code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from sklearn.metrics import matthews_corrcoef
import datasets
__lowerCAmelCase : Union[str, Any] = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'
__lowerCAmelCase : Dict = '\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results[\'matthews_correlation\'], 2))\n -0.25\n'
__lowerCAmelCase : List[str] = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self : Optional[int] ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any]=None ) -> Optional[Any]:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(__UpperCamelCase , __UpperCamelCase , sample_weight=__UpperCamelCase ) ),
}
| 88 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
A = generate_pascal_triangle(UpperCamelCase )
for row_idx in range(UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
# Print row values
for col_idx in range(row_idx + 1 ):
if col_idx != row_idx:
print(triangle[row_idx][col_idx] , end=" " )
else:
print(triangle[row_idx][col_idx] , end="" )
print()
def A__ ( UpperCamelCase ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError("The input value of 'num_rows' should be 'int'" )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
"The input value of 'num_rows' should be greater than or equal to 0" )
A = []
for current_row_idx in range(UpperCamelCase ):
A = populate_current_row(UpperCamelCase , UpperCamelCase )
triangle.append(UpperCamelCase )
return triangle
def A__ ( UpperCamelCase , UpperCamelCase ):
A = [-1] * (current_row_idx + 1)
# first and last elements of current row are equal to 1
A, A = 1, 1
for current_col_idx in range(1 , UpperCamelCase ):
calculate_current_element(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
return current_row
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , ):
A = triangle[current_row_idx - 1][current_col_idx - 1]
A = triangle[current_row_idx - 1][current_col_idx]
A = above_to_left_elt + above_to_right_elt
def A__ ( UpperCamelCase ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError("The input value of 'num_rows' should be 'int'" )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
"The input value of 'num_rows' should be greater than or equal to 0" )
A = [[1]]
for row_index in range(1 , UpperCamelCase ):
A = [0] + result[-1] + [0]
A = row_index + 1
# Calculate the number of distinct elements in a row
A = sum(divmod(UpperCamelCase , 2 ) )
A = [
temp_row[i - 1] + temp_row[i] for i in range(1 , distinct_elements + 1 )
]
A = row_first_half[: (row_index + 1) // 2]
row_second_half.reverse()
A = row_first_half + row_second_half
result.append(UpperCamelCase )
return result
def A__ ( ):
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(UpperCamelCase , UpperCamelCase ) -> None:
A = F"{func.__name__}({value})"
A = timeit(F"__main__.{call}" , setup="import __main__" )
# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
print(F"{call:38} -- {timing:.4f} seconds" )
for value in range(15 ): # (1, 7, 14):
for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
benchmark_a_function(UpperCamelCase , UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 292 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A: Dict = logging.get_logger(__name__)
A: Optional[int] = {
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Union[str, Any] = 'efficientnet'
def __init__( self , _SCREAMING_SNAKE_CASE = 3 , _SCREAMING_SNAKE_CASE = 600 , _SCREAMING_SNAKE_CASE = 2.0 , _SCREAMING_SNAKE_CASE = 3.1 , _SCREAMING_SNAKE_CASE = 8 , _SCREAMING_SNAKE_CASE = [3, 3, 5, 3, 5, 5, 3] , _SCREAMING_SNAKE_CASE = [32, 16, 24, 40, 80, 112, 192] , _SCREAMING_SNAKE_CASE = [16, 24, 40, 80, 112, 192, 320] , _SCREAMING_SNAKE_CASE = [] , _SCREAMING_SNAKE_CASE = [1, 2, 2, 2, 1, 2, 1] , _SCREAMING_SNAKE_CASE = [1, 2, 2, 3, 3, 4, 1] , _SCREAMING_SNAKE_CASE = [1, 6, 6, 6, 6, 6, 6] , _SCREAMING_SNAKE_CASE = 0.25 , _SCREAMING_SNAKE_CASE = "swish" , _SCREAMING_SNAKE_CASE = 2560 , _SCREAMING_SNAKE_CASE = "mean" , _SCREAMING_SNAKE_CASE = 0.02 , _SCREAMING_SNAKE_CASE = 0.001 , _SCREAMING_SNAKE_CASE = 0.99 , _SCREAMING_SNAKE_CASE = 0.5 , _SCREAMING_SNAKE_CASE = 0.2 , **_SCREAMING_SNAKE_CASE , ) -> str:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : Union[str, Any] = image_size
UpperCAmelCase : int = width_coefficient
UpperCAmelCase : int = depth_coefficient
UpperCAmelCase : Optional[Any] = depth_divisor
UpperCAmelCase : Optional[int] = kernel_sizes
UpperCAmelCase : str = in_channels
UpperCAmelCase : Dict = out_channels
UpperCAmelCase : str = depthwise_padding
UpperCAmelCase : Tuple = strides
UpperCAmelCase : List[str] = num_block_repeats
UpperCAmelCase : Tuple = expand_ratios
UpperCAmelCase : int = squeeze_expansion_ratio
UpperCAmelCase : Tuple = hidden_act
UpperCAmelCase : Optional[int] = hidden_dim
UpperCAmelCase : int = pooling_type
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : int = batch_norm_eps
UpperCAmelCase : Any = batch_norm_momentum
UpperCAmelCase : List[Any] = dropout_rate
UpperCAmelCase : Optional[int] = drop_connect_rate
UpperCAmelCase : Optional[int] = sum(_SCREAMING_SNAKE_CASE ) * 4
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Dict = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE ( self ) -> 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 ) -> float:
'''simple docstring'''
return 1E-5
| 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number | (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number & ~(1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number ^ (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return ((number >> position) & 1) == 1
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 76 | 1 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 2 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
lowerCamelCase : Any = None
lowerCamelCase : Dict = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
lowerCamelCase : List[str] = {
'vocab_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
},
'tokenizer_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
},
}
# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase : Any = {
't5-small': 512,
't5-base': 512,
't5-large': 512,
't5-3b': 512,
't5-11b': 512,
}
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = VOCAB_FILES_NAMES
lowerCAmelCase__ : str = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ : int = ["""input_ids""", """attention_mask"""]
lowerCAmelCase__ : Optional[int] = TaTokenizer
lowerCAmelCase__ : List[int] = []
def __init__(self : Dict , UpperCamelCase : str=None , UpperCamelCase : Optional[Any]=None , UpperCamelCase : Any="</s>" , UpperCamelCase : str="<unk>" , UpperCamelCase : List[str]="<pad>" , UpperCamelCase : List[str]=100 , UpperCamelCase : Tuple=None , **UpperCamelCase : List[str] , ):
'''simple docstring'''
if extra_ids > 0 and additional_special_tokens is None:
lowercase__ = [f"<extra_id_{i}>" for i in range(UpperCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
lowercase__ = len(set(filter(lambda UpperCamelCase : bool('''extra_id_''' in str(UpperCamelCase ) ) , UpperCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
super().__init__(
UpperCamelCase , tokenizer_file=UpperCamelCase , eos_token=UpperCamelCase , unk_token=UpperCamelCase , pad_token=UpperCamelCase , extra_ids=UpperCamelCase , additional_special_tokens=UpperCamelCase , **UpperCamelCase , )
lowercase__ = vocab_file
lowercase__ = False if not self.vocab_file else True
lowercase__ = extra_ids
@staticmethod
def UpperCamelCase__ (UpperCamelCase : List[Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
lowercase__ = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f" {pretrained_model_name_or_path} automatically truncating your input to"
f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , UpperCamelCase , )
return max_model_length
def UpperCamelCase__ (self : Any , UpperCamelCase : str , UpperCamelCase : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(UpperCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
lowercase__ = os.path.join(
UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase ):
copyfile(self.vocab_file , UpperCamelCase )
logger.info(f"Copy vocab file to {out_vocab_file}" )
return (out_vocab_file,)
def UpperCamelCase__ (self : Any , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
lowercase__ = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def UpperCamelCase__ (self : Optional[Any] , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCamelCase__ (self : List[Any] ):
'''simple docstring'''
return list(
set(filter(lambda UpperCamelCase : bool(re.search(R'''<extra_id_\d+>''' , UpperCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
return [self.convert_tokens_to_ids(UpperCamelCase ) for token in self.get_sentinel_tokens()]
| 2 | 1 |
"""simple docstring"""
import math
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Tuple:
"""simple docstring"""
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__snake_case )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError('''This should never happen''' )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
_a = """Enter the base and the power separated by a comma: """
_a , _a = map(int, input(prompt).split(""","""))
_a , _a = map(int, input(prompt).split(""","""))
# We find the log of each number, using the function res(), which takes two
# arguments.
_a = res(xa, ya)
_a = res(xa, ya)
# We check for the largest number
if resa > resa:
print("""Largest number is""", xa, """^""", ya)
elif resa > resa:
print("""Largest number is""", xa, """^""", ya)
else:
print("""Both are equal""")
| 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_a = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
_a = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 100 | 1 |
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase = f"""Input value of [number={number}] must be an integer"""
raise TypeError(UpperCamelCase_ )
if number < 1:
UpperCamelCase = f"""Input value of [number={number}] must be > 0"""
raise ValueError(UpperCamelCase_ )
UpperCamelCase = 1
for i in range(1 , UpperCamelCase_ ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 343 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue_model_parallelism.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
] )
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=lowerCamelCase_ , )
assert hasattr(self , """env""" )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[str] ):
"""simple docstring"""
UpperCamelCase = {
"""enabled""": True,
"""processes_per_host""": 8,
}
UpperCamelCase = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
UpperCamelCase = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
UpperCamelCase = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=lowerCamelCase_ , instance_type=self.instance_type , debugger_hook_config=lowerCamelCase_ , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=lowerCamelCase_ , py_version="""py36""" , )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[Any] ):
"""simple docstring"""
TrainingJobAnalytics(lowerCamelCase_ ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : int ):
"""simple docstring"""
UpperCamelCase = self.create_estimator(lowerCamelCase_ )
# run training
estimator.fit()
# result dataframe
UpperCamelCase = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCamelCase = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
UpperCamelCase = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCamelCase = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , lowerCamelCase_ )
| 343 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
__A : Any = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase : str = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
lowerCAmelCase : Optional[int] = os.path.join(_UpperCAmelCase, 'words.txt' )
lowerCAmelCase : Tuple = ''
with open(_UpperCAmelCase ) as f:
lowerCAmelCase : List[str] = f.readline()
lowerCAmelCase : Dict = [word.strip('"' ) for word in words.strip('\r\n' ).split(',' )]
lowerCAmelCase : Union[str, Any] = [
word
for word in [sum(ord(_UpperCAmelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(_UpperCAmelCase )
if __name__ == "__main__":
print(solution())
| 323 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase=False ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase : int = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
('module.cls_token', 'vit.embeddings.cls_token'),
('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('module.pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('module.norm.weight', 'layernorm.weight'),
('module.norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowerCAmelCase : Any = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase=False ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowerCAmelCase : Optional[Any] = ''
else:
lowerCAmelCase : Optional[Any] = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCAmelCase : Union[str, Any] = state_dict.pop(f"module.blocks.{i}.attn.qkv.weight" )
lowerCAmelCase : List[Any] = state_dict.pop(f"module.blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase : str = in_proj_weight[
: config.hidden_size, :
]
lowerCAmelCase : int = in_proj_bias[: config.hidden_size]
lowerCAmelCase : Dict = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCAmelCase : str = in_proj_weight[
-config.hidden_size :, :
]
lowerCAmelCase : Any = in_proj_bias[-config.hidden_size :]
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Any:
'''simple docstring'''
lowerCAmelCase : Optional[int] = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase, _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : Optional[int] = [
'module.fc.fc1.weight',
'module.fc.fc1.bias',
'module.fc.bn1.weight',
'module.fc.bn1.bias',
'module.fc.bn1.running_mean',
'module.fc.bn1.running_var',
'module.fc.bn1.num_batches_tracked',
'module.fc.fc2.weight',
'module.fc.fc2.bias',
'module.fc.bn2.weight',
'module.fc.bn2.bias',
'module.fc.bn2.running_mean',
'module.fc.bn2.running_var',
'module.fc.bn2.num_batches_tracked',
'module.fc.fc3.weight',
'module.fc.fc3.bias',
]
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase, _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowerCAmelCase : List[str] = dct.pop(_UpperCAmelCase )
lowerCAmelCase : Dict = val
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
lowerCAmelCase : str = ViTMSNConfig()
lowerCAmelCase : str = 1_000
lowerCAmelCase : List[str] = 'datasets/huggingface/label-files'
lowerCAmelCase : int = 'imagenet-1k-id2label.json'
lowerCAmelCase : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase, _UpperCAmelCase ), 'r' ) )
lowerCAmelCase : Optional[Any] = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
lowerCAmelCase : List[str] = idalabel
lowerCAmelCase : List[Any] = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowerCAmelCase : Optional[Any] = 384
lowerCAmelCase : List[Any] = 1_536
lowerCAmelCase : Union[str, Any] = 6
elif "l16" in checkpoint_url:
lowerCAmelCase : List[Any] = 1_024
lowerCAmelCase : Any = 4_096
lowerCAmelCase : str = 24
lowerCAmelCase : Optional[int] = 16
lowerCAmelCase : Any = 0.1
elif "b4" in checkpoint_url:
lowerCAmelCase : Any = 4
elif "l7" in checkpoint_url:
lowerCAmelCase : int = 7
lowerCAmelCase : str = 1_024
lowerCAmelCase : Tuple = 4_096
lowerCAmelCase : str = 24
lowerCAmelCase : Tuple = 16
lowerCAmelCase : Dict = 0.1
lowerCAmelCase : List[str] = ViTMSNModel(_UpperCAmelCase )
lowerCAmelCase : int = torch.hub.load_state_dict_from_url(_UpperCAmelCase, map_location='cpu' )['target_encoder']
lowerCAmelCase : int = ViTImageProcessor(size=config.image_size )
remove_projection_head(_UpperCAmelCase )
lowerCAmelCase : Tuple = create_rename_keys(_UpperCAmelCase, base_model=_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase )
read_in_q_k_v(_UpperCAmelCase, _UpperCAmelCase, base_model=_UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
lowerCAmelCase : Optional[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowerCAmelCase : Dict = Image.open(requests.get(_UpperCAmelCase, stream=_UpperCAmelCase ).raw )
lowerCAmelCase : Any = ViTImageProcessor(
size=config.image_size, image_mean=_UpperCAmelCase, image_std=_UpperCAmelCase )
lowerCAmelCase : List[Any] = image_processor(images=_UpperCAmelCase, return_tensors='pt' )
# forward pass
torch.manual_seed(2 )
lowerCAmelCase : Union[str, Any] = model(**_UpperCAmelCase )
lowerCAmelCase : List[str] = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
lowerCAmelCase : Optional[int] = torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]] )
elif "b16" in checkpoint_url:
lowerCAmelCase : int = torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]] )
elif "l16" in checkpoint_url:
lowerCAmelCase : Union[str, Any] = torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]] )
elif "b4" in checkpoint_url:
lowerCAmelCase : int = torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]] )
else:
lowerCAmelCase : Union[str, Any] = torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3], _UpperCAmelCase, atol=1e-4 )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_UpperCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
__A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__A : List[str] = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 323 | 1 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
__UpperCamelCase : int = get_tests_dir("""fixtures/dummy-config.json""")
class __SCREAMING_SNAKE_CASE( unittest.TestCase ):
def lowerCAmelCase_ ( self: Any ) -> int:
snake_case__ = 0
def lowerCAmelCase_ ( self: str ) -> int:
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec('transformers.models.auto' ) )
def lowerCAmelCase_ ( self: Any ) -> Tuple:
snake_case__ = AutoConfig.from_pretrained('bert-base-uncased' )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
def lowerCAmelCase_ ( self: Optional[Any] ) -> int:
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
def lowerCAmelCase_ ( self: Optional[Any] ) -> Dict:
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
def lowerCAmelCase_ ( self: List[Any] ) -> Optional[Any]:
snake_case__ = AutoConfig.for_model('roberta' )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
def lowerCAmelCase_ ( self: List[Any] ) -> Dict:
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
snake_case__ = os.path.join(UpperCamelCase , 'fake-roberta' )
os.makedirs(UpperCamelCase , exist_ok=UpperCamelCase )
with open(os.path.join(UpperCamelCase , 'config.json' ) , 'w' ) as f:
f.write(json.dumps({} ) )
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertEqual(type(UpperCamelCase ) , UpperCamelCase )
def lowerCAmelCase_ ( self: int ) -> Dict:
try:
AutoConfig.register('custom' , UpperCamelCase )
# Wrong model type will raise an error
with self.assertRaises(UpperCamelCase ):
AutoConfig.register('model' , UpperCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCamelCase ):
AutoConfig.register('bert' , UpperCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
snake_case__ = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCamelCase )
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def lowerCAmelCase_ ( self: Dict ) -> Tuple:
with self.assertRaisesRegex(
UpperCamelCase , 'bert-base is not a local folder and is not a valid model identifier' ):
snake_case__ = AutoConfig.from_pretrained('bert-base' )
def lowerCAmelCase_ ( self: Any ) -> Optional[Any]:
with self.assertRaisesRegex(
UpperCamelCase , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase , revision='aaaaaa' )
def lowerCAmelCase_ ( self: List[str] ) -> Tuple:
with self.assertRaisesRegex(
UpperCamelCase , 'hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.' , ):
snake_case__ = AutoConfig.from_pretrained('hf-internal-testing/no-config-test-repo' )
def lowerCAmelCase_ ( self: Optional[Any] ) -> Tuple:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(UpperCamelCase ):
snake_case__ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(UpperCamelCase ):
snake_case__ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=UpperCamelCase )
snake_case__ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=UpperCamelCase )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCamelCase )
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase , trust_remote_code=UpperCamelCase )
self.assertEqual(reloaded_config.__class__.__name__ , 'NewModelConfig' )
def lowerCAmelCase_ ( self: Optional[int] ) -> Tuple:
class __SCREAMING_SNAKE_CASE( a_ ):
_UpperCAmelCase = "new-model"
try:
AutoConfig.register('new-model' , UpperCamelCase )
# If remote code is not set, the default is to use local
snake_case__ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote code is disabled, we load the local one.
snake_case__ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=UpperCamelCase )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote is enabled, we load from the Hub
snake_case__ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=UpperCamelCase )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 307 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE_CASE( a_ ):
_UpperCAmelCase = ["image_processor", "tokenizer"]
_UpperCAmelCase = "LayoutLMv2ImageProcessor"
_UpperCAmelCase = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
def __init__( self: int , UpperCamelCase: Optional[int]=None , UpperCamelCase: Optional[Any]=None , **UpperCamelCase: Union[str, Any] ) -> int:
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , UpperCamelCase , )
snake_case__ = kwargs.pop('feature_extractor' )
snake_case__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(UpperCamelCase , UpperCamelCase )
def __call__( self: Any , UpperCamelCase: Optional[Any] , UpperCamelCase: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCamelCase: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCamelCase: Union[List[List[int]], List[List[List[int]]]] = None , UpperCamelCase: Optional[Union[List[int], List[List[int]]]] = None , UpperCamelCase: bool = True , UpperCamelCase: Union[bool, str, PaddingStrategy] = False , UpperCamelCase: Union[bool, str, TruncationStrategy] = None , UpperCamelCase: Optional[int] = None , UpperCamelCase: int = 0 , UpperCamelCase: Optional[int] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = True , UpperCamelCase: Optional[Union[str, TensorType]] = None , **UpperCamelCase: Any , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes '
'if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.' )
# first, apply the image processor
snake_case__ = self.image_processor(images=UpperCamelCase , return_tensors=UpperCamelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(UpperCamelCase , UpperCamelCase ):
snake_case__ = [text] # add batch dimension (as the image processor always adds a batch dimension)
snake_case__ = features['words']
snake_case__ = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=UpperCamelCase , add_special_tokens=UpperCamelCase , padding=UpperCamelCase , truncation=UpperCamelCase , max_length=UpperCamelCase , stride=UpperCamelCase , pad_to_multiple_of=UpperCamelCase , return_token_type_ids=UpperCamelCase , return_attention_mask=UpperCamelCase , return_overflowing_tokens=UpperCamelCase , return_special_tokens_mask=UpperCamelCase , return_offsets_mapping=UpperCamelCase , return_length=UpperCamelCase , verbose=UpperCamelCase , return_tensors=UpperCamelCase , **UpperCamelCase , )
# add pixel values
snake_case__ = features.pop('pixel_values' )
if return_overflowing_tokens is True:
snake_case__ = self.get_overflowing_images(UpperCamelCase , encoded_inputs['overflow_to_sample_mapping'] )
snake_case__ = images
return encoded_inputs
def lowerCAmelCase_ ( self: Any , UpperCamelCase: Optional[int] , UpperCamelCase: Any ) -> Tuple:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
snake_case__ = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
F''' {len(UpperCamelCase )} and {len(UpperCamelCase )}''' )
return images_with_overflow
def lowerCAmelCase_ ( self: Dict , *UpperCamelCase: Dict , **UpperCamelCase: Optional[int] ) -> List[Any]:
return self.tokenizer.batch_decode(*UpperCamelCase , **UpperCamelCase )
def lowerCAmelCase_ ( self: List[Any] , *UpperCamelCase: Optional[Any] , **UpperCamelCase: int ) -> Optional[Any]:
return self.tokenizer.decode(*UpperCamelCase , **UpperCamelCase )
@property
def lowerCAmelCase_ ( self: str ) -> List[Any]:
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def lowerCAmelCase_ ( self: Any ) -> List[Any]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , UpperCamelCase , )
return self.image_processor_class
@property
def lowerCAmelCase_ ( self: Optional[int] ) -> Dict:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , UpperCamelCase , )
return self.image_processor
| 307 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
lowerCAmelCase__ : str = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['''pixel_values''']
def __init__( self : Any , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Optional[Dict[str, int]] = None , UpperCAmelCase_ : PILImageResampling = PILImageResampling.BILINEAR , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Dict[str, int] = None , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Union[int, float] = 1 / 255 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , **UpperCAmelCase_ : Optional[int] , ):
"""simple docstring"""
super().__init__(**__snake_case )
__UpperCAmelCase : Union[str, Any] = size if size is not None else {"shortest_edge": 256}
__UpperCAmelCase : List[Any] = get_size_dict(__snake_case , default_to_square=__snake_case )
__UpperCAmelCase : Union[str, Any] = crop_size if crop_size is not None else {"height": 224, "width": 224}
__UpperCAmelCase : Dict = get_size_dict(__snake_case , param_name="crop_size" )
__UpperCAmelCase : Union[str, Any] = do_resize
__UpperCAmelCase : int = size
__UpperCAmelCase : Union[str, Any] = resample
__UpperCAmelCase : Optional[Any] = do_center_crop
__UpperCAmelCase : Tuple = crop_size
__UpperCAmelCase : Dict = do_rescale
__UpperCAmelCase : str = rescale_factor
__UpperCAmelCase : List[Any] = do_normalize
__UpperCAmelCase : Tuple = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__UpperCAmelCase : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Dict[str, int] , UpperCAmelCase_ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : str , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = get_size_dict(__snake_case , default_to_square=__snake_case )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
__UpperCAmelCase : Tuple = get_resize_output_image_size(__snake_case , size=size["shortest_edge"] , default_to_square=__snake_case )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : int , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Dict[str, int] , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : Tuple , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(__snake_case , size=(size["height"], size["width"]) , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : float , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : str ):
"""simple docstring"""
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Union[float, List[float]] , UpperCAmelCase_ : Union[float, List[float]] , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : Tuple , ):
"""simple docstring"""
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase_ : ImageInput , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Dict[str, int] = None , UpperCAmelCase_ : PILImageResampling = None , UpperCAmelCase_ : bool = None , UpperCAmelCase_ : Dict[str, int] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[float] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[str, TensorType]] = None , UpperCAmelCase_ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase_ : List[str] , ):
"""simple docstring"""
__UpperCAmelCase : Any = do_resize if do_resize is not None else self.do_resize
__UpperCAmelCase : Tuple = size if size is not None else self.size
__UpperCAmelCase : str = get_size_dict(__snake_case , default_to_square=__snake_case )
__UpperCAmelCase : int = resample if resample is not None else self.resample
__UpperCAmelCase : List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
__UpperCAmelCase : Tuple = crop_size if crop_size is not None else self.crop_size
__UpperCAmelCase : List[str] = get_size_dict(__snake_case , param_name="crop_size" )
__UpperCAmelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
__UpperCAmelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCAmelCase : Tuple = do_normalize if do_normalize is not None else self.do_normalize
__UpperCAmelCase : Tuple = image_mean if image_mean is not None else self.image_mean
__UpperCAmelCase : Any = image_std if image_std is not None else self.image_std
__UpperCAmelCase : Optional[int] = make_list_of_images(__snake_case )
if not valid_images(__snake_case ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
__UpperCAmelCase : Dict = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
__UpperCAmelCase : List[Any] = [self.resize(image=__snake_case , size=__snake_case , resample=__snake_case ) for image in images]
if do_center_crop:
__UpperCAmelCase : str = [self.center_crop(image=__snake_case , size=__snake_case ) for image in images]
if do_rescale:
__UpperCAmelCase : Optional[int] = [self.rescale(image=__snake_case , scale=__snake_case ) for image in images]
if do_normalize:
__UpperCAmelCase : str = [self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case ) for image in images]
__UpperCAmelCase : Union[str, Any] = [to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
__UpperCAmelCase : Tuple = {"pixel_values": images}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[Tuple] = None ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__snake_case ) != len(__snake_case ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(__snake_case ):
__UpperCAmelCase : Union[str, Any] = target_sizes.numpy()
__UpperCAmelCase : Optional[int] = []
for idx in range(len(__snake_case ) ):
__UpperCAmelCase : Optional[int] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=__snake_case )
__UpperCAmelCase : Union[str, Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__snake_case )
else:
__UpperCAmelCase : Dict = logits.argmax(dim=1 )
__UpperCAmelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 370 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if not scores:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1, node_index * 2, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ), )
if is_max
else min(
minimax(depth + 1, node_index * 2, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ), )
)
def __UpperCamelCase ( ):
__UpperCAmelCase : Dict = [90, 23, 6, 33, 21, 65, 123, 34423]
__UpperCAmelCase : Optional[Any] = math.log(len(_UpperCAmelCase ), 2 )
print(F"Optimal value : {minimax(0, 0, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase )}" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 37 | 0 |
def _lowercase ( UpperCamelCase_ ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
SCREAMING_SNAKE_CASE__ = hex_num[0] == '-'
if is_negative:
SCREAMING_SNAKE_CASE__ = hex_num[1:]
try:
SCREAMING_SNAKE_CASE__ = int(UpperCamelCase_ , 16 )
except ValueError:
raise ValueError('Invalid value was passed to the function' )
SCREAMING_SNAKE_CASE__ = ''
while int_num > 0:
SCREAMING_SNAKE_CASE__ = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(('-' + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 176 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {"""vocab_file""": """vocab.txt"""}
__snake_case = {
"""vocab_file""": {
"""facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
"""facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
},
}
__snake_case = {
"""facebook/esm2_t6_8M_UR50D""": 10_24,
"""facebook/esm2_t12_35M_UR50D""": 10_24,
}
def _lowercase ( UpperCamelCase_ ) -> List[str]:
'''simple docstring'''
with open(UpperCamelCase_ , 'r' ) as f:
SCREAMING_SNAKE_CASE__ = f.read().splitlines()
return [l.strip() for l in lines]
class lowercase__ ( _UpperCAmelCase ):
A__ : Tuple =VOCAB_FILES_NAMES
A__ : Union[str, Any] =PRETRAINED_VOCAB_FILES_MAP
A__ : Any =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Any =["""input_ids""", """attention_mask"""]
def __init__( self : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple="<unk>" , UpperCAmelCase_ : Optional[Any]="<cls>" , UpperCAmelCase_ : List[Any]="<pad>" , UpperCAmelCase_ : Optional[int]="<mask>" , UpperCAmelCase_ : Optional[int]="<eos>" , **UpperCAmelCase_ : Optional[int] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = load_vocab_file(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE__ = {tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE__ = unk_token
SCREAMING_SNAKE_CASE__ = cls_token
SCREAMING_SNAKE_CASE__ = pad_token
SCREAMING_SNAKE_CASE__ = mask_token
SCREAMING_SNAKE_CASE__ = eos_token
SCREAMING_SNAKE_CASE__ = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def A_ ( self : Any , UpperCAmelCase_ : int ):
return self._id_to_token.get(UpperCAmelCase_ , self.unk_token )
def A_ ( self : Dict , UpperCAmelCase_ : str ):
return self._token_to_id.get(UpperCAmelCase_ , self._token_to_id.get(self.unk_token ) )
def A_ ( self : List[str] , UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : List[Any] ):
return text.split()
def A_ ( self : str , UpperCAmelCase_ : Optional[Any]=False ):
return len(self._id_to_token )
def A_ ( self : Union[str, Any] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def A_ ( self : Any , UpperCAmelCase_ : str ):
return self._token_to_id.get(UpperCAmelCase_ , self._token_to_id.get(self.unk_token ) )
def A_ ( self : List[str] , UpperCAmelCase_ : int ):
return self._id_to_token.get(UpperCAmelCase_ , self.unk_token )
def A_ ( self : List[str] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE__ = [self.cls_token_id]
SCREAMING_SNAKE_CASE__ = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def A_ ( self : Dict , UpperCAmelCase_ : List , UpperCAmelCase_ : Optional[List] = None , UpperCAmelCase_ : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
SCREAMING_SNAKE_CASE__ = [1] + ([0] * len(UpperCAmelCase_ )) + [1]
if token_ids_a is not None:
mask += [0] * len(UpperCAmelCase_ ) + [1]
return mask
def A_ ( self : int , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE__ = os.path.join(UpperCAmelCase_ , (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(UpperCAmelCase_ , 'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def A_ ( self : int ):
return self.get_vocab_size(with_added_tokens=UpperCAmelCase_ )
def A_ ( self : List[str] , UpperCAmelCase_ : Union[List[str], List[AddedToken]] , UpperCAmelCase_ : bool = False ):
return super()._add_tokens(UpperCAmelCase_ , special_tokens=UpperCAmelCase_ )
| 176 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
__a = logging.get_logger(__name__)
@add_end_docstrings(_a )
class __a( _a ):
"""simple docstring"""
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> List[str]:
super().__init__(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
self.check_model_type(_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,**_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ : Optional[Any] = {}, {}
if padding is not None:
UpperCAmelCase_ : str = padding
if truncation is not None:
UpperCAmelCase_ : Optional[Any] = truncation
if top_k is not None:
UpperCAmelCase_ : Union[str, Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE ,(Image.Image, str) ) and isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : Dict = {'''image''': image, '''question''': question}
else:
UpperCAmelCase_ : List[Any] = image
UpperCAmelCase_ : Union[str, Any] = super().__call__(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
return results
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ) -> Union[str, Any]:
UpperCAmelCase_ : str = load_image(inputs['''image'''] )
UpperCAmelCase_ : Dict = self.tokenizer(
inputs['''question'''] ,return_tensors=self.framework ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = self.image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors=self.framework )
model_inputs.update(_SCREAMING_SNAKE_CASE )
return model_inputs
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> Dict:
UpperCAmelCase_ : int = self.model(**_SCREAMING_SNAKE_CASE )
return model_outputs
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=5 ) -> Union[str, Any]:
if top_k > self.model.config.num_labels:
UpperCAmelCase_ : Tuple = self.model.config.num_labels
if self.framework == "pt":
UpperCAmelCase_ : Union[str, Any] = model_outputs.logits.sigmoid()[0]
UpperCAmelCase_ : Optional[int] = probs.topk(_SCREAMING_SNAKE_CASE )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
UpperCAmelCase_ : str = scores.tolist()
UpperCAmelCase_ : int = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )] | 371 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
__a = logging.get_logger(__name__)
__a = {
'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = '''layoutlmv3'''
def __init__( self ,_SCREAMING_SNAKE_CASE=50_265 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=512 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.02 ,_SCREAMING_SNAKE_CASE=1e-5 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=64 ,_SCREAMING_SNAKE_CASE=256 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=None ,**_SCREAMING_SNAKE_CASE ,) -> Dict:
super().__init__(
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 ,initializer_range=_SCREAMING_SNAKE_CASE ,layer_norm_eps=_SCREAMING_SNAKE_CASE ,pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
UpperCAmelCase_ : Dict = max_ad_position_embeddings
UpperCAmelCase_ : Any = coordinate_size
UpperCAmelCase_ : Tuple = shape_size
UpperCAmelCase_ : Optional[int] = has_relative_attention_bias
UpperCAmelCase_ : Union[str, Any] = rel_pos_bins
UpperCAmelCase_ : Dict = max_rel_pos
UpperCAmelCase_ : Union[str, Any] = has_spatial_attention_bias
UpperCAmelCase_ : Any = rel_ad_pos_bins
UpperCAmelCase_ : Tuple = max_rel_ad_pos
UpperCAmelCase_ : List[str] = text_embed
UpperCAmelCase_ : int = visual_embed
UpperCAmelCase_ : int = input_size
UpperCAmelCase_ : Dict = num_channels
UpperCAmelCase_ : int = patch_size
UpperCAmelCase_ : Dict = classifier_dropout
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = version.parse('''1.12''' )
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
else:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
] )
@property
def a__ ( self ) -> float:
return 1e-5
@property
def a__ ( self ) -> int:
return 12
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 3 ,_SCREAMING_SNAKE_CASE = 40 ,_SCREAMING_SNAKE_CASE = 40 ,) -> Mapping[str, Any]:
setattr(processor.image_processor ,'''apply_ocr''' ,_SCREAMING_SNAKE_CASE )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCAmelCase_ : List[str] = compute_effective_axis_dimension(
_SCREAMING_SNAKE_CASE ,fixed_dimension=OnnxConfig.default_fixed_batch ,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase_ : Optional[Any] = processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = compute_effective_axis_dimension(
_SCREAMING_SNAKE_CASE ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=_SCREAMING_SNAKE_CASE )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase_ : Optional[Any] = [[''' '''.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
UpperCAmelCase_ : Tuple = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
UpperCAmelCase_ : Union[str, Any] = self._generate_dummy_images(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = dict(
processor(
_SCREAMING_SNAKE_CASE ,text=_SCREAMING_SNAKE_CASE ,boxes=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,) )
return inputs | 235 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : Any , _snake_case : Tuple=False ) -> List[Any]:
'''simple docstring'''
_A = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'vit.embeddings.cls_token'),
('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
_A = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def _snake_case ( _snake_case : str , _snake_case : List[Any] , _snake_case : List[Any]=False ) -> Optional[int]:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
_A = ''
else:
_A = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_A = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
_A = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
_A = in_proj_weight[
: config.hidden_size, :
]
_A = in_proj_bias[: config.hidden_size]
_A = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_A = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_A = in_proj_weight[
-config.hidden_size :, :
]
_A = in_proj_bias[-config.hidden_size :]
def _snake_case ( _snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
_A = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def _snake_case ( _snake_case : str , _snake_case : int , _snake_case : Union[str, Any] ) -> int:
'''simple docstring'''
_A = dct.pop(_snake_case )
_A = val
def _snake_case ( ) -> int:
'''simple docstring'''
_A = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_A = Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : Any=True ) -> str:
'''simple docstring'''
_A = ViTConfig()
# patch_size
if model_name[-1] == "8":
_A = 8
# set labels if required
if not base_model:
_A = 10_00
_A = 'huggingface/label-files'
_A = 'imagenet-1k-id2label.json'
_A = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type='dataset' ) , 'r' ) )
_A = {int(_snake_case ): v for k, v in idalabel.items()}
_A = idalabel
_A = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
_A = 3_84
_A = 15_36
_A = 12
_A = 6
# load original model from torch hub
_A = torch.hub.load('facebookresearch/dino:main' , _snake_case )
original_model.eval()
# load state_dict of original model, remove and rename some keys
_A = original_model.state_dict()
if base_model:
remove_classification_head_(_snake_case )
_A = create_rename_keys(_snake_case , base_model=_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , _snake_case )
# load HuggingFace model
if base_model:
_A = ViTModel(_snake_case , add_pooling_layer=_snake_case ).eval()
else:
_A = ViTForImageClassification(_snake_case ).eval()
model.load_state_dict(_snake_case )
# Check outputs on an image, prepared by ViTImageProcessor
_A = ViTImageProcessor()
_A = image_processor(images=prepare_img() , return_tensors='pt' )
_A = encoding['pixel_values']
_A = model(_snake_case )
if base_model:
_A = original_model(_snake_case )
assert torch.allclose(_snake_case , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
_A = original_model(_snake_case )
assert logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1E-3 )
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_snake_case )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''dino_vitb16''',
type=str,
help='''Name of the model trained with DINO you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--base_model''',
action='''store_true''',
help='''Whether to only convert the base model (no projection head weights).''',
)
parser.set_defaults(base_model=True)
a = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_A = Rectangle(height=0.25 , width=0.25 )
_A = [mem.copy() for i in range(6 )]
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('CPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(4 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('GPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Model' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(_UpperCAmelCase )
_A = []
_A = []
for i, rect in enumerate(_UpperCAmelCase ):
_A = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8 )
target.move_to(_UpperCAmelCase )
model_arr.append(_UpperCAmelCase )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_UpperCAmelCase )
self.add(*_UpperCAmelCase , *_UpperCAmelCase )
_A = [meta_mem.copy() for i in range(6 )]
_A = [meta_mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Disk' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_A = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_UpperCAmelCase )
_A = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase ) )
_A = Square(0.3 )
input.set_fill(_UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5 )
self.play(Write(_UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(_UpperCAmelCase ) )
self.play(FadeOut(_UpperCAmelCase ) )
_A = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_A = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) )
_A = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase ) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_A = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_A = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5 ) , MoveToTarget(_UpperCAmelCase , run_time=0.5 ) , FadeIn(_UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_UpperCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_A = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_A = a_c
_A = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_UpperCAmelCase ) , FadeOut(_UpperCAmelCase , run_time=0.5 ) , )
_A = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) , MoveToTarget(_UpperCAmelCase ) )
self.wait()
| 315 | 1 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
UpperCAmelCase = logging.getLogger(__name__)
UpperCAmelCase = 'pytorch_model.bin'
@dataclasses.dataclass
class __snake_case:
'''simple docstring'''
UpperCAmelCase : str = dataclasses.field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
UpperCAmelCase : Optional[str] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} , )
@dataclasses.dataclass
class __snake_case:
'''simple docstring'''
UpperCAmelCase : str = dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
UpperCAmelCase : str = dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
UpperCAmelCase : Optional[str] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "A csv or a json file containing the validation data."} )
UpperCAmelCase : Optional[str] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "The name of the task to train on."} , )
UpperCAmelCase : Optional[List[str]] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "The list of labels for the task."} )
@dataclasses.dataclass
class __snake_case:
'''simple docstring'''
UpperCAmelCase : str = dataclasses.field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
UpperCAmelCase : Optional[str] = dataclasses.field(
default="accuracy" , metadata={"help": "The evaluation metric used for the task."} )
UpperCAmelCase : Optional[str] = dataclasses.field(
default="no" , metadata={
"help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
} , )
UpperCAmelCase : Optional[int] = dataclasses.field(
default=10 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
UpperCAmelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={
"help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
} , )
UpperCAmelCase : Optional[bool] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} , )
UpperCAmelCase : Optional[bool] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} , )
UpperCAmelCase : Optional[bool] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} , )
UpperCAmelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} , )
UpperCAmelCase : Optional[int] = dataclasses.field(
default=1_00 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
UpperCAmelCase : Optional[int] = dataclasses.field(
default=_lowerCAmelCase , metadata={"help": "Random seed for initialization."} , )
def _snake_case ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
lowerCAmelCase = dataset.filter(lambda _SCREAMING_SNAKE_CASE : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
lowerCAmelCase = int(eval_result * len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = dataset.sort("""probability""" , reverse=_SCREAMING_SNAKE_CASE )
lowerCAmelCase = dataset.select(range(_SCREAMING_SNAKE_CASE ) )
lowerCAmelCase = dataset.remove_columns(["""label""", """probability"""] )
lowerCAmelCase = dataset.rename_column("""prediction""" , """label""" )
lowerCAmelCase = dataset.map(lambda _SCREAMING_SNAKE_CASE : {"label": idalabel[example["label"]]} )
lowerCAmelCase = dataset.shuffle(seed=args.seed )
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , f'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(_SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE )
else:
dataset.to_json(_SCREAMING_SNAKE_CASE )
def _snake_case ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Any , **_SCREAMING_SNAKE_CASE : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
lowerCAmelCase = STModelArguments(model_name_or_path=_SCREAMING_SNAKE_CASE )
lowerCAmelCase = STDataArguments(train_file=_SCREAMING_SNAKE_CASE , infer_file=_SCREAMING_SNAKE_CASE )
lowerCAmelCase = STTrainingArguments(output_dir=_SCREAMING_SNAKE_CASE )
lowerCAmelCase = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_SCREAMING_SNAKE_CASE ).items():
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for key, value in kwargs.items():
if hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Sanity checks
lowerCAmelCase = {}
lowerCAmelCase = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
lowerCAmelCase = args.train_file
lowerCAmelCase = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
lowerCAmelCase = args.eval_file
for key in data_files:
lowerCAmelCase = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], f'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
lowerCAmelCase = extension
else:
assert extension == args.data_file_extension, f'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), f'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
lowerCAmelCase = f'{args.output_dir}/self-train_iter-{{}}'.format
lowerCAmelCase = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_SCREAMING_SNAKE_CASE )
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = 0
lowerCAmelCase = False
# Show the progress bar
lowerCAmelCase = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
lowerCAmelCase = data_dir_format(_SCREAMING_SNAKE_CASE )
assert os.path.exists(_SCREAMING_SNAKE_CASE )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """stage-1""" )
lowerCAmelCase = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
arguments_dict.update({key: value} )
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """best-checkpoint""" , _SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , _SCREAMING_SNAKE_CASE )
finetune(**_SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
assert os.path.exists(_SCREAMING_SNAKE_CASE )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , _SCREAMING_SNAKE_CASE )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """best-checkpoint""" )
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """stage-2""" )
# Update arguments_dict
lowerCAmelCase = model_path
lowerCAmelCase = data_files["""train"""]
lowerCAmelCase = current_output_dir
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """best-checkpoint""" , _SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , _SCREAMING_SNAKE_CASE )
finetune(**_SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
assert os.path.exists(_SCREAMING_SNAKE_CASE )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = iteration
lowerCAmelCase = data_dir_format(iteration + 1 )
lowerCAmelCase = AutoConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , """best-checkpoint""" ) )
lowerCAmelCase = config.idalabel
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """eval_results_best-checkpoint.json""" )
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """test_results_best-checkpoint.json""" )
assert os.path.exists(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , """r""" ) as f:
lowerCAmelCase = float(json.load(_SCREAMING_SNAKE_CASE )[args.eval_metric] )
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(_SCREAMING_SNAKE_CASE )
# Loading the dataset from local csv or json files.
lowerCAmelCase = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
lowerCAmelCase = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
shutil.copy(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , f'eval_results_iter-{iteration}.json' ) )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
shutil.copy(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , f'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
lowerCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , f'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
lowerCAmelCase = eval_result
if best_iteration is None:
lowerCAmelCase = new_iteration
lowerCAmelCase = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
lowerCAmelCase = new_iteration
lowerCAmelCase = new_eval_result
lowerCAmelCase = 0
else:
if new_eval_result == best_eval_result:
lowerCAmelCase = new_iteration
lowerCAmelCase = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
lowerCAmelCase = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , _SCREAMING_SNAKE_CASE )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , _SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_SCREAMING_SNAKE_CASE , f'eval_results_iter-{iteration}.json' ) , os.path.join(_SCREAMING_SNAKE_CASE , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , _SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_SCREAMING_SNAKE_CASE , f'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(_SCREAMING_SNAKE_CASE , """eval_results_best-iteration.json""" ) , ) | 364 |
'''simple docstring'''
import cmath
import math
def _snake_case ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> complex:
"""simple docstring"""
lowerCAmelCase = math.radians(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = math.radians(_SCREAMING_SNAKE_CASE )
# Convert voltage and current to rectangular form
lowerCAmelCase = cmath.rect(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = cmath.rect(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod() | 187 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( _A , _A , _A , _A ):
a : Union[str, Any] = []
a , a : Optional[Any] = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
a : Dict = result + left + right
return input_list
def lowerCamelCase__ ( _A ):
if len(_A ) <= 1:
return input_list
a : Optional[Any] = list(_A )
# iteration for two-way merging
a : int = 2
while p <= len(_A ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(_A ) , _A ):
a : List[str] = i
a : int = i + p - 1
a : List[Any] = (low + high + 1) // 2
a : Union[str, Any] = merge(_A , _A , _A , _A )
# final merge of last two parts
if p * 2 >= len(_A ):
a : Any = i
a : Any = merge(_A , 0 , _A , len(_A ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
lowerCAmelCase: Optional[Any] = input('Enter numbers separated by a comma:\n').strip()
if user_input == "":
lowerCAmelCase: Tuple = []
else:
lowerCAmelCase: List[str] = [int(item.strip()) for item in user_input.split(',')]
print(iter_merge_sort(unsorted)) | 297 |
'''simple docstring'''
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase: str = {
'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
'processing_mgp_str': ['MgpstrProcessor'],
'tokenization_mgp_str': ['MgpstrTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Optional[Any] = [
'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
'MgpstrModel',
'MgpstrPreTrainedModel',
'MgpstrForSceneTextRecognition',
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
lowerCAmelCase: Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 297 | 1 |
from sklearn.metrics import recall_score
import datasets
__UpperCAmelCase : Optional[Any] = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
__UpperCAmelCase : Optional[int] = "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
__UpperCAmelCase : Dict = "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class UpperCAmelCase_ ( datasets.Metric):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
'''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
}
if self.config_name == '''multilabel'''
else {
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE="binary" , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE="warn" , ):
"""simple docstring"""
UpperCamelCase : int = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 363 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int] = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__UpperCAmelCase : List[Any] = re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
__UpperCAmelCase : List[Any] = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
__UpperCAmelCase : str = re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__UpperCAmelCase : Union[str, Any] = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
__UpperCAmelCase : Dict = re.compile(r"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
__UpperCAmelCase : str = re.compile(r"^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
__UpperCAmelCase : str = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
__UpperCAmelCase : Any = re.compile(r"^\s*try:")
# Catches a line with else:
__UpperCAmelCase : List[Any] = re.compile(r"^\s*else:")
def a ( SCREAMING_SNAKE_CASE_ : Dict ):
"""simple docstring"""
if _re_test_backend.search(SCREAMING_SNAKE_CASE_ ) is None:
return None
UpperCamelCase : Union[str, Any] = [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE_ )]
backends.sort()
return "_and_".join(SCREAMING_SNAKE_CASE_ )
def a ( SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCamelCase : Tuple = f.readlines()
UpperCamelCase : Tuple = 0
while line_index < len(SCREAMING_SNAKE_CASE_ ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(SCREAMING_SNAKE_CASE_ ):
return None
# First grab the objects without a specific backend in _import_structure
UpperCamelCase : List[Any] = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
UpperCamelCase : Optional[int] = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ).groups()[0]
UpperCamelCase : str = re.findall(R'''\[([^\]]+)\]''' , SCREAMING_SNAKE_CASE_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
UpperCamelCase : List[Any] = _re_import_struct_key_value.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
UpperCamelCase : List[str] = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
UpperCamelCase : Dict = {'''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 : Dict = 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 : Optional[Any] = 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 : str = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
UpperCamelCase : str = lines[line_index]
if _re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ).groups()[0] )
elif _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ) is not None:
UpperCamelCase : Union[str, Any] = _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(''', ''' )
UpperCamelCase : List[Any] = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ) is not None:
UpperCamelCase : str = _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(''', ''' )
UpperCamelCase : Dict = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_quote_object.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_quote_object.search(SCREAMING_SNAKE_CASE_ ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 1_2 + '''"''' ):
objects.append(line[1_3:-3] )
line_index += 1
UpperCamelCase : Tuple = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
UpperCamelCase : int = []
while (
line_index < len(SCREAMING_SNAKE_CASE_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
UpperCamelCase : Tuple = lines[line_index]
UpperCamelCase : Any = _re_import.search(SCREAMING_SNAKE_CASE_ )
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 : Any = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(SCREAMING_SNAKE_CASE_ ):
# If the line is an if is_backend_available, we grab all objects associated.
UpperCamelCase : Optional[Any] = 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 : Dict = 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 : Dict = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
UpperCamelCase : Optional[Any] = lines[line_index]
UpperCamelCase : str = _re_import.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 1_2 ):
objects.append(line[1_2:-2] )
line_index += 1
UpperCamelCase : str = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] ):
"""simple docstring"""
def find_duplicates(SCREAMING_SNAKE_CASE_ : Any ):
return [k for k, v in collections.Counter(SCREAMING_SNAKE_CASE_ ).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 : Dict = []
for key in import_dict_objects.keys():
UpperCamelCase : Union[str, Any] = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
UpperCamelCase : Dict = 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 : List[str] = '''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 a ( ):
"""simple docstring"""
UpperCamelCase : Any = []
for root, _, files in os.walk(SCREAMING_SNAKE_CASE_ ):
if "__init__.py" in files:
UpperCamelCase : int = os.path.join(SCREAMING_SNAKE_CASE_ , '''__init__.py''' )
UpperCamelCase : Optional[int] = parse_init(SCREAMING_SNAKE_CASE_ )
if objects is not None:
UpperCamelCase : str = analyze_results(*SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : List[Any] = F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append('''\n'''.join(SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise ValueError('''\n\n'''.join(SCREAMING_SNAKE_CASE_ ) )
def a ( ):
"""simple docstring"""
UpperCamelCase : Dict = []
for path, directories, files in os.walk(SCREAMING_SNAKE_CASE_ ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(SCREAMING_SNAKE_CASE_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(SCREAMING_SNAKE_CASE_ ) / folder).glob('''*.py''' ) ) ) == 0:
continue
UpperCamelCase : List[str] = str((Path(SCREAMING_SNAKE_CASE_ ) / folder).relative_to(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : str = short_path.replace(os.path.sep , '''.''' )
submodules.append(SCREAMING_SNAKE_CASE_ )
for fname in files:
if fname == "__init__.py":
continue
UpperCamelCase : Tuple = str((Path(SCREAMING_SNAKE_CASE_ ) / fname).relative_to(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : int = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(SCREAMING_SNAKE_CASE_ )
return submodules
__UpperCAmelCase : Optional[int] = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
"models.esm.openfold_utils",
]
def a ( ):
"""simple docstring"""
from transformers.utils import direct_transformers_import
UpperCamelCase : Tuple = direct_transformers_import(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(SCREAMING_SNAKE_CASE_ , '''__init__.py''' ) , '''r''' ) as f:
UpperCamelCase : List[Any] = f.read()
import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]''' , SCREAMING_SNAKE_CASE_ ) ) )
UpperCamelCase : Union[str, Any] = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : str = '''\n'''.join(F"""- {module}""" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed 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()
| 315 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Union[str, Any] = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(_a) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
# Calculate the determinant of the matrix
SCREAMING_SNAKE_CASE : Tuple = float(
d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creates a copy of the matrix with swapped positions of the elements
SCREAMING_SNAKE_CASE : int = [[0.0, 0.0], [0.0, 0.0]]
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = matrix[1][1], matrix[0][0]
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(_a)) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(_a) == 3
and len(matrix[0]) == 3
and len(matrix[1]) == 3
and len(matrix[2]) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
SCREAMING_SNAKE_CASE : Optional[Any] = float(
(
(d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
+ (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
+ (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
)
- (
(d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
+ (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
+ (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
))
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creating cofactor matrix
SCREAMING_SNAKE_CASE : List[str] = [
[d(0.0), d(0.0), d(0.0)],
[d(0.0), d(0.0), d(0.0)],
[d(0.0), d(0.0), d(0.0)],
]
SCREAMING_SNAKE_CASE : Any = (d(matrix[1][1]) * d(matrix[2][2])) - (
d(matrix[1][2]) * d(matrix[2][1])
)
SCREAMING_SNAKE_CASE : Tuple = -(
(d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
)
SCREAMING_SNAKE_CASE : List[str] = (d(matrix[1][0]) * d(matrix[2][1])) - (
d(matrix[1][1]) * d(matrix[2][0])
)
SCREAMING_SNAKE_CASE : List[Any] = -(
(d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
)
SCREAMING_SNAKE_CASE : Optional[int] = (d(matrix[0][0]) * d(matrix[2][2])) - (
d(matrix[0][2]) * d(matrix[2][0])
)
SCREAMING_SNAKE_CASE : Union[str, Any] = -(
(d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
)
SCREAMING_SNAKE_CASE : Dict = (d(matrix[0][1]) * d(matrix[1][2])) - (
d(matrix[0][2]) * d(matrix[1][1])
)
SCREAMING_SNAKE_CASE : str = -(
(d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
)
SCREAMING_SNAKE_CASE : List[Any] = (d(matrix[0][0]) * d(matrix[1][1])) - (
d(matrix[0][1]) * d(matrix[1][0])
)
# Transpose the cofactor matrix (Adjoint matrix)
SCREAMING_SNAKE_CASE : Tuple = array(_a)
for i in range(3):
for j in range(3):
SCREAMING_SNAKE_CASE : Union[str, Any] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
SCREAMING_SNAKE_CASE : List[str] = array(_a)
for i in range(3):
for j in range(3):
inverse_matrix[i][j] /= d(_a)
# Calculate the inverse of the matrix
return [[float(d(_a)) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("Please provide a matrix of size 2x2 or 3x3.") | 76 |
a_ = 8.314_4598
def lowerCamelCase__ ( _a , _a):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K")
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol")
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
a_ = 300
a_ = 28
a_ = rms_speed_of_molecule(temperature, molar_mass)
print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''') | 76 | 1 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A : Optional[int] = '''.'''
