snake_case = image_processing(lowerCAmelCase_ , 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.size['height'],
self.image_processor_tester.size['width'],
) , )
| 42 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(lowercase__ , lowercase__ ) or not all(
isinstance(lowercase__ , lowercase__ ) and len(lowercase__ ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
_lowerCamelCase : dict[str, Any] = {}
_lowerCamelCase : List[Any] = 'WORD_KEEPER'
for word in words:
_lowerCamelCase : Dict = trie
for c in word:
if c not in trie_node:
_lowerCamelCase : Any = {}
_lowerCamelCase : str = trie_node[c]
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Dict = len(lowercase__ )
# Dynamic programming method
@functools.cache
def is_breakable(lowercase__ ) -> bool:
if index == len_string:
return True
_lowerCamelCase : List[Any] = trie
for i in range(lowercase__ , lowercase__ ):
_lowerCamelCase : Any = trie_node.get(string[i] , lowercase__ )
if trie_node is None:
return False
if trie_node.get(lowercase__ , lowercase__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = tmp_path / '''file.csv'''
__UpperCamelCase :Any = textwrap.dedent(
'''\
header1,header2
1,2
10,20
''' )
with open(SCREAMING_SNAKE_CASE , '''w''' ) as f:
f.write(SCREAMING_SNAKE_CASE )
return str(SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :List[str] = tmp_path / '''malformed_file.csv'''
__UpperCamelCase :Tuple = textwrap.dedent(
'''\
header1,header2
1,2
10,20,
''' )
with open(SCREAMING_SNAKE_CASE , '''w''' ) as f:
f.write(SCREAMING_SNAKE_CASE )
return str(SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :List[Any] = tmp_path / '''csv_with_image.csv'''
__UpperCamelCase :str = textwrap.dedent(
f"""\
image
{image_file}
""" )
with open(SCREAMING_SNAKE_CASE , '''w''' ) as f:
f.write(SCREAMING_SNAKE_CASE )
return str(SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = tmp_path / '''csv_with_label.csv'''
__UpperCamelCase :Tuple = textwrap.dedent(
'''\
label
good
bad
good
''' )
with open(SCREAMING_SNAKE_CASE , '''w''' ) as f:
f.write(SCREAMING_SNAKE_CASE )
return str(SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = tmp_path / '''csv_with_int_list.csv'''
__UpperCamelCase :Optional[Any] = textwrap.dedent(
'''\
int_list
1 2 3
4 5 6
7 8 9
''' )
with open(SCREAMING_SNAKE_CASE , '''w''' ) as f:
f.write(SCREAMING_SNAKE_CASE )
return str(SCREAMING_SNAKE_CASE )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = Csv()
__UpperCamelCase :Optional[int] = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(SCREAMING_SNAKE_CASE , match='''Error tokenizing data''' ):
for _ in generator:
pass
assert any(
record.levelname == '''ERROR'''
and '''Failed to read file''' in record.message
and os.path.basename(SCREAMING_SNAKE_CASE ) in record.message
for record in caplog.records )
@require_pil
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as f:
__UpperCamelCase :List[Any] = f.read().splitlines()[1]
__UpperCamelCase :List[Any] = Csv(encoding='''utf-8''' , features=Features({'''image''': Image()} ) )
__UpperCamelCase :Optional[int] = csv._generate_tables([[csv_file_with_image]] )
__UpperCamelCase :Optional[Any] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''image''' ).type == Image()()
__UpperCamelCase :Tuple = pa_table.to_pydict()['''image''']
assert generated_content == [{"path": image_file, "bytes": None}]
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as f:
__UpperCamelCase :List[Any] = f.read().splitlines()[1:]
__UpperCamelCase :Optional[int] = Csv(encoding='''utf-8''' , features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
__UpperCamelCase :List[Any] = csv._generate_tables([[csv_file_with_label]] )
__UpperCamelCase :List[str] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
__UpperCamelCase :List[str] = pa_table.to_pydict()['''label''']
assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(SCREAMING_SNAKE_CASE ) for label in labels]
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :int = Csv(encoding='''utf-8''' , sep=''',''' , converters={'''int_list''': lambda SCREAMING_SNAKE_CASE : [int(SCREAMING_SNAKE_CASE ) for i in x.split()]} )
__UpperCamelCase :Union[str, Any] = csv._generate_tables([[csv_file_with_int_list]] )
__UpperCamelCase :Optional[int] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
__UpperCamelCase :str = pa_table.to_pydict()['''int_list''']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 43 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
if len(lowercase__ ) == 1:
return True
_lowerCamelCase : List[Any] = series[1] - series[0]
for index in range(len(lowercase__ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
_lowerCamelCase : Optional[int] = 0
for val in series:
answer += val
return answer / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a : int = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __A :
_UpperCamelCase : Dict = PegasusConfig
_UpperCamelCase : List[Any] = {}
_UpperCamelCase : Any = "gelu"
def __init__( self , a__ , a__=13 , a__=7 , a__=True , a__=False , a__=99 , a__=32 , a__=5 , a__=4 , a__=37 , a__=0.1 , a__=0.1 , a__=20 , a__=2 , a__=1 , a__=0 , ):
_lowerCAmelCase : List[str] = parent
_lowerCAmelCase : Optional[int] = batch_size
_lowerCAmelCase : str = seq_length
_lowerCAmelCase : Dict = is_training
_lowerCAmelCase : int = use_labels
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : Optional[Any] = hidden_size
_lowerCAmelCase : int = num_hidden_layers
_lowerCAmelCase : Optional[int] = num_attention_heads
_lowerCAmelCase : str = intermediate_size
_lowerCAmelCase : List[str] = hidden_dropout_prob
_lowerCAmelCase : int = attention_probs_dropout_prob
_lowerCAmelCase : Union[str, Any] = max_position_embeddings
_lowerCAmelCase : List[str] = eos_token_id
_lowerCAmelCase : List[str] = pad_token_id
_lowerCAmelCase : Tuple = bos_token_id
def __A ( self ):
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
_lowerCAmelCase : List[Any] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
_lowerCAmelCase : Dict = np.concatenate([input_ids, eos_tensor] , axis=1 )
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase : List[Any] = 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 , **self.config_updates , )
_lowerCAmelCase : List[Any] = prepare_pegasus_inputs_dict(a__ , a__ , a__ )
return config, inputs_dict
def __A ( self , a__ , a__ , a__ ):
_lowerCAmelCase : str = 20
_lowerCAmelCase : Tuple = model_class_name(a__ )
_lowerCAmelCase : Optional[Any] = model.encode(inputs_dict["""input_ids"""] )
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
_lowerCAmelCase : Tuple = model.init_cache(decoder_input_ids.shape[0] , a__ , a__ )
_lowerCAmelCase : Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
_lowerCAmelCase : Any = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowerCAmelCase : Tuple = model.decode(
decoder_input_ids[:, :-1] , a__ , decoder_attention_mask=a__ , past_key_values=a__ , decoder_position_ids=a__ , )
_lowerCAmelCase : str = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase : List[Any] = model.decode(
decoder_input_ids[:, -1:] , a__ , decoder_attention_mask=a__ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=a__ , )
_lowerCAmelCase : Optional[int] = model.decode(a__ , a__ )
_lowerCAmelCase : Optional[int] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F"Max diff is {diff}" )
def __A ( self , a__ , a__ , a__ ):
_lowerCAmelCase : Optional[Any] = 20
_lowerCAmelCase : Optional[Any] = model_class_name(a__ )
_lowerCAmelCase : Tuple = model.encode(inputs_dict["""input_ids"""] )
_lowerCAmelCase , _lowerCAmelCase : List[Any] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
_lowerCAmelCase : Tuple = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
_lowerCAmelCase : List[Any] = model.init_cache(decoder_input_ids.shape[0] , a__ , a__ )
_lowerCAmelCase : Tuple = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowerCAmelCase : int = model.decode(
decoder_input_ids[:, :-1] , a__ , decoder_attention_mask=a__ , past_key_values=a__ , decoder_position_ids=a__ , )
_lowerCAmelCase : Dict = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase : int = model.decode(
decoder_input_ids[:, -1:] , a__ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=a__ , decoder_position_ids=a__ , )
_lowerCAmelCase : Any = model.decode(a__ , a__ , decoder_attention_mask=a__ )
_lowerCAmelCase : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F"Max diff is {diff}" )
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Tuple ,_lowerCamelCase : List[str] ,_lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Union[str, Any]=None ,_lowerCamelCase : Dict=None ,) -> Union[str, Any]:
if attention_mask is None:
_lowerCAmelCase : int = np.not_equal(_lowerCamelCase ,config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_lowerCAmelCase : Union[str, Any] = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape ,dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] ,config.pad_token_id ).astype(np.inta ),
] ,axis=-1 ,)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __A ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
_UpperCamelCase : int = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
_UpperCamelCase : Tuple = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
_UpperCamelCase : str = True
_UpperCamelCase : str = False
_UpperCamelCase : List[Any] = False
_UpperCamelCase : str = False
def __A ( self ):
_lowerCAmelCase : Dict = FlaxPegasusModelTester(self )
_lowerCAmelCase : List[str] = ConfigTester(self , config_class=a__ )
def __A ( self ):
self.config_tester.run_common_tests()
def __A ( self ):
_lowerCAmelCase , _lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(a__ , a__ , a__ )
def __A ( self ):
_lowerCAmelCase , _lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(a__ , a__ , a__ )
def __A ( self ):
_lowerCAmelCase , _lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase : List[str] = self._prepare_for_class(a__ , a__ )
_lowerCAmelCase : List[Any] = model_class(a__ )
@jax.jit
def encode_jitted(a__ , a__=None , **a__ ):
return model.encode(input_ids=a__ , attention_mask=a__ )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase : List[str] = encode_jitted(**a__ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase : Optional[Any] = encode_jitted(**a__ ).to_tuple()
self.assertEqual(len(a__ ) , len(a__ ) )
for jitted_output, output in zip(a__ , a__ ):
self.assertEqual(jitted_output.shape , output.shape )
def __A ( self ):
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase : str = model_class(a__ )
_lowerCAmelCase : Optional[int] = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
_lowerCAmelCase : Optional[Any] = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(a__ , a__ , a__ ):
return model.decode(
decoder_input_ids=a__ , decoder_attention_mask=a__ , encoder_outputs=a__ , )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase : Optional[int] = decode_jitted(**a__ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase : Tuple = decode_jitted(**a__ ).to_tuple()
self.assertEqual(len(a__ ) , len(a__ ) )
for jitted_output, output in zip(a__ , a__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def __A ( self ):
for model_class_name in self.all_model_classes:
_lowerCAmelCase : Any = model_class_name.from_pretrained("""google/pegasus-large""" , from_pt=a__ )
_lowerCAmelCase : List[str] = np.ones((1, 1) )
_lowerCAmelCase : Optional[Any] = model(a__ )
self.assertIsNotNone(a__ )
@slow
def __A ( self ):
_lowerCAmelCase : int = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
_lowerCAmelCase : int = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )
_lowerCAmelCase : List[str] = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
_lowerCAmelCase : List[Any] = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
_lowerCAmelCase : Optional[int] = tokenizer(a__ , return_tensors="""np""" , truncation=a__ , max_length=512 , padding=a__ )
_lowerCAmelCase : Optional[Any] = model.generate(**a__ , num_beams=2 ).sequences
_lowerCAmelCase : List[Any] = tokenizer.batch_decode(a__ , skip_special_tokens=a__ )
assert tgt_text == decoded
| 44 |
"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase__ = 16
lowercase__ = 32
def _snake_case ( lowercase__ , lowercase__ = 16 , lowercase__ = "bert-base-cased" ):
_lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(lowercase__ )
_lowerCamelCase : Tuple = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowercase__ ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Union[str, Any] = 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
_lowerCamelCase : int = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCamelCase : Optional[int] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowercase__ ):
# 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(lowercase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(lowercase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
_lowerCamelCase : List[str] = DataLoader(
tokenized_datasets['train'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
_lowerCamelCase : int = DataLoader(
tokenized_datasets['validation'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ , lowercase__ ):
# Initialize accelerator
_lowerCamelCase : Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCamelCase : Optional[int] = config['lr']
_lowerCamelCase : Optional[int] = int(config['num_epochs'] )
_lowerCamelCase : Union[str, Any] = int(config['seed'] )
_lowerCamelCase : Optional[int] = int(config['batch_size'] )
_lowerCamelCase : Dict = args.model_name_or_path
set_seed(lowercase__ )
_lowerCamelCase, _lowerCamelCase : Optional[int] = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCamelCase : int = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
_lowerCamelCase : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowerCamelCase : Union[str, Any] = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
_lowerCamelCase : str = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
_lowerCamelCase : Tuple = 1
_lowerCamelCase : List[Any] = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowerCamelCase : Tuple = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
_lowerCamelCase : Any = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , 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.
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
_lowerCamelCase : Union[str, Any] = 0
# We also need to keep track of the stating epoch so files are named properly
_lowerCamelCase : Dict = 0
# Now we train the model
_lowerCamelCase : Dict = evaluate.load('glue' , 'mrpc' )
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
_lowerCamelCase : List[Any] = model(**lowercase__ )
_lowerCamelCase : int = outputs.loss
_lowerCamelCase : Dict = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_lowerCamelCase : Union[str, Any] = 0
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():
_lowerCamelCase : Optional[int] = model(**lowercase__ )
_lowerCamelCase : Dict = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowerCamelCase, _lowerCamelCase : List[str] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
_lowerCamelCase : Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCamelCase : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
_lowerCamelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , lowercase__ )
_lowerCamelCase : Tuple = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
_lowerCamelCase : str = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ):
_lowerCamelCase : Any = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=lowercase__ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=lowercase__ , )
parser.add_argument(
'--output_dir' , type=lowercase__ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=lowercase__ , default=lowercase__ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=lowercase__ , default=3 , help='Number of train epochs.' , )
_lowerCamelCase : Optional[Any] = parser.parse_args()
_lowerCamelCase : str = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main() | 96 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : int ) -> float:
if digit_amount > 0:
return round(number - int(lowerCAmelCase__ ) , lowerCAmelCase__ )
return number - int(lowerCAmelCase__ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 45 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[Any] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 ) | 96 | 0 |
"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = 'philschmid/bart-large-cnn-samsum'
_SCREAMING_SNAKE_CASE = (
'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
'and returns a summary of the text.'
)
_SCREAMING_SNAKE_CASE = 'summarizer'
_SCREAMING_SNAKE_CASE = AutoTokenizer
_SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM
_SCREAMING_SNAKE_CASE = ['text']
_SCREAMING_SNAKE_CASE = ['text']
def _snake_case ( self , lowercase ) -> List[Any]:
return self.pre_processor(lowercase , return_tensors="""pt""" , truncation=lowercase )
def _snake_case ( self , lowercase ) -> Dict:
return self.model.generate(**lowercase )[0]
def _snake_case ( self , lowercase ) -> str:
return self.pre_processor.decode(lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase )
| 46 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""IBertForMaskedLM""",
"""IBertForMultipleChoice""",
"""IBertForQuestionAnswering""",
"""IBertForSequenceClassification""",
"""IBertForTokenClassification""",
"""IBertModel""",
"""IBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A__ :
def __init__( self : Any , _a : int , _a : Union[str, Any]=2 , _a : Optional[Any]=3 , _a : Optional[int]=4 , _a : Dict=2 , _a : List[str]=7 , _a : int=True , _a : List[str]=True , _a : Optional[Any]=True , _a : str=True , _a : Any=99 , _a : Optional[int]=36 , _a : Optional[int]=2 , _a : int=4 , _a : List[str]=37 , _a : Union[str, Any]="gelu" , _a : str=0.1 , _a : Any=0.1 , _a : Union[str, Any]=512 , _a : List[str]=16 , _a : List[Any]=2 , _a : Any=0.02 , _a : Optional[int]=6 , _a : Any=6 , _a : Tuple=3 , _a : Union[str, Any]=4 , _a : Optional[int]=None , _a : Dict=1000 , ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =parent
_SCREAMING_SNAKE_CASE =batch_size
_SCREAMING_SNAKE_CASE =num_channels
_SCREAMING_SNAKE_CASE =image_size
_SCREAMING_SNAKE_CASE =patch_size
_SCREAMING_SNAKE_CASE =is_training
_SCREAMING_SNAKE_CASE =use_input_mask
_SCREAMING_SNAKE_CASE =use_token_type_ids
_SCREAMING_SNAKE_CASE =use_labels
_SCREAMING_SNAKE_CASE =vocab_size
_SCREAMING_SNAKE_CASE =hidden_size
_SCREAMING_SNAKE_CASE =num_hidden_layers
_SCREAMING_SNAKE_CASE =num_attention_heads
_SCREAMING_SNAKE_CASE =intermediate_size
_SCREAMING_SNAKE_CASE =hidden_act
_SCREAMING_SNAKE_CASE =hidden_dropout_prob
_SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE =max_position_embeddings
_SCREAMING_SNAKE_CASE =type_vocab_size
_SCREAMING_SNAKE_CASE =type_sequence_label_size
_SCREAMING_SNAKE_CASE =initializer_range
_SCREAMING_SNAKE_CASE =coordinate_size
_SCREAMING_SNAKE_CASE =shape_size
_SCREAMING_SNAKE_CASE =num_labels
_SCREAMING_SNAKE_CASE =num_choices
_SCREAMING_SNAKE_CASE =scope
_SCREAMING_SNAKE_CASE =range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
_SCREAMING_SNAKE_CASE =text_seq_length
_SCREAMING_SNAKE_CASE =(image_size // patch_size) ** 2 + 1
_SCREAMING_SNAKE_CASE =self.text_seq_length + self.image_seq_length
def A ( self : int ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
_SCREAMING_SNAKE_CASE =bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_SCREAMING_SNAKE_CASE =bbox[i, j, 3]
_SCREAMING_SNAKE_CASE =bbox[i, j, 1]
_SCREAMING_SNAKE_CASE =tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
_SCREAMING_SNAKE_CASE =bbox[i, j, 2]
_SCREAMING_SNAKE_CASE =bbox[i, j, 0]
_SCREAMING_SNAKE_CASE =tmp_coordinate
_SCREAMING_SNAKE_CASE =tf.constant(_a )
_SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.text_seq_length] )
_SCREAMING_SNAKE_CASE =None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE =None
_SCREAMING_SNAKE_CASE =None
if self.use_labels:
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE =LayoutLMvaConfig(
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 , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def A ( self : Optional[int] , _a : List[str] , _a : str , _a : Any , _a : Any , _a : Tuple , _a : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFLayoutLMvaModel(config=_a )
# text + image
_SCREAMING_SNAKE_CASE =model(_a , pixel_values=_a , training=_a )
_SCREAMING_SNAKE_CASE =model(
_a , bbox=_a , pixel_values=_a , attention_mask=_a , token_type_ids=_a , training=_a , )
_SCREAMING_SNAKE_CASE =model(_a , bbox=_a , pixel_values=_a , training=_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
_SCREAMING_SNAKE_CASE =model(_a , training=_a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
_SCREAMING_SNAKE_CASE =model({'pixel_values': pixel_values} , training=_a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def A ( self : List[Any] , _a : Optional[int] , _a : Optional[int] , _a : Dict , _a : List[str] , _a : Any , _a : Tuple , _a : List[str] ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.num_labels
_SCREAMING_SNAKE_CASE =TFLayoutLMvaForSequenceClassification(config=_a )
_SCREAMING_SNAKE_CASE =model(
_a , bbox=_a , pixel_values=_a , attention_mask=_a , token_type_ids=_a , labels=_a , training=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , _a : Dict , _a : List[str] , _a : List[str] , _a : Optional[Any] , _a : Dict , _a : str , _a : str ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.num_labels
_SCREAMING_SNAKE_CASE =TFLayoutLMvaForTokenClassification(config=_a )
_SCREAMING_SNAKE_CASE =model(
_a , bbox=_a , pixel_values=_a , attention_mask=_a , token_type_ids=_a , labels=_a , training=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def A ( self : Union[str, Any] , _a : int , _a : Dict , _a : Optional[int] , _a : Optional[int] , _a : Union[str, Any] , _a : Any , _a : str ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =2
_SCREAMING_SNAKE_CASE =TFLayoutLMvaForQuestionAnswering(config=_a )
_SCREAMING_SNAKE_CASE =model(
_a , bbox=_a , pixel_values=_a , attention_mask=_a , token_type_ids=_a , start_positions=_a , end_positions=_a , training=_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 A ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
((_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE)) =config_and_inputs
_SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A__ ( A__ , A__ , unittest.TestCase ):
A__ = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
A__ = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
A__ = False
A__ = False
A__ = False
def A ( self : Optional[Any] , _a : List[str] , _a : int , _a : Any , _a : Optional[Any] , _a : str ) -> Union[str, Any]:
'''simple docstring'''
return True
def A ( self : Dict , _a : Dict , _a : Union[str, Any] , _a : Optional[int]=False ) -> dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =copy.deepcopy(_a )
if model_class in get_values(_a ):
_SCREAMING_SNAKE_CASE ={
k: tf.tile(tf.expand_dims(_a , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(_a , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(_a ):
_SCREAMING_SNAKE_CASE =tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(_a ):
_SCREAMING_SNAKE_CASE =tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
_SCREAMING_SNAKE_CASE =tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(_a ):
_SCREAMING_SNAKE_CASE =tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(_a ):
_SCREAMING_SNAKE_CASE =tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def A ( self : int ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFLayoutLMvaModelTester(self )
_SCREAMING_SNAKE_CASE =ConfigTester(self , config_class=_a , hidden_size=37 )
def A ( self : Any ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE =model_class(_a )
if getattr(_a , 'hf_compute_loss' , _a ):
# The number of elements in the loss should be the same as the number of elements in the label
_SCREAMING_SNAKE_CASE =self._prepare_for_class(inputs_dict.copy() , _a , return_labels=_a )
_SCREAMING_SNAKE_CASE =prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=_a )[0]
]
_SCREAMING_SNAKE_CASE =added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
_SCREAMING_SNAKE_CASE =self._prepare_for_class(inputs_dict.copy() , _a , return_labels=_a )
_SCREAMING_SNAKE_CASE =prepared_for_class.pop('input_ids' )
_SCREAMING_SNAKE_CASE =model(_a , **_a )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
_SCREAMING_SNAKE_CASE =self._prepare_for_class(inputs_dict.copy() , _a , return_labels=_a )
_SCREAMING_SNAKE_CASE =prepared_for_class.pop('input_ids' )
if "labels" in prepared_for_class:
_SCREAMING_SNAKE_CASE =prepared_for_class['labels'].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
_SCREAMING_SNAKE_CASE =-100
_SCREAMING_SNAKE_CASE =tf.convert_to_tensor(_a )
_SCREAMING_SNAKE_CASE =model(_a , **_a )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
_SCREAMING_SNAKE_CASE =self._prepare_for_class(inputs_dict.copy() , _a , return_labels=_a )
_SCREAMING_SNAKE_CASE =model(_a )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
_SCREAMING_SNAKE_CASE =self._prepare_for_class(inputs_dict.copy() , _a , return_labels=_a )
# Get keys that were added with the _prepare_for_class function
_SCREAMING_SNAKE_CASE =prepared_for_class.keys() - inputs_dict.keys()
_SCREAMING_SNAKE_CASE =inspect.signature(model.call ).parameters
_SCREAMING_SNAKE_CASE =list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
_SCREAMING_SNAKE_CASE ={0: 'input_ids'}
for label_key in label_keys:
_SCREAMING_SNAKE_CASE =signature_names.index(_a )
_SCREAMING_SNAKE_CASE =label_key
_SCREAMING_SNAKE_CASE =sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
_SCREAMING_SNAKE_CASE =[]
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
_SCREAMING_SNAKE_CASE =prepared_for_class[value]
_SCREAMING_SNAKE_CASE =tuple(_a )
# Send to model
_SCREAMING_SNAKE_CASE =model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def A ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(_a , _a , _a , _a , _a , _a )
def A ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_SCREAMING_SNAKE_CASE =type
self.model_tester.create_and_check_model(_a , _a , _a , _a , _a , _a )
def A ( self : str ) -> Dict:
'''simple docstring'''
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
_a , _a , _a , _a , _a , _a , _a )
def A ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
_a , _a , _a , _a , _a , _a , _a )
def A ( self : str ) -> Tuple:
'''simple docstring'''
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
_a , _a , _a , _a , _a , _a , _a )
@slow
def A ( self : Any ) -> str:
'''simple docstring'''
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE =TFLayoutLMvaModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def _lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
class A__ ( unittest.TestCase ):
@cached_property
def A ( self : Dict ) -> List[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=_a ) if is_vision_available() else None
@slow
def A ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' )
_SCREAMING_SNAKE_CASE =self.default_image_processor
_SCREAMING_SNAKE_CASE =prepare_img()
_SCREAMING_SNAKE_CASE =image_processor(images=_a , return_tensors='tf' ).pixel_values
_SCREAMING_SNAKE_CASE =tf.constant([[1, 2]] )
_SCREAMING_SNAKE_CASE =tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
_SCREAMING_SNAKE_CASE =model(input_ids=_a , bbox=_a , pixel_values=_a , training=_a )
# verify the logits
_SCREAMING_SNAKE_CASE =(1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape , _a )
_SCREAMING_SNAKE_CASE =tf.constant(
[[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _a , atol=1e-4 ) )
| 47 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{sampling_rate}'''
_lowerCamelCase : str = '1'
_lowerCamelCase : str = 'f32le'
_lowerCamelCase : Union[str, Any] = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowercase__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
_lowerCamelCase : str = ffmpeg_process.communicate(lowercase__ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
_lowerCamelCase : List[Any] = output_stream[0]
_lowerCamelCase : Tuple = np.frombuffer(lowercase__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = "f32le" , ):
_lowerCamelCase : Optional[Any] = f'''{sampling_rate}'''
_lowerCamelCase : List[str] = '1'
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
_lowerCamelCase : Dict = platform.system()
if system == "Linux":
_lowerCamelCase : Optional[int] = 'alsa'
_lowerCamelCase : Optional[Any] = 'default'
elif system == "Darwin":
_lowerCamelCase : Optional[int] = 'avfoundation'
_lowerCamelCase : Any = ':0'
elif system == "Windows":
_lowerCamelCase : Tuple = 'dshow'
_lowerCamelCase : Tuple = 'default'
_lowerCamelCase : Optional[int] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
_lowerCamelCase : Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_lowerCamelCase : List[Any] = _ffmpeg_stream(lowercase__ , lowercase__ )
for item in iterator:
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = "f32le" , ):
if stream_chunk_s is not None:
_lowerCamelCase : int = stream_chunk_s
else:
_lowerCamelCase : Optional[Any] = chunk_length_s
_lowerCamelCase : Optional[Any] = ffmpeg_microphone(lowercase__ , lowercase__ , format_for_conversion=lowercase__ )
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = np.intaa
_lowerCamelCase : str = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : Any = np.floataa
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
_lowerCamelCase : Union[str, Any] = chunk_length_s / 6
_lowerCamelCase : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowercase__ , (int, float) ):
_lowerCamelCase : Any = [stride_length_s, stride_length_s]
_lowerCamelCase : Tuple = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_lowerCamelCase : Optional[Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_lowerCamelCase : List[Any] = datetime.datetime.now()
_lowerCamelCase : Optional[int] = datetime.timedelta(seconds=lowercase__ )
for item in chunk_bytes_iter(lowercase__ , lowercase__ , stride=(stride_left, stride_right) , stream=lowercase__ ):
# Put everything back in numpy scale
_lowerCamelCase : List[Any] = np.frombuffer(item['raw'] , dtype=lowercase__ )
_lowerCamelCase : int = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
_lowerCamelCase : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = False ):
_lowerCamelCase : int = B''
_lowerCamelCase, _lowerCamelCase : Dict = 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}''' )
_lowerCamelCase : str = 0
for raw in iterator:
acc += raw
if stream and len(lowercase__ ) < chunk_len:
_lowerCamelCase : Optional[int] = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowercase__ ) >= chunk_len:
# We are flushing the accumulator
_lowerCamelCase : str = (_stride_left, stride_right)
_lowerCamelCase : str = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
_lowerCamelCase : List[Any] = False
yield item
_lowerCamelCase : Optional[Any] = stride_left
_lowerCamelCase : str = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowercase__ ) > stride_left:
_lowerCamelCase : Optional[Any] = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
_lowerCamelCase : Tuple = False
yield item
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = 2**24 # 16Mo
try:
with subprocess.Popen(lowercase__ , stdout=subprocess.PIPE , bufsize=lowercase__ ) as ffmpeg_process:
while True:
_lowerCamelCase : Optional[Any] = ffmpeg_process.stdout.read(lowercase__ )
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 | 96 | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class UpperCamelCase__ (lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = """pixel_values"""
lowerCamelCase_ : Optional[Any] = False
lowerCamelCase_ : str = TimmBackboneConfig
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
requires_backends(self , "timm" )
super().__init__(UpperCamelCase__ )
lowerCamelCase : Any = config
if config.backbone is None:
raise ValueError("backbone is not set in the config. Please set it to a timm model name." )
if config.backbone not in timm.list_models():
raise ValueError(F'''backbone {config.backbone} is not supported by timm.''' )
if hasattr(UpperCamelCase__ , "out_features" ) and config.out_features is not None:
raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )
lowerCamelCase : List[Any] = getattr(UpperCamelCase__ , "use_pretrained_backbone" , UpperCamelCase__ )
if pretrained is None:
raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )
# We just take the final layer by default. This matches the default for the transformers models.
lowerCamelCase : str = config.out_indices if getattr(UpperCamelCase__ , "out_indices" , UpperCamelCase__ ) is not None else (-1,)
lowerCamelCase : Optional[Any] = timm.create_model(
config.backbone , pretrained=UpperCamelCase__ , features_only=config.features_only , in_chans=config.num_channels , out_indices=UpperCamelCase__ , **UpperCamelCase__ , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
lowerCamelCase : List[Any] = self._backbone.return_layers
lowerCamelCase : Optional[int] = {layer["module"]: str(UpperCamelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(UpperCamelCase__ )
@classmethod
def _lowercase ( cls , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
requires_backends(cls , ["vision", "timm"] )
from ...models.timm_backbone import TimmBackboneConfig
lowerCamelCase : Dict = kwargs.pop("config" , TimmBackboneConfig() )
lowerCamelCase : List[Any] = kwargs.pop("use_timm_backbone" , UpperCamelCase__ )
if not use_timm:
raise ValueError("use_timm_backbone must be True for timm backbones" )
lowerCamelCase : Any = kwargs.pop("num_channels" , config.num_channels )
lowerCamelCase : Tuple = kwargs.pop("features_only" , config.features_only )
lowerCamelCase : Optional[Any] = kwargs.pop("use_pretrained_backbone" , config.use_pretrained_backbone )
lowerCamelCase : Optional[int] = kwargs.pop("out_indices" , config.out_indices )
lowerCamelCase : Optional[Any] = TimmBackboneConfig(
backbone=UpperCamelCase__ , num_channels=UpperCamelCase__ , features_only=UpperCamelCase__ , use_pretrained_backbone=UpperCamelCase__ , out_indices=UpperCamelCase__ , )
return super()._from_config(UpperCamelCase__ , **UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ ) -> Union[str, Any]:
pass
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
lowerCamelCase : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase : Tuple = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase : Tuple = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("Cannot output attentions for timm backbones at the moment" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
lowerCamelCase : Any = self._all_layers
lowerCamelCase : Union[str, Any] = self._backbone(UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : int = self._return_layers
lowerCamelCase : List[str] = tuple(hidden_states[i] for i in self.out_indices )
else:
lowerCamelCase : Optional[int] = self._backbone(UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : Optional[Any] = None
lowerCamelCase : Optional[int] = tuple(UpperCamelCase__ )
lowerCamelCase : List[str] = tuple(UpperCamelCase__ ) if hidden_states is not None else None
if not return_dict:
lowerCamelCase : Dict = (feature_maps,)
if output_hidden_states:
lowerCamelCase : List[Any] = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=UpperCamelCase__ , hidden_states=UpperCamelCase__ , attentions=UpperCamelCase__ )
| 48 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """ctrl"""
lowerCamelCase__ = ["""past_key_values"""]
lowerCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=246534 , lowercase=256 , lowercase=1280 , lowercase=8192 , lowercase=48 , lowercase=16 , lowercase=0.1 , lowercase=0.1 , lowercase=1E-6 , lowercase=0.02 , lowercase=True , **lowercase , ):
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Dict = n_positions
_lowerCamelCase : Optional[int] = n_embd
_lowerCamelCase : str = n_layer
_lowerCamelCase : Union[str, Any] = n_head
_lowerCamelCase : Any = dff
_lowerCamelCase : int = resid_pdrop
_lowerCamelCase : Dict = embd_pdrop
_lowerCamelCase : Union[str, Any] = layer_norm_epsilon
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : str = use_cache
super().__init__(**lowercase ) | 96 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __snake_case ( _UpperCAmelCase ):
__a = []
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
for v in tree.values():
shapes.extend(_fetch_dims(_UpperCAmelCase ) )
elif isinstance(_UpperCAmelCase , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(_UpperCAmelCase ) )
elif isinstance(_UpperCAmelCase , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = []
for d in reversed(_UpperCAmelCase ):
idx.append(flat_idx % d )
__a = flat_idx // d
return tuple(reversed(_UpperCAmelCase ) )
@torch.jit.ignore
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , ):
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(_UpperCAmelCase ) -> None:
__a = True
for i in range(len(_UpperCAmelCase ) ):
__a = -1 * (i + 1)
l[reversed_idx] &= tally
__a = l[reversed_idx]
if start_edges is None:
__a = [s == 0 for s in start]
reduce_edge_list(_UpperCAmelCase )
if end_edges is None:
__a = [e == (d - 1) for e, d in zip(_UpperCAmelCase , _UpperCAmelCase )]
reduce_edge_list(_UpperCAmelCase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(_UpperCAmelCase ) == 0:
return [()]
elif len(_UpperCAmelCase ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
__a = []
__a = []
# Dimensions common to start and end can be selected directly
for s, e in zip(_UpperCAmelCase , _UpperCAmelCase ):
if s == e:
path_list.append(slice(_UpperCAmelCase , s + 1 ) )
else:
break
__a = tuple(_UpperCAmelCase )
__a = len(_UpperCAmelCase )
# start == end, and we're done
if divergence_idx == len(_UpperCAmelCase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__a = start[divergence_idx]
return tuple(
path + (slice(_UpperCAmelCase , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__a = end[divergence_idx]
return tuple(
path + (slice(_UpperCAmelCase , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
__a = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = t.shape[:no_batch_dims]
__a = list(_flat_idx_to_idx(_UpperCAmelCase , _UpperCAmelCase ) )
# _get_minimal_slice_set is inclusive
__a = list(_flat_idx_to_idx(flat_end - 1 , _UpperCAmelCase ) )
# Get an ordered list of slices to perform
__a = _get_minimal_slice_set(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
__a = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = False , ):
if not (len(_UpperCAmelCase ) > 0):
raise ValueError('''Must provide at least one input''' )
__a = [shape[:no_batch_dims] for shape in _fetch_dims(_UpperCAmelCase )]
__a = tuple([max(_UpperCAmelCase ) for s in zip(*_UpperCAmelCase )] )
def _prep_inputs(_UpperCAmelCase ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
__a = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
__a = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
__a = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
__a = tensor_tree_map(_prep_inputs , _UpperCAmelCase )
__a = None
if _out is not None:
__a = tensor_tree_map(lambda _UpperCAmelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
__a = 1
for d in orig_batch_dims:
flat_batch_dim *= d
__a = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(_UpperCAmelCase ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
__a = 0
__a = prepped_outputs
for _ in range(_UpperCAmelCase ):
# Chunk the input
if not low_mem:
__a = _select_chunk
else:
__a = partial(
_chunk_slice , flat_start=_UpperCAmelCase , flat_end=min(_UpperCAmelCase , i + chunk_size ) , no_batch_dims=len(_UpperCAmelCase ) , )
__a = tensor_tree_map(_UpperCAmelCase , _UpperCAmelCase )
# Run the layer on the chunk
__a = layer(**_UpperCAmelCase )
# Allocate space for the output
if out is None:
__a = tensor_tree_map(lambda _UpperCAmelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , _UpperCAmelCase )
# Put the chunk in its pre-allocated space
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
def assign(_UpperCAmelCase , _UpperCAmelCase ) -> None:
for k, v in da.items():
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
assign(_UpperCAmelCase , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
__a = da[k]
assign(_UpperCAmelCase , _UpperCAmelCase )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
for xa, xa in zip(_UpperCAmelCase , _UpperCAmelCase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
__a = xa
elif isinstance(_UpperCAmelCase , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
__a = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
__a = tensor_tree_map(lambda _UpperCAmelCase : t.view(orig_batch_dims + t.shape[1:] ) , _UpperCAmelCase )
return out
class _A :
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int = 512 , ):
'''simple docstring'''
__a = max_chunk_size
__a = None
__a = None
def _lowerCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Callable , __SCREAMING_SNAKE_CASE : tuple , __SCREAMING_SNAKE_CASE : int):
'''simple docstring'''
logging.info('''Tuning chunk size...''')