if __name__ == "__main__":
A : str = os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
A : Tuple = []
A : Optional[Any] = []
with open(doctest_file_path) as fp:
for line in fp:
A : Tuple = line.strip()
A : List[str] = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
A : Optional[Any] = '''\n'''.join(non_existent_paths)
raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
if all_paths != sorted(all_paths):
raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')
| 276 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
A : Dict = random.Random()
def __lowerCamelCase ( __a :Dict , __a :str=1.0 , __a :List[Any]=None , __a :List[str]=None ) -> Optional[int]:
"""simple docstring"""
if rng is None:
A__ = global_rng
A__ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class A (unittest.TestCase ):
'''simple docstring'''
def __init__( self : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : str=7 , __lowerCAmelCase : List[Any]=4_00 , __lowerCAmelCase : Optional[Any]=20_00 , __lowerCAmelCase : Dict=10 , __lowerCAmelCase : Union[str, Any]=1_60 , __lowerCAmelCase : List[Any]=8 , __lowerCAmelCase : List[Any]=0.0 , __lowerCAmelCase : int=40_00 , __lowerCAmelCase : Any=False , __lowerCAmelCase : List[str]=True , ) -> Optional[int]:
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = min_seq_length
A__ = max_seq_length
A__ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
A__ = padding_value
A__ = sampling_rate
A__ = return_attention_mask
A__ = do_normalize
A__ = feature_size
A__ = chunk_length
A__ = hop_length
def a_ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def a_ ( self : List[str] , __lowerCAmelCase : List[Any]=False , __lowerCAmelCase : int=False ) -> str:
"""simple docstring"""
def _flatten(__lowerCAmelCase : Optional[int] ):
return list(itertools.chain(*__lowerCAmelCase ) )
if equal_length:
A__ = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
A__ = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
A__ = [np.asarray(__lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class A (SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : int = WhisperFeatureExtractor if is_speech_available() else None
def a_ ( self : Any ) -> str:
"""simple docstring"""
A__ = WhisperFeatureExtractionTester(self )
def a_ ( self : Dict ) -> Tuple:
"""simple docstring"""
A__ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A__ = feat_extract_first.save_pretrained(__lowerCAmelCase )[0]
check_json_file_has_correct_format(__lowerCAmelCase )
A__ = self.feature_extraction_class.from_pretrained(__lowerCAmelCase )
A__ = feat_extract_first.to_dict()
A__ = feat_extract_second.to_dict()
A__ = feat_extract_first.mel_filters
A__ = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def a_ ( self : Dict ) -> str:
"""simple docstring"""
A__ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A__ = os.path.join(__lowerCAmelCase , """feat_extract.json""" )
feat_extract_first.to_json_file(__lowerCAmelCase )
A__ = self.feature_extraction_class.from_json_file(__lowerCAmelCase )
A__ = feat_extract_first.to_dict()
A__ = feat_extract_second.to_dict()
A__ = feat_extract_first.mel_filters
A__ = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def a_ ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
A__ = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
A__ = [np.asarray(__lowerCAmelCase ) for speech_input in speech_inputs]
# Test feature size
A__ = feature_extractor(__lowerCAmelCase , padding="""max_length""" , return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
A__ = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_features
A__ = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1e-3 ) )
# Test batched
A__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
A__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
A__ = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
A__ = np.asarray(__lowerCAmelCase )
A__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
A__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1e-3 ) )
# Test truncation required
A__ = [floats_list((1, x) )[0] for x in range(2_00 , (feature_extractor.n_samples + 5_00) , 2_00 )]
A__ = [np.asarray(__lowerCAmelCase ) for speech_input in speech_inputs]
A__ = [x[: feature_extractor.n_samples] for x in speech_inputs]
A__ = [np.asarray(__lowerCAmelCase ) for speech_input in speech_inputs_truncated]
A__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
A__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1e-3 ) )
def a_ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
import torch
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = np.random.rand(1_00 , 32 ).astype(np.floataa )
A__ = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
A__ = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
A__ = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def a_ ( self : List[Any] , __lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
A__ = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
A__ = ds.sort("""id""" ).select(range(__lowerCAmelCase ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def a_ ( self : Dict ) -> Any:
"""simple docstring"""
A__ = torch.tensor(
[
0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
-0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
] )
# fmt: on
A__ = self._load_datasamples(1 )
A__ = WhisperFeatureExtractor()
A__ = feature_extractor(__lowerCAmelCase , return_tensors="""pt""" ).input_features
self.assertEqual(input_features.shape , (1, 80, 30_00) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __lowerCAmelCase , atol=1e-4 ) )
def a_ ( self : Dict ) -> Tuple:
"""simple docstring"""
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = self._load_datasamples(1 )[0]
A__ = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35 # Rescale to [0, 65535] to show issue
A__ = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__lowerCAmelCase )[0]
self.assertTrue(np.all(np.mean(__lowerCAmelCase ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__lowerCAmelCase ) - 1 ) < 1e-3 ) )
| 276 | 1 |
"""simple docstring"""
import os
import sys
import unittest
__magic_name__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__magic_name__ = os.path.join(git_repo_path, "src", "diffusers")
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = find_backend(""" if not is_torch_available():""")
self.assertEqual(lowerCAmelCase__ , """torch""")
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__SCREAMING_SNAKE_CASE = find_backend(""" if not (is_torch_available() and is_transformers_available()):""")
self.assertEqual(lowerCAmelCase__ , """torch_and_transformers""")
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__SCREAMING_SNAKE_CASE = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""")
self.assertEqual(lowerCAmelCase__ , """torch_and_transformers_and_onnx""")
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""" , lowerCAmelCase__)
self.assertIn("""torch_and_transformers""" , lowerCAmelCase__)
self.assertIn("""flax_and_transformers""" , lowerCAmelCase__)
self.assertIn("""torch_and_transformers_and_onnx""" , lowerCAmelCase__)
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""" , objects["""torch"""])
self.assertIn("""FlaxUNet2DConditionModel""" , objects["""flax"""])
self.assertIn("""StableDiffusionPipeline""" , objects["""torch_and_transformers"""])
self.assertIn("""FlaxStableDiffusionPipeline""" , objects["""flax_and_transformers"""])
self.assertIn("""LMSDiscreteScheduler""" , objects["""torch_and_scipy"""])
self.assertIn("""OnnxStableDiffusionPipeline""" , objects["""torch_and_transformers_and_onnx"""])
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = create_dummy_object("""CONSTANT""" , """'torch'""")
self.assertEqual(lowerCAmelCase__ , """\nCONSTANT = None\n""")
__SCREAMING_SNAKE_CASE = create_dummy_object("""function""" , """'torch'""")
self.assertEqual(
lowerCAmelCase__ , """\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""")
__SCREAMING_SNAKE_CASE = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
__SCREAMING_SNAKE_CASE = create_dummy_object("""FakeClass""" , """'torch'""")
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
__SCREAMING_SNAKE_CASE = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]})
self.assertEqual(dummy_files["""torch"""] , lowerCAmelCase__)
| 100 |
"""simple docstring"""
from collections import defaultdict
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = first_str.lower().strip()
__SCREAMING_SNAKE_CASE = second_str.lower().strip()
# Remove whitespace
__SCREAMING_SNAKE_CASE = first_str.replace(""" """ , """""" )
__SCREAMING_SNAKE_CASE = second_str.replace(""" """ , """""" )
# Strings of different lengths are not anagrams
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
return False
# Default values for count should be 0
__SCREAMING_SNAKE_CASE = defaultdict(UpperCamelCase_ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(UpperCamelCase_ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
__magic_name__ = input("Enter the first string ").strip()
__magic_name__ = input("Enter the second string ").strip()
__magic_name__ = check_anagrams(input_a, input_b)
print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 100 | 1 |
def __snake_case ( _UpperCAmelCase ):
__a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( _UpperCAmelCase ):
__a = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__a = remove_duplicates(key.upper() )
__a = len(_UpperCAmelCase )
# First fill cipher with key characters
__a = {alphabet[i]: char for i, char in enumerate(_UpperCAmelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_UpperCAmelCase ) , 26 ):
__a = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__a = alphabet[i - offset]
__a = char
return cipher_alphabet
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
return "".join(cipher_map.get(_UpperCAmelCase , _UpperCAmelCase ) for ch in message.upper() )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_UpperCAmelCase , _UpperCAmelCase ) for ch in message.upper() )
def __snake_case ( ):
__a = input('''Enter message to encode or decode: ''' ).strip()
__a = input('''Enter keyword: ''' ).strip()
__a = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
__a = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
__a = create_cipher_map(_UpperCAmelCase )
print(func(_UpperCAmelCase , _UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 131 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__snake_case :Dict = logging.get_logger(__name__)
__snake_case :List[str] = {'''vocab_file''': '''spiece.model'''}
__snake_case :Dict = {
'''vocab_file''': {
'''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
}
}
class _A ( __UpperCAmelCase ):
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[Any]=False , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : List[Any]=False , __SCREAMING_SNAKE_CASE : Optional[int]="<s>" , __SCREAMING_SNAKE_CASE : Optional[Any]="</s>" , __SCREAMING_SNAKE_CASE : Union[str, Any]="<unk>" , __SCREAMING_SNAKE_CASE : Optional[int]="<sep>" , __SCREAMING_SNAKE_CASE : Optional[int]="<pad>" , __SCREAMING_SNAKE_CASE : List[Any]="<cls>" , __SCREAMING_SNAKE_CASE : Optional[int]="<mask>" , __SCREAMING_SNAKE_CASE : Any=["<eop>", "<eod>"] , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ):
'''simple docstring'''
__a = AddedToken(__SCREAMING_SNAKE_CASE , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) else mask_token
__a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , additional_special_tokens=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__a = 3
__a = do_lower_case
__a = remove_space
__a = keep_accents
__a = vocab_file
__a = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(__SCREAMING_SNAKE_CASE)
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
'''See https://pypi.org/project/jieba/ for installation.''')
__a = jieba
__a = str.maketrans(''' \n''' , '''\u2582\u2583''')
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def _lowerCamelCase ( self : int):
'''simple docstring'''
return len(self.sp_model)
def _lowerCamelCase ( self : str):
'''simple docstring'''
__a = {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 : Any):
'''simple docstring'''
__a = self.__dict__.copy()
__a = None
return state
def __setstate__( self : Tuple , __SCREAMING_SNAKE_CASE : List[str]):
'''simple docstring'''
__a = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs'''):
__a = {}
__a = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
if self.remove_space:
__a = ''' '''.join(inputs.strip().split())
else:
__a = inputs
__a = outputs.replace('''``''' , '''"''').replace('''\'\'''' , '''"''')
if not self.keep_accents:
__a = unicodedata.normalize('''NFKD''' , __SCREAMING_SNAKE_CASE)
__a = ''''''.join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE)])
if self.do_lower_case:
__a = outputs.lower()
return outputs
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
__a = self.preprocess_text(__SCREAMING_SNAKE_CASE)
__a = self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE)
__a = []
for piece in pieces:
if len(__SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
__a = self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE , ''''''))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
__a = cur_pieces[1:]
else:
__a = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(__SCREAMING_SNAKE_CASE)
else:
new_pieces.append(__SCREAMING_SNAKE_CASE)
return new_pieces
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : List[Any]):
'''simple docstring'''
return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int]):
'''simple docstring'''
return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
__a = ''''''.join(__SCREAMING_SNAKE_CASE).replace(__SCREAMING_SNAKE_CASE , ''' ''').strip()
return out_string
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None):
'''simple docstring'''
__a = [self.sep_token_id]
__a = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None , __SCREAMING_SNAKE_CASE : bool = False):
'''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)
if token_ids_a is not None:
return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]
return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None):
'''simple docstring'''
__a = [self.sep_token_id]
__a = [2]
if token_ids_a is None:
return len(token_ids_a + sep) * [0] + cls_segment_id
return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(__SCREAMING_SNAKE_CASE):
logger.error(F'Vocabulary path ({save_directory}) should be a directory')
return
__a = 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 = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE)
return (out_vocab_file,)
def _lowerCamelCase ( self : Any , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = super()._decode(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
__a = text.replace(''' ''' , '''''').replace('''\u2582''' , ''' ''').replace('''\u2583''' , '''\n''')
return text
| 131 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class snake_case ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[str], _lowerCamelCase : Optional[Any], _lowerCamelCase : Union[str, Any]=13, _lowerCamelCase : Any=3, _lowerCamelCase : Optional[int]=2_24, _lowerCamelCase : str=30, _lowerCamelCase : Dict=4_00, _lowerCamelCase : Union[str, Any]=True, _lowerCamelCase : Any=None, _lowerCamelCase : Optional[Any]=True, _lowerCamelCase : Any=[0.5, 0.5, 0.5], _lowerCamelCase : List[str]=[0.5, 0.5, 0.5], ):
'''simple docstring'''
__A = size if size is not None else {'''height''': 18, '''width''': 18}
__A = parent
__A = batch_size
__A = num_channels
__A = image_size
__A = min_resolution
__A = max_resolution
__A = do_resize
__A = size
__A = do_normalize
__A = image_mean
__A = image_std
def _SCREAMING_SNAKE_CASE ( self : int ):
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class snake_case ( _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
A_ : str = ViTImageProcessor if is_vision_available() else None
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
__A = EfficientFormerImageProcessorTester(self )
@property
def _SCREAMING_SNAKE_CASE ( self : Dict ):
'''simple docstring'''
return self.image_proc_tester.prepare_image_processor_dict()
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
'''simple docstring'''
__A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase, '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase, '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase, '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase, '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase, '''size''' ) )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
'''simple docstring'''
# Initialize image_processor
__A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__A = prepare_image_inputs(self.image_proc_tester, equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase, Image.Image )
# Test not batched input
__A = image_processor(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
), )
# Test batched
__A = image_processor(_lowerCamelCase, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
), )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
'''simple docstring'''
# Initialize image_processor
__A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__A = prepare_image_inputs(self.image_proc_tester, equal_resolution=_lowerCamelCase, numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase, np.ndarray )
# Test not batched input
__A = image_processor(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
), )
# Test batched
__A = image_processor(_lowerCamelCase, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
), )
def _SCREAMING_SNAKE_CASE ( self : str ):
'''simple docstring'''
# Initialize image_processor
__A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__A = prepare_image_inputs(self.image_proc_tester, equal_resolution=_lowerCamelCase, torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase, torch.Tensor )
# Test not batched input
__A = image_processor(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
), )
# Test batched
__A = image_processor(_lowerCamelCase, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
), )
| 266 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
__A = current_set.copy()
for row_index, row in enumerate(__UpperCamelCase ):
__A = row[0]
for column_index, column in enumerate(__UpperCamelCase ):
if magnitude == 0:
__A = column
continue
__A = column / magnitude
# Subtract to cancel term
__A = current_set[0]
__A = [first_row]
__A = current_set[1::]
for row in current_set:
__A = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(__UpperCamelCase )
continue
for column_index in range(len(__UpperCamelCase ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(__UpperCamelCase )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
__A = final_set[0]
__A = []
__A = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
__A = simplify(__UpperCamelCase )
for i in range(len(__UpperCamelCase ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , __UpperCamelCase )
__A = resultant
return final_set
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
if len(__UpperCamelCase ) == 0:
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
__A = len(__UpperCamelCase ) + 1
if any(len(__UpperCamelCase ) != _length for item in equations ):
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
for row in equations:
if any(not isinstance(__UpperCamelCase , (int, float) ) for column in row ):
raise ValueError('''solve_simultaneous() requires lists of integers''' )
if len(__UpperCamelCase ) == 1:
return [equations[0][-1] / equations[0][0]]
__A = equations.copy()
if any(0 in row for row in data_set ):
__A = data_set.copy()
__A = []
for row_index, row in enumerate(__UpperCamelCase ):
if 0 not in row:
__A = data_set.pop(__UpperCamelCase )
break
if not full_row:
raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
data_set.insert(0 , __UpperCamelCase )
__A = data_set.copy()
__A = simplify(__UpperCamelCase )
__A = simplified[::-1]
__A = []
for row in simplified:
__A = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
__A = row.copy()[: len(__UpperCamelCase ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(__UpperCamelCase ) == 0:
solutions.append(0 )
continue
__A = temp_row[1::]
__A = temp_row[::-1]
for column_index, column in enumerate(__UpperCamelCase ):
current_solution -= column * solutions[column_index]
solutions.append(__UpperCamelCase )
__A = []
for item in solutions:
final.append(float(round(__UpperCamelCase , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase_ = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 266 | 1 |
"""simple docstring"""
from __future__ import annotations
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , ) ->Dict:
a__: Optional[int] = cipher_alphabet or [chr(_lowercase ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
a__: str = {
'a': 0.08_497,
'b': 0.01_492,
'c': 0.02_202,
'd': 0.04_253,
'e': 0.11_162,
'f': 0.02_228,
'g': 0.02_015,
'h': 0.06_094,
'i': 0.07_546,
'j': 0.00_153,
'k': 0.01_292,
'l': 0.04_025,
'm': 0.02_406,
'n': 0.06_749,
'o': 0.07_507,
'p': 0.01_929,
'q': 0.00_095,
'r': 0.07_587,
's': 0.06_327,
't': 0.09_356,
'u': 0.02_758,
'v': 0.00_978,
'w': 0.02_560,
'x': 0.00_150,
'y': 0.01_994,
'z': 0.00_077,
}
else:
# Custom frequencies dictionary
a__: Dict = frequencies_dict
if not case_sensitive:
a__: List[Any] = ciphertext.lower()
# Chi squared statistic values
a__: Any = {}
# cycle through all of the shifts
for shift in range(len(_lowercase ) ):
a__: Union[str, Any] = ''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
a__: List[str] = (alphabet_letters.index(letter.lower() ) - shift) % len(
_lowercase )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
a__: List[Any] = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
a__: List[Any] = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
a__: Optional[int] = decrypted_with_shift.lower().count(_lowercase )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
a__: List[str] = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
a__: Optional[int] = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
a__: str = decrypted_with_shift.count(_lowercase )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
a__: Tuple = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
a__: Dict = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
a__: str = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(_SCREAMING_SNAKE_CASE ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
a__: str = min(
_lowercase , key=_lowercase , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
a__
) , (
a__
) ,
): List[str] = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 359 | """simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __a ( _SCREAMING_SNAKE_CASE ) ->Tuple:
a__: Tuple = {}
a__: Tuple = job['started_at']
a__: int = job['completed_at']
a__: Any = date_parser.parse(_SCREAMING_SNAKE_CASE )
a__: Tuple = date_parser.parse(_SCREAMING_SNAKE_CASE )
a__: str = round((end_datetime - start_datetime).total_seconds() / 60.0 )
a__: Any = start
a__: Dict = end
a__: Optional[int] = duration_in_min
return job_info
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: Tuple = None
if token is not None:
a__: List[str] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: int = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: str = {}
try:
job_time.update({job['name']: extract_time_from_single_job(_SCREAMING_SNAKE_CASE ) for job in result['jobs']} )
a__: Dict = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: str = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
job_time.update({job['name']: extract_time_from_single_job(_SCREAMING_SNAKE_CASE ) for job in result['jobs']} )
return job_time
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
lowercase__ = parser.parse_args()
lowercase__ = get_job_time(args.workflow_run_id)
lowercase__ = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f"{k}: {v['duration']}")
| 203 | 0 |
"""simple docstring"""
import sys
def a__ ( SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = len(SCREAMING_SNAKE_CASE )
lowerCAmelCase : Any = [[0 for x in range(SCREAMING_SNAKE_CASE )] for x in range(SCREAMING_SNAKE_CASE )]
lowerCAmelCase : Tuple = [[0 for x in range(SCREAMING_SNAKE_CASE )] for x in range(SCREAMING_SNAKE_CASE )]
for chain_length in range(2 , SCREAMING_SNAKE_CASE ):
for a in range(1 , n - chain_length + 1 ):
lowerCAmelCase : List[Any] = a + chain_length - 1
lowerCAmelCase : List[Any] = sys.maxsize
for c in range(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
lowerCAmelCase : Union[str, Any] = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
lowerCAmelCase : str = cost
lowerCAmelCase : Optional[int] = c
return matrix, sol
def a__ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
if i == j:
print("A" + str(SCREAMING_SNAKE_CASE ) , end=" " )
else:
print("(" , end=" " )
print_optiomal_solution(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , optimal_solution[i][j] )
print_optiomal_solution(SCREAMING_SNAKE_CASE , optimal_solution[i][j] + 1 , SCREAMING_SNAKE_CASE )
print(")" , end=" " )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = [3_0, 3_5, 1_5, 5, 1_0, 2_0, 2_5]
lowerCAmelCase : List[Any] = len(SCREAMING_SNAKE_CASE )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
lowerCAmelCase , lowerCAmelCase : List[Any] = matrix_chain_order(SCREAMING_SNAKE_CASE )
print("No. of Operation required: " + str(matrix[1][n - 1] ) )
print_optiomal_solution(SCREAMING_SNAKE_CASE , 1 , n - 1 )
if __name__ == "__main__":
main()
| 108 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_lowerCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase = None ,__UpperCAmelCase = None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ) -> str:
if not conversation_id:
lowerCAmelCase__ : List[str] = uuid.uuida()
if past_user_inputs is None:
lowerCAmelCase__ : List[Any] = []
if generated_responses is None:
lowerCAmelCase__ : str = []
lowerCAmelCase__ : uuid.UUID = conversation_id
lowerCAmelCase__ : List[str] = past_user_inputs
lowerCAmelCase__ : List[str] = generated_responses
lowerCAmelCase__ : Optional[str] = text
def __eq__( self ,__UpperCAmelCase ) -> Dict:
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase = False ) -> Optional[Any]:
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
lowerCAmelCase__ : Optional[int] = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
lowerCAmelCase__ : Optional[Any] = text
def UpperCAmelCase_ ( self ) -> List[Any]:
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
lowerCAmelCase__ : Union[str, Any] = None
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Tuple:
self.generated_responses.append(__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> List[str]:
for user_input, generated_response in zip(self.past_user_inputs ,self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ) -> Tuple:
lowerCAmelCase__ : Tuple = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
lowerCAmelCase__ : Any = """user""" if is_user else """bot"""
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
SCREAMING_SNAKE_CASE_ , R'''
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
''' , )
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> Tuple:
super().__init__(*__UpperCAmelCase ,**__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
lowerCAmelCase__ : Tuple = self.tokenizer.eos_token
def UpperCAmelCase_ ( self ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,**__UpperCAmelCase ) -> Optional[int]:
lowerCAmelCase__ : List[Any] = {}
lowerCAmelCase__ : Optional[int] = {}
lowerCAmelCase__ : List[str] = {}
if min_length_for_response is not None:
lowerCAmelCase__ : Any = min_length_for_response
if minimum_tokens is not None:
lowerCAmelCase__ : Optional[int] = minimum_tokens
if "max_length" in generate_kwargs:
lowerCAmelCase__ : Optional[Any] = generate_kwargs["""max_length"""]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
lowerCAmelCase__ : int = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self ,__UpperCAmelCase ,__UpperCAmelCase=0 ,**__UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : Optional[int] = super().__call__(__UpperCAmelCase ,num_workers=__UpperCAmelCase ,**__UpperCAmelCase )
if isinstance(__UpperCAmelCase ,__UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase=32 ) -> Dict[str, Any]:
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
raise ValueError("""ConversationalPipeline, expects Conversation as inputs""" )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
"""Add user inputs with the conversation's `add_user_input` method""" )
if hasattr(self.tokenizer ,"""_build_conversation_input_ids""" ):
lowerCAmelCase__ : str = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
lowerCAmelCase__ : List[Any] = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
lowerCAmelCase__ : List[Any] = torch.LongTensor([input_ids] )
elif self.framework == "tf":
lowerCAmelCase__ : Dict = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase=10 ,**__UpperCAmelCase ) -> Dict:
lowerCAmelCase__ : Optional[Any] = generate_kwargs.get("""max_length""" ,self.model.config.max_length )
lowerCAmelCase__ : Optional[Any] = model_inputs["""input_ids"""].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
lowerCAmelCase__ : str = max_length - minimum_tokens
lowerCAmelCase__ : Union[str, Any] = model_inputs["""input_ids"""][:, -trim:]
if "attention_mask" in model_inputs:
lowerCAmelCase__ : Tuple = model_inputs["""attention_mask"""][:, -trim:]
lowerCAmelCase__ : str = model_inputs.pop("""conversation""" )
lowerCAmelCase__ : Union[str, Any] = max_length
lowerCAmelCase__ : Any = self.model.generate(**__UpperCAmelCase ,**__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
lowerCAmelCase__ : int = 1
else:
lowerCAmelCase__ : int = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase=True ) -> List[str]:
lowerCAmelCase__ : Optional[int] = model_outputs["""output_ids"""]
lowerCAmelCase__ : Tuple = self.tokenizer.decode(
output_ids[0] ,skip_special_tokens=__UpperCAmelCase ,clean_up_tokenization_spaces=__UpperCAmelCase ,)
lowerCAmelCase__ : Union[str, Any] = model_outputs["""conversation"""]
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Dict:
lowerCAmelCase__ : Dict = self.tokenizer.eos_token_id
lowerCAmelCase__ : int = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase ,add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase ,add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
lowerCAmelCase__ : Optional[Any] = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 37 | 0 |
"""simple docstring"""
import re
def _snake_case ( lowerCamelCase__ : str ) -> bool:
lowerCamelCase_ : Tuple =re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(lowerCamelCase__ , lowerCamelCase__ ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('+918827897895'))
| 209 |
"""simple docstring"""
# Copyright 2021 The HuggingFace 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 argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _snake_case ( ) -> Tuple:
lowerCamelCase_ : Optional[int] =ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=lowerCamelCase__ )
lowerCamelCase_ : Optional[int] =parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=lowerCamelCase__ )
env_command_parser(subparsers=lowerCamelCase__ )
launch_command_parser(subparsers=lowerCamelCase__ )
tpu_command_parser(subparsers=lowerCamelCase__ )
test_command_parser(subparsers=lowerCamelCase__ )
# Let's go
lowerCamelCase_ : int =parser.parse_args()
if not hasattr(lowerCamelCase__ , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(lowerCamelCase__ )
if __name__ == "__main__":
main()
| 209 | 1 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> Any:
'''simple docstring'''
if isinstance(__lowercase , torch.Tensor ):
return image
elif isinstance(__lowercase , PIL.Image.Image ):
_A = [image]
if isinstance(image[0] , PIL.Image.Image ):
_A = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
_A = np.concatenate(__lowercase , axis=0 )
_A = np.array(__lowercase ).astype(np.floataa ) / 255.0
_A = image.transpose(0 , 3 , 1 , 2 )
_A = 2.0 * image - 1.0
_A = torch.from_numpy(__lowercase )
elif isinstance(image[0] , torch.Tensor ):
_A = torch.cat(__lowercase , dim=0 )
return image
def __lowercase ( __lowercase , __lowercase , __lowercase , __lowercase=0.9995 ) -> Any:
'''simple docstring'''
if not isinstance(__lowercase , np.ndarray ):
_A = True
_A = va.device
_A = va.cpu().numpy()
_A = va.cpu().numpy()
_A = np.sum(va * va / (np.linalg.norm(__lowercase ) * np.linalg.norm(__lowercase )) )
if np.abs(__lowercase ) > DOT_THRESHOLD:
_A = (1 - t) * va + t * va
else:
_A = np.arccos(__lowercase )
_A = np.sin(__lowercase )
_A = theta_a * t
_A = np.sin(__lowercase )
_A = np.sin(theta_a - theta_t ) / sin_theta_a
_A = sin_theta_t / sin_theta_a
_A = sa * va + sa * va
if inputs_are_torch:
_A = torch.from_numpy(__lowercase ).to(__lowercase )
return va
def __lowercase ( __lowercase , __lowercase ) -> Optional[int]:
'''simple docstring'''
_A = F.normalize(__lowercase , dim=-1 )
_A = F.normalize(__lowercase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def __lowercase ( __lowercase , __lowercase ) -> List[Any]:
'''simple docstring'''
for param in model.parameters():
_A = value
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self : int , __UpperCAmelCase : AutoencoderKL , __UpperCAmelCase : CLIPTextModel , __UpperCAmelCase : CLIPModel , __UpperCAmelCase : CLIPTokenizer , __UpperCAmelCase : UNetaDConditionModel , __UpperCAmelCase : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , __UpperCAmelCase : CLIPFeatureExtractor , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Tuple=None , ):
'''simple docstring'''
super().__init__()
self.register_modules(
vae=__UpperCAmelCase , text_encoder=__UpperCAmelCase , clip_model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , unet=__UpperCAmelCase , scheduler=__UpperCAmelCase , feature_extractor=__UpperCAmelCase , coca_model=__UpperCAmelCase , coca_tokenizer=__UpperCAmelCase , coca_transform=__UpperCAmelCase , )
_A = (
feature_extractor.size
if isinstance(feature_extractor.size , __UpperCAmelCase )
else feature_extractor.size["shortest_edge"]
)
_A = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , __UpperCAmelCase )
set_requires_grad(self.clip_model , __UpperCAmelCase )
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : Optional[Union[str, int]] = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_A = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__UpperCAmelCase )
def lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
self.enable_attention_slicing(__UpperCAmelCase )
def lowerCAmelCase ( self : int ):
'''simple docstring'''
set_requires_grad(self.vae , __UpperCAmelCase )
def lowerCAmelCase ( self : int ):
'''simple docstring'''
set_requires_grad(self.vae , __UpperCAmelCase )
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
set_requires_grad(self.unet , __UpperCAmelCase )
def lowerCAmelCase ( self : int ):
'''simple docstring'''
set_requires_grad(self.unet , __UpperCAmelCase )
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Any ):
'''simple docstring'''
_A = min(int(num_inference_steps * strength ) , __UpperCAmelCase )
_A = max(num_inference_steps - init_timestep , 0 )
_A = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCAmelCase ( self : Any , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : Dict , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : Dict=None ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(__UpperCAmelCase )}''' )
_A = image.to(device=__UpperCAmelCase , dtype=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_A = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__UpperCAmelCase )
]
_A = torch.cat(__UpperCAmelCase , dim=0 )
else:
_A = self.vae.encode(__UpperCAmelCase ).latent_dist.sample(__UpperCAmelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_A = 0.18215 * init_latents
_A = init_latents.repeat_interleave(__UpperCAmelCase , dim=0 )
_A = randn_tensor(init_latents.shape , generator=__UpperCAmelCase , device=__UpperCAmelCase , dtype=__UpperCAmelCase )
# get latents
_A = self.scheduler.add_noise(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
_A = init_latents
return latents
def lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Dict ):
'''simple docstring'''
_A = self.coca_transform(__UpperCAmelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_A = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_A = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," )
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Any , __UpperCAmelCase : int ):
'''simple docstring'''
_A = self.feature_extractor.preprocess(__UpperCAmelCase )
_A = torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
_A = self.clip_model.get_image_features(__UpperCAmelCase )
_A = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__UpperCAmelCase )
_A = image_embeddings_clip.repeat_interleave(__UpperCAmelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def lowerCAmelCase ( self : int , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : int , __UpperCAmelCase : Dict , __UpperCAmelCase : Any , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Dict , ):
'''simple docstring'''
_A = latents.detach().requires_grad_()
_A = self.scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
# predict the noise residual
_A = self.unet(__UpperCAmelCase , __UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_A = self.scheduler.alphas_cumprod[timestep]
_A = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_A = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_A = torch.sqrt(__UpperCAmelCase )
_A = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , __UpperCAmelCase ):
_A = self.scheduler.sigmas[index]
_A = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_A = 1 / 0.18215 * sample
_A = self.vae.decode(__UpperCAmelCase ).sample
_A = (image / 2 + 0.5).clamp(0 , 1 )
_A = transforms.Resize(self.feature_extractor_size )(__UpperCAmelCase )
_A = self.normalize(__UpperCAmelCase ).to(latents.dtype )
_A = self.clip_model.get_image_features(__UpperCAmelCase )
_A = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__UpperCAmelCase )
_A = spherical_dist_loss(__UpperCAmelCase , __UpperCAmelCase ).mean() * clip_guidance_scale
_A = -torch.autograd.grad(__UpperCAmelCase , __UpperCAmelCase )[0]
if isinstance(self.scheduler , __UpperCAmelCase ):
_A = latents.detach() + grads * (sigma**2)
_A = noise_pred_original
else:
_A = noise_pred_original - torch.sqrt(__UpperCAmelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Union[str, Any] , __UpperCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , __UpperCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , __UpperCAmelCase : Optional[str] = None , __UpperCAmelCase : Optional[str] = None , __UpperCAmelCase : Optional[int] = 512 , __UpperCAmelCase : Optional[int] = 512 , __UpperCAmelCase : float = 0.6 , __UpperCAmelCase : Optional[int] = 50 , __UpperCAmelCase : Optional[float] = 7.5 , __UpperCAmelCase : Optional[int] = 1 , __UpperCAmelCase : float = 0.0 , __UpperCAmelCase : Optional[float] = 100 , __UpperCAmelCase : Optional[torch.Generator] = None , __UpperCAmelCase : Optional[str] = "pil" , __UpperCAmelCase : bool = True , __UpperCAmelCase : float = 0.8 , __UpperCAmelCase : float = 0.1 , __UpperCAmelCase : float = 0.1 , ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(__UpperCAmelCase )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(__UpperCAmelCase , torch.Generator ) and batch_size > 1:
_A = [generator] + [None] * (batch_size - 1)
_A = [
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
_A = [x[0] for x in coca_is_none if x[1]]
_A = ", ".join(__UpperCAmelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(__UpperCAmelCase ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_A = self.get_image_description(__UpperCAmelCase )
if style_prompt is None:
if len(__UpperCAmelCase ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_A = self.get_image_description(__UpperCAmelCase )
# get prompt text embeddings for content and style
_A = self.tokenizer(
__UpperCAmelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=__UpperCAmelCase , return_tensors="pt" , )
_A = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_A = self.tokenizer(
__UpperCAmelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=__UpperCAmelCase , return_tensors="pt" , )
_A = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_A = slerp(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# duplicate text embeddings for each generation per prompt
_A = text_embeddings.repeat_interleave(__UpperCAmelCase , dim=0 )
# set timesteps
_A = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_A = {}
if accepts_offset:
_A = 1
self.scheduler.set_timesteps(__UpperCAmelCase , **__UpperCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_A , _A = self.get_timesteps(__UpperCAmelCase , __UpperCAmelCase , self.device )
_A = timesteps[:1].repeat(__UpperCAmelCase )
# Preprocess image
_A = preprocess(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
_A = self.prepare_latents(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , text_embeddings.dtype , self.device , __UpperCAmelCase )
_A = preprocess(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
_A = self.prepare_latents(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , text_embeddings.dtype , self.device , __UpperCAmelCase )
_A = slerp(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if clip_guidance_scale > 0:
_A = self.get_clip_image_embeddings(__UpperCAmelCase , __UpperCAmelCase )
_A = self.get_clip_image_embeddings(__UpperCAmelCase , __UpperCAmelCase )
_A = slerp(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_A = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_A = content_text_input.input_ids.shape[-1]
_A = self.tokenizer([""] , padding="max_length" , max_length=__UpperCAmelCase , return_tensors="pt" )
_A = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_A = uncond_embeddings.repeat_interleave(__UpperCAmelCase , dim=0 )
# 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 = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_A = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_A = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_A = torch.randn(__UpperCAmelCase , generator=__UpperCAmelCase , device="cpu" , dtype=__UpperCAmelCase ).to(
self.device )
else:
_A = torch.randn(__UpperCAmelCase , generator=__UpperCAmelCase , device=self.device , dtype=__UpperCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_A = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_A = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_A = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_A = {}
if accepts_eta:
_A = eta
# check if the scheduler accepts generator
_A = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_A = generator
with self.progress_bar(total=__UpperCAmelCase ):
for i, t in enumerate(__UpperCAmelCase ):
# expand the latents if we are doing classifier free guidance
_A = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_A = self.scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
# predict the noise residual
_A = self.unet(__UpperCAmelCase , __UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_A , _A = noise_pred.chunk(2 )
_A = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_A = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_A , _A = self.cond_fn(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )
# compute the previous noisy sample x_t -> x_t-1
_A = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_A = 1 / 0.18215 * latents
_A = self.vae.decode(__UpperCAmelCase ).sample
_A = (image / 2 + 0.5).clamp(0 , 1 )
_A = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_A = self.numpy_to_pil(__UpperCAmelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=__UpperCAmelCase , nsfw_content_detected=__UpperCAmelCase )
| 79 |
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case , snake_case , snake_case , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif stress < 0:
raise ValueError("Stress cannot be negative" )
elif tangential_force < 0:
raise ValueError("Tangential Force cannot be negative" )
elif area < 0:
raise ValueError("Area cannot be negative" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 139 | 0 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
lowerCAmelCase_ = 2
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : str , *, # begin keyword-only arguments
UpperCamelCase : Dict="<s>" , UpperCamelCase : Optional[Any]="<pad>" , UpperCamelCase : List[Any]="</s>" , UpperCamelCase : Tuple="<unk>" , UpperCamelCase : Tuple=None , ):
'''simple docstring'''
_snake_case : Optional[int] = bos, unk, pad, eos
_snake_case : Any = []
_snake_case : Dict = []
_snake_case : Union[str, Any] = {}
_snake_case : List[str] = self.add_symbol(lowerCAmelCase__ )
_snake_case : Optional[int] = self.add_symbol(lowerCAmelCase__ )
_snake_case : int = self.add_symbol(lowerCAmelCase__ )
_snake_case : List[str] = self.add_symbol(lowerCAmelCase__ )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(lowerCAmelCase__ )
_snake_case : Tuple = len(self.symbols )
def __eq__( self : int , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
return self.indices == other.indices
def __getitem__( self : Dict , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : Dict ):
'''simple docstring'''
return len(self.symbols )
def __contains__( self : Tuple , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
return sym in self.indices
@classmethod
def UpperCamelCase_ ( cls : Any , UpperCamelCase : Any ):
'''simple docstring'''
_snake_case : Optional[int] = cls()
d.add_from_file(lowerCAmelCase__ )
return d
def UpperCamelCase_ ( self : Tuple , UpperCamelCase : Dict , UpperCamelCase : str=1 , UpperCamelCase : List[str]=False ):
'''simple docstring'''
if word in self.indices and not overwrite:
_snake_case : Dict = self.indices[word]
_snake_case : Optional[Any] = self.count[idx] + n
return idx
else:
_snake_case : Dict = len(self.symbols )
_snake_case : Dict = idx
self.symbols.append(lowerCAmelCase__ )
self.count.append(lowerCAmelCase__ )
return idx
def UpperCamelCase_ ( self : str , UpperCamelCase : int ):
'''simple docstring'''
return 0
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : Any ):
'''simple docstring'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
try:
with open(lowerCAmelCase__ , 'r' , encoding='utf-8' ) as fd:
self.add_from_file(lowerCAmelCase__ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(lowerCAmelCase__ ) )
return
_snake_case : List[str] = f.readlines()
_snake_case : Any = self._load_meta(lowerCAmelCase__ )
for line in lines[indices_start_line:]:
try:
_snake_case : Union[str, Any] = line.rstrip().rsplit(' ' , 1 )
if field == "#fairseq:overwrite":
_snake_case : Optional[Any] = True
_snake_case : List[str] = line.rsplit(' ' , 1 )
else:
_snake_case : Tuple = False
_snake_case : List[str] = int(lowerCAmelCase__ )
_snake_case : int = line
if word in self and not overwrite:
raise RuntimeError(
'Duplicate word found when loading Dictionary: \'{}\'. '
'Duplicate words can overwrite earlier ones by adding the '
'#fairseq:overwrite flag at the end of the corresponding row '
'in the dictionary file. If using the Camembert model, please '
'download an updated copy of the model file.'.format(lowerCAmelCase__ ) )
self.add_symbol(lowerCAmelCase__ , n=lowerCAmelCase__ , overwrite=lowerCAmelCase__ )
except ValueError:
raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'' )
def lowerCamelCase_ ( lowerCAmelCase: List[Any] )-> str:
"""simple docstring"""
_snake_case : Any = dict((re.sub(R'@@$' , '' , a_ ), v) if k.endswith('@@' ) else (re.sub(R'$' , '</w>' , a_ ), v) for k, v in d.items() )
_snake_case : Union[str, Any] = "<s> <pad> </s> <unk>".split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
_snake_case : Tuple = d[k] # restore
return da
def lowerCamelCase_ ( lowerCAmelCase: Any , lowerCAmelCase: List[str] )-> Optional[Any]:
"""simple docstring"""
if not os.path.exists(a_ ):
raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""" )
os.makedirs(a_ , exist_ok=a_ )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
_snake_case : List[Any] = os.path.join(a_ , 'checkpoint.pt' )
if not os.path.isfile(a_ ):
raise ValueError(F"""path to the file {checkpoint_file} does not exist!""" )
_snake_case : Dict = torch.load(a_ , map_location='cpu' )
_snake_case : List[Any] = chkpt["cfg"]["model"]
# dicts
_snake_case : Any = os.path.join(a_ , 'dict.txt' )
if not os.path.isfile(a_ ):
raise ValueError(F"""path to the file {dict_file} does not exist!""" )
_snake_case : Any = Dictionary.load(a_ )
_snake_case : Dict = rewrite_dict_keys(src_dict.indices )
_snake_case : List[str] = len(a_ )
_snake_case : int = os.path.join(a_ , VOCAB_FILES_NAMES['vocab_file'] )
print(F"""Generating {src_vocab_file} of {src_vocab_size} records""" )
with open(a_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(a_ , ensure_ascii=a_ , indent=a_ ) )
# merges_file (bpecodes)
_snake_case : int = os.path.join(a_ , 'bpecodes' )
if not os.path.isfile(a_ ):
raise ValueError(F"""path to the file {bpecodes_file} does not exist!""" )
_snake_case : int = os.path.join(a_ , VOCAB_FILES_NAMES['merges_file'] )
shutil.copyfile(a_ , a_ )
# model config
_snake_case : int = os.path.join(a_ , 'config.json' )
_snake_case : Dict = {
"activation_dropout": args["activation_dropout"],
"architectures": ["BioGptForCausalLM"],
"attention_probs_dropout_prob": args["attention_dropout"],
"bos_token_id": 0,
"eos_token_id": 2,
"hidden_act": args["activation_fn"],
"hidden_dropout_prob": args["dropout"],
"hidden_size": args["decoder_embed_dim"],
"initializer_range": 0.0_2,
"intermediate_size": args["decoder_ffn_embed_dim"],
"layer_norm_eps": 1E-12,
"layerdrop": args["decoder_layerdrop"],
"max_position_embeddings": args["max_target_positions"],
"model_type": "biogpt",
"num_attention_heads": args["decoder_attention_heads"],
"num_hidden_layers": args["decoder_layers"],
"pad_token_id": 1,
"scale_embedding": not args["no_scale_embedding"],
"tie_word_embeddings": args["share_decoder_input_output_embed"],
"vocab_size": src_vocab_size,
}
# good hparam defaults to start with
print(F"""Generating {biogpt_model_config_file}""" )
with open(a_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(a_ , ensure_ascii=a_ , indent=a_ ) )
# tokenizer config
_snake_case : Dict = os.path.join(a_ , a_ )
_snake_case : Optional[Any] = {
"bos_token": "<s>",
"eos_token": "</s>",
"model_max_length": 10_24,
"pad_token": "<pad>",
"special_tokens_map_file": None,
"tokenizer_class": "BioGptTokenizer",
"unk_token": "<unk>",
}
print(F"""Generating {biogpt_tokenizer_config_file}""" )
with open(a_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(a_ , ensure_ascii=a_ , indent=a_ ) )
# model
_snake_case : Tuple = chkpt["model"]
# remove unneeded keys
_snake_case : int = [
"decoder.version",
]
for k in ignore_keys:
model_state_dict.pop(a_ , a_ )
_snake_case : Any = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('output_projection.weight' ):
_snake_case : int = model_state_dict.pop(a_ )
else:
_snake_case : str = model_state_dict.pop(a_ )
_snake_case : Union[str, Any] = BioGptConfig.from_pretrained(a_ )
_snake_case : Optional[int] = BioGptForCausalLM(a_ )
# check that it loads ok
model_new.load_state_dict(a_ )
# save
_snake_case : Tuple = os.path.join(a_ , a_ )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(a_ , a_ )
print('Conversion is done!' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--biogpt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
lowerCAmelCase_ = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 367 |
import qiskit
def lowerCamelCase_ ( lowerCAmelCase: int = 2 )-> qiskit.result.counts.Counts:
_snake_case : Dict = qubits
# Using Aer's simulator
_snake_case : List[str] = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q register
_snake_case : Tuple = qiskit.QuantumCircuit(lowerCAmelCase , lowerCAmelCase )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , lowerCAmelCase ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , lowerCAmelCase )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(lowerCAmelCase ) ) , list(range(lowerCAmelCase ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
_snake_case : Any = qiskit.execute(lowerCAmelCase , lowerCAmelCase , shots=10_00 )
return job.result().get_counts(lowerCAmelCase )
if __name__ == "__main__":
print(F"""Total count for various states are: {quantum_entanglement(3)}""")
| 260 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
'''tokenization_deberta''': ['''DebertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : List[str] = ['''DebertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : str = [
'''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DebertaForMaskedLM''',
'''DebertaForQuestionAnswering''',
'''DebertaForSequenceClassification''',
'''DebertaForTokenClassification''',
'''DebertaModel''',
'''DebertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDebertaForMaskedLM''',
'''TFDebertaForQuestionAnswering''',
'''TFDebertaForSequenceClassification''',
'''TFDebertaForTokenClassification''',
'''TFDebertaModel''',
'''TFDebertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
__lowerCamelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 219 | def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
SCREAMING_SNAKE_CASE__ = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE__ = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE__ = max(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase ) , b_binary.zfill(__UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 219 | 1 |
# 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 typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : Dict = "dandelin/vilt-b32-finetuned-vqa"
A__ : Optional[int] = (
"This is a tool that answers a question about an image. It takes an input named `image` which should be the "
"image containing the information, as well as a `question` which should be the question in English. It "
"returns a text that is the answer to the question."
)
A__ : str = "image_qa"
A__ : int = AutoProcessor
A__ : Any = AutoModelForVisualQuestionAnswering
A__ : Dict = ["image", "text"]
A__ : Any = ["text"]
def __init__( self: Dict ,*lowerCamelCase_: str ,**lowerCamelCase_: str ) -> Optional[Any]:
requires_backends(self ,["""vision"""] )
super().__init__(*lowerCamelCase_ ,**lowerCamelCase_ )
def A__ ( self: int ,lowerCamelCase_: "Image" ,lowerCamelCase_: str ) -> int:
return self.pre_processor(lowerCamelCase_ ,lowerCamelCase_ ,return_tensors="""pt""" )
def A__ ( self: List[Any] ,lowerCamelCase_: Optional[int] ) -> List[str]:
with torch.no_grad():
return self.model(**lowerCamelCase_ ).logits
def A__ ( self: str ,lowerCamelCase_: Optional[int] ) -> Union[str, Any]:
UpperCAmelCase_ : Optional[int] = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 352 |
def lowerCamelCase_ ( _a : int = 50 ):
'''simple docstring'''
UpperCAmelCase_ : Dict = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F"{solution() = }")
| 59 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""InstructBlipQFormerConfig""",
"""InstructBlipVisionConfig""",
],
"""processing_instructblip""": ["""InstructBlipProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""InstructBlipQFormerModel""",
"""InstructBlipPreTrainedModel""",
"""InstructBlipForConditionalGeneration""",
"""InstructBlipVisionModel""",
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = OpenAIGPTTokenizer
A__ = OpenAIGPTTokenizerFast
A__ = True
A__ = False
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase__ = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
lowercase__ = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
lowercase__ = ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(_UpperCAmelCase ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(_UpperCAmelCase ) )
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
return "lower newer", "lower newer"
def lowerCamelCase__ (self : Dict ) -> Tuple:
"""simple docstring"""
lowercase__ = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase__ = """lower"""
lowercase__ = ["""low""", """er</w>"""]
lowercase__ = tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
lowercase__ = tokens + ["""<unk>"""]
lowercase__ = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : Union[str, Any]=15 ) -> Optional[int]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
# Simple input
lowercase__ = """This is a simple input"""
lowercase__ = ["""This is a simple input 1""", """This is a simple input 2"""]
lowercase__ = ("""This is a simple input""", """This is a pair""")
lowercase__ = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" )
# Simple input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" )
# Simple input
self.assertRaises(
_UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" , )
# Pair input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" )
# Pair input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" )
# Pair input
self.assertRaises(
_UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" , )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A ( UpperCAmelCase__ ):
'''simple docstring'''
pass
| 367 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A :
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=13 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Dict=3 , _UpperCAmelCase : Dict=4 , _UpperCAmelCase : Union[str, Any]=[10, 20, 30, 40] , _UpperCAmelCase : Optional[int]=[2, 2, 3, 2] , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=37 , _UpperCAmelCase : str="gelu" , _UpperCAmelCase : Dict=10 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : List[Any]=["stage2", "stage3", "stage4"] , _UpperCAmelCase : List[Any]=[2, 3, 4] , _UpperCAmelCase : Optional[Any]=None , ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = num_stages
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = num_labels
lowercase__ = initializer_range
lowercase__ = out_features
lowercase__ = out_indices
lowercase__ = scope
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Any:
"""simple docstring"""
lowercase__ = ConvNextVaModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
# 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 lowerCamelCase__ (self : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = ConvNextVaForImageClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = ConvNextVaBackbone(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase__ = None
lowercase__ = ConvNextVaBackbone(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase__ (self : List[Any] ) -> int:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
def lowerCamelCase__ (self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
A__ = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
A__ = False
A__ = False
A__ = False
A__ = False
A__ = False
def lowerCamelCase__ (self : int ) -> List[Any]:
"""simple docstring"""
lowercase__ = ConvNextVaModelTester(self )
lowercase__ = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase__ (self : int ) -> Tuple:
"""simple docstring"""
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 lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds""" )
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings""" )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking""" )
def lowerCamelCase__ (self : int ) -> str:
"""simple docstring"""
pass
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_with_labels()
lowercase__ = True
if model_class.__name__ in [
*get_values(_UpperCAmelCase ),
*get_values(_UpperCAmelCase ),
]:
continue
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.train()
lowercase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
lowercase__ = model(**_UpperCAmelCase ).loss
loss.backward()
def lowerCamelCase__ (self : Optional[Any] ) -> int:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_with_labels()
lowercase__ = False
lowercase__ = True
if (
model_class.__name__
in [*get_values(_UpperCAmelCase ), *get_values(_UpperCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.gradient_checkpointing_enable()
model.train()
lowercase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
lowercase__ = model(**_UpperCAmelCase ).loss
loss.backward()
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCamelCase__ (self : int ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
def check_hidden_states_output(_UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str ):
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowercase__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase__ = self.model_tester.num_stages
self.assertEqual(len(_UpperCAmelCase ) , expected_num_stages + 1 )
# ConvNextV2'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 // 4, self.model_tester.image_size // 4] , )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )
@slow
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = ConvNextVaModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def UpperCamelCase ( ) -> int:
"""simple docstring"""
lowercase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase__ (self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ) if is_vision_available() else None
@slow
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
lowercase__ = ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ).to(_UpperCAmelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = preprocessor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_UpperCAmelCase )
# verify the logits
lowercase__ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
lowercase__ = torch.tensor([0.9_996, 0.1_966, -0.4_386] ).to(_UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
| 146 | 0 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class A__ ( enum.Enum ):
__UpperCamelCase : str = 0
__UpperCamelCase : Any = 1
__UpperCamelCase : List[Any] = 2
@add_end_docstrings(UpperCAmelCase__ )
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : Tuple = "\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n "
def __init__( self :Optional[int] , *SCREAMING_SNAKE_CASE :Any , **SCREAMING_SNAKE_CASE :Any ) -> str:
'''simple docstring'''
super().__init__(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
_a : int =None
if self.model.config.prefix is not None:
_a : Any =self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
_a : Any =self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
_a , _a , _a : Tuple =self._sanitize_parameters(prefix=SCREAMING_SNAKE_CASE , **self._forward_params )
_a : Optional[int] ={**self._preprocess_params, **preprocess_params}
_a : Tuple ={**self._forward_params, **forward_params}
def __UpperCAmelCase ( self :Optional[int] , SCREAMING_SNAKE_CASE :Tuple=None , SCREAMING_SNAKE_CASE :Optional[int]=None , SCREAMING_SNAKE_CASE :Any=None , SCREAMING_SNAKE_CASE :List[Any]=None , SCREAMING_SNAKE_CASE :str=None , SCREAMING_SNAKE_CASE :Dict=None , SCREAMING_SNAKE_CASE :Optional[int]=None , SCREAMING_SNAKE_CASE :Tuple=None , **SCREAMING_SNAKE_CASE :Any , ) -> Optional[int]:
'''simple docstring'''
_a : Optional[int] ={}
if prefix is not None:
_a : Union[str, Any] =prefix
if prefix:
_a : Union[str, Any] =self.tokenizer(
SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE , return_tensors=self.framework )
_a : List[Any] =prefix_inputs["""input_ids"""].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
""" [None, 'hole']""" )
_a : List[Any] =handle_long_generation
preprocess_params.update(SCREAMING_SNAKE_CASE )
_a : Tuple =generate_kwargs
_a : List[str] ={}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("""`return_text` is mutually exclusive with `return_full_text`""" )
if return_tensors is not None:
raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`""" )
_a : List[Any] =ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("""`return_text` is mutually exclusive with `return_tensors`""" )
_a : Any =ReturnType.TENSORS
if return_type is not None:
_a : Union[str, Any] =return_type
if clean_up_tokenization_spaces is not None:
_a : Tuple =clean_up_tokenization_spaces
if stop_sequence is not None:
_a : str =self.tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 1:
warnings.warn(
"""Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
""" the stop sequence will be used as the stop sequence string in the interim.""" )
_a : Optional[int] =stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __UpperCAmelCase ( self :str , *SCREAMING_SNAKE_CASE :Any , **SCREAMING_SNAKE_CASE :Dict ) -> Optional[int]:
'''simple docstring'''
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"""add_space_before_punct_symbol""": True} )
return super()._parse_and_tokenize(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __call__( self :Dict , SCREAMING_SNAKE_CASE :Optional[int] , **SCREAMING_SNAKE_CASE :Optional[int] ) -> int:
'''simple docstring'''
return super().__call__(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :Optional[int] , SCREAMING_SNAKE_CASE :Dict , SCREAMING_SNAKE_CASE :Optional[Any]="" , SCREAMING_SNAKE_CASE :Optional[Any]=None , **SCREAMING_SNAKE_CASE :List[str] ) -> Any:
'''simple docstring'''
_a : Union[str, Any] =self.tokenizer(
prefix + prompt_text , padding=SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE , return_tensors=self.framework )
_a : Tuple =prompt_text
if handle_long_generation == "hole":
_a : Optional[Any] =inputs["""input_ids"""].shape[-1]
if "max_new_tokens" in generate_kwargs:
_a : List[str] =generate_kwargs["""max_new_tokens"""]
else:
_a : Optional[Any] =generate_kwargs.get("""max_length""" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("""We cannot infer how many new tokens are expected""" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
_a : List[Any] =self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"""We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
""" models max length""" )
_a : int =inputs["""input_ids"""][:, -keep_length:]
if "attention_mask" in inputs:
_a : List[str] =inputs["""attention_mask"""][:, -keep_length:]
return inputs
def __UpperCAmelCase ( self :Tuple , SCREAMING_SNAKE_CASE :Tuple , **SCREAMING_SNAKE_CASE :int ) -> List[str]:
'''simple docstring'''
_a : int =model_inputs["""input_ids"""]
_a : List[str] =model_inputs.get("""attention_mask""" , SCREAMING_SNAKE_CASE )
# Allow empty prompts
if input_ids.shape[1] == 0:
_a : int =None
_a : Optional[Any] =None
_a : Any =1
else:
_a : Dict =input_ids.shape[0]
_a : List[str] =model_inputs.pop("""prompt_text""" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
_a : str =generate_kwargs.pop("""prefix_length""" , 0 )
if prefix_length > 0:
_a : Optional[int] ="""max_new_tokens""" in generate_kwargs or (
"""generation_config""" in generate_kwargs
and generate_kwargs["""generation_config"""].max_new_tokens is not None
)
if not has_max_new_tokens:
_a : Union[str, Any] =generate_kwargs.get("""max_length""" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
_a : List[Any] ="""min_new_tokens""" in generate_kwargs or (
"""generation_config""" in generate_kwargs
and generate_kwargs["""generation_config"""].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
_a : Tuple =self.model.generate(input_ids=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
_a : int =generated_sequence.shape[0]
if self.framework == "pt":
_a : List[str] =generated_sequence.reshape(SCREAMING_SNAKE_CASE , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
_a : List[str] =tf.reshape(SCREAMING_SNAKE_CASE , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def __UpperCAmelCase ( self :Optional[int] , SCREAMING_SNAKE_CASE :Optional[Any] , SCREAMING_SNAKE_CASE :Tuple=ReturnType.FULL_TEXT , SCREAMING_SNAKE_CASE :Any=True ) -> Optional[Any]:
'''simple docstring'''
_a : Optional[int] =model_outputs["""generated_sequence"""][0]
_a : Tuple =model_outputs["""input_ids"""]
_a : Tuple =model_outputs["""prompt_text"""]
_a : List[Any] =generated_sequence.numpy().tolist()
_a : Dict =[]
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
_a : Union[str, Any] ={"""generated_token_ids""": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
_a : int =self.tokenizer.decode(
SCREAMING_SNAKE_CASE , skip_special_tokens=SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
_a : Tuple =0
else:
_a : List[Any] =len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE , ) )
if return_type == ReturnType.FULL_TEXT:
_a : Dict =prompt_text + text[prompt_length:]
else:
_a : int =text[prompt_length:]
_a : str ={"""generated_text""": all_text}
records.append(SCREAMING_SNAKE_CASE )
return records
| 276 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 276 | 1 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : List[str] = False, False, False
@dataclass
class __magic_name__ :
'''simple docstring'''
__UpperCamelCase = None
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = None
# Automatically constructed
__UpperCamelCase = "dict"
__UpperCamelCase = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
__UpperCamelCase = field(default="Audio" , init=UpperCAmelCase__ , repr=UpperCAmelCase__ )
def __call__( self ):
"""simple docstring"""
return self.pa_type
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("""To support encoding audio data, please install 'soundfile'.""" ) from err
if isinstance(_a , _a ):
return {"bytes": None, "path": value}
elif isinstance(_a , _a ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
lowerCamelCase = BytesIO()
sf.write(_a , value["""array"""] , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("""pcm""" ):
# "PCM" only has raw audio bytes
if value.get("""sampling_rate""" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object""" )
if value.get("""bytes""" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
lowerCamelCase = np.frombuffer(value["""bytes"""] , dtype=np.intaa ).astype(np.floataa ) / 32_767
else:
lowerCamelCase = np.memmap(value["""path"""] , dtype="""h""" , mode="""r""" ).astype(np.floataa ) / 32_767
lowerCamelCase = BytesIO(bytes() )
sf.write(_a , _a , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""" )
lowerCamelCase , lowerCamelCase = (value["""path"""], BytesIO(value["""bytes"""] )) if value["""bytes"""] is not None else (value["""path"""], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'.""" ) from err
lowerCamelCase = xsplitext(_a )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"""Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"""Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
if file is None:
lowerCamelCase = token_per_repo_id or {}
lowerCamelCase = path.split("""::""" )[-1]
try:
lowerCamelCase = string_to_dict(_a , config.HUB_DATASETS_URL )["""repo_id"""]
lowerCamelCase = token_per_repo_id[repo_id]
except (ValueError, KeyError):
lowerCamelCase = None
with xopen(_a , """rb""" , use_auth_token=_a ) as f:
lowerCamelCase , lowerCamelCase = sf.read(_a )
else:
lowerCamelCase , lowerCamelCase = sf.read(_a )
lowerCamelCase = array.T
if self.mono:
lowerCamelCase = librosa.to_mono(_a )
if self.sampling_rate and self.sampling_rate != sampling_rate:
lowerCamelCase = librosa.resample(_a , orig_sr=_a , target_sr=self.sampling_rate )
lowerCamelCase = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def _lowerCAmelCase ( self ):
"""simple docstring"""
from .features import Value
if self.decode:
raise ValueError("""Cannot flatten a decoded Audio feature.""" )
return {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if pa.types.is_string(storage.type ):
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.binary() )
lowerCamelCase = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.string() )
lowerCamelCase = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("""array""" ):
lowerCamelCase = pa.array([Audio().encode_example(_a ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowerCamelCase = storage.field("""bytes""" )
else:
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowerCamelCase = storage.field("""path""" )
else:
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.string() )
lowerCamelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
return array_cast(_a , self.pa_type )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
@no_op_if_value_is_null
def path_to_bytes(_a ):
with xopen(_a , """rb""" ) as f:
lowerCamelCase = f.read()
return bytes_
lowerCamelCase = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowerCamelCase = pa.array(
[os.path.basename(_a ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowerCamelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_a , self.pa_type )
| 168 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : List[str] = False, False, False
@dataclass
class __magic_name__ :
'''simple docstring'''
__UpperCamelCase = None
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = None
# Automatically constructed
__UpperCamelCase = "dict"
__UpperCamelCase = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
__UpperCamelCase = field(default="Audio" , init=UpperCAmelCase__ , repr=UpperCAmelCase__ )
def __call__( self ):
"""simple docstring"""
return self.pa_type
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("""To support encoding audio data, please install 'soundfile'.""" ) from err
if isinstance(_a , _a ):
return {"bytes": None, "path": value}
elif isinstance(_a , _a ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
lowerCamelCase = BytesIO()
sf.write(_a , value["""array"""] , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("""pcm""" ):
# "PCM" only has raw audio bytes
if value.get("""sampling_rate""" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object""" )
if value.get("""bytes""" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
lowerCamelCase = np.frombuffer(value["""bytes"""] , dtype=np.intaa ).astype(np.floataa ) / 32_767
else:
lowerCamelCase = np.memmap(value["""path"""] , dtype="""h""" , mode="""r""" ).astype(np.floataa ) / 32_767
lowerCamelCase = BytesIO(bytes() )
sf.write(_a , _a , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""" )
lowerCamelCase , lowerCamelCase = (value["""path"""], BytesIO(value["""bytes"""] )) if value["""bytes"""] is not None else (value["""path"""], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'.""" ) from err
lowerCamelCase = xsplitext(_a )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"""Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"""Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
if file is None:
lowerCamelCase = token_per_repo_id or {}
lowerCamelCase = path.split("""::""" )[-1]
try:
lowerCamelCase = string_to_dict(_a , config.HUB_DATASETS_URL )["""repo_id"""]
lowerCamelCase = token_per_repo_id[repo_id]
except (ValueError, KeyError):
lowerCamelCase = None
with xopen(_a , """rb""" , use_auth_token=_a ) as f:
lowerCamelCase , lowerCamelCase = sf.read(_a )
else:
lowerCamelCase , lowerCamelCase = sf.read(_a )
lowerCamelCase = array.T
if self.mono:
lowerCamelCase = librosa.to_mono(_a )
if self.sampling_rate and self.sampling_rate != sampling_rate:
lowerCamelCase = librosa.resample(_a , orig_sr=_a , target_sr=self.sampling_rate )
lowerCamelCase = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def _lowerCAmelCase ( self ):
"""simple docstring"""
from .features import Value
if self.decode:
raise ValueError("""Cannot flatten a decoded Audio feature.""" )
return {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if pa.types.is_string(storage.type ):
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.binary() )
lowerCamelCase = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.string() )
lowerCamelCase = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("""array""" ):
lowerCamelCase = pa.array([Audio().encode_example(_a ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowerCamelCase = storage.field("""bytes""" )
else:
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowerCamelCase = storage.field("""path""" )
else:
lowerCamelCase = pa.array([None] * len(_a ) , type=pa.string() )
lowerCamelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
return array_cast(_a , self.pa_type )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
@no_op_if_value_is_null
def path_to_bytes(_a ):
with xopen(_a , """rb""" ) as f:
lowerCamelCase = f.read()
return bytes_
lowerCamelCase = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowerCamelCase = pa.array(
[os.path.basename(_a ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowerCamelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_a , self.pa_type )
| 168 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class _a ( _lowercase):
_a : str = '''vit_msn'''
def __init__( self : str , _SCREAMING_SNAKE_CASE : int=768 , _SCREAMING_SNAKE_CASE : Union[str, Any]=12 , _SCREAMING_SNAKE_CASE : str=12 , _SCREAMING_SNAKE_CASE : List[Any]=3072 , _SCREAMING_SNAKE_CASE : List[Any]="gelu" , _SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , _SCREAMING_SNAKE_CASE : List[Any]=0.0 , _SCREAMING_SNAKE_CASE : Any=0.02 , _SCREAMING_SNAKE_CASE : List[str]=1E-06 , _SCREAMING_SNAKE_CASE : Any=224 , _SCREAMING_SNAKE_CASE : Any=16 , _SCREAMING_SNAKE_CASE : Any=3 , _SCREAMING_SNAKE_CASE : List[Any]=True , **_SCREAMING_SNAKE_CASE : Dict , )-> List[Any]:
super().__init__(**_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[int] = hidden_size
lowerCAmelCase__ : List[Any] = num_hidden_layers
lowerCAmelCase__ : List[Any] = num_attention_heads
lowerCAmelCase__ : Dict = intermediate_size
lowerCAmelCase__ : List[str] = hidden_act
lowerCAmelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase__ : List[str] = attention_probs_dropout_prob
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Any = layer_norm_eps
lowerCAmelCase__ : Any = image_size
lowerCAmelCase__ : Optional[int] = patch_size
lowerCAmelCase__ : Dict = num_channels
lowerCAmelCase__ : List[str] = qkv_bias
| 131 |
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Any = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
lowerCAmelCase__ : Union[str, Any] = remove_duplicates(key.upper() )
lowerCAmelCase__ : Dict = len(_a )
# First fill cipher with key characters
lowerCAmelCase__ : Any = {alphabet[i]: char for i, char in enumerate(_a )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_a ) , 26 ):
lowerCAmelCase__ : List[str] = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
lowerCAmelCase__ : str = alphabet[i - offset]
lowerCAmelCase__ : Dict = char
return cipher_alphabet
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
return "".join(cipher_map.get(_a , _a ) for ch in message.upper() )
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : int = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_a , _a ) for ch in message.upper() )
def lowerCamelCase_ ( ):
"""simple docstring"""
lowerCAmelCase__ : Any = input('''Enter message to encode or decode: ''' ).strip()
lowerCAmelCase__ : Tuple = input('''Enter keyword: ''' ).strip()
lowerCAmelCase__ : List[str] = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
lowerCAmelCase__ : List[Any] = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
lowerCAmelCase__ : Dict = create_cipher_map(_a )
print(func(_a , _a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 131 | 1 |
def __lowercase ( __lowerCAmelCase : str ):
return " ".join(
''.join(word[::-1] ) if len(__lowerCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 366 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : int = logging.get_logger(__name__)
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str ):
a__ = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
] )
return rename_keys
def __lowercase ( __lowerCAmelCase : Dict , __lowerCAmelCase : Dict ):
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
a__ = state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight' )
a__ = in_proj_weight[
: encoder_config.hidden_size, :
]
a__ = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
a__ = in_proj_weight[
-encoder_config.hidden_size :, :
]
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ):
a__ = dct.pop(__lowerCAmelCase )
a__ = val
def __lowercase ( __lowerCAmelCase : Optional[Any] ):
if "handwritten" in checkpoint_url:
a__ = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
a__ = 'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
a__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw ).convert('RGB' )
return im
@torch.no_grad()
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : int ):
a__ = ViTConfig(image_size=3_8_4 , qkv_bias=__lowerCAmelCase )
a__ = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
a__ = 7_6_8
elif "large" in checkpoint_url:
# use ViT-large encoder
a__ = 1_0_2_4
a__ = 4_0_9_6
a__ = 2_4
a__ = 1_6
a__ = 1_0_2_4
else:
raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL' )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
a__ = False
a__ = 'relu'
a__ = 1_0_2_4
a__ = True
a__ = False
a__ = False
# load HuggingFace model
a__ = ViTModel(__lowerCAmelCase , add_pooling_layer=__lowerCAmelCase )
a__ = TrOCRForCausalLM(__lowerCAmelCase )
a__ = VisionEncoderDecoderModel(encoder=__lowerCAmelCase , decoder=__lowerCAmelCase )
model.eval()
# load state_dict of original model, rename some keys
a__ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location='cpu' , check_hash=__lowerCAmelCase )['model']
a__ = create_rename_keys(__lowerCAmelCase , __lowerCAmelCase )
for src, dest in rename_keys:
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
a__ = state_dict.pop(__lowerCAmelCase )
if key.startswith('decoder' ) and "output_projection" not in key:
a__ = val
else:
a__ = val
# load state dict
model.load_state_dict(__lowerCAmelCase )
# Check outputs on an image
a__ = ViTImageProcessor(size=encoder_config.image_size )
a__ = RobertaTokenizer.from_pretrained('roberta-large' )
a__ = TrOCRProcessor(__lowerCAmelCase , __lowerCAmelCase )
a__ = processor(images=prepare_img(__lowerCAmelCase ) , return_tensors='pt' ).pixel_values
# verify logits
a__ = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
a__ = model(pixel_values=__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase )
a__ = outputs.logits
a__ = torch.Size([1, 1, 5_0_2_6_5] )
if "trocr-base-handwritten" in checkpoint_url:
a__ = torch.tensor(
[-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
elif "trocr-large-handwritten" in checkpoint_url:
a__ = torch.tensor(
[-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
elif "trocr-base-printed" in checkpoint_url:
a__ = torch.tensor(
[-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
elif "trocr-large-printed" in checkpoint_url:
a__ = torch.tensor(
[-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :1_0] , __lowerCAmelCase , atol=1E-3 ), "First elements of logits not as expected"
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCAmelCase )
print(F'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
snake_case : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
type=str,
help='''URL to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
snake_case : int = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 109 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
A: Optional[int] = logging.get_logger(__name__)
A: Optional[int] = {
"microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Optional[Any] = 'layoutlmv3'
def __init__( self , _SCREAMING_SNAKE_CASE=50265 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1E-5 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=256 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=224 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> List[str]:
'''simple docstring'''
super().__init__(
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 , initializer_range=_SCREAMING_SNAKE_CASE , layer_norm_eps=_SCREAMING_SNAKE_CASE , pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCAmelCase : Dict = max_ad_position_embeddings
UpperCAmelCase : Union[str, Any] = coordinate_size
UpperCAmelCase : str = shape_size
UpperCAmelCase : Dict = has_relative_attention_bias
UpperCAmelCase : List[Any] = rel_pos_bins
UpperCAmelCase : List[Any] = max_rel_pos
UpperCAmelCase : Any = has_spatial_attention_bias
UpperCAmelCase : Union[str, Any] = rel_ad_pos_bins
UpperCAmelCase : Optional[int] = max_rel_ad_pos
UpperCAmelCase : List[Any] = text_embed
UpperCAmelCase : List[Any] = visual_embed
UpperCAmelCase : Union[str, Any] = input_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : str = classifier_dropout
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : int = version.parse('1.12' )
@property
def SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
("""bbox""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
else:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""bbox""", {0: """batch""", 1: """sequence"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
] )
@property
def SCREAMING_SNAKE_CASE ( self ) -> float:
'''simple docstring'''
return 1E-5
@property
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return 12
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = -1 , _SCREAMING_SNAKE_CASE = -1 , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 3 , _SCREAMING_SNAKE_CASE = 40 , _SCREAMING_SNAKE_CASE = 40 , ) -> Mapping[str, Any]:
'''simple docstring'''
setattr(processor.image_processor , """apply_ocr""" , _SCREAMING_SNAKE_CASE )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCAmelCase : Tuple = compute_effective_axis_dimension(
_SCREAMING_SNAKE_CASE , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase : List[str] = processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Union[str, Any] = compute_effective_axis_dimension(
_SCREAMING_SNAKE_CASE , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_SCREAMING_SNAKE_CASE )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase : Tuple = [[""" """.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
UpperCAmelCase : Union[str, Any] = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
UpperCAmelCase : Optional[Any] = self._generate_dummy_images(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase : Optional[Any] = dict(
processor(
_SCREAMING_SNAKE_CASE , text=_SCREAMING_SNAKE_CASE , boxes=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , ) )
return inputs
| 109 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
A: Optional[Any] = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n"
A: Optional[int] = "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"
A: int = "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair as given in the references (see below)\n - 'prediction_text': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair (see above),\n - 'answers': a Dict in the CUAD dataset format\n {\n 'text': list of possible texts for the answer, as a list of strings\n 'answer_start': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n 'exact_match': Exact match (the normalized answer exactly match the gold answer)\n 'f1': The F-score of predicted tokens versus the gold answer\n 'aupr': Area Under the Precision-Recall curve\n 'prec_at_80_recall': Precision at 80% recall\n 'prec_at_90_recall': Precision at 90% recall\nExamples:\n >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> cuad_metric = datasets.load_metric(\"cuad\")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCAmelCase : Tuple = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCAmelCase : Optional[Any] = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 109 | 1 |
def UpperCAmelCase_ (_lowerCAmelCase : list[list[int]] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : set ):
__UpperCamelCase , __UpperCamelCase : Tuple = len(_a ), len(grid[0] )
if (
min(_a , _a ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
__UpperCamelCase : Optional[int] = 0
count += depth_first_search(_a , row + 1 , _a , _a )
count += depth_first_search(_a , row - 1 , _a , _a )
count += depth_first_search(_a , _a , col + 1 , _a )
count += depth_first_search(_a , _a , col - 1 , _a )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 361 |
from math import sqrt
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00_00_00 ):
__UpperCamelCase : int = 0
__UpperCamelCase : int = 0
__UpperCamelCase : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(_lowerCAmelCase , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F"""{solution() = }""") | 171 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _a ( SCREAMING_SNAKE_CASE_ : Any ):
def wrapper(*SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : str ):
__lowerCAmelCase = timeit.default_timer()
__lowerCAmelCase = func(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = timeit.default_timer() - starttime
return delta
__lowerCAmelCase = func.__name__
return wrapper
def _a ( SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : Any=1_00 , SCREAMING_SNAKE_CASE_ : Dict=None ):
__lowerCAmelCase = []
__lowerCAmelCase = seq_shapes or {}
for i in range(SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(SCREAMING_SNAKE_CASE_ , _ArrayXD ):
__lowerCAmelCase = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(SCREAMING_SNAKE_CASE_ , datasets.Value ):
if v.dtype == "string":
__lowerCAmelCase = "The small grey turtle was surprisingly fast when challenged."
else:
__lowerCAmelCase = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(SCREAMING_SNAKE_CASE_ , datasets.Sequence ):
while isinstance(SCREAMING_SNAKE_CASE_ , datasets.Sequence ):
__lowerCAmelCase = v.feature
__lowerCAmelCase = seq_shapes[k]
__lowerCAmelCase = np.random.rand(*SCREAMING_SNAKE_CASE_ ).astype(v.dtype )
__lowerCAmelCase = data
dummy_data.append((i, example) )
return dummy_data
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any]=1_00 , SCREAMING_SNAKE_CASE_ : int=None ):
__lowerCAmelCase = generate_examples(SCREAMING_SNAKE_CASE_ , num_examples=SCREAMING_SNAKE_CASE_ , seq_shapes=SCREAMING_SNAKE_CASE_ )
with ArrowWriter(features=SCREAMING_SNAKE_CASE_ , path=SCREAMING_SNAKE_CASE_ ) as writer:
for key, record in dummy_data:
__lowerCAmelCase = features.encode_example(SCREAMING_SNAKE_CASE_ )
writer.write(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.""" )
__lowerCAmelCase = datasets.Dataset.from_file(filename=SCREAMING_SNAKE_CASE_ , info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_ ) )
return dataset
| 92 |
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_ : Optional[int] ):
__lowerCAmelCase = filter(lambda SCREAMING_SNAKE_CASE_ : p.requires_grad , model.parameters() )
__lowerCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
if metric == "rouge2":
__lowerCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__lowerCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__lowerCAmelCase = "{val_avg_em:.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." )
__lowerCAmelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"""val_{metric}""" , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
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 __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCAmelCase = 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
__lowerCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCAmelCase = od / "test_results.txt"
__lowerCAmelCase = 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.
__lowerCAmelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCAmelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCAmelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
__lowerCAmelCase = val.item()
__lowerCAmelCase = f"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
__lowerCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
__lowerCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCAmelCase = pl_module.model.num_parameters()
__lowerCAmelCase = count_trainable_parameters(_A )
# 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 __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""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")
| 92 | 1 |
from itertools import count
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 50 ) -> int:
UpperCamelCase__ : Dict = [1] * min_block_length
for n in count(__lowerCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(__lowerCAmelCase , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 100_0000:
break
return n
if __name__ == "__main__":
print(F"""{solution() = }""") | 196 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ), f'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
UpperCamelCase__ : List[Any] = f'The input value of [n={number}] has to be > 0'
raise ValueError(__lowerCAmelCase )
else:
UpperCamelCase__ : Optional[Any] = sylvester(number - 1 )
UpperCamelCase__ : str = num - 1
UpperCamelCase__ : int = num
return lower * upper + 1
if __name__ == "__main__":
print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""") | 196 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {'''vocab_file''': '''vocab.txt'''}
a__ = {
'''vocab_file''': {
'''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
},
}
a__ = {
'''openbmb/cpm-ant-10b''': 1024,
}
def __UpperCAmelCase ( __a : List[Any] ) -> Tuple:
"""simple docstring"""
_a : Union[str, Any] = collections.OrderedDict()
with open(__a ,'''r''' ,encoding='''utf-8''' ) as reader:
_a : Dict = reader.readlines()
for index, token in enumerate(__a ):
_a : Optional[int] = token.rstrip('''\n''' )
_a : Any = index
return vocab
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
def __init__( self , _a , _a="<unk>" , _a=2_0_0 ) -> Optional[Any]:
_a : Union[str, Any] = vocab
_a : Tuple = unk_token
_a : List[str] = max_input_chars_per_word
def __lowercase ( self , _a ) -> List[str]:
_a : Union[str, Any] = list(_a )
if len(_a ) > self.max_input_chars_per_word:
return [self.unk_token]
_a : List[str] = 0
_a : Any = []
while start < len(_a ):
_a : Optional[int] = len(_a )
_a : Tuple = None
while start < end:
_a : Dict = ''''''.join(chars[start:end] )
if substr in self.vocab:
_a : Any = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(_a )
_a : str = end
return sub_tokens
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = VOCAB_FILES_NAMES
UpperCAmelCase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ : int = ["input_ids", "attention_mask"]
UpperCAmelCase__ : Any = False
def __init__( self , _a , _a="<d>" , _a="</d>" , _a="<s>" , _a="</s>" , _a="<pad>" , _a="<unk>" , _a="</n>" , _a="</_>" , _a="left" , **_a , ) -> Any:
requires_backends(self , ['''jieba'''] )
super().__init__(
bod_token=_a , eod_token=_a , bos_token=_a , eos_token=_a , pad_token=_a , unk_token=_a , line_token=_a , space_token=_a , padding_side=_a , **_a , )
_a : Optional[Any] = bod_token
_a : Tuple = eod_token
_a : Dict = load_vocab(_a )
_a : List[Any] = self.encoder[space_token]
_a : Any = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
_a : Tuple = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _a : x[1] ) )
_a : Dict = {v: k for k, v in self.encoder.items()}
_a : List[Any] = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def __lowercase ( self ) -> Dict:
return self.encoder[self.bod_token]
@property
def __lowercase ( self ) -> Dict:
return self.encoder[self.eod_token]
@property
def __lowercase ( self ) -> Union[str, Any]:
return self.encoder["\n"]
@property
def __lowercase ( self ) -> int:
return len(self.encoder )
def __lowercase ( self ) -> Tuple:
return dict(self.encoder , **self.added_tokens_encoder )
def __lowercase ( self , _a ) -> List[str]:
_a : str = []
for x in jieba.cut(_a , cut_all=_a ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(_a ) )
return output_tokens
def __lowercase ( self , _a , **_a ) -> str:
_a : List[str] = [i for i in token_ids if i >= 0]
_a : Tuple = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(_a , **_a )
def __lowercase ( self , _a ) -> List[str]:
return token in self.encoder
def __lowercase ( self , _a ) -> str:
return "".join(_a )
def __lowercase ( self , _a ) -> Optional[int]:
return self.encoder.get(_a , self.encoder.get(self.unk_token ) )
def __lowercase ( self , _a ) -> str:
return self.decoder.get(_a , self.unk_token )
def __lowercase ( self , _a , _a = None ) -> Tuple[str]:
if os.path.isdir(_a ):
_a : List[str] = os.path.join(
_a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
_a : int = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
_a : Optional[Any] = 0
if " " in self.encoder:
_a : str = self.encoder[''' ''']
del self.encoder[" "]
if "\n" in self.encoder:
_a : int = self.encoder['''\n''']
del self.encoder["\n"]
_a : List[Any] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _a : x[1] ) )
with open(_a , '''w''' , encoding='''utf-8''' ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
''' Please check that the vocabulary is not corrupted!''' )
_a : str = token_index
writer.write(token + '''\n''' )
index += 1
return (vocab_file,)
def __lowercase ( self , _a , _a = None ) -> List[int]:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def __lowercase ( self , _a , _a = None , _a = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a ))
return [1] + ([0] * len(_a ))
| 235 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : np.ndarray ,__a : Union[int, Iterable[int]] ,__a : bool ,__a : int ) -> Tuple[int, int]:
"""simple docstring"""
def constraint_to_multiple_of(__a : List[str] ,__a : Dict ,__a : Any=0 ,__a : int=None ):
_a : Dict = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_a : Any = math.floor(val / multiple ) * multiple
if x < min_val:
_a : Dict = math.ceil(val / multiple ) * multiple
return x
_a : Union[str, Any] = (output_size, output_size) if isinstance(__a ,__a ) else output_size
_a , _a : List[Any] = get_image_size(__a )
_a , _a : Any = output_size
# determine new height and width
_a : Union[str, Any] = output_height / input_height
_a : Tuple = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_a : Optional[Any] = scale_width
else:
# fit height
_a : Tuple = scale_height
_a : Optional[Any] = constraint_to_multiple_of(scale_height * input_height ,multiple=__a )
_a : int = constraint_to_multiple_of(scale_width * input_width ,multiple=__a )
return (new_height, new_width)
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = ["pixel_values"]
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BILINEAR , _a = False , _a = 1 , _a = True , _a = 1 / 2_5_5 , _a = True , _a = None , _a = None , **_a , ) -> None:
super().__init__(**_a )
_a : Optional[int] = size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
_a : Optional[Any] = get_size_dict(_a )
_a : Any = do_resize
_a : Dict = size
_a : str = keep_aspect_ratio
_a : Any = ensure_multiple_of
_a : Optional[Any] = resample
_a : List[Any] = do_rescale
_a : int = rescale_factor
_a : Any = do_normalize
_a : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_a : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowercase ( self , _a , _a , _a = False , _a = 1 , _a = PILImageResampling.BICUBIC , _a = None , **_a , ) -> np.ndarray:
_a : str = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_a : Optional[Any] = get_resize_output_image_size(
_a , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_a , multiple=_a , )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a , _a = None , **_a , ) -> int:
return rescale(_a , scale=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a , _a , _a = None , **_a , ) -> np.ndarray:
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ) -> PIL.Image.Image:
_a : Optional[int] = do_resize if do_resize is not None else self.do_resize
_a : Union[str, Any] = size if size is not None else self.size
_a : str = get_size_dict(_a )
_a : List[Any] = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_a : Optional[Any] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_a : str = resample if resample is not None else self.resample
_a : str = do_rescale if do_rescale is not None else self.do_rescale
_a : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_a : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
_a : str = image_mean if image_mean is not None else self.image_mean
_a : Tuple = image_std if image_std is not None else self.image_std
_a : Dict = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_a : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
_a : int = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_rescale:
_a : Optional[Any] = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
_a : Optional[int] = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
_a : int = [to_channel_dimension_format(_a , _a ) for image in images]
_a : Tuple = {'''pixel_values''': images}
return BatchFeature(data=_a , tensor_type=_a )
def __lowercase ( self , _a , _a = None ) -> Any:
_a : Optional[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_a ) != len(_a ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_a ):
_a : List[Any] = target_sizes.numpy()
_a : str = []
for idx in range(len(_a ) ):
_a : str = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_a )
_a : Union[str, Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_a )
else:
_a : Tuple = logits.argmax(dim=1 )
_a : Union[str, Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 235 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> list[int]:
lowerCamelCase__ : Union[str, Any] = [0 for i in range(len(_UpperCAmelCase ) )]
# initialize interval's left pointer and right pointer
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = 0, 0
for i in range(1 , len(_UpperCAmelCase ) ):
# case when current index is inside the interval
if i <= right_pointer:
lowerCamelCase__ : Any = min(right_pointer - i + 1 , z_result[i - left_pointer] )
lowerCamelCase__ : Union[str, Any] = min_edge
while go_next(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
lowerCamelCase__ , lowerCamelCase__ : Any = i, i + z_result[i] - 1
return z_result
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> bool:
return i + z_result[i] < len(_UpperCAmelCase ) and s[z_result[i]] == s[i + z_result[i]]
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
lowerCamelCase__ : Any = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
lowerCamelCase__ : List[str] = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_UpperCAmelCase ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 45 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
}
_UpperCAmelCase : Optional[Any] = {
"""vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
"""merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}
_UpperCAmelCase : Dict = {
"""ctrl""": 2_56,
}
_UpperCAmelCase : str = {
"""Pregnancy""": 16_86_29,
"""Christianity""": 76_75,
"""Explain""": 10_64_23,
"""Fitness""": 6_34_40,
"""Saving""": 6_31_63,
"""Ask""": 2_71_71,
"""Ass""": 9_59_85,
"""Joke""": 16_35_09,
"""Questions""": 4_56_22,
"""Thoughts""": 4_96_05,
"""Retail""": 5_23_42,
"""Feminism""": 16_43_38,
"""Writing""": 1_19_92,
"""Atheism""": 19_22_63,
"""Netflix""": 4_86_16,
"""Computing""": 3_96_39,
"""Opinion""": 4_32_13,
"""Alone""": 4_49_67,
"""Funny""": 5_89_17,
"""Gaming""": 4_03_58,
"""Human""": 40_88,
"""India""": 13_31,
"""Joker""": 7_71_38,
"""Diet""": 3_62_06,
"""Legal""": 1_18_59,
"""Norman""": 49_39,
"""Tip""": 7_26_89,
"""Weight""": 5_23_43,
"""Movies""": 4_62_73,
"""Running""": 2_34_25,
"""Science""": 20_90,
"""Horror""": 3_77_93,
"""Confession""": 6_05_72,
"""Finance""": 1_22_50,
"""Politics""": 1_63_60,
"""Scary""": 19_19_85,
"""Support""": 1_26_54,
"""Technologies""": 3_25_16,
"""Teenage""": 6_61_60,
"""Event""": 3_27_69,
"""Learned""": 6_74_60,
"""Notion""": 18_27_70,
"""Wikipedia""": 3_75_83,
"""Books""": 66_65,
"""Extract""": 7_60_50,
"""Confessions""": 10_27_01,
"""Conspiracy""": 7_59_32,
"""Links""": 6_36_74,
"""Narcissus""": 15_04_25,
"""Relationship""": 5_47_66,
"""Relationships""": 13_47_96,
"""Reviews""": 4_16_71,
"""News""": 42_56,
"""Translation""": 2_68_20,
"""multilingual""": 12_84_06,
}
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase__ : Tuple = set()
lowerCamelCase__ : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase__ : Optional[Any] = char
lowerCamelCase__ : Any = set(_UpperCAmelCase )
return pairs
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = CONTROL_CODES
def __init__( self : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str]="<unk>" , **UpperCAmelCase : List[Any] ) -> Union[str, Any]:
super().__init__(unk_token=UpperCAmelCase , **UpperCAmelCase )
with open(UpperCAmelCase , encoding='utf-8' ) as vocab_handle:
lowerCamelCase__ : List[Any] = json.load(UpperCAmelCase )
lowerCamelCase__ : Dict = {v: k for k, v in self.encoder.items()}
with open(UpperCAmelCase , encoding='utf-8' ) as merges_handle:
lowerCamelCase__ : Any = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase__ : Any = [tuple(merge.split() ) for merge in merges]
lowerCamelCase__ : List[str] = dict(zip(UpperCAmelCase , range(len(UpperCAmelCase ) ) ) )
lowerCamelCase__ : Any = {}
@property
def A_ ( self : int ) -> Dict:
return len(self.encoder )
def A_ ( self : List[str] ) -> str:
return dict(self.encoder , **self.added_tokens_encoder )
def A_ ( self : Any , UpperCAmelCase : Any ) -> Union[str, Any]:
if token in self.cache:
return self.cache[token]
lowerCamelCase__ : List[str] = tuple(UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
lowerCamelCase__ : Optional[Any] = get_pairs(UpperCAmelCase )
if not pairs:
return token
while True:
lowerCamelCase__ : Optional[Any] = min(UpperCAmelCase , key=lambda UpperCAmelCase : self.bpe_ranks.get(UpperCAmelCase , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase__ , lowerCamelCase__ : str = bigram
lowerCamelCase__ : List[Any] = []
lowerCamelCase__ : Dict = 0
while i < len(UpperCAmelCase ):
try:
lowerCamelCase__ : Any = word.index(UpperCAmelCase , UpperCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase__ : int = j
if word[i] == first and i < len(UpperCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase__ : Dict = tuple(UpperCAmelCase )
lowerCamelCase__ : str = new_word
if len(UpperCAmelCase ) == 1:
break
else:
lowerCamelCase__ : Any = get_pairs(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = '@@ '.join(UpperCAmelCase )
lowerCamelCase__ : int = word[:-4]
lowerCamelCase__ : str = word
return word
def A_ ( self : Dict , UpperCAmelCase : Optional[int] ) -> Optional[int]:
lowerCamelCase__ : Tuple = []
lowerCamelCase__ : Tuple = re.findall(R'\S+\n?' , UpperCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(UpperCAmelCase ).split(' ' ) ) )
return split_tokens
def A_ ( self : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
return self.encoder.get(UpperCAmelCase , self.encoder.get(self.unk_token ) )
def A_ ( self : List[Any] , UpperCAmelCase : Union[str, Any] ) -> List[Any]:
return self.decoder.get(UpperCAmelCase , self.unk_token )
def A_ ( self : str , UpperCAmelCase : Tuple ) -> Optional[int]:
lowerCamelCase__ : Tuple = ' '.join(UpperCAmelCase ).replace('@@ ' , '' ).strip()
return out_string
def A_ ( self : Any , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase__ : List[Any] = os.path.join(
UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase__ : str = os.path.join(
UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCAmelCase , ensure_ascii=UpperCAmelCase ) + '\n' )
lowerCamelCase__ : str = 0
with open(UpperCAmelCase , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda UpperCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase__ : str = token_index
writer.write(' '.join(UpperCAmelCase ) + '\n' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 45 | 1 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
__lowerCAmelCase : List[Any] =logging.getLogger(__name__)
__lowerCAmelCase : List[str] ="pytorch_model.bin"
@dataclasses.dataclass
class UpperCAmelCase :
__lowercase = dataclasses.field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""} )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""} , )
@dataclasses.dataclass
class UpperCAmelCase :
__lowercase = dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""} )
__lowercase = dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""} )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """The name of the task to train on."""} , )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """The list of labels for the task."""} )
@dataclasses.dataclass
class UpperCAmelCase :
__lowercase = dataclasses.field(
metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""} )
__lowercase = dataclasses.field(
default="""accuracy""" , metadata={"""help""": """The evaluation metric used for the task."""} )
__lowercase = dataclasses.field(
default="""no""" , metadata={
"""help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
} , )
__lowercase = dataclasses.field(
default=10 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
__lowercase = dataclasses.field(
default=0.0 , metadata={
"""help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
} , )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""} , )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""} , )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""} , )
__lowercase = dataclasses.field(
default=0.0 , metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""} , )
__lowercase = dataclasses.field(
default=100 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
__lowercase = dataclasses.field(
default=A_ , metadata={"""help""": """Random seed for initialization."""} , )
def UpperCamelCase ( _lowerCamelCase : int , _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple , _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ):
A__ = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
A__ = dataset.filter(lambda _lowerCamelCase : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
A__ = int(eval_result * len(__lowerCamelCase ) )
print(__lowerCamelCase )
A__ = dataset.sort("probability" , reverse=__lowerCamelCase )
A__ = dataset.select(range(__lowerCamelCase ) )
A__ = dataset.remove_columns(["label", "probability"] )
A__ = dataset.rename_column("prediction" , "label" )
A__ = dataset.map(lambda _lowerCamelCase : {"label": idalabel[example["label"]]} )
A__ = dataset.shuffle(seed=args.seed )
A__ = os.path.join(__lowerCamelCase , F"train_pseudo.{args.data_file_extension}" )
if args.data_file_extension == "csv":
dataset.to_csv(__lowerCamelCase , index=__lowerCamelCase )
else:
dataset.to_json(__lowerCamelCase )
def UpperCamelCase ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Tuple , **_lowerCamelCase : List[Any] ):
A__ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
A__ = STModelArguments(model_name_or_path=__lowerCamelCase )
A__ = STDataArguments(train_file=__lowerCamelCase , infer_file=__lowerCamelCase )
A__ = STTrainingArguments(output_dir=__lowerCamelCase )
A__ = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(__lowerCamelCase ).items():
setattr(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for key, value in kwargs.items():
if hasattr(__lowerCamelCase , __lowerCamelCase ):
setattr(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Sanity checks
A__ = {}
A__ = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
A__ = args.train_file
A__ = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
A__ = args.eval_file
for key in data_files:
A__ = data_files[key].split("." )[-1]
assert extension in ["csv", "json"], F"`{key}_file` should be a csv or a json file."
if args.data_file_extension is None:
A__ = extension
else:
assert extension == args.data_file_extension, F"`{key}_file` should be a {args.data_file_extension} file`."
assert (
args.eval_metric in datasets.list_metrics()
), F"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("Creating the initial data directory for self-training..." )
A__ = F"{args.output_dir}/self-train_iter-{{}}".format
A__ = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=__lowerCamelCase )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
accelerator.wait_for_everyone()
A__ = None
A__ = None
A__ = 0
A__ = False
# Show the progress bar
A__ = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
A__ = data_dir_format(__lowerCamelCase )
assert os.path.exists(__lowerCamelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
A__ = os.path.join(__lowerCamelCase , "stage-1" )
A__ = {
"accelerator": accelerator,
"model_name_or_path": args.model_name_or_path,
"cache_dir": args.cache_dir,
"do_train": True,
"train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
"do_eval": True if args.eval_file is not None else False,
"eval_file": data_files["eval"],
"do_predict": True,
"infer_file": data_files["infer"],
"task_name": args.task_name,
"label_list": args.label_list,
"output_dir": current_output_dir,
"eval_metric": args.eval_metric,
"evaluation_strategy": args.evaluation_strategy,
"early_stopping_patience": args.early_stopping_patience,
"early_stopping_threshold": args.early_stopping_threshold,
"seed": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(__lowerCamelCase , __lowerCamelCase ):
arguments_dict.update({key: value} )
A__ = os.path.join(__lowerCamelCase , "best-checkpoint" , __lowerCamelCase )
if os.path.exists(__lowerCamelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1." , __lowerCamelCase , __lowerCamelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 1 *****" , __lowerCamelCase )
finetune(**__lowerCamelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__lowerCamelCase )
logger.info("Self-training job completed: iteration: %d, stage: 1." , __lowerCamelCase )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
A__ = os.path.join(__lowerCamelCase , "best-checkpoint" )
A__ = os.path.join(__lowerCamelCase , "stage-2" )
# Update arguments_dict
A__ = model_path
A__ = data_files["train"]
A__ = current_output_dir
A__ = os.path.join(__lowerCamelCase , "best-checkpoint" , __lowerCamelCase )
if os.path.exists(__lowerCamelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2." , __lowerCamelCase , __lowerCamelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 2 *****" , __lowerCamelCase )
finetune(**__lowerCamelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__lowerCamelCase )
logger.info("Self-training job completed: iteration: %d, stage: 2." , __lowerCamelCase )
A__ = iteration
A__ = data_dir_format(iteration + 1 )
A__ = AutoConfig.from_pretrained(os.path.join(__lowerCamelCase , "best-checkpoint" ) )
A__ = config.idalabel
A__ = os.path.join(__lowerCamelCase , "eval_results_best-checkpoint.json" )
A__ = os.path.join(__lowerCamelCase , "test_results_best-checkpoint.json" )
assert os.path.exists(__lowerCamelCase )
with open(__lowerCamelCase , "r" ) as f:
A__ = float(json.load(__lowerCamelCase )[args.eval_metric] )
A__ = os.path.join(__lowerCamelCase , "infer_output_best-checkpoint.csv" )
assert os.path.exists(__lowerCamelCase )
# Loading the dataset from local csv or json files.
A__ = load_dataset(args.data_file_extension , data_files={"data": data_files["infer"]} )["data"]
A__ = load_dataset("csv" , data_files={"data": infer_output_file} )["data"]
if accelerator.is_main_process:
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
shutil.copy(__lowerCamelCase , os.path.join(__lowerCamelCase , F"eval_results_iter-{iteration}.json" ) )
if os.path.exists(__lowerCamelCase ):
shutil.copy(__lowerCamelCase , os.path.join(__lowerCamelCase , F"test_results_iter-{iteration}.json" ) )
create_pseudo_labeled_data(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
accelerator.wait_for_everyone()
A__ = os.path.join(__lowerCamelCase , F"train_pseudo.{args.data_file_extension}" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
A__ = eval_result
if best_iteration is None:
A__ = new_iteration
A__ = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
A__ = new_iteration
A__ = new_eval_result
A__ = 0
else:
if new_eval_result == best_eval_result:
A__ = new_iteration
A__ = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
A__ = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("Best iteration: %d" , __lowerCamelCase )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , __lowerCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__lowerCamelCase , F"eval_results_iter-{iteration}.json" ) , os.path.join(__lowerCamelCase , "eval_results_best-iteration.json" ) , )
else:
# Assume that the last iteration is the best
logger.info("Best iteration: %d" , args.max_selftrain_iterations - 1 )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , __lowerCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__lowerCamelCase , F"eval_results_iter-{args.max_selftrain_iterations - 1}.json" ) , os.path.join(__lowerCamelCase , "eval_results_best-iteration.json" ) , )
| 237 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def UpperCamelCase ( __lowerCamelCase : Optional[int] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def UpperCamelCase ( __lowerCamelCase : str ):
class UpperCAmelCase :
def __init__(self : Optional[int] , snake_case__ : str ) -> Any:
'''simple docstring'''
snake_case : List[str] = metric_id
class UpperCAmelCase :
A__ : List[str] = [MetricMock(A_ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def _SCREAMING_SNAKE_CASE (self : int ) -> List[str]:
'''simple docstring'''
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def UpperCamelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Any , __lowerCamelCase : int , __lowerCamelCase : Any ):
if "tmp_path" in args:
snake_case : str = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(__lowerCamelCase , match="https://huggingface.co/docs/evaluate" ):
func(*__lowerCamelCase )
| 59 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ["FNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ["FNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
"FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FNetForMaskedLM",
"FNetForMultipleChoice",
"FNetForNextSentencePrediction",
"FNetForPreTraining",
"FNetForQuestionAnswering",
"FNetForSequenceClassification",
"FNetForTokenClassification",
"FNetLayer",
"FNetModel",
"FNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 357 |
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''' )
snake_case_ : List[Any] = 0
snake_case_ : Tuple = str(_UpperCamelCase )
while len(_UpperCamelCase ) != 1:
snake_case_ : Tuple = [int(_UpperCamelCase ) for i in num_string]
snake_case_ : Dict = 1
for i in range(0 , len(_UpperCamelCase ) ):
total *= numbers[i]
snake_case_ : str = str(_UpperCamelCase )
steps += 1
return steps
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError('''additive_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''' )
snake_case_ : Any = 0
snake_case_ : Tuple = str(_UpperCamelCase )
while len(_UpperCamelCase ) != 1:
snake_case_ : List[str] = [int(_UpperCamelCase ) for i in num_string]
snake_case_ : Optional[int] = 0
for i in range(0 , len(_UpperCamelCase ) ):
total += numbers[i]
snake_case_ : Tuple = str(_UpperCamelCase )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowercase_ : Union[str, Any] = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self : int ):
"""simple docstring"""
_UpperCAmelCase = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
_UpperCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe.dual_guided(
prompt="first prompt" , image=snake_case__ , text_to_image_strength=0.75 , generator=snake_case__ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(snake_case__ )
_UpperCAmelCase = VersatileDiffusionPipeline.from_pretrained(snake_case__ , torch_dtype=torch.floataa )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
_UpperCAmelCase = generator.manual_seed(0 )
_UpperCAmelCase = pipe.dual_guided(
prompt="first prompt" , image=snake_case__ , text_to_image_strength=0.75 , generator=snake_case__ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def UpperCamelCase ( self : str ):
"""simple docstring"""
_UpperCAmelCase = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
_UpperCAmelCase = "cyberpunk 2077"
_UpperCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe.dual_guided(
prompt=snake_case__ , image=snake_case__ , text_to_image_strength=0.75 , generator=snake_case__ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" , ).images
_UpperCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
_UpperCAmelCase = "A painting of a squirrel eating a burger "
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe.text_to_image(
prompt=snake_case__ , generator=snake_case__ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" ).images
_UpperCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
_UpperCAmelCase = pipe.image_variation(snake_case__ , generator=snake_case__ , output_type="numpy" ).images
_UpperCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 133 |
def __SCREAMING_SNAKE_CASE ( snake_case_ = 1000 ):
'''simple docstring'''
_UpperCAmelCase = 2**power
_UpperCAmelCase = 0
while n:
_UpperCAmelCase , _UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 133 | 1 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , lowercase = None , lowercase = None , lowercase=None , lowercase=None ):
"""simple docstring"""
if not conversation_id:
A_ : str = uuid.uuida()
if past_user_inputs is None:
A_ : int = []
if generated_responses is None:
A_ : int = []
A_ : uuid.UUID = conversation_id
A_ : List[str] = past_user_inputs
A_ : List[str] = generated_responses
A_ : Optional[str] = text
def __eq__( self , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCAmelCase_ ( self , lowercase , lowercase = False ):
"""simple docstring"""
if self.new_user_input:
if overwrite:
logger.warning(
F'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
F'''with: "{text}".''' )
A_ : List[Any] = text
else:
logger.warning(
F'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
F'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
A_ : Any = text
def lowerCAmelCase_ ( self ):
"""simple docstring"""
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
A_ : List[str] = None
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
self.generated_responses.append(lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
"""simple docstring"""
A_ : str = F'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
A_ : Dict = 'user' if is_user else 'bot'
output += F'''{name} >> {text} \n'''
return output
@add_end_docstrings(
__A , R'''
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
''' , )
class UpperCAmelCase ( __A ):
'''simple docstring'''
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
super().__init__(*lowercase , **lowercase )
if self.tokenizer.pad_token_id is None:
A_ : Tuple = self.tokenizer.eos_token
def lowerCAmelCase_ ( self , lowercase=None , lowercase=None , lowercase=None , **lowercase ):
"""simple docstring"""
A_ : Union[str, Any] = {}
A_ : Dict = {}
A_ : List[str] = {}
if min_length_for_response is not None:
A_ : int = min_length_for_response
if minimum_tokens is not None:
A_ : List[str] = minimum_tokens
if "max_length" in generate_kwargs:
A_ : Union[str, Any] = generate_kwargs['max_length']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
A_ : int = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(lowercase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , lowercase , lowercase=0 , **lowercase ):
"""simple docstring"""
A_ : Optional[int] = super().__call__(lowercase , num_workers=lowercase , **lowercase )
if isinstance(lowercase , lowercase ) and len(lowercase ) == 1:
return outputs[0]
return outputs
def lowerCAmelCase_ ( self , lowercase , lowercase=3_2 ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise ValueError('ConversationalPipeline, expects Conversation as inputs' )
if conversation.new_user_input is None:
raise ValueError(
F'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
'Add user inputs with the conversation\'s `add_user_input` method' )
if hasattr(self.tokenizer , '_build_conversation_input_ids' ):
A_ : int = self.tokenizer._build_conversation_input_ids(lowercase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
A_ : int = self._legacy_parse_and_tokenize(lowercase )
if self.framework == "pt":
A_ : int = torch.LongTensor([input_ids] )
elif self.framework == "tf":
A_ : Dict = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCAmelCase_ ( self , lowercase , lowercase=1_0 , **lowercase ):
"""simple docstring"""
A_ : str = generate_kwargs.get('max_length' , self.model.config.max_length )
A_ : Union[str, Any] = model_inputs['input_ids'].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
A_ : List[Any] = max_length - minimum_tokens
A_ : int = model_inputs['input_ids'][:, -trim:]
if "attention_mask" in model_inputs:
A_ : List[str] = model_inputs['attention_mask'][:, -trim:]
A_ : Union[str, Any] = model_inputs.pop('conversation' )
A_ : Union[str, Any] = max_length
A_ : Union[str, Any] = self.model.generate(**lowercase , **lowercase )
if self.model.config.is_encoder_decoder:
A_ : str = 1
else:
A_ : Optional[Any] = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCAmelCase_ ( self , lowercase , lowercase=True ):
"""simple docstring"""
A_ : Tuple = model_outputs['output_ids']
A_ : Optional[int] = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase , )
A_ : List[Any] = model_outputs['conversation']
conversation.mark_processed()
conversation.append_response(lowercase )
return conversation
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
A_ : int = self.tokenizer.eos_token_id
A_ : str = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(lowercase , add_special_tokens=lowercase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(lowercase , add_special_tokens=lowercase ) )
if len(lowercase ) > self.tokenizer.model_max_length:
A_ : str = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 353 | import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""tensor(bool)""": np.bool_,
"""tensor(int8)""": np.inta,
"""tensor(uint8)""": np.uinta,
"""tensor(int16)""": np.intaa,
"""tensor(uint16)""": np.uintaa,
"""tensor(int32)""": np.intaa,
"""tensor(uint32)""": np.uintaa,
"""tensor(int64)""": np.intaa,
"""tensor(uint64)""": np.uintaa,
"""tensor(float16)""": np.floataa,
"""tensor(float)""": np.floataa,
"""tensor(double)""": np.floataa,
}
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , lowercase=None , **lowercase ):
"""simple docstring"""
logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
A_ : List[Any] = model
A_ : Dict = kwargs.get('model_save_dir' , lowercase )
A_ : List[str] = kwargs.get('latest_model_name' , lowercase )
def __call__( self , **lowercase ):
"""simple docstring"""
A_ : str = {k: np.array(lowercase ) for k, v in kwargs.items()}
return self.model.run(lowercase , lowercase )
@staticmethod
def lowerCAmelCase_ ( lowercase , lowercase=None , lowercase=None ):
"""simple docstring"""
if provider is None:
logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
A_ : List[Any] = 'CPUExecutionProvider'
return ort.InferenceSession(lowercase , providers=[provider] , sess_options=lowercase )
def lowerCAmelCase_ ( self , lowercase , lowercase = None , **lowercase ):
"""simple docstring"""
A_ : str = file_name if file_name is not None else ONNX_WEIGHTS_NAME
A_ : Optional[int] = self.model_save_dir.joinpath(self.latest_model_name )
A_ : int = Path(lowercase ).joinpath(lowercase )
try:
shutil.copyfile(lowercase , lowercase )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
A_ : Optional[Any] = self.model_save_dir.joinpath(lowercase )
if src_path.exists():
A_ : int = Path(lowercase ).joinpath(lowercase )
try:
shutil.copyfile(lowercase , lowercase )
except shutil.SameFileError:
pass
def lowerCAmelCase_ ( self , lowercase , **lowercase , ):
"""simple docstring"""
if os.path.isfile(lowercase ):
logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''' )
return
os.makedirs(lowercase , exist_ok=lowercase )
# saving model weights/files
self._save_pretrained(lowercase , **lowercase )
@classmethod
def lowerCAmelCase_ ( cls , lowercase , lowercase = None , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , lowercase = None , **lowercase , ):
"""simple docstring"""
A_ : Any = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(lowercase ):
A_ : Optional[int] = OnnxRuntimeModel.load_model(
os.path.join(lowercase , lowercase ) , provider=lowercase , sess_options=lowercase )
A_ : Dict = Path(lowercase )
# load model from hub
else:
# download model
A_ : List[str] = hf_hub_download(
repo_id=lowercase , filename=lowercase , use_auth_token=lowercase , revision=lowercase , cache_dir=lowercase , force_download=lowercase , )
A_ : int = Path(lowercase ).parent
A_ : Optional[Any] = Path(lowercase ).name
A_ : Any = OnnxRuntimeModel.load_model(lowercase , provider=lowercase , sess_options=lowercase )
return cls(model=lowercase , **lowercase )
@classmethod
def lowerCAmelCase_ ( cls , lowercase , lowercase = True , lowercase = None , lowercase = None , **lowercase , ):
"""simple docstring"""
A_ : List[Any] = None
if len(str(lowercase ).split('@' ) ) == 2:
A_ , A_ : int = model_id.split('@' )
return cls._from_pretrained(
model_id=lowercase , revision=lowercase , cache_dir=lowercase , force_download=lowercase , use_auth_token=lowercase , **lowercase , )
| 192 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def A_ ( _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowercase :
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : int):
pass
def _SCREAMING_SNAKE_CASE ( self : str):
pass
def _SCREAMING_SNAKE_CASE ( self : Dict):
pass
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict=None , **lowerCAmelCase__ : Tuple):
SCREAMING_SNAKE_CASE_: int = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = TFVisionTextDualEncoderModel(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim))
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim))
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : str , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any=None , **lowerCAmelCase__ : List[str]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: int = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim))
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim))
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int]=None , **lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Dict = {"vision_model": vision_model, "text_model": text_model}
SCREAMING_SNAKE_CASE_: Dict = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim))
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim))
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[str, Any]=None , **lowerCAmelCase__ : Dict):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = after_output[0].numpy()
SCREAMING_SNAKE_CASE_: Union[str, Any] = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(lowerCAmelCase__ , 1E-5)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[Any]=None , **lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Tuple = model(
input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , output_attentions=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , vision_config.num_hidden_layers)
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE_: List[str] = to_atuple(vision_model.config.image_size)
SCREAMING_SNAKE_CASE_: Dict = to_atuple(vision_model.config.patch_size)
SCREAMING_SNAKE_CASE_: int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
SCREAMING_SNAKE_CASE_: str = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len))
SCREAMING_SNAKE_CASE_: Dict = output.text_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , text_config.num_hidden_layers)
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : float):
SCREAMING_SNAKE_CASE_: int = np.abs((a - b)).max()
self.assertLessEqual(lowerCAmelCase__ , lowerCAmelCase__ , F"Difference between torch and flax is {diff} (>= {tol}).")