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
__a = [2**l for l in range(int(math.log(self.max_chunk_size , 2)) + 1)]
__a = [c for c in candidates if c > min_chunk_size]
__a = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(__SCREAMING_SNAKE_CASE : int) -> bool:
try:
with torch.no_grad():
fn(*__SCREAMING_SNAKE_CASE , chunk_size=__SCREAMING_SNAKE_CASE)
return True
except RuntimeError:
return False
__a = 0
__a = len(__SCREAMING_SNAKE_CASE) - 1
while i > min_viable_chunk_size_index:
__a = test_chunk_size(candidates[i])
if not viable:
__a = (min_viable_chunk_size_index + i) // 2
else:
__a = i
__a = (i + len(__SCREAMING_SNAKE_CASE) - 1) // 2
return candidates[min_viable_chunk_size_index]
def _lowerCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Iterable , __SCREAMING_SNAKE_CASE : Iterable):
'''simple docstring'''
__a = True
for aa, aa in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
assert type(__SCREAMING_SNAKE_CASE) == type(__SCREAMING_SNAKE_CASE)
if isinstance(__SCREAMING_SNAKE_CASE , (list, tuple)):
consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
__a = [v for _, v in sorted(aa.items() , key=lambda __SCREAMING_SNAKE_CASE: x[0])]
__a = [v for _, v in sorted(aa.items() , key=lambda __SCREAMING_SNAKE_CASE: x[0])]
consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
else:
consistent &= aa == aa
return consistent
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : Callable , __SCREAMING_SNAKE_CASE : tuple , __SCREAMING_SNAKE_CASE : int , ):
'''simple docstring'''
__a = True
__a = tree_map(lambda __SCREAMING_SNAKE_CASE: a.shape if isinstance(__SCREAMING_SNAKE_CASE , torch.Tensor) else a , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data) == len(__SCREAMING_SNAKE_CASE)
__a = self._compare_arg_caches(self.cached_arg_data , __SCREAMING_SNAKE_CASE)
else:
# Otherwise, we can reuse the precomputed value
__a = False
if not consistent:
__a = self._determine_favorable_chunk_size(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )
__a = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 49 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = data
_lowerCamelCase : Node | None = None
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : str = None
_lowerCamelCase : str = None
def __iter__( self ):
_lowerCamelCase : List[str] = self.head
while self.head:
yield node.data
_lowerCamelCase : Optional[int] = node.next
if node == self.head:
break
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join(str(lowercase ) for item in iter(self ) )
def A_ ( self , lowercase ):
self.insert_nth(len(self ) , lowercase )
def A_ ( self , lowercase ):
self.insert_nth(0 , lowercase )
def A_ ( self , lowercase , lowercase ):
if index < 0 or index > len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : List[Any] = Node(lowercase )
if self.head is None:
_lowerCamelCase : str = new_node # first node points itself
_lowerCamelCase : Union[str, Any] = new_node
elif index == 0: # insert at head
_lowerCamelCase : List[str] = self.head
_lowerCamelCase : str = new_node
else:
_lowerCamelCase : Union[str, Any] = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : Union[str, Any] = temp.next
_lowerCamelCase : List[str] = new_node
if index == len(self ) - 1: # insert at tail
_lowerCamelCase : Any = new_node
def A_ ( self ):
return self.delete_nth(0 )
def A_ ( self ):
return self.delete_nth(len(self ) - 1 )
def A_ ( self , lowercase = 0 ):
if not 0 <= index < len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : Any = self.head
if self.head == self.tail: # just one node
_lowerCamelCase : List[str] = None
elif index == 0: # delete head node
_lowerCamelCase : List[str] = self.tail.next.next
_lowerCamelCase : Optional[int] = self.head.next
else:
_lowerCamelCase : Dict = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : int = temp.next
_lowerCamelCase : Optional[int] = temp.next.next
if index == len(self ) - 1: # delete at tail
_lowerCamelCase : List[Any] = temp
return delete_node.data
def A_ ( self ):
return len(self ) == 0
def _snake_case ( ):
_lowerCamelCase : Union[str, Any] = CircularLinkedList()
assert len(lowercase__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(lowercase__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(lowercase__ ) == i
circular_linked_list.insert_nth(lowercase__ , i + 1 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """glpn"""
def __init__( self : int , UpperCAmelCase : List[str]=3 , UpperCAmelCase : Tuple=4 , UpperCAmelCase : List[Any]=[2, 2, 2, 2] , UpperCAmelCase : int=[8, 4, 2, 1] , UpperCAmelCase : Tuple=[32, 64, 160, 256] , UpperCAmelCase : str=[7, 3, 3, 3] , UpperCAmelCase : Union[str, Any]=[4, 2, 2, 2] , UpperCAmelCase : List[Any]=[1, 2, 5, 8] , UpperCAmelCase : Tuple=[4, 4, 4, 4] , UpperCAmelCase : Optional[int]="gelu" , UpperCAmelCase : Any=0.0 , UpperCAmelCase : Any=0.0 , UpperCAmelCase : int=0.0_2 , UpperCAmelCase : str=0.1 , UpperCAmelCase : List[str]=1e-6 , UpperCAmelCase : Dict=64 , UpperCAmelCase : Optional[int]=10 , UpperCAmelCase : Any=-1 , **UpperCAmelCase : List[Any] , ) -> str:
super().__init__(**UpperCAmelCase )
lowerCamelCase__ : int = num_channels
lowerCamelCase__ : str = num_encoder_blocks
lowerCamelCase__ : Any = depths
lowerCamelCase__ : int = sr_ratios
lowerCamelCase__ : List[Any] = hidden_sizes
lowerCamelCase__ : List[str] = patch_sizes
lowerCamelCase__ : int = strides
lowerCamelCase__ : List[str] = mlp_ratios
lowerCamelCase__ : Union[str, Any] = num_attention_heads
lowerCamelCase__ : List[Any] = hidden_act
lowerCamelCase__ : Dict = hidden_dropout_prob
lowerCamelCase__ : Dict = attention_probs_dropout_prob
lowerCamelCase__ : Union[str, Any] = initializer_range
lowerCamelCase__ : Optional[Any] = drop_path_rate
lowerCamelCase__ : int = layer_norm_eps
lowerCamelCase__ : List[Any] = decoder_hidden_size
lowerCamelCase__ : List[Any] = max_depth
lowerCamelCase__ : Tuple = head_in_index
| 50 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowercase__ = get_logger(__name__)
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = """dummy_data"""
lowerCamelCase__ = """datasets"""
lowerCamelCase__ = False
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = False , lowercase = True , lowercase = None , ):
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Dict = dataset_name
_lowerCamelCase : Union[str, Any] = cache_dir
_lowerCamelCase : Dict = use_local_dummy_data
_lowerCamelCase : Tuple = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : Any = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : str = str(lowercase )
# to be downloaded
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : int = None
@property
def A_ ( self ):
if self._dummy_file is None:
_lowerCamelCase : Tuple = self.download_dummy_data()
return self._dummy_file
@property
def A_ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def A_ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def A_ ( self ):
_lowerCamelCase : List[str] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : int = cached_path(
lowercase , cache_dir=self.cache_dir , extract_compressed_file=lowercase , force_extract=lowercase )
return os.path.join(lowercase , self.dummy_file_name )
@property
def A_ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def A_ ( self ):
if self._bucket_url is None:
_lowerCamelCase : List[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def A_ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def A_ ( self , lowercase , *lowercase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Union[str, Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Union[str, Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowercase , lowercase ):
return self.create_dummy_data_dict(lowercase , lowercase )
elif isinstance(lowercase , (list, tuple) ):
return self.create_dummy_data_list(lowercase , lowercase )
else:
return self.create_dummy_data_single(lowercase , lowercase )
def A_ ( self , lowercase , *lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , *lowercase , **lowercase ):
return path
def A_ ( self ):
return {}
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowercase , lowercase ):
for single_url in single_urls:
download_callback(lowercase )
else:
_lowerCamelCase : List[Any] = single_urls
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowercase , lowercase ):
_lowerCamelCase : List[Any] = [os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : Optional[int] = single_urls
_lowerCamelCase : List[Any] = os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) )
_lowerCamelCase : int = value
# make sure that values are unique
if all(isinstance(lowercase , lowercase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : List[str] = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , lowercase ) ) for url in data_url )
_lowerCamelCase : int = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : List[str] = [data_url[0]] * len(lowercase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : str = os.path.join(lowercase , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(lowercase )
return dummy_data_list
def A_ ( self , lowercase , lowercase ):
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Tuple = os.path.join(lowercase , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(lowercase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self , lowercase ):
def _iter_archive_members(lowercase ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : str = Path(self.dummy_file ).parent
_lowerCamelCase : Union[str, Any] = path.relative_to(lowercase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : List[str] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowercase )
_lowerCamelCase : Optional[int] = Path(lowercase )
_lowerCamelCase : Dict = _iter_archive_members(lowercase ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(lowercase ).as_posix(), file_path.open('rb' )
def A_ ( self , lowercase ):
if not isinstance(lowercase , lowercase ):
_lowerCamelCase : List[str] = [paths]
for path in paths:
if os.path.isfile(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(lowercase ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(lowercase , lowercase ) | 96 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : List[Any] = {
"configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : int = ["RemBertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[Any] = ["RemBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = [
"REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RemBertForCausalLM",
"RemBertForMaskedLM",
"RemBertForMultipleChoice",
"RemBertForQuestionAnswering",
"RemBertForSequenceClassification",
"RemBertForTokenClassification",
"RemBertLayer",
"RemBertModel",
"RemBertPreTrainedModel",
"load_tf_weights_in_rembert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : int = [
"TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRemBertForCausalLM",
"TFRemBertForMaskedLM",
"TFRemBertForMultipleChoice",
"TFRemBertForQuestionAnswering",
"TFRemBertForSequenceClassification",
"TFRemBertForTokenClassification",
"TFRemBertLayer",
"TFRemBertModel",
"TFRemBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
snake_case_ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 51 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
_lowerCamelCase, _lowerCamelCase : Optional[Any] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
_lowerCamelCase : Union[str, Any] = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(lowercase__ , i + t , (lowercase__) )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
if __name__ == "__main__":
lowercase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowercase__ = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted)) | 96 | 0 |
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). ' , __snake_case , )
class A__ ( __snake_case ):
_UpperCAmelCase :str = RobertaConfig
_UpperCAmelCase :Any = 'roberta'
def __init__( self , A_ ):
'''simple docstring'''
super().__init__(A_ )
UpperCamelCase : Union[str, Any] = RobertaEmbeddings(A_ )
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. ' , __snake_case , )
class A__ ( __snake_case ):
_UpperCAmelCase :Optional[int] = RobertaConfig
_UpperCAmelCase :List[Any] = 'roberta'
def __init__( self , A_ ):
'''simple docstring'''
super().__init__(A_ )
UpperCamelCase : Any = config.num_labels
UpperCamelCase : int = config.num_hidden_layers
UpperCamelCase : Union[str, Any] = DeeRobertaModel(A_ )
UpperCamelCase : str = nn.Dropout(config.hidden_dropout_prob )
UpperCamelCase : List[str] = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(A_ )
def __UpperCamelCase( self , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , A_=-1 , A_=False , ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.num_layers
try:
UpperCamelCase : Any = self.roberta(
A_ , attention_mask=A_ , token_type_ids=A_ , position_ids=A_ , head_mask=A_ , inputs_embeds=A_ , )
UpperCamelCase : Optional[int] = outputs[1]
UpperCamelCase : Tuple = self.dropout(A_ )
UpperCamelCase : str = self.classifier(A_ )
UpperCamelCase : Union[str, Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
UpperCamelCase : Optional[Any] = e.message
UpperCamelCase : Dict = e.exit_layer
UpperCamelCase : Optional[Any] = outputs[0]
if not self.training:
UpperCamelCase : Optional[int] = entropy(A_ )
UpperCamelCase : str = []
UpperCamelCase : Dict = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
UpperCamelCase : List[Any] = MSELoss()
UpperCamelCase : Any = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
UpperCamelCase : List[str] = CrossEntropyLoss()
UpperCamelCase : Tuple = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
UpperCamelCase : Optional[int] = []
for highway_exit in outputs[-1]:
UpperCamelCase : Optional[Any] = highway_exit[0]
if not self.training:
highway_logits_all.append(A_ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
UpperCamelCase : Dict = MSELoss()
UpperCamelCase : int = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
UpperCamelCase : Union[str, Any] = CrossEntropyLoss()
UpperCamelCase : str = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(A_ )
if train_highway:
UpperCamelCase : Union[str, Any] = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
UpperCamelCase : int = (loss,) + outputs
if not self.training:
UpperCamelCase : List[str] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
UpperCamelCase : 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
| 52 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""image_processor""", """tokenizer"""]
lowerCamelCase__ = """BlipImageProcessor"""
lowerCamelCase__ = """AutoTokenizer"""
def __init__( self , lowercase , lowercase , lowercase ):
super().__init__(lowercase , lowercase )
# add QFormer tokenizer
_lowerCamelCase : int = qformer_tokenizer
def __call__( self , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ):
if images is None and text is None:
raise ValueError('You have to specify at least images or text.' )
_lowerCamelCase : int = BatchFeature()
if text is not None:
_lowerCamelCase : List[str] = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
encoding.update(lowercase )
_lowerCamelCase : List[str] = self.qformer_tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
_lowerCamelCase : List[Any] = qformer_text_encoding.pop('input_ids' )
_lowerCamelCase : Tuple = qformer_text_encoding.pop('attention_mask' )
if images is not None:
_lowerCamelCase : int = self.image_processor(lowercase , return_tensors=lowercase )
encoding.update(lowercase )
return encoding
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.decode(*lowercase , **lowercase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = self.tokenizer.model_input_names
_lowerCamelCase : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def A_ ( self , lowercase , **lowercase ):
if os.path.isfile(lowercase ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(lowercase , exist_ok=lowercase )
_lowerCamelCase : Optional[Any] = os.path.join(lowercase , 'qformer_tokenizer' )
self.qformer_tokenizer.save_pretrained(lowercase )
return super().save_pretrained(lowercase , **lowercase )
@classmethod
def A_ ( cls , lowercase , **lowercase ):
_lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(lowercase , subfolder='qformer_tokenizer' )
_lowerCamelCase : Dict = cls._get_arguments_from_pretrained(lowercase , **lowercase )
args.append(lowercase )
return cls(*lowercase ) | 96 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : int = 1000000 ) -> int:
"""simple docstring"""
__UpperCamelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , __lowercase ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 53 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowercase__ = logging.getLogger()
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[Any] = {}
_lowerCamelCase : List[Any] = os.path.join(lowercase__ , 'all_results.json' )
if os.path.exists(lowercase__ ):
with open(lowercase__ , 'r' ) as f:
_lowerCamelCase : List[Any] = json.load(lowercase__ )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
lowercase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def A_ ( self ):
import xla_spawn
_lowerCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
_lowerCamelCase : List[Any] = F'''
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(lowercase , 'argv' , lowercase ):
_lowerCamelCase : Dict = time()
xla_spawn.main()
_lowerCamelCase : Any = time()
_lowerCamelCase : Optional[int] = get_results(lowercase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def A_ ( self ):
import xla_spawn
_lowerCamelCase : Tuple = '\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n '.split()
with patch.object(lowercase , 'argv' , lowercase ):
xla_spawn.main() | 96 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def UpperCAmelCase__ (lowerCAmelCase_="" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
return os.path.join(lowerCAmelCase_ , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : Optional[int] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
__SCREAMING_SNAKE_CASE = AgentAudio(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(UpperCAmelCase__ , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(UpperCAmelCase__ ) )
# Ensure that the file contains the same value as the original tensor
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = sf.read(UpperCAmelCase__ )
self.assertTrue(torch.allclose(UpperCAmelCase__ , torch.tensor(UpperCAmelCase__ ) , atol=1E-4 ) )
def UpperCAmelCase_ ( self : Tuple ) -> str:
__SCREAMING_SNAKE_CASE = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
__SCREAMING_SNAKE_CASE = get_new_path(suffix=".wav" )
sf.write(UpperCAmelCase__ , UpperCAmelCase__ , 1_6_0_0_0 )
__SCREAMING_SNAKE_CASE = AgentAudio(UpperCAmelCase__ )
self.assertTrue(torch.allclose(UpperCAmelCase__ , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , UpperCAmelCase__ )
@require_vision
@require_torch
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = torch.randint(0 , 2_5_6 , (6_4, 6_4, 3) )
__SCREAMING_SNAKE_CASE = AgentImage(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(UpperCAmelCase__ , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(UpperCAmelCase__ ) )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
__SCREAMING_SNAKE_CASE = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
__SCREAMING_SNAKE_CASE = Image.open(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = AgentImage(UpperCAmelCase__ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(UpperCAmelCase__ ) )
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
__SCREAMING_SNAKE_CASE = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
__SCREAMING_SNAKE_CASE = Image.open(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = AgentImage(UpperCAmelCase__ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(UpperCAmelCase__ ) )
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> Tuple:
__SCREAMING_SNAKE_CASE = "Hey!"
__SCREAMING_SNAKE_CASE = AgentText(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , agent_type.to_string() )
self.assertEqual(UpperCAmelCase__ , agent_type.to_raw() )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
| 54 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( lowercase__ , lowercase__ ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase__ , lowercase__ ) ) )
def _snake_case ( lowercase__ , lowercase__ ):
if dataset.ndim != value_array.ndim:
_lowerCamelCase : Tuple = (
'Wrong input data\'s dimensions... '
f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(lowercase__ )
try:
if dataset.shape[1] != value_array.shape[1]:
_lowerCamelCase : Optional[int] = (
'Wrong input data\'s shape... '
f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(lowercase__ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('Wrong shape' )
if dataset.dtype != value_array.dtype:
_lowerCamelCase : int = (
'Input data have different datatype... '
f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(lowercase__ )
_lowerCamelCase : Optional[int] = []
for value in value_array:
_lowerCamelCase : Tuple = euclidean(lowercase__ , dataset[0] )
_lowerCamelCase : Union[str, Any] = dataset[0].tolist()
for dataset_value in dataset[1:]:
_lowerCamelCase : Optional[Any] = euclidean(lowercase__ , lowercase__ )
if dist > temp_dist:
_lowerCamelCase : List[Any] = temp_dist
_lowerCamelCase : List[str] = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( lowercase__ , lowercase__ ):
return np.dot(lowercase__ , lowercase__ ) / (norm(lowercase__ ) * norm(lowercase__ ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
a_ : Dict = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 55 |
"""simple docstring"""
import socket
def _snake_case ( ):
_lowerCamelCase : List[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase : Union[str, Any] = socket.gethostname()
_lowerCamelCase : List[Any] = 12312
sock.connect((host, port) )
sock.send(B'Hello server!' )
with open('Received_file' , 'wb' ) as out_file:
print('File opened' )
print('Receiving data...' )
while True:
_lowerCamelCase : int = sock.recv(1024 )
if not data:
break
out_file.write(lowercase__ )
print('Successfully received the file' )
sock.close()
print('Connection closed' )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
if years_to_repay <= 0 or not isinstance(__UpperCAmelCase, __UpperCAmelCase ):
raise Exception('''Years to repay must be an integer > 0''' )
# Yearly rate is divided by 12 to get monthly rate
snake_case_ = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
snake_case_ = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase__ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
"""
lowercase__ = """\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""
lowercase__ = """
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for 'record': list of question-answer dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'prediction_text': the predicted answer text
- for 'multirc': list of question-answer dictionaries with the following keys:
- 'idx': index of the question-answer pair as specified by the dataset
- 'prediction': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for 'record': list of question-answers dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'answers': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for 'record':
- 'exact_match': Exact match between answer and gold answer
- 'f1': F1 score
- for 'multirc':
- 'exact_match': Exact match between answer and gold answer
- 'f1_m': Per-question macro-F1 score
- 'f1_a': Average F1 score over all answers
- for 'axb':
'matthews_correlation': Matthew Correlation
- for 'cb':
- 'accuracy': Accuracy
- 'f1': F1 score
- for all others:
- 'accuracy': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'record')
>>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
>>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
>>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def _snake_case ( lowercase__ , lowercase__ ):
return float((preds == labels).mean() )
def _snake_case ( lowercase__ , lowercase__ , lowercase__="binary" ):
_lowerCamelCase : str = simple_accuracy(lowercase__ , lowercase__ )
_lowerCamelCase : Any = float(fa_score(y_true=lowercase__ , y_pred=lowercase__ , average=lowercase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Any = {}
for id_pred, label in zip(lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
_lowerCamelCase : Union[str, Any] = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
_lowerCamelCase : Optional[Any] = [(pred, label)]
_lowerCamelCase, _lowerCamelCase : Optional[int] = [], []
for question, preds_labels in question_map.items():
_lowerCamelCase, _lowerCamelCase : Tuple = zip(*lowercase__ )
_lowerCamelCase : List[str] = fa_score(y_true=lowercase__ , y_pred=lowercase__ , average='macro' )
fas.append(lowercase__ )
_lowerCamelCase : int = int(sum(pred == label for pred, label in preds_labels ) == len(lowercase__ ) )
ems.append(lowercase__ )
_lowerCamelCase : Optional[Any] = float(sum(lowercase__ ) / len(lowercase__ ) )
_lowerCamelCase : Optional[int] = sum(lowercase__ ) / len(lowercase__ )
_lowerCamelCase : List[Any] = float(fa_score(y_true=lowercase__ , y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def A_ ( self ):
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None , )
def A_ ( self ):
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"prediction_text": datasets.Value('string' ),
},
"references": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"answers": datasets.Sequence(datasets.Value('string' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64' ),
"paragraph": datasets.Value('int64' ),
"question": datasets.Value('int64' ),
},
"prediction": datasets.Value('int64' ),
},
"references": datasets.Value('int64' ),
}
else:
return {
"predictions": datasets.Value('int64' ),
"references": datasets.Value('int64' ),
}
def A_ ( self , lowercase , lowercase ):
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowercase , lowercase )}
elif self.config_name == "cb":
return acc_and_fa(lowercase , lowercase , fa_avg='macro' )
elif self.config_name == "record":
_lowerCamelCase : List[str] = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
_lowerCamelCase : Union[str, Any] = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(lowercase , lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowercase , lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowercase , lowercase )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' ) | 96 | 0 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
A : List[str] = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase=None ):
'''simple docstring'''
require_version(deps[pkg] , _UpperCamelCase )
| 57 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = DDIMPipeline
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCamelCase__ = False
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
_lowerCamelCase : List[str] = DDIMScheduler()
_lowerCamelCase : Optional[int] = {'unet': unet, 'scheduler': scheduler}
return components
def A_ ( self , lowercase , lowercase=0 ):
if str(lowercase ).startswith('mps' ):
_lowerCamelCase : Dict = torch.manual_seed(lowercase )
else:
_lowerCamelCase : List[str] = torch.Generator(device=lowercase ).manual_seed(lowercase )
_lowerCamelCase : Tuple = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def A_ ( self ):
_lowerCamelCase : Any = 'cpu'
_lowerCamelCase : Tuple = self.get_dummy_components()
_lowerCamelCase : Optional[Any] = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : str = self.get_dummy_inputs(lowercase )
_lowerCamelCase : int = pipe(**lowercase ).images
_lowerCamelCase : Any = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
_lowerCamelCase : Tuple = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
_lowerCamelCase : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase , 1E-3 )
def A_ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
_lowerCamelCase : Optional[Any] = 'google/ddpm-cifar10-32'
_lowerCamelCase : Optional[Any] = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : Dict = DDIMScheduler()
_lowerCamelCase : Dict = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddim.to(lowercase )
ddim.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : List[str] = torch.manual_seed(0 )
_lowerCamelCase : str = ddim(generator=lowercase , eta=0.0 , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowerCamelCase : List[Any] = np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : Optional[int] = 'google/ddpm-ema-bedroom-256'
_lowerCamelCase : str = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : str = DDIMScheduler.from_pretrained(lowercase )
_lowerCamelCase : Optional[int] = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddpm.to(lowercase )
ddpm.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Tuple = torch.manual_seed(0 )
_lowerCamelCase : int = ddpm(generator=lowercase , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_lowerCamelCase : str = np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 96 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
lowercase_ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
lowercase_ = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def lowerCamelCase ( __lowerCamelCase : list[list[int]] ) ->list[list[int]]:
_SCREAMING_SNAKE_CASE = []
for i in range(len(__lowerCamelCase ) ):
_SCREAMING_SNAKE_CASE = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
_SCREAMING_SNAKE_CASE = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(__lowerCamelCase ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(__lowerCamelCase ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(__lowerCamelCase ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
_SCREAMING_SNAKE_CASE = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(__lowerCamelCase )
return next_generation
def lowerCamelCase ( __lowerCamelCase : list[list[int]] , __lowerCamelCase : int ) ->list[Image.Image]:
_SCREAMING_SNAKE_CASE = []
for _ in range(__lowerCamelCase ):
# Create output image
_SCREAMING_SNAKE_CASE = Image.new("""RGB""" , (len(cells[0] ), len(__lowerCamelCase )) )
_SCREAMING_SNAKE_CASE = img.load()
# Save cells to image
for x in range(len(__lowerCamelCase ) ):
for y in range(len(cells[0] ) ):
_SCREAMING_SNAKE_CASE = 255 - cells[y][x] * 255
_SCREAMING_SNAKE_CASE = (colour, colour, colour)
# Save image
images.append(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = new_generation(__lowerCamelCase )
return images
if __name__ == "__main__":
lowercase_ = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 58 |
"""simple docstring"""
# Imports
import numpy as np
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
def A_ ( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
if red is not None:
_lowerCamelCase : Optional[int] = red
if green is not None:
_lowerCamelCase : Optional[Any] = green
if blue is not None:
_lowerCamelCase : Tuple = blue
if red_edge is not None:
_lowerCamelCase : Optional[Any] = red_edge
if nir is not None:
_lowerCamelCase : Union[str, Any] = nir
return True
def A_ ( self , lowercase="" , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
_lowerCamelCase : str = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def A_ ( self ):
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def A_ ( self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def A_ ( self ):
return self.nir * (self.red / (self.green**2))
def A_ ( self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def A_ ( self ):
return (self.nir - self.red) / (self.nir + self.red)
def A_ ( self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def A_ ( self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def A_ ( self ):
return (self.nir - self.green) / (self.nir + self.green)
def A_ ( self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def A_ ( self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def A_ ( self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def A_ ( self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def A_ ( self , lowercase=0.08 , lowercase=1.22 , lowercase=0.03 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def A_ ( self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def A_ ( self ):
return (self.nir / self.green) - 1
def A_ ( self ):
return (self.nir / self.redEdge) - 1
def A_ ( self ):
return (self.red - self.blue) / self.red
def A_ ( self ):
_lowerCamelCase : Any = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def A_ ( self ):
return self.nir - self.green
def A_ ( self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def A_ ( self ):
_lowerCamelCase : Any = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)
def A_ ( self , lowercase=0.16 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def A_ ( self , lowercase=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def A_ ( self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def A_ ( self , lowercase=None , lowercase=None ):
return (self.nir - b) / (a * self.red)
def A_ ( self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def A_ ( self ):
return (self.red + self.green + self.blue) / 30.5
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def A_ ( self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def A_ ( self ):
return self.green / (self.nir + self.red + self.green)
def A_ ( self ):
return self.nir / (self.nir + self.red + self.green)
def A_ ( self ):
return self.red / (self.nir + self.red + self.green)
def A_ ( self ):
return (self.green - self.red) / (self.green + self.red)
def A_ ( self ):
return (self.red - self.green) / (self.red + self.green)
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
_lowerCamelCase : Dict = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def A_ ( self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def A_ ( self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge) | 96 | 0 |
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
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCAmelCase ( A_ ,A_ ):
A__ : Optional[Any] = "swin"
A__ : str = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(self : Optional[Any] , snake_case__ : Optional[Any]=2_24 , snake_case__ : List[str]=4 , snake_case__ : Union[str, Any]=3 , snake_case__ : Tuple=96 , snake_case__ : List[Any]=[2, 2, 6, 2] , snake_case__ : Dict=[3, 6, 12, 24] , snake_case__ : Optional[Any]=7 , snake_case__ : int=4.0 , snake_case__ : Tuple=True , snake_case__ : List[str]=0.0 , snake_case__ : Optional[int]=0.0 , snake_case__ : Tuple=0.1 , snake_case__ : Dict="gelu" , snake_case__ : int=False , snake_case__ : Dict=0.02 , snake_case__ : List[str]=1e-5 , snake_case__ : Union[str, Any]=32 , snake_case__ : Any=None , snake_case__ : Tuple=None , **snake_case__ : List[str] , ) -> Tuple:
'''simple docstring'''
super().__init__(**snake_case__ )
snake_case : Union[str, Any] = image_size
snake_case : List[Any] = patch_size
snake_case : str = num_channels
snake_case : List[Any] = embed_dim
snake_case : Dict = depths
snake_case : Optional[Any] = len(snake_case__ )
snake_case : Optional[Any] = num_heads
snake_case : Any = window_size
snake_case : str = mlp_ratio
snake_case : Optional[int] = qkv_bias
snake_case : Union[str, Any] = hidden_dropout_prob
snake_case : Optional[Any] = attention_probs_dropout_prob
snake_case : Optional[int] = drop_path_rate
snake_case : Optional[Any] = hidden_act
snake_case : Optional[int] = use_absolute_embeddings
snake_case : List[Any] = layer_norm_eps
snake_case : Any = initializer_range
snake_case : Optional[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case : Any = int(embed_dim * 2 ** (len(snake_case__ ) - 1) )
snake_case : Union[str, Any] = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(snake_case__ ) + 1 )]
snake_case , snake_case : Optional[Any] = get_aligned_output_features_output_indices(
out_features=snake_case__ , out_indices=snake_case__ , stage_names=self.stage_names )
class UpperCAmelCase ( A_ ):
A__ : Dict = version.parse("1.11" )
@property
def _SCREAMING_SNAKE_CASE (self : Any ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _SCREAMING_SNAKE_CASE (self : int ) -> float:
'''simple docstring'''
return 1e-4
| 59 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self , lowercase , lowercase=768 ):
super().__init__(lowercase )
_lowerCamelCase : Any = proj_size
_lowerCamelCase : Dict = CLIPVisionModel(lowercase )
_lowerCamelCase : List[str] = PaintByExampleMapper(lowercase )
_lowerCamelCase : Optional[Any] = nn.LayerNorm(config.hidden_size )
_lowerCamelCase : int = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
_lowerCamelCase : str = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def A_ ( self , lowercase , lowercase=False ):
_lowerCamelCase : Union[str, Any] = self.model(pixel_values=lowercase )
_lowerCamelCase : int = clip_output.pooler_output
_lowerCamelCase : str = self.mapper(latent_states[:, None] )
_lowerCamelCase : List[Any] = self.final_layer_norm(lowercase )
_lowerCamelCase : Dict = self.proj_out(lowercase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , lowercase ):
super().__init__()
_lowerCamelCase : Tuple = (config.num_hidden_layers + 1) // 5
_lowerCamelCase : int = config.hidden_size
_lowerCamelCase : Optional[Any] = 1
_lowerCamelCase : str = nn.ModuleList(
[
BasicTransformerBlock(lowercase , lowercase , lowercase , activation_fn='gelu' , attention_bias=lowercase )
for _ in range(lowercase )
] )
def A_ ( self , lowercase ):
for block in self.blocks:
_lowerCamelCase : Tuple = block(lowercase )
return hidden_states | 96 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
snake_case__ : Optional[int] = logging.get_logger(__name__)
snake_case__ : List[Any] = {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class snake_case_( a__ ):
__UpperCamelCase = '''longformer'''
def __init__( self : Optional[int] , UpperCamelCase_ : Union[List[int], int] = 5_1_2 , UpperCamelCase_ : int = 2 , UpperCamelCase_ : int = 1 , UpperCamelCase_ : int = 0 , UpperCamelCase_ : int = 2 , UpperCamelCase_ : int = 3_0_5_2_2 , UpperCamelCase_ : int = 7_6_8 , UpperCamelCase_ : int = 1_2 , UpperCamelCase_ : int = 1_2 , UpperCamelCase_ : int = 3_0_7_2 , UpperCamelCase_ : str = "gelu" , UpperCamelCase_ : float = 0.1 , UpperCamelCase_ : float = 0.1 , UpperCamelCase_ : int = 5_1_2 , UpperCamelCase_ : int = 2 , UpperCamelCase_ : float = 0.02 , UpperCamelCase_ : float = 1E-12 , UpperCamelCase_ : bool = False , **UpperCamelCase_ : List[Any] , ):
super().__init__(pad_token_id=UpperCamelCase_ , **UpperCamelCase_ )
lowerCAmelCase : Union[str, Any] = attention_window
lowerCAmelCase : List[str] = sep_token_id
lowerCAmelCase : Tuple = bos_token_id
lowerCAmelCase : str = eos_token_id
lowerCAmelCase : Any = vocab_size
lowerCAmelCase : Union[str, Any] = hidden_size
lowerCAmelCase : str = num_hidden_layers
lowerCAmelCase : Any = num_attention_heads
lowerCAmelCase : List[str] = hidden_act
lowerCAmelCase : Dict = intermediate_size
lowerCAmelCase : Optional[int] = hidden_dropout_prob
lowerCAmelCase : Any = attention_probs_dropout_prob
lowerCAmelCase : Union[str, Any] = max_position_embeddings
lowerCAmelCase : List[str] = type_vocab_size
lowerCAmelCase : int = initializer_range
lowerCAmelCase : List[Any] = layer_norm_eps
lowerCAmelCase : Tuple = onnx_export
class snake_case_( a__ ):
def __init__( self : Union[str, Any] , UpperCamelCase_ : "PretrainedConfig" , UpperCamelCase_ : str = "default" , UpperCamelCase_ : "List[PatchingSpec]" = None ):
super().__init__(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase : int = True
@property
def lowerCamelCase__ ( self : Optional[Any] ):
if self.task == "multiple-choice":
lowerCAmelCase : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCAmelCase : List[str] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''global_attention_mask''', dynamic_axis),
] )
@property
def lowerCamelCase__ ( self : str ):
lowerCAmelCase : Optional[int] = super().outputs
if self.task == "default":
lowerCAmelCase : List[str] = {0: '''batch'''}
return outputs
@property
def lowerCamelCase__ ( self : List[Any] ):
return 1E-4
@property
def lowerCamelCase__ ( self : Any ):
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 1_4 )
def lowerCamelCase__ ( self : int , UpperCamelCase_ : "PreTrainedTokenizerBase" , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
lowerCAmelCase : Optional[int] = super().generate_dummy_inputs(
preprocessor=UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
lowerCAmelCase : Optional[int] = torch.zeros_like(inputs['''input_ids'''] )
# make every second token global
lowerCAmelCase : Union[str, Any] = 1
return inputs
| 60 |
"""simple docstring"""
lowercase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowercase__ = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : List[Any] = from_type.lower().strip('s' )
_lowerCamelCase : List[Any] = to_type.lower().strip('s' )
_lowerCamelCase : Optional[int] = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
_lowerCamelCase : Any = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
if from_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Tuple = (
f'''Invalid \'from_type\' value: {from_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
if to_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Any = (
f'''Invalid \'to_type\' value: {to_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
_lowerCamelCase : List[Any] = METRIC_CONVERSION[from_sanitized]
_lowerCamelCase : int = METRIC_CONVERSION[to_sanitized]
_lowerCamelCase : List[str] = 1
if from_exponent > to_exponent:
_lowerCamelCase : List[str] = from_exponent - to_exponent
else:
_lowerCamelCase : List[Any] = -(to_exponent - from_exponent)
return value * pow(10 , lowercase__ )
if __name__ == "__main__":
from doctest import testmod
testmod() | 96 | 0 |
"""simple docstring"""
import string
from math import logaa
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Optional[Any] = document.translate(
str.maketrans("", "", string.punctuation ) ).replace("\n", "" )
UpperCAmelCase_ : str = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : List[Any] = corpus.lower().translate(
str.maketrans("", "", string.punctuation ) ) # strip all punctuation and replace it with ''
UpperCAmelCase_ : str = corpus_without_punctuation.split("\n" )
UpperCAmelCase_ : Union[str, Any] = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__lowerCamelCase ))
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase=False ):
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ), 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ), 3 )
def __a ( __lowerCamelCase, __lowerCamelCase ):
return round(tf * idf, 3 )
| 61 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMSNModel""",
"""ViTMSNForImageClassification""",
"""ViTMSNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] ):
# Check if the input is valid
if not len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) == 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
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase =equationa
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase =equationa
# Calculate the determinants of the matrices
__UpperCamelCase =aa * ba - aa * ba
__UpperCamelCase =ca * ba - ca * ba
__UpperCamelCase =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:
__UpperCamelCase =determinant_x / determinant
__UpperCamelCase =determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 62 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : List[Any] = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Tuple = boundary[0]
_lowerCamelCase : Dict = boundary[1]
_lowerCamelCase : List[Any] = make_points(lowercase__ , lowercase__ , lowercase__ )
_lowerCamelCase : List[Any] = 0.0
y += (h / 2.0) * f(lowercase__ )
for i in x_i:
# print(i)
y += h * f(lowercase__ )
y += (h / 2.0) * f(lowercase__ )
return y
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : str = a + h
while x < (b - h):
yield x
_lowerCamelCase : int = x + h
def _snake_case ( lowercase__ ): # enter your function here
_lowerCamelCase : Optional[Any] = (x - 0) * (x - 0)
return y
def _snake_case ( ):
_lowerCamelCase : int = 0.0 # Lower bound of integration
_lowerCamelCase : Optional[int] = 1.0 # Upper bound of integration
_lowerCamelCase : List[str] = 1_0.0 # define number of steps or resolution
_lowerCamelCase : List[Any] = [a, b] # define boundary of integration
_lowerCamelCase : Optional[Any] = method_a(lowercase__ , lowercase__ )
print(f'''y = {y}''' )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase_ : str = tuple[int, int, int]
lowerCAmelCase_ : int = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase_ : Union[str, Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCAmelCase_ : Any = 'EGZWVONAHDCLFQMSIPJBYUKXTR'
lowerCAmelCase_ : Optional[Any] = 'FOBHMDKEXQNRAULPGSJVTYICZW'
lowerCAmelCase_ : Any = 'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
lowerCAmelCase_ : int = {
'A': 'N',
'N': 'A',
'B': 'O',
'O': 'B',
'C': 'P',
'P': 'C',
'D': 'Q',
'Q': 'D',
'E': 'R',
'R': 'E',
'F': 'S',
'S': 'F',
'G': 'T',
'T': 'G',
'H': 'U',
'U': 'H',
'I': 'V',
'V': 'I',
'J': 'W',
'W': 'J',
'K': 'X',
'X': 'K',
'L': 'Y',
'Y': 'L',
'M': 'Z',
'Z': 'M',
}
# -------------------------- extra rotors --------------------------
lowerCAmelCase_ : int = 'RMDJXFUWGISLHVTCQNKYPBEZOA'
lowerCAmelCase_ : str = 'SGLCPQWZHKXAREONTFBVIYJUDM'
lowerCAmelCase_ : List[Any] = 'HVSICLTYKQUBXDWAJZOMFGPREN'
lowerCAmelCase_ : Optional[Any] = 'RZWQHFMVDBKICJLNTUXAGYPSOE'
lowerCAmelCase_ : Optional[int] = 'LFKIJODBEGAMQPXVUHYSTCZRWN'
lowerCAmelCase_ : Optional[Any] = 'KOAEGVDHXPQZMLFTYWJNBRCIUS'
def _lowerCamelCase ( lowercase : RotorPositionT , lowercase : RotorSelectionT , lowercase : str ) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(lowercase ) )) < 3:
_a = F'Please use 3 unique rotors (not {unique_rotsel})'
raise Exception(lowercase )
# Checks if rotor positions are valid
_a , _a , _a = rotpos
if not 0 < rotorposa <= len(lowercase ):
_a = F'First rotor position is not within range of 1..26 ({rotorposa}'
raise ValueError(lowercase )
if not 0 < rotorposa <= len(lowercase ):
_a = F'Second rotor position is not within range of 1..26 ({rotorposa})'
raise ValueError(lowercase )
if not 0 < rotorposa <= len(lowercase ):
_a = F'Third rotor position is not within range of 1..26 ({rotorposa})'
raise ValueError(lowercase )
# Validates string and returns dict
_a = _plugboard(lowercase )
return rotpos, rotsel, pbdict
def _lowerCamelCase ( lowercase : str ) -> dict[str, str]:
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(lowercase , lowercase ):
_a = F'Plugboard setting isn\'t type string ({type(lowercase )})'
raise TypeError(lowercase )
elif len(lowercase ) % 2 != 0:
_a = F'Odd number of symbols ({len(lowercase )})'
raise Exception(lowercase )
elif pbstring == "":
return {}
pbstring.replace(" " , "" )
# Checks if all characters are unique
_a = set()
for i in pbstring:
if i not in abc:
_a = F'\'{i}\' not in list of symbols'
raise Exception(lowercase )
elif i in tmppbl:
_a = F'Duplicate symbol ({i})'
raise Exception(lowercase )
else:
tmppbl.add(lowercase )
del tmppbl
# Created the dictionary
_a = {}
for j in range(0 , len(lowercase ) - 1 , 2 ):
_a = pbstring[j + 1]
_a = pbstring[j]
return pb
def _lowerCamelCase ( lowercase : str , lowercase : RotorPositionT , lowercase : RotorSelectionT = (rotora, rotora, rotora) , lowercase : str = "" , ) -> str:
_a = text.upper()
_a , _a , _a = _validator(
lowercase , lowercase , plugb.upper() )
_a , _a , _a = rotor_position
_a , _a , _a = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
_a = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
_a = plugboard[symbol]
# rotor ra --------------------------
_a = abc.index(lowercase ) + rotorposa
_a = rotora[index % len(lowercase )]
# rotor rb --------------------------
_a = abc.index(lowercase ) + rotorposa
_a = rotora[index % len(lowercase )]
# rotor rc --------------------------
_a = abc.index(lowercase ) + rotorposa
_a = rotora[index % len(lowercase )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
_a = reflector[symbol]
# 2nd rotors
_a = abc[rotora.index(lowercase ) - rotorposa]
_a = abc[rotora.index(lowercase ) - rotorposa]
_a = abc[rotora.index(lowercase ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
_a = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(lowercase ):
_a = 0
rotorposa += 1
if rotorposa >= len(lowercase ):
_a = 0
rotorposa += 1
if rotorposa >= len(lowercase ):
_a = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(lowercase )
return "".join(lowercase )
if __name__ == "__main__":
lowerCAmelCase_ : Union[str, Any] = 'This is my Python script that emulates the Enigma machine from WWII.'
lowerCAmelCase_ : Optional[Any] = (1, 1, 1)
lowerCAmelCase_ : List[str] = 'pictures'
lowerCAmelCase_ : List[str] = (rotora, rotora, rotora)
lowerCAmelCase_ : List[Any] = enigma(message, rotor_pos, rotor_sel, pb)
print('Encrypted message:', en)
print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))
| 63 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ ):
return math.sqrt(lowercase__ ) * math.sqrt(lowercase__ ) == num
def _snake_case ( lowercase__ ):
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : List[Any] = n
while left <= right:
_lowerCamelCase : str = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_lowerCamelCase : str = mid - 1
else:
_lowerCamelCase : Optional[int] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str , snake_case__ : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = Path(snake_case__ )
_snake_case : int = Path(snake_case__ )
dest_dir.mkdir(exist_ok=snake_case__ )
for path in src_dir.iterdir():
_snake_case : List[Any] = [x.rstrip() for x in list(path.open().readlines() )][:n]
_snake_case : Tuple = dest_dir.joinpath(path.name )
print(snake_case__ )
dest_path.open("""w""" ).write("""\n""".join(snake_case__ ) )
if __name__ == "__main__":
fire.Fire(minify)
| 64 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(lowercase__ , lowercase__ ) or not all(
isinstance(lowercase__ , lowercase__ ) and len(lowercase__ ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
_lowerCamelCase : dict[str, Any] = {}
_lowerCamelCase : List[Any] = 'WORD_KEEPER'
for word in words:
_lowerCamelCase : Dict = trie
for c in word:
if c not in trie_node:
_lowerCamelCase : Any = {}
_lowerCamelCase : str = trie_node[c]
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Dict = len(lowercase__ )
# Dynamic programming method
@functools.cache
def is_breakable(lowercase__ ) -> bool:
if index == len_string:
return True
_lowerCamelCase : List[Any] = trie
for i in range(lowercase__ , lowercase__ ):
_lowerCamelCase : Any = trie_node.get(string[i] , lowercase__ )
if trie_node is None:
return False
if trie_node.get(lowercase__ , lowercase__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __A ) -> float:
'''simple docstring'''
UpperCAmelCase__ = 0.00
UpperCAmelCase__ = 0
for resistor in resistors:
if resistor <= 0:
UpperCAmelCase__ = f"""Resistor at index {index} has a negative or zero value!"""