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : str):
SCREAMING_SNAKE_CASE_: Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : str):
SCREAMING_SNAKE_CASE_: Any = self.prepare_config_and_inputs()
self.check_save_load(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_: Any = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowerCAmelCase__)
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: int = self.get_pretrained_model_and_inputs()
SCREAMING_SNAKE_CASE_: Dict = model_a(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Tuple = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Any = model_a(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = after_outputs[0].numpy()
SCREAMING_SNAKE_CASE_: Optional[Any] = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(lowerCAmelCase__ , 1E-5)
@require_tf
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: Tuple = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert")
SCREAMING_SNAKE_CASE_: List[str] = 13
SCREAMING_SNAKE_CASE_: int = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
])
SCREAMING_SNAKE_CASE_: List[str] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size)
SCREAMING_SNAKE_CASE_: Union[str, Any] = random_attention_mask([batch_size, 4])
SCREAMING_SNAKE_CASE_: Dict = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any]):
SCREAMING_SNAKE_CASE_: Any = TFViTModel(lowerCAmelCase__ , name="vision_model")
SCREAMING_SNAKE_CASE_: Optional[Any] = TFBertModel(lowerCAmelCase__ , name="text_model")
return vision_model, text_model
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: Union[str, Any] = TFViTModelTester(self)
SCREAMING_SNAKE_CASE_: Union[str, Any] = TFBertModelTester(self)
SCREAMING_SNAKE_CASE_: str = vit_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_: List[Any] = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] = vision_config_and_inputs
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
): List[Any] = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Tuple):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
SCREAMING_SNAKE_CASE_: Any = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta")
SCREAMING_SNAKE_CASE_: Optional[int] = 13
SCREAMING_SNAKE_CASE_: Optional[int] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
])
SCREAMING_SNAKE_CASE_: Union[str, Any] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size)
SCREAMING_SNAKE_CASE_: Dict = random_attention_mask([batch_size, 4])
SCREAMING_SNAKE_CASE_: Optional[int] = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[Any]=None , **lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = model(
input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , output_attentions=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = output.vision_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , vision_config.num_hidden_layers)
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
SCREAMING_SNAKE_CASE_: Dict = to_atuple(vision_model.config.image_size)
SCREAMING_SNAKE_CASE_: int = to_atuple(vision_model.config.patch_size)
SCREAMING_SNAKE_CASE_: List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
SCREAMING_SNAKE_CASE_: Union[str, Any] = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len))
SCREAMING_SNAKE_CASE_: List[Any] = output.text_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , text_config.num_hidden_layers)
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase__ : int , lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_: Optional[int] = TFDeiTModel(lowerCAmelCase__ , name="vision_model")
SCREAMING_SNAKE_CASE_: Optional[int] = TFRobertaModel(lowerCAmelCase__ , name="text_model")
return vision_model, text_model
def _SCREAMING_SNAKE_CASE ( self : Any):
SCREAMING_SNAKE_CASE_: Dict = TFDeiTModelTester(self)
SCREAMING_SNAKE_CASE_: List[str] = TFRobertaModelTester(self)
SCREAMING_SNAKE_CASE_: Optional[Any] = vit_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_: Union[str, Any] = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str = vision_config_and_inputs
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
): Tuple = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Tuple):
SCREAMING_SNAKE_CASE_: str = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert")
SCREAMING_SNAKE_CASE_: List[str] = 13
SCREAMING_SNAKE_CASE_: Tuple = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
])
SCREAMING_SNAKE_CASE_: Tuple = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size)
SCREAMING_SNAKE_CASE_: Dict = random_attention_mask([batch_size, 4])
SCREAMING_SNAKE_CASE_: Any = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: List[str] = TFCLIPVisionModel(lowerCAmelCase__ , name="vision_model")
SCREAMING_SNAKE_CASE_: List[str] = TFBertModel(lowerCAmelCase__ , name="text_model")
return vision_model, text_model
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: Optional[int] = TFCLIPVisionModelTester(self)
SCREAMING_SNAKE_CASE_: Any = TFBertModelTester(self)
SCREAMING_SNAKE_CASE_: Optional[int] = clip_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_: str = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = vision_config_and_inputs
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
): List[Any] = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
SCREAMING_SNAKE_CASE_: Optional[int] = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")
SCREAMING_SNAKE_CASE_: Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
SCREAMING_SNAKE_CASE_: int = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=lowerCAmelCase__ , padding=lowerCAmelCase__ , return_tensors="np")
SCREAMING_SNAKE_CASE_: List[str] = model(**lowerCAmelCase__)
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
SCREAMING_SNAKE_CASE_: Optional[Any] = np.array([[1.228_4727, 0.310_4122]])
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , lowerCAmelCase__ , atol=1E-3))
| 13 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'ClapFeatureExtractor'
SCREAMING_SNAKE_CASE__ = ('RobertaTokenizer', 'RobertaTokenizerFast')
def __init__( self , _lowerCamelCase , _lowerCamelCase ):
super().__init__(_lowerCamelCase , _lowerCamelCase )
def __call__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase ):
a :Dict = kwargs.pop('''sampling_rate''' , _lowerCamelCase )
if text is None and audios is None:
raise ValueError('''You have to specify either text or audios. Both cannot be none.''' )
if text is not None:
a :Optional[int] = self.tokenizer(_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if audios is not None:
a :Tuple = self.feature_extractor(
_lowerCamelCase , sampling_rate=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if text is not None and audios is not None:
a :Union[str, Any] = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCamelCase ) , tensor_type=_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , *_lowerCamelCase , **_lowerCamelCase ):
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , *_lowerCamelCase , **_lowerCamelCase ):
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
a :List[Any] = self.tokenizer.model_input_names
a :str = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 94 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__lowercase = logging.get_logger(__name__)
# General docstring
__lowercase = '''MobileNetV1Config'''
# Base docstring
__lowercase = '''google/mobilenet_v1_1.0_224'''
__lowercase = [1, 1024, 7, 7]
# Image classification docstring
__lowercase = '''google/mobilenet_v1_1.0_224'''
__lowercase = '''tabby, tabby cat'''
__lowercase = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__UpperCamelCase :Tuple = {}
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Dict = model.mobilenet_va
else:
__UpperCamelCase :str = model
__UpperCamelCase :int = '''MobilenetV1/Conv2d_0/'''
__UpperCamelCase :str = backbone.conv_stem.convolution.weight
__UpperCamelCase :int = backbone.conv_stem.normalization.bias
__UpperCamelCase :Union[str, Any] = backbone.conv_stem.normalization.weight
__UpperCamelCase :Optional[int] = backbone.conv_stem.normalization.running_mean
__UpperCamelCase :Optional[int] = backbone.conv_stem.normalization.running_var
for i in range(13 ):
__UpperCamelCase :Optional[Any] = i + 1
__UpperCamelCase :Optional[int] = i * 2
__UpperCamelCase :List[Any] = backbone.layer[pt_index]
__UpperCamelCase :Tuple = f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
__UpperCamelCase :Any = pointer.convolution.weight
__UpperCamelCase :Dict = pointer.normalization.bias
__UpperCamelCase :List[str] = pointer.normalization.weight
__UpperCamelCase :Any = pointer.normalization.running_mean
__UpperCamelCase :List[str] = pointer.normalization.running_var
__UpperCamelCase :Union[str, Any] = backbone.layer[pt_index + 1]
__UpperCamelCase :List[str] = f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
__UpperCamelCase :Optional[Any] = pointer.convolution.weight
__UpperCamelCase :Dict = pointer.normalization.bias
__UpperCamelCase :int = pointer.normalization.weight
__UpperCamelCase :Optional[int] = pointer.normalization.running_mean
__UpperCamelCase :Optional[int] = pointer.normalization.running_var
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Any = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
__UpperCamelCase :Union[str, Any] = model.classifier.weight
__UpperCamelCase :int = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
__UpperCamelCase :Any = tf.train.list_variables(SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = {}
for name, shape in init_vars:
logger.info(f"""Loading TF weight {name} with shape {shape}""" )
__UpperCamelCase :str = tf.train.load_variable(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = array
# Build TF to PyTorch weights loading map
__UpperCamelCase :Optional[Any] = _build_tf_to_pytorch_map(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for name, pointer in tf_to_pt_map.items():
logger.info(f"""Importing {name}""" )
if name not in tf_weights:
logger.info(f"""{name} not in tf pre-trained weights, skipping""" )
continue
__UpperCamelCase :Optional[Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
__UpperCamelCase :Optional[int] = np.transpose(SCREAMING_SNAKE_CASE , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
__UpperCamelCase :Tuple = array.squeeze().transpose()
else:
__UpperCamelCase :Union[str, Any] = np.transpose(SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(f"""Initialize PyTorch weight {name} {array.shape}""" )
__UpperCamelCase :Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE )
tf_weights.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '''/RMSProp''' , SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '''/RMSProp_1''' , SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , SCREAMING_SNAKE_CASE )
logger.info(f"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}""" )
return model
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase :str = features.shape[-2:]
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = conv_layer.stride
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = conv_layer.kernel_size
if in_height % stride_height == 0:
__UpperCamelCase :Optional[int] = max(kernel_height - stride_height , 0 )
else:
__UpperCamelCase :List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__UpperCamelCase :List[str] = max(kernel_width - stride_width , 0 )
else:
__UpperCamelCase :Tuple = max(kernel_width - (in_width % stride_width) , 0 )
__UpperCamelCase :Any = pad_along_width // 2
__UpperCamelCase :Tuple = pad_along_width - pad_left
__UpperCamelCase :Union[str, Any] = pad_along_height // 2
__UpperCamelCase :str = pad_along_height - pad_top
__UpperCamelCase :Optional[Any] = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''constant''' , 0.0 )
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase = 1 , __lowercase = 1 , __lowercase = False , __lowercase = True , __lowercase = True , ) -> None:
super().__init__()
__UpperCamelCase :str = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""")
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""")
__UpperCamelCase :Any = 0 if config.tf_padding else int((kernel_size - 1) / 2)
__UpperCamelCase :List[Any] = nn.Convad(
in_channels=__lowercase , out_channels=__lowercase , kernel_size=__lowercase , stride=__lowercase , padding=__lowercase , groups=__lowercase , bias=__lowercase , padding_mode='''zeros''' , )
if use_normalization:
__UpperCamelCase :str = nn.BatchNormad(
num_features=__lowercase , eps=config.layer_norm_eps , momentum=0.99_97 , affine=__lowercase , track_running_stats=__lowercase , )
else:
__UpperCamelCase :Tuple = None
if use_activation:
if isinstance(__lowercase , __lowercase):
__UpperCamelCase :Union[str, Any] = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __lowercase):
__UpperCamelCase :Dict = ACTaFN[config.hidden_act]
else:
__UpperCamelCase :List[Any] = config.hidden_act
else:
__UpperCamelCase :Optional[Any] = None
def UpperCamelCase__ ( self , __lowercase) -> torch.Tensor:
if self.config.tf_padding:
__UpperCamelCase :Any = apply_tf_padding(__lowercase , self.convolution)
__UpperCamelCase :str = self.convolution(__lowercase)
if self.normalization is not None:
__UpperCamelCase :Any = self.normalization(__lowercase)
if self.activation is not None:
__UpperCamelCase :List[str] = self.activation(__lowercase)
return features
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : List[str] = MobileNetVaConfig
a__ : Dict = load_tf_weights_in_mobilenet_va
a__ : Tuple = """mobilenet_v1"""
a__ : Optional[Any] = """pixel_values"""
a__ : int = False
def UpperCamelCase__ ( self , __lowercase) -> None:
if isinstance(__lowercase , (nn.Linear, nn.Convad)):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__lowercase , nn.BatchNormad):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
__lowercase = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__lowercase = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.""" , UpperCAmelCase_ , )
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase = True) -> Optional[Any]:
super().__init__(__lowercase)
__UpperCamelCase :List[str] = config
__UpperCamelCase :Any = 32
__UpperCamelCase :List[str] = max(int(depth * config.depth_multiplier) , config.min_depth)
__UpperCamelCase :Union[str, Any] = MobileNetVaConvLayer(
__lowercase , in_channels=config.num_channels , out_channels=__lowercase , kernel_size=3 , stride=2 , )
__UpperCamelCase :str = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__UpperCamelCase :Any = nn.ModuleList()
for i in range(13):
__UpperCamelCase :str = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__UpperCamelCase :Tuple = max(int(depth * config.depth_multiplier) , config.min_depth)
self.layer.append(
MobileNetVaConvLayer(
__lowercase , in_channels=__lowercase , out_channels=__lowercase , kernel_size=3 , stride=strides[i] , groups=__lowercase , ))
self.layer.append(
MobileNetVaConvLayer(
__lowercase , in_channels=__lowercase , out_channels=__lowercase , kernel_size=1 , ))
__UpperCamelCase :str = nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def UpperCamelCase__ ( self , __lowercase) -> Union[str, Any]:
raise NotImplementedError
@add_start_docstrings_to_model_forward(__lowercase)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCamelCase__ ( self , __lowercase = None , __lowercase = None , __lowercase = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
__UpperCamelCase :Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCamelCase :str = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''')
__UpperCamelCase :int = self.conv_stem(__lowercase)
__UpperCamelCase :List[str] = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
__UpperCamelCase :Optional[Any] = layer_module(__lowercase)
if output_hidden_states:
__UpperCamelCase :int = all_hidden_states + (hidden_states,)
__UpperCamelCase :Any = hidden_states
if self.pooler is not None:
__UpperCamelCase :str = torch.flatten(self.pooler(__lowercase) , start_dim=1)
else:
__UpperCamelCase :Tuple = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__lowercase , pooler_output=__lowercase , hidden_states=__lowercase , )
@add_start_docstrings(
"""
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , UpperCAmelCase_ , )
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase) -> None:
super().__init__(__lowercase)
__UpperCamelCase :int = config.num_labels
__UpperCamelCase :Optional[int] = MobileNetVaModel(__lowercase)
__UpperCamelCase :Optional[Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__UpperCamelCase :str = nn.Dropout(config.classifier_dropout_prob , inplace=__lowercase)
__UpperCamelCase :Dict = nn.Linear(__lowercase , config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__lowercase)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCamelCase__ ( self , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
__UpperCamelCase :List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase :Tuple = self.mobilenet_va(__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase)
__UpperCamelCase :List[str] = outputs.pooler_output if return_dict else outputs[1]
__UpperCamelCase :Union[str, Any] = self.classifier(self.dropout(__lowercase))
__UpperCamelCase :int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__UpperCamelCase :Tuple = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__UpperCamelCase :Union[str, Any] = '''single_label_classification'''
else:
__UpperCamelCase :Optional[Any] = '''multi_label_classification'''
if self.config.problem_type == "regression":
__UpperCamelCase :Any = MSELoss()
if self.num_labels == 1:
__UpperCamelCase :List[str] = loss_fct(logits.squeeze() , labels.squeeze())
else:
__UpperCamelCase :Dict = loss_fct(__lowercase , __lowercase)
elif self.config.problem_type == "single_label_classification":
__UpperCamelCase :Optional[int] = CrossEntropyLoss()
__UpperCamelCase :str = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
__UpperCamelCase :Dict = BCEWithLogitsLoss()
__UpperCamelCase :List[str] = loss_fct(__lowercase , __lowercase)
if not return_dict:
__UpperCamelCase :Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__lowercase , logits=__lowercase , hidden_states=outputs.hidden_states , )
| 105 | import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
__lowercase = logging.get_logger(__name__)
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if "xprophetnet" in prophetnet_checkpoint_path:
__UpperCamelCase :Dict = XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :int = XLMProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Dict = ['''key_proj''', '''value_proj''', '''query_proj''']
__UpperCamelCase :Optional[Any] = {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
__UpperCamelCase :Tuple = key.split('''.''' )
if attributes[0] == "lm_head":
__UpperCamelCase :Union[str, Any] = prophet
__UpperCamelCase :Any = prophet_old
else:
__UpperCamelCase :Any = prophet.prophetnet
__UpperCamelCase :int = prophet_old.model
__UpperCamelCase :Optional[Any] = False
for attribute in attributes:
if attribute in mapping:
__UpperCamelCase :str = mapping[attribute]
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
__UpperCamelCase :Optional[int] = attribute
elif hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :List[Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
__UpperCamelCase :Tuple = old_model.weight
logger.info(f"""{attribute} is initialized.""" )
__UpperCamelCase :Union[str, Any] = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
__UpperCamelCase :Union[str, Any] = old_model.bias
logger.info(f"""{attribute} is initialized""" )
__UpperCamelCase :List[Any] = True
break
elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE , '''in_proj_weight''' ):
__UpperCamelCase :str = old_model.in_proj_weight.shape[0] // 3
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
__UpperCamelCase :List[Any] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
__UpperCamelCase :Optional[int] = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.embed_positions.weight[:512, :] )
__UpperCamelCase :List[Any] = True
break
if attribute.isdigit():
__UpperCamelCase :List[Any] = model[int(SCREAMING_SNAKE_CASE )]
__UpperCamelCase :Optional[int] = old_model[int(SCREAMING_SNAKE_CASE )]
else:
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if old_attribute == "":
__UpperCamelCase :Any = old_model
else:
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError(f"""{old_model} does not have {old_attribute}""" )
__UpperCamelCase :Any = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if not is_key_init:
raise ValueError(f"""{key} was not correctly initialized!""" )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 105 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list ):
if not postfix_notation:
return 0
__a : Optional[Any] = {'+', '-', '*', '/'}
__a : list[Any] = []
for token in postfix_notation:
if token in operations:
__a , __a : List[Any] = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(_SCREAMING_SNAKE_CASE ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( lowerCAmelCase__ :list[int] ) -> int:
'''simple docstring'''
if not nums:
return 0
lowercase = nums[0]
lowercase = 0
for num in nums[1:]:
lowercase , lowercase = (
max_excluding + num,
max(lowerCAmelCase__ , lowerCAmelCase__ ),
)
return max(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 197 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A = True
except ImportError:
__A = False
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCamelCase_ ( UpperCamelCase__ : Namespace ) -> List[str]:
"""simple docstring"""
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@staticmethod
def lowercase_ ( lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = parser.add_parser('add-new-model' )
add_new_model_parser.add_argument('--testing' , action='store_true' , help='If in testing mode.' )
add_new_model_parser.add_argument('--testing_file' , type=lowerCamelCase__ , help='Configuration file on which to run.' )
add_new_model_parser.add_argument(
'--path' , type=lowerCamelCase__ , help='Path to cookiecutter. Should only be used for testing purposes.' )
add_new_model_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , *lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = testing
__lowerCamelCase = testing_file
__lowerCamelCase = path
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
warnings.warn(
'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
'checks, you should use `transformers-cli add-new-model-like` instead.' )
if not _has_cookiecutter:
raise ImportError(
'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n' )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
__lowerCamelCase = [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
if len(lowerCamelCase__ ) > 0:
raise ValueError(
'Several directories starting with `cookiecutter-template-` in current working directory. '
'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
'change your working directory.' )
__lowerCamelCase = (
Path(lowerCamelCase__ ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
__lowerCamelCase = path_to_transformer_root / 'templates' / 'adding_a_new_model'
# Execute cookiecutter
if not self._testing:
cookiecutter(str(lowerCamelCase__ ) )
else:
with open(self._testing_file , 'r' ) as configuration_file:
__lowerCamelCase = json.load(lowerCamelCase__ )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=lowerCamelCase__ , extra_context=lowerCamelCase__ , )
__lowerCamelCase = [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]
# Retrieve configuration
with open(directory + '/configuration.json' , 'r' ) as configuration_file:
__lowerCamelCase = json.load(lowerCamelCase__ )
__lowerCamelCase = configuration['lowercase_modelname']
__lowerCamelCase = configuration['generate_tensorflow_pytorch_and_flax']
os.remove(f"""{directory}/configuration.json""" )
__lowerCamelCase = 'PyTorch' in generate_tensorflow_pytorch_and_flax
__lowerCamelCase = 'TensorFlow' in generate_tensorflow_pytorch_and_flax
__lowerCamelCase = 'Flax' in generate_tensorflow_pytorch_and_flax
__lowerCamelCase = f"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
os.makedirs(lowerCamelCase__ , exist_ok=lowerCamelCase__ )
os.makedirs(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}""" , exist_ok=lowerCamelCase__ )
# Tests require submodules as they have parent imports
with open(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py""" , 'w' ):
pass
shutil.move(
f"""{directory}/__init__.py""" , f"""{model_dir}/__init__.py""" , )
shutil.move(
f"""{directory}/configuration_{lowercase_model_name}.py""" , f"""{model_dir}/configuration_{lowercase_model_name}.py""" , )
def remove_copy_lines(lowerCamelCase__ ):
with open(lowerCamelCase__ , 'r' ) as f:
__lowerCamelCase = f.readlines()
with open(lowerCamelCase__ , 'w' ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(lowerCamelCase__ )
if output_pytorch:
if not self._testing:
remove_copy_lines(f"""{directory}/modeling_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/modeling_{lowercase_model_name}.py""" , f"""{model_dir}/modeling_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/test_modeling_{lowercase_model_name}.py""" , f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py""" , )
else:
os.remove(f"""{directory}/modeling_{lowercase_model_name}.py""" )
os.remove(f"""{directory}/test_modeling_{lowercase_model_name}.py""" )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"""{directory}/modeling_tf_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/modeling_tf_{lowercase_model_name}.py""" , f"""{model_dir}/modeling_tf_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/test_modeling_tf_{lowercase_model_name}.py""" , f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py""" , )
else:
os.remove(f"""{directory}/modeling_tf_{lowercase_model_name}.py""" )
os.remove(f"""{directory}/test_modeling_tf_{lowercase_model_name}.py""" )
if output_flax:
if not self._testing:
remove_copy_lines(f"""{directory}/modeling_flax_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/modeling_flax_{lowercase_model_name}.py""" , f"""{model_dir}/modeling_flax_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/test_modeling_flax_{lowercase_model_name}.py""" , f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py""" , )
else:
os.remove(f"""{directory}/modeling_flax_{lowercase_model_name}.py""" )
os.remove(f"""{directory}/test_modeling_flax_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/{lowercase_model_name}.md""" , f"""{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md""" , )
shutil.move(
f"""{directory}/tokenization_{lowercase_model_name}.py""" , f"""{model_dir}/tokenization_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/tokenization_fast_{lowercase_model_name}.py""" , f"""{model_dir}/tokenization_{lowercase_model_name}_fast.py""" , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
# Create temp file
__lowerCamelCase , __lowerCamelCase = mkstemp()
__lowerCamelCase = False
with fdopen(lowerCamelCase__ , 'w' ) as new_file:
with open(lowerCamelCase__ ) as old_file:
for line in old_file:
new_file.write(lowerCamelCase__ )
if line_to_copy_below in line:
__lowerCamelCase = True
for line_to_copy in lines_to_copy:
new_file.write(lowerCamelCase__ )
if not line_found:
raise ValueError(f"""Line {line_to_copy_below} was not found in file.""" )
# Copy the file permissions from the old file to the new file
copymode(lowerCamelCase__ , lowerCamelCase__ )
# Remove original file
remove(lowerCamelCase__ )
# Move new file
move(lowerCamelCase__ , lowerCamelCase__ )
def skip_units(lowerCamelCase__ ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(lowerCamelCase__ ):
with open(lowerCamelCase__ ) as datafile:
__lowerCamelCase = []
__lowerCamelCase = False
__lowerCamelCase = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
__lowerCamelCase = line.split('"' )[1]
__lowerCamelCase = skip_units(lowerCamelCase__ )
elif "# Below: " in line and "##" not in line:
__lowerCamelCase = line.split('"' )[1]
__lowerCamelCase = skip_units(lowerCamelCase__ )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = []
elif "# Replace with" in line and "##" not in line:
__lowerCamelCase = []
elif "##" not in line:
lines_to_copy.append(lowerCamelCase__ )
remove(lowerCamelCase__ )
replace_in_files(f"""{directory}/to_replace_{lowercase_model_name}.py""" )
os.rmdir(lowerCamelCase__ )
| 364 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = [False] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ )
def dfs(UpperCamelCase__ : int , UpperCamelCase__ : int ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCamelCase__ , 1 - c )
for i in range(len(UpperCamelCase__ ) ):
if not visited[i]:
dfs(UpperCamelCase__ , 0 )
for i in range(len(UpperCamelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__A = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
UpperCAmelCase__ : Union[str, Any] = TypeVar('T')
class UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
__UpperCamelCase : deque[T] # Cache store of keys
__UpperCamelCase : set[T] # References of the keys in cache
__UpperCamelCase : int = 10 # Maximum capacity of cache
def __init__( self : List[str] , lowerCAmelCase_ : int ):
"""simple docstring"""
_A: Tuple = deque()
_A: List[Any] = set()
if not n:
_A: str = sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
_A: Dict = n
def __magic_name__ ( self : Optional[int] , lowerCAmelCase_ : T ):
"""simple docstring"""
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
_A: Optional[Any] = self.dq_store.pop()
self.key_reference.remove(lowerCAmelCase_ )
else:
self.dq_store.remove(lowerCAmelCase_ )
self.dq_store.appendleft(lowerCAmelCase_ )
self.key_reference.add(lowerCAmelCase_ )
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
for k in self.dq_store:
print(lowerCAmelCase_ )
def __repr__( self : Dict ):
"""simple docstring"""
return F"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase__ : LRUCache[str | int] = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 121 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Any = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : List[Any] = '''informer'''
__UpperCamelCase : List[str] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[Any] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : str = "student_t" , lowerCAmelCase_ : str = "nll" , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : List[int] = None , lowerCAmelCase_ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : int = 6_4 , lowerCAmelCase_ : int = 3_2 , lowerCAmelCase_ : int = 3_2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : str = "gelu" , lowerCAmelCase_ : float = 0.05 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : int = 1_0_0 , lowerCAmelCase_ : float = 0.02 , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : str = "prob" , lowerCAmelCase_ : int = 5 , lowerCAmelCase_ : bool = True , **lowerCAmelCase_ : str , ):
"""simple docstring"""
# time series specific configuration
_A: Optional[Any] = prediction_length
_A: Optional[Any] = context_length or prediction_length
_A: Dict = distribution_output
_A: List[str] = loss
_A: int = input_size
_A: List[str] = num_time_features
_A: Optional[Any] = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
_A: str = scaling
_A: Optional[Any] = num_dynamic_real_features
_A: List[Any] = num_static_real_features
_A: Tuple = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
_A: str = cardinality
else:
_A: Union[str, Any] = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
_A: List[str] = embedding_dimension
else:
_A: Union[str, Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_A: int = num_parallel_samples
# Transformer architecture configuration
_A: Union[str, Any] = input_size * len(self.lags_sequence ) + self._number_of_features
_A: Union[str, Any] = d_model
_A: Optional[Any] = encoder_attention_heads
_A: Optional[Any] = decoder_attention_heads
_A: Optional[Any] = encoder_ffn_dim
_A: Union[str, Any] = decoder_ffn_dim
_A: Any = encoder_layers
_A: str = decoder_layers
_A: List[str] = dropout
_A: Any = attention_dropout
_A: Optional[int] = activation_dropout
_A: List[Any] = encoder_layerdrop
_A: str = decoder_layerdrop
_A: int = activation_function
_A: Tuple = init_std
_A: Union[str, Any] = use_cache
# Informer
_A: Union[str, Any] = attention_type
_A: str = sampling_factor
_A: List[str] = distil
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 121 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class A__ :
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str]=0.2 , lowerCAmelCase__ : str=0.2 ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = bp_numa
_UpperCAmelCase : Any = bp_numa
_UpperCAmelCase : Union[str, Any] = bp_numa
_UpperCAmelCase : str = conva_get[:2]
_UpperCAmelCase : Union[str, Any] = conva_get[2]
_UpperCAmelCase : Dict = size_pa
_UpperCAmelCase : List[Any] = rate_w
_UpperCAmelCase : Dict = rate_t
_UpperCAmelCase : List[Any] = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
_UpperCAmelCase : int = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
_UpperCAmelCase : Optional[int] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
_UpperCAmelCase : Any = -2 * np.random.rand(self.conva[1] ) + 1
_UpperCAmelCase : Optional[Any] = -2 * np.random.rand(self.num_bpa ) + 1
_UpperCAmelCase : Optional[int] = -2 * np.random.rand(self.num_bpa ) + 1
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : str ) -> int:
"""simple docstring"""
_UpperCAmelCase : Tuple = {
"num_bp1": self.num_bpa,
"num_bp2": self.num_bpa,
"num_bp3": self.num_bpa,
"conv1": self.conva,
"step_conv1": self.step_conva,
"size_pooling1": self.size_poolinga,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conva,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conva,
"thre_bp2": self.thre_bpa,
"thre_bp3": self.thre_bpa,
}
with open(lowerCAmelCase__ , "wb" ) as f:
pickle.dump(lowerCAmelCase__ , lowerCAmelCase__ )
print(F"""Model saved： {save_path}""" )
@classmethod
def _lowerCAmelCase ( cls : List[str] , lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
with open(lowerCAmelCase__ , "rb" ) as f:
_UpperCAmelCase : int = pickle.load(lowerCAmelCase__ ) # noqa: S301
_UpperCAmelCase : int = model_dic.get("conv1" )
conv_get.append(model_dic.get("step_conv1" ) )
_UpperCAmelCase : List[str] = model_dic.get("size_pooling1" )
_UpperCAmelCase : List[Any] = model_dic.get("num_bp1" )
_UpperCAmelCase : Optional[Any] = model_dic.get("num_bp2" )
_UpperCAmelCase : List[str] = model_dic.get("num_bp3" )
_UpperCAmelCase : Tuple = model_dic.get("rate_weight" )
_UpperCAmelCase : str = model_dic.get("rate_thre" )
# create model instance
_UpperCAmelCase : Tuple = CNN(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# modify model parameter
_UpperCAmelCase : List[Any] = model_dic.get("w_conv1" )
_UpperCAmelCase : int = model_dic.get("wkj" )
_UpperCAmelCase : Dict = model_dic.get("vji" )
_UpperCAmelCase : int = model_dic.get("thre_conv1" )
_UpperCAmelCase : Any = model_dic.get("thre_bp2" )
_UpperCAmelCase : Optional[int] = model_dic.get("thre_bp3" )
return conv_ins
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : List[str] ) -> Optional[int]:
"""simple docstring"""
return 1 / (1 + np.exp(-1 * x ))
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : List[Any] ) -> int:
"""simple docstring"""
return round(lowerCAmelCase__ , 3 )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Any = convs[0]
_UpperCAmelCase : str = convs[1]
_UpperCAmelCase : Optional[int] = np.shape(lowerCAmelCase__ )[0]
# get the data slice of original image data, data_focus
_UpperCAmelCase : Tuple = []
for i_focus in range(0 , size_data - size_conv + 1 , lowerCAmelCase__ ):
for j_focus in range(0 , size_data - size_conv + 1 , lowerCAmelCase__ ):
_UpperCAmelCase : Tuple = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(lowerCAmelCase__ )
# calculate the feature map of every single kernel, and saved as list of matrix
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(lowerCAmelCase__ ):
_UpperCAmelCase : List[Any] = []
for i_focus in range(len(lowerCAmelCase__ ) ):
_UpperCAmelCase : Tuple = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(lowerCAmelCase__ ) )
_UpperCAmelCase : List[str] = np.asmatrix(lowerCAmelCase__ ).reshape(
lowerCAmelCase__ , lowerCAmelCase__ )
data_featuremap.append(lowerCAmelCase__ )
# expanding the data slice to One dimenssion
_UpperCAmelCase : Optional[int] = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(lowerCAmelCase__ ) )
_UpperCAmelCase : Tuple = np.asarray(lowerCAmelCase__ )
return focus_list, data_featuremap
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple="average_pool" ) -> str:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = len(featuremaps[0] )
_UpperCAmelCase : Optional[Any] = int(size_map / size_pooling )
_UpperCAmelCase : List[str] = []
for i_map in range(len(lowerCAmelCase__ ) ):
_UpperCAmelCase : int = featuremaps[i_map]
_UpperCAmelCase : Union[str, Any] = []
for i_focus in range(0 , lowerCAmelCase__ , lowerCAmelCase__ ):
for j_focus in range(0 , lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(lowerCAmelCase__ ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(lowerCAmelCase__ ) )
_UpperCAmelCase : str = np.asmatrix(lowerCAmelCase__ ).reshape(lowerCAmelCase__ , lowerCAmelCase__ )
featuremap_pooled.append(lowerCAmelCase__ )
return featuremap_pooled
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = []
for i in range(len(lowerCAmelCase__ ) ):
_UpperCAmelCase : Dict = np.shape(data[i] )
_UpperCAmelCase : Optional[int] = data[i].reshape(1 , shapes[0] * shapes[1] )
_UpperCAmelCase : Dict = data_listed.getA().tolist()[0]
data_expanded.extend(lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = np.asarray(lowerCAmelCase__ )
return data_expanded
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = np.asarray(lowerCAmelCase__ )
_UpperCAmelCase : Any = np.shape(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Optional[Any] = 0
for i_map in range(lowerCAmelCase__ ):
_UpperCAmelCase : List[str] = np.ones((size_map, size_map) )
for i in range(0 , lowerCAmelCase__ , lowerCAmelCase__ ):
for j in range(0 , lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Optional[Any] = pd_pool[
i_pool
]
_UpperCAmelCase : Tuple = i_pool + 1
_UpperCAmelCase : Union[str, Any] = np.multiply(
lowerCAmelCase__ , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(lowerCAmelCase__ )
return pd_all
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[int]=bool ) -> str:
"""simple docstring"""
print("----------------------Start Training-------------------------" )
print((" - - Shape: Train_Data ", np.shape(lowerCAmelCase__ )) )
print((" - - Shape: Teach_Data ", np.shape(lowerCAmelCase__ )) )
_UpperCAmelCase : str = 0
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Tuple = 1_0_0_0_0
while rp < n_repeat and mse >= error_accuracy:
_UpperCAmelCase : List[str] = 0
print(F"""-------------Learning Time {rp}--------------""" )
for p in range(len(lowerCAmelCase__ ) ):
# print('------------Learning Image: %d--------------'%p)
_UpperCAmelCase : Optional[int] = np.asmatrix(datas_train[p] )
_UpperCAmelCase : Tuple = np.asarray(datas_teach[p] )
_UpperCAmelCase : Tuple = self.convolute(
lowerCAmelCase__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
_UpperCAmelCase : Optional[Any] = self.pooling(lowerCAmelCase__ , self.size_poolinga )
_UpperCAmelCase : List[Any] = np.shape(lowerCAmelCase__ )
_UpperCAmelCase : Any = self._expand(lowerCAmelCase__ )
_UpperCAmelCase : str = data_bp_input
_UpperCAmelCase : Union[str, Any] = np.dot(lowerCAmelCase__ , self.vji.T ) - self.thre_bpa
_UpperCAmelCase : str = self.sig(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = np.dot(lowerCAmelCase__ , self.wkj.T ) - self.thre_bpa
_UpperCAmelCase : List[Any] = self.sig(lowerCAmelCase__ )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
_UpperCAmelCase : Optional[Any] = np.multiply(
(data_teach - bp_outa) , np.multiply(lowerCAmelCase__ , (1 - bp_outa) ) )
_UpperCAmelCase : int = np.multiply(
np.dot(lowerCAmelCase__ , self.wkj ) , np.multiply(lowerCAmelCase__ , (1 - bp_outa) ) )
_UpperCAmelCase : List[Any] = np.dot(lowerCAmelCase__ , self.vji )
_UpperCAmelCase : List[str] = pd_i_all / (self.size_poolinga * self.size_poolinga)
_UpperCAmelCase : Optional[int] = pd_conva_pooled.T.getA().tolist()
_UpperCAmelCase : Optional[Any] = self._calculate_gradient_from_pool(
lowerCAmelCase__ , lowerCAmelCase__ , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
_UpperCAmelCase : str = self._expand_mat(pd_conva_all[k_conv] )
_UpperCAmelCase : Tuple = self.rate_weight * np.dot(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
_UpperCAmelCase : Dict = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
_UpperCAmelCase : int = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
_UpperCAmelCase : Optional[int] = self.vji + pd_j_all.T * bp_outa * self.rate_weight
_UpperCAmelCase : Dict = self.thre_bpa - pd_k_all * self.rate_thre
_UpperCAmelCase : Union[str, Any] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
_UpperCAmelCase : Tuple = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
_UpperCAmelCase : Optional[int] = rp + 1
_UpperCAmelCase : Optional[int] = error_count / patterns
all_mse.append(lowerCAmelCase__ )
def draw_error():
_UpperCAmelCase : Tuple = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(lowerCAmelCase__ , "+-" )
plt.plot(lowerCAmelCase__ , "r--" )
plt.xlabel("Learning Times" )
plt.ylabel("All_mse" )
plt.grid(lowerCAmelCase__ , alpha=0.5 )
plt.show()
print("------------------Training Complished---------------------" )
print((" - - Training epoch: ", rp, F""" - - Mse: {mse:.6f}""") )
if draw_e:
draw_error()
return mse
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = []
print("-------------------Start Testing-------------------------" )
print((" - - Shape: Test_Data ", np.shape(lowerCAmelCase__ )) )
for p in range(len(lowerCAmelCase__ ) ):
_UpperCAmelCase : int = np.asmatrix(datas_test[p] )
_UpperCAmelCase : Dict = self.convolute(
lowerCAmelCase__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
_UpperCAmelCase : Union[str, Any] = self.pooling(lowerCAmelCase__ , self.size_poolinga )
_UpperCAmelCase : List[Any] = self._expand(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = data_bp_input
_UpperCAmelCase : Union[str, Any] = bp_outa * self.vji.T - self.thre_bpa
_UpperCAmelCase : int = self.sig(lowerCAmelCase__ )
_UpperCAmelCase : Dict = bp_outa * self.wkj.T - self.thre_bpa
_UpperCAmelCase : List[str] = self.sig(lowerCAmelCase__ )
produce_out.extend(bp_outa.getA().tolist() )
_UpperCAmelCase : List[str] = [list(map(self.do_round , lowerCAmelCase__ ) ) for each in produce_out]
return np.asarray(lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Any = np.asmatrix(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = self.convolute(
lowerCAmelCase__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
_UpperCAmelCase : Tuple = self.pooling(lowerCAmelCase__ , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass | 352 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCase : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : int = plt.imshow(a_ )
fig.axes.get_xaxis().set_visible(a_ )
fig.axes.get_yaxis().set_visible(a_ )
plt.show()
def __UpperCAmelCase ( ):
_UpperCAmelCase : Dict = datetime.now()
_UpperCAmelCase : List[str] = current_time.strftime("%H:%M:%S" )
return timestamp | 17 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case , snake_case=False ) -> Optional[Any]:
lowercase__: int = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'blocks.{i}.norm1.weight', f'deit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((f'blocks.{i}.norm1.bias', f'deit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((f'blocks.{i}.attn.proj.weight', f'deit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((f'blocks.{i}.attn.proj.bias', f'deit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((f'blocks.{i}.norm2.weight', f'deit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((f'blocks.{i}.norm2.bias', f'deit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'deit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'deit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'deit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'deit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'deit.embeddings.cls_token'),
('dist_token', 'deit.embeddings.distillation_token'),
('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'deit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
('pre_logits.fc.weight', 'pooler.dense.weight'),
('pre_logits.fc.bias', 'pooler.dense.bias'),
] )
# if just the base model, we should remove "deit" from all keys that start with "deit"
lowercase__: Tuple = [(pair[0], pair[1][4:]) if pair[1].startswith('deit' ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
('norm.weight', 'deit.layernorm.weight'),
('norm.bias', 'deit.layernorm.bias'),
('head.weight', 'cls_classifier.weight'),
('head.bias', 'cls_classifier.bias'),
('head_dist.weight', 'distillation_classifier.weight'),
('head_dist.bias', 'distillation_classifier.bias'),
] )
return rename_keys
def snake_case_ ( snake_case , snake_case , snake_case=False ) -> Optional[int]:
for i in range(config.num_hidden_layers ):
if base_model:
lowercase__: str = ''
else:
lowercase__: Optional[Any] = 'deit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase__: int = state_dict.pop(f'blocks.{i}.attn.qkv.weight' )
lowercase__: Tuple = state_dict.pop(f'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
lowercase__: List[str] = in_proj_weight[
: config.hidden_size, :
]
lowercase__: Dict = in_proj_bias[: config.hidden_size]
lowercase__: List[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase__: Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase__: Dict = in_proj_weight[
-config.hidden_size :, :
]
lowercase__: Dict = in_proj_bias[-config.hidden_size :]
def snake_case_ ( snake_case , snake_case , snake_case ) -> Dict:
lowercase__: Optional[int] = dct.pop(snake_case )
lowercase__: List[str] = val
def snake_case_ ( ) -> Union[str, Any]:
lowercase__: List[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase__: Dict = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return im
@torch.no_grad()
def snake_case_ ( snake_case , snake_case ) -> Dict:
lowercase__: Tuple = DeiTConfig()
# all deit models have fine-tuned heads
lowercase__: Optional[int] = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
lowercase__: Optional[Any] = 10_00
lowercase__: Dict = 'huggingface/label-files'
lowercase__: Any = 'imagenet-1k-id2label.json'
lowercase__: Any = json.load(open(hf_hub_download(snake_case , snake_case , repo_type='dataset' ) , 'r' ) )
lowercase__: Optional[int] = {int(snake_case ): v for k, v in idalabel.items()}
lowercase__: int = idalabel
lowercase__: Tuple = {v: k for k, v in idalabel.items()}
lowercase__: Optional[int] = int(deit_name[-6:-4] )
lowercase__: Optional[Any] = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith('tiny' ):
lowercase__: List[Any] = 1_92
lowercase__: int = 7_68
lowercase__: Dict = 12
lowercase__: Union[str, Any] = 3
elif deit_name[9:].startswith('small' ):
lowercase__: Union[str, Any] = 3_84
lowercase__: Tuple = 15_36
lowercase__: Optional[int] = 12
lowercase__: Tuple = 6
if deit_name[9:].startswith('base' ):
pass
elif deit_name[4:].startswith('large' ):
lowercase__: Optional[int] = 10_24
lowercase__: Tuple = 40_96
lowercase__: List[str] = 24
lowercase__: Any = 16
# load original model from timm
lowercase__: Optional[Any] = timm.create_model(snake_case , pretrained=snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowercase__: str = timm_model.state_dict()
lowercase__: Tuple = create_rename_keys(snake_case , snake_case )
for src, dest in rename_keys:
rename_key(snake_case , snake_case , snake_case )
read_in_q_k_v(snake_case , snake_case , snake_case )
# load HuggingFace model
lowercase__: List[Any] = DeiTForImageClassificationWithTeacher(snake_case ).eval()
model.load_state_dict(snake_case )
# Check outputs on an image, prepared by DeiTImageProcessor
lowercase__: Any = int(
(2_56 / 2_24) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
lowercase__: Dict = DeiTImageProcessor(size=snake_case , crop_size=config.image_size )
lowercase__: Any = image_processor(images=prepare_img() , return_tensors='pt' )
lowercase__: Tuple = encoding['pixel_values']
lowercase__: Union[str, Any] = model(snake_case )
lowercase__: Any = timm_model(snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(snake_case , outputs.logits , atol=1e-3 )
Path(snake_case ).mkdir(exist_ok=snake_case )
print(f'Saving model {deit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(snake_case )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--deit_name''',
default='''vit_deit_base_distilled_patch16_224''',
type=str,
help='''Name of the DeiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowerCAmelCase = parser.parse_args()
convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 196 |
__lowerCAmelCase = range(2, 20 + 1)
__lowerCAmelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase = {}
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> Optional[int]:
lowercase__: str = sum(a_i[j] for j in range(snake_case , len(snake_case ) ) )
lowercase__: Optional[int] = sum(a_i[j] * base[j] for j in range(min(len(snake_case ) , snake_case ) ) )
lowercase__ , lowercase__: str = 0, 0
lowercase__: Tuple = n - i
lowercase__: Dict = memo.get(snake_case )
if sub_memo is not None:
lowercase__: Optional[Any] = sub_memo.get(snake_case )
if jumps is not None and len(snake_case ) > 0:
# find and make the largest jump without going over
lowercase__: int = -1
for _k in range(len(snake_case ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
lowercase__: Union[str, Any] = _k
break
if max_jump >= 0:
lowercase__ , lowercase__ , lowercase__: Any = jumps[max_jump]
# since the difference between jumps is cached, add c
lowercase__: str = diff + c
for j in range(min(snake_case , len(snake_case ) ) ):
lowercase__ , lowercase__: Dict = divmod(snake_case , 10 )
if new_c > 0:
add(snake_case , snake_case , snake_case )
else:
lowercase__: List[Any] = []
else:
lowercase__: Optional[Any] = {c: []}
lowercase__: Union[str, Any] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
lowercase__ , lowercase__: Union[str, Any] = next_term(snake_case , k - 1 , i + dn , snake_case )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
lowercase__ , lowercase__: Dict = compute(snake_case , snake_case , i + dn , snake_case )
diff += _diff
dn += terms_jumped
lowercase__: Any = sub_memo[c]
# keep jumps sorted by # of terms skipped
lowercase__: str = 0
while j < len(snake_case ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(snake_case , (diff, dn, k) )
return (diff, dn)
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> str:
if i >= n:
return 0, i
if k > len(snake_case ):
a_i.extend([0 for _ in range(k - len(snake_case ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
lowercase__: List[Any] = i
lowercase__ , lowercase__ , lowercase__: Any = 0, 0, 0
for j in range(len(snake_case ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
lowercase__: str = ds_c + ds_b
diff += addend
lowercase__: List[str] = 0
for j in range(snake_case ):
lowercase__: Any = a_i[j] + addend
lowercase__ , lowercase__: List[Any] = divmod(snake_case , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(snake_case , snake_case , snake_case )
return diff, i - start_i
def snake_case_ ( snake_case , snake_case , snake_case ) -> int:
for j in range(snake_case , len(snake_case ) ):
lowercase__: str = digits[j] + addend
if s >= 10:
lowercase__ , lowercase__: Any = divmod(snake_case , 10 )
lowercase__: Any = addend // 10 + quotient
else:
lowercase__: Union[str, Any] = s
lowercase__: Union[str, Any] = addend // 10
if addend == 0:
break
while addend > 0:
lowercase__ , lowercase__: Union[str, Any] = divmod(snake_case , 10 )
digits.append(snake_case )
def snake_case_ ( snake_case = 10**15 ) -> int:
lowercase__: Optional[Any] = [1]
lowercase__: int = 1
lowercase__: Tuple = 0
while True:
lowercase__ , lowercase__: str = next_term(snake_case , 20 , i + dn , snake_case )
dn += terms_jumped
if dn == n - i:
break
lowercase__: Dict = 0
for j in range(len(snake_case ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'''{solution() = }''')
| 196 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : List[Any] ,_a : Optional[int] ):
'''simple docstring'''
_a : str = str(id_ )
_a : int = None
_a : Optional[int] = None
_a : List[str] = []
_a : Optional[Any] = {} # {vertex:distance}
def __lt__( self : Any ,_a : List[Any] ):
'''simple docstring'''
return self.key < other.key
def __repr__( self : int ):
'''simple docstring'''
return self.id
def __lowercase ( self : Optional[Any] ,_a : str ):
'''simple docstring'''
self.neighbors.append(_a )
def __lowercase ( self : Optional[int] ,_a : str ,_a : Tuple ):
'''simple docstring'''
_a : List[str] = weight
def UpperCAmelCase_ (__a : Union[str, Any] , __a : int , __a : int , __a : List[Any] ):
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __a )
graph[b - 1].add_edge(graph[a - 1] , __a )
def UpperCAmelCase_ (__a : list , __a : Vertex ):
"""simple docstring"""
_a : int = []
for u in graph:
_a : Any = math.inf
_a : Optional[int] = None
_a : Any = 0
_a : Tuple = graph[:]
while q:
_a : Optional[Any] = min(__a )
q.remove(__a )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_a : Tuple = u
_a : int = u.edges[v.id]
for i in range(1 , len(__a ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase_ (__a : list , __a : Vertex ):
"""simple docstring"""
for u in graph:
_a : Any = math.inf
_a : List[str] = None
_a : Any = 0
_a : Union[str, Any] = list(__a )
hq.heapify(__a )
while h:
_a : int = hq.heappop(__a )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_a : Optional[Any] = u
_a : List[str] = u.edges[v.id]
hq.heapify(__a )
for i in range(1 , len(__a ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase_ ():
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
__lowerCAmelCase = {
"""169M""": 1_2,
"""430M""": 2_4,
"""1B5""": 2_4,
"""3B""": 3_2,
"""7B""": 3_2,
"""14B""": 4_0,
}
__lowerCAmelCase = {
"""169M""": 7_6_8,
"""430M""": 1_0_2_4,
"""1B5""": 2_0_4_8,
"""3B""": 2_5_6_0,
"""7B""": 4_0_9_6,
"""14B""": 5_1_2_0,
}
def UpperCAmelCase_ (__a : Dict ):
"""simple docstring"""
_a : List[Any] = list(state_dict.keys() )
for name in state_dict_keys:
_a : List[Any] = state_dict.pop(__a )
# emb -> embedding
if name.startswith('emb.' ):
_a : List[str] = name.replace('emb.' , 'embeddings.' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('blocks.0.ln0' ):
_a : Dict = name.replace('blocks.0.ln0' , 'blocks.0.pre_ln' )
# att -> attention
_a : int = re.sub(R'blocks\.(\d+)\.att' , R'blocks.\1.attention' , __a )
# ffn -> feed_forward
_a : str = re.sub(R'blocks\.(\d+)\.ffn' , R'blocks.\1.feed_forward' , __a )
# time_mix_k -> time_mix_key and reshape
if name.endswith('.time_mix_k' ):
_a : Any = name.replace('.time_mix_k' , '.time_mix_key' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('.time_mix_v' ):
_a : int = name.replace('.time_mix_v' , '.time_mix_value' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('.time_mix_r' ):
_a : Tuple = name.replace('.time_mix_r' , '.time_mix_receptance' )
if name != "head.weight":
_a : Tuple = 'rwkv.' + name
_a : List[Any] = weight
return state_dict
def UpperCAmelCase_ (__a : Tuple , __a : Union[str, Any] , __a : List[str] , __a : str=None , __a : List[str]=None , __a : int=False , __a : int=None ):
"""simple docstring"""
if tokenizer_file is None:
print('No `--tokenizer_file` provided, we will use the default tokenizer.' )
_a : List[Any] = 5_0_2_7_7
_a : Optional[Any] = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b' )
else:
_a : Optional[Any] = PreTrainedTokenizerFast(tokenizer_file=__a )
_a : List[Any] = len(__a )
tokenizer.save_pretrained(__a )
# 2. Build the config
_a : List[str] = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
_a : str = candidate
break
if size is None:
raise ValueError('Could not infer the size, please provide it with the `--size` argument.' )
if size not in possible_sizes:
raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}.""" )
_a : str = RwkvConfig(
vocab_size=__a , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(__a )
# 3. Download model file then convert state_dict
_a : Tuple = hf_hub_download(__a , __a )
_a : Optional[int] = torch.load(__a , map_location='cpu' )
_a : Dict = convert_state_dict(__a )
# 4. Split in shards and save
_a, _a : List[Any] = shard_checkpoint(__a )
for shard_file, shard in shards.items():
torch.save(__a , os.path.join(__a , __a ) )
if index is not None:
_a : Dict = os.path.join(__a , __a )
# Save the index as well
with open(__a , 'w' , encoding='utf-8' ) as f:
_a : List[Any] = json.dumps(__a , indent=2 , sort_keys=__a ) + '\n'
f.write(__a )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.' )
_a : List[Any] = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
_a : Optional[Any] = torch.load(os.path.join(__a , __a ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(__a , __a ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('Please provide a `model_name` to push the model to the Hub.' )
_a : List[str] = AutoModelForCausalLM.from_pretrained(__a )
model.push_to_hub(__a , max_shard_size='2GB' )
tokenizer.push_to_hub(__a )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
)
parser.add_argument(
"""--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
)
parser.add_argument(
"""--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
)
parser.add_argument(
"""--tokenizer_file""",
default=None,
type=str,
help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
)
parser.add_argument(
"""--size""",
default=None,
type=str,
help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Push to the Hub the converted model.""",
)
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help="""Name of the pushed model on the Hub, including the username / organization.""",
)
__lowerCAmelCase = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 5 | 1 |
"""simple docstring"""
from copy import deepcopy
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self , _a = None , _a = None ):
if arr is None and size is not None:
__a = size
__a = [0] * size
elif arr is not None:
self.init(_a )
else:
raise ValueError('''Either arr or size must be specified''' )
def __UpperCAmelCase ( self , _a ):
__a = len(_a )
__a = deepcopy(_a )
for i in range(1 , self.size ):
__a = self.next_(_a )
if j < self.size:
self.tree[j] += self.tree[i]
def __UpperCAmelCase ( self ):
__a = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
__a = self.next_(_a )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def __UpperCAmelCase ( _a ):
return index + (index & (-index))
@staticmethod
def __UpperCAmelCase ( _a ):
return index - (index & (-index))
def __UpperCAmelCase ( self , _a , _a ):
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
__a = self.next_(_a )
def __UpperCAmelCase ( self , _a , _a ):
self.add(_a , value - self.get(_a ) )
def __UpperCAmelCase ( self , _a ):
if right == 0:
return 0
__a = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
__a = self.prev(_a )
return result
def __UpperCAmelCase ( self , _a , _a ):
return self.prefix(_a ) - self.prefix(_a )
def __UpperCAmelCase ( self , _a ):
return self.query(_a , index + 1 )
def __UpperCAmelCase ( self , _a ):
value -= self.tree[0]
if value < 0:
return -1
__a = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
__a = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> List[Any]:
_enforce_args(lowerCAmelCase__ , lowerCAmelCase__ )
if n == 0:
return 0
__a = float('''-inf''' )
for i in range(1 , n + 1 ):
__a = max(
lowerCAmelCase__ , prices[i - 1] + naive_cut_rod_recursive(n - i , lowerCAmelCase__ ) )
return max_revue
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> List[str]:
_enforce_args(lowerCAmelCase__ , lowerCAmelCase__ )
__a = [float('''-inf''' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list , lowerCAmelCase__ : list ) -> Union[str, Any]:
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
__a = float('''-inf''' )
for i in range(1 , n + 1 ):
__a = max(
lowerCAmelCase__ , prices[i - 1] + _top_down_cut_rod_recursive(n - i , lowerCAmelCase__ , lowerCAmelCase__ ) , )
__a = max_revenue
return max_rev[n]
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> Dict:
_enforce_args(lowerCAmelCase__ , lowerCAmelCase__ )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
__a = [float('''-inf''' ) for _ in range(n + 1 )]
__a = 0
for i in range(1 , n + 1 ):
__a = max_rev[i]
for j in range(1 , i + 1 ):
__a = max(lowerCAmelCase__ , prices[j - 1] + max_rev[i - j] )
__a = max_revenue_i
return max_rev[n]
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> str:
if n < 0:
__a = f'''n must be greater than or equal to 0. Got n = {n}'''
raise ValueError(lowerCAmelCase__ )
if n > len(lowerCAmelCase__ ):
__a = (
'''Each integral piece of rod must have a corresponding price. '''
f'''Got n = {n} but length of prices = {len(lowerCAmelCase__ )}'''
)
raise ValueError(lowerCAmelCase__ )
def lowercase ( ) -> int:
__a = [6, 10, 12, 15, 20, 23]
__a = len(lowerCAmelCase__ )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
__a = 36
__a = top_down_cut_rod(lowerCAmelCase__ , lowerCAmelCase__ )
__a = bottom_up_cut_rod(lowerCAmelCase__ , lowerCAmelCase__ )
__a = naive_cut_rod_recursive(lowerCAmelCase__ , lowerCAmelCase__ )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 45 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple=13 , SCREAMING_SNAKE_CASE_ : Tuple=30 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=2 , SCREAMING_SNAKE_CASE_ : Any=3 , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=32 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=2 , SCREAMING_SNAKE_CASE_ : Optional[int]=4 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=37 , SCREAMING_SNAKE_CASE_ : Tuple="gelu" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE_ : Tuple=0.1 , SCREAMING_SNAKE_CASE_ : Optional[int]=10 , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : List[str]=3 , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Optional[int]=2 , ) -> str:
'''simple docstring'''
A: Dict = parent
A: Optional[int] = batch_size
A: List[Any] = image_size
A: Tuple = patch_size
A: Union[str, Any] = num_channels
A: List[Any] = is_training
A: List[Any] = use_labels
A: Any = hidden_size
A: Union[str, Any] = num_hidden_layers
A: List[Any] = num_attention_heads
A: Tuple = intermediate_size
A: Any = hidden_act
A: Union[str, Any] = hidden_dropout_prob
A: int = attention_probs_dropout_prob
A: Dict = type_sequence_label_size
A: List[str] = initializer_range
A: Union[str, Any] = scope
A: Dict = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
A: Optional[int] = (image_size // patch_size) ** 2
A: Optional[int] = num_patches + 2
def _snake_case ( self : Optional[Any] ) -> int:
'''simple docstring'''
A: int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A: Tuple = None
if self.use_labels:
A: Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A: Optional[int] = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_a , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Any ) -> int:
'''simple docstring'''
A: str = TFDeiTModel(config=_a )
A: Dict = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
A: Optional[int] = TFDeiTForMaskedImageModeling(config=_a )
A: List[Any] = model(_a )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A: int = 1
A: Optional[int] = TFDeiTForMaskedImageModeling(_a )
A: str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A: int = model(_a )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _snake_case ( self : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str] ) -> List[Any]:
'''simple docstring'''
A: int = self.type_sequence_label_size
A: Optional[Any] = TFDeiTForImageClassification(_a )
A: Dict = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A: List[Any] = 1
A: str = TFDeiTForImageClassification(_a )
A: Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A: List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _snake_case ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
A: Union[str, Any] = self.prepare_config_and_inputs()
A: List[Any] = config_and_inputs
A: int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( _a , _a , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : str = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : List[str] = (
{
"""feature-extraction""": TFDeiTModel,
"""image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Dict = False
UpperCamelCase_ : List[str] = False
UpperCamelCase_ : Tuple = False
def _snake_case ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
A: Optional[int] = TFDeiTModelTester(self )
A: Optional[Any] = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def _snake_case ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''DeiT does not use inputs_embeds''' )
def _snake_case ( self : int ) -> Any:
'''simple docstring'''
pass
def _snake_case ( self : Tuple ) -> List[Any]:
'''simple docstring'''
A: Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A: List[str] = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
A: int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , tf.keras.layers.Dense ) )
def _snake_case ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
A: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A: str = model_class(_a )
A: Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A: Union[str, Any] = [*signature.parameters.keys()]
A: List[str] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _a )
def _snake_case ( self : int ) -> Optional[int]:
'''simple docstring'''
A: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _snake_case ( self : int ) -> int:
'''simple docstring'''
A: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_a )
def _snake_case ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
A: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
def _snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str]=False ) -> Optional[int]:
'''simple docstring'''
A: List[Any] = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def _snake_case ( self : List[Any] ) -> Any:
'''simple docstring'''
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A: Tuple = TFDeiTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def SCREAMING_SNAKE_CASE( ) -> Optional[Any]:
A: List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
if is_vision_available()
else None
)
@slow
def _snake_case ( self : List[Any] ) -> Any:
'''simple docstring'''
A: List[str] = TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
A: Optional[Any] = self.default_image_processor
A: str = prepare_img()
A: List[Any] = image_processor(images=_a , return_tensors='''tf''' )
# forward pass
A: Optional[int] = model(**_a )
# verify the logits
A: Optional[Any] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , _a )
A: List[Any] = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _a , atol=1E-4 ) )
| 370 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ["""input_features""", """attention_mask"""]
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1_60_00 , SCREAMING_SNAKE_CASE_ : int=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> List[Any]:
'''simple docstring'''
super().__init__(feature_size=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , padding_value=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
A: Union[str, Any] = num_mel_bins
A: str = do_ceptral_normalize
A: int = normalize_means
A: List[Any] = normalize_vars
A: Any = True
def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : np.ndarray , ) -> np.ndarray:
'''simple docstring'''
A: Optional[int] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
A: Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 )
A: List[Any] = ta_kaldi.fbank(SCREAMING_SNAKE_CASE_ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _snake_case ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : float = 0.0 , ) -> np.ndarray:
'''simple docstring'''
if normalize_means:
A: str = x[:input_length].mean(axis=0 )
A: Dict = np.subtract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if normalize_vars:
A: Tuple = x[:input_length].std(axis=0 )
A: List[Any] = np.divide(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if input_length < x.shape[0]:
A: Optional[int] = padding_value
# make sure array is in float32
A: Optional[Any] = x.astype(np.floataa )
return x
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[np.ndarray] , SCREAMING_SNAKE_CASE_ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
'''simple docstring'''
A: int = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
]
def __call__( self : Tuple , SCREAMING_SNAKE_CASE_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , SCREAMING_SNAKE_CASE_ : Union[bool, str, PaddingStrategy] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> BatchFeature:
'''simple docstring'''
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 `raw_speech` 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.''' )
A: Any = isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
A: Optional[Any] = is_batched_numpy or (
isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
A: Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ):
A: int = np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa )
elif isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A: Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A: Union[str, Any] = [raw_speech]
# extract fbank features
A: str = [self._extract_fbank_features(SCREAMING_SNAKE_CASE_ ) for waveform in raw_speech]
# convert into correct format for padding
A: int = BatchFeature({'''input_features''': features} )
A: int = self.pad(
SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
# make sure list is in array format
A: List[str] = padded_inputs.get('''input_features''' )
if isinstance(input_features[0] , SCREAMING_SNAKE_CASE_ ):
A: Optional[Any] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for feature in input_features]
A: List[Any] = padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
A: Dict = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
A: Dict = (
np.array(SCREAMING_SNAKE_CASE_ , dtype=np.intaa )
if self._get_padding_strategies(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
A: List[Any] = self.normalize(
padded_inputs['''input_features'''] , attention_mask=SCREAMING_SNAKE_CASE_ )
if return_tensors is not None:
A: Dict = padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_ )
return padded_inputs
| 334 | 0 |
"""simple docstring"""
import math
import random
def _lowerCAmelCase ( lowercase_ , lowercase_ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
snake_case_ = 0.02
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
UpperCAmelCase = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(lowercase_ ):
# Forward propagation
UpperCAmelCase = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
UpperCAmelCase = (expected / 100) - layer_a
# Error delta
UpperCAmelCase = layer_1_error * sigmoid_function(lowercase_ , lowercase_ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case_ = int(input("""Expected value: """))
snake_case_ = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 78 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCAmelCase__ = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
lowerCAmelCase__ = {
'''distilbert-base-uncased''': 5_1_2,
'''distilbert-base-uncased-distilled-squad''': 5_1_2,
'''distilbert-base-cased''': 5_1_2,
'''distilbert-base-cased-distilled-squad''': 5_1_2,
'''distilbert-base-german-cased''': 5_1_2,
'''distilbert-base-multilingual-cased''': 5_1_2,
}
lowerCAmelCase__ = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PRETRAINED_INIT_CONFIGURATION
lowercase_ = ["""input_ids""", """attention_mask"""]
lowercase_ = DistilBertTokenizer
def __init__( self : Tuple , SCREAMING_SNAKE_CASE : str=None , SCREAMING_SNAKE_CASE : Any=None , SCREAMING_SNAKE_CASE : Tuple=True , SCREAMING_SNAKE_CASE : Union[str, Any]="[UNK]" , SCREAMING_SNAKE_CASE : str="[SEP]" , SCREAMING_SNAKE_CASE : Dict="[PAD]" , SCREAMING_SNAKE_CASE : List[str]="[CLS]" , SCREAMING_SNAKE_CASE : List[str]="[MASK]" , SCREAMING_SNAKE_CASE : Tuple=True , SCREAMING_SNAKE_CASE : int=None , **SCREAMING_SNAKE_CASE : Dict , ):
super().__init__(
SCREAMING_SNAKE_CASE , tokenizer_file=SCREAMING_SNAKE_CASE , do_lower_case=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , sep_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , cls_token=SCREAMING_SNAKE_CASE , mask_token=SCREAMING_SNAKE_CASE , tokenize_chinese_chars=SCREAMING_SNAKE_CASE , strip_accents=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
lowercase__ : List[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , SCREAMING_SNAKE_CASE ) != do_lower_case
or normalizer_state.get("strip_accents" , SCREAMING_SNAKE_CASE ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , SCREAMING_SNAKE_CASE ) != tokenize_chinese_chars
):
lowercase__ : Optional[Any] = getattr(SCREAMING_SNAKE_CASE , normalizer_state.pop("type" ) )
lowercase__ : Any = do_lower_case
lowercase__ : Optional[int] = strip_accents
lowercase__ : List[Any] = tokenize_chinese_chars
lowercase__ : List[Any] = normalizer_class(**SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = do_lower_case
def snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict=None ):
lowercase__ : List[str] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case ( self : List[str] , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None ):
lowercase__ : Dict = [self.sep_token_id]
lowercase__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case ( self : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[str] = None ):
lowercase__ : Union[str, Any] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE , name=SCREAMING_SNAKE_CASE )
return tuple(SCREAMING_SNAKE_CASE )
| 130 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
@slow
def __a ( self :Any) -> Any:
UpperCAmelCase_ = XLMRobertaModel.from_pretrained('''xlm-roberta-base''')
UpperCAmelCase_ = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
UpperCAmelCase_ = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = torch.tensor(
[[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]])
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase_ = model(_lowercase)['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _lowercase)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _lowercase , atol=1E-3))
@slow
def __a ( self :Union[str, Any]) -> Dict:
UpperCAmelCase_ = XLMRobertaModel.from_pretrained('''xlm-roberta-large''')
UpperCAmelCase_ = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
UpperCAmelCase_ = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = torch.tensor(
[[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]])
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase_ = model(_lowercase)['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _lowercase)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _lowercase , atol=1E-3))
| 361 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class a_ ( _snake_case ):
UpperCamelCase__ : Optional[Any] =(DPMSolverSinglestepScheduler,)
UpperCamelCase__ : Tuple =(("num_inference_steps", 25),)
def __a ( self :List[Any] , **_lowercase :Optional[Any]) -> int:
UpperCAmelCase_ = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
'''sample_max_value''': 1.0,
'''algorithm_type''': '''dpmsolver++''',
'''solver_type''': '''midpoint''',
'''lambda_min_clipped''': -float('''inf'''),
'''variance_type''': None,
}
config.update(**_lowercase)
return config
def __a ( self :Union[str, Any] , _lowercase :List[Any]=0 , **_lowercase :Optional[int]) -> List[Any]:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config(**_lowercase)
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowercase)
UpperCAmelCase_ = scheduler_class.from_pretrained(_lowercase)
new_scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase_ , UpperCAmelCase_ = sample, sample
for t in range(_lowercase , time_step + scheduler.config.solver_order + 1):
UpperCAmelCase_ = scheduler.step(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __a ( self :Union[str, Any]) -> List[Any]:
pass
def __a ( self :Optional[Any] , _lowercase :str=0 , **_lowercase :Union[str, Any]) -> Dict:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowercase)
UpperCAmelCase_ = scheduler_class.from_pretrained(_lowercase)
# copy over dummy past residuals
new_scheduler.set_timesteps(_lowercase)
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase_ = scheduler.step(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __a ( self :Dict , _lowercase :Union[str, Any]=None , **_lowercase :List[Any]) -> int:
if scheduler is None:
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(**_lowercase)
UpperCAmelCase_ = scheduler_class(**_lowercase)
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(**_lowercase)
UpperCAmelCase_ = scheduler_class(**_lowercase)
UpperCAmelCase_ = 10
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter
scheduler.set_timesteps(_lowercase)
for i, t in enumerate(scheduler.timesteps):
UpperCAmelCase_ = model(_lowercase , _lowercase)
UpperCAmelCase_ = scheduler.step(_lowercase , _lowercase , _lowercase).prev_sample
return sample
def __a ( self :int) -> Tuple:
UpperCAmelCase_ = DPMSolverSinglestepScheduler(**self.get_scheduler_config())
UpperCAmelCase_ = 50
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter
scheduler.set_timesteps(_lowercase)
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:]):
UpperCAmelCase_ = model(_lowercase , _lowercase)
UpperCAmelCase_ = scheduler.step(_lowercase , _lowercase , _lowercase).prev_sample
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_mean.item() - 0.2_574) < 1E-3
def __a ( self :List[Any]) -> List[Any]:
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=_lowercase)
def __a ( self :int) -> Optional[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
UpperCAmelCase_ = DPMSolverSinglestepScheduler(**self.get_scheduler_config())
UpperCAmelCase_ = self.full_loop(scheduler=_lowercase)
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_mean.item() - 0.2_791) < 1E-3
UpperCAmelCase_ = DEISMultistepScheduler.from_config(scheduler.config)
UpperCAmelCase_ = DPMSolverMultistepScheduler.from_config(scheduler.config)
UpperCAmelCase_ = UniPCMultistepScheduler.from_config(scheduler.config)
UpperCAmelCase_ = DPMSolverSinglestepScheduler.from_config(scheduler.config)
UpperCAmelCase_ = self.full_loop(scheduler=_lowercase)
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_mean.item() - 0.2_791) < 1E-3
def __a ( self :Tuple) -> int:
self.check_over_configs(thresholding=_lowercase)
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , algorithm_type='''dpmsolver++''' , solver_order=_lowercase , solver_type=_lowercase , )
def __a ( self :List[Any]) -> Any:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase)
def __a ( self :Any) -> Optional[int]:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=_lowercase , solver_type=_lowercase , prediction_type=_lowercase , algorithm_type=_lowercase , )
UpperCAmelCase_ = self.full_loop(
solver_order=_lowercase , solver_type=_lowercase , prediction_type=_lowercase , algorithm_type=_lowercase , )
assert not torch.isnan(_lowercase).any(), "Samples have nan numbers"
def __a ( self :Tuple) -> int:
self.check_over_configs(lower_order_final=_lowercase)
self.check_over_configs(lower_order_final=_lowercase)
def __a ( self :Tuple) -> Optional[Any]:
self.check_over_configs(lambda_min_clipped=-float('''inf'''))
self.check_over_configs(lambda_min_clipped=-5.1)
def __a ( self :Any) -> List[str]:
self.check_over_configs(variance_type=_lowercase)
self.check_over_configs(variance_type='''learned_range''')
def __a ( self :Any) -> Dict:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=_lowercase , time_step=0)
def __a ( self :Dict) -> Union[str, Any]:
UpperCAmelCase_ = self.full_loop()
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_mean.item() - 0.2_791) < 1E-3
def __a ( self :Any) -> Union[str, Any]:
UpperCAmelCase_ = self.full_loop(use_karras_sigmas=_lowercase)
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_mean.item() - 0.2_248) < 1E-3
def __a ( self :str) -> Optional[int]:
UpperCAmelCase_ = self.full_loop(prediction_type='''v_prediction''')
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_mean.item() - 0.1_453) < 1E-3
def __a ( self :List[Any]) -> Dict:
UpperCAmelCase_ = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=_lowercase)
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_mean.item() - 0.0_649) < 1E-3
def __a ( self :Any) -> Optional[Any]:
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(thresholding=_lowercase , dynamic_thresholding_ratio=0)
UpperCAmelCase_ = scheduler_class(**_lowercase)
UpperCAmelCase_ = 10
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter.half()
scheduler.set_timesteps(_lowercase)
for i, t in enumerate(scheduler.timesteps):
UpperCAmelCase_ = model(_lowercase , _lowercase)
UpperCAmelCase_ = scheduler.step(_lowercase , _lowercase , _lowercase).prev_sample
assert sample.dtype == torch.floataa
| 344 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 293 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowerCAmelCase ( yaml.SafeLoader ):
"""simple docstring"""
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = [self.constructed_objects[key_node] for key_node, _ in node.value]
lowerCAmelCase__ :str = [tuple(__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else key for key in keys]
lowerCAmelCase__ :Optional[int] = Counter(__UpperCAmelCase )
lowerCAmelCase__ :int = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}" )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = super().construct_mapping(__UpperCAmelCase , deep=__UpperCAmelCase )
self._check_no_duplicates_on_constructed_node(__UpperCAmelCase )
return mapping
def __A (_SCREAMING_SNAKE_CASE ) ->Tuple[Optional[str], str]:
"""simple docstring"""
lowerCAmelCase__ :Optional[Any] = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
lowerCAmelCase__ :Optional[int] = full_content[1:].index('---' ) + 1
lowerCAmelCase__ :Union[str, Any] = '\n'.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(_SCREAMING_SNAKE_CASE )
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :List[str] = {"""train_eval_index"""} # train-eval-index in the YAML metadata
@classmethod
def snake_case ( cls , __UpperCAmelCase ):
'''simple docstring'''
with open(__UpperCAmelCase , encoding='utf-8' ) as readme_file:
lowerCAmelCase__ , lowerCAmelCase__ :Union[str, Any] = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(__UpperCAmelCase )
else:
return cls()
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
if path.exists():
with open(__UpperCAmelCase , encoding='utf-8' ) as readme_file:
lowerCAmelCase__ :Optional[Any] = readme_file.read()
else:
lowerCAmelCase__ :Union[str, Any] = None
lowerCAmelCase__ :Union[str, Any] = self._to_readme(__UpperCAmelCase )
with open(__UpperCAmelCase , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase = None ):
'''simple docstring'''
if readme_content is not None:
lowerCAmelCase__ , lowerCAmelCase__ :Optional[int] = _split_yaml_from_readme(__UpperCAmelCase )
lowerCAmelCase__ :Optional[Any] = '---\n' + self.to_yaml_string() + '---\n' + content
else:
lowerCAmelCase__ :str = '---\n' + self.to_yaml_string() + '---\n'
return full_content
@classmethod
def snake_case ( cls , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Dict = yaml.load(__UpperCAmelCase , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
lowerCAmelCase__ :int = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=__UpperCAmelCase , allow_unicode=__UpperCAmelCase , encoding='utf-8' , ).decode('utf-8' )
__A = {
"""image-classification""": [],
"""translation""": [],
"""image-segmentation""": [],
"""fill-mask""": [],
"""automatic-speech-recognition""": [],
"""token-classification""": [],
"""sentence-similarity""": [],
"""audio-classification""": [],
"""question-answering""": [],
"""summarization""": [],
"""zero-shot-classification""": [],
"""table-to-text""": [],
"""feature-extraction""": [],
"""other""": [],
"""multiple-choice""": [],
"""text-classification""": [],
"""text-to-image""": [],
"""text2text-generation""": [],
"""zero-shot-image-classification""": [],
"""tabular-classification""": [],
"""tabular-regression""": [],
"""image-to-image""": [],
"""tabular-to-text""": [],
"""unconditional-image-generation""": [],
"""text-retrieval""": [],
"""text-to-speech""": [],
"""object-detection""": [],
"""audio-to-audio""": [],
"""text-generation""": [],
"""conversational""": [],
"""table-question-answering""": [],
"""visual-question-answering""": [],
"""image-to-text""": [],
"""reinforcement-learning""": [],
"""voice-activity-detection""": [],
"""time-series-forecasting""": [],
"""document-question-answering""": [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
__A = ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
ap.add_argument("""readme_filepath""")
__A = ap.parse_args()
__A = Path(args.readme_filepath)
__A = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 293 | 1 |
from math import asin, atan, cos, radians, sin, sqrt, tan
_SCREAMING_SNAKE_CASE : Optional[int] = 6378137.0
_SCREAMING_SNAKE_CASE : Dict = 6356752.314245
_SCREAMING_SNAKE_CASE : Optional[int] = 6_37_81_37
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = (AXIS_A - AXIS_B) / AXIS_A
snake_case = atan((1 - flattening) * tan(radians(UpperCamelCase_ ) ) )
snake_case = atan((1 - flattening) * tan(radians(UpperCamelCase_ ) ) )
snake_case = radians(UpperCamelCase_ )
snake_case = radians(UpperCamelCase_ )
# Equation
snake_case = sin((phi_a - phi_a) / 2 )
snake_case = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
snake_case = sqrt(sin_sq_phi + (cos(UpperCamelCase_ ) * cos(UpperCamelCase_ ) * sin_sq_lambda) )
return 2 * RADIUS * asin(UpperCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 213 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = 'efficientnet'
def __init__( self , __snake_case = 3 , __snake_case = 6_0_0 , __snake_case = 2.0 , __snake_case = 3.1 , __snake_case = 8 , __snake_case = [3, 3, 5, 3, 5, 5, 3] , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , __snake_case = [] , __snake_case = [1, 2, 2, 2, 1, 2, 1] , __snake_case = [1, 2, 2, 3, 3, 4, 1] , __snake_case = [1, 6, 6, 6, 6, 6, 6] , __snake_case = 0.25 , __snake_case = "swish" , __snake_case = 2_5_6_0 , __snake_case = "mean" , __snake_case = 0.02 , __snake_case = 0.001 , __snake_case = 0.99 , __snake_case = 0.5 , __snake_case = 0.2 , **__snake_case , ):
super().__init__(**__snake_case )
snake_case = num_channels
snake_case = image_size
snake_case = width_coefficient
snake_case = depth_coefficient
snake_case = depth_divisor
snake_case = kernel_sizes
snake_case = in_channels
snake_case = out_channels
snake_case = depthwise_padding
snake_case = strides
snake_case = num_block_repeats
snake_case = expand_ratios
snake_case = squeeze_expansion_ratio
snake_case = hidden_act
snake_case = hidden_dim
snake_case = pooling_type
snake_case = initializer_range
snake_case = batch_norm_eps
snake_case = batch_norm_momentum
snake_case = dropout_rate
snake_case = drop_connect_rate
snake_case = sum(__snake_case ) * 4
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = version.parse('1.11' )
@property
def a_ ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def a_ ( self ):
return 1E-5
| 213 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: Any = logging.get_logger(__name__)
__a: Optional[int] = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "mgp-str"
def __init__( self , __lowerCAmelCase=[32, 128] , __lowerCAmelCase=4 , __lowerCAmelCase=3 , __lowerCAmelCase=27 , __lowerCAmelCase=38 , __lowerCAmelCase=50257 , __lowerCAmelCase=30522 , __lowerCAmelCase=768 , __lowerCAmelCase=12 , __lowerCAmelCase=12 , __lowerCAmelCase=4.0 , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase=1E-5 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=False , __lowerCAmelCase=0.0_2 , **__lowerCAmelCase , ) -> Optional[int]:
super().__init__(**__lowerCAmelCase )
lowercase__ : Dict = image_size
lowercase__ : List[str] = patch_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Any = max_token_length
lowercase__ : Dict = num_character_labels
lowercase__ : Optional[Any] = num_bpe_labels
lowercase__ : List[str] = num_wordpiece_labels
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : List[str] = num_attention_heads
lowercase__ : List[Any] = mlp_ratio
lowercase__ : Optional[int] = distilled
lowercase__ : Any = layer_norm_eps
lowercase__ : List[str] = drop_rate
lowercase__ : List[str] = qkv_bias
lowercase__ : List[Any] = attn_drop_rate
lowercase__ : List[str] = drop_path_rate
lowercase__ : int = output_aa_attentions
lowercase__ : Optional[Any] = initializer_range
| 198 | '''simple docstring'''
import requests
__a: str = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __UpperCamelCase ( UpperCAmelCase ):
# fetching a list of articles in json format
lowercase__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['''articles'''] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
| 198 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
a__ = trt.Logger(trt.Logger.WARNING)
a__ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
a__ = logging.getLogger(__name__)
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=384,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=128,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
a__ = parser.parse_args()
if args.tokenizer_name:
a__ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
a__ = args.per_device_eval_batch_size
a__ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
a__ = True
a__ = """temp_engine/bert-fp32.engine"""
if args.fpaa:
a__ = """temp_engine/bert-fp16.engine"""
if args.inta:
a__ = """temp_engine/bert-int8.engine"""
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
a__ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
a__ = [network.get_input(i) for i in range(network.num_inputs)]
a__ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
a__ = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
a__ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
a__ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Optional[Any] ,__a : str ,__a : Optional[int] ,__a : Optional[Any] ,__a : Dict ,__a : List[str] ,__a : List[Any] ) -> Any:
"""simple docstring"""
_a : Optional[int] = np.asarray(inputs['''input_ids'''] ,dtype=np.intaa )
_a : Optional[int] = np.asarray(inputs['''attention_mask'''] ,dtype=np.intaa )
_a : Tuple = np.asarray(inputs['''token_type_ids'''] ,dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] ,input_ids.ravel() ,_snake_case )
cuda.memcpy_htod_async(d_inputs[1] ,attention_mask.ravel() ,_snake_case )
cuda.memcpy_htod_async(d_inputs[2] ,token_type_ids.ravel() ,_snake_case )
# start time
_a : int = time.time()
# Run inference
context.execute_async(
bindings=[int(_snake_case ) for d_inp in d_inputs] + [int(_snake_case ), int(_snake_case )] ,stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(_snake_case ,_snake_case ,_snake_case )
cuda.memcpy_dtoh_async(_snake_case ,_snake_case ,_snake_case )
# Synchronize the stream and take time
stream.synchronize()
# end time
_a : List[str] = time.time()
_a : str = end_time - start_time
_a : List[Any] = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
a__ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
a__ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
a__ = raw_datasets["""validation"""].column_names
a__ = """question""" if """question""" in column_names else column_names[0]
a__ = """context""" if """context""" in column_names else column_names[1]
a__ = """answers""" if """answers""" in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
a__ = tokenizer.padding_side == """right"""
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
)
a__ = min(args.max_seq_length, tokenizer.model_max_length)
def __UpperCAmelCase ( __a : List[str] ) -> Tuple:
"""simple docstring"""
_a : Optional[Any] = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
_a : Union[str, Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] ,examples[context_column_name if pad_on_right else question_column_name] ,truncation='''only_second''' if pad_on_right else '''only_first''' ,max_length=_snake_case ,stride=args.doc_stride ,return_overflowing_tokens=_snake_case ,return_offsets_mapping=_snake_case ,padding='''max_length''' ,)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
_a : Optional[Any] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
_a : Dict = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
_a : Tuple = tokenized_examples.sequence_ids(_snake_case )
_a : int = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
_a : List[str] = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
_a : Optional[Any] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
a__ = raw_datasets["""validation"""]
# Validation Feature Creation
a__ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
a__ = default_data_collator
a__ = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
a__ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def __UpperCAmelCase ( __a : List[Any] ,__a : Dict ,__a : Tuple ,__a : Tuple="eval" ) -> Any:
"""simple docstring"""
_a : Optional[Any] = postprocess_qa_predictions(
examples=_snake_case ,features=_snake_case ,predictions=_snake_case ,version_2_with_negative=args.version_2_with_negative ,n_best_size=args.n_best_size ,max_answer_length=args.max_answer_length ,null_score_diff_threshold=args.null_score_diff_threshold ,output_dir=args.output_dir ,prefix=_snake_case ,)
# Format the result to the format the metric expects.
if args.version_2_with_negative:
_a : Tuple = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
_a : str = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
_a : List[Any] = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=_snake_case ,label_ids=_snake_case )
a__ = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def __UpperCAmelCase ( __a : str ) -> Dict:
"""simple docstring"""
return trt.volume(engine.get_binding_shape(_snake_case ) ) * engine.get_binding_dtype(_snake_case ).itemsize
# Allocate device memory for inputs and outputs.
a__ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
a__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
a__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
a__ = cuda.mem_alloc(h_outputa.nbytes)
a__ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
a__ = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f''' Num examples = {len(eval_dataset)}''')
logger.info(f''' Batch size = {args.per_device_eval_batch_size}''')
a__ = 0.0
a__ = 0
a__ = timeit.default_timer()
a__ = None
for step, batch in enumerate(eval_dataloader):
a__ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
a__ = outputs
a__ = torch.tensor(start_logits)
a__ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
a__ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
a__ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
a__ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
a__ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
a__ = nested_truncate(all_preds, len(eval_dataset))
a__ = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 1000))
logger.info('''Total Number of Inference = %d''', niter)
a__ = post_processing_function(eval_examples, eval_dataset, all_preds)
a__ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'''Evaluation metrics: {eval_metric}''')
| 351 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : Dict = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : int = 2 * i + 1
_a : str = 2 * i
_a : Any = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
a__ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number''')
| 15 | 0 |
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ,_UpperCamelCase : int ):
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
__lowerCamelCase = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
__lowerCamelCase = 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()
| 330 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
a_ = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
a_ = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : Optional[Any] ):
for tf_name, hf_name in patterns:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = BigBirdPegasusConfig(**_UpperCamelCase )
__lowerCamelCase = BigBirdPegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
__lowerCamelCase = {}
# separating decoder weights
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = DECODER_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = REMAINING_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
__lowerCamelCase = mapping['''model.embed_positions.weight''']
__lowerCamelCase = mapping.pop('''model.embed_positions.weight''' )
__lowerCamelCase ,__lowerCamelCase = torch_model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : dict ):
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = convert_bigbird_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
a_ = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 330 | 1 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if (len(UpperCamelCase__ ) % 2) != 0:
raise ValueError(
'Base16 encoded data is invalid:\nData does not have an even number of hex digits.' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(UpperCamelCase__ ) <= set('0123456789ABCDEF' ):
raise ValueError(
'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(UpperCamelCase__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356 |
import requests
_UpperCAmelCase : Union[str, Any] = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F'''{i}.) {article["title"]}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
| 200 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__lowerCAmelCase )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : str = field(default='''image-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
UpperCAmelCase : ClassVar[Features] = Features({'''image''': Image()} )
UpperCAmelCase : ClassVar[Features] = Features({'''labels''': ClassLabel} )
UpperCAmelCase : str = "image"
UpperCAmelCase : str = "labels"
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Optional[int] ):
if self.label_column not in features:
raise ValueError(F'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , UpperCAmelCase__ ):
raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' )
_A = copy.deepcopy(self )
_A = self.label_schema.copy()
_A = features[self.label_column]
_A = label_schema
return task_template
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
return {
self.image_column: "image",
self.label_column: "labels",
}
| 315 |
"""simple docstring"""
from math import sqrt
def _A ( UpperCamelCase_ : int) -> int:
'''simple docstring'''
__lowercase = 0
for i in range(1, int(sqrt(UpperCamelCase_) + 1)):
if n % i == 0 and i != sqrt(UpperCamelCase_):
total += i + n // i
elif i == sqrt(UpperCamelCase_):
total += i
return total - n
def _A ( UpperCamelCase_ : int = 10000) -> int:
'''simple docstring'''
__lowercase = sum(
i
for i in range(1, UpperCamelCase_)
if sum_of_divisors(sum_of_divisors(UpperCamelCase_)) == i and sum_of_divisors(UpperCamelCase_) != i)
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 17 | 0 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
__snake_case : int = logging.get_logger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> int:
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , _SCREAMING_SNAKE_CASE , )
super().__init__(args=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 353 | '''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=[0, 1, 2, 3] , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=[1, 384, 24, 24] , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , ) -> Tuple:
A_ = parent
A_ = batch_size
A_ = image_size
A_ = patch_size
A_ = num_channels
A_ = is_training
A_ = use_labels
A_ = hidden_size
A_ = num_hidden_layers
A_ = backbone_out_indices
A_ = num_attention_heads
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = initializer_range
A_ = num_labels
A_ = backbone_featmap_shape
A_ = scope
A_ = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
A_ = (image_size // patch_size) ** 2
A_ = num_patches + 1
def __A ( self ) -> Optional[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.image_size, self.image_size] , self.num_labels )
A_ = self.get_config()
return config, pixel_values, labels
def __A ( self ) -> Optional[Any]:
A_ = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [96, 192, 384, 768],
'''num_groups''': 2,
}
return DPTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=_SCREAMING_SNAKE_CASE , backbone_featmap_shape=self.backbone_featmap_shape , )
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
A_ = DPTModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
A_ = self.num_labels
A_ = DPTForDepthEstimation(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
A_ = self.num_labels
A_ = DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def __A ( self ) -> Optional[int]:
A_ = self.prepare_config_and_inputs()
A_ ,A_ ,A_ = config_and_inputs
A_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__lowercase : Optional[int] = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__lowercase : Optional[int] = (
{
'depth-estimation': DPTForDepthEstimation,
'feature-extraction': DPTModel,
'image-segmentation': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__lowercase : Any = False
__lowercase : Tuple = False
__lowercase : List[Any] = False
def __A ( self ) -> Tuple:
A_ = DPTModelTester(self )
A_ = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def __A ( self ) -> Dict:
self.config_tester.run_common_tests()
@unittest.skip(reason='''DPT does not use inputs_embeds''' )
def __A ( self ) -> Union[str, Any]:
pass
def __A ( self ) -> Dict:
A_ ,A_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ = model_class(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , nn.Linear ) )
def __A ( self ) -> Optional[int]:
A_ ,A_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ = model_class(_SCREAMING_SNAKE_CASE )
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] , _SCREAMING_SNAKE_CASE )
def __A ( self ) -> str:
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __A ( self ) -> str:
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE )
def __A ( self ) -> Optional[Any]:
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE )
def __A ( self ) -> Any:
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
A_ ,A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = True
if model_class in get_values(_SCREAMING_SNAKE_CASE ):
continue
A_ = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.train()
A_ = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
A_ = model(**_SCREAMING_SNAKE_CASE ).loss
loss.backward()
def __A ( self ) -> Any:
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
A_ ,A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = False
A_ = True
if model_class in get_values(_SCREAMING_SNAKE_CASE ) or not model_class.supports_gradient_checkpointing:
continue
A_ = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.gradient_checkpointing_enable()
model.train()
A_ = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
A_ = model(**_SCREAMING_SNAKE_CASE ).loss
loss.backward()
def __A ( self ) -> Tuple:
A_ ,A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = _config_zero_init(_SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
A_ = model_class(config=_SCREAMING_SNAKE_CASE )
# Skip the check for the backbone
A_ = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
A_ = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self ) -> int:
pass
@slow
def __A ( self ) -> Dict:
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
A_ = DPTModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def __A ( self ) -> Optional[int]:
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
A_ ,A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = '''add'''
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
A_ = DPTForDepthEstimation(_SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( ) -> Optional[int]:
A_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
@slow
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __A ( self ) -> Any:
A_ = DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''' )
A_ = DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''' ).to(_SCREAMING_SNAKE_CASE )
A_ = prepare_img()
A_ = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(_SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
A_ = model(**_SCREAMING_SNAKE_CASE )
A_ = outputs.predicted_depth
# verify the predicted depth
A_ = torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape , _SCREAMING_SNAKE_CASE )
A_ = torch.tensor(
[[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]] ).to(_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , _SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 18 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase__ :
def __init__(self , UpperCAmelCase ) -> Any:
_lowercase =str(id_ )
_lowercase =None
_lowercase =None
_lowercase =[]
_lowercase ={} # {vertex:distance}
def __lt__(self , UpperCAmelCase ) -> List[str]:
return self.key < other.key
def __repr__(self ) -> str:
return self.id
def __A (self , UpperCAmelCase ) -> Dict:
self.neighbors.append(UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase =weight
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case ) -> List[str]:
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __snake_case )
graph[b - 1].add_edge(graph[a - 1] , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> list:
"""simple docstring"""
_lowercase =[]
for u in graph:
_lowercase =math.inf
_lowercase =None
_lowercase =0
_lowercase =graph[:]
while q:
_lowercase =min(__snake_case )
q.remove(__snake_case )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_lowercase =u
_lowercase =u.edges[v.id]
for i in range(1 , len(__snake_case ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Iterator[tuple]:
"""simple docstring"""
for u in graph:
_lowercase =math.inf
_lowercase =None
_lowercase =0
_lowercase =list(__snake_case )
hq.heapify(__snake_case )
while h:
_lowercase =hq.heappop(__snake_case )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_lowercase =u
_lowercase =u.edges[v.id]
hq.heapify(__snake_case )
for i in range(1 , len(__snake_case ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase_ ( ) -> None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
def UpperCAmelCase_ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
_lowercase =MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
_lowercase =[144, 192, 240]
_lowercase =[16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
_lowercase =[96, 120, 144]
_lowercase =[16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
_lowercase =[64, 80, 96]
_lowercase =[16, 16, 24, 48, 64, 80, 320]
_lowercase =0.05
_lowercase =2.0
if mobilevit_name.startswith('''deeplabv3_''' ):
_lowercase =512
_lowercase =16
_lowercase =21
_lowercase ='''pascal-voc-id2label.json'''
else:
_lowercase =1000
_lowercase ='''imagenet-1k-id2label.json'''
_lowercase ='''huggingface/label-files'''
_lowercase =json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='''dataset''' ) , '''r''' ) )
_lowercase ={int(__snake_case ): v for k, v in idalabel.items()}
_lowercase =idalabel
_lowercase ={v: k for k, v in idalabel.items()}
return config
def UpperCAmelCase_ ( __snake_case , __snake_case=False ) -> Tuple:
"""simple docstring"""
for i in range(1 , 6 ):
if F"layer_{i}." in name:
_lowercase =name.replace(F"layer_{i}." , F"encoder.layer.{i - 1}." )
if "conv_1." in name:
_lowercase =name.replace('''conv_1.''' , '''conv_stem.''' )
if ".block." in name:
_lowercase =name.replace('''.block.''' , '''.''' )
if "exp_1x1" in name:
_lowercase =name.replace('''exp_1x1''' , '''expand_1x1''' )
if "red_1x1" in name:
_lowercase =name.replace('''red_1x1''' , '''reduce_1x1''' )
if ".local_rep.conv_3x3." in name:
_lowercase =name.replace('''.local_rep.conv_3x3.''' , '''.conv_kxk.''' )
if ".local_rep.conv_1x1." in name:
_lowercase =name.replace('''.local_rep.conv_1x1.''' , '''.conv_1x1.''' )
if ".norm." in name:
_lowercase =name.replace('''.norm.''' , '''.normalization.''' )
if ".conv." in name:
_lowercase =name.replace('''.conv.''' , '''.convolution.''' )
if ".conv_proj." in name:
_lowercase =name.replace('''.conv_proj.''' , '''.conv_projection.''' )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F".{i}.{j}." in name:
_lowercase =name.replace(F".{i}.{j}." , F".{i}.layer.{j}." )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F".{i}.{j}." in name:
_lowercase =name.replace(F".{i}.{j}." , F".{i}." )
if "expand_1x1" in name:
_lowercase =name.replace('''expand_1x1''' , '''downsampling_layer.expand_1x1''' )
if "conv_3x3" in name:
_lowercase =name.replace('''conv_3x3''' , '''downsampling_layer.conv_3x3''' )
if "reduce_1x1" in name:
_lowercase =name.replace('''reduce_1x1''' , '''downsampling_layer.reduce_1x1''' )
for i in range(2 , 5 ):
if F".global_rep.{i}.weight" in name:
_lowercase =name.replace(F".global_rep.{i}.weight" , '''.layernorm.weight''' )
if F".global_rep.{i}.bias" in name:
_lowercase =name.replace(F".global_rep.{i}.bias" , '''.layernorm.bias''' )
if ".global_rep." in name:
_lowercase =name.replace('''.global_rep.''' , '''.transformer.''' )
if ".pre_norm_mha.0." in name:
_lowercase =name.replace('''.pre_norm_mha.0.''' , '''.layernorm_before.''' )
if ".pre_norm_mha.1.out_proj." in name:
_lowercase =name.replace('''.pre_norm_mha.1.out_proj.''' , '''.attention.output.dense.''' )
if ".pre_norm_ffn.0." in name:
_lowercase =name.replace('''.pre_norm_ffn.0.''' , '''.layernorm_after.''' )
if ".pre_norm_ffn.1." in name:
_lowercase =name.replace('''.pre_norm_ffn.1.''' , '''.intermediate.dense.''' )
if ".pre_norm_ffn.4." in name:
_lowercase =name.replace('''.pre_norm_ffn.4.''' , '''.output.dense.''' )
if ".transformer." in name:
_lowercase =name.replace('''.transformer.''' , '''.transformer.layer.''' )
if ".aspp_layer." in name:
_lowercase =name.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in name:
_lowercase =name.replace('''.aspp_pool.''' , '''.''' )
if "seg_head." in name:
_lowercase =name.replace('''seg_head.''' , '''segmentation_head.''' )
if "segmentation_head.classifier.classifier." in name:
_lowercase =name.replace('''segmentation_head.classifier.classifier.''' , '''segmentation_head.classifier.''' )
if "classifier.fc." in name:
_lowercase =name.replace('''classifier.fc.''' , '''classifier.''' )
elif (not base_model) and ("segmentation_head." not in name):
_lowercase ='''mobilevit.''' + name
return name
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case=False ) -> Optional[Any]:
"""simple docstring"""
if base_model:
_lowercase =''''''
else:
_lowercase ='''mobilevit.'''
for key in orig_state_dict.copy().keys():
_lowercase =orig_state_dict.pop(__snake_case )
if key[:8] == "encoder.":
_lowercase =key[8:]
if "qkv" in key:
_lowercase =key.split('''.''' )
_lowercase =int(key_split[0][6:] ) - 1
_lowercase =int(key_split[3] )
_lowercase =model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}" )
_lowercase =layer.transformer.layer[transformer_num].attention.attention.all_head_size
_lowercase =(
F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
)
if "weight" in key:
_lowercase =val[:dim, :]
_lowercase =val[dim : dim * 2, :]
_lowercase =val[-dim:, :]
else:
_lowercase =val[:dim]
_lowercase =val[dim : dim * 2]
_lowercase =val[-dim:]
else:
_lowercase =val
return orig_state_dict
def UpperCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
_lowercase ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
_lowercase =Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
@torch.no_grad()
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case=False ) -> int:
"""simple docstring"""
_lowercase =get_mobilevit_config(__snake_case )
# load original state_dict
_lowercase =torch.load(__snake_case , map_location='''cpu''' )
# load 🤗 model
if mobilevit_name.startswith('''deeplabv3_''' ):
_lowercase =MobileViTForSemanticSegmentation(__snake_case ).eval()
else:
_lowercase =MobileViTForImageClassification(__snake_case ).eval()
_lowercase =convert_state_dict(__snake_case , __snake_case )
model.load_state_dict(__snake_case )
# Check outputs on an image, prepared by MobileViTImageProcessor
_lowercase =MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_lowercase =image_processor(images=prepare_img() , return_tensors='''pt''' )
_lowercase =model(**__snake_case )
_lowercase =outputs.logits
if mobilevit_name.startswith('''deeplabv3_''' ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
_lowercase =torch.tensor(
[
[[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
[[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
[[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
_lowercase =torch.tensor(
[
[[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
[[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
[[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
_lowercase =torch.tensor(
[
[[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
[[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
[[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
] )
else:
raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3, :3, :3] , __snake_case , atol=1e-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
_lowercase =torch.tensor([-0.98_66, 0.23_92, -1.12_41] )
elif mobilevit_name == "mobilevit_xs":
_lowercase =torch.tensor([-2.47_61, -0.93_99, -1.95_87] )
elif mobilevit_name == "mobilevit_xxs":
_lowercase =torch.tensor([-1.93_64, -1.23_27, -0.46_53] )
else:
raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3] , __snake_case , atol=1e-4 )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(__snake_case )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__snake_case )
if push_to_hub:
_lowercase ={
'''mobilevit_s''': '''mobilevit-small''',
'''mobilevit_xs''': '''mobilevit-x-small''',
'''mobilevit_xxs''': '''mobilevit-xx-small''',
'''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
'''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
'''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
}
print('''Pushing to the hub...''' )
_lowercase =model_mapping[mobilevit_name]
image_processor.push_to_hub(__snake_case , organization='''apple''' )
model.push_to_hub(__snake_case , organization='''apple''' )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, 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 to the 🤗 hub.'''