raise ValueError(__A )
first_sum += 1 / float(__A )
index += 1
return 1 / first_sum
def lowerCAmelCase_ ( __A ) -> float:
'''simple docstring'''
UpperCAmelCase__ = 0.00
UpperCAmelCase__ = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
UpperCAmelCase__ = f"""Resistor at index {index} has a negative value!"""
raise ValueError(__A )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
if len(lowercase__ ) == 1:
return True
_lowerCamelCase : List[Any] = series[1] - series[0]
for index in range(len(lowercase__ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
_lowerCamelCase : Optional[int] = 0
for val in series:
answer += val
return answer / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( _lowerCAmelCase ):
'''simple docstring'''
_A : Dict = (IPNDMScheduler,)
_A : Any = (("""num_inference_steps""", 5_0),)
def lowerCAmelCase_ ( self: Union[str, Any] , **snake_case: str ) -> Optional[int]:
snake_case_ :Tuple = {"""num_train_timesteps""": 1_000}
config.update(**snake_case )
return config
def lowerCAmelCase_ ( self: List[str] , snake_case: int=0 , **snake_case: str ) -> Optional[Any]:
snake_case_ :List[Any] = dict(self.forward_default_kwargs )
snake_case_ :str = kwargs.pop("""num_inference_steps""" , snake_case )
snake_case_ :Optional[int] = self.dummy_sample
snake_case_ :Tuple = 0.1 * sample
snake_case_ :str = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
snake_case_ :int = self.get_scheduler_config(**snake_case )
snake_case_ :str = scheduler_class(**snake_case )
scheduler.set_timesteps(snake_case )
# copy over dummy past residuals
snake_case_ :str = dummy_past_residuals[:]
if time_step is None:
snake_case_ :Optional[Any] = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case )
snake_case_ :List[str] = scheduler_class.from_pretrained(snake_case )
new_scheduler.set_timesteps(snake_case )
# copy over dummy past residuals
snake_case_ :str = dummy_past_residuals[:]
snake_case_ :int = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
snake_case_ :Optional[int] = new_scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
snake_case_ :Optional[int] = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
snake_case_ :Tuple = new_scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCAmelCase_ ( self: str ) -> List[str]:
pass
def lowerCAmelCase_ ( self: Union[str, Any] , snake_case: str=0 , **snake_case: Dict ) -> Tuple:
snake_case_ :List[str] = dict(self.forward_default_kwargs )
snake_case_ :Optional[int] = kwargs.pop("""num_inference_steps""" , snake_case )
snake_case_ :Dict = self.dummy_sample
snake_case_ :Optional[Any] = 0.1 * sample
snake_case_ :Dict = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
snake_case_ :Tuple = self.get_scheduler_config()
snake_case_ :List[str] = scheduler_class(**snake_case )
scheduler.set_timesteps(snake_case )
# copy over dummy past residuals (must be after setting timesteps)
snake_case_ :Any = dummy_past_residuals[:]
if time_step is None:
snake_case_ :Dict = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case )
snake_case_ :int = scheduler_class.from_pretrained(snake_case )
# copy over dummy past residuals
new_scheduler.set_timesteps(snake_case )
# copy over dummy past residual (must be after setting timesteps)
snake_case_ :Union[str, Any] = dummy_past_residuals[:]
snake_case_ :List[Any] = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
snake_case_ :Union[str, Any] = new_scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
snake_case_ :Tuple = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
snake_case_ :List[str] = new_scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCAmelCase_ ( self: Optional[int] , **snake_case: Tuple ) -> str:
snake_case_ :int = self.scheduler_classes[0]
snake_case_ :List[Any] = self.get_scheduler_config(**snake_case )
snake_case_ :int = scheduler_class(**snake_case )
snake_case_ :str = 10
snake_case_ :Optional[Any] = self.dummy_model()
snake_case_ :int = self.dummy_sample_deter
scheduler.set_timesteps(snake_case )
for i, t in enumerate(scheduler.timesteps ):
snake_case_ :Tuple = model(snake_case , snake_case )
snake_case_ :Dict = scheduler.step(snake_case , snake_case , snake_case ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
snake_case_ :Optional[int] = model(snake_case , snake_case )
snake_case_ :Any = scheduler.step(snake_case , snake_case , snake_case ).prev_sample
return sample
def lowerCAmelCase_ ( self: str ) -> List[str]:
snake_case_ :str = dict(self.forward_default_kwargs )
snake_case_ :int = kwargs.pop("""num_inference_steps""" , snake_case )
for scheduler_class in self.scheduler_classes:
snake_case_ :List[str] = self.get_scheduler_config()
snake_case_ :Any = scheduler_class(**snake_case )
snake_case_ :List[str] = self.dummy_sample
snake_case_ :Dict = 0.1 * sample
if num_inference_steps is not None and hasattr(snake_case , """set_timesteps""" ):
scheduler.set_timesteps(snake_case )
elif num_inference_steps is not None and not hasattr(snake_case , """set_timesteps""" ):
snake_case_ :str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case_ :Union[str, Any] = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
snake_case_ :Optional[int] = dummy_past_residuals[:]
snake_case_ :Optional[int] = scheduler.timesteps[5]
snake_case_ :str = scheduler.timesteps[6]
snake_case_ :int = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
snake_case_ :List[str] = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
snake_case_ :str = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
snake_case_ :Optional[int] = scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCAmelCase_ ( self: Tuple ) -> Dict:
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=snake_case , time_step=snake_case )
def lowerCAmelCase_ ( self: int ) -> Optional[int]:
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=snake_case , time_step=snake_case )
def lowerCAmelCase_ ( self: Optional[Any] ) -> Optional[Any]:
snake_case_ :List[Any] = self.full_loop()
snake_case_ :str = torch.mean(torch.abs(snake_case ) )
assert abs(result_mean.item() - 2_540_529 ) < 10
| 66 |
"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase__ = 16
lowercase__ = 32
def _snake_case ( lowercase__ , lowercase__ = 16 , lowercase__ = "bert-base-cased" ):
_lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(lowercase__ )
_lowerCamelCase : Tuple = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowercase__ ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Union[str, Any] = 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
_lowerCamelCase : int = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCamelCase : Optional[int] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowercase__ ):
# 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(lowercase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(lowercase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
_lowerCamelCase : List[str] = DataLoader(
tokenized_datasets['train'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
_lowerCamelCase : int = DataLoader(
tokenized_datasets['validation'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ , lowercase__ ):
# Initialize accelerator
_lowerCamelCase : Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCamelCase : Optional[int] = config['lr']
_lowerCamelCase : Optional[int] = int(config['num_epochs'] )
_lowerCamelCase : Union[str, Any] = int(config['seed'] )
_lowerCamelCase : Optional[int] = int(config['batch_size'] )
_lowerCamelCase : Dict = args.model_name_or_path
set_seed(lowercase__ )
_lowerCamelCase, _lowerCamelCase : Optional[int] = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCamelCase : int = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
_lowerCamelCase : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowerCamelCase : Union[str, Any] = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
_lowerCamelCase : str = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
_lowerCamelCase : Tuple = 1
_lowerCamelCase : List[Any] = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowerCamelCase : Tuple = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
_lowerCamelCase : Any = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , 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.
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
_lowerCamelCase : Union[str, Any] = 0
# We also need to keep track of the stating epoch so files are named properly
_lowerCamelCase : Dict = 0
# Now we train the model
_lowerCamelCase : Dict = evaluate.load('glue' , 'mrpc' )
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
_lowerCamelCase : List[Any] = model(**lowercase__ )
_lowerCamelCase : int = outputs.loss
_lowerCamelCase : Dict = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_lowerCamelCase : Union[str, Any] = 0
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():
_lowerCamelCase : Optional[int] = model(**lowercase__ )
_lowerCamelCase : Dict = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowerCamelCase, _lowerCamelCase : List[str] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
_lowerCamelCase : Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCamelCase : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
_lowerCamelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , lowercase__ )
_lowerCamelCase : Tuple = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
_lowerCamelCase : str = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ):
_lowerCamelCase : Any = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=lowercase__ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=lowercase__ , )
parser.add_argument(
'--output_dir' , type=lowercase__ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=lowercase__ , default=lowercase__ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=lowercase__ , default=3 , help='Number of train epochs.' , )
_lowerCamelCase : Optional[Any] = parser.parse_args()
_lowerCamelCase : str = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class a__ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
lowerCamelCase : int =StableDiffusionPanoramaPipeline
lowerCamelCase : Dict =TEXT_TO_IMAGE_PARAMS
lowerCamelCase : Union[str, Any] =TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase : Dict =TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase : int =TEXT_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCamelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
__lowerCamelCase = DDIMScheduler()
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
__lowerCamelCase = CLIPTextModel(a )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : List[str] , a : Dict , a : Union[str, Any]=0 ):
"""simple docstring"""
__lowerCamelCase = torch.manual_seed(a )
__lowerCamelCase = {
'''prompt''': '''a photo of the dolomites''',
'''generator''': generator,
# Setting height and width to None to prevent OOMs on CPU.
'''height''': None,
'''width''': None,
'''num_inference_steps''': 1,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionPanoramaPipeline(**a )
__lowerCamelCase = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
__lowerCamelCase = self.get_dummy_inputs(a )
__lowerCamelCase = sd_pipe(**a ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.2_5e-3 )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionPanoramaPipeline(**a )
__lowerCamelCase = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
__lowerCamelCase = self.get_dummy_inputs(a )
__lowerCamelCase = '''french fries'''
__lowerCamelCase = sd_pipe(**a , negative_prompt=a )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionPanoramaPipeline(**a )
__lowerCamelCase = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
__lowerCamelCase = self.get_dummy_inputs(a )
__lowerCamelCase = sd_pipe(**a , view_batch_size=2 )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = EulerAncestralDiscreteScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' )
__lowerCamelCase = StableDiffusionPanoramaPipeline(**a )
__lowerCamelCase = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
__lowerCamelCase = self.get_dummy_inputs(a )
__lowerCamelCase = sd_pipe(**a ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = PNDMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , skip_prk_steps=a )
__lowerCamelCase = StableDiffusionPanoramaPipeline(**a )
__lowerCamelCase = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
__lowerCamelCase = self.get_dummy_inputs(a )
__lowerCamelCase = sd_pipe(**a ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : str , a : str=0 ):
"""simple docstring"""
__lowerCamelCase = torch.manual_seed(a )
__lowerCamelCase = {
'''prompt''': '''a photo of the dolomites''',
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase = '''stabilityai/stable-diffusion-2-base'''
__lowerCamelCase = DDIMScheduler.from_pretrained(a , subfolder='''scheduler''' )
__lowerCamelCase = StableDiffusionPanoramaPipeline.from_pretrained(a , scheduler=a , safety_checker=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
pipe.enable_attention_slicing()
__lowerCamelCase = self.get_inputs()
__lowerCamelCase = pipe(**a ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 20_48, 3)
__lowerCamelCase = np.array(
[
0.36_96_83_92,
0.27_02_53_72,
0.32_44_67_66,
0.28_37_93_87,
0.36_36_32_74,
0.30_73_33_47,
0.27_10_00_27,
0.27_05_41_25,
0.25_53_60_96,
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
__lowerCamelCase = StableDiffusionPanoramaPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-base''' , safety_checker=a )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
pipe.enable_attention_slicing()
__lowerCamelCase = self.get_inputs()
__lowerCamelCase = pipe(**a ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 20_48, 3)
__lowerCamelCase = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
__lowerCamelCase = 0
def callback_fn(a : int , a : int , a : torch.FloatTensor ) -> None:
__lowerCamelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__lowerCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 2_56)
__lowerCamelCase = latents[0, -3:, -3:, -1]
__lowerCamelCase = np.array(
[
0.18_68_18_69,
0.33_90_78_16,
0.5_36_12_76,
0.14_43_28_65,
-0.02_85_66_11,
-0.73_94_11_23,
0.23_39_79_87,
0.47_32_26_82,
-0.37_82_31_64,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
__lowerCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 2_56)
__lowerCamelCase = latents[0, -3:, -3:, -1]
__lowerCamelCase = np.array(
[
0.18_53_96_45,
0.33_98_72_48,
0.5_37_85_59,
0.14_43_71_42,
-0.02_45_52_61,
-0.7_33_83_17,
0.23_99_07_55,
0.47_35_62_72,
-0.3_78_65_05,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
__lowerCamelCase = False
__lowerCamelCase = '''stabilityai/stable-diffusion-2-base'''
__lowerCamelCase = DDIMScheduler.from_pretrained(a , subfolder='''scheduler''' )
__lowerCamelCase = StableDiffusionPanoramaPipeline.from_pretrained(a , scheduler=a , safety_checker=a )
__lowerCamelCase = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
pipe.enable_attention_slicing()
__lowerCamelCase = self.get_inputs()
pipe(**a , callback=a , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCamelCase = '''stabilityai/stable-diffusion-2-base'''
__lowerCamelCase = DDIMScheduler.from_pretrained(a , subfolder='''scheduler''' )
__lowerCamelCase = StableDiffusionPanoramaPipeline.from_pretrained(a , scheduler=a , safety_checker=a )
__lowerCamelCase = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__lowerCamelCase = self.get_inputs()
__lowerCamelCase = pipe(**a )
__lowerCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 67 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[Any] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 ) | 96 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'gpt_neox_japanese'
def __init__( self , lowercase=32000 , lowercase=2560 , lowercase=32 , lowercase=32 , lowercase=4 , lowercase="gelu" , lowercase=1.00 , lowercase=10000 , lowercase=2048 , lowercase=0.02 , lowercase=1e-5 , lowercase=True , lowercase=31996 , lowercase=31999 , lowercase=0.1 , lowercase=0.0 , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_multiple_size
A__ = hidden_act
A__ = rotary_pct
A__ = rotary_emb_base
A__ = initializer_range
A__ = layer_norm_eps
A__ = use_cache
A__ = attention_dropout
A__ = hidden_dropout
| 68 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""IBertForMaskedLM""",
"""IBertForMultipleChoice""",
"""IBertForQuestionAnswering""",
"""IBertForSequenceClassification""",
"""IBertForTokenClassification""",
"""IBertModel""",
"""IBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def UpperCAmelCase ( UpperCAmelCase ) -> Dict:
snake_case_ = []
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
f'stage{idx}.patch_embed.proj.weight',
) )
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
f'stage{idx}.patch_embed.proj.bias',
) )
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
f'stage{idx}.patch_embed.norm.weight',
) )
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
f'stage{idx}.patch_embed.norm.bias',
) )
return embed
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> Union[str, Any]:
snake_case_ = []
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
f'stage{idx}.blocks.{cnt}.attn.proj.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
f'stage{idx}.blocks.{cnt}.attn.proj.bias',
) )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias') )
return attention_weights
def UpperCAmelCase ( UpperCAmelCase ) -> Dict:
snake_case_ = []
token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token') )
return token
def UpperCAmelCase ( ) -> Optional[Any]:
snake_case_ = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
snake_case_ = 'imagenet-1k-id2label.json'
snake_case_ = 1000
snake_case_ = 'huggingface/label-files'
snake_case_ = num_labels
snake_case_ = json.load(open(cached_download(hf_hub_url(UpperCAmelCase , UpperCAmelCase , repo_type='dataset' ) ) , 'r' ) )
snake_case_ = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = snake_case_ = CvtConfig(num_labels=UpperCAmelCase , idalabel=UpperCAmelCase , labelaid=UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
snake_case_ = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
snake_case_ = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
snake_case_ = [2, 2, 20]
snake_case_ = [3, 12, 16]
snake_case_ = [192, 768, 1024]
snake_case_ = CvtForImageClassification(UpperCAmelCase )
snake_case_ = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
snake_case_ = image_size
snake_case_ = torch.load(UpperCAmelCase , map_location=torch.device('cpu' ) )
snake_case_ = OrderedDict()
snake_case_ = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
snake_case_ = list_of_state_dict + cls_token(UpperCAmelCase )
snake_case_ = list_of_state_dict + embeddings(UpperCAmelCase )
for cnt in range(config.depth[idx] ):
snake_case_ = list_of_state_dict + attention(UpperCAmelCase , UpperCAmelCase )
snake_case_ = list_of_state_dict + final()
for gg in list_of_state_dict:
print(UpperCAmelCase )
for i in range(len(UpperCAmelCase ) ):
snake_case_ = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(UpperCAmelCase )
model.save_pretrained(UpperCAmelCase )
image_processor.save_pretrained(UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=384,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__UpperCamelCase = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 69 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{sampling_rate}'''
_lowerCamelCase : str = '1'
_lowerCamelCase : str = 'f32le'
_lowerCamelCase : Union[str, Any] = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowercase__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
_lowerCamelCase : str = ffmpeg_process.communicate(lowercase__ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
_lowerCamelCase : List[Any] = output_stream[0]
_lowerCamelCase : Tuple = np.frombuffer(lowercase__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = "f32le" , ):
_lowerCamelCase : Optional[Any] = f'''{sampling_rate}'''
_lowerCamelCase : List[str] = '1'
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
_lowerCamelCase : Dict = platform.system()
if system == "Linux":
_lowerCamelCase : Optional[int] = 'alsa'
_lowerCamelCase : Optional[Any] = 'default'
elif system == "Darwin":
_lowerCamelCase : Optional[int] = 'avfoundation'
_lowerCamelCase : Any = ':0'
elif system == "Windows":
_lowerCamelCase : Tuple = 'dshow'
_lowerCamelCase : Tuple = 'default'
_lowerCamelCase : Optional[int] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
_lowerCamelCase : Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_lowerCamelCase : List[Any] = _ffmpeg_stream(lowercase__ , lowercase__ )
for item in iterator:
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = "f32le" , ):
if stream_chunk_s is not None:
_lowerCamelCase : int = stream_chunk_s
else:
_lowerCamelCase : Optional[Any] = chunk_length_s
_lowerCamelCase : Optional[Any] = ffmpeg_microphone(lowercase__ , lowercase__ , format_for_conversion=lowercase__ )
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = np.intaa
_lowerCamelCase : str = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : Any = np.floataa
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
_lowerCamelCase : Union[str, Any] = chunk_length_s / 6
_lowerCamelCase : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowercase__ , (int, float) ):
_lowerCamelCase : Any = [stride_length_s, stride_length_s]
_lowerCamelCase : Tuple = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_lowerCamelCase : Optional[Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_lowerCamelCase : List[Any] = datetime.datetime.now()
_lowerCamelCase : Optional[int] = datetime.timedelta(seconds=lowercase__ )
for item in chunk_bytes_iter(lowercase__ , lowercase__ , stride=(stride_left, stride_right) , stream=lowercase__ ):
# Put everything back in numpy scale
_lowerCamelCase : List[Any] = np.frombuffer(item['raw'] , dtype=lowercase__ )
_lowerCamelCase : int = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
_lowerCamelCase : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = False ):
_lowerCamelCase : int = B''
_lowerCamelCase, _lowerCamelCase : Dict = 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}''' )
_lowerCamelCase : str = 0
for raw in iterator:
acc += raw
if stream and len(lowercase__ ) < chunk_len:
_lowerCamelCase : Optional[int] = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowercase__ ) >= chunk_len:
# We are flushing the accumulator
_lowerCamelCase : str = (_stride_left, stride_right)
_lowerCamelCase : str = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
_lowerCamelCase : List[Any] = False
yield item
_lowerCamelCase : Optional[Any] = stride_left
_lowerCamelCase : str = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowercase__ ) > stride_left:
_lowerCamelCase : Optional[Any] = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
_lowerCamelCase : Tuple = False
yield item
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = 2**24 # 16Mo
try:
with subprocess.Popen(lowercase__ , stdout=subprocess.PIPE , bufsize=lowercase__ ) as ffmpeg_process:
while True:
_lowerCamelCase : Optional[Any] = ffmpeg_process.stdout.read(lowercase__ )
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 | 96 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A__ : str ={
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Union[str, Any] =['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[str] =[
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[Any] =[
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
A__ : Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 70 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """ctrl"""
lowerCamelCase__ = ["""past_key_values"""]
lowerCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=246534 , lowercase=256 , lowercase=1280 , lowercase=8192 , lowercase=48 , lowercase=16 , lowercase=0.1 , lowercase=0.1 , lowercase=1E-6 , lowercase=0.02 , lowercase=True , **lowercase , ):
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Dict = n_positions
_lowerCamelCase : Optional[int] = n_embd
_lowerCamelCase : str = n_layer
_lowerCamelCase : Union[str, Any] = n_head
_lowerCamelCase : Any = dff
_lowerCamelCase : int = resid_pdrop
_lowerCamelCase : Dict = embd_pdrop
_lowerCamelCase : Union[str, Any] = layer_norm_epsilon
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : str = use_cache
super().__init__(**lowercase ) | 96 | 0 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 71 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = data
_lowerCamelCase : Node | None = None
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : str = None
_lowerCamelCase : str = None
def __iter__( self ):
_lowerCamelCase : List[str] = self.head
while self.head:
yield node.data
_lowerCamelCase : Optional[int] = node.next
if node == self.head:
break
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join(str(lowercase ) for item in iter(self ) )
def A_ ( self , lowercase ):
self.insert_nth(len(self ) , lowercase )
def A_ ( self , lowercase ):
self.insert_nth(0 , lowercase )
def A_ ( self , lowercase , lowercase ):
if index < 0 or index > len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : List[Any] = Node(lowercase )
if self.head is None:
_lowerCamelCase : str = new_node # first node points itself
_lowerCamelCase : Union[str, Any] = new_node
elif index == 0: # insert at head
_lowerCamelCase : List[str] = self.head
_lowerCamelCase : str = new_node
else:
_lowerCamelCase : Union[str, Any] = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : Union[str, Any] = temp.next
_lowerCamelCase : List[str] = new_node
if index == len(self ) - 1: # insert at tail
_lowerCamelCase : Any = new_node
def A_ ( self ):
return self.delete_nth(0 )
def A_ ( self ):
return self.delete_nth(len(self ) - 1 )
def A_ ( self , lowercase = 0 ):
if not 0 <= index < len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : Any = self.head
if self.head == self.tail: # just one node
_lowerCamelCase : List[str] = None
elif index == 0: # delete head node
_lowerCamelCase : List[str] = self.tail.next.next
_lowerCamelCase : Optional[int] = self.head.next
else:
_lowerCamelCase : Dict = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : int = temp.next
_lowerCamelCase : Optional[int] = temp.next.next
if index == len(self ) - 1: # delete at tail
_lowerCamelCase : List[Any] = temp
return delete_node.data
def A_ ( self ):
return len(self ) == 0
def _snake_case ( ):
_lowerCamelCase : Union[str, Any] = CircularLinkedList()
assert len(lowercase__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(lowercase__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(lowercase__ ) == i
circular_linked_list.insert_nth(lowercase__ , i + 1 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""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 __snake_case :
def __init__( self : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any=1_3 , __lowerCAmelCase : Tuple=7 , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Tuple=9_9 , __lowerCAmelCase : Tuple=3_2 , __lowerCAmelCase : Any=2 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : List[str]=3_7 , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : str=0.1 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : Dict=5_1_2 , __lowerCAmelCase : List[Any]=1_6 , __lowerCAmelCase : List[Any]=2 , __lowerCAmelCase : List[str]=0.02 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : str=None , ):
"""simple docstring"""
_lowerCamelCase : Dict = parent
_lowerCamelCase : Any = 1_3
_lowerCamelCase : List[str] = 7
_lowerCamelCase : List[str] = True
_lowerCamelCase : Optional[int] = True
_lowerCamelCase : Dict = True
_lowerCamelCase : Dict = True
_lowerCamelCase : Union[str, Any] = 9_9
_lowerCamelCase : str = 3_2
_lowerCamelCase : Optional[int] = 2
_lowerCamelCase : Union[str, Any] = 4
_lowerCamelCase : Tuple = 3_7
_lowerCamelCase : Optional[int] = '''gelu'''
_lowerCamelCase : List[str] = 0.1
_lowerCamelCase : str = 0.1
_lowerCamelCase : List[str] = 5_1_2
_lowerCamelCase : Union[str, Any] = 1_6
_lowerCamelCase : Any = 2
_lowerCamelCase : Any = 0.02
_lowerCamelCase : int = 3
_lowerCamelCase : Optional[Any] = 4
_lowerCamelCase : Optional[Any] = None
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
_lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCamelCase : List[str] = None
if self.use_input_mask:
_lowerCamelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Optional[int] = None
if self.use_token_type_ids:
_lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCamelCase : Any = None
_lowerCamelCase : Any = None
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCamelCase : str = ids_tensor([self.batch_size] , self.num_choices )
_lowerCamelCase : Dict = 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=__lowerCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : str = TFRoFormerModel(config=__lowerCAmelCase )
_lowerCamelCase : str = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_lowerCamelCase : Dict = [input_ids, input_mask]
_lowerCamelCase : Optional[Any] = model(__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Tuple = True
_lowerCamelCase : Any = TFRoFormerForCausalLM(config=__lowerCAmelCase )
_lowerCamelCase : Tuple = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCamelCase : Union[str, Any] = model(__lowerCAmelCase )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Any = TFRoFormerForMaskedLM(config=__lowerCAmelCase )
_lowerCamelCase : Tuple = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCamelCase : Any = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = self.num_labels
_lowerCamelCase : str = TFRoFormerForSequenceClassification(config=__lowerCAmelCase )
_lowerCamelCase : Any = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCamelCase : Union[str, Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = self.num_choices
_lowerCamelCase : Union[str, Any] = TFRoFormerForMultipleChoice(config=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = tf.tile(tf.expand_dims(__lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
_lowerCamelCase : Union[str, Any] = tf.tile(tf.expand_dims(__lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
_lowerCamelCase : List[Any] = tf.tile(tf.expand_dims(__lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
_lowerCamelCase : str = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
_lowerCamelCase : Union[str, Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Tuple = self.num_labels
_lowerCamelCase : Dict = TFRoFormerForTokenClassification(config=__lowerCAmelCase )
_lowerCamelCase : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCamelCase : Optional[Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = TFRoFormerForQuestionAnswering(config=__lowerCAmelCase )
_lowerCamelCase : str = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCamelCase : Dict = model(__lowerCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
_lowerCamelCase : List[str] = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : str = config_and_inputs
_lowerCamelCase : Optional[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( _lowercase , _lowercase , unittest.TestCase):
snake_case__ : Tuple = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
snake_case__ : int = (
{
"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 {}
)
snake_case__ : Tuple = False
snake_case__ : Optional[Any] = False
def SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str] ):
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
_lowerCamelCase : Tuple = TFRoFormerModelTester(self )
_lowerCamelCase : Any = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
_lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Tuple = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(__lowerCAmelCase )
@require_tf
class __snake_case ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : Tuple = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
_lowerCamelCase : List[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
_lowerCamelCase : List[Any] = model(__lowerCAmelCase )[0]
# TODO Replace vocab size
_lowerCamelCase : int = 5_0_0_0_0
_lowerCamelCase : Optional[int] = [1, 6, vocab_size]
self.assertEqual(output.shape , __lowerCAmelCase )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_lowerCamelCase : str = tf.constant(
[
[
[-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
[-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
[-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __lowerCAmelCase , atol=1E-4 )
@require_tf
class __snake_case ( unittest.TestCase):
snake_case__ : Union[str, Any] = 1e-4
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
_lowerCamelCase : int = tf.constant([[4, 1_0]] )
_lowerCamelCase : Tuple = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_lowerCamelCase : Any = emba(input_ids.shape )
_lowerCamelCase : Union[str, Any] = tf.constant(
[[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]] )
tf.debugging.assert_near(__lowerCAmelCase , __lowerCAmelCase , atol=self.tolerance )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
_lowerCamelCase : List[str] = tf.constant(
[
[0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
[0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
[0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
] )
_lowerCamelCase : Any = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2 , embedding_dim=5_1_2 )
emba([2, 1_6, 5_1_2] )
_lowerCamelCase : Optional[Any] = emba.weight[:3, :5]
tf.debugging.assert_near(__lowerCAmelCase , __lowerCAmelCase , atol=self.tolerance )
@require_tf
class __snake_case ( unittest.TestCase):
snake_case__ : List[str] = 1e-4
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase : List[str] = 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
_lowerCamelCase : Optional[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
_lowerCamelCase : Optional[Any] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2 , embedding_dim=6_4 )
_lowerCamelCase : Optional[Any] = embed_positions([2, 1_6, 7_6_8] )[None, None, :, :]
_lowerCamelCase , _lowerCamelCase : str = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = tf.constant(
[
[0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
[-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
[-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
[-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
[0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
[3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
] )
_lowerCamelCase : Any = tf.constant(
[
[0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
[0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
[1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
[2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
[-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
[-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __lowerCAmelCase , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __lowerCAmelCase , atol=self.tolerance )
| 72 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowercase__ = get_logger(__name__)
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = """dummy_data"""
lowerCamelCase__ = """datasets"""
lowerCamelCase__ = False
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = False , lowercase = True , lowercase = None , ):
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Dict = dataset_name
_lowerCamelCase : Union[str, Any] = cache_dir
_lowerCamelCase : Dict = use_local_dummy_data
_lowerCamelCase : Tuple = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : Any = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : str = str(lowercase )
# to be downloaded
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : int = None
@property
def A_ ( self ):
if self._dummy_file is None:
_lowerCamelCase : Tuple = self.download_dummy_data()
return self._dummy_file
@property
def A_ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def A_ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def A_ ( self ):
_lowerCamelCase : List[str] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : int = cached_path(
lowercase , cache_dir=self.cache_dir , extract_compressed_file=lowercase , force_extract=lowercase )
return os.path.join(lowercase , self.dummy_file_name )
@property
def A_ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def A_ ( self ):
if self._bucket_url is None:
_lowerCamelCase : List[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def A_ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def A_ ( self , lowercase , *lowercase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Union[str, Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Union[str, Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowercase , lowercase ):
return self.create_dummy_data_dict(lowercase , lowercase )
elif isinstance(lowercase , (list, tuple) ):
return self.create_dummy_data_list(lowercase , lowercase )
else:
return self.create_dummy_data_single(lowercase , lowercase )
def A_ ( self , lowercase , *lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , *lowercase , **lowercase ):
return path
def A_ ( self ):
return {}
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowercase , lowercase ):
for single_url in single_urls:
download_callback(lowercase )
else:
_lowerCamelCase : List[Any] = single_urls
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowercase , lowercase ):
_lowerCamelCase : List[Any] = [os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : Optional[int] = single_urls
_lowerCamelCase : List[Any] = os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) )
_lowerCamelCase : int = value
# make sure that values are unique
if all(isinstance(lowercase , lowercase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : List[str] = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , lowercase ) ) for url in data_url )
_lowerCamelCase : int = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : List[str] = [data_url[0]] * len(lowercase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : str = os.path.join(lowercase , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(lowercase )
return dummy_data_list
def A_ ( self , lowercase , lowercase ):
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Tuple = os.path.join(lowercase , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(lowercase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self , lowercase ):
def _iter_archive_members(lowercase ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : str = Path(self.dummy_file ).parent
_lowerCamelCase : Union[str, Any] = path.relative_to(lowercase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : List[str] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowercase )
_lowerCamelCase : Optional[int] = Path(lowercase )
_lowerCamelCase : Dict = _iter_archive_members(lowercase ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(lowercase ).as_posix(), file_path.open('rb' )
def A_ ( self , lowercase ):
if not isinstance(lowercase , lowercase ):
_lowerCamelCase : List[str] = [paths]
for path in paths:
if os.path.isfile(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(lowercase ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(lowercase , lowercase ) | 96 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Union[str, Any] = '''mgp-str'''
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : List[str]=[3_2, 1_2_8] ,SCREAMING_SNAKE_CASE__ : Any=4 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=3 ,SCREAMING_SNAKE_CASE__ : Tuple=2_7 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=3_8 ,SCREAMING_SNAKE_CASE__ : Tuple=5_0_2_5_7 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=3_0_5_2_2 ,SCREAMING_SNAKE_CASE__ : Optional[int]=7_6_8 ,SCREAMING_SNAKE_CASE__ : Dict=1_2 ,SCREAMING_SNAKE_CASE__ : Optional[int]=1_2 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=4.0 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=True ,SCREAMING_SNAKE_CASE__ : List[Any]=False ,SCREAMING_SNAKE_CASE__ : int=1E-5 ,SCREAMING_SNAKE_CASE__ : List[str]=0.0 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0 ,SCREAMING_SNAKE_CASE__ : Any=0.0 ,SCREAMING_SNAKE_CASE__ : int=False ,SCREAMING_SNAKE_CASE__ : Optional[Any]=0.02 ,**SCREAMING_SNAKE_CASE__ : int ,):
super().__init__(**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Tuple = image_size
__lowerCamelCase : Optional[Any] = patch_size
__lowerCamelCase : Any = num_channels
__lowerCamelCase : Tuple = max_token_length
__lowerCamelCase : Tuple = num_character_labels
__lowerCamelCase : List[str] = num_bpe_labels
__lowerCamelCase : int = num_wordpiece_labels
__lowerCamelCase : Any = hidden_size
__lowerCamelCase : int = num_hidden_layers
__lowerCamelCase : int = num_attention_heads
__lowerCamelCase : List[str] = mlp_ratio
__lowerCamelCase : List[str] = distilled
__lowerCamelCase : Optional[Any] = layer_norm_eps
__lowerCamelCase : Any = drop_rate
__lowerCamelCase : Tuple = qkv_bias
__lowerCamelCase : Any = attn_drop_rate
__lowerCamelCase : int = drop_path_rate
__lowerCamelCase : str = output_aa_attentions
__lowerCamelCase : int = initializer_range
| 73 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
_lowerCamelCase, _lowerCamelCase : Optional[Any] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
_lowerCamelCase : Union[str, Any] = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(lowercase__ , i + t , (lowercase__) )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
if __name__ == "__main__":
lowercase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowercase__ = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted)) | 96 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure) | 74 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""image_processor""", """tokenizer"""]
lowerCamelCase__ = """BlipImageProcessor"""
lowerCamelCase__ = """AutoTokenizer"""
def __init__( self , lowercase , lowercase , lowercase ):
super().__init__(lowercase , lowercase )
# add QFormer tokenizer
_lowerCamelCase : int = qformer_tokenizer
def __call__( self , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ):
if images is None and text is None:
raise ValueError('You have to specify at least images or text.' )
_lowerCamelCase : int = BatchFeature()
if text is not None:
_lowerCamelCase : List[str] = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
encoding.update(lowercase )
_lowerCamelCase : List[str] = self.qformer_tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
_lowerCamelCase : List[Any] = qformer_text_encoding.pop('input_ids' )
_lowerCamelCase : Tuple = qformer_text_encoding.pop('attention_mask' )
if images is not None:
_lowerCamelCase : int = self.image_processor(lowercase , return_tensors=lowercase )
encoding.update(lowercase )
return encoding
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.decode(*lowercase , **lowercase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = self.tokenizer.model_input_names
_lowerCamelCase : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def A_ ( self , lowercase , **lowercase ):
if os.path.isfile(lowercase ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(lowercase , exist_ok=lowercase )
_lowerCamelCase : Optional[Any] = os.path.join(lowercase , 'qformer_tokenizer' )
self.qformer_tokenizer.save_pretrained(lowercase )
return super().save_pretrained(lowercase , **lowercase )
@classmethod
def A_ ( cls , lowercase , **lowercase ):
_lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(lowercase , subfolder='qformer_tokenizer' )
_lowerCamelCase : Dict = cls._get_arguments_from_pretrained(lowercase , **lowercase )
args.append(lowercase )
return cls(*lowercase ) | 96 | 0 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def a_ ( __snake_case : int , __snake_case : Optional[int] , __snake_case : Optional[Any]=None , **__snake_case : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[x.strip() for x in open(__snake_case ).readlines()]
lowerCamelCase_ =[x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
lowerCamelCase_ =calculate_rouge(__snake_case , __snake_case , **__snake_case )
if save_path is not None:
save_json(__snake_case , __snake_case , indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 75 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowercase__ = logging.getLogger()
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[Any] = {}
_lowerCamelCase : List[Any] = os.path.join(lowercase__ , 'all_results.json' )
if os.path.exists(lowercase__ ):
with open(lowercase__ , 'r' ) as f:
_lowerCamelCase : List[Any] = json.load(lowercase__ )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
lowercase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def A_ ( self ):
import xla_spawn
_lowerCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
_lowerCamelCase : List[Any] = F'''
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(lowercase , 'argv' , lowercase ):
_lowerCamelCase : Dict = time()
xla_spawn.main()
_lowerCamelCase : Any = time()
_lowerCamelCase : Optional[int] = get_results(lowercase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def A_ ( self ):
import xla_spawn
_lowerCamelCase : Tuple = '\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n '.split()
with patch.object(lowercase , 'argv' , lowercase ):
xla_spawn.main() | 96 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Union[str, Any] , a : Optional[int] , a : List[str] , a : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = dataset
SCREAMING_SNAKE_CASE : str = process
SCREAMING_SNAKE_CASE : List[str] = params
def __len__( self : int ) -> int:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self : Union[str, Any] , a : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.dataset[i]
SCREAMING_SNAKE_CASE : Tuple = self.process(a , **self.params )
return processed
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Optional[int] , a : Any , a : Any , a : Union[str, Any] , a : Union[str, Any]=None ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = loader
SCREAMING_SNAKE_CASE : Union[str, Any] = infer
SCREAMING_SNAKE_CASE : List[str] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : List[Any] = loader_batch_size
# Internal bookkeeping
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : Union[str, Any] = None
def __len__( self : List[Any] ) -> List[str]:
"""simple docstring"""
return len(self.loader )
def __iter__( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(self.loader )
return self
def __UpperCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
SCREAMING_SNAKE_CASE : str = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
SCREAMING_SNAKE_CASE : str = {}
for k, element in self._loader_batch_data.items():
if isinstance(a , a ):
# Convert ModelOutput to tuple first
SCREAMING_SNAKE_CASE : Optional[Any] = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE : str = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
SCREAMING_SNAKE_CASE : Union[str, Any] = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(a , a ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE : Dict = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
SCREAMING_SNAKE_CASE : Dict = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
SCREAMING_SNAKE_CASE : str = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
SCREAMING_SNAKE_CASE : Optional[Any] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
SCREAMING_SNAKE_CASE : Optional[Any] = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
SCREAMING_SNAKE_CASE : Optional[int] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
SCREAMING_SNAKE_CASE : List[Any] = self._loader_batch_data.__class__(a )
self._loader_batch_index += 1
return result
def __UpperCamelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
SCREAMING_SNAKE_CASE : Union[str, Any] = next(self.iterator )
SCREAMING_SNAKE_CASE : List[Any] = self.infer(a , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(a , torch.Tensor ):
SCREAMING_SNAKE_CASE : List[Any] = processed
else:
SCREAMING_SNAKE_CASE : Optional[Any] = list(processed.keys() )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = processed[key]
if isinstance(a , a ):
SCREAMING_SNAKE_CASE : List[str] = len(a )
else:
SCREAMING_SNAKE_CASE : int = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
SCREAMING_SNAKE_CASE : Union[str, Any] = observed_batch_size
# Setting internal index to unwrap the batch
SCREAMING_SNAKE_CASE : List[Any] = processed
SCREAMING_SNAKE_CASE : Optional[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Any , a : Optional[int] , a : Union[str, Any] , a : List[Any] , a : List[str]=None ) -> Dict:
"""simple docstring"""
super().__init__(a , a , a )
def __iter__( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(self.loader )
SCREAMING_SNAKE_CASE : List[Any] = None
return self
def __UpperCamelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
if self.subiterator is None:
SCREAMING_SNAKE_CASE : int = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
SCREAMING_SNAKE_CASE : Union[str, Any] = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
SCREAMING_SNAKE_CASE : Any = self.infer(next(self.iterator ) , **self.params )
SCREAMING_SNAKE_CASE : Union[str, Any] = next(self.subiterator )
return processed
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __iter__( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = iter(self.loader )
return self
def __UpperCamelCase ( self : str ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : List[str] = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
SCREAMING_SNAKE_CASE : Any = self.loader_batch_item()
SCREAMING_SNAKE_CASE : List[str] = item.pop("is_last" )
accumulator.append(a )
if is_last:
return accumulator
while not is_last:
SCREAMING_SNAKE_CASE : Optional[int] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(a , torch.Tensor ):
SCREAMING_SNAKE_CASE : Optional[int] = processed
else:
SCREAMING_SNAKE_CASE : List[Any] = list(processed.keys() )[0]
SCREAMING_SNAKE_CASE : List[Any] = processed[key]
if isinstance(a , a ):
SCREAMING_SNAKE_CASE : Any = len(a )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
SCREAMING_SNAKE_CASE : Union[str, Any] = observed_batch_size
SCREAMING_SNAKE_CASE : Optional[Any] = processed
SCREAMING_SNAKE_CASE : Dict = 0
while self._loader_batch_index < self.loader_batch_size:
SCREAMING_SNAKE_CASE : Tuple = self.loader_batch_item()
SCREAMING_SNAKE_CASE : Dict = item.pop("is_last" )
accumulator.append(a )
if is_last:
return accumulator
else:
SCREAMING_SNAKE_CASE : List[Any] = processed
SCREAMING_SNAKE_CASE : Optional[Any] = item.pop("is_last" )
accumulator.append(a )
return accumulator
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : List[str] , a : Dataset , a : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = dataset
SCREAMING_SNAKE_CASE : List[Any] = key
def __len__( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self : Dict , a : List[Any] ) -> List[str]:
"""simple docstring"""
return self.dataset[i][self.key]
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Optional[int] , a : Dataset , a : str , a : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = dataset
SCREAMING_SNAKE_CASE : Dict = keya
SCREAMING_SNAKE_CASE : Optional[int] = keya
def __len__( self : Optional[Any] ) -> Any:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self : Tuple , a : Optional[int] ) -> List[str]:
"""simple docstring"""
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]} | 76 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( lowercase__ , lowercase__ ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase__ , lowercase__ ) ) )
def _snake_case ( lowercase__ , lowercase__ ):
if dataset.ndim != value_array.ndim:
_lowerCamelCase : Tuple = (
'Wrong input data\'s dimensions... '
f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(lowercase__ )
try:
if dataset.shape[1] != value_array.shape[1]:
_lowerCamelCase : Optional[int] = (
'Wrong input data\'s shape... '
f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(lowercase__ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('Wrong shape' )
if dataset.dtype != value_array.dtype:
_lowerCamelCase : int = (
'Input data have different datatype... '
f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(lowercase__ )
_lowerCamelCase : Optional[int] = []
for value in value_array:
_lowerCamelCase : Tuple = euclidean(lowercase__ , dataset[0] )
_lowerCamelCase : Union[str, Any] = dataset[0].tolist()
for dataset_value in dataset[1:]:
_lowerCamelCase : Optional[Any] = euclidean(lowercase__ , lowercase__ )
if dist > temp_dist:
_lowerCamelCase : List[Any] = temp_dist
_lowerCamelCase : List[str] = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( lowercase__ , lowercase__ ):
return np.dot(lowercase__ , lowercase__ ) / (norm(lowercase__ ) * norm(lowercase__ ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
import torch
from transformers import AutoModel
class UpperCAmelCase_ ( torch.nn.Module):
def __init__( self , a="sayef/fsner-bert-base-uncased" ) -> Tuple:
super(a , self ).__init__()
lowercase__ : str = AutoModel.from_pretrained(a , return_dict=a )
lowercase__ : Any = torch.nn.CosineSimilarity(3 , 1e-08 )
lowercase__ : Union[str, Any] = torch.nn.Softmax(dim=1 )
def _UpperCAmelCase ( self , **a ) -> List[str]:
return self.bert(**a ).last_hidden_state
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
return token_embeddings.sum(2 , keepdim=a )
def _UpperCAmelCase ( self , a , a , a=1 ) -> str:
return self.softmax(T * self.cos(a , a ) )
def _UpperCAmelCase ( self , a , a ) -> str:
lowercase__ : Union[str, Any] = W_supports['sizes'].tolist()
lowercase__ : str = W_supports['start_token_id'].item()
lowercase__ : str = W_supports['end_token_id'].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
lowercase__ : Any = self.BERT(**a )
lowercase__ : Any = self.BERT(**a )
lowercase__ : Tuple = None
lowercase__ : Dict = None
lowercase__ : str = W_supports['input_ids'] == start_token_id
lowercase__ : List[str] = W_supports['input_ids'] == end_token_id
for i, size in enumerate(a ):
if i == 0:
lowercase__ : int = 0
else:
lowercase__ : Dict = support_sizes[i - 1]
lowercase__ : Dict = S[s : s + size][start_token_masks[s : s + size]]
lowercase__ : int = S[s : s + size][end_token_masks[s : s + size]]
lowercase__ : Any = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
lowercase__ : int = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
lowercase__ : Tuple = torch.vstack((p_starts, p_start) )
lowercase__ : Tuple = torch.vstack((p_ends, p_end) )
else:
lowercase__ : Optional[int] = p_start
lowercase__ : Optional[int] = p_end
return p_starts, p_ends
| 77 |
"""simple docstring"""
import socket
def _snake_case ( ):
_lowerCamelCase : List[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase : Union[str, Any] = socket.gethostname()
_lowerCamelCase : List[Any] = 12312
sock.connect((host, port) )
sock.send(B'Hello server!' )
with open('Received_file' , 'wb' ) as out_file:
print('File opened' )
print('Receiving data...' )
while True:
_lowerCamelCase : int = sock.recv(1024 )
if not data:
break
out_file.write(lowercase__ )
print('Successfully received the file' )
sock.close()
print('Connection closed' )
if __name__ == "__main__":
main() | 96 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case_ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
"""processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
"""tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ["""LayoutLMv2TokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ["""LayoutLMv2FeatureExtractor"""]
snake_case_ = ["""LayoutLMv2ImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LayoutLMv2ForQuestionAnswering""",
"""LayoutLMv2ForSequenceClassification""",
"""LayoutLMv2ForTokenClassification""",
"""LayoutLMv2Layer""",
"""LayoutLMv2Model""",
"""LayoutLMv2PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase__ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
"""
lowercase__ = """\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""
lowercase__ = """
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for 'record': list of question-answer dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'prediction_text': the predicted answer text
- for 'multirc': list of question-answer dictionaries with the following keys:
- 'idx': index of the question-answer pair as specified by the dataset
- 'prediction': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for 'record': list of question-answers dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'answers': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for 'record':
- 'exact_match': Exact match between answer and gold answer
- 'f1': F1 score
- for 'multirc':
- 'exact_match': Exact match between answer and gold answer
- 'f1_m': Per-question macro-F1 score
- 'f1_a': Average F1 score over all answers
- for 'axb':
'matthews_correlation': Matthew Correlation
- for 'cb':
- 'accuracy': Accuracy
- 'f1': F1 score
- for all others:
- 'accuracy': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'record')
>>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
>>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
>>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def _snake_case ( lowercase__ , lowercase__ ):
return float((preds == labels).mean() )
def _snake_case ( lowercase__ , lowercase__ , lowercase__="binary" ):
_lowerCamelCase : str = simple_accuracy(lowercase__ , lowercase__ )
_lowerCamelCase : Any = float(fa_score(y_true=lowercase__ , y_pred=lowercase__ , average=lowercase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Any = {}
for id_pred, label in zip(lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
_lowerCamelCase : Union[str, Any] = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
_lowerCamelCase : Optional[Any] = [(pred, label)]
_lowerCamelCase, _lowerCamelCase : Optional[int] = [], []
for question, preds_labels in question_map.items():
_lowerCamelCase, _lowerCamelCase : Tuple = zip(*lowercase__ )
_lowerCamelCase : List[str] = fa_score(y_true=lowercase__ , y_pred=lowercase__ , average='macro' )
fas.append(lowercase__ )
_lowerCamelCase : int = int(sum(pred == label for pred, label in preds_labels ) == len(lowercase__ ) )
ems.append(lowercase__ )
_lowerCamelCase : Optional[Any] = float(sum(lowercase__ ) / len(lowercase__ ) )
_lowerCamelCase : Optional[int] = sum(lowercase__ ) / len(lowercase__ )
_lowerCamelCase : List[Any] = float(fa_score(y_true=lowercase__ , y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def A_ ( self ):
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None , )
def A_ ( self ):
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"prediction_text": datasets.Value('string' ),
},
"references": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"answers": datasets.Sequence(datasets.Value('string' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64' ),
"paragraph": datasets.Value('int64' ),
"question": datasets.Value('int64' ),
},
"prediction": datasets.Value('int64' ),
},
"references": datasets.Value('int64' ),
}
else:
return {
"predictions": datasets.Value('int64' ),
"references": datasets.Value('int64' ),
}
def A_ ( self , lowercase , lowercase ):
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowercase , lowercase )}
elif self.config_name == "cb":
return acc_and_fa(lowercase , lowercase , fa_avg='macro' )
elif self.config_name == "record":
_lowerCamelCase : List[str] = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
_lowerCamelCase : Union[str, Any] = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(lowercase , lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowercase , lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowercase , lowercase )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' ) | 96 | 0 |
'''simple docstring'''
import math
def __lowercase ( __lowercase ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__lowercase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowercase ( __lowercase = 0.1 ) -> int:
'''simple docstring'''
_A = 3
_A = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__lowercase )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = DDIMPipeline
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCamelCase__ = False
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
_lowerCamelCase : List[str] = DDIMScheduler()
_lowerCamelCase : Optional[int] = {'unet': unet, 'scheduler': scheduler}
return components
def A_ ( self , lowercase , lowercase=0 ):
if str(lowercase ).startswith('mps' ):
_lowerCamelCase : Dict = torch.manual_seed(lowercase )
else:
_lowerCamelCase : List[str] = torch.Generator(device=lowercase ).manual_seed(lowercase )
_lowerCamelCase : Tuple = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def A_ ( self ):
_lowerCamelCase : Any = 'cpu'
_lowerCamelCase : Tuple = self.get_dummy_components()
_lowerCamelCase : Optional[Any] = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : str = self.get_dummy_inputs(lowercase )
_lowerCamelCase : int = pipe(**lowercase ).images
_lowerCamelCase : Any = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
_lowerCamelCase : Tuple = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
_lowerCamelCase : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase , 1E-3 )
def A_ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
_lowerCamelCase : Optional[Any] = 'google/ddpm-cifar10-32'
_lowerCamelCase : Optional[Any] = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : Dict = DDIMScheduler()
_lowerCamelCase : Dict = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddim.to(lowercase )
ddim.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : List[str] = torch.manual_seed(0 )
_lowerCamelCase : str = ddim(generator=lowercase , eta=0.0 , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowerCamelCase : List[Any] = np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : Optional[int] = 'google/ddpm-ema-bedroom-256'
_lowerCamelCase : str = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : str = DDIMScheduler.from_pretrained(lowercase )
_lowerCamelCase : Optional[int] = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddpm.to(lowercase )
ddpm.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Tuple = torch.manual_seed(0 )
_lowerCamelCase : int = ddpm(generator=lowercase , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_lowerCamelCase : str = np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 96 | 0 |
'''simple docstring'''
from ....utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
class lowercase_ ( a__ ):
def __init__( self , a , a=None , a=20_48 ):
UpperCamelCase__ = config.__dict__
UpperCamelCase__ = modal_hidden_size
if num_labels:
UpperCamelCase__ = num_labels
| 80 |
"""simple docstring"""
# Imports
import numpy as np
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
def A_ ( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
if red is not None:
_lowerCamelCase : Optional[int] = red
if green is not None:
_lowerCamelCase : Optional[Any] = green
if blue is not None:
_lowerCamelCase : Tuple = blue
if red_edge is not None:
_lowerCamelCase : Optional[Any] = red_edge
if nir is not None:
_lowerCamelCase : Union[str, Any] = nir
return True
def A_ ( self , lowercase="" , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
_lowerCamelCase : str = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def A_ ( self ):
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def A_ ( self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def A_ ( self ):
return self.nir * (self.red / (self.green**2))
def A_ ( self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def A_ ( self ):
return (self.nir - self.red) / (self.nir + self.red)
def A_ ( self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def A_ ( self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def A_ ( self ):
return (self.nir - self.green) / (self.nir + self.green)
def A_ ( self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def A_ ( self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def A_ ( self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def A_ ( self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def A_ ( self , lowercase=0.08 , lowercase=1.22 , lowercase=0.03 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def A_ ( self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def A_ ( self ):
return (self.nir / self.green) - 1
def A_ ( self ):
return (self.nir / self.redEdge) - 1
def A_ ( self ):
return (self.red - self.blue) / self.red
def A_ ( self ):
_lowerCamelCase : Any = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def A_ ( self ):
return self.nir - self.green
def A_ ( self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def A_ ( self ):
_lowerCamelCase : Any = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)
def A_ ( self , lowercase=0.16 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def A_ ( self , lowercase=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def A_ ( self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def A_ ( self , lowercase=None , lowercase=None ):
return (self.nir - b) / (a * self.red)
def A_ ( self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def A_ ( self ):
return (self.red + self.green + self.blue) / 30.5
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def A_ ( self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def A_ ( self ):
return self.green / (self.nir + self.red + self.green)
def A_ ( self ):
return self.nir / (self.nir + self.red + self.green)
def A_ ( self ):
return self.red / (self.nir + self.red + self.green)
def A_ ( self ):
return (self.green - self.red) / (self.green + self.red)
def A_ ( self ):
return (self.red - self.green) / (self.red + self.green)
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
_lowerCamelCase : Dict = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def A_ ( self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def A_ ( self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge) | 96 | 0 |
"""simple docstring"""
import cva
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , __A , __A ) -> int:
if k in (0.04, 0.06):
a =k
a =window_size
else:
raise ValueError('''invalid k value''' )
def __str__( self ) -> str:
return str(self.k )
def SCREAMING_SNAKE_CASE ( self , __A ) -> tuple[cva.Mat, list[list[int]]]:
a =cva.imread(__A , 0 )
a , a =img.shape
a =[]
a =img.copy()
a =cva.cvtColor(__A , cva.COLOR_GRAY2RGB )
a , a =np.gradient(__A )
a =dx**2
a =dy**2
a =dx * dy
a =0.04
a =self.window_size // 2
for y in range(__A , h - offset ):
for x in range(__A , w - offset ):
a =ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
a =iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
a =ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
a =(wxx * wyy) - (wxy**2)
a =wxx + wyy
a =det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
lowerCamelCase_ : List[Any] = HarrisCorner(0.04, 3)
lowerCamelCase_ , lowerCamelCase_ : int = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img) | 81 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self , lowercase , lowercase=768 ):
super().__init__(lowercase )
_lowerCamelCase : Any = proj_size
_lowerCamelCase : Dict = CLIPVisionModel(lowercase )
_lowerCamelCase : List[str] = PaintByExampleMapper(lowercase )
_lowerCamelCase : Optional[Any] = nn.LayerNorm(config.hidden_size )
_lowerCamelCase : int = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
_lowerCamelCase : str = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def A_ ( self , lowercase , lowercase=False ):
_lowerCamelCase : Union[str, Any] = self.model(pixel_values=lowercase )
_lowerCamelCase : int = clip_output.pooler_output
_lowerCamelCase : str = self.mapper(latent_states[:, None] )
_lowerCamelCase : List[Any] = self.final_layer_norm(lowercase )
_lowerCamelCase : Dict = self.proj_out(lowercase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , lowercase ):
super().__init__()
_lowerCamelCase : Tuple = (config.num_hidden_layers + 1) // 5
_lowerCamelCase : int = config.hidden_size
_lowerCamelCase : Optional[Any] = 1
_lowerCamelCase : str = nn.ModuleList(
[
BasicTransformerBlock(lowercase , lowercase , lowercase , activation_fn='gelu' , attention_bias=lowercase )
for _ in range(lowercase )
] )
def A_ ( self , lowercase ):
for block in self.blocks:
_lowerCamelCase : Tuple = block(lowercase )
return hidden_states | 96 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowerCamelCase__ ) , '''Tatoeba directory does not exist.''' )
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
self.resolver.convert_models(["""heb-eng"""] )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.resolver.write_model_card("""opus-mt-he-en""" , dry_run=_snake_case )
assert mmeta["long_pair"] == "heb-eng"
| 82 |
"""simple docstring"""
lowercase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowercase__ = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : List[Any] = from_type.lower().strip('s' )
_lowerCamelCase : List[Any] = to_type.lower().strip('s' )
_lowerCamelCase : Optional[int] = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
_lowerCamelCase : Any = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
if from_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Tuple = (
f'''Invalid \'from_type\' value: {from_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
if to_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Any = (
f'''Invalid \'to_type\' value: {to_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
_lowerCamelCase : List[Any] = METRIC_CONVERSION[from_sanitized]
_lowerCamelCase : int = METRIC_CONVERSION[to_sanitized]
_lowerCamelCase : List[str] = 1
if from_exponent > to_exponent:
_lowerCamelCase : List[str] = from_exponent - to_exponent
else:
_lowerCamelCase : List[Any] = -(to_exponent - from_exponent)
return value * pow(10 , lowercase__ )
if __name__ == "__main__":
from doctest import testmod
testmod() | 96 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class lowercase__ :
def __init__( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : str = {}
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : str ):
'''simple docstring'''
_UpperCamelCase : List[Any] = {}
def UpperCamelCase_ ( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : str ,lowerCamelCase__ : float ):
'''simple docstring'''
if nodea not in self.connections:
self.add_node(lowerCamelCase__ )
if nodea not in self.connections:
self.add_node(lowerCamelCase__ )
_UpperCamelCase : str = probability
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return list(self.connections )
def UpperCamelCase_ ( self : Dict ,lowerCamelCase__ : str ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = 0
_UpperCamelCase : List[Any] = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : Dict = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : Union[str, Any] = Counter(graph.get_nodes() )
_UpperCamelCase : Dict = start
for _ in range(UpperCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = graph.transition(UpperCAmelCase_ )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 83 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMSNModel""",
"""ViTMSNForImageClassification""",
"""ViTMSNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _snake_case ( ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
lowerCAmelCase_ :Tuple = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowercase__ )
# Let's go
lowerCAmelCase_ :Tuple = parser.parse_args()
if not hasattr(lowercase__ , """func""" ):
parser.print_help()
exit(1 )
# Run
lowerCAmelCase_ :List[str] = args.func(lowercase__ )
service.run()
if __name__ == "__main__":
main()
| 84 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : List[Any] = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Tuple = boundary[0]
_lowerCamelCase : Dict = boundary[1]
_lowerCamelCase : List[Any] = make_points(lowercase__ , lowercase__ , lowercase__ )
_lowerCamelCase : List[Any] = 0.0
y += (h / 2.0) * f(lowercase__ )
for i in x_i:
# print(i)
y += h * f(lowercase__ )
y += (h / 2.0) * f(lowercase__ )
return y
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : str = a + h
while x < (b - h):
yield x
_lowerCamelCase : int = x + h
def _snake_case ( lowercase__ ): # enter your function here
_lowerCamelCase : Optional[Any] = (x - 0) * (x - 0)
return y
def _snake_case ( ):
_lowerCamelCase : int = 0.0 # Lower bound of integration
_lowerCamelCase : Optional[int] = 1.0 # Upper bound of integration
_lowerCamelCase : List[str] = 1_0.0 # define number of steps or resolution
_lowerCamelCase : List[Any] = [a, b] # define boundary of integration
_lowerCamelCase : Optional[Any] = method_a(lowercase__ , lowercase__ )
print(f'''y = {y}''' )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
class _snake_case ( lowercase_ ):
def __init__( self , *a__ , **a__ ) -> None:
'''simple docstring'''
warnings.warn(
"The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use DeformableDetrImageProcessor instead." , a__ , )
super().__init__(*a__ , **a__ )
| 85 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ ):
return math.sqrt(lowercase__ ) * math.sqrt(lowercase__ ) == num
def _snake_case ( lowercase__ ):
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : List[Any] = n
while left <= right:
_lowerCamelCase : str = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_lowerCamelCase : str = mid - 1
else:
_lowerCamelCase : Optional[int] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
lowerCamelCase__ = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase__ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class A__ :
A_ : Optional[str] = field(
default='cifar10' , metadata={'help': 'Name of a dataset from the datasets package'})
A_ : Optional[str] = field(
default=_lowerCamelCase , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
A_ : Optional[str] = field(
default=_lowerCamelCase , metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'} , )
A_ : Optional[str] = field(default=_lowerCamelCase , metadata={'help': 'A folder containing the training data.'})
A_ : Optional[str] = field(default=_lowerCamelCase , metadata={'help': 'A folder containing the validation data.'})
A_ : Optional[float] = field(
default=0.15 , metadata={'help': 'Percent to split off of train for validation.'})
A_ : int = field(default=3_2 , metadata={'help': 'The size of the square patches to use for masking.'})
A_ : float = field(
default=0.6 , metadata={'help': 'Percentage of patches to mask.'} , )
A_ : Optional[int] = field(
default=_lowerCamelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
A_ : Optional[int] = field(
default=_lowerCamelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[int] = {}
if self.train_dir is not None:
__lowerCAmelCase : Dict = self.train_dir
if self.validation_dir is not None:
__lowerCAmelCase : Dict = self.validation_dir
__lowerCAmelCase : str = data_files if data_files else None
@dataclass
class A__ :
A_ : str = field(
default=_lowerCamelCase , metadata={
'help': (
'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
'checkpoint identifier on the hub. '
'Don\'t set if you want to train a model from scratch.'
)
} , )
A_ : Optional[str] = field(
default=_lowerCamelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCamelCase)} , )
A_ : Optional[str] = field(
default=_lowerCamelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'})
A_ : Optional[str] = field(
default=_lowerCamelCase , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
A_ : Optional[str] = field(
default=_lowerCamelCase , metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'} , )
A_ : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
A_ : str = field(default=_lowerCamelCase , metadata={'help': 'Name or path of preprocessor config.'})
A_ : bool = field(
default=_lowerCamelCase , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
A_ : Optional[int] = field(
default=_lowerCamelCase , metadata={
'help': (
'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
)
} , )
A_ : Optional[int] = field(
default=_lowerCamelCase , metadata={
'help': (
'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
)
} , )
A_ : Optional[int] = field(
default=_lowerCamelCase , metadata={'help': 'Stride to use for the encoder.'} , )
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE=1_92 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=0.6 ):
__lowerCAmelCase : List[str] = input_size
__lowerCAmelCase : str = mask_patch_size
__lowerCAmelCase : Tuple = model_patch_size
__lowerCAmelCase : int = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('Input size must be divisible by mask patch size' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('Mask patch size must be divisible by model patch size' )
__lowerCAmelCase : str = self.input_size // self.mask_patch_size
__lowerCAmelCase : Dict = self.mask_patch_size // self.model_patch_size
__lowerCAmelCase : Union[str, Any] = self.rand_size**2
__lowerCAmelCase : Any = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self ):
__lowerCAmelCase : List[Any] = np.random.permutation(self.token_count )[: self.mask_count]
__lowerCAmelCase : Union[str, Any] = np.zeros(self.token_count , dtype=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = 1
__lowerCAmelCase : Any = mask.reshape((self.rand_size, self.rand_size) )
__lowerCAmelCase : int = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def __lowerCAmelCase (_UpperCamelCase ):
__lowerCAmelCase : Optional[int] = torch.stack([example['pixel_values'] for example in examples] )
__lowerCAmelCase : int = torch.stack([example['mask'] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def __lowerCAmelCase ():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Any = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mim' , _UpperCamelCase , _UpperCamelCase )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__lowerCAmelCase : Dict = training_args.get_process_log_level()
logger.setLevel(_UpperCamelCase )
transformers.utils.logging.set_verbosity(_UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
__lowerCAmelCase : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
__lowerCAmelCase : str = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
__lowerCAmelCase : Dict = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _UpperCamelCase ) and data_args.train_val_split > 0.0:
__lowerCAmelCase : int = ds['train'].train_test_split(data_args.train_val_split )
__lowerCAmelCase : Optional[Any] = split['train']
__lowerCAmelCase : Dict = split['test']
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase : List[str] = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
__lowerCAmelCase : Union[str, Any] = AutoConfig.from_pretrained(model_args.config_name_or_path , **_UpperCamelCase )
elif model_args.model_name_or_path:
__lowerCAmelCase : Dict = AutoConfig.from_pretrained(model_args.model_name_or_path , **_UpperCamelCase )
else:
__lowerCAmelCase : Optional[int] = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(_UpperCamelCase , 'decoder_type' ):
__lowerCAmelCase : List[str] = 'simmim'
# adapt config
__lowerCAmelCase : List[str] = model_args.image_size if model_args.image_size is not None else config.image_size
__lowerCAmelCase : Tuple = model_args.patch_size if model_args.patch_size is not None else config.patch_size
__lowerCAmelCase : Dict = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'image_size': model_args.image_size,
'patch_size': model_args.patch_size,
'encoder_stride': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
__lowerCAmelCase : Any = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **_UpperCamelCase )
elif model_args.model_name_or_path:
__lowerCAmelCase : Dict = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **_UpperCamelCase )
else:
__lowerCAmelCase : Optional[Any] = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
__lowerCAmelCase : Optional[Any] = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
__lowerCAmelCase : Optional[Any] = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
__lowerCAmelCase : Optional[Any] = AutoModelForMaskedImageModeling.from_config(_UpperCamelCase )
if training_args.do_train:
__lowerCAmelCase : Any = ds['train'].column_names
else:
__lowerCAmelCase : List[str] = ds['validation'].column_names
if data_args.image_column_name is not None:
__lowerCAmelCase : List[Any] = data_args.image_column_name
elif "image" in column_names:
__lowerCAmelCase : Dict = 'image'
elif "img" in column_names:
__lowerCAmelCase : Optional[int] = 'img'
else:
__lowerCAmelCase : Dict = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
__lowerCAmelCase : Optional[Any] = Compose(
[
Lambda(lambda _UpperCamelCase : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
__lowerCAmelCase : List[str] = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(_UpperCamelCase ):
__lowerCAmelCase : List[Any] = [transforms(_UpperCamelCase ) for image in examples[image_column_name]]
__lowerCAmelCase : int = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
__lowerCAmelCase : Optional[Any] = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
__lowerCAmelCase : Union[str, Any] = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_UpperCamelCase )
# Initialize our trainer
__lowerCAmelCase : List[str] = Trainer(
model=_UpperCamelCase , args=_UpperCamelCase , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_UpperCamelCase , data_collator=_UpperCamelCase , )
# Training
if training_args.do_train:
__lowerCAmelCase : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase : List[str] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase : str = last_checkpoint
__lowerCAmelCase : Union[str, Any] = trainer.train(resume_from_checkpoint=_UpperCamelCase )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__lowerCAmelCase : List[str] = trainer.evaluate()
trainer.log_metrics('eval' , _UpperCamelCase )
trainer.save_metrics('eval' , _UpperCamelCase )
# Write model card and (optionally) push to hub
__lowerCAmelCase : int = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'masked-image-modeling',
'dataset': data_args.dataset_name,
'tags': ['masked-image-modeling'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCamelCase )
else:
trainer.create_model_card(**_UpperCamelCase )
if __name__ == "__main__":
main() | 86 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(lowercase__ , lowercase__ ) or not all(
isinstance(lowercase__ , lowercase__ ) and len(lowercase__ ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
_lowerCamelCase : dict[str, Any] = {}
_lowerCamelCase : List[Any] = 'WORD_KEEPER'
for word in words:
_lowerCamelCase : Dict = trie
for c in word:
if c not in trie_node:
_lowerCamelCase : Any = {}
_lowerCamelCase : str = trie_node[c]
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Dict = len(lowercase__ )
# Dynamic programming method
@functools.cache
def is_breakable(lowercase__ ) -> bool:
if index == len_string:
return True
_lowerCamelCase : List[Any] = trie
for i in range(lowercase__ , lowercase__ ):
_lowerCamelCase : Any = trie_node.get(string[i] , lowercase__ )
if trie_node is None:
return False
if trie_node.get(lowercase__ , lowercase__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case_ ( unittest.TestCase ,__A ):
def __UpperCamelCase ( self : Tuple ) -> Dict:
lowercase__ : Optional[Any] = load_tool("text-classification" )
self.tool.setup()
lowercase__ : Tuple = load_tool("text-classification" , remote=lowercase_ )
def __UpperCamelCase ( self : List[Any] ) -> str:
lowercase__ : Optional[int] = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(lowercase_ , "positive" )
def __UpperCamelCase ( self : Optional[int] ) -> int:
lowercase__ : Tuple = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(lowercase_ , "positive" )
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
lowercase__ : Union[str, Any] = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(lowercase_ , "positive" )
def __UpperCamelCase ( self : List[Any] ) -> Union[str, Any]:
lowercase__ : List[Any] = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(lowercase_ , "positive" )
| 87 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
if len(lowercase__ ) == 1:
return True
_lowerCamelCase : List[Any] = series[1] - series[0]
for index in range(len(lowercase__ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
_lowerCamelCase : Optional[int] = 0
for val in series:
answer += val
return answer / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ):
'''simple docstring'''
__magic_name__ = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""", type=A_, default=1, help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""", type=A_, help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
), )
# rest from the training program
parser.add_argument("""training_script_args""", nargs=A_ )
return parser.parse_args()
def a__ ( ):
'''simple docstring'''
__magic_name__ = parse_args()
# Import training_script as a module.
__magic_name__ = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
__magic_name__ = script_fpath.stem
__magic_name__ = importlib.import_module(A_ )
# Patch sys.argv
__magic_name__ = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 88 |
"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase__ = 16
lowercase__ = 32
def _snake_case ( lowercase__ , lowercase__ = 16 , lowercase__ = "bert-base-cased" ):
_lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(lowercase__ )
_lowerCamelCase : Tuple = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowercase__ ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Union[str, Any] = 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
_lowerCamelCase : int = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCamelCase : Optional[int] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowercase__ ):
# 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(lowercase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(lowercase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
_lowerCamelCase : List[str] = DataLoader(
tokenized_datasets['train'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
_lowerCamelCase : int = DataLoader(
tokenized_datasets['validation'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ , lowercase__ ):
# Initialize accelerator
_lowerCamelCase : Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCamelCase : Optional[int] = config['lr']
_lowerCamelCase : Optional[int] = int(config['num_epochs'] )
_lowerCamelCase : Union[str, Any] = int(config['seed'] )
_lowerCamelCase : Optional[int] = int(config['batch_size'] )
_lowerCamelCase : Dict = args.model_name_or_path
set_seed(lowercase__ )
_lowerCamelCase, _lowerCamelCase : Optional[int] = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCamelCase : int = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
_lowerCamelCase : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowerCamelCase : Union[str, Any] = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
_lowerCamelCase : str = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
_lowerCamelCase : Tuple = 1
_lowerCamelCase : List[Any] = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowerCamelCase : Tuple = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
_lowerCamelCase : Any = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , 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.
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
_lowerCamelCase : Union[str, Any] = 0
# We also need to keep track of the stating epoch so files are named properly
_lowerCamelCase : Dict = 0
# Now we train the model
_lowerCamelCase : Dict = evaluate.load('glue' , 'mrpc' )
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
_lowerCamelCase : List[Any] = model(**lowercase__ )
_lowerCamelCase : int = outputs.loss
_lowerCamelCase : Dict = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_lowerCamelCase : Union[str, Any] = 0
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():
_lowerCamelCase : Optional[int] = model(**lowercase__ )
_lowerCamelCase : Dict = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowerCamelCase, _lowerCamelCase : List[str] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
_lowerCamelCase : Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCamelCase : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
_lowerCamelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , lowercase__ )
_lowerCamelCase : Tuple = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
_lowerCamelCase : str = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ):
_lowerCamelCase : Any = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=lowercase__ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=lowercase__ , )
parser.add_argument(
'--output_dir' , type=lowercase__ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=lowercase__ , default=lowercase__ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=lowercase__ , default=3 , help='Number of train epochs.' , )
_lowerCamelCase : Optional[Any] = parser.parse_args()
_lowerCamelCase : str = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
__lowerCAmelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__lowerCAmelCase = (((515, 22, 13), 555), ((61, 35, 49), 150))
__lowerCAmelCase = [2, 4, 1, 5]
__lowerCAmelCase = len(train_data)
__lowerCAmelCase = 0.009
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_="train" ) -> Tuple:
return calculate_hypothesis_value(lowerCAmelCase_ , lowerCAmelCase_ ) - output(
lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCamelCase ( lowerCAmelCase_ ) -> Dict:
_a : int = 0
for i in range(len(lowerCAmelCase_ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=m ) -> List[str]:
_a : Any = 0
for i in range(lowerCAmelCase_ ):
if index == -1:
summation_value += _error(lowerCAmelCase_ )
else:
summation_value += _error(lowerCAmelCase_ ) * train_data[i][0][index]
return summation_value
def __lowerCamelCase ( lowerCAmelCase_ ) -> Tuple:
_a : Tuple = summation_of_cost_derivative(lowerCAmelCase_ , lowerCAmelCase_ ) / m
return cost_derivative_value
def __lowerCamelCase ( ) -> List[str]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
_a : int = 0.000_002
_a : str = 0
_a : Optional[Any] = 0
while True:
j += 1
_a : Tuple = [0, 0, 0, 0]
for i in range(0 , len(lowerCAmelCase_ ) ):
_a : Union[str, Any] = get_cost_derivative(i - 1 )
_a : Any = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
lowerCAmelCase_ , lowerCAmelCase_ , atol=lowerCAmelCase_ , rtol=lowerCAmelCase_ , ):
break
_a : Any = temp_parameter_vector
print(('Number of iterations:', j) )
def __lowerCamelCase ( ) -> str:
for i in range(len(lowerCAmelCase_ ) ):
print(('Actual output value:', output(lowerCAmelCase_ , 'test' )) )
print(('Hypothesis output:', calculate_hypothesis_value(lowerCAmelCase_ , 'test' )) )
if __name__ == "__main__":
run_gradient_descent()
print('''\nTesting gradient descent for a linear hypothesis function.\n''')
test_gradient_descent()
| 89 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[Any] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 ) | 96 | 0 |
from itertools import permutations
def lowerCamelCase_ ( UpperCamelCase__ : tuple ) -> bool:
"""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
__lowerCamelCase = [7, 11, 13, 17]
for i, test in enumerate(UpperCamelCase__ ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def lowerCamelCase_ ( UpperCamelCase__ : int = 10 ) -> int:
"""simple docstring"""
return sum(
int(''.join(map(UpperCamelCase__ , UpperCamelCase__ ) ) )
for num in permutations(range(UpperCamelCase__ ) )
if is_substring_divisible(UpperCamelCase__ ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 90 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""IBertForMaskedLM""",
"""IBertForMultipleChoice""",
"""IBertForQuestionAnswering""",
"""IBertForSequenceClassification""",
"""IBertForTokenClassification""",
"""IBertModel""",
"""IBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : str = """▁"""
UpperCAmelCase_ : List[Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__ ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = BigBirdTokenizer
__UpperCamelCase = BigBirdTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = True
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
super().setUp()
SCREAMING_SNAKE_CASE_ : Any = self.tokenizer_class(lowercase_ , keep_accents=lowercase_)
tokenizer.save_pretrained(self.tmpdirname)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = '''<s>'''
SCREAMING_SNAKE_CASE_ : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_) , lowercase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_) , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '''<unk>''')
self.assertEqual(vocab_keys[1] , '''<s>''')
self.assertEqual(vocab_keys[-1] , '''[MASK]''')
self.assertEqual(len(lowercase_) , 1004)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1000)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE_ : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : int = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ : Optional[Any] = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = tokenizer.tokenize(lowercase_)
SCREAMING_SNAKE_CASE_ : str = rust_tokenizer.tokenize(lowercase_)
self.assertListEqual(lowercase_ , lowercase_)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_)
self.assertListEqual(lowercase_ , lowercase_)
SCREAMING_SNAKE_CASE_ : List[str] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ : Optional[Any] = tokenizer.encode(lowercase_)
SCREAMING_SNAKE_CASE_ : str = rust_tokenizer.encode(lowercase_)
self.assertListEqual(lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = BigBirdTokenizer(lowercase_ , keep_accents=lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = tokenizer.tokenize('''This is a test''')
self.assertListEqual(lowercase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_) , [285, 46, 10, 170, 382] , )
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
SCREAMING_SNAKE_CASE_ : Dict = tokenizer.convert_tokens_to_ids(lowercase_)
self.assertListEqual(
lowercase_ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
SCREAMING_SNAKE_CASE_ : Dict = tokenizer.convert_ids_to_tokens(lowercase_)
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
'''simple docstring'''
return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
@slow
def _SCREAMING_SNAKE_CASE ( self : str):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = '''Hello World!'''