)
UpperCAmelCase__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 5 | 1 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
lowercase : Optional[int] = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
"cache_dir",
"data_dir",
"all_configs",
"save_infos",
"ignore_verifications",
"force_redownload",
"clear_cache",
],
defaults=[None, None, None, False, False, False, False, False],
)
def UpperCAmelCase_ (_lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ):
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def UpperCAmelCase_ (_lowerCAmelCase : List[Any] ):
__UpperCamelCase : Optional[Any] = _TestCommandArgs(dataset=_lowerCAmelCase , all_configs=_lowerCAmelCase , save_infos=_lowerCAmelCase )
__UpperCamelCase : Optional[Any] = TestCommand(*_lowerCAmelCase )
test_command.run()
__UpperCamelCase : Union[str, Any] = os.path.join(_lowerCAmelCase , "README.md" )
assert os.path.exists(_lowerCAmelCase )
__UpperCamelCase : Optional[Any] = DatasetInfosDict.from_directory(_lowerCAmelCase )
__UpperCamelCase : List[str] = DatasetInfosDict(
{
"default": DatasetInfo(
features=Features(
{
"tokens": Sequence(Value("string" ) ),
"ner_tags": Sequence(
ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"] ) ),
"langs": Sequence(Value("string" ) ),
"spans": Sequence(Value("string" ) ),
} ) , splits=[
{
"name": "train",
"num_bytes": 2_35_15_63,
"num_examples": 1_00_00,
},
{
"name": "validation",
"num_bytes": 23_84_18,
"num_examples": 10_00,
},
] , download_size=3_94_06_80 , dataset_size=2_58_99_81 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
__UpperCamelCase , __UpperCamelCase : List[Any] = getattr(dataset_infos["default"] , _lowerCAmelCase ), getattr(expected_dataset_infos["default"] , _lowerCAmelCase )
if key == "num_bytes":
assert is_apercent_close(_lowerCAmelCase , _lowerCAmelCase )
elif key == "splits":
assert list(_lowerCAmelCase ) == list(_lowerCAmelCase )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected | 171 |
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00 ):
__UpperCamelCase : Tuple = n * (n + 1) * (2 * n + 1) / 6
__UpperCamelCase : List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""") | 171 | 1 |
UpperCAmelCase__ = [0, 2, 4, 6, 8]
UpperCAmelCase__ = [1, 3, 5, 7, 9]
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_UpperCAmelCase = 0
for digit in range(10 ):
_UpperCAmelCase = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , _UpperCAmelCase , _UpperCAmelCase )
return result
_UpperCAmelCase = 0
for digita in range(10 ):
_UpperCAmelCase = digita
if (remainder + digita) % 2 == 0:
_UpperCAmelCase = ODD_DIGITS
else:
_UpperCAmelCase = EVEN_DIGITS
for digita in other_parity_digits:
_UpperCAmelCase = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , _UpperCAmelCase , _UpperCAmelCase , )
return result
def A ( _UpperCAmelCase : int = 9 ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(_UpperCAmelCase , 0 , [0] * length , _UpperCAmelCase )
return result
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A_ : List[Any] ={"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
A_ : Any =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
"""simple docstring"""
from math import factorial
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 20 )-> int:
_lowerCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_lowerCamelCase = n // 2
return int(factorial(snake_case ) / (factorial(snake_case ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(2_0))
else:
try:
A_ : Optional[Any] =int(sys.argv[1])
print(solution(n))
except ValueError:
print("""Invalid entry - please enter a number.""")
| 80 | 1 |
from __future__ import annotations
def UpperCamelCase( __UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Any ):
if len(__lowerCAmelCase ) == 0:
return False
lowerCAmelCase_ : List[str] = len(__lowerCAmelCase ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] ,__lowerCAmelCase )
else:
return binary_search(a_list[midpoint + 1 :] ,__lowerCAmelCase )
if __name__ == "__main__":
A__ : Union[str, Any] = input('''Enter numbers separated by comma:\n''').strip()
A__ : Optional[int] = [int(item.strip()) for item in user_input.split(''',''')]
A__ : str = int(input('''Enter the number to be found in the list:\n''').strip())
A__ : Any = '''''' if binary_search(sequence, target) else '''not '''
print(F'''{target} was {not_str}found in {sequence}''')
| 103 |
'''simple docstring'''
def __lowerCAmelCase ():
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
lowerCamelCase__ = generate_large_matrix()
lowerCamelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def __lowerCAmelCase (__lowerCAmelCase ):
assert all(row == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for row in grid )
assert all(list(__lowerCAmelCase ) == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for col in zip(*__lowerCAmelCase ) )
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : Any = 0
_UpperCAmelCase : str = len(__lowerCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_UpperCAmelCase : Union[str, Any] = (left + right) // 2
_UpperCAmelCase : List[str] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_UpperCAmelCase : Tuple = mid + 1
else:
_UpperCAmelCase : Optional[Any] = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__lowerCAmelCase )
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : int = len(grid[0] )
for i in range(len(__lowerCAmelCase ) ):
_UpperCAmelCase : Dict = find_negative_index(grid[i][:bound] )
total += bound
return (len(__lowerCAmelCase ) * len(grid[0] )) - total
def __lowerCAmelCase (__lowerCAmelCase ):
return len([number for row in grid for number in row if number < 0] )
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : Tuple = 0
for row in grid:
for i, number in enumerate(__lowerCAmelCase ):
if number < 0:
total += len(__lowerCAmelCase ) - i
break
return total
def __lowerCAmelCase ():
from timeit import timeit
print("Running benchmarks" )
_UpperCAmelCase : Tuple = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_UpperCAmelCase : str = timeit(F"""{func}(grid=grid)""" , setup=__lowerCAmelCase , number=500 )
print(F"""{func}() took {time:0.4f} seconds""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 234 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''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
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 352 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class snake_case__(unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[int]=100 , SCREAMING_SNAKE_CASE : Tuple=13 , SCREAMING_SNAKE_CASE : str=30 , SCREAMING_SNAKE_CASE : Union[str, Any]=2 , SCREAMING_SNAKE_CASE : Tuple=3 , SCREAMING_SNAKE_CASE : List[Any]=True , SCREAMING_SNAKE_CASE : int=True , SCREAMING_SNAKE_CASE : Dict=32 , SCREAMING_SNAKE_CASE : str=5 , SCREAMING_SNAKE_CASE : Any=4 , SCREAMING_SNAKE_CASE : str=37 , SCREAMING_SNAKE_CASE : Any="gelu" , SCREAMING_SNAKE_CASE : Tuple=0.1 , SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE : Union[str, Any]=10 , SCREAMING_SNAKE_CASE : Dict=0.02 , SCREAMING_SNAKE_CASE : Any=3 , ):
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = vocab_size
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Tuple = num_channels
lowercase__ : Any = is_training
lowercase__ : str = use_labels
lowercase__ : List[Any] = hidden_size
lowercase__ : Optional[int] = num_hidden_layers
lowercase__ : Dict = num_attention_heads
lowercase__ : Optional[int] = intermediate_size
lowercase__ : int = hidden_act
lowercase__ : str = hidden_dropout_prob
lowercase__ : Union[str, Any] = attention_probs_dropout_prob
lowercase__ : int = type_sequence_label_size
lowercase__ : Optional[int] = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase__ : str = (image_size // patch_size) ** 2
lowercase__ : List[str] = num_patches + 1
def snake_case ( self : Tuple ):
lowercase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Union[str, Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : int = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[Any] ):
lowercase__ : Optional[Any] = FlaxBeitModel(config=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ):
lowercase__ : int = FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any ):
lowercase__ : Tuple = self.type_sequence_label_size
lowercase__ : Optional[int] = FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE )
lowercase__ : str = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ : int = 1
lowercase__ : List[str] = FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE )
lowercase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ : List[str] = model(SCREAMING_SNAKE_CASE )
def snake_case ( self : Any ):
lowercase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : str = config_and_inputs
lowercase__ : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class snake_case__(_UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowercase_ = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def snake_case ( self : Any ):
lowercase__ : List[Any] = FlaxBeitModelTester(self )
lowercase__ : str = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def snake_case ( self : int ):
self.config_tester.run_common_tests()
def snake_case ( self : int ):
lowercase__ , lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : str = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[int] = [*signature.parameters.keys()]
lowercase__ : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def snake_case ( self : List[Any] ):
lowercase__ , lowercase__ : 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__ ):
lowercase__ : Optional[int] = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = model_class(SCREAMING_SNAKE_CASE )
@jax.jit
def model_jitted(SCREAMING_SNAKE_CASE : Optional[Any] , **SCREAMING_SNAKE_CASE : List[Any] ):
return model(pixel_values=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
with self.subTest("JIT Enabled" ):
lowercase__ : Union[str, Any] = model_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowercase__ : Optional[int] = model_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , len(SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
def snake_case ( self : Optional[int] ):
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def snake_case ( self : int ):
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[Any] ):
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : Optional[int] ):
for model_class_name in self.all_model_classes:
lowercase__ : Any = model_class_name.from_pretrained("microsoft/beit-base-patch16-224" )
lowercase__ : Optional[int] = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@require_flax
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self : int ):
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def snake_case ( self : Union[str, Any] ):
lowercase__ : Tuple = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" )
lowercase__ : int = self.default_image_processor
lowercase__ : Union[str, Any] = prepare_img()
lowercase__ : str = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="np" ).pixel_values
# prepare bool_masked_pos
lowercase__ : Optional[Any] = np.ones((1, 196) , dtype=SCREAMING_SNAKE_CASE )
# forward pass
lowercase__ : Any = model(pixel_values=SCREAMING_SNAKE_CASE , bool_masked_pos=SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = outputs.logits
# verify the logits
lowercase__ : List[str] = (1, 196, 8_192)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : Dict = np.array(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , SCREAMING_SNAKE_CASE , atol=1E-2 ) )
@slow
def snake_case ( self : Any ):
lowercase__ : Union[str, Any] = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : List[Any] = prepare_img()
lowercase__ : Optional[Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="np" )
# forward pass
lowercase__ : str = model(**SCREAMING_SNAKE_CASE )
lowercase__ : Dict = outputs.logits
# verify the logits
lowercase__ : List[str] = (1, 1_000)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = np.array([-1.2_385, -1.0_987, -1.0_108] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
lowercase__ : str = 281
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : str ):
lowercase__ : List[Any] = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" )
lowercase__ : Dict = self.default_image_processor
lowercase__ : Dict = prepare_img()
lowercase__ : List[Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="np" )
# forward pass
lowercase__ : Union[str, Any] = model(**SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = outputs.logits
# verify the logits
lowercase__ : int = (1, 21_841)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : str = np.array([1.6_881, -0.2_787, 0.5_901] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
lowercase__ : Union[str, Any] = 2_396
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE )
| 121 | 0 |
def UpperCamelCase__ ( A__ ) -> list[list]:
snake_case__ : Union[str, Any] = current_set.copy()
for row_index, row in enumerate(A__ ):
snake_case__ : Any = row[0]
for column_index, column in enumerate(A__ ):
if magnitude == 0:
snake_case__ : List[str] = column
continue
snake_case__ : int = column / magnitude
# Subtract to cancel term
snake_case__ : Union[str, Any] = current_set[0]
snake_case__ : List[Any] = [first_row]
snake_case__ : Dict = current_set[1::]
for row in current_set:
snake_case__ : str = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(A__ )
continue
for column_index in range(len(A__ ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(A__ )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
snake_case__ : Dict = final_set[0]
snake_case__ : List[str] = []
snake_case__ : Dict = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
snake_case__ : Any = simplify(A__ )
for i in range(len(A__ ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , A__ )
snake_case__ : Any = resultant
return final_set
def UpperCamelCase__ ( A__ ) -> list:
if len(A__ ) == 0:
raise IndexError('solve_simultaneous() requires n lists of length n+1' )
snake_case__ : List[Any] = len(A__ ) + 1
if any(len(A__ ) != _length for item in equations ):
raise IndexError('solve_simultaneous() requires n lists of length n+1' )
for row in equations:
if any(not isinstance(A__ , (int, float) ) for column in row ):
raise ValueError('solve_simultaneous() requires lists of integers' )
if len(A__ ) == 1:
return [equations[0][-1] / equations[0][0]]
snake_case__ : Dict = equations.copy()
if any(0 in row for row in data_set ):
snake_case__ : Dict = data_set.copy()
snake_case__ : Tuple = []
for row_index, row in enumerate(A__ ):
if 0 not in row:
snake_case__ : str = data_set.pop(A__ )
break
if not full_row:
raise ValueError('solve_simultaneous() requires at least 1 full equation' )
data_set.insert(0 , A__ )
snake_case__ : Tuple = data_set.copy()
snake_case__ : Optional[int] = simplify(A__ )
snake_case__ : Tuple = simplified[::-1]
snake_case__ : list = []
for row in simplified:
snake_case__ : Tuple = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
snake_case__ : Any = row.copy()[: len(A__ ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(A__ ) == 0:
solutions.append(0 )
continue
snake_case__ : Dict = temp_row[1::]
snake_case__ : str = temp_row[::-1]
for column_index, column in enumerate(A__ ):
current_solution -= column * solutions[column_index]
solutions.append(A__ )
snake_case__ : List[Any] = []
for item in solutions:
final.append(float(round(A__ , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ : List[Any] = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 143 | from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __snake_case :
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=30 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=10 , __UpperCamelCase=0.0_2 , __UpperCamelCase=3 , __UpperCamelCase=None , ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Any = parent
snake_case__ : List[Any] = batch_size
snake_case__ : str = image_size
snake_case__ : Union[str, Any] = patch_size
snake_case__ : int = num_channels
snake_case__ : Union[str, Any] = is_training
snake_case__ : Optional[int] = use_labels
snake_case__ : str = hidden_size
snake_case__ : Any = num_hidden_layers
snake_case__ : Union[str, Any] = num_attention_heads
snake_case__ : int = intermediate_size
snake_case__ : Any = hidden_act
snake_case__ : int = hidden_dropout_prob
snake_case__ : Optional[int] = attention_probs_dropout_prob
snake_case__ : Optional[Any] = type_sequence_label_size
snake_case__ : int = initializer_range
snake_case__ : Dict = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case__ : int = (image_size // patch_size) ** 2
snake_case__ : Union[str, Any] = num_patches + 1
def __a ( self ) -> List[str]:
'''simple docstring'''
snake_case__ : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ : Union[str, Any] = None
if self.use_labels:
snake_case__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case__ : Any = self.get_config()
return config, pixel_values, labels
def __a ( self ) -> int:
'''simple docstring'''
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Tuple:
'''simple docstring'''
snake_case__ : Optional[Any] = TFViTModel(config=__UpperCamelCase )
snake_case__ : Union[str, Any] = model(__UpperCamelCase , training=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
snake_case__ : Any = self.image_size // 2
snake_case__ : str = pixel_values[:, :, :image_size, :image_size]
snake_case__ : Dict = model(__UpperCamelCase , interpolate_pos_encoding=__UpperCamelCase , training=__UpperCamelCase )
snake_case__ : Any = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Tuple:
'''simple docstring'''
snake_case__ : int = self.type_sequence_label_size
snake_case__ : Optional[int] = TFViTForImageClassification(__UpperCamelCase )
snake_case__ : Tuple = model(__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
snake_case__ : str = self.image_size // 2
snake_case__ : Dict = pixel_values[:, :, :image_size, :image_size]
snake_case__ : Tuple = model(__UpperCamelCase , interpolate_pos_encoding=__UpperCamelCase , training=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case__ : Union[str, Any] = 1
snake_case__ : List[str] = TFViTForImageClassification(__UpperCamelCase )
snake_case__ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case__ : str = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __a ( self ) -> Any:
'''simple docstring'''
snake_case__ : Optional[Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ : Optional[int] = config_and_inputs
snake_case__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class __snake_case ( _lowerCamelCase ,_lowerCamelCase ,unittest.TestCase ):
__lowerCamelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
__lowerCamelCase = (
{"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
if is_tf_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case__ : Dict = TFViTModelTester(self )
snake_case__ : int = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def __a ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='ViT does not use inputs_embeds' )
def __a ( self ) -> Tuple:
'''simple docstring'''
pass
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case__ , snake_case__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Union[str, Any] = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , tf.keras.layers.Layer ) )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ , snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : List[Any] = model_class(__UpperCamelCase )
snake_case__ : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : str = [*signature.parameters.keys()]
snake_case__ : Union[str, Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __a ( self ) -> List[str]:
'''simple docstring'''
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __a ( self ) -> Optional[int]:
'''simple docstring'''
snake_case__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def __a ( self ) -> str:
'''simple docstring'''
snake_case__ : str = TFViTModel.from_pretrained('google/vit-base-patch16-224' )
self.assertIsNotNone(__UpperCamelCase )
def UpperCamelCase__ ( ) -> int:
snake_case__ : str = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class __snake_case ( unittest.TestCase ):
@cached_property
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None
@slow
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Any = TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224' )
snake_case__ : Any = self.default_image_processor
snake_case__ : Union[str, Any] = prepare_img()
snake_case__ : Tuple = image_processor(images=__UpperCamelCase , return_tensors='tf' )
# forward pass
snake_case__ : str = model(**__UpperCamelCase )
# verify the logits
snake_case__ : Optional[int] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case__ : List[Any] = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 )
| 143 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ = SwinConfig()
UpperCAmelCase_ = swin_name.split('_' )
UpperCAmelCase_ = name_split[1]
UpperCAmelCase_ = int(name_split[4] )
UpperCAmelCase_ = int(name_split[3][-1] )
if model_size == "tiny":
UpperCAmelCase_ = 96
UpperCAmelCase_ = (2, 2, 6, 2)
UpperCAmelCase_ = (3, 6, 12, 24)
elif model_size == "small":
UpperCAmelCase_ = 96
UpperCAmelCase_ = (2, 2, 18, 2)
UpperCAmelCase_ = (3, 6, 12, 24)
elif model_size == "base":
UpperCAmelCase_ = 128
UpperCAmelCase_ = (2, 2, 18, 2)
UpperCAmelCase_ = (4, 8, 16, 32)
else:
UpperCAmelCase_ = 192
UpperCAmelCase_ = (2, 2, 18, 2)
UpperCAmelCase_ = (6, 12, 24, 48)
if "in22k" in swin_name:
UpperCAmelCase_ = 21_841
else:
UpperCAmelCase_ = 1_000
UpperCAmelCase_ = """huggingface/label-files"""
UpperCAmelCase_ = """imagenet-1k-id2label.json"""
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_ = img_size
UpperCAmelCase_ = num_classes
UpperCAmelCase_ = embed_dim
UpperCAmelCase_ = depths
UpperCAmelCase_ = num_heads
UpperCAmelCase_ = window_size
return config
def lowercase__ ( __snake_case : List[str] ):
'''simple docstring'''
if "patch_embed.proj" in name:
UpperCAmelCase_ = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
UpperCAmelCase_ = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
UpperCAmelCase_ = """encoder.""" + name
if "attn.proj" in name:
UpperCAmelCase_ = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
UpperCAmelCase_ = name.replace('attn' , 'attention.self' )
if "norm1" in name:
UpperCAmelCase_ = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
UpperCAmelCase_ = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
UpperCAmelCase_ = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
UpperCAmelCase_ = name.replace('mlp.fc2' , 'output.dense' )
if name == "norm.weight":
UpperCAmelCase_ = """layernorm.weight"""
if name == "norm.bias":
UpperCAmelCase_ = """layernorm.bias"""
if "head" in name:
UpperCAmelCase_ = name.replace('head' , 'classifier' )
else:
UpperCAmelCase_ = """swin.""" + name
return name
def lowercase__ ( __snake_case : Any , __snake_case : Any ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCAmelCase_ = orig_state_dict.pop(lowercase__ )
if "mask" in key:
continue
elif "qkv" in key:
UpperCAmelCase_ = key.split('.' )
UpperCAmelCase_ = int(key_split[1] )
UpperCAmelCase_ = int(key_split[3] )
UpperCAmelCase_ = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
UpperCAmelCase_ = val[:dim, :]
UpperCAmelCase_ = val[
dim : dim * 2, :
]
UpperCAmelCase_ = val[-dim:, :]
else:
UpperCAmelCase_ = val[
:dim
]
UpperCAmelCase_ = val[
dim : dim * 2
]
UpperCAmelCase_ = val[
-dim:
]
else:
UpperCAmelCase_ = val
return orig_state_dict
def lowercase__ ( __snake_case : str , __snake_case : Any ):
'''simple docstring'''
UpperCAmelCase_ = timm.create_model(lowercase__ , pretrained=lowercase__ )
timm_model.eval()
UpperCAmelCase_ = get_swin_config(lowercase__ )
UpperCAmelCase_ = SwinForImageClassification(lowercase__ )
model.eval()
UpperCAmelCase_ = convert_state_dict(timm_model.state_dict() , lowercase__ )
model.load_state_dict(lowercase__ )
UpperCAmelCase_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCAmelCase_ = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' , '-' ) ) )
UpperCAmelCase_ = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
UpperCAmelCase_ = image_processor(images=lowercase__ , return_tensors='pt' )
UpperCAmelCase_ = timm_model(inputs['pixel_values'] )
UpperCAmelCase_ = model(**lowercase__ ).logits
assert torch.allclose(lowercase__ , lowercase__ , atol=1E-3 )
print(F"Saving model {swin_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(lowercase__ )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swin_name',
default='swin_tiny_patch4_window7_224',
type=str,
help='Name of the Swin timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
__UpperCAmelCase = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 356 |
from __future__ import annotations
def lowercase__ ( __snake_case : list[int] , __snake_case : int ):
'''simple docstring'''
if len(__snake_case ) == 0:
return False
UpperCAmelCase_ : Optional[int] = len(__snake_case ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __snake_case )
else:
return binary_search(a_list[midpoint + 1 :] , __snake_case )
if __name__ == "__main__":
__UpperCAmelCase = input('Enter numbers separated by comma:\n').strip()
__UpperCAmelCase = [int(item.strip()) for item in user_input.split(',')]
__UpperCAmelCase = int(input('Enter the number to be found in the list:\n').strip())
__UpperCAmelCase = '' if binary_search(sequence, target) else 'not '
print(F'{target} was {not_str}found in {sequence}')
| 145 | 0 |
import re
def lowerCamelCase__ ( snake_case_ : str ) -> list:
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def lowerCamelCase__ ( snake_case_ : str ) -> str:
__snake_case = split_input(str_ )
return "".join(
[''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : bool , snake_case_ : str ) -> str:
try:
__snake_case = split_input(snake_case_ )
if upper:
__snake_case = ''''''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
__snake_case = ''''''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def lowerCamelCase__ ( snake_case_ : str ) -> str:
return to_simple_case(snake_case_ )
def lowerCamelCase__ ( snake_case_ : str ) -> str:
try:
__snake_case = to_simple_case(snake_case_ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : bool ) -> str:
return to_complex_case(snake_case_ , snake_case_ , '''_''' )
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : bool ) -> str:
return to_complex_case(snake_case_ , snake_case_ , '''-''' )
if __name__ == "__main__":
__import__('doctest').testmod()
| 24 |
# Copyright 2021 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 packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
SCREAMING_SNAKE_CASE :List[str] = 'pytorch_model.bin'
SCREAMING_SNAKE_CASE :str = 'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE :Optional[int] = 'adapter_config.json'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.bin'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.safetensors'
SCREAMING_SNAKE_CASE :str = 'tf_model.h5'
SCREAMING_SNAKE_CASE :List[Any] = 'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE :str = 'model.ckpt'
SCREAMING_SNAKE_CASE :List[Any] = 'flax_model.msgpack'
SCREAMING_SNAKE_CASE :Optional[int] = 'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE :Tuple = 'model.safetensors'
SCREAMING_SNAKE_CASE :List[Any] = 'model.safetensors.index.json'
SCREAMING_SNAKE_CASE :str = 'config.json'
SCREAMING_SNAKE_CASE :int = 'preprocessor_config.json'
SCREAMING_SNAKE_CASE :Optional[Any] = FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE :Optional[int] = 'generation_config.json'
SCREAMING_SNAKE_CASE :List[str] = 'modelcard.json'
SCREAMING_SNAKE_CASE :Optional[int] = '▁'
SCREAMING_SNAKE_CASE :Optional[Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
SCREAMING_SNAKE_CASE :str = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE :Optional[Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE :List[Any] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
__A = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__A = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 15 | 0 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase : List[str] = 637_8137.0
UpperCAmelCase : List[str] = 635_6752.31_4245
UpperCAmelCase : Any = 637_8137
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
lowercase_ = atan((1 - flattening) * tan(radians(lowerCAmelCase__ ) ) )
lowercase_ = atan((1 - flattening) * tan(radians(lowerCAmelCase__ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
lowercase_ = haversine_distance(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
lowercase_ = (b_lata + b_lata) / 2
lowercase_ = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
lowercase_ = (sin(lowerCAmelCase__ ) ** 2) * (cos(lowerCAmelCase__ ) ** 2)
lowercase_ = cos(sigma / 2 ) ** 2
lowercase_ = (sigma - sin(lowerCAmelCase__ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
lowercase_ = (cos(lowerCAmelCase__ ) ** 2) * (sin(lowerCAmelCase__ ) ** 2)
lowercase_ = sin(sigma / 2 ) ** 2
lowercase_ = (sigma + sin(lowerCAmelCase__ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase : Dict = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[MinHash]:
'''simple docstring'''
if len(__lowerCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase_ = MinHash(num_perm=__lowerCAmelCase )
for token in set(__lowerCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(__lowerCAmelCase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , *,
lowerCAmelCase_ : float = 0.85 , ):
"""simple docstring"""
lowercase_ = duplication_jaccard_threshold
lowercase_ = NUM_PERM
lowercase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm)
lowercase_ = defaultdict(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : MinHash):
"""simple docstring"""
lowercase_ = self._index.query(lowerCAmelCase_)
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''')
return
self._index.insert(lowerCAmelCase_ , lowerCAmelCase_)
if len(lowerCAmelCase_) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(lowerCAmelCase_)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowercase_ = [base] + list(lowerCAmelCase_)
# reformat the cluster to be a list of dict
lowercase_ = [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
duplicate_clusters.append(lowerCAmelCase_)
return duplicate_clusters
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.get_duplicate_clusters()
with open(lowerCAmelCase_ , """w""") as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ = element
lowercase_ = get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__lowerCAmelCase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase ) ) , max_queue_size=1_00 ) ):
di.add(__lowerCAmelCase , __lowerCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase_ = get_tokens(__lowerCAmelCase )
lowercase_ = get_tokens(__lowerCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for elementa in cluster:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
for elementa in extremes:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
if jaccard_similarity(__lowerCAmelCase , __lowerCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase_ = 1
extremes.append(__lowerCAmelCase )
return extremes
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
global _shared_dataset
lowercase_ = dataset
lowercase_ = []
lowercase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=__lowerCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__lowerCAmelCase , __lowerCAmelCase , ) , total=len(__lowerCAmelCase ) , ):
extremes_list.append(__lowerCAmelCase )
return extremes_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
lowercase_ = make_duplicate_clusters(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
lowercase_ = {}
lowercase_ = find_extremes(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase_ = element
lowercase_ = duplicate_indices - set(extreme_dict.keys() )
lowercase_ = dataset.filter(lambda __lowerCAmelCase , __lowerCAmelCase : idx not in remove_indices , with_indices=__lowerCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase_ = element["""base_index"""] in extreme_dict
if element["is_extreme"]:
lowercase_ = extreme_dict[element["""base_index"""]]["""copies"""]
print(F'''Original dataset size: {len(__lowerCAmelCase )}''' )
print(F'''Number of duplicate clusters: {len(__lowerCAmelCase )}''' )
print(F'''Files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Filtered dataset size: {len(__lowerCAmelCase )}''' )
return ds_filter, duplicate_clusters
| 313 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
UpperCamelCase__ = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCamelCase__ = "CIDAS/clipseg-rd64-refined"
UpperCamelCase__ = "image_segmenter"
UpperCamelCase__ = CLIPSegForImageSegmentation
UpperCamelCase__ = ["image", "text"]
UpperCamelCase__ = ["image"]
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ):
"""simple docstring"""
requires_backends(self , ['vision'] )
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=UpperCAmelCase , return_tensors='pt' )
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
with torch.no_grad():
_UpperCAmelCase = self.model(**UpperCAmelCase ).logits
return logits
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = outputs.cpu().detach().numpy()
_UpperCAmelCase = 0
_UpperCAmelCase = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 39 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase = "\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)[\"depth\"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline(\"depth-estimation\")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n\n >>> img = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n >>> prompt = \"A robot, 4k photo\"\n >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save(\"robot_cat.png\")\n ```\n"
def __UpperCAmelCase ( a_ , a_ , a_=8):
snake_case_ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
snake_case_ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , a , a , a , ) -> Tuple:
super().__init__()
self.register_modules(
unet=a , scheduler=a , movq=a , )
snake_case_ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def _UpperCamelCase ( self , a , a , a , a , a , a ) -> Any:
if latents is None:
snake_case_ = randn_tensor(a , generator=a , device=a , dtype=a )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
snake_case_ = latents.to(a )
snake_case_ = latents * scheduler.init_noise_sigma
return latents
def _UpperCamelCase ( self , a=0 ) -> str:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
snake_case_ = torch.device(F'''cuda:{gpu_id}''' )
snake_case_ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(a , a )
def _UpperCamelCase ( self , a=0 ) -> List[str]:
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
snake_case_ = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=a )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
snake_case_ = None
for cpu_offloaded_model in [self.unet, self.movq]:
snake_case_ , snake_case_ = cpu_offload_with_hook(a , a , prev_module_hook=a )
# We'll offload the last model manually.
snake_case_ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _UpperCamelCase ( self ) -> Any:
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(a , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(a )
def __call__( self , a , a , a , a = 5_12 , a = 5_12 , a = 1_00 , a = 4.0 , a = 1 , a = None , a = None , a = "pil" , a = True , ) -> List[str]:
snake_case_ = self._execution_device
snake_case_ = guidance_scale > 1.0
if isinstance(a , a ):
snake_case_ = torch.cat(a , dim=0 )
if isinstance(a , a ):
snake_case_ = torch.cat(a , dim=0 )
if isinstance(a , a ):
snake_case_ = torch.cat(a , dim=0 )
snake_case_ = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
snake_case_ = image_embeds.repeat_interleave(a , dim=0 )
snake_case_ = negative_image_embeds.repeat_interleave(a , dim=0 )
snake_case_ = hint.repeat_interleave(a , dim=0 )
snake_case_ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=a )
snake_case_ = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=a )
self.scheduler.set_timesteps(a , device=a )
snake_case_ = self.scheduler.timesteps
snake_case_ = self.movq.config.latent_channels
snake_case_ , snake_case_ = downscale_height_and_width(a , a , self.movq_scale_factor )
# create initial latent
snake_case_ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , a , a , a , self.scheduler , )
for i, t in enumerate(self.progress_bar(a ) ):
# expand the latents if we are doing classifier free guidance
snake_case_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case_ = {'image_embeds': image_embeds, 'hint': hint}
snake_case_ = self.unet(
sample=a , timestep=a , encoder_hidden_states=a , added_cond_kwargs=a , return_dict=a , )[0]
if do_classifier_free_guidance:
snake_case_ , snake_case_ = noise_pred.split(latents.shape[1] , dim=1 )
snake_case_ , snake_case_ = noise_pred.chunk(2 )
snake_case_ , snake_case_ = variance_pred.chunk(2 )
snake_case_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
snake_case_ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
snake_case_ , snake_case_ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(
a , a , a , generator=a , )[0]
# post-processing
snake_case_ = self.movq.decode(a , force_not_quantize=a )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
snake_case_ = image * 0.5 + 0.5
snake_case_ = image.clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a )
| 178 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _lowerCAmelCase:
"""simple docstring"""
@staticmethod
def _a ( *_lowerCamelCase , **_lowerCamelCase ):
pass
def snake_case (UpperCAmelCase__ ) -> List[Any]:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
A_ : Any = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
a : Union[str, Any] =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: List[Any] = pipeline(
'document-question-answering' , model=_lowerCamelCase , tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
UpperCamelCase_: List[Any] = INVOICE_URL
UpperCamelCase_: Tuple = list(zip(*apply_tesseract(load_image(_lowerCamelCase ) , _lowerCamelCase , '' ) ) )
UpperCamelCase_: int = 'What is the placebo?'
UpperCamelCase_: List[Any] = [
{
'image': load_image(_lowerCamelCase ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Optional[Any] = dqa_pipeline(_lowerCamelCase , top_k=2 )
self.assertEqual(
_lowerCamelCase , [
[
{'score': ANY(_lowerCamelCase ), 'answer': ANY(_lowerCamelCase ), 'start': ANY(_lowerCamelCase ), 'end': ANY(_lowerCamelCase )},
{'score': ANY(_lowerCamelCase ), 'answer': ANY(_lowerCamelCase ), 'start': ANY(_lowerCamelCase ), 'end': ANY(_lowerCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self ):
UpperCamelCase_: Optional[Any] = pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
UpperCamelCase_: Tuple = INVOICE_URL
UpperCamelCase_: Optional[Any] = 'How many cats are there?'
UpperCamelCase_: str = [
{'score': 0.0_0_0_1, 'answer': 'oy 2312/2019', 'start': 3_8, 'end': 3_9},
{'score': 0.0_0_0_1, 'answer': 'oy 2312/2019 DUE', 'start': 3_8, 'end': 4_0},
]
UpperCamelCase_: Dict = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(_lowerCamelCase , decimals=4 ) , _lowerCamelCase )
UpperCamelCase_: List[Any] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(_lowerCamelCase , decimals=4 ) , _lowerCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
UpperCamelCase_: List[Any] = './tests/fixtures/tests_samples/COCO/000000039769.png'
UpperCamelCase_: int = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(_lowerCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
UpperCamelCase_: Optional[int] = './tests/fixtures/tests_samples/COCO/000000039769.png'
UpperCamelCase_: Any = []
UpperCamelCase_: Union[str, Any] = []
UpperCamelCase_: str = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , words=_lowerCamelCase , boxes=_lowerCamelCase , top_k=2 )
self.assertEqual(_lowerCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self ):
UpperCamelCase_: Any = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
UpperCamelCase_: List[str] = INVOICE_URL
UpperCamelCase_: Union[str, Any] = 'What is the invoice number?'
UpperCamelCase_: Optional[int] = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
UpperCamelCase_: str = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
UpperCamelCase_: Any = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self ):
UpperCamelCase_: Optional[int] = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=5_0 , )
UpperCamelCase_: Union[str, Any] = INVOICE_URL
UpperCamelCase_: List[str] = 'What is the invoice number?'
UpperCamelCase_: List[Any] = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
UpperCamelCase_: Optional[int] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
UpperCamelCase_: List[str] = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _a ( self ):
UpperCamelCase_: List[Any] = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=_lowerCamelCase )
UpperCamelCase_: Optional[int] = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=_lowerCamelCase , revision='3dc6de3' , )
UpperCamelCase_: Optional[Any] = INVOICE_URL
UpperCamelCase_: Tuple = 'What is the invoice number?'
UpperCamelCase_: Dict = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
] , )
UpperCamelCase_: Tuple = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
] , )
UpperCamelCase_: Tuple = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
]
]
* 2 , )
UpperCamelCase_: str = list(zip(*apply_tesseract(load_image(_lowerCamelCase ) , _lowerCamelCase , '' ) ) )
# This model should also work if `image` is set to None
UpperCamelCase_: List[str] = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _a ( self ):
UpperCamelCase_: int = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=_lowerCamelCase )
UpperCamelCase_: List[Any] = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=_lowerCamelCase , revision='3dc6de3' , max_seq_len=5_0 , )
UpperCamelCase_: List[str] = INVOICE_URL
UpperCamelCase_: int = 'What is the invoice number?'
UpperCamelCase_: List[Any] = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
UpperCamelCase_: str = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
]
]
* 2 , )
UpperCamelCase_: Tuple = list(zip(*apply_tesseract(load_image(_lowerCamelCase ) , _lowerCamelCase , '' ) ) )
# This model should also work if `image` is set to None
UpperCamelCase_: Any = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
@slow
@require_torch
def _a ( self ):
UpperCamelCase_: Tuple = pipeline(
'document-question-answering' , model='naver-clova-ix/donut-base-finetuned-docvqa' , tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) , feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' , )
UpperCamelCase_: Optional[int] = INVOICE_URL
UpperCamelCase_: List[Any] = 'What is the invoice number?'
UpperCamelCase_: Tuple = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(_lowerCamelCase , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def _a ( self ):
pass | 292 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _lowerCAmelCase( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
a : Optional[int] =RoFormerTokenizer
a : int =RoFormerTokenizerFast
a : int =True
a : Optional[int] =True
def _a ( self ):
super().setUp()
def _a ( self , **_lowerCamelCase ):
return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **_lowerCamelCase )
def _a ( self , **_lowerCamelCase ):
return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: Optional[int] = '永和服装饰品有限公司,今天天气非常好'
UpperCamelCase_: Any = '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
return input_text, output_text
def _a ( self ):
UpperCamelCase_: int = self.get_tokenizer()
UpperCamelCase_ ,UpperCamelCase_: int = self.get_chinese_input_output_texts()
UpperCamelCase_: Tuple = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , output_text.split() )
UpperCamelCase_: Dict = tokens + [tokenizer.unk_token]
UpperCamelCase_: Dict = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
def _a ( self ):
UpperCamelCase_: Optional[Any] = self.get_rust_tokenizer()
UpperCamelCase_ ,UpperCamelCase_: Tuple = self.get_chinese_input_output_texts()
UpperCamelCase_: Optional[Any] = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , output_text.split() )
UpperCamelCase_: str = tokens + [tokenizer.unk_token]
UpperCamelCase_: Optional[Any] = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
def _a ( self ):
pass
def _a ( self ):
pass
def _a ( self ):
pass | 292 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
__UpperCamelCase = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase = '''cuda''' if torch.cuda.is_available() else '''cpu'''
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase=100 , UpperCAmelCase=" " ) -> List[str]:
snake_case_ = text.split(UpperCAmelCase )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(UpperCAmelCase ) , UpperCAmelCase )]
def UpperCAmelCase ( UpperCAmelCase ) -> dict:
snake_case_ , snake_case_ = [], []
for title, text in zip(documents['title'] , documents['text'] ):
if text is not None:
for passage in split_text(UpperCAmelCase ):
titles.append(title if title is not None else '' )
texts.append(UpperCAmelCase )
return {"title": titles, "text": texts}
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> dict:
snake_case_ = ctx_tokenizer(
documents['title'] , documents['text'] , truncation=UpperCAmelCase , padding='longest' , return_tensors='pt' )['input_ids']
snake_case_ = ctx_encoder(input_ids.to(device=UpperCAmelCase ) , return_dict=UpperCAmelCase ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) -> int:
######################################
logger.info('Step 1 - Create the dataset' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
snake_case_ = load_dataset(
'csv' , data_files=[rag_example_args.csv_path] , split='train' , delimiter='\t' , column_names=['title', 'text'] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
snake_case_ = dataset.map(UpperCAmelCase , batched=UpperCAmelCase , num_proc=processing_args.num_proc )
# And compute the embeddings
snake_case_ = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCAmelCase )
snake_case_ = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
snake_case_ = Features(
{'text': Value('string' ), 'title': Value('string' ), 'embeddings': Sequence(Value('float32' ) )} ) # optional, save as float32 instead of float64 to save space
snake_case_ = dataset.map(
partial(UpperCAmelCase , ctx_encoder=UpperCAmelCase , ctx_tokenizer=UpperCAmelCase ) , batched=UpperCAmelCase , batch_size=processing_args.batch_size , features=UpperCAmelCase , )
# And finally save your dataset
snake_case_ = os.path.join(rag_example_args.output_dir , 'my_knowledge_dataset' )
dataset.save_to_disk(UpperCAmelCase )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('Step 2 - Index the dataset' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
snake_case_ = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('embeddings' , custom_index=UpperCAmelCase )
# And save the index
snake_case_ = os.path.join(rag_example_args.output_dir , 'my_knowledge_dataset_hnsw_index.faiss' )
dataset.get_index('embeddings' ).save(UpperCAmelCase )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class UpperCamelCase :
SCREAMING_SNAKE_CASE_ = field(
default=str(Path(lowerCAmelCase__ ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) , metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} , )
SCREAMING_SNAKE_CASE_ = field(
default=lowerCAmelCase__ , metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} , )
SCREAMING_SNAKE_CASE_ = field(
default="facebook/rag-sequence-nq" , metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} , )
SCREAMING_SNAKE_CASE_ = field(
default="facebook/dpr-ctx_encoder-multiset-base" , metadata={
"help": (
"The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
" 'facebook/dpr-ctx_encoder-multiset-base'"
)
} , )
SCREAMING_SNAKE_CASE_ = field(
default=str(Path(lowerCAmelCase__ ).parent / "test_run" / "dummy-kb" ) , metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} , )
@dataclass
class UpperCamelCase :
SCREAMING_SNAKE_CASE_ = field(
default=lowerCAmelCase__ , metadata={
"help": "The number of processes to use to split the documents into passages. Default is single process."
} , )
SCREAMING_SNAKE_CASE_ = field(
default=1_6 , metadata={
"help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
} , )
@dataclass
class UpperCamelCase :
SCREAMING_SNAKE_CASE_ = field(
default=7_6_8 , metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} , )
SCREAMING_SNAKE_CASE_ = field(
default=1_2_8 , metadata={
"help": (
"The number of bi-directional links created for every new element during the HNSW index construction."
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
__UpperCamelCase = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
__UpperCamelCase = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 69 | from math import factorial, radians
def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 1_8 , lowerCAmelCase : int = 1_0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
SCREAMING_SNAKE_CASE_ : Tuple = radians(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = angle_in_radians
SCREAMING_SNAKE_CASE_ : List[str] = 3
SCREAMING_SNAKE_CASE_ : str = -1
for _ in range(lowerCAmelCase ):
result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowerCAmelCase , lowerCAmelCase )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 18 | 0 |
"""simple docstring"""
from math import factorial, radians
def _UpperCamelCase (a__ :float , a__ :int = 18 , a__ :int = 10 ):
"""simple docstring"""
UpperCamelCase__ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
UpperCamelCase__ = radians(a__ )
UpperCamelCase__ = angle_in_radians
UpperCamelCase__ = 3
UpperCamelCase__ = -1
for _ in range(a__ ):
result += (b * (angle_in_radians**a)) / factorial(a__ )
UpperCamelCase__ = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(a__ , a__ )
if __name__ == "__main__":
__import__("doctest").testmod()
| 356 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = """ZinengTang/tvlt-base"""
UpperCamelCase__ = tempfile.mkdtemp()
def _lowerCamelCase ( self , **__lowerCAmelCase ):
return TvltImageProcessor.from_pretrained(self.checkpoint , **__lowerCAmelCase )
def _lowerCamelCase ( self , **__lowerCAmelCase ):
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **__lowerCAmelCase )
def _lowerCamelCase ( self ):
shutil.rmtree(self.tmpdirname )
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.get_image_processor()
UpperCamelCase__ = self.get_feature_extractor()
UpperCamelCase__ = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase__ = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , __lowerCAmelCase )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.get_image_processor()
UpperCamelCase__ = self.get_feature_extractor()
UpperCamelCase__ = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
UpperCamelCase__ = np.ones([12000] )
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" )
UpperCamelCase__ = processor(audio=__lowerCAmelCase , return_tensors="""np""" )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.get_image_processor()
UpperCamelCase__ = self.get_feature_extractor()
UpperCamelCase__ = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
UpperCamelCase__ = np.ones([3, 224, 224] )
UpperCamelCase__ = image_processor(__lowerCAmelCase , return_tensors="""np""" )
UpperCamelCase__ = processor(images=__lowerCAmelCase , return_tensors="""np""" )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.get_image_processor()
UpperCamelCase__ = self.get_feature_extractor()
UpperCamelCase__ = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
UpperCamelCase__ = np.ones([12000] )
UpperCamelCase__ = np.ones([3, 224, 224] )
UpperCamelCase__ = processor(audio=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.get_image_processor()
UpperCamelCase__ = self.get_feature_extractor()
UpperCamelCase__ = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg="""`processor` and `image_processor`+`feature_extractor` model input names do not match""" , )
| 87 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class lowerCAmelCase_ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Optional[int] ):
"""simple docstring"""
pass
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Image ):
'''simple docstring'''
UpperCAmelCase__ = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Image ):
'''simple docstring'''
UpperCAmelCase__ = np.array(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = npimg.shape
return {"hash": hashimage(SCREAMING_SNAKE_CASE__ ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
lowerCAmelCase_ : Dict = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = MaskGenerationPipeline(model=_UpperCAmelCase , image_processor=_UpperCAmelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
pass
@require_tf
@unittest.skip("""Image segmentation not implemented in TF""" )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = pipeline("""mask-generation""" , model="""facebook/sam-vit-huge""" )
UpperCAmelCase__ = image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg""" , points_per_batch=2_56 )
# Shortening by hashing
UpperCAmelCase__ = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.021},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
{"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9967},
{"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.993},
{"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9909},
{"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9879},
{"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9834},
{"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9716},
{"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9612},
{"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9599},
{"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9552},
{"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9532},
{"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9516},
{"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9499},
{"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9483},
{"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9464},
{"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
{"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
{"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9408},
{"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9335},
{"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9326},
{"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9262},
{"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8999},
{"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8986},
{"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8984},
{"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8873},
{"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8871}
] , )
# fmt: on
@require_torch
@slow
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = """facebook/sam-vit-huge"""
UpperCAmelCase__ = pipeline("""mask-generation""" , model=_UpperCAmelCase )
UpperCAmelCase__ = image_segmenter(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
UpperCAmelCase__ = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0210},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
] , )
| 346 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase_ )
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self : Optional[Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Dict ):
"""simple docstring"""
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : List[Any]=None ):
"""simple docstring"""
UpperCAmelCase__ = {}
if top_k is not None:
UpperCAmelCase__ = top_k
return {}, {}, postprocess_params
def __call__( self : Any , _UpperCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **_UpperCAmelCase : str ):
"""simple docstring"""
return super().__call__(_UpperCAmelCase , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = load_image(_UpperCAmelCase )
UpperCAmelCase__ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = self.model(**_UpperCAmelCase )
return model_outputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : str=5 ):
"""simple docstring"""
if top_k > self.model.config.num_labels:
UpperCAmelCase__ = self.model.config.num_labels
if self.framework == "pt":
UpperCAmelCase__ = model_outputs.logits.softmax(-1 )[0]
UpperCAmelCase__ , UpperCAmelCase__ = probs.topk(_UpperCAmelCase )
elif self.framework == "tf":
UpperCAmelCase__ = stable_softmax(model_outputs.logits , axis=-1 )[0]
UpperCAmelCase__ = tf.math.top_k(_UpperCAmelCase , k=_UpperCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
UpperCAmelCase__ = scores.tolist()
UpperCAmelCase__ = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase , _UpperCAmelCase )]
| 346 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
A : Any = 0
@slow
def _lowerCAmelCase ( self ):
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
A : Any = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(lowerCamelCase__ ), 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
A : Any = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(lowerCamelCase__ ), 0 )
def _lowerCAmelCase ( self ):
A : Union[str, Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size, 12 )
def _lowerCAmelCase ( self ):
A : Union[str, Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size, 20 )
def _lowerCAmelCase ( self ):
A : List[Any] = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
# Check that tokenizer_type ≠ model_type
A : Union[str, Any] = AutoTokenizer.from_pretrained(lowerCamelCase__, config=lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size, 12 )
def _lowerCAmelCase ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.txt""", os.path.join(lowerCamelCase__, """vocab.txt""" ) )
A : List[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__, tokenizer_type="""bert""", use_fast=lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.json""", os.path.join(lowerCamelCase__, """vocab.json""" ) )
shutil.copy("""./tests/fixtures/merges.txt""", os.path.join(lowerCamelCase__, """merges.txt""" ) )
A : str = AutoTokenizer.from_pretrained(lowerCamelCase__, tokenizer_type="""gpt2""", use_fast=lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
@require_tokenizers
def _lowerCAmelCase ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.txt""", os.path.join(lowerCamelCase__, """vocab.txt""" ) )
A : List[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__, tokenizer_type="""bert""" )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.json""", os.path.join(lowerCamelCase__, """vocab.json""" ) )
shutil.copy("""./tests/fixtures/merges.txt""", os.path.join(lowerCamelCase__, """merges.txt""" ) )
A : int = AutoTokenizer.from_pretrained(lowerCamelCase__, tokenizer_type="""gpt2""" )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
with pytest.raises(lowerCamelCase__ ):
AutoTokenizer.from_pretrained("""./""", tokenizer_type="""xxx""" )
@require_tokenizers
def _lowerCAmelCase ( self ):
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
A : Optional[int] = tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased""" )
self.assertIsInstance(lowerCamelCase__, (BertTokenizer, BertTokenizerFast) )
if isinstance(lowerCamelCase__, lowerCamelCase__ ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case, lowerCamelCase__ )
else:
self.assertEqual(tokenizer.do_lower_case, lowerCamelCase__ )
self.assertEqual(tokenizer.model_max_length, 512 )
@require_tokenizers
def _lowerCAmelCase ( self ):
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
lowerCamelCase__, """julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier""", ):
A : int = tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists""" )
def _lowerCAmelCase ( self ):
# tests: https://github.com/huggingface/transformers/pull/13251
# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
A : List[Any] = TOKENIZER_MAPPING.values()
A : str = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(lowerCamelCase__ )
@require_tokenizers
def _lowerCAmelCase ( self ):
self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased""", use_fast=lowerCamelCase__ ), lowerCamelCase__ )
self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased""" ), lowerCamelCase__ )
@require_tokenizers
def _lowerCAmelCase ( self ):
A : List[Any] = AutoTokenizer.from_pretrained("""distilbert-base-uncased""", do_lower_case=lowerCamelCase__ )
A : Dict = """Hello, world. How are you?"""