SCREAMING_SNAKE_CASE_ : List[Any] = [65, 18536, 2260, 101, 66]
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_))
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
# fmt: off
SCREAMING_SNAKE_CASE_ : List[str] = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66] # noqa: E231
# fmt: on
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_))
@require_torch
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
SCREAMING_SNAKE_CASE_ : List[Any] = list(self.big_tokenizer.get_vocab().keys())[:10]
SCREAMING_SNAKE_CASE_ : str = ''' '''.join(lowercase_)
SCREAMING_SNAKE_CASE_ : str = self.big_tokenizer.encode_plus(lowercase_ , return_tensors='''pt''' , return_token_type_ids=lowercase_)
SCREAMING_SNAKE_CASE_ : int = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=lowercase_)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = BigBirdConfig(attention_type='''original_full''')
SCREAMING_SNAKE_CASE_ : int = BigBirdModel(lowercase_)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**lowercase_)
model(**lowercase_)
@slow
def _SCREAMING_SNAKE_CASE ( self : Any):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
SCREAMING_SNAKE_CASE_ : List[Any] = tokenizer.decode(tokenizer('''Paris is the [MASK].''').input_ids)
self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]''')
@slow
def _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = {'''input_ids''': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='''google/bigbird-roberta-base''' , revision='''215c99f1600e06f83acce68422f2035b2b5c3510''' , )
| 91 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{sampling_rate}'''
_lowerCamelCase : str = '1'
_lowerCamelCase : str = 'f32le'
_lowerCamelCase : Union[str, Any] = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowercase__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
_lowerCamelCase : str = ffmpeg_process.communicate(lowercase__ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
_lowerCamelCase : List[Any] = output_stream[0]
_lowerCamelCase : Tuple = np.frombuffer(lowercase__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = "f32le" , ):
_lowerCamelCase : Optional[Any] = f'''{sampling_rate}'''
_lowerCamelCase : List[str] = '1'
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
_lowerCamelCase : Dict = platform.system()
if system == "Linux":
_lowerCamelCase : Optional[int] = 'alsa'
_lowerCamelCase : Optional[Any] = 'default'
elif system == "Darwin":
_lowerCamelCase : Optional[int] = 'avfoundation'
_lowerCamelCase : Any = ':0'
elif system == "Windows":
_lowerCamelCase : Tuple = 'dshow'
_lowerCamelCase : Tuple = 'default'
_lowerCamelCase : Optional[int] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
_lowerCamelCase : Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_lowerCamelCase : List[Any] = _ffmpeg_stream(lowercase__ , lowercase__ )
for item in iterator:
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = "f32le" , ):
if stream_chunk_s is not None:
_lowerCamelCase : int = stream_chunk_s
else:
_lowerCamelCase : Optional[Any] = chunk_length_s
_lowerCamelCase : Optional[Any] = ffmpeg_microphone(lowercase__ , lowercase__ , format_for_conversion=lowercase__ )
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = np.intaa
_lowerCamelCase : str = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : Any = np.floataa
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
_lowerCamelCase : Union[str, Any] = chunk_length_s / 6
_lowerCamelCase : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowercase__ , (int, float) ):
_lowerCamelCase : Any = [stride_length_s, stride_length_s]
_lowerCamelCase : Tuple = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_lowerCamelCase : Optional[Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_lowerCamelCase : List[Any] = datetime.datetime.now()
_lowerCamelCase : Optional[int] = datetime.timedelta(seconds=lowercase__ )
for item in chunk_bytes_iter(lowercase__ , lowercase__ , stride=(stride_left, stride_right) , stream=lowercase__ ):
# Put everything back in numpy scale
_lowerCamelCase : List[Any] = np.frombuffer(item['raw'] , dtype=lowercase__ )
_lowerCamelCase : int = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
_lowerCamelCase : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = False ):
_lowerCamelCase : int = B''
_lowerCamelCase, _lowerCamelCase : Dict = 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}''' )
_lowerCamelCase : str = 0
for raw in iterator:
acc += raw
if stream and len(lowercase__ ) < chunk_len:
_lowerCamelCase : Optional[int] = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowercase__ ) >= chunk_len:
# We are flushing the accumulator
_lowerCamelCase : str = (_stride_left, stride_right)
_lowerCamelCase : str = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
_lowerCamelCase : List[Any] = False
yield item
_lowerCamelCase : Optional[Any] = stride_left
_lowerCamelCase : str = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowercase__ ) > stride_left:
_lowerCamelCase : Optional[Any] = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
_lowerCamelCase : Tuple = False
yield item
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = 2**24 # 16Mo
try:
with subprocess.Popen(lowercase__ , stdout=subprocess.PIPE , bufsize=lowercase__ ) as ffmpeg_process:
while True:
_lowerCamelCase : Optional[Any] = ffmpeg_process.stdout.read(lowercase__ )
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 | 96 | 0 |
from __future__ import annotations
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : np.ndarray ):
__lowerCAmelCase , __lowerCAmelCase = np.shape(SCREAMING_SNAKE_CASE_ )
if rows != columns:
__lowerCAmelCase = (
"'table' has to be of square shaped array but got a "
F"""{rows}x{columns} array:\n{table}"""
)
raise ValueError(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = np.zeros((rows, columns) )
__lowerCAmelCase = np.zeros((rows, columns) )
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_ ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
__lowerCAmelCase = (table[i][j] - total) / upper[j][j]
__lowerCAmelCase = 1
for j in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_ ) )
__lowerCAmelCase = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """ctrl"""
lowerCamelCase__ = ["""past_key_values"""]
lowerCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=246534 , lowercase=256 , lowercase=1280 , lowercase=8192 , lowercase=48 , lowercase=16 , lowercase=0.1 , lowercase=0.1 , lowercase=1E-6 , lowercase=0.02 , lowercase=True , **lowercase , ):
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Dict = n_positions
_lowerCamelCase : Optional[int] = n_embd
_lowerCamelCase : str = n_layer
_lowerCamelCase : Union[str, Any] = n_head
_lowerCamelCase : Any = dff
_lowerCamelCase : int = resid_pdrop
_lowerCamelCase : Dict = embd_pdrop
_lowerCamelCase : Union[str, Any] = layer_norm_epsilon
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : str = use_cache
super().__init__(**lowercase ) | 96 | 0 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class lowerCAmelCase__ ( nn.Module ):
def __init__( self ):
"""simple docstring"""
super().__init__()
lowercase_ : Optional[int] = nn.Linear(3 , 4 )
lowercase_ : Optional[Any] = nn.BatchNormad(4 )
lowercase_ : Dict = nn.Linear(4 , 5 )
def _snake_case ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(__SCREAMING_SNAKE_CASE ) ) )
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : List[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(__SCREAMING_SNAKE_CASE , model.state_dict() )
lowercase_ : List[str] = os.path.join(__SCREAMING_SNAKE_CASE , '''index.json''' )
self.assertTrue(os.path.isfile(__SCREAMING_SNAKE_CASE ) )
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
lowercase_ : Any = os.path.join(__SCREAMING_SNAKE_CASE , F'''{key}.dat''' )
self.assertTrue(os.path.isfile(__SCREAMING_SNAKE_CASE ) )
# TODO: add tests on the fact weights are properly loaded
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Tuple = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
lowercase_ : Optional[int] = torch.randn(2 , 3 , dtype=__SCREAMING_SNAKE_CASE )
with TemporaryDirectory() as tmp_dir:
lowercase_ : Optional[Any] = offload_weight(__SCREAMING_SNAKE_CASE , '''weight''' , __SCREAMING_SNAKE_CASE , {} )
lowercase_ : str = os.path.join(__SCREAMING_SNAKE_CASE , '''weight.dat''' )
self.assertTrue(os.path.isfile(__SCREAMING_SNAKE_CASE ) )
self.assertDictEqual(__SCREAMING_SNAKE_CASE , {'''weight''': {'''shape''': [2, 3], '''dtype''': str(__SCREAMING_SNAKE_CASE ).split('''.''' )[1]}} )
lowercase_ : int = load_offloaded_weight(__SCREAMING_SNAKE_CASE , index['''weight'''] )
self.assertTrue(torch.equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : str = ModelForTest()
lowercase_ : Any = model.state_dict()
lowercase_ : Dict = {k: v for k, v in state_dict.items() if '''linear2''' not in k}
lowercase_ : int = {k: v for k, v in state_dict.items() if '''linear2''' in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : Dict = OffloadedWeightsLoader(state_dict=__SCREAMING_SNAKE_CASE , save_folder=__SCREAMING_SNAKE_CASE )
# Every key is there with the right value
self.assertEqual(sorted(__SCREAMING_SNAKE_CASE ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE , weight_map[key] ) )
lowercase_ : Dict = {k: v for k, v in state_dict.items() if '''weight''' in k}
lowercase_ : str = {k: v for k, v in state_dict.items() if '''weight''' not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : Any = OffloadedWeightsLoader(state_dict=__SCREAMING_SNAKE_CASE , save_folder=__SCREAMING_SNAKE_CASE )
# Every key is there with the right value
self.assertEqual(sorted(__SCREAMING_SNAKE_CASE ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE , weight_map[key] ) )
with TemporaryDirectory() as tmp_dir:
offload_state_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Duplicates are removed
lowercase_ : Dict = OffloadedWeightsLoader(state_dict=__SCREAMING_SNAKE_CASE , save_folder=__SCREAMING_SNAKE_CASE )
# Every key is there with the right value
self.assertEqual(sorted(__SCREAMING_SNAKE_CASE ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE , weight_map[key] ) )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[Any] = {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
lowercase_ : Dict = extract_submodules_state_dict(__SCREAMING_SNAKE_CASE , ['''a.1''', '''a.2'''] )
self.assertDictEqual(__SCREAMING_SNAKE_CASE , {'''a.1''': 0, '''a.2''': 2} )
lowercase_ : Any = {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
lowercase_ : Optional[int] = extract_submodules_state_dict(__SCREAMING_SNAKE_CASE , ['''a.1''', '''a.2'''] )
self.assertDictEqual(__SCREAMING_SNAKE_CASE , {'''a.1.a''': 0, '''a.2.a''': 2} )
| 93 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = data
_lowerCamelCase : Node | None = None
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : str = None
_lowerCamelCase : str = None
def __iter__( self ):
_lowerCamelCase : List[str] = self.head
while self.head:
yield node.data
_lowerCamelCase : Optional[int] = node.next
if node == self.head:
break
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join(str(lowercase ) for item in iter(self ) )
def A_ ( self , lowercase ):
self.insert_nth(len(self ) , lowercase )
def A_ ( self , lowercase ):
self.insert_nth(0 , lowercase )
def A_ ( self , lowercase , lowercase ):
if index < 0 or index > len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : List[Any] = Node(lowercase )
if self.head is None:
_lowerCamelCase : str = new_node # first node points itself
_lowerCamelCase : Union[str, Any] = new_node
elif index == 0: # insert at head
_lowerCamelCase : List[str] = self.head
_lowerCamelCase : str = new_node
else:
_lowerCamelCase : Union[str, Any] = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : Union[str, Any] = temp.next
_lowerCamelCase : List[str] = new_node
if index == len(self ) - 1: # insert at tail
_lowerCamelCase : Any = new_node
def A_ ( self ):
return self.delete_nth(0 )
def A_ ( self ):
return self.delete_nth(len(self ) - 1 )
def A_ ( self , lowercase = 0 ):
if not 0 <= index < len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : Any = self.head
if self.head == self.tail: # just one node
_lowerCamelCase : List[str] = None
elif index == 0: # delete head node
_lowerCamelCase : List[str] = self.tail.next.next
_lowerCamelCase : Optional[int] = self.head.next
else:
_lowerCamelCase : Dict = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : int = temp.next
_lowerCamelCase : Optional[int] = temp.next.next
if index == len(self ) - 1: # delete at tail
_lowerCamelCase : List[Any] = temp
return delete_node.data
def A_ ( self ):
return len(self ) == 0
def _snake_case ( ):
_lowerCamelCase : Union[str, Any] = CircularLinkedList()
assert len(lowercase__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(lowercase__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(lowercase__ ) == i
circular_linked_list.insert_nth(lowercase__ , i + 1 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
from __future__ import annotations
snake_case : Any = list[list[int]]
# assigning initial values to the grid
snake_case : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
snake_case : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def __lowerCamelCase ( UpperCAmelCase_ : Matrix , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""simple docstring"""
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def __lowerCamelCase ( UpperCAmelCase_ : Matrix ):
"""simple docstring"""
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def __lowerCamelCase ( UpperCAmelCase_ : Matrix ):
"""simple docstring"""
if location := find_empty_location(UpperCAmelCase_ ):
a , a :Dict = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
a :Optional[Any] = digit
if sudoku(UpperCAmelCase_ ) is not None:
return grid
a :Optional[int] = 0
return None
def __lowerCamelCase ( UpperCAmelCase_ : Matrix ):
"""simple docstring"""
for row in grid:
for cell in row:
print(UpperCAmelCase_ , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 20)
print_solution(example_grid)
print('''\nExample grid solution:''')
snake_case : Optional[Any] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 94 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowercase__ = get_logger(__name__)
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = """dummy_data"""
lowerCamelCase__ = """datasets"""
lowerCamelCase__ = False
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = False , lowercase = True , lowercase = None , ):
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Dict = dataset_name
_lowerCamelCase : Union[str, Any] = cache_dir
_lowerCamelCase : Dict = use_local_dummy_data
_lowerCamelCase : Tuple = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : Any = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : str = str(lowercase )
# to be downloaded
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : int = None
@property
def A_ ( self ):
if self._dummy_file is None:
_lowerCamelCase : Tuple = self.download_dummy_data()
return self._dummy_file
@property
def A_ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def A_ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def A_ ( self ):
_lowerCamelCase : List[str] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : int = cached_path(
lowercase , cache_dir=self.cache_dir , extract_compressed_file=lowercase , force_extract=lowercase )
return os.path.join(lowercase , self.dummy_file_name )
@property
def A_ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def A_ ( self ):
if self._bucket_url is None:
_lowerCamelCase : List[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def A_ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def A_ ( self , lowercase , *lowercase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Union[str, Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Union[str, Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowercase , lowercase ):
return self.create_dummy_data_dict(lowercase , lowercase )
elif isinstance(lowercase , (list, tuple) ):
return self.create_dummy_data_list(lowercase , lowercase )
else:
return self.create_dummy_data_single(lowercase , lowercase )
def A_ ( self , lowercase , *lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , *lowercase , **lowercase ):
return path
def A_ ( self ):
return {}
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowercase , lowercase ):
for single_url in single_urls:
download_callback(lowercase )
else:
_lowerCamelCase : List[Any] = single_urls
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowercase , lowercase ):
_lowerCamelCase : List[Any] = [os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : Optional[int] = single_urls
_lowerCamelCase : List[Any] = os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) )
_lowerCamelCase : int = value
# make sure that values are unique
if all(isinstance(lowercase , lowercase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : List[str] = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , lowercase ) ) for url in data_url )
_lowerCamelCase : int = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : List[str] = [data_url[0]] * len(lowercase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : str = os.path.join(lowercase , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(lowercase )
return dummy_data_list
def A_ ( self , lowercase , lowercase ):
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Tuple = os.path.join(lowercase , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(lowercase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self , lowercase ):
def _iter_archive_members(lowercase ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : str = Path(self.dummy_file ).parent
_lowerCamelCase : Union[str, Any] = path.relative_to(lowercase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : List[str] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowercase )
_lowerCamelCase : Optional[int] = Path(lowercase )
_lowerCamelCase : Dict = _iter_archive_members(lowercase ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(lowercase ).as_posix(), file_path.open('rb' )
def A_ ( self , lowercase ):
if not isinstance(lowercase , lowercase ):
_lowerCamelCase : List[str] = [paths]
for path in paths:
if os.path.isfile(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(lowercase ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(lowercase , lowercase ) | 96 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 95 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
_lowerCamelCase, _lowerCamelCase : Optional[Any] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
_lowerCamelCase : Union[str, Any] = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(lowercase__ , i + t , (lowercase__) )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
if __name__ == "__main__":
lowercase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowercase__ = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted)) | 96 | 0 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class lowercase ( A__ ):
"""simple docstring"""
_a = CustomTokenizer
pass | 97 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""image_processor""", """tokenizer"""]
lowerCamelCase__ = """BlipImageProcessor"""
lowerCamelCase__ = """AutoTokenizer"""
def __init__( self , lowercase , lowercase , lowercase ):
super().__init__(lowercase , lowercase )
# add QFormer tokenizer
_lowerCamelCase : int = qformer_tokenizer
def __call__( self , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ):
if images is None and text is None:
raise ValueError('You have to specify at least images or text.' )
_lowerCamelCase : int = BatchFeature()
if text is not None:
_lowerCamelCase : List[str] = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
encoding.update(lowercase )
_lowerCamelCase : List[str] = self.qformer_tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
_lowerCamelCase : List[Any] = qformer_text_encoding.pop('input_ids' )
_lowerCamelCase : Tuple = qformer_text_encoding.pop('attention_mask' )
if images is not None:
_lowerCamelCase : int = self.image_processor(lowercase , return_tensors=lowercase )
encoding.update(lowercase )
return encoding
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.decode(*lowercase , **lowercase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = self.tokenizer.model_input_names
_lowerCamelCase : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def A_ ( self , lowercase , **lowercase ):
if os.path.isfile(lowercase ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(lowercase , exist_ok=lowercase )
_lowerCamelCase : Optional[Any] = os.path.join(lowercase , 'qformer_tokenizer' )
self.qformer_tokenizer.save_pretrained(lowercase )
return super().save_pretrained(lowercase , **lowercase )
@classmethod
def A_ ( cls , lowercase , **lowercase ):
_lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(lowercase , subfolder='qformer_tokenizer' )
_lowerCamelCase : Dict = cls._get_arguments_from_pretrained(lowercase , **lowercase )
args.append(lowercase )
return cls(*lowercase ) | 96 | 0 |
"""simple docstring"""
import os
def a_ ( ):
UpperCAmelCase__ = os.path.dirname(os.path.realpath(lowerCamelCase ) )
UpperCAmelCase__ = os.path.join(lowerCamelCase , 'triangle.txt' )
with open(lowerCamelCase ) as f:
UpperCAmelCase__ = f.readlines()
UpperCAmelCase__ = []
for line in triangle:
UpperCAmelCase__ = []
for number in line.strip().split(' ' ):
numbers_from_line.append(int(lowerCamelCase ) )
a.append(lowerCamelCase )
for i in range(1 , len(lowerCamelCase ) ):
for j in range(len(a[i] ) ):
UpperCAmelCase__ = a[i - 1][j] if j != len(a[i - 1] ) else 0
UpperCAmelCase__ = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowerCamelCase , lowerCamelCase )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 98 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowercase__ = logging.getLogger()
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[Any] = {}
_lowerCamelCase : List[Any] = os.path.join(lowercase__ , 'all_results.json' )
if os.path.exists(lowercase__ ):
with open(lowercase__ , 'r' ) as f:
_lowerCamelCase : List[Any] = json.load(lowercase__ )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
lowercase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def A_ ( self ):
import xla_spawn
_lowerCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
_lowerCamelCase : List[Any] = F'''
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(lowercase , 'argv' , lowercase ):
_lowerCamelCase : Dict = time()
xla_spawn.main()
_lowerCamelCase : Any = time()
_lowerCamelCase : Optional[int] = get_results(lowercase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def A_ ( self ):
import xla_spawn
_lowerCamelCase : Tuple = '\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n '.split()
with patch.object(lowercase , 'argv' , lowercase ):
xla_spawn.main() | 96 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
lowercase : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase : Optional[int] = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Union[PIL.Image.Image, np.ndarray]
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(
prior=lowercase , image_encoder=lowercase , image_processor=lowercase , scheduler=lowercase , renderer=lowercase , )
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Any:
'''simple docstring'''
if latents is None:
a__ : List[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase)
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}')
a__ : List[str] = latents.to(lowercase)
a__ : Dict = latents * scheduler.init_noise_sigma
return latents
def __lowercase ( self , lowercase=0) -> Optional[int]:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`')
a__ : Any = torch.device(F'cuda:{gpu_id}')
a__ : Optional[Any] = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase , lowercase)
@property
def __lowercase ( self) -> Any:
'''simple docstring'''
if self.device != torch.device('meta') or not hasattr(self.image_encoder , '_hf_hook'):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowercase , '_hf_hook')
and hasattr(module._hf_hook , 'execution_device')
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , ) -> Any:
'''simple docstring'''
if isinstance(lowercase , lowercase) and isinstance(image[0] , torch.Tensor):
a__ : Dict = torch.cat(lowercase , axis=0) if image[0].ndim == 4 else torch.stack(lowercase , axis=0)
if not isinstance(lowercase , torch.Tensor):
a__ : List[Any] = self.image_processor(lowercase , return_tensors='pt').pixel_values[0].unsqueeze(0)
a__ : Optional[Any] = image.to(dtype=self.image_encoder.dtype , device=lowercase)
a__ : int = self.image_encoder(lowercase)['last_hidden_state']
a__ : Optional[Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
a__ : Optional[int] = image_embeds.repeat_interleave(lowercase , dim=0)
if do_classifier_free_guidance:
a__ : Tuple = torch.zeros_like(lowercase)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
a__ : int = torch.cat([negative_image_embeds, image_embeds])
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowercase)
def __call__( self , lowercase , lowercase = 1 , lowercase = 25 , lowercase = None , lowercase = None , lowercase = 4.0 , lowercase = 64 , lowercase = "pil" , lowercase = True , ) -> Tuple:
'''simple docstring'''
if isinstance(lowercase , PIL.Image.Image):
a__ : List[str] = 1
elif isinstance(lowercase , torch.Tensor):
a__ : List[str] = image.shape[0]
elif isinstance(lowercase , lowercase) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image)):
a__ : List[str] = len(lowercase)
else:
raise ValueError(
F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase)}')
a__ : Tuple = self._execution_device
a__ : List[Any] = batch_size * num_images_per_prompt
a__ : Optional[Any] = guidance_scale > 1.0
a__ : Optional[int] = self._encode_image(lowercase , lowercase , lowercase , lowercase)
# prior
self.scheduler.set_timesteps(lowercase , device=lowercase)
a__ : str = self.scheduler.timesteps
a__ : Tuple = self.prior.config.num_embeddings
a__ : Optional[int] = self.prior.config.embedding_dim
a__ : Dict = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , lowercase , lowercase , lowercase , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
a__ : Tuple = latents.reshape(latents.shape[0] , lowercase , lowercase)
for i, t in enumerate(self.progress_bar(lowercase)):
# expand the latents if we are doing classifier free guidance
a__ : List[Any] = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
a__ : Optional[int] = self.scheduler.scale_model_input(lowercase , lowercase)
a__ : Tuple = self.prior(
lowercase , timestep=lowercase , proj_embedding=lowercase , ).predicted_image_embedding
# remove the variance
a__ , a__ : Any = noise_pred.split(
scaled_model_input.shape[2] , dim=2) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
a__ , a__ : Any = noise_pred.chunk(2)
a__ : str = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
a__ : List[str] = self.scheduler.step(
lowercase , timestep=lowercase , sample=lowercase , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowercase)
a__ : List[Any] = []
for i, latent in enumerate(lowercase):
print()
a__ : Dict = self.renderer.decode(
latent[None, :] , lowercase , size=lowercase , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(lowercase)
a__ : Union[str, Any] = torch.stack(lowercase)
if output_type not in ["np", "pil"]:
raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}')
a__ : List[Any] = images.cpu().numpy()
if output_type == "pil":
a__ : Any = [self.numpy_to_pil(lowercase) for image in images]
# Offload last model to CPU
if hasattr(self , 'final_offload_hook') and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowercase)
| 99 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( lowercase__ , lowercase__ ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase__ , lowercase__ ) ) )
def _snake_case ( lowercase__ , lowercase__ ):
if dataset.ndim != value_array.ndim:
_lowerCamelCase : Tuple = (
'Wrong input data\'s dimensions... '
f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(lowercase__ )
try:
if dataset.shape[1] != value_array.shape[1]:
_lowerCamelCase : Optional[int] = (
'Wrong input data\'s shape... '
f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(lowercase__ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('Wrong shape' )
if dataset.dtype != value_array.dtype:
_lowerCamelCase : int = (
'Input data have different datatype... '
f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(lowercase__ )
_lowerCamelCase : Optional[int] = []
for value in value_array:
_lowerCamelCase : Tuple = euclidean(lowercase__ , dataset[0] )
_lowerCamelCase : Union[str, Any] = dataset[0].tolist()
for dataset_value in dataset[1:]:
_lowerCamelCase : Optional[Any] = euclidean(lowercase__ , lowercase__ )
if dist > temp_dist:
_lowerCamelCase : List[Any] = temp_dist
_lowerCamelCase : List[str] = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( lowercase__ , lowercase__ ):
return np.dot(lowercase__ , lowercase__ ) / (norm(lowercase__ ) * norm(lowercase__ ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__magic_name__ = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n month = sep,\n year = \"2015\",\n address = \"Lisbon, Portugal\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W15-3049\",\n doi = \"10.18653/v1/W15-3049\",\n pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n title = \"chr{F}++: words helping character n-grams\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n month = sep,\n year = \"2017\",\n address = \"Copenhagen, Denmark\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W17-4770\",\n doi = \"10.18653/v1/W17-4770\",\n pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
__magic_name__ = "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"
__magic_name__ = "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n predictions (list of str): The predicted sentences.\n references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n char_order (int): Character n-gram order. Defaults to `6`.\n word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n to reference chrF++.py, NLTK and Moses implementations. If `False`,\n it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n 'score' (float): The chrF (chrF++) score,\n 'char_order' (int): The character n-gram order,\n 'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n 'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n Example 1--a simple example of calculating chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction, references=reference)\n >>> print(results)\n {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2)\n >>> print(results)\n {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2,\n ... lowercase=True)\n >>> print(results)\n {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE_ ( datasets.Metric ):
"""simple docstring"""
def snake_case_ ( self):
if version.parse(scb.__version__) < version.parse("""1.4.12"""):
raise ImportWarning(
"""To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
"""You can install it with `pip install \"sacrebleu>=1.4.12\"`.""")
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence"""),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""") , id="""references"""),
}) , codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""] , reference_urls=[
"""https://github.com/m-popovic/chrF""",
] , )
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = CHRF.CHAR_ORDER , lowerCAmelCase__ = CHRF.WORD_ORDER , lowerCAmelCase__ = CHRF.BETA , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , ):
__SCREAMING_SNAKE_CASE = len(references[0])
if any(len(lowerCAmelCase__) != references_per_prediction for refs in references):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""")
__SCREAMING_SNAKE_CASE = [[refs[i] for refs in references] for i in range(lowerCAmelCase__)]
__SCREAMING_SNAKE_CASE = CHRF(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = sb_chrf.corpus_score(lowerCAmelCase__ , lowerCAmelCase__)
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 100 |
"""simple docstring"""
import socket
def _snake_case ( ):
_lowerCamelCase : List[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase : Union[str, Any] = socket.gethostname()
_lowerCamelCase : List[Any] = 12312
sock.connect((host, port) )
sock.send(B'Hello server!' )
with open('Received_file' , 'wb' ) as out_file:
print('File opened' )
print('Receiving data...' )
while True:
_lowerCamelCase : int = sock.recv(1024 )
if not data:
break
out_file.write(lowercase__ )
print('Successfully received the file' )
sock.close()
print('Connection closed' )
if __name__ == "__main__":
main() | 96 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowercase__ :List[Any] = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
lowercase__ :List[Any] = parser.parse_args()
lowercase__ :List[str] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
lowercase__ :Dict = CLIPImageProcessor()
lowercase__ :Dict = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
lowercase__ :Any = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 101 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase__ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
"""
lowercase__ = """\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""
lowercase__ = """
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for 'record': list of question-answer dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'prediction_text': the predicted answer text
- for 'multirc': list of question-answer dictionaries with the following keys:
- 'idx': index of the question-answer pair as specified by the dataset
- 'prediction': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for 'record': list of question-answers dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'answers': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for 'record':
- 'exact_match': Exact match between answer and gold answer
- 'f1': F1 score
- for 'multirc':
- 'exact_match': Exact match between answer and gold answer
- 'f1_m': Per-question macro-F1 score
- 'f1_a': Average F1 score over all answers
- for 'axb':
'matthews_correlation': Matthew Correlation
- for 'cb':
- 'accuracy': Accuracy
- 'f1': F1 score
- for all others:
- 'accuracy': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'record')
>>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
>>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
>>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def _snake_case ( lowercase__ , lowercase__ ):
return float((preds == labels).mean() )
def _snake_case ( lowercase__ , lowercase__ , lowercase__="binary" ):
_lowerCamelCase : str = simple_accuracy(lowercase__ , lowercase__ )
_lowerCamelCase : Any = float(fa_score(y_true=lowercase__ , y_pred=lowercase__ , average=lowercase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Any = {}
for id_pred, label in zip(lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
_lowerCamelCase : Union[str, Any] = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
_lowerCamelCase : Optional[Any] = [(pred, label)]
_lowerCamelCase, _lowerCamelCase : Optional[int] = [], []
for question, preds_labels in question_map.items():
_lowerCamelCase, _lowerCamelCase : Tuple = zip(*lowercase__ )
_lowerCamelCase : List[str] = fa_score(y_true=lowercase__ , y_pred=lowercase__ , average='macro' )
fas.append(lowercase__ )
_lowerCamelCase : int = int(sum(pred == label for pred, label in preds_labels ) == len(lowercase__ ) )
ems.append(lowercase__ )
_lowerCamelCase : Optional[Any] = float(sum(lowercase__ ) / len(lowercase__ ) )
_lowerCamelCase : Optional[int] = sum(lowercase__ ) / len(lowercase__ )
_lowerCamelCase : List[Any] = float(fa_score(y_true=lowercase__ , y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def A_ ( self ):
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None , )
def A_ ( self ):
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"prediction_text": datasets.Value('string' ),
},
"references": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"answers": datasets.Sequence(datasets.Value('string' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64' ),
"paragraph": datasets.Value('int64' ),
"question": datasets.Value('int64' ),
},
"prediction": datasets.Value('int64' ),
},
"references": datasets.Value('int64' ),
}
else:
return {
"predictions": datasets.Value('int64' ),
"references": datasets.Value('int64' ),
}
def A_ ( self , lowercase , lowercase ):
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowercase , lowercase )}
elif self.config_name == "cb":
return acc_and_fa(lowercase , lowercase , fa_avg='macro' )
elif self.config_name == "record":
_lowerCamelCase : List[str] = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
_lowerCamelCase : Union[str, Any] = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(lowercase , lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowercase , lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowercase , lowercase )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' ) | 96 | 0 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
SCREAMING_SNAKE_CASE : int = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE : Dict = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
SCREAMING_SNAKE_CASE : Any = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
lowerCamelCase__ =field(
default=__snake_case, metadata={
'help': (
'The model checkpoint for weights initialization. Leave None if you want to train a model from'
' scratch.'
)
}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__snake_case )}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}, )
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'The input training data file (a text file).'} )
lowerCamelCase__ =field(
default=__snake_case, metadata={
'help': (
'The input training data files (multiple files in glob format). '
'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
)
}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
lowerCamelCase__ =field(default=__snake_case, metadata={'help': 'Whether ot not to use whole word mask.'} )
lowerCamelCase__ =field(
default=0.1_5, metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
lowerCamelCase__ =field(
default=1 / 6, metadata={
'help': (
'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
' modeling.'
)
}, )
lowerCamelCase__ =field(
default=5, metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )
lowerCamelCase__ =field(
default=-1, metadata={
'help': (
'Optional input sequence length after tokenization.'
'The training dataset will be truncated in block of this size for training.'
'Default to the model max input length for single sentence inputs (take into account special tokens).'
)
}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def lowercase ( _snake_case : DataTrainingArguments , _snake_case : PreTrainedTokenizer , _snake_case : bool = False , _snake_case : Optional[str] = None , ) ->Any:
"""simple docstring"""
def _dataset(_snake_case : List[Any] , _snake_case : str=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask''' )
return LineByLineWithRefDataset(
tokenizer=_snake_case , file_path=_snake_case , block_size=args.block_size , ref_path=_snake_case , )
return LineByLineTextDataset(tokenizer=_snake_case , file_path=_snake_case , block_size=args.block_size )
else:
return TextDataset(
tokenizer=_snake_case , file_path=_snake_case , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=_snake_case , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(_snake_case ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def lowercase ( ) ->List[Any]:
"""simple docstring"""
__snake_case : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__snake_case , __snake_case , __snake_case : Union[str, Any] = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
'''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
'''or remove the --do_eval argument.''' )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , _snake_case )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
__snake_case : Optional[Any] = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__snake_case : Optional[Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
__snake_case : Tuple = CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.tokenizer_name:
__snake_case : Dict = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__snake_case : List[Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
''' script, save it,and load it from here, using --tokenizer_name''' )
if model_args.model_name_or_path:
__snake_case : int = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , )
else:
logger.info('''Training new model from scratch''' )
__snake_case : List[Any] = AutoModelWithLMHead.from_config(_snake_case )
model.resize_token_embeddings(len(_snake_case ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
'''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
'''--mlm flag (masked language modeling).''' )
if data_args.block_size <= 0:
__snake_case : List[str] = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
__snake_case : Optional[int] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
__snake_case : Optional[Any] = (
get_dataset(_snake_case , tokenizer=_snake_case , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
__snake_case : Any = (
get_dataset(_snake_case , tokenizer=_snake_case , evaluate=_snake_case , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
__snake_case : List[Any] = DataCollatorForPermutationLanguageModeling(
tokenizer=_snake_case , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
__snake_case : Optional[Any] = DataCollatorForWholeWordMask(
tokenizer=_snake_case , mlm_probability=data_args.mlm_probability )
else:
__snake_case : Union[str, Any] = DataCollatorForLanguageModeling(
tokenizer=_snake_case , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__snake_case : Optional[int] = Trainer(
model=_snake_case , args=_snake_case , data_collator=_snake_case , train_dataset=_snake_case , eval_dataset=_snake_case , prediction_loss_only=_snake_case , )
# Training
if training_args.do_train:
__snake_case : Dict = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=_snake_case )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__snake_case : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
__snake_case : Dict = trainer.evaluate()
__snake_case : Dict = math.exp(eval_output['''eval_loss'''] )
__snake_case : List[Any] = {'''perplexity''': perplexity}
__snake_case : str = os.path.join(training_args.output_dir , '''eval_results_lm.txt''' )
if trainer.is_world_master():
with open(_snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , _snake_case , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
results.update(_snake_case )
return results
def lowercase ( _snake_case : Optional[int] ) ->Tuple:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 102 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = DDIMPipeline
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCamelCase__ = False
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
_lowerCamelCase : List[str] = DDIMScheduler()
_lowerCamelCase : Optional[int] = {'unet': unet, 'scheduler': scheduler}
return components
def A_ ( self , lowercase , lowercase=0 ):
if str(lowercase ).startswith('mps' ):
_lowerCamelCase : Dict = torch.manual_seed(lowercase )
else:
_lowerCamelCase : List[str] = torch.Generator(device=lowercase ).manual_seed(lowercase )
_lowerCamelCase : Tuple = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def A_ ( self ):
_lowerCamelCase : Any = 'cpu'
_lowerCamelCase : Tuple = self.get_dummy_components()
_lowerCamelCase : Optional[Any] = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : str = self.get_dummy_inputs(lowercase )
_lowerCamelCase : int = pipe(**lowercase ).images
_lowerCamelCase : Any = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
_lowerCamelCase : Tuple = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
_lowerCamelCase : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase , 1E-3 )
def A_ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
_lowerCamelCase : Optional[Any] = 'google/ddpm-cifar10-32'
_lowerCamelCase : Optional[Any] = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : Dict = DDIMScheduler()
_lowerCamelCase : Dict = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddim.to(lowercase )
ddim.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : List[str] = torch.manual_seed(0 )
_lowerCamelCase : str = ddim(generator=lowercase , eta=0.0 , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowerCamelCase : List[Any] = np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : Optional[int] = 'google/ddpm-ema-bedroom-256'
_lowerCamelCase : str = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : str = DDIMScheduler.from_pretrained(lowercase )
_lowerCamelCase : Optional[int] = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddpm.to(lowercase )
ddpm.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Tuple = torch.manual_seed(0 )
_lowerCamelCase : int = ddpm(generator=lowercase , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_lowerCamelCase : str = np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 96 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
A__ : Any = '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('''importlib_metadata''')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def UpperCamelCase( __UpperCamelCase : List[str] ,__UpperCamelCase : Optional[Any]=None ):
require_version(deps[pkg] ,__UpperCamelCase )
| 103 |
"""simple docstring"""
# Imports
import numpy as np
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
def A_ ( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
if red is not None:
_lowerCamelCase : Optional[int] = red
if green is not None:
_lowerCamelCase : Optional[Any] = green
if blue is not None:
_lowerCamelCase : Tuple = blue
if red_edge is not None:
_lowerCamelCase : Optional[Any] = red_edge
if nir is not None:
_lowerCamelCase : Union[str, Any] = nir
return True
def A_ ( self , lowercase="" , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
_lowerCamelCase : str = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def A_ ( self ):
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def A_ ( self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def A_ ( self ):
return self.nir * (self.red / (self.green**2))
def A_ ( self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def A_ ( self ):
return (self.nir - self.red) / (self.nir + self.red)
def A_ ( self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def A_ ( self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def A_ ( self ):
return (self.nir - self.green) / (self.nir + self.green)
def A_ ( self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def A_ ( self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def A_ ( self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def A_ ( self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def A_ ( self , lowercase=0.08 , lowercase=1.22 , lowercase=0.03 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def A_ ( self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def A_ ( self ):
return (self.nir / self.green) - 1
def A_ ( self ):
return (self.nir / self.redEdge) - 1
def A_ ( self ):
return (self.red - self.blue) / self.red
def A_ ( self ):
_lowerCamelCase : Any = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def A_ ( self ):
return self.nir - self.green
def A_ ( self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def A_ ( self ):
_lowerCamelCase : Any = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)
def A_ ( self , lowercase=0.16 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def A_ ( self , lowercase=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def A_ ( self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def A_ ( self , lowercase=None , lowercase=None ):
return (self.nir - b) / (a * self.red)
def A_ ( self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def A_ ( self ):
return (self.red + self.green + self.blue) / 30.5
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def A_ ( self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def A_ ( self ):
return self.green / (self.nir + self.red + self.green)
def A_ ( self ):
return self.nir / (self.nir + self.red + self.green)
def A_ ( self ):
return self.red / (self.nir + self.red + self.green)
def A_ ( self ):
return (self.green - self.red) / (self.green + self.red)
def A_ ( self ):
return (self.red - self.green) / (self.red + self.green)
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
_lowerCamelCase : Dict = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def A_ ( self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def A_ ( self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge) | 96 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ = logging.getLogger()
def _A ( ):
"""simple docstring"""
__lowercase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
__lowercase = parser.parse_args()
return args.f
def _A ( A__ ):
"""simple docstring"""
__lowercase = {}
__lowercase = os.path.join(A__ , '''all_results.json''' )
if os.path.exists(A__ ):
with open(A__ , '''r''' ) as f:
__lowercase = json.load(A__ )
else:
raise ValueError(F"can't find {path}" )
return results
def _A ( ):
"""simple docstring"""
__lowercase = torch.cuda.is_available() and torch_device == '''cuda'''
return is_using_cuda and is_apex_available()
lowerCAmelCase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
@classmethod
def SCREAMING_SNAKE_CASE ( cls : List[str] ):
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__lowercase = tempfile.mkdtemp()
__lowercase = os.path.join(cls.tmpdir ,'''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__lowercase = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE ( cls : str ):
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --seed=42\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.7_5 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''glue_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --block_size 128\n --per_device_train_batch_size 5\n --per_device_eval_batch_size 5\n --num_train_epochs 2\n --output_dir {tmp_dir}\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertLess(result['''perplexity'''] ,1_0_0 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''clm_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --num_train_epochs=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertLess(result['''perplexity'''] ,4_2 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''mlm_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__lowercase = 7 if get_gpu_count() > 1 else 2
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.7_5 )
self.assertLess(result['''train_loss'''] ,0.5 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''ner_no_trainer''' ) ) )
@unittest.skip(reason='''Fix me @muellerzr''' )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --seed=42\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['''eval_f1'''] ,2_8 )
self.assertGreaterEqual(result['''eval_exact'''] ,2_8 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''qa_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/swag/sample.json\n --validation_file tests/fixtures/tests_samples/swag/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=20\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.8 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''swag_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_rouge1'''] ,1_0 )
self.assertGreaterEqual(result['''eval_rouge2'''] ,2 )
self.assertGreaterEqual(result['''eval_rougeL'''] ,7 )
self.assertGreaterEqual(result['''eval_rougeLsum'''] ,7 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''summarization_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n --model_name_or_path sshleifer/student_marian_en_ro_6_1\n --source_lang en\n --target_lang ro\n --train_file tests/fixtures/tests_samples/wmt16/sample.json\n --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --num_beams=6\n --learning_rate=3e-3\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --source_lang en_XX\n --target_lang ro_RO\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_bleu'''] ,3_0 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''translation_no_trainer''' ) ) )
@slow
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = logging.StreamHandler(sys.stdout )
logger.addHandler(lowercase__ )
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n --dataset_name huggingface/semantic-segmentation-test-sample\n --output_dir {tmp_dir}\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,0.1_0 )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n --model_name_or_path google/vit-base-patch16-224-in21k\n --dataset_name hf-internal-testing/cats_vs_dogs_sample\n --learning_rate 1e-4\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 1\n --max_train_steps 2\n --train_val_split 0.1\n --seed 42\n --output_dir {tmp_dir}\n --with_tracking\n --checkpointing_steps 1\n ".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
# The base model scores a 25%
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.6 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''step_1''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''image_classification_no_trainer''' ) ) )
| 104 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self , lowercase , lowercase=768 ):
super().__init__(lowercase )
_lowerCamelCase : Any = proj_size
_lowerCamelCase : Dict = CLIPVisionModel(lowercase )
_lowerCamelCase : List[str] = PaintByExampleMapper(lowercase )
_lowerCamelCase : Optional[Any] = nn.LayerNorm(config.hidden_size )
_lowerCamelCase : int = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
_lowerCamelCase : str = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def A_ ( self , lowercase , lowercase=False ):
_lowerCamelCase : Union[str, Any] = self.model(pixel_values=lowercase )
_lowerCamelCase : int = clip_output.pooler_output
_lowerCamelCase : str = self.mapper(latent_states[:, None] )
_lowerCamelCase : List[Any] = self.final_layer_norm(lowercase )
_lowerCamelCase : Dict = self.proj_out(lowercase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , lowercase ):
super().__init__()
_lowerCamelCase : Tuple = (config.num_hidden_layers + 1) // 5
_lowerCamelCase : int = config.hidden_size
_lowerCamelCase : Optional[Any] = 1
_lowerCamelCase : str = nn.ModuleList(
[
BasicTransformerBlock(lowercase , lowercase , lowercase , activation_fn='gelu' , attention_bias=lowercase )
for _ in range(lowercase )
] )
def A_ ( self , lowercase ):
for block in self.blocks:
_lowerCamelCase : Tuple = block(lowercase )
return hidden_states | 96 | 0 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a : str = logging.get_logger(__name__)
class __UpperCamelCase ( a__ ):
lowerCamelCase : List[Any] =["""input_values""", """attention_mask"""]
def __init__( self , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 1_6000 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = False , lowerCAmelCase__ = 80 , lowerCAmelCase__ = 16 , lowerCAmelCase__ = 64 , lowerCAmelCase__ = "hann_window" , lowerCAmelCase__ = 1.0 , lowerCAmelCase__ = 80 , lowerCAmelCase__ = 7600 , lowerCAmelCase__ = 1E-10 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = True , **lowerCAmelCase__ , ) -> Tuple:
super().__init__(feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ )
a : Tuple = do_normalize
a : Optional[int] = return_attention_mask
a : Optional[Any] = num_mel_bins
a : Optional[int] = hop_length
a : str = win_length
a : Optional[int] = win_function
a : str = frame_signal_scale
a : int = fmin
a : Any = fmax
a : Dict = mel_floor
a : Tuple = reduction_factor
a : List[Any] = win_length * sampling_rate // 1000
a : Dict = hop_length * sampling_rate // 1000
a : Optional[int] = optimal_fft_length(self.sample_size )
a : List[str] = (self.n_fft // 2) + 1
a : str = window_function(window_length=self.sample_size , name=self.win_function , periodic=lowerCAmelCase__ )
a : Optional[int] = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm="slaney" , mel_scale="slaney" , )
if frame_signal_scale != 1.0:
warnings.warn(
"The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers" , lowerCAmelCase__ , )
if reduction_factor != 2.0:
warnings.warn(
"The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers" , lowerCAmelCase__ , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __a ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
a : int = np.array(lowerCAmelCase__ , np.intaa )
a : Any = []
for vector, length in zip(lowerCAmelCase__ , attention_mask.sum(-1 ) ):
a : str = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
a : Optional[int] = padding_value
normed_input_values.append(lowerCAmelCase__ )
else:
a : Dict = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __a ( self , lowerCAmelCase__ , ) -> np.ndarray:
a : Optional[int] = spectrogram(
lowerCAmelCase__ , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel="log10" , )
return log_mel_spec.T
def __call__( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> BatchFeature:
if audio is None and audio_target is None:
raise ValueError("You must provide either `audio` or `audio_target` values." )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
f""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
if audio is not None:
a : Tuple = self._process_audio(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ , )
else:
a : Any = None
if audio_target is not None:
a : Tuple = self._process_audio(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ , )
if inputs is None:
return inputs_target
else:
a : Dict = inputs_target["input_values"]
a : List[Any] = inputs_target.get("attention_mask" )
if decoder_attention_mask is not None:
a : int = decoder_attention_mask
return inputs
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> BatchFeature:
a : Union[str, Any] = isinstance(lowerCAmelCase__ , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a : Optional[int] = is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a : List[str] = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
a : str = np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
a : int = speech.astype(np.floataa )
# always return batch
if not is_batched:
a : Optional[Any] = [speech]
# needed to make pad() work on spectrogram inputs
a : Union[str, Any] = self.feature_size
# convert into correct format for padding
if is_target:
a : Union[str, Any] = [self._extract_mel_features(lowerCAmelCase__ ) for waveform in speech]
a : str = BatchFeature({"input_values": features} )
a : Dict = self.num_mel_bins
else:
a : List[Any] = BatchFeature({"input_values": speech} )
a : Any = self.pad(
lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
a : int = feature_size_hack
# convert input values to correct format
a : Optional[int] = padded_inputs["input_values"]
if not isinstance(input_values[0] , np.ndarray ):
a : Dict = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(lowerCAmelCase__ , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
a : Tuple = [array.astype(np.floataa ) for array in input_values]
elif isinstance(lowerCAmelCase__ , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
a : Optional[Any] = input_values.astype(np.floataa )
# convert attention_mask to correct format
a : Tuple = padded_inputs.get("attention_mask" )
if attention_mask is not None:
a : str = [np.asarray(lowerCAmelCase__ , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
a : Union[str, Any] = (
attention_mask
if self._get_padding_strategies(lowerCAmelCase__ , max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
a : Optional[Any] = self.zero_mean_unit_var_norm(
padded_inputs["input_values"] , attention_mask=lowerCAmelCase__ , padding_value=self.padding_value )
if return_tensors is not None:
a : Optional[Any] = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
def __a ( self ) -> Dict[str, Any]:
a : Dict = super().to_dict()