A : Optional[int] = tokenizer.tokenize(lowerCamelCase__ )
self.assertEqual("""[UNK]""", tokens[0] )
A : List[str] = AutoTokenizer.from_pretrained("""microsoft/mpnet-base""", do_lower_case=lowerCamelCase__ )
A : Tuple = tokenizer.tokenize(lowerCamelCase__ )
self.assertEqual("""[UNK]""", tokens[0] )
@require_tokenizers
def _lowerCAmelCase ( self ):
A : List[Any] = AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config""" )
self.assertEqual(type(lowerCamelCase__ ), lowerCamelCase__ )
self.assertEqual(tokenizer.model_max_length, 512 )
self.assertEqual(tokenizer.vocab_size, 3_0000 )
self.assertEqual(tokenizer.unk_token, """[UNK]""" )
self.assertEqual(tokenizer.padding_side, """right""" )
self.assertEqual(tokenizer.truncation_side, """right""" )
def _lowerCAmelCase ( self ):
A : List[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowerCamelCase__ )
A : Any = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size, 12 )
def _lowerCAmelCase ( self ):
A : int = AutoTokenizer.from_pretrained("""ctrl""" )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
# Check we can load the tokenizer config of an online model.
A : Optional[Any] = get_tokenizer_config("""bert-base-cased""" )
A : Optional[int] = config.pop("""_commit_hash""", lowerCamelCase__ )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(lowerCamelCase__, {"""do_lower_case""": False} )
# This model does not have a tokenizer_config so we get back an empty dict.
A : int = get_tokenizer_config(lowerCamelCase__ )
self.assertDictEqual(lowerCamelCase__, {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
A : Any = AutoTokenizer.from_pretrained(lowerCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowerCamelCase__ )
A : Optional[int] = get_tokenizer_config(lowerCamelCase__ )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config["""tokenizer_class"""], """BertTokenizer""" )
def _lowerCAmelCase ( self ):
try:
AutoConfig.register("""custom""", lowerCamelCase__ )
AutoTokenizer.register(lowerCamelCase__, slow_tokenizer_class=lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoTokenizer.register(lowerCamelCase__, slow_tokenizer_class=lowerCamelCase__ )
A : int = CustomTokenizer.from_pretrained(lowerCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowerCamelCase__ )
A : Tuple = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def _lowerCAmelCase ( self ):
try:
AutoConfig.register("""custom""", lowerCamelCase__ )
# Can register in two steps
AutoTokenizer.register(lowerCamelCase__, slow_tokenizer_class=lowerCamelCase__ )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig], (CustomTokenizer, None) )
AutoTokenizer.register(lowerCamelCase__, fast_tokenizer_class=lowerCamelCase__ )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig], (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
lowerCamelCase__, slow_tokenizer_class=lowerCamelCase__, fast_tokenizer_class=lowerCamelCase__ )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig], (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoTokenizer.register(lowerCamelCase__, fast_tokenizer_class=lowerCamelCase__ )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
A : Tuple = BertTokenizerFast.from_pretrained(lowerCamelCase__ )
bert_tokenizer.save_pretrained(lowerCamelCase__ )
A : Optional[int] = CustomTokenizerFast.from_pretrained(lowerCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowerCamelCase__ )
A : List[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
A : Dict = AutoTokenizer.from_pretrained(lowerCamelCase__, use_fast=lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__, lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def _lowerCAmelCase ( self ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowerCamelCase__ ):
A : List[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
A : int = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""", trust_remote_code=lowerCamelCase__ )
A : Tuple = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""", trust_remote_code=lowerCamelCase__ )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowerCamelCase__ )
A : int = AutoTokenizer.from_pretrained(lowerCamelCase__, trust_remote_code=lowerCamelCase__ )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizerFast""" )
self.assertEqual(reloaded_tokenizer.__class__.__name__, """NewTokenizerFast""" )
# Test we can also load the slow version
A : Tuple = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""", trust_remote_code=lowerCamelCase__, use_fast=lowerCamelCase__ )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizer""" )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowerCamelCase__ )
A : Union[str, Any] = AutoTokenizer.from_pretrained(lowerCamelCase__, trust_remote_code=lowerCamelCase__, use_fast=lowerCamelCase__ )
self.assertEqual(reloaded_tokenizer.__class__.__name__, """NewTokenizer""" )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizer""" )
self.assertEqual(reloaded_tokenizer.__class__.__name__, """NewTokenizer""" )
@require_tokenizers
def _lowerCAmelCase ( self ):
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : str = False
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : List[Any] = NewTokenizer
__lowerCamelCase : List[str] = False
try:
AutoConfig.register("""custom""", lowerCamelCase__ )
AutoTokenizer.register(lowerCamelCase__, slow_tokenizer_class=lowerCamelCase__ )
AutoTokenizer.register(lowerCamelCase__, fast_tokenizer_class=lowerCamelCase__ )
# If remote code is not set, the default is to use local
A : Optional[int] = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizerFast""" )
self.assertFalse(tokenizer.special_attribute_present )
A : List[str] = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""", use_fast=lowerCamelCase__ )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizer""" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
A : Union[str, Any] = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""", trust_remote_code=lowerCamelCase__ )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizerFast""" )
self.assertFalse(tokenizer.special_attribute_present )
A : Any = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""", trust_remote_code=lowerCamelCase__, use_fast=lowerCamelCase__ )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizer""" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
A : Tuple = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""", trust_remote_code=lowerCamelCase__ )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizerFast""" )
self.assertTrue(tokenizer.special_attribute_present )
A : Optional[int] = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""", trust_remote_code=lowerCamelCase__, use_fast=lowerCamelCase__ )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizer""" )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def _lowerCAmelCase ( self ):
A : Optional[Any] = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer_legacy""", trust_remote_code=lowerCamelCase__ )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizerFast""" )
# Test we can also load the slow version
A : Tuple = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer_legacy""", trust_remote_code=lowerCamelCase__, use_fast=lowerCamelCase__ )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__, """NewTokenizer""" )
def _lowerCAmelCase ( self ):
with self.assertRaisesRegex(
lowerCamelCase__, """bert-base is not a local folder and is not a valid model identifier""" ):
A : Tuple = AutoTokenizer.from_pretrained("""bert-base""" )
def _lowerCAmelCase ( self ):
with self.assertRaisesRegex(
lowerCamelCase__, R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
A : Any = AutoTokenizer.from_pretrained(lowerCamelCase__, revision="""aaaaaa""" )
def _lowerCAmelCase ( self ):
# Make sure we have cached the tokenizer.
A : Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
with RequestCounter() as counter:
A : Optional[int] = AutoTokenizer.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 )
| 115 |
from jiwer import compute_measures
import datasets
SCREAMING_SNAKE_CASE_:str = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""
SCREAMING_SNAKE_CASE_:Union[str, Any] = """\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""
SCREAMING_SNAKE_CASE_:List[Any] = """
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = [\"this is the prediction\", \"there is an other sample\"]
>>> references = [\"this is the reference\", \"there is another one\"]
>>> wer = datasets.load_metric(\"wer\")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
"""predictions""": datasets.Value("""string""", id="""sequence""" ),
"""references""": datasets.Value("""string""", id="""sequence""" ),
} ), codebase_urls=["""https://github.com/jitsi/jiwer/"""], reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
], )
def _lowerCAmelCase ( self, lowerCamelCase__=None, lowerCamelCase__=None, lowerCamelCase__=False ):
if concatenate_texts:
return compute_measures(lowerCamelCase__, lowerCamelCase__ )["wer"]
else:
A : str = 0
A : int = 0
for prediction, reference in zip(lowerCamelCase__, lowerCamelCase__ ):
A : Tuple = compute_measures(lowerCamelCase__, lowerCamelCase__ )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 115 | 1 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
def __a ( self ):
UpperCamelCase__ = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(a ) )
def __a ( self ):
UpperCamelCase__ = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(a ) )
def __a ( self ):
UpperCamelCase__ = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(a ) )
def __a ( self ):
UpperCamelCase__ = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(a ) )
def __a ( self ):
UpperCamelCase__ = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(a ) )
def __a ( self ):
UpperCamelCase__ = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCamelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __a ( self ):
UpperCamelCase__ = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCamelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __a ( self ):
# pass variant but use the non-variant filenames
UpperCamelCase__ = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
UpperCamelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __a ( self ):
UpperCamelCase__ = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
UpperCamelCase__ = "fp16"
self.assertFalse(is_safetensors_compatible(a , variant=a ) )
def __a ( self ):
UpperCamelCase__ = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
UpperCamelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __a ( self ):
# pass variant but use the non-variant filenames
UpperCamelCase__ = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
UpperCamelCase__ = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __a ( self ):
UpperCamelCase__ = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCamelCase__ = "fp16"
self.assertFalse(is_safetensors_compatible(a , variant=a ) )
| 80 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class lowercase_ ( a__ ):
def __init__( self , a , a , a = None , a = None , a = False , **a , ):
super().__init__(features=a , cache_dir=a , keep_in_memory=a , **a )
UpperCamelCase__ = Sql(
cache_dir=a , features=a , sql=a , con=a , **a , )
def __a ( self ):
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = None
self.builder.download_and_prepare(
download_config=a , download_mode=a , verification_mode=a , base_path=a , )
# Build dataset for splits
UpperCamelCase__ = self.builder.as_dataset(
split="train" , verification_mode=a , in_memory=self.keep_in_memory )
return dataset
class lowercase_ :
def __init__( self , a , a , a , a = None , a = None , **a , ):
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCamelCase__ = dataset
UpperCamelCase__ = name
UpperCamelCase__ = con
UpperCamelCase__ = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCamelCase__ = num_proc
UpperCamelCase__ = to_sql_kwargs
def __a ( self ):
UpperCamelCase__ = self.to_sql_kwargs.pop("sql" , a )
UpperCamelCase__ = self.to_sql_kwargs.pop("con" , a )
UpperCamelCase__ = self.to_sql_kwargs.pop("index" , a )
UpperCamelCase__ = self._write(index=a , **self.to_sql_kwargs )
return written
def __a ( self , a ):
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = args
UpperCamelCase__ = {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
UpperCamelCase__ = query_table(
table=self.dataset.data , key=slice(a , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCamelCase__ = batch.to_pandas()
UpperCamelCase__ = df.to_sql(self.name , self.con , index=a , **a )
return num_rows or len(a )
def __a ( self , a , **a ):
UpperCamelCase__ = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCamelCase__ , UpperCamelCase__ = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , a , a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += num_rows
return written
| 80 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __snake_case:
'''simple docstring'''
def __init__( self , A_ , A_=13 , A_=32 , A_=2 , A_=3 , A_=16 , A_=[1, 2, 1] , A_=[2, 2, 4] , A_=2 , A_=2.0 , A_=True , A_=0.0 , A_=0.0 , A_=0.1 , A_="gelu" , A_=False , A_=True , A_=0.0_2 , A_=1e-5 , A_=True , A_=None , A_=True , A_=10 , A_=8 , A_=["stage1", "stage2", "stage3"] , A_=[1, 2, 3] , ) -> List[str]:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = image_size
lowerCAmelCase = patch_size
lowerCAmelCase = num_channels
lowerCAmelCase = embed_dim
lowerCAmelCase = depths
lowerCAmelCase = num_heads
lowerCAmelCase = window_size
lowerCAmelCase = mlp_ratio
lowerCAmelCase = qkv_bias
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = drop_path_rate
lowerCAmelCase = hidden_act
lowerCAmelCase = use_absolute_embeddings
lowerCAmelCase = patch_norm
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = initializer_range
lowerCAmelCase = is_training
lowerCAmelCase = scope
lowerCAmelCase = use_labels
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = encoder_stride
lowerCAmelCase = out_features
lowerCAmelCase = out_indices
def __snake_case ( self ) -> Tuple:
lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def __snake_case ( self ) -> str:
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def __snake_case ( self , A_ , A_ , A_ ) -> Tuple:
lowerCAmelCase = MaskFormerSwinModel(config=A_ )
model.to(A_ )
model.eval()
lowerCAmelCase = model(A_ )
lowerCAmelCase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowerCAmelCase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __snake_case ( self , A_ , A_ , A_ ) -> Optional[Any]:
lowerCAmelCase = MaskFormerSwinBackbone(config=A_ )
model.to(A_ )
model.eval()
lowerCAmelCase = model(A_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(A_ ):
lowerCAmelCase = ["""stem"""]
lowerCAmelCase = MaskFormerSwinBackbone(config=A_ )
def __snake_case ( self ) -> List[str]:
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = config_and_inputs
lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : int = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
UpperCAmelCase : str = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Dict = False
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : Union[str, Any] = False
def __snake_case ( self ) -> Dict:
lowerCAmelCase = MaskFormerSwinModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=A_ , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def __snake_case ( self ) -> List[Any]:
pass
def __snake_case ( self ) -> Tuple:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __snake_case ( self ) -> List[Any]:
return
def __snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*A_ )
@unittest.skip("""Swin does not use inputs_embeds""" )
def __snake_case ( self ) -> str:
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def __snake_case ( self ) -> Optional[int]:
pass
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(A_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_ , nn.Linear ) )
def __snake_case ( self ) -> str:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(A_ )
lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase = [*signature.parameters.keys()]
lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A_ )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def __snake_case ( self ) -> int:
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def __snake_case ( self ) -> List[Any]:
pass
def __snake_case ( self , A_ , A_ , A_ , A_ ) -> Union[str, Any]:
lowerCAmelCase = model_class(A_ )
model.to(A_ )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(A_ , A_ ) )
lowerCAmelCase = outputs.hidden_states
lowerCAmelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(A_ ) , A_ )
# Swin has a different seq_length
lowerCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
lowerCAmelCase = True
self.check_hidden_states_output(A_ , A_ , A_ , A_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase = True
self.check_hidden_states_output(A_ , A_ , A_ , A_ )
def __snake_case ( self ) -> Any:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = 3
lowerCAmelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowerCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCAmelCase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowerCAmelCase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowerCAmelCase = True
self.check_hidden_states_output(A_ , A_ , A_ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase = True
self.check_hidden_states_output(A_ , A_ , A_ , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def __snake_case ( self ) -> List[str]:
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def __snake_case ( self ) -> Any:
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def __snake_case ( self ) -> List[str]:
pass
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(A_ ):
lowerCAmelCase = 0
return t
def check_equivalence(A_ , A_ , A_ , A_={} ):
with torch.no_grad():
lowerCAmelCase = model(**A_ , return_dict=A_ , **A_ )
lowerCAmelCase = model(**A_ , return_dict=A_ , **A_ ).to_tuple()
def recursive_check(A_ , A_ ):
if isinstance(A_ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(A_ , A_ ):
recursive_check(A_ , A_ )
elif isinstance(A_ , A_ ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(A_ , A_ )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(A_ ) , set_nan_tensor_to_zero(A_ ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
f' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'
f' {torch.isnan(A_ ).any()} and `inf`: {torch.isinf(A_ )}. Dict has'
f' `nan`: {torch.isnan(A_ ).any()} and `inf`: {torch.isinf(A_ )}.'
) , )
recursive_check(A_ , A_ )
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(A_ )
model.to(A_ )
model.eval()
lowerCAmelCase = self._prepare_for_class(A_ , A_ )
lowerCAmelCase = self._prepare_for_class(A_ , A_ )
check_equivalence(A_ , A_ , A_ )
lowerCAmelCase = self._prepare_for_class(A_ , A_ , return_labels=A_ )
lowerCAmelCase = self._prepare_for_class(A_ , A_ , return_labels=A_ )
check_equivalence(A_ , A_ , A_ )
lowerCAmelCase = self._prepare_for_class(A_ , A_ )
lowerCAmelCase = self._prepare_for_class(A_ , A_ )
check_equivalence(A_ , A_ , A_ , {"""output_hidden_states""": True} )
lowerCAmelCase = self._prepare_for_class(A_ , A_ , return_labels=A_ )
lowerCAmelCase = self._prepare_for_class(A_ , A_ , return_labels=A_ )
check_equivalence(A_ , A_ , A_ , {"""output_hidden_states""": True} )
@require_torch
class __snake_case( unittest.TestCase , _lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = (MaskFormerSwinBackbone,) if is_torch_available() else ()
UpperCAmelCase : str = MaskFormerSwinConfig
def __snake_case ( self ) -> Dict:
lowerCAmelCase = MaskFormerSwinModelTester(self )
def __snake_case ( self ) -> Any:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
lowerCAmelCase = backbone_class(A_ )
backbone.to(A_ )
backbone.eval()
lowerCAmelCase = backbone(**A_ )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , A_ )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowerCAmelCase = backbone(**A_ , output_hidden_states=A_ )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowerCAmelCase = backbone(**A_ , output_attentions=A_ )
self.assertIsNotNone(outputs.attentions ) | 366 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> bool:
lowerCAmelCase = False
if low == high:
return swapped
lowerCAmelCase = low
lowerCAmelCase = high
while left < right:
if collection[left] > collection[right]:
lowerCAmelCase, lowerCAmelCase = (
collection[right],
collection[left],
)
lowerCAmelCase = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
lowerCAmelCase, lowerCAmelCase = (
collection[right + 1],
collection[left],
)
lowerCAmelCase = True
lowerCAmelCase = low + int((high - low) / 2 )
lowerCAmelCase = circle_sort_util(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = circle_sort_util(_SCREAMING_SNAKE_CASE , mid + 1 , _SCREAMING_SNAKE_CASE )
return swapped or left_swap or right_swap
lowerCAmelCase = True
while is_not_sorted is True:
lowerCAmelCase = circle_sort_util(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return collection
if __name__ == "__main__":
UpperCAmelCase = input('Enter numbers separated by a comma:\n').strip()
UpperCAmelCase = [int(item) for item in user_input.split(',')]
print(circle_sort(unsorted)) | 187 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[Any], UpperCAmelCase__ : List[str] ):
__lowercase = parent
def _lowercase ( self : Optional[Any] ):
return {}
def _A ( ) -> int:
'''simple docstring'''
__lowercase = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
__lowercase = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class _lowerCAmelCase ( lowercase ,unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = MarkupLMFeatureExtractor if is_bsa_available() else None
def _lowercase ( self : List[str] ):
__lowercase = MarkupLMFeatureExtractionTester(self )
@property
def _lowercase ( self : Optional[int] ):
return self.feature_extract_tester.prepare_feat_extract_dict()
def _lowercase ( self : List[Any] ):
# Initialize feature_extractor
__lowercase = self.feature_extraction_class()
# Test not batched input
__lowercase = get_html_strings()[0]
__lowercase = feature_extractor(UpperCAmelCase__ )
# fmt: off
__lowercase = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
__lowercase = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
# fmt: on
self.assertEqual(encoding.nodes, UpperCAmelCase__ )
self.assertEqual(encoding.xpaths, UpperCAmelCase__ )
# Test batched
__lowercase = get_html_strings()
__lowercase = feature_extractor(UpperCAmelCase__ )
# fmt: off
__lowercase = expected_nodes + [["My First Heading", "My first paragraph."]]
__lowercase = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes ), 2 )
self.assertEqual(len(encoding.xpaths ), 2 )
self.assertEqual(encoding.nodes, UpperCAmelCase__ )
self.assertEqual(encoding.xpaths, UpperCAmelCase__ )
| 17 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
UpperCAmelCase__ : Union[str, Any] = TypeVar('T')
class UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
__UpperCamelCase : deque[T] # Cache store of keys
__UpperCamelCase : set[T] # References of the keys in cache
__UpperCamelCase : int = 10 # Maximum capacity of cache
def __init__( self : List[str] , lowerCAmelCase_ : int ):
"""simple docstring"""
_A: Tuple = deque()
_A: List[Any] = set()
if not n:
_A: str = sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
_A: Dict = n
def __magic_name__ ( self : Optional[int] , lowerCAmelCase_ : T ):
"""simple docstring"""
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
_A: Optional[Any] = self.dq_store.pop()
self.key_reference.remove(lowerCAmelCase_ )
else:
self.dq_store.remove(lowerCAmelCase_ )
self.dq_store.appendleft(lowerCAmelCase_ )
self.key_reference.add(lowerCAmelCase_ )
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
for k in self.dq_store:
print(lowerCAmelCase_ )
def __repr__( self : Dict ):
"""simple docstring"""
return F"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase__ : LRUCache[str | int] = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 121 | 0 |
def _UpperCamelCase ( UpperCamelCase_ : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 122 |
import qiskit
def _UpperCamelCase ( UpperCamelCase_ : int , UpperCamelCase_ : int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
lowerCAmelCase__ = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
lowerCAmelCase__ = qiskit.QuantumCircuit(UpperCamelCase_ , UpperCamelCase_ )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
lowerCAmelCase__ = qiskit.execute(UpperCamelCase_ , UpperCamelCase_ , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(UpperCamelCase_ )
if __name__ == "__main__":
print(f'Total count for various states are: {single_qubit_measure(1, 1)}')
| 122 | 1 |
def A ( a_ ) -> List[str]:
__UpperCamelCase : Any =0
__UpperCamelCase : Union[str, Any] =len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 ,__snake_case ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def A ( a_ ) -> Any:
if len(__snake_case ) <= 1:
return arr, 0
__UpperCamelCase : int =len(__snake_case ) // 2
__UpperCamelCase : str =arr[0:mid]
__UpperCamelCase : Union[str, Any] =arr[mid:]
__UpperCamelCase : str =count_inversions_recursive(__snake_case )
__UpperCamelCase : Any =count_inversions_recursive(__snake_case )
__UpperCamelCase : int =_count_cross_inversions(__snake_case ,__snake_case )
__UpperCamelCase : str =inversion_p + inversions_q + cross_inversions
return c, num_inversions
def A ( a_ ,a_ ) -> Dict:
__UpperCamelCase : Dict =[]
__UpperCamelCase : List[str] =0
while i < len(__snake_case ) and j < len(__snake_case ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__snake_case ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__snake_case ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def A ( ) -> str:
__UpperCamelCase : str =[10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__UpperCamelCase : Optional[Any] =count_inversions_bf(__snake_case )
__UpperCamelCase : Optional[Any] =count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' ,__snake_case )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__UpperCamelCase : Optional[Any] =count_inversions_bf(__snake_case )
__UpperCamelCase : List[str] =count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' ,__snake_case )
# an empty list should also have zero inversions
__UpperCamelCase : Optional[Any] =[]
__UpperCamelCase : Any =count_inversions_bf(__snake_case )
__UpperCamelCase : List[Any] =count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' ,__snake_case )
if __name__ == "__main__":
main()
| 71 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class SCREAMING_SNAKE_CASE (a__ ):
def __init__( self , _UpperCAmelCase = "▁" , _UpperCAmelCase = True , _UpperCAmelCase = "<unk>" , _UpperCAmelCase = "</s>" , _UpperCAmelCase = "<pad>" , ):
'''simple docstring'''
__A : Dict = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
__A : List[Any] = [None] * len(self.special_tokens)
for token_dict in self.special_tokens.values():
__A : List[str] = token_dict['token']
__A : str = Tokenizer(Unigram())
__A : Dict = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}') , ' '),
normalizers.Lowercase(),
])
__A : Union[str, Any] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase),
pre_tokenizers.Digits(individual_digits=_UpperCAmelCase),
pre_tokenizers.Punctuation(),
])
__A : Any = decoders.Metaspace(replacement=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase)
__A : Dict = TemplateProcessing(
single=F'$A {self.special_tokens["eos"]["token"]}' , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
__A : Any = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(_UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = 8000 , _UpperCAmelCase = True , ):
'''simple docstring'''
__A : str = trainers.UnigramTrainer(
vocab_size=_UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=_UpperCAmelCase , )
if isinstance(_UpperCAmelCase , _UpperCAmelCase):
__A : Union[str, Any] = [files]
self._tokenizer.train(_UpperCAmelCase , trainer=_UpperCAmelCase)
self.add_unk_id()
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = 8000 , _UpperCAmelCase = True , ):
'''simple docstring'''
__A : Dict = trainers.UnigramTrainer(
vocab_size=_UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=_UpperCAmelCase , )
self._tokenizer.train_from_iterator(_UpperCAmelCase , trainer=_UpperCAmelCase)
self.add_unk_id()
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Any = json.loads(self._tokenizer.to_str())
__A : Union[str, Any] = self.special_tokens['unk']['id']
__A : str = Tokenizer.from_str(json.dumps(_UpperCAmelCase)) | 190 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__snake_case = 25_00_04
__snake_case = 25_00_20
@require_sentencepiece
@require_tokenizers
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Union[str, Any] = MBartaaTokenizer
__lowerCamelCase : List[Any] = MBartaaTokenizerFast
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Optional[Any] = True
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase : Union[str, Any] =MBartaaTokenizer(snake_case__ , src_lang='''en_XX''' , tgt_lang='''ro_RO''' , keep_accents=snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[int] ='''<s>'''
UpperCAmelCase : Union[str, Any] =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[Any] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(snake_case__ ) , 1054 )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1054 )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Tuple =MBartaaTokenizer(snake_case__ , src_lang='''en_XX''' , tgt_lang='''ro_RO''' , keep_accents=snake_case__ )
UpperCAmelCase : int =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(snake_case__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCAmelCase : Tuple =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
snake_case__ , [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''', '''.'''] , )
UpperCAmelCase : Union[str, Any] =tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
UpperCAmelCase : Union[str, Any] =tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [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>''', '''.'''] , )
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] ={'''input_ids''': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]], '''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, 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, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 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=snake_case__ , model_name='''facebook/mbart-large-50''' , revision='''d3913889c59cd5c9e456b269c376325eabad57e2''' , )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCAmelCase : Optional[int] =(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCAmelCase : Union[str, Any] =self.rust_tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
UpperCAmelCase : str =self.tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
UpperCAmelCase : Optional[int] =tempfile.mkdtemp()
UpperCAmelCase : Tuple =tokenizer_r.save_pretrained(snake_case__ )
UpperCAmelCase : Tuple =tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
UpperCAmelCase : Any =tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
UpperCAmelCase : List[str] =tokenizer_r.from_pretrained(snake_case__ )
UpperCAmelCase : str =tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=True
UpperCAmelCase : Dict =tempfile.mkdtemp()
UpperCAmelCase : Any =tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
UpperCAmelCase : int =tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
UpperCAmelCase : str =tokenizer_r.from_pretrained(snake_case__ )
UpperCAmelCase : Optional[int] =tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=False
UpperCAmelCase : List[str] =tempfile.mkdtemp()
UpperCAmelCase : Any =tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
UpperCAmelCase : List[str] =tokenizer_p.save_pretrained(snake_case__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCAmelCase : Union[str, Any] =tokenizer_r.from_pretrained(snake_case__ )
UpperCAmelCase : Dict =tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case ( unittest.TestCase ):
__lowerCamelCase : List[Any] = """facebook/mbart-large-50-one-to-many-mmt"""
__lowerCamelCase : str = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
__lowerCamelCase : int = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
__lowerCamelCase : str = [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]
@classmethod
def UpperCAmelCase__ ( cls ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : MBartaaTokenizer =MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
UpperCAmelCase : List[Any] =1
return cls
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 25_0001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 25_0004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 25_0020 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN'''] , 25_0038 )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
self.assertIn(snake_case__ , self.tokenizer.all_special_ids )
UpperCAmelCase : Optional[int] =[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
UpperCAmelCase : str =self.tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ )
UpperCAmelCase : Any =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
self.assertNotIn(self.tokenizer.eos_token , snake_case__ )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , snake_case__ )
UpperCAmelCase : Optional[int] =10
UpperCAmelCase : Optional[int] =self.tokenizer(snake_case__ , max_length=snake_case__ , truncation=snake_case__ ).input_ids[0]
self.assertEqual(ids[0] , snake_case__ )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(snake_case__ ) , snake_case__ )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [25_0053, 25_0001] )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =tempfile.mkdtemp()
UpperCAmelCase : int =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(snake_case__ )
UpperCAmelCase : Optional[Any] =MBartaaTokenizer.from_pretrained(snake_case__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , snake_case__ )
@require_torch
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : str =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=snake_case__ , return_tensors='''pt''' )
UpperCAmelCase : Dict =shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : int =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
UpperCAmelCase : Union[str, Any] =shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
UpperCAmelCase : Any =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : str =self.tokenizer(self.src_text , padding=snake_case__ , truncation=snake_case__ , max_length=3 , return_tensors='''pt''' )
UpperCAmelCase : List[str] =self.tokenizer(
text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=10 , return_tensors='''pt''' )
UpperCAmelCase : Any =targets['''input_ids''']
UpperCAmelCase : str =shift_tokens_right(snake_case__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(snake_case__ ) , {
# en_XX, A, test, EOS
'''input_ids''': [[25_0004, 62, 3034, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 25_0001,
} , )
| 78 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.json'''}
__snake_case = {
'''vocab_file''': {
'''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
}
}
__snake_case = {'''mgp-str''': 27}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , snake_case__ , snake_case__="[GO]" , snake_case__="[GO]" , snake_case__="[s]" , snake_case__="[GO]" , **snake_case__ ) -> Any:
'''simple docstring'''
super().__init__(
unk_token=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , pad_token=snake_case__ , **snake_case__ , )
with open(snake_case__ , encoding='''utf-8''' ) as vocab_handle:
UpperCAmelCase : int =json.load(snake_case__ )
UpperCAmelCase : List[str] ={v: k for k, v in self.vocab.items()}
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return len(self.vocab )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self , snake_case__ ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =[]
for s in text:
char_tokens.extend(snake_case__ )
return char_tokens
def UpperCAmelCase__ ( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
return self.vocab.get(snake_case__ , self.vocab.get(self.unk_token ) )
def UpperCAmelCase__ ( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(snake_case__ ) )
return
UpperCAmelCase : List[Any] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=snake_case__ , ensure_ascii=snake_case__ ) + '''\n''' )
return (vocab_file,)
| 78 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __lowerCamelCase ( A__ ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def __lowerCamelCase ( A__ , A__ , A__ ) -> np.ndarray:
"""simple docstring"""
UpperCamelCase = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(A__ , A__ )
# Predict target for test data
UpperCamelCase = xgb.predict(A__ )
UpperCamelCase = predictions.reshape(len(A__ ) , 1 )
return predictions
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
UpperCamelCase = fetch_california_housing()
UpperCamelCase , UpperCamelCase = data_handling(A__ )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = train_test_split(
A__ , A__ , test_size=0.25 , random_state=1 )
UpperCamelCase = xgboost(A__ , A__ , A__ )
# Error printing
print(F"""Mean Absolute Error : {mean_absolute_error(A__ , A__ )}""" )
print(F"""Mean Square Error : {mean_squared_error(A__ , A__ )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 28 |
"""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 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
raise Exception("requires fairseq >= 1.0.0a")
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = 'Hello world! cécé herlolip'
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
__snake_case : str = FairseqRobertaModel.from_pretrained(snake_case_ )
roberta.eval() # disable dropout
__snake_case : Tuple = roberta.model.encoder.sentence_encoder
__snake_case : List[str] = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
__snake_case : List[str] = roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our RoBERTa config:""" , snake_case_ )
__snake_case : str = XLMRobertaXLForSequenceClassification(snake_case_ ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_ )
model.eval()
# Now let's copy all the weights.
# Embeddings
__snake_case : Dict = roberta_sent_encoder.embed_tokens.weight
__snake_case : Any = roberta_sent_encoder.embed_positions.weight
__snake_case : Optional[int] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
__snake_case : Dict = roberta_sent_encoder.layer_norm.weight
__snake_case : int = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
__snake_case : int = model.roberta.encoder.layer[i]
__snake_case : List[str] = roberta_sent_encoder.layers[i]
__snake_case : int = layer.attention
__snake_case : int = roberta_layer.self_attn_layer_norm.weight
__snake_case : Union[str, Any] = roberta_layer.self_attn_layer_norm.bias
# self attention
__snake_case : List[str] = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
__snake_case : str = roberta_layer.self_attn.q_proj.weight
__snake_case : int = roberta_layer.self_attn.q_proj.bias
__snake_case : Any = roberta_layer.self_attn.k_proj.weight
__snake_case : Optional[Any] = roberta_layer.self_attn.k_proj.bias
__snake_case : Dict = roberta_layer.self_attn.v_proj.weight
__snake_case : str = roberta_layer.self_attn.v_proj.bias
# self-attention output
__snake_case : List[Any] = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
__snake_case : List[Any] = roberta_layer.self_attn.out_proj.weight
__snake_case : Any = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
__snake_case : Optional[Any] = roberta_layer.final_layer_norm.weight
__snake_case : Optional[int] = roberta_layer.final_layer_norm.bias
# intermediate
__snake_case : List[Any] = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
__snake_case : Optional[Any] = roberta_layer.fca.weight
__snake_case : Optional[int] = roberta_layer.fca.bias
# output
__snake_case : Any = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
__snake_case : Tuple = roberta_layer.fca.weight
__snake_case : Optional[int] = roberta_layer.fca.bias
# end of layer
if classification_head:
__snake_case : Union[str, Any] = roberta.model.classification_heads["""mnli"""].dense.weight
__snake_case : int = roberta.model.classification_heads["""mnli"""].dense.bias
__snake_case : Union[str, Any] = roberta.model.classification_heads["""mnli"""].out_proj.weight
__snake_case : List[str] = roberta.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
__snake_case : str = roberta.model.encoder.lm_head.dense.weight
__snake_case : Optional[int] = roberta.model.encoder.lm_head.dense.bias
__snake_case : Optional[int] = roberta.model.encoder.lm_head.layer_norm.weight
__snake_case : Optional[int] = roberta.model.encoder.lm_head.layer_norm.bias
__snake_case : List[str] = roberta.model.encoder.lm_head.weight
__snake_case : str = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
__snake_case : List[str] = roberta.encode(snake_case_ ).unsqueeze(0 ) # batch of size 1
__snake_case : int = model(snake_case_ )[0]
if classification_head:
__snake_case : Tuple = roberta.model.classification_heads["""mnli"""](roberta.extract_features(snake_case_ ) )
else:
__snake_case : str = roberta.model(snake_case_ )[0]
print(our_output.shape , their_output.shape )
__snake_case : Optional[int] = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
__snake_case : Dict = torch.allclose(snake_case_ , snake_case_ , atol=1E-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
pathlib.Path(snake_case_ ).mkdir(parents=snake_case_ , exist_ok=snake_case_ )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(snake_case_ )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
__UpperCamelCase = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 369 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__UpperCamelCase = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class _A :
lowercase__: str
lowercase__: Optional[str] = None
lowercase__: Optional[Union[str, int]] = None
lowercase__: Optional[Union[str, int]] = None
lowercase__: Optional[Union[str, int]] = None
def lowercase__ ( self : str ) -> List[str]:
"""simple docstring"""
__snake_case , __snake_case , __snake_case : List[Any] = _str_to_version_tuple(self.version_str )
def __repr__( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return f'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''
@property
def lowercase__ ( self : Tuple ) -> Dict:
"""simple docstring"""
return self.major, self.minor, self.patch
def lowercase__ ( self : Any , __magic_name__ : Dict ) -> Optional[int]:
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ):
return Version(__magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ):
return other
raise TypeError(f'''{other} (type {type(__magic_name__ )}) cannot be compared to version.''' )
def __eq__( self : Optional[Any] , __magic_name__ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
try:
__snake_case : Union[str, Any] = self._validate_operand(__magic_name__ )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self : Union[str, Any] , __magic_name__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__snake_case : Union[str, Any] = self._validate_operand(__magic_name__ )
return self.tuple < other.tuple
def __hash__( self : Any ) -> Any:
"""simple docstring"""
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def lowercase__ ( cls : List[str] , __magic_name__ : Tuple ) -> str:
"""simple docstring"""
__snake_case : List[str] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def lowercase__ ( self : str ) -> str:
"""simple docstring"""
return self.version_str
def _a ( _lowerCamelCase ) -> List[str]:
"""simple docstring"""
__snake_case : List[Any] = _VERSION_REG.match(_lowerCamelCase )
if not res:
raise ValueError(F'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.''' )
return tuple(int(_lowerCamelCase ) for v in [res.group("""major""" ), res.group("""minor""" ), res.group("""patch""" )] )
def _a ( _lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
return ".".join(str(_lowerCamelCase ) for v in version_tuple )
| 13 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Optional[int]= logging.get_logger(__name__)
_a : Optional[Any]= {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class UpperCamelCase ( __lowerCAmelCase ):
UpperCAmelCase : List[Any] = """camembert"""
def __init__(self : Dict , _A : Tuple=3_05_22 , _A : Any=7_68 , _A : List[Any]=12 , _A : Optional[int]=12 , _A : int=30_72 , _A : List[Any]="gelu" , _A : Any=0.1 , _A : List[Any]=0.1 , _A : List[Any]=5_12 , _A : List[Any]=2 , _A : Any=0.02 , _A : Tuple=1E-12 , _A : Optional[Any]=1 , _A : str=0 , _A : Union[str, Any]=2 , _A : Any="absolute" , _A : Dict=True , _A : List[Any]=None , **_A : Any , ) -> Any:
super().__init__(pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , **lowerCamelCase_)
__snake_case : int = vocab_size
__snake_case : Optional[int] = hidden_size
__snake_case : List[Any] = num_hidden_layers
__snake_case : Optional[Any] = num_attention_heads
__snake_case : Any = hidden_act
__snake_case : List[str] = intermediate_size
__snake_case : int = hidden_dropout_prob
__snake_case : Any = attention_probs_dropout_prob
__snake_case : Tuple = max_position_embeddings
__snake_case : Optional[int] = type_vocab_size
__snake_case : Dict = initializer_range
__snake_case : int = layer_norm_eps
__snake_case : Union[str, Any] = position_embedding_type
__snake_case : Union[str, Any] = use_cache
__snake_case : str = classifier_dropout
class UpperCamelCase ( __lowerCAmelCase ):
@property
def _lowercase (self : List[str]) -> Any:
if self.task == "multiple-choice":
__snake_case : Tuple = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__snake_case : Optional[Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 172 | from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE_ :
def __init__( self : Tuple , lowerCamelCase_ : Any , lowerCamelCase_ : Dict=13 , lowerCamelCase_ : str=30 , lowerCamelCase_ : List[str]=2 , lowerCamelCase_ : Union[str, Any]=3 , lowerCamelCase_ : Any=True , lowerCamelCase_ : int=True , lowerCamelCase_ : Tuple=32 , lowerCamelCase_ : Optional[Any]=2 , lowerCamelCase_ : int=4 , lowerCamelCase_ : str=37 , lowerCamelCase_ : Optional[Any]="gelu" , lowerCamelCase_ : Optional[int]=0.1 , lowerCamelCase_ : List[Any]=0.1 , lowerCamelCase_ : List[Any]=10 , lowerCamelCase_ : List[Any]=0.0_2 , lowerCamelCase_ : Optional[int]=3 , lowerCamelCase_ : List[Any]=0.6 , lowerCamelCase_ : Optional[Any]=None , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = mask_ratio
UpperCamelCase = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCamelCase = (image_size // patch_size) ** 2
UpperCamelCase = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase_ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = TFViTMAEModel(config=lowerCamelCase_ )
UpperCamelCase = model(lowerCamelCase_ , training=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : str ):
"""simple docstring"""
UpperCamelCase = TFViTMAEForPreTraining(lowerCamelCase_ )
UpperCamelCase = model(lowerCamelCase_ , training=lowerCamelCase_ )
# expected sequence length = num_patches
UpperCamelCase = (self.image_size // self.patch_size) ** 2
UpperCamelCase = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCamelCase = 1
UpperCamelCase = TFViTMAEForPreTraining(lowerCamelCase_ )
UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase = model(lowerCamelCase_ , training=lowerCamelCase_ )
UpperCamelCase = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) = config_and_inputs
UpperCamelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
__lowerCAmelCase = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
__lowerCAmelCase = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = TFViTMAEModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=lowerCamelCase_ , has_text_modality=lowerCamelCase_ , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
pass
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(lowerCamelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase_ , tf.keras.layers.Layer ) )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(lowerCamelCase_ )
UpperCamelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase_ )
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCamelCase = model_class(lowerCamelCase_ )
UpperCamelCase = self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ )
UpperCamelCase = model(lowerCamelCase_ , noise=lowerCamelCase_ )
UpperCamelCase = copy.deepcopy(self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ ) )
UpperCamelCase = model(**lowerCamelCase_ , noise=lowerCamelCase_ )
UpperCamelCase = outputs_dict[0].numpy()
UpperCamelCase = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1E-6 )
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(lowerCamelCase_ : List[Any] ):
UpperCamelCase = {}
for k, v in inputs_dict.items():
if tf.is_tensor(lowerCamelCase_ ):
UpperCamelCase = v.numpy()
else:
UpperCamelCase = np.array(lowerCamelCase_ )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCamelCase = model_class(lowerCamelCase_ )
UpperCamelCase = self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ )
UpperCamelCase = prepare_numpy_arrays(lowerCamelCase_ )
UpperCamelCase = model(lowerCamelCase_ , noise=lowerCamelCase_ )
UpperCamelCase = model(**lowerCamelCase_ , noise=lowerCamelCase_ )
self.assert_outputs_same(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[int] ):
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCamelCase = tf.constant(lowerCamelCase_ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCamelCase = tf_noise
super().check_pt_tf_models(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(lowerCamelCase_ )
if module_member_name.endswith("""MainLayer""" )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("""MainLayer""" )] == model_class.__name__[: -len("""Model""" )]
for module_member in (getattr(lowerCamelCase_ , lowerCamelCase_ ),)
if isinstance(lowerCamelCase_ , lowerCamelCase_ )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(lowerCamelCase_ , """_keras_serializable""" , lowerCamelCase_ )
}
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCamelCase = tf.convert_to_tensor(lowerCamelCase_ )
inputs_dict.update({"""noise""": noise} )
for main_layer_class in tf_main_layer_classes:
UpperCamelCase = main_layer_class(lowerCamelCase_ )
UpperCamelCase = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCamelCase = tf.keras.Model(lowerCamelCase_ , outputs=main_layer(lowerCamelCase_ ) )
UpperCamelCase = model(lowerCamelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = os.path.join(lowerCamelCase_ , """keras_model.h5""" )
model.save(lowerCamelCase_ )
UpperCamelCase = tf.keras.models.load_model(
lowerCamelCase_ , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(lowerCamelCase_ , tf.keras.Model )
UpperCamelCase = model(lowerCamelCase_ )
self.assert_outputs_same(lowerCamelCase_ , lowerCamelCase_ )
@slow
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCamelCase = model_class(lowerCamelCase_ )
UpperCamelCase = self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ )
UpperCamelCase = model(lowerCamelCase_ , noise=lowerCamelCase_ )
if model_class.__name__ == "TFViTMAEModel":
UpperCamelCase = outputs.last_hidden_state.numpy()
UpperCamelCase = 0
else:
UpperCamelCase = outputs.logits.numpy()
UpperCamelCase = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCamelCase_ , saved_model=lowerCamelCase_ )
UpperCamelCase = model_class.from_pretrained(lowerCamelCase_ )
UpperCamelCase = model(lowerCamelCase_ , noise=lowerCamelCase_ )
if model_class.__name__ == "TFViTMAEModel":
UpperCamelCase = after_outputs["""last_hidden_state"""].numpy()
UpperCamelCase = 0
else:
UpperCamelCase = after_outputs["""logits"""].numpy()
UpperCamelCase = 0
UpperCamelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCamelCase_ , 1E-5 )
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCamelCase = model_class(lowerCamelCase_ )
UpperCamelCase = self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ )
UpperCamelCase = model(lowerCamelCase_ , noise=lowerCamelCase_ )
UpperCamelCase = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(lowerCamelCase_ )
UpperCamelCase = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCamelCase = model_class.from_config(model.config )
UpperCamelCase = new_model(lowerCamelCase_ ) # Build model
new_model.set_weights(model.get_weights() )
UpperCamelCase = new_model(lowerCamelCase_ , noise=lowerCamelCase_ )
self.assert_outputs_same(lowerCamelCase_ , lowerCamelCase_ )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
pass
@slow
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(lowerCamelCase_ )
def lowercase( ) -> int:
'''simple docstring'''
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase = TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=lowerCamelCase_ , return_tensors="""tf""" )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCamelCase = ViTMAEConfig()
UpperCamelCase = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCamelCase = model(**lowerCamelCase_ , noise=lowerCamelCase_ )
# verify the logits
UpperCamelCase = tf.convert_to_tensor([1, 196, 768] )
self.assertEqual(outputs.logits.shape , lowerCamelCase_ )
UpperCamelCase = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , lowerCamelCase_ , atol=1E-4 )
| 343 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE_ = {
'do_resize': True,
'size': {'height': 224, 'width': 224},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
'do_convert_rgb': True,
}
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , _lowerCAmelCase )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , **_lowerCAmelCase : str ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Dict , **_lowerCAmelCase : Optional[int] ):
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , **_lowerCAmelCase : List[Any] ):
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self : str ):
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(_lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase_ ( self : Any ):
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=_lowerCAmelCase )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(_lowerCAmelCase , return_tensors='np' )
SCREAMING_SNAKE_CASE_ = processor(images=_lowerCAmelCase , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCAmelCase_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE_ = processor(text=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer(_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase_ ( self : Tuple ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : str ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 210 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
lowerCamelCase__ : str = get_tests_dir('fixtures/dummy-config.json')
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
SCREAMING_SNAKE_CASE_ = 0
def lowerCAmelCase_ ( self : Optional[int] ):
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec('transformers.models.auto' ) )
def lowerCAmelCase_ ( self : Any ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('bert-base-uncased' )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
SCREAMING_SNAKE_CASE_ = AutoConfig.for_model('roberta' )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
SCREAMING_SNAKE_CASE_ = os.path.join(_lowerCAmelCase , 'fake-roberta' )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
with open(os.path.join(_lowerCAmelCase , 'config.json' ) , 'w' ) as f:
f.write(json.dumps({} ) )
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertEqual(type(_lowerCAmelCase ) , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
try:
AutoConfig.register('custom' , _lowerCAmelCase )
# Wrong model type will raise an error
with self.assertRaises(_lowerCAmelCase ):
AutoConfig.register('model' , _lowerCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCAmelCase ):
AutoConfig.register('bert' , _lowerCAmelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE_ = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def lowerCAmelCase_ ( self : Optional[int] ):
with self.assertRaisesRegex(
_lowerCAmelCase , 'bert-base is not a local folder and is not a valid model identifier' ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('bert-base' )
def lowerCAmelCase_ ( self : int ):
with self.assertRaisesRegex(
_lowerCAmelCase , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(_lowerCAmelCase , revision='aaaaaa' )
def lowerCAmelCase_ ( self : Tuple ):
with self.assertRaisesRegex(
_lowerCAmelCase , 'hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.' , ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('hf-internal-testing/no-config-test-repo' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=_lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(_lowerCAmelCase , trust_remote_code=_lowerCAmelCase )
self.assertEqual(reloaded_config.__class__.__name__ , 'NewModelConfig' )
def lowerCAmelCase_ ( self : Any ):
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "new-model"
try:
AutoConfig.register('new-model' , _lowerCAmelCase )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=_lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=_lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"] | 210 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class a ( _lowerCamelCase ):
snake_case_ = ["image_processor"]
snake_case_ = "SamImageProcessor"
def __init__( self : List[Any] , lowercase_ : List[Any] ):
super().__init__(lowercase_ )
snake_case_ = self.image_processor
snake_case_ = -10
snake_case_ = self.image_processor.size['''longest_edge''']
def __call__( self : List[str] , lowercase_ : Tuple=None , lowercase_ : Tuple=None , lowercase_ : Optional[int]=None , lowercase_ : Dict=None , lowercase_ : Optional[Union[str, TensorType]] = None , **lowercase_ : Any , ):
snake_case_ = self.image_processor(
lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
# pop arguments that are not used in the foward but used nevertheless
snake_case_ = encoding_image_processor['''original_sizes''']
if hasattr(lowercase_ , '''numpy''' ): # Checks if Torch or TF tensor
snake_case_ = original_sizes.numpy()
snake_case_ ,snake_case_ ,snake_case_ = self._check_and_preprocess_points(
input_points=lowercase_ , input_labels=lowercase_ , input_boxes=lowercase_ , )
snake_case_ = self._normalize_and_convert(
lowercase_ , lowercase_ , input_points=lowercase_ , input_labels=lowercase_ , input_boxes=lowercase_ , return_tensors=lowercase_ , )
return encoding_image_processor
def A_ ( self : Dict , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Optional[Any]=None , lowercase_ : Tuple=None , lowercase_ : Optional[Any]=None , lowercase_ : Optional[Any]="pt" , ):
if input_points is not None:
if len(lowercase_ ) != len(lowercase_ ):
snake_case_ = [
self._normalize_coordinates(self.target_size , lowercase_ , original_sizes[0] ) for point in input_points
]
else:
snake_case_ = [
self._normalize_coordinates(self.target_size , lowercase_ , lowercase_ )
for point, original_size in zip(lowercase_ , lowercase_ )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
snake_case_ ,snake_case_ = self._pad_points_and_labels(lowercase_ , lowercase_ )
snake_case_ = np.array(lowercase_ )
if input_labels is not None:
snake_case_ = np.array(lowercase_ )
if input_boxes is not None:
if len(lowercase_ ) != len(lowercase_ ):
snake_case_ = [
self._normalize_coordinates(self.target_size , lowercase_ , original_sizes[0] , is_bounding_box=lowercase_ )
for box in input_boxes
]
else:
snake_case_ = [
self._normalize_coordinates(self.target_size , lowercase_ , lowercase_ , is_bounding_box=lowercase_ )
for box, original_size in zip(lowercase_ , lowercase_ )
]
snake_case_ = np.array(lowercase_ )
if input_boxes is not None:
if return_tensors == "pt":
snake_case_ = torch.from_numpy(lowercase_ )
# boxes batch size of 1 by default
snake_case_ = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
snake_case_ = tf.convert_to_tensor(lowercase_ )
# boxes batch size of 1 by default
snake_case_ = tf.expand_dims(lowercase_ , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'''input_boxes''': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
snake_case_ = torch.from_numpy(lowercase_ )
# point batch size of 1 by default
snake_case_ = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
snake_case_ = tf.convert_to_tensor(lowercase_ )
# point batch size of 1 by default
snake_case_ = tf.expand_dims(lowercase_ , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'''input_points''': input_points} )
if input_labels is not None:
if return_tensors == "pt":
snake_case_ = torch.from_numpy(lowercase_ )
# point batch size of 1 by default
snake_case_ = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
snake_case_ = tf.convert_to_tensor(lowercase_ )
# point batch size of 1 by default
snake_case_ = tf.expand_dims(lowercase_ , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'''input_labels''': input_labels} )
return encoding_image_processor
def A_ ( self : Union[str, Any] , lowercase_ : Any , lowercase_ : Dict ):
snake_case_ = max([point.shape[0] for point in input_points] )
snake_case_ = []
for i, point in enumerate(lowercase_ ):
if point.shape[0] != expected_nb_points:
snake_case_ = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
snake_case_ = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(lowercase_ )
snake_case_ = processed_input_points
return input_points, input_labels
def A_ ( self : int , lowercase_ : int , lowercase_ : np.ndarray , lowercase_ : List[str] , lowercase_ : Tuple=False ):
snake_case_ ,snake_case_ = original_size
snake_case_ ,snake_case_ = self.image_processor._get_preprocess_shape(lowercase_ , longest_edge=lowercase_ )
snake_case_ = deepcopy(lowercase_ ).astype(lowercase_ )
if is_bounding_box:
snake_case_ = coords.reshape(-1 , 2 , 2 )
snake_case_ = coords[..., 0] * (new_w / old_w)
snake_case_ = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
snake_case_ = coords.reshape(-1 , 4 )
return coords
def A_ ( self : Any , lowercase_ : int=None , lowercase_ : int=None , lowercase_ : Optional[Any]=None , ):
if input_points is not None:
if hasattr(lowercase_ , '''numpy''' ): # Checks for TF or Torch tensor
snake_case_ = input_points.numpy().tolist()
if not isinstance(lowercase_ , lowercase_ ) or not isinstance(input_points[0] , lowercase_ ):
raise ValueError('''Input points must be a list of list of floating points.''' )
snake_case_ = [np.array(lowercase_ ) for input_point in input_points]
else:
snake_case_ = None
if input_labels is not None:
if hasattr(lowercase_ , '''numpy''' ):
snake_case_ = input_labels.numpy().tolist()
if not isinstance(lowercase_ , lowercase_ ) or not isinstance(input_labels[0] , lowercase_ ):
raise ValueError('''Input labels must be a list of list integers.''' )
snake_case_ = [np.array(lowercase_ ) for label in input_labels]
else:
snake_case_ = None
if input_boxes is not None:
if hasattr(lowercase_ , '''numpy''' ):
snake_case_ = input_boxes.numpy().tolist()
if (
not isinstance(lowercase_ , lowercase_ )
or not isinstance(input_boxes[0] , lowercase_ )
or not isinstance(input_boxes[0][0] , lowercase_ )
):
raise ValueError('''Input boxes must be a list of list of list of floating points.''' )
snake_case_ = [np.array(lowercase_ ).astype(np.floataa ) for box in input_boxes]
else:
snake_case_ = None
return input_points, input_labels, input_boxes
@property
def A_ ( self : Optional[int] ):
snake_case_ = self.image_processor.model_input_names
return list(dict.fromkeys(lowercase_ ) )
def A_ ( self : Optional[Any] , *lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
return self.image_processor.post_process_masks(*lowercase_ , **lowercase_ )
| 56 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase_ = 256
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
a : List[str] = ["melgan"]
def __init__( self, __magic_name__, __magic_name__, __magic_name__, __magic_name__, __magic_name__, ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase__ : Optional[int] = math.log(1E-5 ) # Matches MelGAN training.