# Don't serialize these as they are derived from the other properties.
a : str = ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
for name in names:
if name in output:
del output[name]
return output
| 105 |
"""simple docstring"""
lowercase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowercase__ = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : List[Any] = from_type.lower().strip('s' )
_lowerCamelCase : List[Any] = to_type.lower().strip('s' )
_lowerCamelCase : Optional[int] = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
_lowerCamelCase : Any = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
if from_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Tuple = (
f'''Invalid \'from_type\' value: {from_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
if to_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Any = (
f'''Invalid \'to_type\' value: {to_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
_lowerCamelCase : List[Any] = METRIC_CONVERSION[from_sanitized]
_lowerCamelCase : int = METRIC_CONVERSION[to_sanitized]
_lowerCamelCase : List[str] = 1
if from_exponent > to_exponent:
_lowerCamelCase : List[str] = from_exponent - to_exponent
else:
_lowerCamelCase : List[Any] = -(to_exponent - from_exponent)
return value * pow(10 , lowercase__ )
if __name__ == "__main__":
from doctest import testmod
testmod() | 96 | 0 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCamelCase : int = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , '''config.json''' ) ) and os.path.isfile(
os.path.join(A_ , '''config.json''' ) ):
os.remove(os.path.join(A_ , '''config.json''' ) )
if os.path.exists(os.path.join(A_ , '''pytorch_model.bin''' ) ) and os.path.isfile(
os.path.join(A_ , '''pytorch_model.bin''' ) ):
os.remove(os.path.join(A_ , '''pytorch_model.bin''' ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def __SCREAMING_SNAKE_CASE ( A_ , A_=False ):
lowerCAmelCase__ : Optional[Any] = 2
if unlogit:
lowerCAmelCase__ : Union[str, Any] = torch.pow(A_ , A_ )
lowerCAmelCase__ : Optional[Any] = p * torch.log(A_ )
lowerCAmelCase__ : List[Any] = 0
return -plogp.sum(dim=-1 )
def __SCREAMING_SNAKE_CASE ( A_ ):
logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:.5f}' for x in tensor[row].cpu().data ) )
else:
logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:d}' for x in tensor[row].cpu().data ) )
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = model.config.num_hidden_layers, model.config.num_attention_heads
lowerCAmelCase__ : Dict = torch.zeros(A_ , A_ ).to(args.device )
lowerCAmelCase__ : int = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
lowerCAmelCase__ : Union[str, Any] = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Optional[int] = 0.0
lowerCAmelCase__ : Optional[int] = 0.0
for step, inputs in enumerate(tqdm(A_ , desc='''Iteration''' , disable=args.local_rank not in [-1, 0] ) ):
lowerCAmelCase__ : Any = tuple(t.to(args.device ) for t in inputs )
((lowerCAmelCase__) ,) : List[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
lowerCAmelCase__ : Any = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Dict = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
lowerCAmelCase__ : Dict = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
lowerCAmelCase__ : Any = 2
lowerCAmelCase__ : Dict = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0
if not args.dont_normalize_global_importance:
lowerCAmelCase__ : List[Any] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''' )
print_ad_tensor(A_ )
if compute_importance:
logger.info('''Head importance scores''' )
print_ad_tensor(A_ )
logger.info('''Head ranked by importance scores''' )
lowerCAmelCase__ : str = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
lowerCAmelCase__ : Optional[int] = torch.arange(
head_importance.numel() , device=args.device )
lowerCAmelCase__ : int = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : List[str] = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
lowerCAmelCase__ : Union[str, Any] = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , A_ , original_score * args.masking_threshold )
lowerCAmelCase__ : Union[str, Any] = torch.ones_like(A_ )
lowerCAmelCase__ : List[str] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
lowerCAmelCase__ : int = original_score
while current_score >= original_score * args.masking_threshold:
lowerCAmelCase__ : Union[str, Any] = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
lowerCAmelCase__ : str = float('''Inf''' )
lowerCAmelCase__ : List[Any] = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print('''BREAK BY num_to_mask''' )
break
# mask heads
lowerCAmelCase__ : List[Any] = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist() ) )
lowerCAmelCase__ : int = new_head_mask.view(-1 )
lowerCAmelCase__ : Optional[int] = 0.0
lowerCAmelCase__ : Union[str, Any] = new_head_mask.view_as(A_ )
lowerCAmelCase__ : Tuple = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
lowerCAmelCase__ : Tuple = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_00 , )
logger.info('''Final head mask''' )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , '''head_mask.npy''' ) , head_mask.detach().cpu().numpy() )
return head_mask
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ : Optional[Any] = datetime.now()
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Optional[Any] = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
lowerCAmelCase__ : Optional[Any] = 1 / loss
lowerCAmelCase__ : Tuple = datetime.now() - before_time
lowerCAmelCase__ : int = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : List[Any] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
lowerCAmelCase__ : int = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
lowerCAmelCase__ : List[Any] = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : Any = datetime.now()
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : int = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
lowerCAmelCase__ : int = 1 / loss
lowerCAmelCase__ : Dict = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , A_ , A_ , pruned_num_params / original_num_params * 1_00 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , A_ , A_ )
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 1_00 )
save_model(A_ , args.output_dir )
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=A_ , type=A_ , required=A_ , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=A_ , type=A_ , required=A_ , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=A_ , type=A_ , required=A_ , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=A_ , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=A_ , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=A_ , type=A_ , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=A_ , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''' )
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''' )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''' )
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''' )
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=A_ , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=A_ , help='''Amount to heads to masking at each masking step.''' )
parser.add_argument('''--metric_name''' , default='''acc''' , type=A_ , help='''Metric to use for head masking.''' )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=A_ , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=A_ , help='''Batch size.''' )
parser.add_argument('''--seed''' , type=A_ , default=42 )
parser.add_argument('''--local_rank''' , type=A_ , default=-1 , help='''local_rank for distributed training on gpus''' )
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''' )
parser.add_argument('''--server_ip''' , type=A_ , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=A_ , default='''''' , help='''Can be used for distant debugging.''' )
lowerCAmelCase__ : Optional[Any] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
lowerCAmelCase__ : Union[str, Any] = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
lowerCAmelCase__ : str = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
lowerCAmelCase__ : Dict = torch.device('''cuda''' , args.local_rank )
lowerCAmelCase__ : Union[str, Any] = 1
torch.distributed.init_process_group(backend='''nccl''' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
lowerCAmelCase__ : List[str] = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
lowerCAmelCase__ : Dict = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
lowerCAmelCase__ : List[Any] = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , '''run_args.bin''' ) )
logger.info('''Training/evaluation parameters %s''' , A_ )
# Prepare dataset
lowerCAmelCase__ : str = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
lowerCAmelCase__ : Union[str, Any] = (torch.from_numpy(A_ ),)
lowerCAmelCase__ : Tuple = TensorDataset(*A_ )
lowerCAmelCase__ : Optional[int] = RandomSampler(A_ )
lowerCAmelCase__ : Dict = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
lowerCAmelCase__ : Tuple = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 106 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMSNModel""",
"""ViTMSNForImageClassification""",
"""ViTMSNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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 RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case__ :
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Optional[Any]=3 , __lowerCamelCase : Union[str, Any]=32 , __lowerCamelCase : Tuple=3 , __lowerCamelCase : Tuple=10 , __lowerCamelCase : List[str]=[10, 20, 30, 40] , __lowerCamelCase : int=[1, 1, 2, 1] , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : int=True , __lowerCamelCase : Union[str, Any]="relu" , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : Any=None , ) -> Optional[Any]:
a = parent
a = batch_size
a = image_size
a = num_channels
a = embeddings_size
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = hidden_act
a = num_labels
a = scope
a = len(__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase ( self : Any ) -> List[Any]:
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Dict , __lowerCamelCase : Dict ) -> Union[str, Any]:
a = RegNetModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __UpperCAmelCase ( self : int , __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : Union[str, Any] ) -> Optional[int]:
a = self.num_labels
a = RegNetForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class snake_case__ (_UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
{"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Any = False
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : str = False
def __UpperCAmelCase ( self : Any ) -> Any:
a = RegNetModelTester(self )
a = ConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase )
def __UpperCAmelCase ( self : Dict ) -> Any:
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 : List[str] ) -> List[Any]:
return
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def __UpperCAmelCase ( self : str ) -> Optional[Any]:
pass
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def __UpperCAmelCase ( self : Any ) -> Union[str, Any]:
pass
def __UpperCAmelCase ( self : Dict ) -> Optional[Any]:
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCamelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def __UpperCAmelCase ( self : int ) -> List[str]:
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(config=__lowerCamelCase )
for name, module in model.named_modules():
if isinstance(__lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def __UpperCAmelCase ( self : Dict ) -> Any:
def check_hidden_states_output(__lowerCamelCase : List[Any] , __lowerCamelCase : Any , __lowerCamelCase : List[str] ):
a = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCamelCase ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
a = layer_type
a = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Any ) -> List[str]:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
@slow
def __UpperCAmelCase ( self : Optional[int] ) -> Any:
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = RegNetModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def __magic_name__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def __UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
a = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__lowerCamelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(images=__lowerCamelCase , return_tensors="pt" ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCamelCase )
# verify the logits
a = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
a = torch.tensor([-0.4_180, -1.5_051, -3.4_836] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1e-4 ) )
| 107 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : List[Any] = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Tuple = boundary[0]
_lowerCamelCase : Dict = boundary[1]
_lowerCamelCase : List[Any] = make_points(lowercase__ , lowercase__ , lowercase__ )
_lowerCamelCase : List[Any] = 0.0
y += (h / 2.0) * f(lowercase__ )
for i in x_i:
# print(i)
y += h * f(lowercase__ )
y += (h / 2.0) * f(lowercase__ )
return y
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : str = a + h
while x < (b - h):
yield x
_lowerCamelCase : int = x + h
def _snake_case ( lowercase__ ): # enter your function here
_lowerCamelCase : Optional[Any] = (x - 0) * (x - 0)
return y
def _snake_case ( ):
_lowerCamelCase : int = 0.0 # Lower bound of integration
_lowerCamelCase : Optional[int] = 1.0 # Upper bound of integration
_lowerCamelCase : List[str] = 1_0.0 # define number of steps or resolution
_lowerCamelCase : List[Any] = [a, b] # define boundary of integration
_lowerCamelCase : Optional[Any] = method_a(lowercase__ , lowercase__ )
print(f'''y = {y}''' )
if __name__ == "__main__":
main() | 96 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Any = size
# approximate the overall size of segment tree with given value
lowerCAmelCase : Optional[int] = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowerCAmelCase : List[str] = [0 for i in range(0 , 4 * size )]
lowerCAmelCase : Dict = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
return idx * 2
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
return idx * 2 + 1
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if left_element == right_element:
lowerCAmelCase : List[str] = a[left_element - 1]
else:
lowerCAmelCase : Tuple = (left_element + right_element) // 2
self.build(self.left(snake_case__ ) , snake_case__ , snake_case__ , snake_case__ )
self.build(self.right(snake_case__ ) , mid + 1 , snake_case__ , snake_case__ )
lowerCAmelCase : Tuple = max(
self.segment_tree[self.left(snake_case__ )] , self.segment_tree[self.right(snake_case__ )] )
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if self.flag[idx] is True:
lowerCAmelCase : Optional[int] = self.lazy[idx]
lowerCAmelCase : List[str] = False
if left_element != right_element:
lowerCAmelCase : Optional[Any] = self.lazy[idx]
lowerCAmelCase : List[Any] = self.lazy[idx]
lowerCAmelCase : List[Any] = True
lowerCAmelCase : Optional[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowerCAmelCase : str = val
if left_element != right_element:
lowerCAmelCase : Optional[Any] = val
lowerCAmelCase : Union[str, Any] = val
lowerCAmelCase : int = True
lowerCAmelCase : int = True
return True
lowerCAmelCase : List[str] = (left_element + right_element) // 2
self.update(self.left(snake_case__ ) , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
self.update(self.right(snake_case__ ) , mid + 1 , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : Optional[int] = max(
self.segment_tree[self.left(snake_case__ )] , self.segment_tree[self.right(snake_case__ )] )
return True
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if self.flag[idx] is True:
lowerCAmelCase : List[Any] = self.lazy[idx]
lowerCAmelCase : str = False
if left_element != right_element:
lowerCAmelCase : Tuple = self.lazy[idx]
lowerCAmelCase : List[Any] = self.lazy[idx]
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : str = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
lowerCAmelCase : Any = (left_element + right_element) // 2
lowerCAmelCase : Optional[int] = self.query(self.left(snake_case__ ) , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : Dict = self.query(self.right(snake_case__ ) , mid + 1 , snake_case__ , snake_case__ , snake_case__ )
return max(snake_case__ , snake_case__ )
def __str__( self ):
"""simple docstring"""
return str([self.query(1 , 1 , self.size , snake_case__ , snake_case__ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
lowerCAmelCase__ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
lowerCAmelCase__ = 15
lowerCAmelCase__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| 108 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ ):
return math.sqrt(lowercase__ ) * math.sqrt(lowercase__ ) == num
def _snake_case ( lowercase__ ):
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : List[Any] = n
while left <= right:
_lowerCamelCase : str = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_lowerCamelCase : str = mid - 1
else:
_lowerCamelCase : Optional[int] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def _snake_case ( UpperCamelCase : Optional[Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : str , UpperCamelCase : Optional[int] ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"
def _snake_case ( UpperCamelCase : List[str] , UpperCamelCase : str , UpperCamelCase : Dict , UpperCamelCase : int , UpperCamelCase : Optional[Any]=True ):
model.train()
UpperCAmelCase : str = model(UpperCamelCase )
UpperCAmelCase : Optional[int] = F.mse_loss(UpperCamelCase , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(UpperCamelCase )
def _snake_case ( UpperCamelCase : Union[str, Any] , UpperCamelCase : List[Any]=False ):
set_seed(42 )
UpperCAmelCase : Tuple = RegressionModel()
UpperCAmelCase : List[Any] = deepcopy(UpperCamelCase )
UpperCAmelCase : Optional[Any] = RegressionDataset(length=80 )
UpperCAmelCase : Union[str, Any] = DataLoader(UpperCamelCase , batch_size=16 )
model.to(accelerator.device )
if sched:
UpperCAmelCase : Union[str, Any] = AdamW(params=model.parameters() , lr=1e-3 )
UpperCAmelCase : List[str] = AdamW(params=ddp_model.parameters() , lr=1e-3 )
UpperCAmelCase : Tuple = LambdaLR(UpperCamelCase , lr_lambda=lambda UpperCamelCase : epoch**0.65 )
UpperCAmelCase : Tuple = LambdaLR(UpperCamelCase , lr_lambda=lambda UpperCamelCase : epoch**0.65 )
# Make a copy of `model`
if sched:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = accelerator.prepare(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
else:
UpperCAmelCase , UpperCAmelCase : int = accelerator.prepare(UpperCamelCase , UpperCamelCase )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def _snake_case ( UpperCamelCase : Tuple ):
# Test when on a single CPU or GPU that the context manager does nothing
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[Any] = get_training_setup(UpperCamelCase )
# Use a single batch
UpperCAmelCase , UpperCAmelCase : Optional[int] = next(iter(UpperCamelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : Optional[Any] = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : Dict = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(UpperCamelCase ):
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
else:
# Sync grads
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
UpperCAmelCase : List[str] = ddp_input[torch.randperm(len(UpperCamelCase ) )]
def _snake_case ( UpperCamelCase : int ):
# Test on distributed setup that context manager behaves properly
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = get_training_setup(UpperCamelCase )
# Use a single batch
UpperCAmelCase , UpperCAmelCase : Any = next(iter(UpperCamelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : Any = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : int = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(UpperCamelCase ):
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
else:
# Sync grads
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
UpperCAmelCase : Optional[int] = ddp_input[torch.randperm(len(UpperCamelCase ) )]
def _snake_case ( UpperCamelCase : Union[str, Any]=False , UpperCamelCase : Any=False ):
UpperCAmelCase : Union[str, Any] = Accelerator(
split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[Any] = get_training_setup(UpperCamelCase )
for iteration, batch in enumerate(UpperCamelCase ):
UpperCAmelCase , UpperCAmelCase : Tuple = batch.values()
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : str = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : int = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(UpperCamelCase ):
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
UpperCAmelCase : str = ddp_input[torch.randperm(len(UpperCamelCase ) )]
GradientState._reset_state()
def _snake_case ( UpperCamelCase : List[Any]=False , UpperCamelCase : List[Any]=False ):
UpperCAmelCase : Optional[Any] = Accelerator(
split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = get_training_setup(UpperCamelCase , UpperCamelCase )
for iteration, batch in enumerate(UpperCamelCase ):
UpperCAmelCase , UpperCAmelCase : Any = batch.values()
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : Any = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : Optional[int] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(UpperCamelCase ):
step_model(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
UpperCAmelCase : List[str] = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase ))
if accelerator.num_processes > 1:
check_model_parameters(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
GradientState._reset_state()
def _snake_case ( ):
UpperCAmelCase : str = Accelerator()
UpperCAmelCase : Any = RegressionDataset(length=80 )
UpperCAmelCase : Tuple = DataLoader(UpperCamelCase , batch_size=16 )
UpperCAmelCase : List[Any] = RegressionDataset(length=96 )
UpperCAmelCase : str = DataLoader(UpperCamelCase , batch_size=16 )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = accelerator.prepare(UpperCamelCase , UpperCamelCase )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(UpperCamelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase )
if iteration < len(UpperCamelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(UpperCamelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase )
if batch_num < len(UpperCamelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def _snake_case ( ):
UpperCAmelCase : Dict = Accelerator()
UpperCAmelCase : Optional[Any] = accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(UpperCamelCase )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(UpperCamelCase )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """ , F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**" , )
test_gradient_accumulation(UpperCamelCase , UpperCamelCase )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""" , """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , """`split_batches=False`, `dispatch_batches=False`**""" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**" , )
test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase , UpperCamelCase )
def _snake_case ( UpperCamelCase : List[str] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 109 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(lowercase__ , lowercase__ ) or not all(
isinstance(lowercase__ , lowercase__ ) and len(lowercase__ ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
_lowerCamelCase : dict[str, Any] = {}
_lowerCamelCase : List[Any] = 'WORD_KEEPER'
for word in words:
_lowerCamelCase : Dict = trie
for c in word:
if c not in trie_node:
_lowerCamelCase : Any = {}
_lowerCamelCase : str = trie_node[c]
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Dict = len(lowercase__ )
# Dynamic programming method
@functools.cache
def is_breakable(lowercase__ ) -> bool:
if index == len_string:
return True
_lowerCamelCase : List[Any] = trie
for i in range(lowercase__ , lowercase__ ):
_lowerCamelCase : Any = trie_node.get(string[i] , lowercase__ )
if trie_node is None:
return False
if trie_node.get(lowercase__ , lowercase__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = filter(lambda SCREAMING_SNAKE_CASE : p.requires_grad , model.parameters() )
lowercase__ = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowerCAmelCase = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if metric == "rouge2":
lowercase__ = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
lowercase__ = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
lowercase__ = '''{val_avg_em:.4f}-{step_count}'''
elif metric == "loss":
lowercase__ = '''{val_avg_loss:.4f}-{step_count}'''
else:
raise NotImplementedError(
f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
''' function.''' )
lowercase__ = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE , filename=SCREAMING_SNAKE_CASE , monitor=f'val_{metric}' , mode='''max''' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return EarlyStopping(
monitor=f'val_{metric}' , mode='''min''' if '''loss''' in metric else '''max''' , patience=SCREAMING_SNAKE_CASE , verbose=SCREAMING_SNAKE_CASE , )
class _a ( pl.Callback ):
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: List[str] , UpperCamelCase_: Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = {f'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(UpperCamelCase_ )
@rank_zero_only
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: pl.Trainer , UpperCamelCase_: pl.LightningModule , UpperCamelCase_: str , UpperCamelCase_: Optional[int]=True ) -> None:
"""simple docstring"""
logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
lowercase__ = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
lowercase__ = Path(pl_module.hparams.output_dir )
if type_path == "test":
lowercase__ = od / '''test_results.txt'''
lowercase__ = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
lowercase__ = od / f'{type_path}_results/{trainer.global_step:05d}.txt'
lowercase__ = od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=UpperCamelCase_ )
generations_file.parent.mkdir(exist_ok=UpperCamelCase_ )
with open(UpperCamelCase_ , '''a+''' ) as writer:
for key in sorted(UpperCamelCase_ ):
if key in ["log", "progress_bar", "preds"]:
continue
lowercase__ = metrics[key]
if isinstance(UpperCamelCase_ , torch.Tensor ):
lowercase__ = val.item()
lowercase__ = f'{key}: {val:.6f}\n'
writer.write(UpperCamelCase_ )
if not save_generations:
return
if "preds" in metrics:
lowercase__ = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(UpperCamelCase_ )
@rank_zero_only
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Tuple , UpperCamelCase_: Tuple ) -> Optional[int]:
"""simple docstring"""
try:
lowercase__ = pl_module.model.model.num_parameters()
except AttributeError:
lowercase__ = pl_module.model.num_parameters()
lowercase__ = count_trainable_parameters(UpperCamelCase_ )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6} )
@rank_zero_only
def lowerCamelCase_ ( self: str , UpperCamelCase_: pl.Trainer , UpperCamelCase_: pl.LightningModule ) -> Tuple:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(UpperCamelCase_ , UpperCamelCase_ , '''test''' )
@rank_zero_only
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase_: pl.Trainer , UpperCamelCase_: List[str] ) -> Union[str, Any]:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 110 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
if len(lowercase__ ) == 1:
return True
_lowerCamelCase : List[Any] = series[1] - series[0]
for index in range(len(lowercase__ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
_lowerCamelCase : Optional[int] = 0
for val in series:
answer += val
return answer / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase__ = 5_0000
UpperCAmelCase__ = 5000
UpperCAmelCase__ , UpperCAmelCase__ = os.path.split(__file__)
UpperCAmelCase__ = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple ) -> Optional[int]:
'''simple docstring'''
for i in range(lowercase__ ):
_UpperCAmelCase = dataset[i]
@get_duration
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : str ) -> List[str]:
'''simple docstring'''
for i in range(0 , len(lowercase__ ) , lowercase__ ):
_UpperCAmelCase = dataset[i : i + batch_size]
@get_duration
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] ) -> Tuple:
'''simple docstring'''
with dataset.formatted_as(type=lowercase__ ):
for i in range(lowercase__ ):
_UpperCAmelCase = dataset[i]
@get_duration
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
with dataset.formatted_as(type=lowercase__ ):
for i in range(0 , lowercase__ , lowercase__ ):
_UpperCAmelCase = dataset[i : i + batch_size]
def A ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = {'num examples': SPEED_TEST_N_EXAMPLES}
_UpperCAmelCase = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
(read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
(read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
]
_UpperCAmelCase = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print('generating dataset' )
_UpperCAmelCase = datasets.Features(
{'list': datasets.Sequence(datasets.Value('float32' ) ), 'numbers': datasets.Value('float32' )} )
_UpperCAmelCase = generate_example_dataset(
os.path.join(lowercase__ , 'dataset.arrow' ) , lowercase__ , num_examples=lowercase__ , seq_shapes={'list': (100,)} , )
print('first set of iterations' )
for func, kwargs in functions:
print(func.__name__ , str(lowercase__ ) )
_UpperCAmelCase = func(lowercase__ , **lowercase__ )
print('shuffling dataset' )
_UpperCAmelCase = dataset.shuffle()
print('Second set of iterations (after shuffling' )
for func, kwargs in functions_shuffled:
print('shuffled ' , func.__name__ , str(lowercase__ ) )
_UpperCAmelCase = func(
lowercase__ , **lowercase__ )
with open(lowercase__ , 'wb' ) as f:
f.write(json.dumps(lowercase__ ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 339 |
"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase__ = 16
lowercase__ = 32
def _snake_case ( lowercase__ , lowercase__ = 16 , lowercase__ = "bert-base-cased" ):
_lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(lowercase__ )
_lowerCamelCase : Tuple = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowercase__ ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Union[str, Any] = 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
_lowerCamelCase : int = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCamelCase : Optional[int] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowercase__ ):
# 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(lowercase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(lowercase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
_lowerCamelCase : List[str] = DataLoader(
tokenized_datasets['train'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
_lowerCamelCase : int = DataLoader(
tokenized_datasets['validation'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ , lowercase__ ):
# Initialize accelerator
_lowerCamelCase : Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCamelCase : Optional[int] = config['lr']
_lowerCamelCase : Optional[int] = int(config['num_epochs'] )
_lowerCamelCase : Union[str, Any] = int(config['seed'] )
_lowerCamelCase : Optional[int] = int(config['batch_size'] )
_lowerCamelCase : Dict = args.model_name_or_path
set_seed(lowercase__ )
_lowerCamelCase, _lowerCamelCase : Optional[int] = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCamelCase : int = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
_lowerCamelCase : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowerCamelCase : Union[str, Any] = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
_lowerCamelCase : str = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
_lowerCamelCase : Tuple = 1
_lowerCamelCase : List[Any] = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowerCamelCase : Tuple = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
_lowerCamelCase : Any = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , 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.
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
_lowerCamelCase : Union[str, Any] = 0
# We also need to keep track of the stating epoch so files are named properly
_lowerCamelCase : Dict = 0
# Now we train the model
_lowerCamelCase : Dict = evaluate.load('glue' , 'mrpc' )
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
_lowerCamelCase : List[Any] = model(**lowercase__ )
_lowerCamelCase : int = outputs.loss
_lowerCamelCase : Dict = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_lowerCamelCase : Union[str, Any] = 0
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():
_lowerCamelCase : Optional[int] = model(**lowercase__ )
_lowerCamelCase : Dict = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowerCamelCase, _lowerCamelCase : List[str] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
_lowerCamelCase : Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCamelCase : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
_lowerCamelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , lowercase__ )
_lowerCamelCase : Tuple = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
_lowerCamelCase : str = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ):
_lowerCamelCase : Any = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=lowercase__ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=lowercase__ , )
parser.add_argument(
'--output_dir' , type=lowercase__ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=lowercase__ , default=lowercase__ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=lowercase__ , default=3 , help='Number of train epochs.' , )
_lowerCamelCase : Optional[Any] = parser.parse_args()
_lowerCamelCase : str = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main() | 96 | 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 PreTrainedTokenizer
from ...utils import logging
A__: List[str] = logging.get_logger(__name__)
A__: int = {'''vocab_file''': '''spm_char.model'''}
A__: Tuple = {
'''vocab_file''': {
'''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
'''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
'''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
}
}
A__: Dict = {
'''microsoft/speecht5_asr''': 1024,
'''microsoft/speecht5_tts''': 1024,
'''microsoft/speecht5_vc''': 1024,
}
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : int = ["input_ids", "attention_mask"]
def __init__( self :Any , SCREAMING_SNAKE_CASE :Optional[Any] , SCREAMING_SNAKE_CASE :List[str]="<s>" , SCREAMING_SNAKE_CASE :Optional[int]="</s>" , SCREAMING_SNAKE_CASE :Tuple="<unk>" , SCREAMING_SNAKE_CASE :int="<pad>" , SCREAMING_SNAKE_CASE :Optional[Any] = None , **SCREAMING_SNAKE_CASE :int , ) -> Any:
'''simple docstring'''
_a : List[str] ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE , )
_a : str =vocab_file
_a : Union[str, Any] =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(SCREAMING_SNAKE_CASE )
@property
def __UpperCAmelCase ( self :Optional[Any] ) -> int:
'''simple docstring'''
return self.sp_model.get_piece_size()
def __UpperCAmelCase ( self :List[Any] ) -> Any:
'''simple docstring'''
_a : Optional[int] ={self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self :int ) -> int:
'''simple docstring'''
_a : Optional[Any] =self.__dict__.copy()
_a : Tuple =None
return state
def __setstate__( self :Any , SCREAMING_SNAKE_CASE :Tuple ) -> Tuple:
'''simple docstring'''
_a : int =d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_a : Tuple ={}
_a : List[str] =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __UpperCAmelCase ( self :Tuple , SCREAMING_SNAKE_CASE :Dict ) -> Union[str, Any]:
'''simple docstring'''
return self.sp_model.encode(SCREAMING_SNAKE_CASE , out_type=SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :Any , SCREAMING_SNAKE_CASE :Dict ) -> List[str]:
'''simple docstring'''
return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :Tuple , SCREAMING_SNAKE_CASE :Dict ) -> str:
'''simple docstring'''
_a : Dict =self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE )
return token
def __UpperCAmelCase ( self :int , SCREAMING_SNAKE_CASE :Any ) -> Union[str, Any]:
'''simple docstring'''
_a : Dict =[]
_a : List[str] =''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE ) + token
_a : List[Any] =[]
else:
current_sub_tokens.append(SCREAMING_SNAKE_CASE )
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE )
return out_string.strip()
def __UpperCAmelCase ( self :Optional[Any] , SCREAMING_SNAKE_CASE :Dict , SCREAMING_SNAKE_CASE :List[str]=None ) -> int:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def __UpperCAmelCase ( self :List[str] , SCREAMING_SNAKE_CASE :Union[str, Any] , SCREAMING_SNAKE_CASE :str = None , SCREAMING_SNAKE_CASE :Union[str, Any] = False ) -> Optional[Any]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE , token_ids_a=SCREAMING_SNAKE_CASE , already_has_special_tokens=SCREAMING_SNAKE_CASE )
_a : Optional[int] =[1]
if token_ids_a is None:
return ([0] * len(SCREAMING_SNAKE_CASE )) + suffix_ones
return ([0] * len(SCREAMING_SNAKE_CASE )) + ([0] * len(SCREAMING_SNAKE_CASE )) + suffix_ones
def __UpperCAmelCase ( self :str , SCREAMING_SNAKE_CASE :List[Any] , SCREAMING_SNAKE_CASE :Union[str, Any] = None ) -> Dict:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
_a : Tuple =os.path.join(
SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE , """wb""" ) as fi:
_a : str =self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 276 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[Any] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 ) | 96 | 0 |
'''simple docstring'''
import numpy as np
a : Union[str, Any] = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class a :
def __init__( self : Tuple ):
snake_case_ = np.array(lowercase_ )
def A_ ( self : List[str] , lowercase_ : Union[str, Any] ):
snake_case_ = np.where(letter == self.SQUARE )
snake_case_ = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def A_ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = self.SQUARE[indexa - 1, indexa - 1]
return letter
def A_ ( self : Optional[Any] , lowercase_ : Any ):
snake_case_ = message.lower()
snake_case_ = message.replace(''' ''' , '''''' )
snake_case_ = message.replace('''j''' , '''i''' )
snake_case_ = np.empty((2, len(lowercase_ )) )
for letter_index in range(len(lowercase_ ) ):
snake_case_ = self.letter_to_numbers(message[letter_index] )
snake_case_ = numbers[0]
snake_case_ = numbers[1]
snake_case_ = first_step.reshape(2 * len(lowercase_ ) )
snake_case_ = ''
for numbers_index in range(len(lowercase_ ) ):
snake_case_ = int(second_step[numbers_index * 2] )
snake_case_ = int(second_step[(numbers_index * 2) + 1] )
snake_case_ = self.numbers_to_letter(lowercase_ , lowercase_ )
snake_case_ = encoded_message + letter
return encoded_message
def A_ ( self : Dict , lowercase_ : Tuple ):
snake_case_ = message.lower()
message.replace(''' ''' , '''''' )
snake_case_ = np.empty(2 * len(lowercase_ ) )
for letter_index in range(len(lowercase_ ) ):
snake_case_ = self.letter_to_numbers(message[letter_index] )
snake_case_ = numbers[0]
snake_case_ = numbers[1]
snake_case_ = first_step.reshape((2, len(lowercase_ )) )
snake_case_ = ''
for numbers_index in range(len(lowercase_ ) ):
snake_case_ = int(second_step[0, numbers_index] )
snake_case_ = int(second_step[1, numbers_index] )
snake_case_ = self.numbers_to_letter(lowercase_ , lowercase_ )
snake_case_ = decoded_message + letter
return decoded_message
| 56 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""IBertForMaskedLM""",
"""IBertForMultipleChoice""",
"""IBertForQuestionAnswering""",
"""IBertForSequenceClassification""",
"""IBertForTokenClassification""",
"""IBertModel""",
"""IBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = tuple[float, float, float]
SCREAMING_SNAKE_CASE__ = tuple[float, float, float]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = end_pointa[0] - end_pointa[0]
UpperCamelCase = end_pointa[1] - end_pointa[1]
UpperCamelCase = end_pointa[2] - end_pointa[2]
return (x, y, z)
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
UpperCamelCase = ab[1] * ac[2] - ab[2] * ac[1] # *i
UpperCamelCase = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
UpperCamelCase = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
return tuple(round(lowercase__ , lowercase__ ) for x in vector ) == (0, 0, 0)
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 10 )-> Dict:
UpperCamelCase = create_vector(lowercase__ , lowercase__ )
UpperCamelCase = create_vector(lowercase__ , lowercase__ )
return is_zero_vector(get_ad_vectors_cross(lowercase__ , lowercase__ ) , lowercase__ )
| 321 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{sampling_rate}'''
_lowerCamelCase : str = '1'
_lowerCamelCase : str = 'f32le'
_lowerCamelCase : Union[str, Any] = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowercase__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
_lowerCamelCase : str = ffmpeg_process.communicate(lowercase__ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
_lowerCamelCase : List[Any] = output_stream[0]
_lowerCamelCase : Tuple = np.frombuffer(lowercase__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = "f32le" , ):
_lowerCamelCase : Optional[Any] = f'''{sampling_rate}'''
_lowerCamelCase : List[str] = '1'
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
_lowerCamelCase : Dict = platform.system()
if system == "Linux":
_lowerCamelCase : Optional[int] = 'alsa'
_lowerCamelCase : Optional[Any] = 'default'
elif system == "Darwin":
_lowerCamelCase : Optional[int] = 'avfoundation'
_lowerCamelCase : Any = ':0'
elif system == "Windows":
_lowerCamelCase : Tuple = 'dshow'
_lowerCamelCase : Tuple = 'default'
_lowerCamelCase : Optional[int] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
_lowerCamelCase : Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_lowerCamelCase : List[Any] = _ffmpeg_stream(lowercase__ , lowercase__ )
for item in iterator:
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = "f32le" , ):
if stream_chunk_s is not None:
_lowerCamelCase : int = stream_chunk_s
else:
_lowerCamelCase : Optional[Any] = chunk_length_s
_lowerCamelCase : Optional[Any] = ffmpeg_microphone(lowercase__ , lowercase__ , format_for_conversion=lowercase__ )
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = np.intaa
_lowerCamelCase : str = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : Any = np.floataa
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
_lowerCamelCase : Union[str, Any] = chunk_length_s / 6
_lowerCamelCase : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowercase__ , (int, float) ):
_lowerCamelCase : Any = [stride_length_s, stride_length_s]
_lowerCamelCase : Tuple = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_lowerCamelCase : Optional[Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_lowerCamelCase : List[Any] = datetime.datetime.now()
_lowerCamelCase : Optional[int] = datetime.timedelta(seconds=lowercase__ )
for item in chunk_bytes_iter(lowercase__ , lowercase__ , stride=(stride_left, stride_right) , stream=lowercase__ ):
# Put everything back in numpy scale
_lowerCamelCase : List[Any] = np.frombuffer(item['raw'] , dtype=lowercase__ )
_lowerCamelCase : int = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
_lowerCamelCase : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = False ):
_lowerCamelCase : int = B''
_lowerCamelCase, _lowerCamelCase : Dict = 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}''' )
_lowerCamelCase : str = 0
for raw in iterator:
acc += raw
if stream and len(lowercase__ ) < chunk_len:
_lowerCamelCase : Optional[int] = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowercase__ ) >= chunk_len:
# We are flushing the accumulator
_lowerCamelCase : str = (_stride_left, stride_right)
_lowerCamelCase : str = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
_lowerCamelCase : List[Any] = False
yield item
_lowerCamelCase : Optional[Any] = stride_left
_lowerCamelCase : str = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowercase__ ) > stride_left:
_lowerCamelCase : Optional[Any] = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
_lowerCamelCase : Tuple = False
yield item
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = 2**24 # 16Mo
try:
with subprocess.Popen(lowercase__ , stdout=subprocess.PIPE , bufsize=lowercase__ ) as ffmpeg_process:
while True:
_lowerCamelCase : Optional[Any] = ffmpeg_process.stdout.read(lowercase__ )
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 | 96 | 0 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def _A ( UpperCamelCase_ : Tuple, UpperCamelCase_ : Union[str, Any]) -> Optional[Any]:
'''simple docstring'''
__lowercase = RobertaPreLayerNormConfig.from_pretrained(
lowercase__, architectures=["RobertaPreLayerNormForMaskedLM"])
# convert state_dict
__lowercase = torch.load(hf_hub_download(repo_id=lowercase__, filename="pytorch_model.bin"))
__lowercase = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("roberta."):
__lowercase = 'roberta_prelayernorm.' + tensor_key[len("roberta.") :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(".self.LayerNorm.weight") or tensor_key.endswith(".self.LayerNorm.bias"):
continue
__lowercase = tensor_value
__lowercase = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=lowercase__, config=lowercase__, state_dict=lowercase__)
model.save_pretrained(lowercase__)
# convert tokenizer
__lowercase = AutoTokenizer.from_pretrained(lowercase__)
tokenizer.save_pretrained(lowercase__)
if __name__ == "__main__":
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint-repo',
default=None,
type=str,
required=True,
help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
_a = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 17 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """ctrl"""
lowerCamelCase__ = ["""past_key_values"""]
lowerCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=246534 , lowercase=256 , lowercase=1280 , lowercase=8192 , lowercase=48 , lowercase=16 , lowercase=0.1 , lowercase=0.1 , lowercase=1E-6 , lowercase=0.02 , lowercase=True , **lowercase , ):
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Dict = n_positions
_lowerCamelCase : Optional[int] = n_embd
_lowerCamelCase : str = n_layer
_lowerCamelCase : Union[str, Any] = n_head
_lowerCamelCase : Any = dff
_lowerCamelCase : int = resid_pdrop
_lowerCamelCase : Dict = embd_pdrop
_lowerCamelCase : Union[str, Any] = layer_norm_epsilon
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : str = use_cache
super().__init__(**lowercase ) | 96 | 0 |
import collections
import importlib.util
import os
import re
from pathlib import Path
_A = 'src/transformers'
# Matches is_xxx_available()
_A = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_A = re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_A = re.compile(R'\s+\"\S*\":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
_A = re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
_A = re.compile(R'^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_A = re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
_A = re.compile('^\s+\"([^\"]+)\",')
# Catches a line with objects between brackets only: ["foo", "bar"],
_A = re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
_A = re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
_A = re.compile(R'^\s*try:')
# Catches a line with else:
_A = re.compile(R'^\s*else:')
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Dict ):
if _re_test_backend.search(lowercase__ ) is None:
return None
__UpperCamelCase =[b[0] for b in _re_backend.findall(lowercase__ )]
backends.sort()
return "_and_".join(lowercase__ )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Dict ):
with open(lowercase__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
__UpperCamelCase =f.readlines()
__UpperCamelCase =0
while line_index < len(lowercase__ ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(lowercase__ ):
return None
# First grab the objects without a specific backend in _import_structure
__UpperCamelCase =[]
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
__UpperCamelCase =lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(lowercase__ ):
__UpperCamelCase =_re_one_line_import_struct.search(lowercase__ ).groups()[0]
__UpperCamelCase =re.findall('\[([^\]]+)\]' , lowercase__ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
__UpperCamelCase =_re_import_struct_key_value.search(lowercase__ )
if single_line_import_search is not None:
__UpperCamelCase =[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(lowercase__ ) > 0]
objects.extend(lowercase__ )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
__UpperCamelCase ={'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
__UpperCamelCase =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__UpperCamelCase =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__UpperCamelCase =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
__UpperCamelCase =lines[line_index]
if _re_import_struct_add_one.search(lowercase__ ) is not None:
objects.append(_re_import_struct_add_one.search(lowercase__ ).groups()[0] )
elif _re_import_struct_add_many.search(lowercase__ ) is not None:
__UpperCamelCase =_re_import_struct_add_many.search(lowercase__ ).groups()[0].split(', ' )
__UpperCamelCase =[obj[1:-1] for obj in imports if len(lowercase__ ) > 0]
objects.extend(lowercase__ )
elif _re_between_brackets.search(lowercase__ ) is not None:
__UpperCamelCase =_re_between_brackets.search(lowercase__ ).groups()[0].split(', ' )
__UpperCamelCase =[obj[1:-1] for obj in imports if len(lowercase__ ) > 0]
objects.extend(lowercase__ )
elif _re_quote_object.search(lowercase__ ) is not None:
objects.append(_re_quote_object.search(lowercase__ ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
__UpperCamelCase =objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
__UpperCamelCase =[]
while (
line_index < len(lowercase__ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
__UpperCamelCase =lines[line_index]
__UpperCamelCase =_re_import.search(lowercase__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
__UpperCamelCase ={'none': objects}
# Let's continue with backend-specific objects
while line_index < len(lowercase__ ):
# If the line is an if is_backend_available, we grab all objects associated.
__UpperCamelCase =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__UpperCamelCase =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__UpperCamelCase =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
__UpperCamelCase =lines[line_index]
__UpperCamelCase =_re_import.search(lowercase__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
__UpperCamelCase =objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] ):
def find_duplicates(SCREAMING_SNAKE_CASE__ : Tuple ):
return [k for k, v in collections.Counter(lowercase__ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
__UpperCamelCase =[]
for key in import_dict_objects.keys():
__UpperCamelCase =find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}' )
__UpperCamelCase =find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
__UpperCamelCase ='base imports' if key == 'none' else F'{key} backend'
errors.append(F'Differences for {name}:' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F' {a} in TYPE_HINT but not in _import_structure.' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F' {a} in _import_structure but not in TYPE_HINT.' )
return errors
def _UpperCAmelCase ( ):
__UpperCamelCase =[]
for root, _, files in os.walk(lowercase__ ):
if "__init__.py" in files:
__UpperCamelCase =os.path.join(lowercase__ , '__init__.py' )
__UpperCamelCase =parse_init(lowercase__ )
if objects is not None:
__UpperCamelCase =analyze_results(*lowercase__ )
if len(lowercase__ ) > 0:
__UpperCamelCase =F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
failures.append('\n'.join(lowercase__ ) )
if len(lowercase__ ) > 0:
raise ValueError('\n\n'.join(lowercase__ ) )
def _UpperCAmelCase ( ):
__UpperCamelCase =[]
for path, directories, files in os.walk(lowercase__ ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(lowercase__ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(lowercase__ ) / folder).glob('*.py' ) ) ) == 0:
continue
__UpperCamelCase =str((Path(lowercase__ ) / folder).relative_to(lowercase__ ) )
__UpperCamelCase =short_path.replace(os.path.sep , '.' )
submodules.append(lowercase__ )
for fname in files:
if fname == "__init__.py":
continue
__UpperCamelCase =str((Path(lowercase__ ) / fname).relative_to(lowercase__ ) )
__UpperCamelCase =short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(lowercase__ )
return submodules
_A = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
]
def _UpperCAmelCase ( ):
# This is to make sure the transformers module imported is the one in the repo.
__UpperCamelCase =importlib.util.spec_from_file_location(
'transformers' , os.path.join(lowercase__ , '__init__.py' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
__UpperCamelCase =spec.loader.load_module()
__UpperCamelCase =[
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(lowercase__ ) > 0:
__UpperCamelCase ='\n'.join(F'- {module}' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
F'{list_of_modules}\n'
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 62 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = data
_lowerCamelCase : Node | None = None
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : str = None
_lowerCamelCase : str = None
def __iter__( self ):
_lowerCamelCase : List[str] = self.head
while self.head:
yield node.data
_lowerCamelCase : Optional[int] = node.next
if node == self.head:
break
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join(str(lowercase ) for item in iter(self ) )
def A_ ( self , lowercase ):
self.insert_nth(len(self ) , lowercase )
def A_ ( self , lowercase ):
self.insert_nth(0 , lowercase )
def A_ ( self , lowercase , lowercase ):
if index < 0 or index > len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : List[Any] = Node(lowercase )
if self.head is None:
_lowerCamelCase : str = new_node # first node points itself
_lowerCamelCase : Union[str, Any] = new_node
elif index == 0: # insert at head
_lowerCamelCase : List[str] = self.head
_lowerCamelCase : str = new_node
else:
_lowerCamelCase : Union[str, Any] = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : Union[str, Any] = temp.next
_lowerCamelCase : List[str] = new_node
if index == len(self ) - 1: # insert at tail
_lowerCamelCase : Any = new_node
def A_ ( self ):
return self.delete_nth(0 )
def A_ ( self ):
return self.delete_nth(len(self ) - 1 )
def A_ ( self , lowercase = 0 ):
if not 0 <= index < len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : Any = self.head
if self.head == self.tail: # just one node
_lowerCamelCase : List[str] = None
elif index == 0: # delete head node
_lowerCamelCase : List[str] = self.tail.next.next
_lowerCamelCase : Optional[int] = self.head.next
else:
_lowerCamelCase : Dict = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : int = temp.next
_lowerCamelCase : Optional[int] = temp.next.next
if index == len(self ) - 1: # delete at tail
_lowerCamelCase : List[Any] = temp
return delete_node.data
def A_ ( self ):
return len(self ) == 0
def _snake_case ( ):
_lowerCamelCase : Union[str, Any] = CircularLinkedList()
assert len(lowercase__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(lowercase__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(lowercase__ ) == i
circular_linked_list.insert_nth(lowercase__ , i + 1 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
import numpy as np
class a :
def __init__( self : Any , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : List[Any]=None ) -> List[str]:
self.set_matricies(red=__SCREAMING_SNAKE_CASE , green=__SCREAMING_SNAKE_CASE , blue=__SCREAMING_SNAKE_CASE , red_edge=__SCREAMING_SNAKE_CASE , nir=__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : str=None ) -> str:
if red is not None:
lowerCamelCase_ = red
if green is not None:
lowerCamelCase_ = green
if blue is not None:
lowerCamelCase_ = blue
if red_edge is not None:
lowerCamelCase_ = red_edge
if nir is not None:
lowerCamelCase_ = nir
return True
def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Tuple="" , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=None ) -> str:
self.set_matricies(red=__SCREAMING_SNAKE_CASE , green=__SCREAMING_SNAKE_CASE , blue=__SCREAMING_SNAKE_CASE , red_edge=__SCREAMING_SNAKE_CASE , nir=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def UpperCamelCase ( self : Optional[int] ) -> str:
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCamelCase ( self : Optional[int] ) -> Tuple:
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
return self.nir * (self.red / (self.green**2))
def UpperCamelCase ( self : str ) -> str:
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCamelCase ( self : Union[str, Any] ) -> Any:
return (self.nir - self.red) / (self.nir + self.red)
def UpperCamelCase ( self : Dict ) -> str:
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCamelCase ( self : str ) -> Optional[int]:
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCamelCase ( self : Tuple ) -> int:
return (self.nir - self.green) / (self.nir + self.green)
def UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCamelCase ( self : Union[str, Any] ) -> int:
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCamelCase ( self : int ) -> str:
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCamelCase ( self : int ) -> int:
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : List[str]=0.08 , __SCREAMING_SNAKE_CASE : Union[str, Any]=1.22 , __SCREAMING_SNAKE_CASE : Optional[int]=0.03 ) -> int:
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCamelCase ( self : str ) -> Optional[Any]:
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCamelCase ( self : Any ) -> List[Any]:
return (self.nir / self.green) - 1
def UpperCamelCase ( self : List[str] ) -> Any:
return (self.nir / self.redEdge) - 1
def UpperCamelCase ( self : int ) -> Optional[Any]:
return (self.red - self.blue) / self.red
def UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
lowerCamelCase_ = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def UpperCamelCase ( self : Tuple ) -> int:
return self.nir - self.green
def UpperCamelCase ( self : Any ) -> Dict:
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCamelCase ( self : str ) -> List[str]:
lowerCamelCase_ = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def UpperCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[str]=0.16 ) -> str:
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : List[Any]=0.5 ) -> Optional[Any]:
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCamelCase ( self : Any ) -> int:
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[Any]=None ) -> List[str]:
return (self.nir - b) / (a * self.red)
def UpperCamelCase ( self : Tuple ) -> Optional[Any]:
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCamelCase ( self : Dict ) -> Union[str, Any]:
return (self.red + self.green + self.blue) / 30.5
def UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
return self.nir / self.red
def UpperCamelCase ( self : Dict ) -> List[str]:
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
return self.green / (self.nir + self.red + self.green)
def UpperCamelCase ( self : Any ) -> str:
return self.nir / (self.nir + self.red + self.green)
def UpperCamelCase ( self : int ) -> str:
return self.red / (self.nir + self.red + self.green)
def UpperCamelCase ( self : Any ) -> Optional[Any]:
return (self.green - self.red) / (self.green + self.red)
def UpperCamelCase ( self : Any ) -> int:
return (self.red - self.green) / (self.red + self.green)
def UpperCamelCase ( self : str ) -> int:
lowerCamelCase_ = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
lowerCamelCase_ = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def UpperCamelCase ( self : List[Any] ) -> Tuple:
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCamelCase ( self : Tuple ) -> Optional[int]:
return self.nir / self.red
def UpperCamelCase ( self : List[Any] ) -> Tuple:
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCamelCase ( self : str ) -> Tuple:
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 183 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowercase__ = get_logger(__name__)
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = """dummy_data"""
lowerCamelCase__ = """datasets"""
lowerCamelCase__ = False
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = False , lowercase = True , lowercase = None , ):
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Dict = dataset_name
_lowerCamelCase : Union[str, Any] = cache_dir
_lowerCamelCase : Dict = use_local_dummy_data
_lowerCamelCase : Tuple = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : Any = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : str = str(lowercase )
# to be downloaded
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : int = None
@property
def A_ ( self ):
if self._dummy_file is None:
_lowerCamelCase : Tuple = self.download_dummy_data()
return self._dummy_file
@property
def A_ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def A_ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def A_ ( self ):
_lowerCamelCase : List[str] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : int = cached_path(
lowercase , cache_dir=self.cache_dir , extract_compressed_file=lowercase , force_extract=lowercase )
return os.path.join(lowercase , self.dummy_file_name )
@property
def A_ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def A_ ( self ):
if self._bucket_url is None:
_lowerCamelCase : List[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def A_ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def A_ ( self , lowercase , *lowercase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Union[str, Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Union[str, Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowercase , lowercase ):
return self.create_dummy_data_dict(lowercase , lowercase )
elif isinstance(lowercase , (list, tuple) ):
return self.create_dummy_data_list(lowercase , lowercase )
else:
return self.create_dummy_data_single(lowercase , lowercase )
def A_ ( self , lowercase , *lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , *lowercase , **lowercase ):
return path
def A_ ( self ):
return {}
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowercase , lowercase ):
for single_url in single_urls:
download_callback(lowercase )
else:
_lowerCamelCase : List[Any] = single_urls
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowercase , lowercase ):
_lowerCamelCase : List[Any] = [os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : Optional[int] = single_urls
_lowerCamelCase : List[Any] = os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) )
_lowerCamelCase : int = value
# make sure that values are unique
if all(isinstance(lowercase , lowercase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : List[str] = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , lowercase ) ) for url in data_url )
_lowerCamelCase : int = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : List[str] = [data_url[0]] * len(lowercase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : str = os.path.join(lowercase , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(lowercase )
return dummy_data_list
def A_ ( self , lowercase , lowercase ):
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Tuple = os.path.join(lowercase , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(lowercase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self , lowercase ):
def _iter_archive_members(lowercase ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : str = Path(self.dummy_file ).parent
_lowerCamelCase : Union[str, Any] = path.relative_to(lowercase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : List[str] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowercase )
_lowerCamelCase : Optional[int] = Path(lowercase )
_lowerCamelCase : Dict = _iter_archive_members(lowercase ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(lowercase ).as_posix(), file_path.open('rb' )
def A_ ( self , lowercase ):
if not isinstance(lowercase , lowercase ):
_lowerCamelCase : List[str] = [paths]
for path in paths:
if os.path.isfile(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(lowercase ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(lowercase , lowercase ) | 96 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Tuple, _snake_case : List[str]=0.0_1, _snake_case : List[Any]=1_0_0_0 ) ->str:
snake_case__ : List[str] = p_stop
snake_case__ : str = max_length
def __iter__( self : Optional[int] ) ->Dict:
snake_case__ : int = 0
snake_case__ : List[Any] = False
while not stop and count < self.max_length:
yield count
count += 1
snake_case__ : Dict = random.random() < self.p_stop
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : int, _snake_case : Tuple, _snake_case : Tuple, _snake_case : Optional[Any]=False, _snake_case : Any=True ) ->Tuple:
snake_case__ : List[Any] = [
BatchSamplerShard(_snake_case, 2, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
for i in range(2 )
]
snake_case__ : List[Any] = [list(_snake_case ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(_snake_case ) for shard in batch_sampler_shards], [len(_snake_case ) for e in expected] )
self.assertListEqual(_snake_case, _snake_case )
def lowercase_ ( self : Tuple ) ->Tuple:
# Check the shards when the dataset is a round multiple of total batch size.
snake_case__ : List[str] = BatchSampler(range(2_4 ), batch_size=3, drop_last=_snake_case )
snake_case__ : int = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case )
snake_case__ : Union[str, Any] = BatchSampler(range(2_4 ), batch_size=3, drop_last=_snake_case )
# Expected shouldn't change
self.check_batch_sampler_shards(_snake_case, _snake_case )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
snake_case__ : List[Any] = BatchSampler(range(2_1 ), batch_size=3, drop_last=_snake_case )
snake_case__ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case )
snake_case__ : Optional[int] = BatchSampler(range(2_1 ), batch_size=3, drop_last=_snake_case )
snake_case__ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
snake_case__ : int = BatchSampler(range(2_2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Any = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case )
snake_case__ : List[str] = BatchSampler(range(2_2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Tuple = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
snake_case__ : Union[str, Any] = BatchSampler(range(2_0 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case )
snake_case__ : Optional[int] = BatchSampler(range(2_0 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Dict = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case )
# Check the shards when the dataset is very small.
snake_case__ : Union[str, Any] = BatchSampler(range(2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Optional[int] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(_snake_case, _snake_case )
snake_case__ : Optional[int] = BatchSampler(range(2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Optional[int] = [[], []]
self.check_batch_sampler_shards(_snake_case, _snake_case )
def lowercase_ ( self : Optional[Any] ) ->Optional[int]:
# Check the shards when the dataset is a round multiple of batch size.
snake_case__ : int = BatchSampler(range(2_4 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
snake_case__ : int = BatchSampler(range(2_4 ), batch_size=4, drop_last=_snake_case )
# Expected shouldn't change
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
# Check the shards when the dataset is not a round multiple of batch size.
snake_case__ : Tuple = BatchSampler(range(2_2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Tuple = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
snake_case__ : int = BatchSampler(range(2_2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : List[str] = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
snake_case__ : Optional[Any] = BatchSampler(range(2_1 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Any = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
snake_case__ : Tuple = BatchSampler(range(2_1 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Any = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
# Check the shards when the dataset is very small.
snake_case__ : str = BatchSampler(range(2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Any = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
snake_case__ : str = BatchSampler(range(2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : List[str] = [[], []]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case )
def lowercase_ ( self : Tuple ) ->Tuple:
# Check the shards when the dataset is a round multiple of total batch size.
snake_case__ : Dict = BatchSampler(range(2_4 ), batch_size=3, drop_last=_snake_case )
snake_case__ : str = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
snake_case__ : Union[str, Any] = BatchSampler(range(2_4 ), batch_size=3, drop_last=_snake_case )
# Expected shouldn't change
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
snake_case__ : str = BatchSampler(range(2_1 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
snake_case__ : Any = BatchSampler(range(2_1 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
snake_case__ : List[str] = BatchSampler(range(2_2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
snake_case__ : Tuple = BatchSampler(range(2_2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : int = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
snake_case__ : Any = BatchSampler(range(2_0 ), batch_size=3, drop_last=_snake_case )
snake_case__ : int = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
snake_case__ : str = BatchSampler(range(2_0 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
# Check the shards when the dataset is very small.
snake_case__ : Union[str, Any] = BatchSampler(range(2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : Optional[int] = [[[0, 1]], []]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
snake_case__ : int = BatchSampler(range(2 ), batch_size=3, drop_last=_snake_case )
snake_case__ : List[Any] = [[], []]
self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case )
def lowercase_ ( self : Optional[Any] ) ->Union[str, Any]:
# Check the shards when the dataset is a round multiple of batch size.
snake_case__ : List[Any] = BatchSampler(range(2_4 ), batch_size=4, drop_last=_snake_case )
snake_case__ : int = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
snake_case__ : List[Any] = BatchSampler(range(2_4 ), batch_size=4, drop_last=_snake_case )
# Expected shouldn't change
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
# Check the shards when the dataset is not a round multiple of batch size.
snake_case__ : int = BatchSampler(range(2_2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Any = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
snake_case__ : List[Any] = BatchSampler(range(2_2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : int = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
snake_case__ : Any = BatchSampler(range(2_1 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Tuple = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
snake_case__ : List[Any] = BatchSampler(range(2_1 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
# Check the shards when the dataset is very small.
snake_case__ : int = BatchSampler(range(2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : Any = [[[0, 1]], []]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
snake_case__ : List[str] = BatchSampler(range(2 ), batch_size=4, drop_last=_snake_case )
snake_case__ : List[Any] = [[], []]
self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case )
def lowercase_ ( self : List[Any] ) ->int:
snake_case__ : List[Any] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
snake_case__ : List[str] = [BatchSamplerShard(_snake_case, 2, _snake_case, even_batches=_snake_case ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ), 3 )
self.assertEqual(len(batch_sampler_shards[1] ), 2 )
self.assertListEqual(list(batch_sampler_shards[0] ), [[0, 1, 2], [5, 6, 7, 8], [1_2, 1_3]] )
self.assertListEqual(list(batch_sampler_shards[1] ), [[3, 4], [9, 1_0, 1_1]] )
def lowercase_ ( self : Union[str, Any], _snake_case : Tuple, _snake_case : List[Any], _snake_case : Optional[Any], _snake_case : List[Any]=False, _snake_case : List[Any]=2, _snake_case : Dict=False ) ->Optional[int]:
random.seed(_snake_case )
snake_case__ : Tuple = list(_snake_case )
snake_case__ : Dict = [
IterableDatasetShard(
_snake_case, batch_size=_snake_case, drop_last=_snake_case, num_processes=_snake_case, process_index=_snake_case, split_batches=_snake_case, )
for i in range(_snake_case )
]
snake_case__ : Optional[int] = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(_snake_case )
iterable_dataset_lists.append(list(_snake_case ) )
snake_case__ : int = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
snake_case__ : Any = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(_snake_case ), len(_snake_case ) )
self.assertTrue(len(_snake_case ) % shard_batch_size == 0 )
snake_case__ : List[Any] = []
for idx in range(0, len(_snake_case ), _snake_case ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(_snake_case ) < len(_snake_case ):
reference += reference
self.assertListEqual(_snake_case, reference[: len(_snake_case )] )
def lowercase_ ( self : Tuple ) ->Union[str, Any]:
snake_case__ : List[Any] = 4_2
snake_case__ : Tuple = RandomIterableDataset()
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
# Edge case with a very small dataset
snake_case__ : Optional[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case )
def lowercase_ ( self : str ) ->Optional[int]:
snake_case__ : List[str] = BatchSampler(range(1_6 ), batch_size=4, drop_last=_snake_case )
snake_case__ : List[Any] = SkipBatchSampler(_snake_case, 2 )
self.assertListEqual(list(_snake_case ), [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]] )
def lowercase_ ( self : List[str] ) ->str:
snake_case__ : Optional[Any] = SkipDataLoader(list(range(1_6 ) ), batch_size=4, skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader], [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]] )
def lowercase_ ( self : Optional[Any] ) ->Union[str, Any]:
snake_case__ : Optional[Any] = DataLoader(list(range(1_6 ) ), batch_size=4 )
snake_case__ : Union[str, Any] = skip_first_batches(_snake_case, num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader], [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]] )
def lowercase_ ( self : Optional[int] ) ->Any:
snake_case__ : List[Any] = DataLoaderShard(list(range(1_6 ) ), batch_size=4 )
for idx, _ in enumerate(_snake_case ):
self.assertEqual(dataloader.end_of_dataloader, idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(_snake_case ):
self.assertEqual(dataloader.end_of_dataloader, idx == 3 )
def lowercase_ ( self : Tuple ) ->Any:
Accelerator()
snake_case__ : List[str] = DataLoaderDispatcher(range(1_6 ), batch_size=4 )
for idx, _ in enumerate(_snake_case ):
self.assertEqual(dataloader.end_of_dataloader, idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(_snake_case ):
self.assertEqual(dataloader.end_of_dataloader, idx == 3 )
| 277 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
_lowerCamelCase, _lowerCamelCase : Optional[Any] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
_lowerCamelCase : Union[str, Any] = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(lowercase__ , i + t , (lowercase__) )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
if __name__ == "__main__":
lowercase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowercase__ = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted)) | 96 | 0 |
"""simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
return math.sqrt(lowercase__ ) * math.sqrt(lowercase__ ) == num
def __a ( _SCREAMING_SNAKE_CASE ) ->int:
a__: Optional[int] = 0
a__: List[Any] = n
while left <= right:
a__: str = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
a__: str = mid - 1
else:
a__: Optional[int] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""image_processor""", """tokenizer"""]
lowerCamelCase__ = """BlipImageProcessor"""
lowerCamelCase__ = """AutoTokenizer"""
def __init__( self , lowercase , lowercase , lowercase ):
super().__init__(lowercase , lowercase )
# add QFormer tokenizer
_lowerCamelCase : int = qformer_tokenizer
def __call__( self , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ):
if images is None and text is None:
raise ValueError('You have to specify at least images or text.' )
_lowerCamelCase : int = BatchFeature()
if text is not None:
_lowerCamelCase : List[str] = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
encoding.update(lowercase )
_lowerCamelCase : List[str] = self.qformer_tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
_lowerCamelCase : List[Any] = qformer_text_encoding.pop('input_ids' )
_lowerCamelCase : Tuple = qformer_text_encoding.pop('attention_mask' )
if images is not None:
_lowerCamelCase : int = self.image_processor(lowercase , return_tensors=lowercase )
encoding.update(lowercase )
return encoding
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.decode(*lowercase , **lowercase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = self.tokenizer.model_input_names
_lowerCamelCase : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def A_ ( self , lowercase , **lowercase ):
if os.path.isfile(lowercase ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(lowercase , exist_ok=lowercase )
_lowerCamelCase : Optional[Any] = os.path.join(lowercase , 'qformer_tokenizer' )
self.qformer_tokenizer.save_pretrained(lowercase )
return super().save_pretrained(lowercase , **lowercase )
@classmethod
def A_ ( cls , lowercase , **lowercase ):
_lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(lowercase , subfolder='qformer_tokenizer' )
_lowerCamelCase : Dict = cls._get_arguments_from_pretrained(lowercase , **lowercase )
args.append(lowercase )
return cls(*lowercase ) | 96 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCamelCase ) , "Tatoeba directory does not exist." )
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase__ : Dict = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__magic_name__ )
@slow
def UpperCamelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
self.resolver.convert_models(['''heb-eng'''] )
@slow
def UpperCamelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ : Dict = self.resolver.write_model_card('''opus-mt-he-en''', dry_run=__magic_name__ )
assert mmeta["long_pair"] == "heb-eng"
| 201 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowercase__ = logging.getLogger()
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[Any] = {}
_lowerCamelCase : List[Any] = os.path.join(lowercase__ , 'all_results.json' )
if os.path.exists(lowercase__ ):
with open(lowercase__ , 'r' ) as f:
_lowerCamelCase : List[Any] = json.load(lowercase__ )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
lowercase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def A_ ( self ):
import xla_spawn
_lowerCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
_lowerCamelCase : List[Any] = F'''
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(lowercase , 'argv' , lowercase ):
_lowerCamelCase : Dict = time()
xla_spawn.main()
_lowerCamelCase : Any = time()
_lowerCamelCase : Optional[int] = get_results(lowercase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def A_ ( self ):
import xla_spawn
_lowerCamelCase : Tuple = '\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n '.split()
with patch.object(lowercase , 'argv' , lowercase ):
xla_spawn.main() | 96 | 0 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=2 , __snake_case=8 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=99 , __snake_case=16 , __snake_case=5 , __snake_case=2 , __snake_case=36 , __snake_case="gelu" , __snake_case=0.0 , __snake_case=0.0 , __snake_case=512 , __snake_case=16 , __snake_case=2 , __snake_case=0.02 , __snake_case=3 , __snake_case=4 , __snake_case=None , ) -> List[Any]:
'''simple docstring'''
__a =parent
__a =batch_size
__a =seq_length
__a =is_training
__a =use_input_mask
__a =use_token_type_ids
__a =use_labels
__a =vocab_size
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =type_vocab_size
__a =type_sequence_label_size
__a =initializer_range
__a =num_labels
__a =num_choices
__a =scope
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a =None
if self.use_input_mask:
__a =random_attention_mask([self.batch_size, self.seq_length] )
__a =None
if self.use_token_type_ids:
__a =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a =None
__a =None
__a =None
if self.use_labels:
__a =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a =ids_tensor([self.batch_size] , self.num_choices )
__a =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
return MraConfig(
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=__snake_case , initializer_range=self.initializer_range , )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.get_config()
__a =300
return config
def __magic_name__ ( self ) -> str:
'''simple docstring'''
(
__a
) =self.prepare_config_and_inputs()
__a =True
__a =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__a =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> List[Any]:
'''simple docstring'''
__a =MraModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case )
__a =model(__snake_case , token_type_ids=__snake_case )
__a =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[str]:
'''simple docstring'''
__a =True
__a =MraModel(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , )
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , )
__a =model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> List[str]:
'''simple docstring'''
__a =MraForMaskedLM(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> List[str]:
'''simple docstring'''
__a =MraForQuestionAnswering(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> List[Any]:
'''simple docstring'''
__a =self.num_labels
__a =MraForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> Tuple:
'''simple docstring'''
__a =self.num_labels
__a =MraForTokenClassification(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> Dict:
'''simple docstring'''
__a =self.num_choices
__a =MraForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =self.prepare_config_and_inputs()
(
__a
) =config_and_inputs
__a ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = ()
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =MraModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , hidden_size=37 )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__a =type
self.model_tester.create_and_check_model(*__snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__snake_case )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__snake_case )
@slow
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a =MraModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@unittest.skip(reason='MRA does not output attentions' )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =MraModel.from_pretrained('uw-madison/mra-base-512-4' )
__a =torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__a =model(__snake_case )[0]
__a =torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __snake_case )
__a =torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) )
@slow
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
__a =torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__a =model(__snake_case )[0]
__a =5_0265
__a =torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __snake_case )
__a =torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) )
@slow
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
__a =torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
__a =model(__snake_case )[0]
__a =5_0265
__a =torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , __snake_case )
__a =torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) )
| 218 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( lowercase__ , lowercase__ ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase__ , lowercase__ ) ) )
def _snake_case ( lowercase__ , lowercase__ ):
if dataset.ndim != value_array.ndim:
_lowerCamelCase : Tuple = (
'Wrong input data\'s dimensions... '
f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(lowercase__ )
try:
if dataset.shape[1] != value_array.shape[1]:
_lowerCamelCase : Optional[int] = (
'Wrong input data\'s shape... '
f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(lowercase__ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('Wrong shape' )
if dataset.dtype != value_array.dtype:
_lowerCamelCase : int = (
'Input data have different datatype... '
f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(lowercase__ )
_lowerCamelCase : Optional[int] = []
for value in value_array:
_lowerCamelCase : Tuple = euclidean(lowercase__ , dataset[0] )
_lowerCamelCase : Union[str, Any] = dataset[0].tolist()
for dataset_value in dataset[1:]:
_lowerCamelCase : Optional[Any] = euclidean(lowercase__ , lowercase__ )
if dist > temp_dist:
_lowerCamelCase : List[Any] = temp_dist
_lowerCamelCase : List[str] = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( lowercase__ , lowercase__ ):
return np.dot(lowercase__ , lowercase__ ) / (norm(lowercase__ ) * norm(lowercase__ ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCAmelCase__ = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n"
UpperCAmelCase__ = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"
UpperCAmelCase__ = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] ) -> Dict:
'''simple docstring'''
return float((preds == labels).mean() )
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any]="binary" ) -> str:
'''simple docstring'''
_UpperCAmelCase = simple_accuracy(lowercase__ , lowercase__ )
_UpperCAmelCase = float(fa_score(y_true=lowercase__ , y_pred=lowercase__ , average=lowercase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = {}
for id_pred, label in zip(lowercase__ , lowercase__ ):
_UpperCAmelCase = F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
_UpperCAmelCase = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
_UpperCAmelCase = [(pred, label)]
_UpperCAmelCase = [], []
for question, preds_labels in question_map.items():
_UpperCAmelCase = zip(*lowercase__ )
_UpperCAmelCase = fa_score(y_true=lowercase__ , y_pred=lowercase__ , average='macro' )
fas.append(lowercase__ )
_UpperCAmelCase = int(sum(pred == label for pred, label in preds_labels ) == len(lowercase__ ) )
ems.append(lowercase__ )
_UpperCAmelCase = float(sum(lowercase__ ) / len(lowercase__ ) )
_UpperCAmelCase = sum(lowercase__ ) / len(lowercase__ )
_UpperCAmelCase = float(fa_score(y_true=lowercase__ , y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> Union[str, Any]:
"""simple docstring"""
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]')
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types()) , codebase_urls=[] , reference_urls=[] , format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None , )
def _lowerCamelCase ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64'),
"query": datasets.Value('int64'),
},
"prediction_text": datasets.Value('string'),
},
"references": {
"idx": {
"passage": datasets.Value('int64'),
"query": datasets.Value('int64'),
},
"answers": datasets.Sequence(datasets.Value('string')),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64'),
"paragraph": datasets.Value('int64'),
"question": datasets.Value('int64'),
},
"prediction": datasets.Value('int64'),
},
"references": datasets.Value('int64'),
}
else:
return {
"predictions": datasets.Value('int64'),
"references": datasets.Value('int64'),
}
def _lowerCamelCase ( self : Optional[int] , A : List[str] , A : int) -> Optional[int]:
"""simple docstring"""
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(A , A)}
elif self.config_name == "cb":
return acc_and_fa(A , A , fa_avg='macro')
elif self.config_name == "record":
_UpperCAmelCase = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
_UpperCAmelCase = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(A , A)[0]
elif self.config_name == "multirc":
return evaluate_multirc(A , A)
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(A , A)}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]')
| 339 |
"""simple docstring"""
import socket
def _snake_case ( ):
_lowerCamelCase : List[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase : Union[str, Any] = socket.gethostname()
_lowerCamelCase : List[Any] = 12312
sock.connect((host, port) )
sock.send(B'Hello server!' )
with open('Received_file' , 'wb' ) as out_file:
print('File opened' )
print('Receiving data...' )
while True:
_lowerCamelCase : int = sock.recv(1024 )
if not data:
break
out_file.write(lowercase__ )
print('Successfully received the file' )
sock.close()
print('Connection closed' )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__: Optional[int] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__: Dict = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
A__: int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 276 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase__ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
"""
lowercase__ = """\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""
lowercase__ = """
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for 'record': list of question-answer dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'prediction_text': the predicted answer text
- for 'multirc': list of question-answer dictionaries with the following keys:
- 'idx': index of the question-answer pair as specified by the dataset
- 'prediction': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for 'record': list of question-answers dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'answers': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for 'record':
- 'exact_match': Exact match between answer and gold answer
- 'f1': F1 score
- for 'multirc':
- 'exact_match': Exact match between answer and gold answer
- 'f1_m': Per-question macro-F1 score
- 'f1_a': Average F1 score over all answers
- for 'axb':
'matthews_correlation': Matthew Correlation
- for 'cb':
- 'accuracy': Accuracy
- 'f1': F1 score
- for all others:
- 'accuracy': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'record')
>>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
>>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
>>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def _snake_case ( lowercase__ , lowercase__ ):
return float((preds == labels).mean() )
def _snake_case ( lowercase__ , lowercase__ , lowercase__="binary" ):
_lowerCamelCase : str = simple_accuracy(lowercase__ , lowercase__ )
_lowerCamelCase : Any = float(fa_score(y_true=lowercase__ , y_pred=lowercase__ , average=lowercase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Any = {}
for id_pred, label in zip(lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
_lowerCamelCase : Union[str, Any] = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
_lowerCamelCase : Optional[Any] = [(pred, label)]
_lowerCamelCase, _lowerCamelCase : Optional[int] = [], []
for question, preds_labels in question_map.items():
_lowerCamelCase, _lowerCamelCase : Tuple = zip(*lowercase__ )
_lowerCamelCase : List[str] = fa_score(y_true=lowercase__ , y_pred=lowercase__ , average='macro' )
fas.append(lowercase__ )
_lowerCamelCase : int = int(sum(pred == label for pred, label in preds_labels ) == len(lowercase__ ) )
ems.append(lowercase__ )
_lowerCamelCase : Optional[Any] = float(sum(lowercase__ ) / len(lowercase__ ) )
_lowerCamelCase : Optional[int] = sum(lowercase__ ) / len(lowercase__ )
_lowerCamelCase : List[Any] = float(fa_score(y_true=lowercase__ , y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def A_ ( self ):
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None , )
def A_ ( self ):
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"prediction_text": datasets.Value('string' ),
},
"references": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"answers": datasets.Sequence(datasets.Value('string' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64' ),
"paragraph": datasets.Value('int64' ),
"question": datasets.Value('int64' ),
},
"prediction": datasets.Value('int64' ),
},
"references": datasets.Value('int64' ),
}
else:
return {
"predictions": datasets.Value('int64' ),
"references": datasets.Value('int64' ),
}
def A_ ( self , lowercase , lowercase ):
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowercase , lowercase )}
elif self.config_name == "cb":
return acc_and_fa(lowercase , lowercase , fa_avg='macro' )
elif self.config_name == "record":
_lowerCamelCase : List[str] = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
_lowerCamelCase : Union[str, Any] = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(lowercase , lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowercase , lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowercase , lowercase )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' ) | 96 | 0 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self : Dict ):
snake_case_ = ''
snake_case_ = ''
snake_case_ = []
snake_case_ = 0
snake_case_ = 256
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
def A_ ( self : List[Any] , lowercase_ : Tuple ):
snake_case_ = cva.imread(lowercase_ , 0 )
snake_case_ = copy.deepcopy(self.img )
snake_case_ = plt.hist(self.img.ravel() , 256 , [0, 256] , label='''x''' )
snake_case_ = np.sum(lowercase_ )
for i in range(len(lowercase_ ) ):
snake_case_ = x[i] / self.k
self.sk += prk
snake_case_ = (self.L - 1) * self.sk
if self.rem != 0:
snake_case_ = int(last % last )
snake_case_ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowercase_ )
snake_case_ = int(np.ma.count(self.img ) / self.img[1].size )
snake_case_ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
snake_case_ = self.img[j][i]
if num != self.last_list[num]:
snake_case_ = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def A_ ( self : Union[str, Any] ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A_ ( self : Optional[Any] ):
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
a : Any = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
a : List[Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 56 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = DDIMPipeline
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
lowerCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCamelCase__ = False
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
_lowerCamelCase : List[str] = DDIMScheduler()
_lowerCamelCase : Optional[int] = {'unet': unet, 'scheduler': scheduler}
return components
def A_ ( self , lowercase , lowercase=0 ):
if str(lowercase ).startswith('mps' ):
_lowerCamelCase : Dict = torch.manual_seed(lowercase )
else:
_lowerCamelCase : List[str] = torch.Generator(device=lowercase ).manual_seed(lowercase )
_lowerCamelCase : Tuple = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def A_ ( self ):
_lowerCamelCase : Any = 'cpu'
_lowerCamelCase : Tuple = self.get_dummy_components()
_lowerCamelCase : Optional[Any] = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : str = self.get_dummy_inputs(lowercase )
_lowerCamelCase : int = pipe(**lowercase ).images
_lowerCamelCase : Any = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
_lowerCamelCase : Tuple = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
_lowerCamelCase : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase , 1E-3 )
def A_ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def A_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
_lowerCamelCase : Optional[Any] = 'google/ddpm-cifar10-32'
_lowerCamelCase : Optional[Any] = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : Dict = DDIMScheduler()
_lowerCamelCase : Dict = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddim.to(lowercase )
ddim.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : List[str] = torch.manual_seed(0 )
_lowerCamelCase : str = ddim(generator=lowercase , eta=0.0 , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowerCamelCase : List[Any] = np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : Optional[int] = 'google/ddpm-ema-bedroom-256'
_lowerCamelCase : str = UNetaDModel.from_pretrained(lowercase )
_lowerCamelCase : str = DDIMScheduler.from_pretrained(lowercase )
_lowerCamelCase : Optional[int] = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddpm.to(lowercase )
ddpm.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Tuple = torch.manual_seed(0 )
_lowerCamelCase : int = ddpm(generator=lowercase , output_type='numpy' ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_lowerCamelCase : str = np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 96 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=None ) -> str:
"""simple docstring"""
UpperCamelCase = np.random.default_rng(_SCREAMING_SNAKE_CASE )
UpperCamelCase = length
UpperCamelCase = rng.normal(size=(length,) ).astype(np.floataa )
UpperCamelCase = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self ) -> str:
"""simple docstring"""
return self.length
def __getitem__( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return {"x": self.x[i], "y": self.y[i]}
class a_ ( torch.nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
"""simple docstring"""
super().__init__()
UpperCamelCase = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCamelCase = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCamelCase = True
def A__ ( self , _SCREAMING_SNAKE_CASE=None ) -> List[Any]:
"""simple docstring"""
if self.first_batch:
print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}" )
UpperCamelCase = False
return x * self.a[0] + self.b[0]
class a_ ( torch.nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
UpperCamelCase = torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE ).float() )
UpperCamelCase = torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE ).float() )
UpperCamelCase = True
def A__ ( self , _SCREAMING_SNAKE_CASE=None ) -> Dict:
"""simple docstring"""
if self.first_batch:
print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}" )
UpperCamelCase = False
return x * self.a + self.b
def lowercase__ ( __UpperCamelCase , __UpperCamelCase = 16 )-> List[str]:
from datasets import load_dataset
from transformers import AutoTokenizer
UpperCamelCase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase = {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
UpperCamelCase = load_dataset("""csv""" , data_files=lowercase__ )
UpperCamelCase = datasets['train'].unique("""label""" )
UpperCamelCase = {v: i for i, v in enumerate(lowercase__ )}
def tokenize_function(__UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase = tokenizer(
examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ , padding="""max_length""" )
if "label" in examples:
UpperCamelCase = [label_to_id[l] for l in examples['label']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCamelCase = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""sentence1""", """sentence2""", """label"""] , )
def collate_fn(__UpperCamelCase ):
# 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(lowercase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowercase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
UpperCamelCase = DataLoader(tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=2 )
UpperCamelCase = DataLoader(tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 321 |
"""simple docstring"""
# Imports
import numpy as np
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
def A_ ( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
if red is not None:
_lowerCamelCase : Optional[int] = red
if green is not None:
_lowerCamelCase : Optional[Any] = green
if blue is not None:
_lowerCamelCase : Tuple = blue
if red_edge is not None:
_lowerCamelCase : Optional[Any] = red_edge
if nir is not None:
_lowerCamelCase : Union[str, Any] = nir
return True
def A_ ( self , lowercase="" , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_matricies(red=lowercase , green=lowercase , blue=lowercase , red_edge=lowercase , nir=lowercase )
_lowerCamelCase : str = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def A_ ( self ):
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def A_ ( self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def A_ ( self ):
return self.nir * (self.red / (self.green**2))
def A_ ( self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def A_ ( self ):
return (self.nir - self.red) / (self.nir + self.red)
def A_ ( self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def A_ ( self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def A_ ( self ):
return (self.nir - self.green) / (self.nir + self.green)
def A_ ( self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def A_ ( self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def A_ ( self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def A_ ( self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def A_ ( self , lowercase=0.08 , lowercase=1.22 , lowercase=0.03 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def A_ ( self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def A_ ( self ):
return (self.nir / self.green) - 1
def A_ ( self ):
return (self.nir / self.redEdge) - 1
def A_ ( self ):
return (self.red - self.blue) / self.red
def A_ ( self ):
_lowerCamelCase : Any = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def A_ ( self ):
return self.nir - self.green
def A_ ( self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def A_ ( self ):
_lowerCamelCase : Any = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)
def A_ ( self , lowercase=0.16 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def A_ ( self , lowercase=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def A_ ( self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def A_ ( self , lowercase=None , lowercase=None ):
return (self.nir - b) / (a * self.red)
def A_ ( self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def A_ ( self ):
return (self.red + self.green + self.blue) / 30.5
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def A_ ( self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def A_ ( self ):
return self.green / (self.nir + self.red + self.green)
def A_ ( self ):
return self.nir / (self.nir + self.red + self.green)
def A_ ( self ):
return self.red / (self.nir + self.red + self.green)
def A_ ( self ):
return (self.green - self.red) / (self.green + self.red)
def A_ ( self ):
return (self.red - self.green) / (self.red + self.green)
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
_lowerCamelCase : Dict = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def A_ ( self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def A_ ( self ):
return self.nir / self.red
def A_ ( self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def A_ ( self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge) | 96 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def _A ( UpperCamelCase_ : int, UpperCamelCase_ : List[Any], UpperCamelCase_ : Optional[Any], UpperCamelCase_ : str) -> List[str]:
'''simple docstring'''
__lowercase = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинное обучение - это здорово, не так ли?',
'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
__lowercase = {
'wmt16-en-de-dist-12-1': [28.3, 27.52],
'wmt16-en-de-dist-6-1': [27.4, 27.11],
'wmt16-en-de-12-1': [26.9, 25.75],
}
__lowercase = F"""{src_lang}-{tgt_lang}"""
__lowercase = F"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 models are available:
* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
-------|---------|----------
{model_name} | {scores[model_name][0]} | {scores[model_name][1]}
The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
"""
model_card_dir.mkdir(parents=lowercase__, exist_ok=lowercase__)
__lowercase = os.path.join(lowercase__, "README.md")
print(F"""Generating {path}""")
with open(lowercase__, "w", encoding="utf-8") as f:
f.write(lowercase__)
# make sure we are under the root of the project
_a = Path(__file__).resolve().parent.parent.parent
_a = repo_dir / 'model_cards'
for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
_a = model_cards_dir / 'allenai' / model_name
write_model_card(model_card_dir, src_lang='en', tgt_lang='de', model_name=model_name)
| 17 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self , lowercase , lowercase=768 ):
super().__init__(lowercase )
_lowerCamelCase : Any = proj_size
_lowerCamelCase : Dict = CLIPVisionModel(lowercase )
_lowerCamelCase : List[str] = PaintByExampleMapper(lowercase )
_lowerCamelCase : Optional[Any] = nn.LayerNorm(config.hidden_size )
_lowerCamelCase : int = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
_lowerCamelCase : str = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def A_ ( self , lowercase , lowercase=False ):
_lowerCamelCase : Union[str, Any] = self.model(pixel_values=lowercase )
_lowerCamelCase : int = clip_output.pooler_output
_lowerCamelCase : str = self.mapper(latent_states[:, None] )
_lowerCamelCase : List[Any] = self.final_layer_norm(lowercase )
_lowerCamelCase : Dict = self.proj_out(lowercase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , lowercase ):
super().__init__()
_lowerCamelCase : Tuple = (config.num_hidden_layers + 1) // 5
_lowerCamelCase : int = config.hidden_size
_lowerCamelCase : Optional[Any] = 1
_lowerCamelCase : str = nn.ModuleList(
[
BasicTransformerBlock(lowercase , lowercase , lowercase , activation_fn='gelu' , attention_bias=lowercase )
for _ in range(lowercase )
] )
def A_ ( self , lowercase ):
for block in self.blocks:
_lowerCamelCase : Tuple = block(lowercase )
return hidden_states | 96 | 0 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_A = {
'cola': 2,
'mnli': 3,
'mrpc': 2,
'sst-2': 2,
'sts-b': 1,
'qqp': 2,
'qnli': 2,
'rte': 2,
'wnli': 2,
}
logging.set_verbosity_info()
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any]=None ):
# Initialise PyTorch model
__UpperCamelCase =XLNetConfig.from_json_file(lowercase__ )
__UpperCamelCase =finetuning_task.lower() if finetuning_task is not None else ''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(F'Building PyTorch XLNetForSequenceClassification model from configuration: {config}' )
__UpperCamelCase =finetuning_task
__UpperCamelCase =GLUE_TASKS_NUM_LABELS[finetuning_task]
__UpperCamelCase =XLNetForSequenceClassification(lowercase__ )
elif "squad" in finetuning_task:
__UpperCamelCase =finetuning_task
__UpperCamelCase =XLNetForQuestionAnswering(lowercase__ )
else:
__UpperCamelCase =XLNetLMHeadModel(lowercase__ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
__UpperCamelCase =os.path.join(lowercase__ , lowercase__ )
__UpperCamelCase =os.path.join(lowercase__ , lowercase__ )
print(F'Save PyTorch model to {os.path.abspath(lowercase__ )}' )
torch.save(model.state_dict() , lowercase__ )
print(F'Save configuration file to {os.path.abspath(lowercase__ )}' )
with open(lowercase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_A = 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(
'--xlnet_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained XLNet model. \n'
'This specifies the model architecture.'
),
)
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(
'--finetuning_task',
default=None,
type=str,
help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
)
_A = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 62 |
"""simple docstring"""
lowercase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowercase__ = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : List[Any] = from_type.lower().strip('s' )
_lowerCamelCase : List[Any] = to_type.lower().strip('s' )
_lowerCamelCase : Optional[int] = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
_lowerCamelCase : Any = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
if from_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Tuple = (
f'''Invalid \'from_type\' value: {from_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
if to_sanitized not in METRIC_CONVERSION:
_lowerCamelCase : Any = (
f'''Invalid \'to_type\' value: {to_type!r}.\n'''
f'''Conversion abbreviations are: {', '.join(lowercase__ )}'''
)
raise ValueError(lowercase__ )
_lowerCamelCase : List[Any] = METRIC_CONVERSION[from_sanitized]
_lowerCamelCase : int = METRIC_CONVERSION[to_sanitized]
_lowerCamelCase : List[str] = 1
if from_exponent > to_exponent:
_lowerCamelCase : List[str] = from_exponent - to_exponent
else:
_lowerCamelCase : List[Any] = -(to_exponent - from_exponent)
return value * pow(10 , lowercase__ )
if __name__ == "__main__":
from doctest import testmod
testmod() | 96 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( _lowerCamelCase : str , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Union[str, Any] ) -> Optional[int]:
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 lowerCamelCase__ ( _lowerCamelCase : List[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[int] , ) -> Tuple:
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 lowerCamelCase__ ( _lowerCamelCase : str , _lowerCamelCase : Any , _lowerCamelCase : Union[str, Any] , ) -> Tuple:
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(
lowercase__ , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 183 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMSNModel""",
"""ViTMSNForImageClassification""",
"""ViTMSNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def lowercase_ (A : Tuple=None , A : Dict=None ):
return field(default_factory=lambda: default , metadata=lowercase__ )
@dataclass
class snake_case__ :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = field(
metadata={"""help""": """The csv file to plot."""} , )
_SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""} , )
_SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""} , )
_SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={"""help""": """Disable logarithmic scale when plotting"""} , )
_SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
"""help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
} , )
_SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""} , )
_SCREAMING_SNAKE_CASE = list_field(
default=lowerCAmelCase_ , metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""} )
def lowercase_ (A : str ):
try:
int(lowercase__ )
return True
except ValueError:
return False
def lowercase_ (A : List[Any] ):
try:
float(lowercase__ )
return True
except ValueError:
return False
class snake_case__ :
"""simple docstring"""
def __init__( self : List[Any], _snake_case : str ) ->int:
snake_case__ : Any = args
snake_case__ : Tuple = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file, newline='' ) as csv_file:
snake_case__ : Optional[Any] = csv.DictReader(_snake_case )
for row in reader:
snake_case__ : Optional[Any] = row['model']
self.result_dict[model_name]["bsz"].append(int(row['batch_size'] ) )
self.result_dict[model_name]["seq_len"].append(int(row['sequence_length'] ) )
if can_convert_to_int(row['result'] ):
# value is not None
snake_case__ : List[str] = int(row['result'] )
elif can_convert_to_float(row['result'] ):
# value is not None
snake_case__ : List[Any] = float(row['result'] )
def lowercase_ ( self : Dict ) ->Dict:
snake_case__ : Tuple = plt.subplots()
snake_case__ : Tuple = 'Time usage' if self.args.is_time else 'Memory usage'
snake_case__ : Optional[int] = title_str + ' for training' if self.args.is_train else title_str + ' for inference'
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('log' )
ax.set_yscale('log' )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
snake_case__ : str = sorted(set(self.result_dict[model_name]['bsz'] ) )
snake_case__ : Union[str, Any] = sorted(set(self.result_dict[model_name]['seq_len'] ) )
snake_case__ : Tuple = self.result_dict[model_name]['result']
(snake_case__) : Optional[int] = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
snake_case__ : Union[str, Any] = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
snake_case__ : Optional[Any] = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results], dtype=_snake_case, )
else:
snake_case__ : Optional[int] = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results], dtype=np.floataa, )
(snake_case__) : int = (
('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
)
snake_case__ : List[Any] = np.asarray(_snake_case, _snake_case )[: len(_snake_case )]
plt.scatter(
_snake_case, _snake_case, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
plt.plot(_snake_case, _snake_case, '--' )
title_str += F''' {label_model_name} vs.'''
snake_case__ : Dict = title_str[:-4]
snake_case__ : List[Any] = 'Time in s' if self.args.is_time else 'Memory in MB'
# plot
plt.title(_snake_case )
plt.xlabel(_snake_case )
plt.ylabel(_snake_case )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def lowercase_ ():
snake_case__ : Any = HfArgumentParser(lowercase__ )
snake_case__ : List[Any] = parser.parse_args_into_dataclasses()[0]
snake_case__ : Dict = Plot(args=lowercase__ )
plot.plot()
if __name__ == "__main__":
main()
| 277 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : List[Any] = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Tuple = boundary[0]
_lowerCamelCase : Dict = boundary[1]
_lowerCamelCase : List[Any] = make_points(lowercase__ , lowercase__ , lowercase__ )
_lowerCamelCase : List[Any] = 0.0
y += (h / 2.0) * f(lowercase__ )
for i in x_i:
# print(i)
y += h * f(lowercase__ )
y += (h / 2.0) * f(lowercase__ )
return y
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : str = a + h
while x < (b - h):
yield x
_lowerCamelCase : int = x + h
def _snake_case ( lowercase__ ): # enter your function here
_lowerCamelCase : Optional[Any] = (x - 0) * (x - 0)
return y
def _snake_case ( ):
_lowerCamelCase : int = 0.0 # Lower bound of integration
_lowerCamelCase : Optional[int] = 1.0 # Upper bound of integration
_lowerCamelCase : List[str] = 1_0.0 # define number of steps or resolution
_lowerCamelCase : List[Any] = [a, b] # define boundary of integration
_lowerCamelCase : Optional[Any] = method_a(lowercase__ , lowercase__ )
print(f'''y = {y}''' )
if __name__ == "__main__":
main() | 96 | 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
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __snake_case ( __lowerCAmelCase ):
a__ = """mobilenet_v1"""
def __init__( self , lowercase=3 , lowercase=2_24 , lowercase=1.0 , lowercase=8 , lowercase="relu6" , lowercase=True , lowercase=0.999 , lowercase=0.02 , lowercase=0.001 , **lowercase , ) -> int:
'''simple docstring'''
super().__init__(**lowercase)
if depth_multiplier <= 0:
raise ValueError('depth_multiplier must be greater than zero.')
a__: Optional[int] = num_channels
a__: Any = image_size
a__: str = depth_multiplier
a__: Dict = min_depth
a__: List[str] = hidden_act
a__: Union[str, Any] = tf_padding
a__: str = classifier_dropout_prob
a__: Tuple = initializer_range
a__: int = layer_norm_eps
class __snake_case ( __lowerCAmelCase ):
a__ = version.parse("""1.11""" )
@property
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
return OrderedDict([('pixel_values', {0: 'batch'})])
@property
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([('logits', {0: 'batch'})])
else:
return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})])
@property
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
return 1e-4
| 290 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ ):
return math.sqrt(lowercase__ ) * math.sqrt(lowercase__ ) == num
def _snake_case ( lowercase__ ):
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : List[Any] = n
while left <= right:
_lowerCamelCase : str = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_lowerCamelCase : str = mid - 1
else:
_lowerCamelCase : Optional[int] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
import math
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] ) -> List[str]:
UpperCamelCase__ : Any = [True] * n
UpperCamelCase__ : List[Any] = False
UpperCamelCase__ : Optional[int] = False
UpperCamelCase__ : Optional[int] = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
UpperCamelCase__ : Union[str, Any] = i * 2
while index < n:
UpperCamelCase__ : List[Any] = False
UpperCamelCase__ : str = index + i
UpperCamelCase__ : Any = [2]
for i in range(3 , lowercase__ , 2 ):
if is_prime[i]:
primes.append(lowercase__ )
return primes
def lowerCAmelCase_ ( __UpperCAmelCase: Tuple = 9999_6666_3333 ) -> Union[str, Any]:
UpperCamelCase__ : Tuple = math.floor(math.sqrt(lowercase__ ) ) + 100
UpperCamelCase__ : Optional[int] = prime_sieve(lowercase__ )
UpperCamelCase__ : List[Any] = 0
UpperCamelCase__ : Optional[int] = 0
UpperCamelCase__ : Tuple = primes[prime_index]
while (last_prime**2) <= limit:
UpperCamelCase__ : List[str] = primes[prime_index + 1]
UpperCamelCase__ : Dict = last_prime**2
UpperCamelCase__ : int = next_prime**2
# Get numbers divisible by lps(current)
UpperCamelCase__ : Any = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
UpperCamelCase__ : str = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
UpperCamelCase__ : int = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
UpperCamelCase__ : str = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 201 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(lowercase__ , lowercase__ ) or not all(
isinstance(lowercase__ , lowercase__ ) and len(lowercase__ ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
_lowerCamelCase : dict[str, Any] = {}
_lowerCamelCase : List[Any] = 'WORD_KEEPER'
for word in words:
_lowerCamelCase : Dict = trie
for c in word:
if c not in trie_node:
_lowerCamelCase : Any = {}
_lowerCamelCase : str = trie_node[c]
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Dict = len(lowercase__ )
# Dynamic programming method
@functools.cache
def is_breakable(lowercase__ ) -> bool:
if index == len_string:
return True
_lowerCamelCase : List[Any] = trie
for i in range(lowercase__ , lowercase__ ):
_lowerCamelCase : Any = trie_node.get(string[i] , lowercase__ )
if trie_node is None:
return False
if trie_node.get(lowercase__ , lowercase__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Union[str, Any] = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"PoolFormerOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Union[str, Any] = ["PoolFormerFeatureExtractor"]
_lowerCAmelCase : List[Any] = ["PoolFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Tuple = [
"POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"PoolFormerForImageClassification",
"PoolFormerModel",
"PoolFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 218 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
if len(lowercase__ ) == 1:
return True
_lowerCamelCase : List[Any] = series[1] - series[0]
for index in range(len(lowercase__ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non empty list' )
_lowerCamelCase : Optional[int] = 0
for val in series:
answer += val
return answer / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger()
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str = True ) -> str:
'''simple docstring'''
print(F"Converting {name}..." )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
_UpperCAmelCase = timm.create_model('levit_128s' , pretrained=lowercase__ )
else:
_UpperCAmelCase = timm.create_model('levit_128' , pretrained=lowercase__ )
if hidden_sizes == 192:
_UpperCAmelCase = timm.create_model('levit_192' , pretrained=lowercase__ )
if hidden_sizes == 256:
_UpperCAmelCase = timm.create_model('levit_256' , pretrained=lowercase__ )
if hidden_sizes == 384:
_UpperCAmelCase = timm.create_model('levit_384' , pretrained=lowercase__ )
from_model.eval()
_UpperCAmelCase = LevitForImageClassificationWithTeacher(lowercase__ ).eval()
_UpperCAmelCase = OrderedDict()
_UpperCAmelCase = from_model.state_dict()
_UpperCAmelCase = list(from_model.state_dict().keys() )
_UpperCAmelCase = list(our_model.state_dict().keys() )
print(len(lowercase__ ) , len(lowercase__ ) )
for i in range(len(lowercase__ ) ):
_UpperCAmelCase = weights[og_keys[i]]
our_model.load_state_dict(lowercase__ )
_UpperCAmelCase = torch.randn((2, 3, 224, 224) )
_UpperCAmelCase = from_model(lowercase__ )
_UpperCAmelCase = our_model(lowercase__ ).logits
assert torch.allclose(lowercase__ , lowercase__ ), "The model logits don't match the original one."
_UpperCAmelCase = name
print(lowercase__ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
_UpperCAmelCase = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F"Pushed {checkpoint_name}" )
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] = None , _UpperCAmelCase : List[str] = True ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = 'imagenet-1k-id2label.json'
_UpperCAmelCase = 1_000
_UpperCAmelCase = (1, num_labels)
_UpperCAmelCase = 'huggingface/label-files'
_UpperCAmelCase = num_labels
_UpperCAmelCase = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
_UpperCAmelCase = {int(lowercase__ ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
_UpperCAmelCase = partial(lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
_UpperCAmelCase = {
'levit-128S': 128,
'levit-128': 128,
'levit-192': 192,
'levit-256': 256,
'levit-384': 384,
}
_UpperCAmelCase = {
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , lowercase__ , names_to_config[model_name] , lowercase__ , lowercase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
return config, expected_shape
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="levit-dump-folder/",
type=Path,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 339 |
"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase__ = 16
lowercase__ = 32
def _snake_case ( lowercase__ , lowercase__ = 16 , lowercase__ = "bert-base-cased" ):
_lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(lowercase__ )
_lowerCamelCase : Tuple = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowercase__ ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Union[str, Any] = 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
_lowerCamelCase : int = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCamelCase : Optional[int] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowercase__ ):
# 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(lowercase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(lowercase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
_lowerCamelCase : List[str] = DataLoader(
tokenized_datasets['train'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
_lowerCamelCase : int = DataLoader(
tokenized_datasets['validation'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ , lowercase__ ):
# Initialize accelerator
_lowerCamelCase : Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCamelCase : Optional[int] = config['lr']
_lowerCamelCase : Optional[int] = int(config['num_epochs'] )
_lowerCamelCase : Union[str, Any] = int(config['seed'] )
_lowerCamelCase : Optional[int] = int(config['batch_size'] )
_lowerCamelCase : Dict = args.model_name_or_path
set_seed(lowercase__ )
_lowerCamelCase, _lowerCamelCase : Optional[int] = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCamelCase : int = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
_lowerCamelCase : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowerCamelCase : Union[str, Any] = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
_lowerCamelCase : str = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
_lowerCamelCase : Tuple = 1
_lowerCamelCase : List[Any] = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowerCamelCase : Tuple = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
_lowerCamelCase : Any = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , 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.
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
_lowerCamelCase : Union[str, Any] = 0
# We also need to keep track of the stating epoch so files are named properly
_lowerCamelCase : Dict = 0
# Now we train the model
_lowerCamelCase : Dict = evaluate.load('glue' , 'mrpc' )
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
_lowerCamelCase : List[Any] = model(**lowercase__ )
_lowerCamelCase : int = outputs.loss
_lowerCamelCase : Dict = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_lowerCamelCase : Union[str, Any] = 0
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():
_lowerCamelCase : Optional[int] = model(**lowercase__ )
_lowerCamelCase : Dict = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowerCamelCase, _lowerCamelCase : List[str] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
_lowerCamelCase : Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCamelCase : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
_lowerCamelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , lowercase__ )
_lowerCamelCase : Tuple = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
_lowerCamelCase : str = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ):
_lowerCamelCase : Any = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=lowercase__ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=lowercase__ , )
parser.add_argument(
'--output_dir' , type=lowercase__ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=lowercase__ , default=lowercase__ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=lowercase__ , default=3 , help='Number of train epochs.' , )
_lowerCamelCase : Optional[Any] = parser.parse_args()
_lowerCamelCase : str = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main() | 96 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Optional[Any] ) -> int:
if not isinstance(lowercase__ ,lowercase__ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(lowercase__ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
if len(lowercase__ ) == 1:
return True
_a : List[Any] =series[1] - series[0]
for index in range(len(lowercase__ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
if not isinstance(lowercase__ ,lowercase__ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(lowercase__ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
_a : Optional[int] =0
for val in series:
answer += val
return answer / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 276 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[Any] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 ) | 96 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class a ( _lowerCamelCase ):
snake_case_ = "Salesforce/blip-image-captioning-base"
snake_case_ = (
"This is a tool that generates a description of an image. It takes an input named `image` which should be the "
"image to caption, and returns a text that contains the description in English."
)
snake_case_ = "image_captioner"
snake_case_ = AutoModelForVisionaSeq
snake_case_ = ["image"]
snake_case_ = ["text"]
def __init__( self : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : Optional[Any] ):
requires_backends(self , ['''vision'''] )
super().__init__(*lowercase_ , **lowercase_ )
def A_ ( self : List[Any] , lowercase_ : Tuple ):
return self.pre_processor(images=lowercase_ , return_tensors='''pt''' )
def A_ ( self : List[str] , lowercase_ : Union[str, Any] ):
return self.model.generate(**lowercase_ )
def A_ ( self : Optional[int] , lowercase_ : Optional[int] ):
return self.pre_processor.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )[0].strip()
| 56 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""IBertForMaskedLM""",
"""IBertForMultipleChoice""",
"""IBertForQuestionAnswering""",
"""IBertForSequenceClassification""",
"""IBertForTokenClassification""",
"""IBertModel""",
"""IBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class a_ ( lowerCamelCase ):
lowercase = 42
@flax_register_to_config
class a_ ( nn.Module , lowerCamelCase , lowerCamelCase ):
lowercase = 32
lowercase = 4
lowercase = 4
lowercase = (
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""DownBlock2D""",
)
lowercase = ("""UpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""")
lowercase = False
lowercase = (3_20, 6_40, 12_80, 12_80)
lowercase = 2
lowercase = 8
lowercase = None
lowercase = 12_80
lowercase = 0.0
lowercase = False
lowercase = jnp.floataa
lowercase = True
lowercase = 0
lowercase = False
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = (1, self.in_channels, self.sample_size, self.sample_size)
UpperCamelCase = jnp.zeros(_SCREAMING_SNAKE_CASE , dtype=jnp.floataa )
UpperCamelCase = jnp.ones((1,) , dtype=jnp.intaa )
UpperCamelCase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
UpperCamelCase = jax.random.split(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )["params"]
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.block_out_channels
UpperCamelCase = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
"""At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""" )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
UpperCamelCase = self.num_attention_heads or self.attention_head_dim
# input
UpperCamelCase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
UpperCamelCase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
UpperCamelCase = FlaxTimestepEmbedding(_SCREAMING_SNAKE_CASE , dtype=self.dtype )
UpperCamelCase = self.only_cross_attention
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = (num_attention_heads,) * len(self.down_block_types )
# down
UpperCamelCase = []
UpperCamelCase = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
UpperCamelCase = output_channel
UpperCamelCase = block_out_channels[i]
UpperCamelCase = i == len(_SCREAMING_SNAKE_CASE ) - 1
if down_block_type == "CrossAttnDownBlock2D":
UpperCamelCase = FlaxCrossAttnDownBlockaD(
in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
UpperCamelCase = FlaxDownBlockaD(
in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase = down_blocks
# mid
UpperCamelCase = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
UpperCamelCase = []
UpperCamelCase = list(reversed(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = list(reversed(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = list(reversed(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
UpperCamelCase = output_channel
UpperCamelCase = reversed_block_out_channels[i]
UpperCamelCase = reversed_block_out_channels[min(i + 1 , len(_SCREAMING_SNAKE_CASE ) - 1 )]
UpperCamelCase = i == len(_SCREAMING_SNAKE_CASE ) - 1
if up_block_type == "CrossAttnUpBlock2D":
UpperCamelCase = FlaxCrossAttnUpBlockaD(
in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , prev_output_channel=_SCREAMING_SNAKE_CASE , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
UpperCamelCase = FlaxUpBlockaD(
in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , prev_output_channel=_SCREAMING_SNAKE_CASE , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase = output_channel
UpperCamelCase = up_blocks
# out
UpperCamelCase = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
UpperCamelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = False , ) -> List[Any]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , jnp.ndarray ):
UpperCamelCase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(_SCREAMING_SNAKE_CASE , jnp.ndarray ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps.astype(dtype=jnp.floataa )
UpperCamelCase = jnp.expand_dims(_SCREAMING_SNAKE_CASE , 0 )
UpperCamelCase = self.time_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.time_embedding(_SCREAMING_SNAKE_CASE )
# 2. pre-process
UpperCamelCase = jnp.transpose(_SCREAMING_SNAKE_CASE , (0, 2, 3, 1) )
UpperCamelCase = self.conv_in(_SCREAMING_SNAKE_CASE )
# 3. down
UpperCamelCase = (sample,)
for down_block in self.down_blocks:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = down_block(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , deterministic=not train )
else:
UpperCamelCase = down_block(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
UpperCamelCase = ()
for down_block_res_sample, down_block_additional_residual in zip(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
UpperCamelCase = new_down_block_res_samples
# 4. mid
UpperCamelCase = self.mid_block(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
UpperCamelCase = down_block_res_samples[-(self.layers_per_block + 1) :]
UpperCamelCase = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = up_block(
_SCREAMING_SNAKE_CASE , temb=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , res_hidden_states_tuple=_SCREAMING_SNAKE_CASE , deterministic=not train , )
else:
UpperCamelCase = up_block(_SCREAMING_SNAKE_CASE , temb=_SCREAMING_SNAKE_CASE , res_hidden_states_tuple=_SCREAMING_SNAKE_CASE , deterministic=not train )
# 6. post-process
UpperCamelCase = self.conv_norm_out(_SCREAMING_SNAKE_CASE )
UpperCamelCase = nn.silu(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.conv_out(_SCREAMING_SNAKE_CASE )
UpperCamelCase = jnp.transpose(_SCREAMING_SNAKE_CASE , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=_SCREAMING_SNAKE_CASE )
| 321 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{sampling_rate}'''
_lowerCamelCase : str = '1'
_lowerCamelCase : str = 'f32le'
_lowerCamelCase : Union[str, Any] = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowercase__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
_lowerCamelCase : str = ffmpeg_process.communicate(lowercase__ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
_lowerCamelCase : List[Any] = output_stream[0]
_lowerCamelCase : Tuple = np.frombuffer(lowercase__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = "f32le" , ):
_lowerCamelCase : Optional[Any] = f'''{sampling_rate}'''
_lowerCamelCase : List[str] = '1'
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
_lowerCamelCase : Dict = platform.system()
if system == "Linux":
_lowerCamelCase : Optional[int] = 'alsa'
_lowerCamelCase : Optional[Any] = 'default'
elif system == "Darwin":
_lowerCamelCase : Optional[int] = 'avfoundation'
_lowerCamelCase : Any = ':0'
elif system == "Windows":
_lowerCamelCase : Tuple = 'dshow'
_lowerCamelCase : Tuple = 'default'
_lowerCamelCase : Optional[int] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
_lowerCamelCase : Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_lowerCamelCase : List[Any] = _ffmpeg_stream(lowercase__ , lowercase__ )
for item in iterator:
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = "f32le" , ):
if stream_chunk_s is not None:
_lowerCamelCase : int = stream_chunk_s
else:
_lowerCamelCase : Optional[Any] = chunk_length_s
_lowerCamelCase : Optional[Any] = ffmpeg_microphone(lowercase__ , lowercase__ , format_for_conversion=lowercase__ )
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = np.intaa
_lowerCamelCase : str = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : Any = np.floataa
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
_lowerCamelCase : Union[str, Any] = chunk_length_s / 6
_lowerCamelCase : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowercase__ , (int, float) ):
_lowerCamelCase : Any = [stride_length_s, stride_length_s]
_lowerCamelCase : Tuple = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_lowerCamelCase : Optional[Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_lowerCamelCase : List[Any] = datetime.datetime.now()
_lowerCamelCase : Optional[int] = datetime.timedelta(seconds=lowercase__ )
for item in chunk_bytes_iter(lowercase__ , lowercase__ , stride=(stride_left, stride_right) , stream=lowercase__ ):
# Put everything back in numpy scale
_lowerCamelCase : List[Any] = np.frombuffer(item['raw'] , dtype=lowercase__ )
_lowerCamelCase : int = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
_lowerCamelCase : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = False ):
_lowerCamelCase : int = B''
_lowerCamelCase, _lowerCamelCase : Dict = 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}''' )
_lowerCamelCase : str = 0
for raw in iterator:
acc += raw
if stream and len(lowercase__ ) < chunk_len:
_lowerCamelCase : Optional[int] = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowercase__ ) >= chunk_len:
# We are flushing the accumulator
_lowerCamelCase : str = (_stride_left, stride_right)
_lowerCamelCase : str = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
_lowerCamelCase : List[Any] = False
yield item
_lowerCamelCase : Optional[Any] = stride_left
_lowerCamelCase : str = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowercase__ ) > stride_left:
_lowerCamelCase : Optional[Any] = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
_lowerCamelCase : Tuple = False
yield item
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = 2**24 # 16Mo
try:
with subprocess.Popen(lowercase__ , stdout=subprocess.PIPE , bufsize=lowercase__ ) as ffmpeg_process:
while True:
_lowerCamelCase : Optional[Any] = ffmpeg_process.stdout.read(lowercase__ )
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 | 96 | 0 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = "t5"
__UpperCAmelCase : Any = ["past_key_values"]
__UpperCAmelCase : str = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : int, UpperCAmelCase__ : str=3_2_1_2_8, UpperCAmelCase__ : List[Any]=5_1_2, UpperCAmelCase__ : List[str]=6_4, UpperCAmelCase__ : List[str]=2_0_4_8, UpperCAmelCase__ : Tuple=6, UpperCAmelCase__ : Dict=None, UpperCAmelCase__ : Tuple=8, UpperCAmelCase__ : List[Any]=3_2, UpperCAmelCase__ : Optional[Any]=1_2_8, UpperCAmelCase__ : str=0.1, UpperCAmelCase__ : int=1E-6, UpperCAmelCase__ : Tuple=1.0, UpperCAmelCase__ : Dict="relu", UpperCAmelCase__ : Optional[Any]=True, UpperCAmelCase__ : List[str]=True, UpperCAmelCase__ : Optional[Any]=0, UpperCAmelCase__ : Any=1, **UpperCAmelCase__ : Any, ):
__lowercase = vocab_size
__lowercase = d_model
__lowercase = d_kv
__lowercase = d_ff
__lowercase = num_layers
__lowercase = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__lowercase = num_heads
__lowercase = relative_attention_num_buckets
__lowercase = relative_attention_max_distance
__lowercase = dropout_rate
__lowercase = layer_norm_epsilon
__lowercase = initializer_factor
__lowercase = feed_forward_proj
__lowercase = use_cache
__lowercase = self.feed_forward_proj.split("-" )
__lowercase = act_info[-1]
__lowercase = act_info[0] == 'gated'
if len(UpperCAmelCase__ ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase__ ) > 2:
raise ValueError(
F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"\'gated-gelu\' or \'relu\'" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
__lowercase = 'gelu_new'
super().__init__(
pad_token_id=UpperCAmelCase__, eos_token_id=UpperCAmelCase__, is_encoder_decoder=UpperCAmelCase__, **UpperCAmelCase__, )
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
@property
def _lowercase ( self : Tuple ):
__lowercase = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__lowercase = 'past_encoder_sequence + sequence'
__lowercase = {0: 'batch'}
__lowercase = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__lowercase = {0: 'batch', 1: 'decoder_sequence'}
__lowercase = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase__, direction="inputs" )
return common_inputs
@property
def _lowercase ( self : Tuple ):
return 1_3
| 17 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """ctrl"""
lowerCamelCase__ = ["""past_key_values"""]
lowerCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=246534 , lowercase=256 , lowercase=1280 , lowercase=8192 , lowercase=48 , lowercase=16 , lowercase=0.1 , lowercase=0.1 , lowercase=1E-6 , lowercase=0.02 , lowercase=True , **lowercase , ):
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Dict = n_positions
_lowerCamelCase : Optional[int] = n_embd
_lowerCamelCase : str = n_layer
_lowerCamelCase : Union[str, Any] = n_head
_lowerCamelCase : Any = dff
_lowerCamelCase : int = resid_pdrop
_lowerCamelCase : Dict = embd_pdrop
_lowerCamelCase : Union[str, Any] = layer_norm_epsilon
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : str = use_cache
super().__init__(**lowercase ) | 96 | 0 |
from PIL import Image
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
__UpperCamelCase =image.size
__UpperCamelCase =0
__UpperCamelCase =image.load()
for i in range(lowercase__ ):
for j in range(lowercase__ ):
__UpperCamelCase =pixels[j, i]
mean += pixel
mean //= width * height
for j in range(lowercase__ ):
for i in range(lowercase__ ):
__UpperCamelCase =2_55 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
_A = mean_threshold(Image.open('path_to_image').convert('L'))
image.save('output_image_path')
| 62 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = data
_lowerCamelCase : Node | None = None
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : str = None
_lowerCamelCase : str = None
def __iter__( self ):
_lowerCamelCase : List[str] = self.head
while self.head:
yield node.data
_lowerCamelCase : Optional[int] = node.next
if node == self.head:
break
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join(str(lowercase ) for item in iter(self ) )
def A_ ( self , lowercase ):
self.insert_nth(len(self ) , lowercase )
def A_ ( self , lowercase ):
self.insert_nth(0 , lowercase )
def A_ ( self , lowercase , lowercase ):
if index < 0 or index > len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : List[Any] = Node(lowercase )
if self.head is None:
_lowerCamelCase : str = new_node # first node points itself
_lowerCamelCase : Union[str, Any] = new_node
elif index == 0: # insert at head
_lowerCamelCase : List[str] = self.head
_lowerCamelCase : str = new_node
else:
_lowerCamelCase : Union[str, Any] = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : Union[str, Any] = temp.next
_lowerCamelCase : List[str] = new_node
if index == len(self ) - 1: # insert at tail
_lowerCamelCase : Any = new_node
def A_ ( self ):
return self.delete_nth(0 )
def A_ ( self ):
return self.delete_nth(len(self ) - 1 )
def A_ ( self , lowercase = 0 ):
if not 0 <= index < len(self ):
raise IndexError('list index out of range.' )
_lowerCamelCase : Any = self.head
if self.head == self.tail: # just one node
_lowerCamelCase : List[str] = None
elif index == 0: # delete head node
_lowerCamelCase : List[str] = self.tail.next.next
_lowerCamelCase : Optional[int] = self.head.next
else:
_lowerCamelCase : Dict = self.head
for _ in range(index - 1 ):
_lowerCamelCase : List[Any] = temp.next
_lowerCamelCase : int = temp.next
_lowerCamelCase : Optional[int] = temp.next.next
if index == len(self ) - 1: # delete at tail
_lowerCamelCase : List[Any] = temp
return delete_node.data
def A_ ( self ):
return len(self ) == 0
def _snake_case ( ):
_lowerCamelCase : Union[str, Any] = CircularLinkedList()
assert len(lowercase__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(lowercase__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(lowercase__ ) == i
circular_linked_list.insert_nth(lowercase__ , i + 1 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 | 0 |
"""simple docstring"""
import math
def lowerCamelCase__ ( _lowerCamelCase : Any ) -> Union[str, Any]:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowercase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase__ ( _lowerCamelCase : List[Any] = 0.1 ) -> Union[str, Any]:
lowerCamelCase_ = 3
lowerCamelCase_ = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(lowercase__ )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 183 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowercase__ = get_logger(__name__)
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = """dummy_data"""
lowerCamelCase__ = """datasets"""
lowerCamelCase__ = False
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = False , lowercase = True , lowercase = None , ):
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Dict = dataset_name
_lowerCamelCase : Union[str, Any] = cache_dir
_lowerCamelCase : Dict = use_local_dummy_data
_lowerCamelCase : Tuple = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : Any = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : str = str(lowercase )
# to be downloaded
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : int = None
@property
def A_ ( self ):
if self._dummy_file is None:
_lowerCamelCase : Tuple = self.download_dummy_data()
return self._dummy_file
@property
def A_ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def A_ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def A_ ( self ):
_lowerCamelCase : List[str] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : int = cached_path(
lowercase , cache_dir=self.cache_dir , extract_compressed_file=lowercase , force_extract=lowercase )
return os.path.join(lowercase , self.dummy_file_name )
@property
def A_ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def A_ ( self ):
if self._bucket_url is None:
_lowerCamelCase : List[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def A_ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def A_ ( self , lowercase , *lowercase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Union[str, Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Union[str, Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowercase , lowercase ):
return self.create_dummy_data_dict(lowercase , lowercase )
elif isinstance(lowercase , (list, tuple) ):
return self.create_dummy_data_list(lowercase , lowercase )
else:
return self.create_dummy_data_single(lowercase , lowercase )
def A_ ( self , lowercase , *lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , *lowercase , **lowercase ):
return path
def A_ ( self ):
return {}
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowercase , lowercase ):
for single_url in single_urls:
download_callback(lowercase )
else:
_lowerCamelCase : List[Any] = single_urls
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowercase , lowercase ):
_lowerCamelCase : List[Any] = [os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : Optional[int] = single_urls
_lowerCamelCase : List[Any] = os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) )
_lowerCamelCase : int = value
# make sure that values are unique
if all(isinstance(lowercase , lowercase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : List[str] = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , lowercase ) ) for url in data_url )
_lowerCamelCase : int = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : List[str] = [data_url[0]] * len(lowercase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : str = os.path.join(lowercase , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(lowercase )
return dummy_data_list
def A_ ( self , lowercase , lowercase ):
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Tuple = os.path.join(lowercase , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(lowercase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self , lowercase ):
def _iter_archive_members(lowercase ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : str = Path(self.dummy_file ).parent
_lowerCamelCase : Union[str, Any] = path.relative_to(lowercase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : List[str] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowercase )
_lowerCamelCase : Optional[int] = Path(lowercase )
_lowerCamelCase : Dict = _iter_archive_members(lowercase ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(lowercase ).as_posix(), file_path.open('rb' )
def A_ ( self , lowercase ):
if not isinstance(lowercase , lowercase ):
_lowerCamelCase : List[str] = [paths]
for path in paths:
if os.path.isfile(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(lowercase ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(lowercase , lowercase ) | 96 | 0 |
import functools
from typing import Any
def lowercase_ (A : Optional[Any] , A : List[str] ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(lowercase__ , lowercase__ ) or not all(
isinstance(lowercase__ , lowercase__ ) and len(lowercase__ ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
snake_case__ : dict[str, Any] = {}
snake_case__ : List[Any] = 'WORD_KEEPER'
for word in words:
snake_case__ : Dict = trie
for c in word:
if c not in trie_node:
snake_case__ : Any = {}
snake_case__ : str = trie_node[c]
snake_case__ : Optional[Any] = True
snake_case__ : Dict = len(lowercase__ )
# Dynamic programming method
@functools.cache
def is_breakable(A : str ) -> bool:
if index == len_string:
return True
snake_case__ : List[Any] = trie
for i in range(lowercase__ , lowercase__ ):
snake_case__ : Any = trie_node.get(string[i] , lowercase__ )
if trie_node is None:
return False
if trie_node.get(lowercase__ , lowercase__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 277 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
_lowerCamelCase, _lowerCamelCase : Optional[Any] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
_lowerCamelCase : Union[str, Any] = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(lowercase__ , i + t , (lowercase__) )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
if __name__ == "__main__":
lowercase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowercase__ = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted)) | 96 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.