UpperCamelCase__ : int = 4.0 # Largest value for most examples
UpperCamelCase__ : Optional[int] = 128
self.register_modules(
notes_encoder=__magic_name__, continuous_encoder=__magic_name__, decoder=__magic_name__, scheduler=__magic_name__, melgan=__magic_name__, )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__=(-1.0, 1.0), __magic_name__=False ) -> Any:
"""simple docstring"""
UpperCamelCase__ ,UpperCamelCase__ : str = output_range
if clip:
UpperCamelCase__ : Union[str, Any] = torch.clip(__magic_name__, self.min_value, self.max_value )
# Scale to [0, 1].
UpperCamelCase__ : List[str] = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def UpperCamelCase__ ( self, __magic_name__, __magic_name__=(-1.0, 1.0), __magic_name__=False ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ ,UpperCamelCase__ : List[str] = input_range
UpperCamelCase__ : Any = torch.clip(__magic_name__, __magic_name__, __magic_name__ ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase__ : Any = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = input_tokens > 0
UpperCamelCase__ ,UpperCamelCase__ : Any = self.notes_encoder(
encoder_input_tokens=__magic_name__, encoder_inputs_mask=__magic_name__ )
UpperCamelCase__ ,UpperCamelCase__ : Optional[Any] = self.continuous_encoder(
encoder_inputs=__magic_name__, encoder_inputs_mask=__magic_name__ )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> str:
"""simple docstring"""
UpperCamelCase__ : Any = noise_time
if not torch.is_tensor(__magic_name__ ):
UpperCamelCase__ : Tuple = torch.tensor([timesteps], dtype=torch.long, device=input_tokens.device )
elif torch.is_tensor(__magic_name__ ) and len(timesteps.shape ) == 0:
UpperCamelCase__ : Union[str, Any] = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase__ : Dict = timesteps * torch.ones(input_tokens.shape[0], dtype=timesteps.dtype, device=timesteps.device )
UpperCamelCase__ : List[str] = self.decoder(
encodings_and_masks=__magic_name__, decoder_input_tokens=__magic_name__, decoder_noise_time=__magic_name__ )
return logits
@torch.no_grad()
def __call__( self, __magic_name__, __magic_name__ = None, __magic_name__ = 100, __magic_name__ = True, __magic_name__ = "numpy", __magic_name__ = None, __magic_name__ = 1, ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__magic_name__, __magic_name__ ) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(__magic_name__ )}." )
UpperCamelCase__ : Dict = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.floataa )
UpperCamelCase__ : Tuple = np.zeros([1, 0, self.n_dims], np.floataa )
UpperCamelCase__ : List[Any] = torch.ones((1, TARGET_FEATURE_LENGTH), dtype=__magic_name__, device=self.device )
for i, encoder_input_tokens in enumerate(__magic_name__ ):
if i == 0:
UpperCamelCase__ : str = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device, dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase__ : Any = torch.zeros((1, TARGET_FEATURE_LENGTH), dtype=__magic_name__, device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase__ : List[str] = ones
UpperCamelCase__ : int = self.scale_features(
__magic_name__, output_range=[-1.0, 1.0], clip=__magic_name__ )
UpperCamelCase__ : Union[str, Any] = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ), continuous_inputs=__magic_name__, continuous_mask=__magic_name__, )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase__ : Optional[int] = randn_tensor(
shape=encoder_continuous_inputs.shape, generator=__magic_name__, device=self.device, dtype=self.decoder.dtype, )
# set step values
self.scheduler.set_timesteps(__magic_name__ )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase__ : Union[str, Any] = self.decode(
encodings_and_masks=__magic_name__, input_tokens=__magic_name__, noise_time=t / self.scheduler.config.num_train_timesteps, )
# Compute previous output: x_t -> x_t-1
UpperCamelCase__ : List[Any] = self.scheduler.step(__magic_name__, __magic_name__, __magic_name__, generator=__magic_name__ ).prev_sample
UpperCamelCase__ : List[Any] = self.scale_to_features(__magic_name__, input_range=[-1.0, 1.0] )
UpperCamelCase__ : List[Any] = mel[:1]
UpperCamelCase__ : int = mel.cpu().float().numpy()
UpperCamelCase__ : Union[str, Any] = np.concatenate([full_pred_mel, pred_mel[:1]], axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__magic_name__, __magic_name__ )
logger.info('''Generated segment''', __magic_name__ )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )
if output_type == "numpy":
UpperCamelCase__ : Optional[int] = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase__ : Any = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=__magic_name__ )
| 201 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
'''JukeboxVQVAEConfig''',
],
'''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''JukeboxModel''',
'''JukeboxPreTrainedModel''',
'''JukeboxVQVAE''',
'''JukeboxPrior''',
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 367 | """simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
def lowercase ( a__ : str ) -> int:
_UpperCamelCase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
_UpperCamelCase = [144, 192, 240]
_UpperCamelCase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
_UpperCamelCase = [96, 120, 144]
_UpperCamelCase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
_UpperCamelCase = [64, 80, 96]
_UpperCamelCase = [16, 16, 24, 48, 64, 80, 320]
_UpperCamelCase = 0.05
_UpperCamelCase = 2.0
if mobilevit_name.startswith('''deeplabv3_''' ):
_UpperCamelCase = 512
_UpperCamelCase = 16
_UpperCamelCase = 21
_UpperCamelCase = '''pascal-voc-id2label.json'''
else:
_UpperCamelCase = 1000
_UpperCamelCase = '''imagenet-1k-id2label.json'''
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = json.load(open(hf_hub_download(a__ , a__ , repo_type='''dataset''' ) , '''r''' ) )
_UpperCamelCase = {int(a__ ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
return config
def lowercase ( a__ : Union[str, Any] , a__ : Optional[int]=False ) -> int:
for i in range(1 , 6 ):
if F'''layer_{i}.''' in name:
_UpperCamelCase = name.replace(F'''layer_{i}.''' , F'''encoder.layer.{i - 1}.''' )
if "conv_1." in name:
_UpperCamelCase = name.replace('''conv_1.''' , '''conv_stem.''' )
if ".block." in name:
_UpperCamelCase = name.replace('''.block.''' , '''.''' )
if "exp_1x1" in name:
_UpperCamelCase = name.replace('''exp_1x1''' , '''expand_1x1''' )
if "red_1x1" in name:
_UpperCamelCase = name.replace('''red_1x1''' , '''reduce_1x1''' )
if ".local_rep.conv_3x3." in name:
_UpperCamelCase = name.replace('''.local_rep.conv_3x3.''' , '''.conv_kxk.''' )
if ".local_rep.conv_1x1." in name:
_UpperCamelCase = name.replace('''.local_rep.conv_1x1.''' , '''.conv_1x1.''' )
if ".norm." in name:
_UpperCamelCase = name.replace('''.norm.''' , '''.normalization.''' )
if ".conv." in name:
_UpperCamelCase = name.replace('''.conv.''' , '''.convolution.''' )
if ".conv_proj." in name:
_UpperCamelCase = name.replace('''.conv_proj.''' , '''.conv_projection.''' )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F'''.{i}.{j}.''' in name:
_UpperCamelCase = name.replace(F'''.{i}.{j}.''' , F'''.{i}.layer.{j}.''' )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F'''.{i}.{j}.''' in name:
_UpperCamelCase = name.replace(F'''.{i}.{j}.''' , F'''.{i}.''' )
if "expand_1x1" in name:
_UpperCamelCase = name.replace('''expand_1x1''' , '''downsampling_layer.expand_1x1''' )
if "conv_3x3" in name:
_UpperCamelCase = name.replace('''conv_3x3''' , '''downsampling_layer.conv_3x3''' )
if "reduce_1x1" in name:
_UpperCamelCase = name.replace('''reduce_1x1''' , '''downsampling_layer.reduce_1x1''' )
for i in range(2 , 5 ):
if F'''.global_rep.{i}.weight''' in name:
_UpperCamelCase = name.replace(F'''.global_rep.{i}.weight''' , '''.layernorm.weight''' )
if F'''.global_rep.{i}.bias''' in name:
_UpperCamelCase = name.replace(F'''.global_rep.{i}.bias''' , '''.layernorm.bias''' )
if ".global_rep." in name:
_UpperCamelCase = name.replace('''.global_rep.''' , '''.transformer.''' )
if ".pre_norm_mha.0." in name:
_UpperCamelCase = name.replace('''.pre_norm_mha.0.''' , '''.layernorm_before.''' )
if ".pre_norm_mha.1.out_proj." in name:
_UpperCamelCase = name.replace('''.pre_norm_mha.1.out_proj.''' , '''.attention.output.dense.''' )
if ".pre_norm_ffn.0." in name:
_UpperCamelCase = name.replace('''.pre_norm_ffn.0.''' , '''.layernorm_after.''' )
if ".pre_norm_ffn.1." in name:
_UpperCamelCase = name.replace('''.pre_norm_ffn.1.''' , '''.intermediate.dense.''' )
if ".pre_norm_ffn.4." in name:
_UpperCamelCase = name.replace('''.pre_norm_ffn.4.''' , '''.output.dense.''' )
if ".transformer." in name:
_UpperCamelCase = name.replace('''.transformer.''' , '''.transformer.layer.''' )
if ".aspp_layer." in name:
_UpperCamelCase = name.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in name:
_UpperCamelCase = name.replace('''.aspp_pool.''' , '''.''' )
if "seg_head." in name:
_UpperCamelCase = name.replace('''seg_head.''' , '''segmentation_head.''' )
if "segmentation_head.classifier.classifier." in name:
_UpperCamelCase = name.replace('''segmentation_head.classifier.classifier.''' , '''segmentation_head.classifier.''' )
if "classifier.fc." in name:
_UpperCamelCase = name.replace('''classifier.fc.''' , '''classifier.''' )
elif (not base_model) and ("segmentation_head." not in name):
_UpperCamelCase = '''mobilevit.''' + name
return name
def lowercase ( a__ : Union[str, Any] , a__ : List[Any] , a__ : Tuple=False ) -> Optional[Any]:
if base_model:
_UpperCamelCase = ''''''
else:
_UpperCamelCase = '''mobilevit.'''
for key in orig_state_dict.copy().keys():
_UpperCamelCase = orig_state_dict.pop(a__ )
if key[:8] == "encoder.":
_UpperCamelCase = key[8:]
if "qkv" in key:
_UpperCamelCase = key.split('''.''' )
_UpperCamelCase = int(key_split[0][6:] ) - 1
_UpperCamelCase = int(key_split[3] )
_UpperCamelCase = model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}''' )
_UpperCamelCase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
_UpperCamelCase = (
F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
)
if "weight" in key:
_UpperCamelCase = val[:dim, :]
_UpperCamelCase = val[dim : dim * 2, :]
_UpperCamelCase = val[-dim:, :]
else:
_UpperCamelCase = val[:dim]
_UpperCamelCase = val[dim : dim * 2]
_UpperCamelCase = val[-dim:]
else:
_UpperCamelCase = val
return orig_state_dict
def lowercase ( ) -> Dict:
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(a__ , stream=a__ ).raw )
return im
@torch.no_grad()
def lowercase ( a__ : Dict , a__ : Optional[int] , a__ : Any , a__ : Tuple=False ) -> Any:
_UpperCamelCase = get_mobilevit_config(a__ )
# load original state_dict
_UpperCamelCase = torch.load(a__ , map_location='''cpu''' )
# load 🤗 model
if mobilevit_name.startswith('''deeplabv3_''' ):
_UpperCamelCase = MobileViTForSemanticSegmentation(a__ ).eval()
else:
_UpperCamelCase = MobileViTForImageClassification(a__ ).eval()
_UpperCamelCase = convert_state_dict(a__ , a__ )
model.load_state_dict(a__ )
# Check outputs on an image, prepared by MobileViTImageProcessor
_UpperCamelCase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_UpperCamelCase = image_processor(images=prepare_img() , return_tensors='''pt''' )
_UpperCamelCase = model(**a__ )
_UpperCamelCase = outputs.logits
if mobilevit_name.startswith('''deeplabv3_''' ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
_UpperCamelCase = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
_UpperCamelCase = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
_UpperCamelCase = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
] )
else:
raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3, :3, :3] , a__ , atol=1e-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
_UpperCamelCase = torch.tensor([-0.9866, 0.2392, -1.1241] )
elif mobilevit_name == "mobilevit_xs":
_UpperCamelCase = torch.tensor([-2.4761, -0.9399, -1.9587] )
elif mobilevit_name == "mobilevit_xxs":
_UpperCamelCase = torch.tensor([-1.9364, -1.2327, -0.4653] )
else:
raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3] , a__ , atol=1e-4 )
Path(a__ ).mkdir(exist_ok=a__ )
print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(a__ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(a__ )
if push_to_hub:
_UpperCamelCase = {
'''mobilevit_s''': '''mobilevit-small''',
'''mobilevit_xs''': '''mobilevit-x-small''',
'''mobilevit_xxs''': '''mobilevit-xx-small''',
'''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
'''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
'''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
}
print('''Pushing to the hub...''' )
_UpperCamelCase = model_mapping[mobilevit_name]
image_processor.push_to_hub(a__ , organization='''apple''' )
model.push_to_hub(a__ , organization='''apple''' )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, 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 to the 🤗 hub."""
)
UpperCAmelCase = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 54 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Optional[int] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Optional[int] = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 314 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {
'''vocab_file''': '''vocab.txt''',
'''merges_file''': '''bpe.codes''',
}
_SCREAMING_SNAKE_CASE : Dict = {
'''vocab_file''': {
'''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
'''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
},
'''merges_file''': {
'''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
'''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
},
}
_SCREAMING_SNAKE_CASE : Optional[int] = {
'''vinai/phobert-base''': 256,
'''vinai/phobert-large''': 256,
}
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = set()
SCREAMING_SNAKE_CASE__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE__ = char
SCREAMING_SNAKE_CASE__ = set(_A )
return pairs
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = VOCAB_FILES_NAMES
a = PRETRAINED_VOCAB_FILES_MAP
a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : str , __lowerCamelCase : Optional[Any]="<s>" , __lowerCamelCase : List[str]="</s>" , __lowerCamelCase : Dict="</s>" , __lowerCamelCase : Dict="<s>" , __lowerCamelCase : List[str]="<unk>" , __lowerCamelCase : Optional[Any]="<pad>" , __lowerCamelCase : Union[str, Any]="<mask>" , **__lowerCamelCase : Optional[int] , ) -> Union[str, Any]:
super().__init__(
bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , **__lowerCamelCase , )
SCREAMING_SNAKE_CASE__ = vocab_file
SCREAMING_SNAKE_CASE__ = merges_file
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = 3
self.add_from_file(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.encoder.items()}
with open(__lowerCamelCase , encoding='''utf-8''' ) as merges_handle:
SCREAMING_SNAKE_CASE__ = merges_handle.read().split('''\n''' )[:-1]
SCREAMING_SNAKE_CASE__ = [tuple(merge.split()[:-1] ) for merge in merges]
SCREAMING_SNAKE_CASE__ = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE__ = {}
def lowercase_ ( self : Dict , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ = [self.cls_token_id]
SCREAMING_SNAKE_CASE__ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase_ ( self : Union[str, Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def lowercase_ ( self : List[Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowercase_ ( self : Dict ) -> str:
return len(self.encoder )
def lowercase_ ( self : List[Any] ) -> str:
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase_ ( self : Any , __lowerCamelCase : Any ) -> Any:
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE__ = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
SCREAMING_SNAKE_CASE__ = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE__ = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = bigram
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
while i < len(__lowerCamelCase ):
try:
SCREAMING_SNAKE_CASE__ = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE__ = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE__ = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = new_word
if len(__lowerCamelCase ) == 1:
break
else:
SCREAMING_SNAKE_CASE__ = get_pairs(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = '''@@ '''.join(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = word[:-4]
SCREAMING_SNAKE_CASE__ = word
return word
def lowercase_ ( self : Optional[Any] , __lowerCamelCase : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = re.findall(r'''\S+\n?''' , __lowerCamelCase )
for token in words:
split_tokens.extend(list(self.bpe(__lowerCamelCase ).split(''' ''' ) ) )
return split_tokens
def lowercase_ ( self : str , __lowerCamelCase : Optional[int] ) -> Optional[int]:
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def lowercase_ ( self : List[Any] , __lowerCamelCase : List[str] ) -> Dict:
return self.decoder.get(__lowerCamelCase , self.unk_token )
def lowercase_ ( self : Union[str, Any] , __lowerCamelCase : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ = ''' '''.join(__lowerCamelCase ).replace('''@@ ''' , '''''' ).strip()
return out_string
def lowercase_ ( self : Dict , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
SCREAMING_SNAKE_CASE__ = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ):
copyfile(self.vocab_file , __lowerCamelCase )
if os.path.abspath(self.merges_file ) != os.path.abspath(__lowerCamelCase ):
copyfile(self.merges_file , __lowerCamelCase )
return out_vocab_file, out_merge_file
def lowercase_ ( self : int , __lowerCamelCase : Tuple ) -> Optional[Any]:
if isinstance(__lowerCamelCase , __lowerCamelCase ):
try:
with open(__lowerCamelCase , '''r''' , encoding='''utf-8''' ) as fd:
self.add_from_file(__lowerCamelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
SCREAMING_SNAKE_CASE__ = f.readlines()
for lineTmp in lines:
SCREAMING_SNAKE_CASE__ = lineTmp.strip()
SCREAMING_SNAKE_CASE__ = line.rfind(''' ''' )
if idx == -1:
raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
SCREAMING_SNAKE_CASE__ = line[:idx]
SCREAMING_SNAKE_CASE__ = len(self.encoder )
| 314 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class lowerCAmelCase_ ( lowerCamelCase__ ):
'''simple docstring'''
__snake_case = "data2vec-audio"
def __init__( self , _UpperCAmelCase=32 , _UpperCAmelCase=7_68 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=30_72 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1E-5 , _UpperCAmelCase="gelu" , _UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , _UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2) , _UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2) , _UpperCAmelCase=False , _UpperCAmelCase=16 , _UpperCAmelCase=19 , _UpperCAmelCase=5 , _UpperCAmelCase=0.05 , _UpperCAmelCase=10 , _UpperCAmelCase=2 , _UpperCAmelCase=0.0 , _UpperCAmelCase=10 , _UpperCAmelCase=0 , _UpperCAmelCase="sum" , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=2_56 , _UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 15_00) , _UpperCAmelCase=(5, 3, 3, 1, 1) , _UpperCAmelCase=(1, 2, 3, 1, 1) , _UpperCAmelCase=5_12 , _UpperCAmelCase=0 , _UpperCAmelCase=1 , _UpperCAmelCase=2 , _UpperCAmelCase=False , _UpperCAmelCase=3 , _UpperCAmelCase=2 , _UpperCAmelCase=3 , _UpperCAmelCase=None , **_UpperCAmelCase , ):
super().__init__(**_UpperCAmelCase , pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase )
snake_case_ = hidden_size
snake_case_ = feat_extract_activation
snake_case_ = list(_UpperCAmelCase )
snake_case_ = list(_UpperCAmelCase )
snake_case_ = list(_UpperCAmelCase )
snake_case_ = conv_bias
snake_case_ = num_conv_pos_embeddings
snake_case_ = num_conv_pos_embedding_groups
snake_case_ = conv_pos_kernel_size
snake_case_ = len(self.conv_dim )
snake_case_ = num_hidden_layers
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = feat_proj_dropout
snake_case_ = final_dropout
snake_case_ = layerdrop
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = vocab_size
snake_case_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
# ctc loss
snake_case_ = ctc_loss_reduction
snake_case_ = ctc_zero_infinity
# adapter
snake_case_ = add_adapter
snake_case_ = adapter_kernel_size
snake_case_ = adapter_stride
snake_case_ = num_adapter_layers
snake_case_ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ = list(_UpperCAmelCase )
snake_case_ = list(_UpperCAmelCase )
snake_case_ = list(_UpperCAmelCase )
snake_case_ = xvector_output_dim
@property
def UpperCamelCase__ ( self ):
return math.prod(self.conv_stride ) | 267 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=99 , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=9 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=32 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=37 , _UpperCAmelCase=8 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.002 , _UpperCAmelCase=1 , _UpperCAmelCase=0 , _UpperCAmelCase=0 , _UpperCAmelCase=None , _UpperCAmelCase=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = encoder_seq_length
snake_case_ = decoder_seq_length
# For common tests
snake_case_ = self.decoder_seq_length
snake_case_ = is_training
snake_case_ = use_attention_mask
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = d_ff
snake_case_ = relative_attention_num_buckets
snake_case_ = dropout_rate
snake_case_ = initializer_factor
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = decoder_start_token_id
snake_case_ = None
snake_case_ = decoder_layers
def UpperCamelCase__ ( self ):
return TaConfig.from_pretrained('''google/umt5-base''' )
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , ):
if attention_mask is None:
snake_case_ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
snake_case_ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
snake_case_ = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_UpperCAmelCase )
if decoder_head_mask is None:
snake_case_ = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_UpperCAmelCase )
if cross_attn_head_mask is None:
snake_case_ = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_UpperCAmelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def UpperCamelCase__ ( self ):
snake_case_ = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
snake_case_ = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
snake_case_ = input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = decoder_input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = self.get_config()
snake_case_ = config.num_attention_heads
snake_case_ = self.prepare_inputs_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return config, input_dict
def UpperCamelCase__ ( self ):
snake_case_ , snake_case_ = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCamelCase__ ( self ):
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def UpperCamelCase__ ( self ):
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
snake_case_ = UMTaModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case_ = model(
input_ids=_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase , attention_mask=_UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , )
snake_case_ = model(input_ids=_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase )
snake_case_ = result.last_hidden_state
snake_case_ = result.past_key_values
snake_case_ = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_UpperCAmelCase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
snake_case_ = UMTaModel(config=_UpperCAmelCase ).get_decoder().to(_UpperCAmelCase ).eval()
# first forward pass
snake_case_ = model(_UpperCAmelCase , use_cache=_UpperCAmelCase )
snake_case_ = model(_UpperCAmelCase )
snake_case_ = model(_UpperCAmelCase , use_cache=_UpperCAmelCase )
self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) )
self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) + 1 )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = model(_UpperCAmelCase )['''last_hidden_state''']
snake_case_ = model(_UpperCAmelCase , past_key_values=_UpperCAmelCase )['''last_hidden_state''']
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -1, random_slice_idx].detach()
snake_case_ = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase , ):
snake_case_ = UMTaModel(config=_UpperCAmelCase ).to(_UpperCAmelCase ).half().eval()
snake_case_ = model(**_UpperCAmelCase )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(_UpperCAmelCase ).any().item() )
@require_torch
class lowerCAmelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
__snake_case = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
__snake_case = (UMTaForConditionalGeneration,) if is_torch_available() else ()
__snake_case = (
{
"conversational": UMTaForConditionalGeneration,
"feature-extraction": UMTaModel,
"summarization": UMTaForConditionalGeneration,
"text2text-generation": UMTaForConditionalGeneration,
"translation": UMTaForConditionalGeneration,
"question-answering": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
__snake_case = True
__snake_case = False
__snake_case = False
__snake_case = True
__snake_case = True
# The small UMT5 model needs higher percentages for CPU/MP tests
__snake_case = [0.8, 0.9]
def UpperCamelCase__ ( self ):
snake_case_ = UMTaModelTester(self )
@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
def UpperCamelCase__ ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
snake_case_ = UMTaModel(config_and_inputs[0] ).to(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_UpperCAmelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'''{tmpdirname}/t5_test.onnx''' , export_params=_UpperCAmelCase , opset_version=9 , input_names=['''input_ids''', '''decoder_input_ids'''] , )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def UpperCamelCase__ ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_UpperCAmelCase )
def UpperCamelCase__ ( self ):
snake_case_ = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
snake_case_ = self.model_tester.prepare_config_and_inputs()
snake_case_ = config_and_inputs[0]
snake_case_ = UMTaForConditionalGeneration(_UpperCAmelCase ).eval()
model.to(_UpperCAmelCase )
snake_case_ = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=_UpperCAmelCase ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=_UpperCAmelCase ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=_UpperCAmelCase ),
}
for attn_name, (name, mask) in zip(_UpperCAmelCase , head_masking.items() ):
snake_case_ = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
snake_case_ = torch.ones(
config.num_decoder_layers , config.num_heads , device=_UpperCAmelCase )
snake_case_ = model.generate(
config_and_inputs[1]['''input_ids'''] , num_beams=1 , max_length=3 , output_attentions=_UpperCAmelCase , return_dict_in_generate=_UpperCAmelCase , **_UpperCAmelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
snake_case_ = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
def UpperCamelCase__ ( self ):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
@unittest.skip(
'''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
def UpperCamelCase__ ( self ):
snake_case_ = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' , return_dict=_UpperCAmelCase ).to(_UpperCAmelCase )
snake_case_ = AutoTokenizer.from_pretrained('''google/umt5-small''' , use_fast=_UpperCAmelCase , legacy=_UpperCAmelCase )
snake_case_ = [
'''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
'''No se como puedo <extra_id_0>.''',
'''This is the reason why we <extra_id_0> them.''',
'''The <extra_id_0> walks in <extra_id_1>, seats''',
'''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
]
snake_case_ = tokenizer(_UpperCAmelCase , return_tensors='''pt''' , padding=_UpperCAmelCase ).input_ids
# fmt: off
snake_case_ = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
] )
# fmt: on
torch.testing.assert_allclose(_UpperCAmelCase , _UpperCAmelCase )
snake_case_ = model.generate(input_ids.to(_UpperCAmelCase ) )
snake_case_ = [
'''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
'''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
]
snake_case_ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase ) | 267 | 1 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def _SCREAMING_SNAKE_CASE ( _lowercase : str=None ) ->Optional[Any]:
'''simple docstring'''
if subparsers is not None:
a : Any = subparsers.add_parser("env" )
else:
a : str = argparse.ArgumentParser("Accelerate env command" )
parser.add_argument(
"--config_file" , default=_lowercase , help="The config file to use for the default values in the launching script." )
if subparsers is not None:
parser.set_defaults(func=_lowercase )
return parser
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->Dict:
'''simple docstring'''
a : Optional[Any] = torch.__version__
a : Union[str, Any] = torch.cuda.is_available()
a : Optional[Any] = is_xpu_available()
a : List[Any] = is_npu_available()
a : str = "Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_lowercase ):
a : Optional[int] = load_config_from_file(args.config_file ).to_dict()
a : int = {
"`Accelerate` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Numpy version": np.__version__,
"PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
"PyTorch XPU available": str(_lowercase ),
"PyTorch NPU available": str(_lowercase ),
"System RAM": F"""{psutil.virtual_memory().total / 1024 ** 3:.2f} GB""",
}
if pt_cuda_available:
a : str = torch.cuda.get_device_name()
print("\nCopy-and-paste the text below in your GitHub issue\n" )
print("\n".join([F"""- {prop}: {val}""" for prop, val in info.items()] ) )
print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:" )
a : List[Any] = (
"\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
if isinstance(_lowercase , _lowercase )
else F"""\t{accelerate_config}"""
)
print(_lowercase )
a : List[Any] = accelerate_config
return info
def _SCREAMING_SNAKE_CASE ( ) ->int:
'''simple docstring'''
a : Optional[int] = env_command_parser()
a : Tuple = parser.parse_args()
env_command(_lowercase )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 105 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class snake_case__ (datasets.BuilderConfig ):
"""simple docstring"""
__lowerCAmelCase :Optional[datasets.Features] = None
class snake_case__ (datasets.ArrowBasedBuilder ):
"""simple docstring"""
__lowerCAmelCase :Dict = PandasConfig
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> Tuple:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
a__ : str = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__lowercase , (str, list, tuple) ):
a__ : Optional[int] = data_files
if isinstance(__lowercase , __lowercase ):
a__ : List[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a__ : str = [dl_manager.iter_files(__lowercase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
a__ : List[str] = []
for split_name, files in data_files.items():
if isinstance(__lowercase , __lowercase ):
a__ : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a__ : Dict = [dl_manager.iter_files(__lowercase ) for file in files]
splits.append(datasets.SplitGenerator(name=__lowercase , gen_kwargs={"""files""": files} ) )
return splits
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
a__ : Tuple = table_cast(__lowercase , self.config.features.arrow_schema )
return pa_table
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> List[Any]:
"""simple docstring"""
for i, file in enumerate(itertools.chain.from_iterable(__lowercase ) ):
with open(__lowercase , """rb""" ) as f:
a__ : str = pa.Table.from_pandas(pd.read_pickle(__lowercase ) )
yield i, self._cast_table(__lowercase )
| 170 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowerCAmelCase__ : int = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n"
lowerCAmelCase__ : Any = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"
lowerCAmelCase__ : Union[str, Any] = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n predictions: list of predictions to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"pearson\": Pearson Correlation\n \"spearmanr\": Spearman Correlation\n \"matthews_correlation\": Matthew Correlation\nExamples:\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\') # \'sst2\' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\') # \'mrpc\' or \'qqp\'\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n >>> references = [0., 1., 2., 3., 4., 5.]\n >>> predictions = [0., 1., 2., 3., 4., 5.]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'matthews_correlation\': 1.0}\n"
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
return float((preds == labels).mean() )
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : int = simple_accuracy(_UpperCAmelCase, _UpperCAmelCase )
__UpperCAmelCase : Any = float(fa_score(y_true=_UpperCAmelCase, y_pred=_UpperCAmelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : str = float(pearsonr(_UpperCAmelCase, _UpperCAmelCase )[0] )
__UpperCAmelCase : Any = float(spearmanr(_UpperCAmelCase, _UpperCAmelCase )[0] )
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
if self.config_name not in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
"\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32" ),
"references": datasets.Value("int64" if self.config_name != "stsb" else "float32" ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" , )
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] ):
"""simple docstring"""
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(UpperCamelCase_ , UpperCamelCase_ )}
elif self.config_name == "stsb":
return pearson_and_spearman(UpperCamelCase_ , UpperCamelCase_ )
elif self.config_name in ["mrpc", "qqp"]:
return acc_and_fa(UpperCamelCase_ , UpperCamelCase_ )
elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
return {"accuracy": simple_accuracy(UpperCamelCase_ , UpperCamelCase_ )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
"\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]" )
| 354 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signature(_UpperCAmelCase )]
lowerCAmelCase__ : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
lowerCAmelCase__ : Optional[int] = sorted({word.strip().lower() for word in data.splitlines()})
lowerCAmelCase__ : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
lowerCAmelCase__ : int = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 37 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger(__name__)
def UpperCamelCase ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
a_ = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
a_ = key.replace("module.encoder" , "glpn.encoder" )
if key.startswith("module.decoder" ):
a_ = key.replace("module.decoder" , "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
a_ = key[key.find("patch_embed" ) + len("patch_embed" )]
a_ = key.replace(F'''patch_embed{idx}''' , F'''patch_embeddings.{int(UpperCAmelCase )-1}''' )
if "norm" in key:
a_ = key.replace("norm" , "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
a_ = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
a_ = key.replace(F'''layer_norm{idx}''' , F'''layer_norm.{int(UpperCAmelCase )-1}''' )
if "layer_norm1" in key:
a_ = key.replace("layer_norm1" , "layer_norm_1" )
if "layer_norm2" in key:
a_ = key.replace("layer_norm2" , "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
a_ = key[key.find("block" ) + len("block" )]
a_ = key.replace(F'''block{idx}''' , F'''block.{int(UpperCAmelCase )-1}''' )
if "attn.q" in key:
a_ = key.replace("attn.q" , "attention.self.query" )
if "attn.proj" in key:
a_ = key.replace("attn.proj" , "attention.output.dense" )
if "attn" in key:
a_ = key.replace("attn" , "attention.self" )
if "fc1" in key:
a_ = key.replace("fc1" , "dense1" )
if "fc2" in key:
a_ = key.replace("fc2" , "dense2" )
if "linear_pred" in key:
a_ = key.replace("linear_pred" , "classifier" )
if "linear_fuse" in key:
a_ = key.replace("linear_fuse.conv" , "linear_fuse" )
a_ = key.replace("linear_fuse.bn" , "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
a_ = key[key.find("linear_c" ) + len("linear_c" )]
a_ = key.replace(F'''linear_c{idx}''' , F'''linear_c.{int(UpperCAmelCase )-1}''' )
if "bot_conv" in key:
a_ = key.replace("bot_conv" , "0.convolution" )
if "skip_conv1" in key:
a_ = key.replace("skip_conv1" , "1.convolution" )
if "skip_conv2" in key:
a_ = key.replace("skip_conv2" , "2.convolution" )
if "fusion1" in key:
a_ = key.replace("fusion1" , "1.fusion" )
if "fusion2" in key:
a_ = key.replace("fusion2" , "2.fusion" )
if "fusion3" in key:
a_ = key.replace("fusion3" , "3.fusion" )
if "fusion" in key and "conv" in key:
a_ = key.replace("conv" , "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
a_ = key.replace("module.last_layer_depth" , "head.head" )
a_ = value
return new_state_dict
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
a_ = state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
a_ = state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
a_ = kv_weight[
: config.hidden_sizes[i], :
]
a_ = kv_bias[: config.hidden_sizes[i]]
a_ = kv_weight[
config.hidden_sizes[i] :, :
]
a_ = kv_bias[config.hidden_sizes[i] :]
def UpperCamelCase ( ) ->Any:
"""simple docstring"""
a_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
a_ = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return image
@torch.no_grad()
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=False , UpperCAmelCase=None ) ->str:
"""simple docstring"""
a_ = GLPNConfig(hidden_sizes=[64, 128, 320, 512] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
a_ = GLPNImageProcessor()
# prepare image
a_ = prepare_img()
a_ = image_processor(images=UpperCAmelCase , return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
a_ = torch.load(UpperCAmelCase , map_location=torch.device("cpu" ) )
# rename keys
a_ = rename_keys(UpperCAmelCase )
# key and value matrices need special treatment
read_in_k_v(UpperCAmelCase , UpperCAmelCase )
# create HuggingFace model and load state dict
a_ = GLPNForDepthEstimation(UpperCAmelCase )
model.load_state_dict(UpperCAmelCase )
model.eval()
# forward pass
a_ = model(UpperCAmelCase )
a_ = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
a_ = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
a_ = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(F'''Unknown model name: {model_name}''' )
a_ = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , UpperCAmelCase , atol=1E-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the hub..." )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=UpperCAmelCase , )
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
UpperCamelCase_ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name) | 243 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class snake_case :
def __init__( self , __UpperCAmelCase = "cpu" , __UpperCAmelCase = "openai/clip-vit-large-patch14") ->None:
a_ = device
a_ = CLIPTokenizerFast.from_pretrained(__UpperCAmelCase)
a_ = [0.48_145_466, 0.4_578_275, 0.40_821_073]
a_ = [0.26_862_954, 0.26_130_258, 0.27_577_711]
a_ = torchvision.transforms.Normalize(self.image_mean , self.image_std)
a_ = torchvision.transforms.Resize(2_24)
a_ = torchvision.transforms.CenterCrop(2_24)
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->List[Any]:
a_ = self.resize(__UpperCAmelCase)
a_ = self.center_crop(__UpperCAmelCase)
a_ = self.normalize(__UpperCAmelCase)
return images
def __call__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase) ->Union[str, Any]:
a_ = self.tokenizer(text=__UpperCAmelCase , **__UpperCAmelCase)
a_ = self.preprocess_img(__UpperCAmelCase)
a_ = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class snake_case ( nn.Module ):
def __init__( self , __UpperCAmelCase=10 , __UpperCAmelCase=0.01 , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase="image" , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=False , ) ->None:
super().__init__()
a_ = None
a_ = device if device else get_device()
if vqgan:
a_ = vqgan
else:
a_ = load_vqgan(self.device , conf_path=__UpperCAmelCase , ckpt_path=__UpperCAmelCase)
self.vqgan.eval()
if clip:
a_ = clip
else:
a_ = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.clip.to(self.device)
a_ = ProcessorGradientFlow(device=self.device)
a_ = iterations
a_ = lr
a_ = log
a_ = make_grid
a_ = return_val
a_ = quantize
a_ = self.vqgan.decoder.z_shape
def UpperCAmelCase__ ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=5 , __UpperCAmelCase=True) ->Any:
a_ = []
if output_path is None:
a_ = "./animation.gif"
if input_path is None:
a_ = self.save_path
a_ = sorted(glob(input_path + "/*"))
if not len(__UpperCAmelCase):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)")
if len(__UpperCAmelCase) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
a_ = total_duration / len(__UpperCAmelCase)
a_ = [frame_duration] * len(__UpperCAmelCase)
if extend_frames:
a_ = 1.5
a_ = 3
for file_name in paths:
if file_name.endswith(".png"):
images.append(imageio.imread(__UpperCAmelCase))
imageio.mimsave(__UpperCAmelCase , __UpperCAmelCase , duration=__UpperCAmelCase)
print(F'''gif saved to {output_path}''')
def UpperCAmelCase__ ( self , __UpperCAmelCase=None , __UpperCAmelCase=None) ->List[Any]:
if not (path or img):
raise ValueError("Input either path or tensor")
if img is not None:
raise NotImplementedError
a_ = preprocess(Image.open(__UpperCAmelCase) , target_image_size=2_56).to(self.device)
a_ = preprocess_vqgan(__UpperCAmelCase)
a_ , *a_ = self.vqgan.encode(__UpperCAmelCase)
return z
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->Tuple:
a_ = self.latent.detach().requires_grad_()
a_ = base_latent + transform_vector
if self.quantize:
a_ , *a_ = self.vqgan.quantize(__UpperCAmelCase)
else:
a_ = trans_latent
return self.vqgan.decode(__UpperCAmelCase)
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None) ->str:
a_ = self.clip_preprocessor(text=__UpperCAmelCase , images=__UpperCAmelCase , return_tensors="pt" , padding=__UpperCAmelCase)
a_ = self.clip(**__UpperCAmelCase)
a_ = clip_outputs.logits_per_image
if weights is not None:
a_ = similarity_logits * weights
return similarity_logits.sum()
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Optional[int]:
a_ = self._get_clip_similarity(pos_prompts["prompts"] , __UpperCAmelCase , weights=(1 / pos_prompts["weights"]))
if neg_prompts:
a_ = self._get_clip_similarity(neg_prompts["prompts"] , __UpperCAmelCase , weights=neg_prompts["weights"])
else:
a_ = torch.tensor([1] , device=self.device)
a_ = -torch.log(__UpperCAmelCase) + torch.log(__UpperCAmelCase)
return loss
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->int:
a_ = torch.randn_like(self.latent , requires_grad=__UpperCAmelCase , device=self.device)
a_ = torch.optim.Adam([vector] , lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
a_ = self._add_vector(__UpperCAmelCase)
a_ = loop_post_process(__UpperCAmelCase)
a_ = self._get_CLIP_loss(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
print("CLIP loss" , __UpperCAmelCase)
if self.log:
wandb.log({"CLIP Loss": clip_loss})
clip_loss.backward(retain_graph=__UpperCAmelCase)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Tuple:
wandb.init(reinit=__UpperCAmelCase , project="face-editor")
wandb.config.update({"Positive Prompts": positive_prompts})
wandb.config.update({"Negative Prompts": negative_prompts})
wandb.config.update({"lr": self.lr, "iterations": self.iterations})
if image_path:
a_ = Image.open(__UpperCAmelCase)
a_ = image.resize((2_56, 2_56))
wandb.log("Original Image" , wandb.Image(__UpperCAmelCase))
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->List[str]:
if not prompts:
return []
a_ = []
a_ = []
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
a_ = [prompt.strip() for prompt in prompts.split("|")]
for prompt in prompts:
if isinstance(__UpperCAmelCase , (tuple, list)):
a_ = prompt[0]
a_ = float(prompt[1])
elif ":" in prompt:
a_ , a_ = prompt.split(":")
a_ = float(__UpperCAmelCase)
else:
a_ = prompt
a_ = 1.0
processed_prompts.append(__UpperCAmelCase)
weights.append(__UpperCAmelCase)
return {
"prompts": processed_prompts,
"weights": torch.tensor(__UpperCAmelCase , device=self.device),
}
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=None , ) ->List[Any]:
if image_path:
a_ = self._get_latent(__UpperCAmelCase)
else:
a_ = torch.randn(self.latent_dim , device=self.device)
if self.log:
self._init_logging(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
assert pos_prompts, "You must provide at least one positive prompt."
a_ = self.process_prompts(__UpperCAmelCase)
a_ = self.process_prompts(__UpperCAmelCase)
if save_final and save_path is None:
a_ = os.path.join("./outputs/" , "_".join(pos_prompts["prompts"]))
if not os.path.exists(__UpperCAmelCase):
os.makedirs(__UpperCAmelCase)
else:
a_ = save_path + "_" + get_timestamp()
os.makedirs(__UpperCAmelCase)
a_ = save_path
a_ = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print("Original Image")
show_pil(custom_to_pil(__UpperCAmelCase))
a_ = loop_post_process(__UpperCAmelCase)
for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)):
if show_intermediate:
show_pil(__UpperCAmelCase)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F'''iter_{iter:03d}.png'''))
if self.log:
wandb.log({"Image": wandb.Image(__UpperCAmelCase)})
if show_final:
show_pil(__UpperCAmelCase)
if save_final:
transformed_img.save(os.path.join(self.save_path , F'''iter_{iter:03d}_final.png''')) | 243 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase__ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : Tuple = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = [
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ) )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Tuple = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ ) )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
_UpperCamelCase : Tuple = [
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : List[Any] = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
# Removed: 'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : Any = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
_UpperCamelCase : int = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ,variant=lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : Dict = [
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
_UpperCamelCase : List[str] = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ,variant=lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_UpperCamelCase : Any = [
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
_UpperCamelCase : Optional[Any] = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ,variant=lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : Any = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
_UpperCamelCase : Any = 'fp16'
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ ,variant=lowerCamelCase__ ) )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = [
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
]
_UpperCamelCase : Any = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ,variant=lowerCamelCase__ ) )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Any = [
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
_UpperCamelCase : Optional[int] = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ,variant=lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : Dict = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
# 'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
_UpperCamelCase : Optional[int] = 'fp16'
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ ,variant=lowerCamelCase__ ) )
| 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case_ : Optional[Any] = {
'configuration_clip': [
'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPConfig',
'CLIPOnnxConfig',
'CLIPTextConfig',
'CLIPVisionConfig',
],
'processing_clip': ['CLIPProcessor'],
'tokenization_clip': ['CLIPTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = ['CLIPTokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = ['CLIPFeatureExtractor']
snake_case_ : Dict = ['CLIPImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Tuple = [
'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPModel',
'CLIPPreTrainedModel',
'CLIPTextModel',
'CLIPTextModelWithProjection',
'CLIPVisionModel',
'CLIPVisionModelWithProjection',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[Any] = [
'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFCLIPModel',
'TFCLIPPreTrainedModel',
'TFCLIPTextModel',
'TFCLIPVisionModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = [
'FlaxCLIPModel',
'FlaxCLIPPreTrainedModel',
'FlaxCLIPTextModel',
'FlaxCLIPTextPreTrainedModel',
'FlaxCLIPVisionModel',
'FlaxCLIPVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
snake_case_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 236 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLMTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLMForMultipleChoice""",
"""XLMForQuestionAnswering""",
"""XLMForQuestionAnsweringSimple""",
"""XLMForSequenceClassification""",
"""XLMForTokenClassification""",
"""XLMModel""",
"""XLMPreTrainedModel""",
"""XLMWithLMHeadModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLMForMultipleChoice""",
"""TFXLMForQuestionAnsweringSimple""",
"""TFXLMForSequenceClassification""",
"""TFXLMForTokenClassification""",
"""TFXLMMainLayer""",
"""TFXLMModel""",
"""TFXLMPreTrainedModel""",
"""TFXLMWithLMHeadModel""",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
snake_case_ = logging.get_logger(__name__)
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__UpperCamelCase = ["""pixel_values"""]
def __init__( self :int , lowercase_ :bool = True , lowercase_ :Dict[str, int] = None , lowercase_ :PILImageResampling = PILImageResampling.BICUBIC , lowercase_ :bool = True , lowercase_ :Union[int, float] = 1 / 2_55 , lowercase_ :bool = True , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :bool = True , **lowercase_ :Union[str, Any] , ) -> None:
super().__init__(**lowercase_ )
UpperCAmelCase = size if size is not None else {'height': 3_84, 'width': 3_84}
UpperCAmelCase = get_size_dict(lowercase_ , default_to_square=lowercase_ )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase = do_convert_rgb
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :np.ndarray , lowercase_ :Dict[str, int] , lowercase_ :PILImageResampling = PILImageResampling.BICUBIC , lowercase_ :Optional[Union[str, ChannelDimension]] = None , **lowercase_ :Any , ) -> np.ndarray:
UpperCAmelCase = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
UpperCAmelCase = (size['height'], size['width'])
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :np.ndarray , lowercase_ :Union[int, float] , lowercase_ :Optional[Union[str, ChannelDimension]] = None , **lowercase_ :Optional[int] , ) -> int:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :Any , lowercase_ :np.ndarray , lowercase_ :Union[float, List[float]] , lowercase_ :Union[float, List[float]] , lowercase_ :Optional[Union[str, ChannelDimension]] = None , **lowercase_ :Optional[Any] , ) -> np.ndarray:
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :ImageInput , lowercase_ :Optional[bool] = None , lowercase_ :Optional[Dict[str, int]] = None , lowercase_ :PILImageResampling = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[float] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :Optional[Union[str, TensorType]] = None , lowercase_ :bool = None , lowercase_ :ChannelDimension = ChannelDimension.FIRST , **lowercase_ :Tuple , ) -> PIL.Image.Image:
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(lowercase_ , default_to_square=lowercase_ )
UpperCAmelCase = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCAmelCase = [convert_to_rgb(lowercase_ ) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
UpperCAmelCase = BatchFeature(data={'pixel_values': images} , tensor_type=lowercase_ )
return encoded_outputs
| 78 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : str = 400_0000 ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase_ : int = []
lowerCAmelCase_ ,lowerCAmelCase_ : Any = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowerCAmelCase__ )
lowerCAmelCase_ ,lowerCAmelCase_ : str = b, a + b
return sum(lowerCAmelCase__ )
if __name__ == "__main__":
print(f'{solution() = }')
| 359 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowercase__ : Tuple = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
booktitle={ACL},
year={2020},
url={https://arxiv.org/abs/2004.04696}
}
"""
lowercase__ : Tuple = """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).
See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""
lowercase__ : List[Any] = """
BLEURT score.
Args:
`predictions` (list of str): prediction/candidate sentences
`references` (list of str): reference sentences
`checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.
Returns:
'scores': List of scores.
Examples:
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> bleurt = datasets.load_metric(\"bleurt\")
>>> results = bleurt.compute(predictions=predictions, references=references)
>>> print([round(v, 2) for v in results[\"scores\"]])
[1.03, 1.04]
"""
lowercase__ : List[Any] = {
"""bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
"""bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
"""bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
"""bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
"""bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
"""bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
"""BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
"""BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
"""BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
"""BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/google-research/bleurt' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/google-research/bleurt'] , reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696'] , )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : str ):
# check that config name specifies a valid BLEURT model
if self.config_name == "default":
logger.warning(
'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
lowerCAmelCase_ : List[Any] = 'bleurt-base-128'
if self.config_name.lower() in CHECKPOINT_URLS:
lowerCAmelCase_ : List[Any] = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
lowerCAmelCase_ : Tuple = self.config_name.upper()
else:
raise KeyError(
F"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}" )
# download the model checkpoint specified by self.config_name and set up the scorer
lowerCAmelCase_ : List[Any] = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
lowerCAmelCase_ : List[str] = score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Any ):
lowerCAmelCase_ : Tuple = self.scorer.score(references=SCREAMING_SNAKE_CASE_ , candidates=SCREAMING_SNAKE_CASE_ )
return {"scores": scores}
| 289 | 0 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = Dict[str, Any]
lowerCamelCase__ = List[Prediction]
@add_end_docstrings(_lowerCamelCase)
class A__ ( _lowerCamelCase):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch." )
requires_backends(self , 'vision' )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def __lowerCamelCase ( self , **_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = {}
if "threshold" in kwargs:
__lowerCAmelCase : int = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return super().__call__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[str] = load_image(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = torch.IntTensor([[image.height, image.width]] )
__lowerCAmelCase : int = self.image_processor(images=[image] , return_tensors='pt' )
if self.tokenizer is not None:
__lowerCAmelCase : Tuple = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt' )
__lowerCAmelCase : str = target_size
return inputs
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = model_inputs.pop('target_size' )
__lowerCAmelCase : int = self.model(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = outputs.__class__({'target_size': target_size, **outputs} )
if self.tokenizer is not None:
__lowerCAmelCase : Dict = model_inputs['bbox']
return model_outputs
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0.9 ):
__lowerCAmelCase : Union[str, Any] = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
__lowerCAmelCase , __lowerCAmelCase : int = target_size[0].tolist()
def unnormalize(_SCREAMING_SNAKE_CASE ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 10_00),
(height * bbox[1] / 10_00),
(width * bbox[2] / 10_00),
(height * bbox[3] / 10_00),
] ) )
__lowerCAmelCase , __lowerCAmelCase : List[Any] = model_outputs['logits'].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
__lowerCAmelCase : Optional[Any] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__lowerCAmelCase : Any = [unnormalize(_SCREAMING_SNAKE_CASE ) for bbox in model_outputs['bbox'].squeeze(0 )]
__lowerCAmelCase : List[str] = ['score', 'label', 'box']
__lowerCAmelCase : Tuple = [dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) for vals in zip(scores.tolist() , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__lowerCAmelCase : Tuple = self.image_processor.post_process_object_detection(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = raw_annotations[0]
__lowerCAmelCase : Dict = raw_annotation['scores']
__lowerCAmelCase : Dict = raw_annotation['labels']
__lowerCAmelCase : int = raw_annotation['boxes']
__lowerCAmelCase : Any = scores.tolist()
__lowerCAmelCase : Any = [self.model.config.idalabel[label.item()] for label in labels]
__lowerCAmelCase : Optional[int] = [self._get_bounding_box(_SCREAMING_SNAKE_CASE ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__lowerCAmelCase : List[Any] = ['score', 'label', 'box']
__lowerCAmelCase : str = [
dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'] )
]
return annotation
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.' )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[int] = box.int().tolist()
__lowerCAmelCase : str = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox | 86 |
from collections.abc import Callable
class __lowercase :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Callable | None = None):
# Stores actual heap items.
SCREAMING_SNAKE_CASE_: list = []
# Stores indexes of each item for supporting updates and deletion.
SCREAMING_SNAKE_CASE_: dict = {}
# Stores current size of heap.
SCREAMING_SNAKE_CASE_: Optional[Any] = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
SCREAMING_SNAKE_CASE_: Any = key or (lambda lowerCAmelCase__: x)
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : int):
return int((i - 1) / 2) if i > 0 else None
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: Union[str, Any] = int(2 * i + 1)
return left if 0 < left < self.size else None
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: Union[str, Any] = int(2 * i + 2)
return right if 0 < right < self.size else None
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = self.arr[j], self.arr[i]
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : int):
return self.arr[i][1] < self.arr[j][1]
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: Any = self._left(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = self._right(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = i
if left is not None and not self._cmp(lowerCAmelCase__ , lowerCAmelCase__):
SCREAMING_SNAKE_CASE_: Optional[int] = left
if right is not None and not self._cmp(lowerCAmelCase__ , lowerCAmelCase__):
SCREAMING_SNAKE_CASE_: Tuple = right
return valid_parent
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: List[Any] = self._parent(lowerCAmelCase__)
while parent is not None and not self._cmp(lowerCAmelCase__ , lowerCAmelCase__):
self._swap(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = parent, self._parent(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: Optional[int] = self._get_valid_parent(lowerCAmelCase__)
while valid_parent != index:
self._swap(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict = valid_parent, self._get_valid_parent(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : int):
if item not in self.pos_map:
return
SCREAMING_SNAKE_CASE_: Any = self.pos_map[item]
SCREAMING_SNAKE_CASE_: int = [item, self.key(lowerCAmelCase__)]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(lowerCAmelCase__)
self._heapify_down(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase__ : int):
if item not in self.pos_map:
return
SCREAMING_SNAKE_CASE_: Optional[Any] = self.pos_map[item]
del self.pos_map[item]
SCREAMING_SNAKE_CASE_: List[str] = self.arr[self.size - 1]
SCREAMING_SNAKE_CASE_: Tuple = 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(lowerCAmelCase__)
self._heapify_down(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: Optional[int] = len(self.arr)
if arr_len == self.size:
self.arr.append([item, self.key(lowerCAmelCase__)])
else:
SCREAMING_SNAKE_CASE_: str = [item, self.key(lowerCAmelCase__)]
SCREAMING_SNAKE_CASE_: List[Any] = self.size
self.size += 1
self._heapify_up(self.size - 1)
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
return self.arr[0] if self.size else None
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: Dict = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0])
return top_item_tuple
def A_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __magic_name__ :
@property
def __a ( self ) -> int:
return self.get_dummy_input()
@property
def __a ( self ) -> str:
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(f"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'." )
def __a ( self , _a=True , _a=False , _a=False , _a=False , ) -> List[Any]:
lowerCAmelCase_ = 4
lowerCAmelCase_ = 32
lowerCAmelCase_ = (32, 32)
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = torch.device(_a )
lowerCAmelCase_ = (batch_size, num_channels) + sizes
lowerCAmelCase_ = randn_tensor(_a , generator=_a , device=_a )
lowerCAmelCase_ = {"hidden_states": hidden_states}
if include_temb:
lowerCAmelCase_ = 128
lowerCAmelCase_ = randn_tensor((batch_size, temb_channels) , generator=_a , device=_a )
if include_res_hidden_states_tuple:
lowerCAmelCase_ = torch.manual_seed(1 )
lowerCAmelCase_ = (randn_tensor(_a , generator=_a , device=_a ),)
if include_encoder_hidden_states:
lowerCAmelCase_ = floats_tensor((batch_size, 32, 32) ).to(_a )
if include_skip_sample:
lowerCAmelCase_ = randn_tensor(((batch_size, 3) + sizes) , generator=_a , device=_a )
return dummy_input
def __a ( self ) -> List[str]:
lowerCAmelCase_ = {
"in_channels": 32,
"out_channels": 32,
"temb_channels": 128,
}
if self.block_type == "up":
lowerCAmelCase_ = 32
if self.block_type == "mid":
init_dict.pop("out_channels" )
lowerCAmelCase_ = self.dummy_input
return init_dict, inputs_dict
def __a ( self , _a ) -> Optional[int]:
lowerCAmelCase_ , lowerCAmelCase_ = self.prepare_init_args_and_inputs_for_common()
lowerCAmelCase_ = self.block_class(**_a )
unet_block.to(_a )
unet_block.eval()
with torch.no_grad():
lowerCAmelCase_ = unet_block(**_a )
if isinstance(_a , _a ):
lowerCAmelCase_ = output[0]
self.assertEqual(output.shape , self.output_shape )
lowerCAmelCase_ = output[0, -1, -3:, -3:]
lowerCAmelCase_ = torch.tensor(_a ).to(_a )
assert torch_all_close(output_slice.flatten() , _a , atol=5E-3 )
@unittest.skipIf(torch_device == "mps" , "Training is not supported in mps" )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ , lowerCAmelCase_ = self.prepare_init_args_and_inputs_for_common()
lowerCAmelCase_ = self.block_class(**_a )
model.to(_a )
model.train()
lowerCAmelCase_ = model(**_a )
if isinstance(_a , _a ):
lowerCAmelCase_ = output[0]
lowerCAmelCase_ = torch.device(_a )
lowerCAmelCase_ = randn_tensor(output.shape , device=_a )
lowerCAmelCase_ = torch.nn.functional.mse_loss(_a , _a )
loss.backward()
| 362 |
def A():
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCamelCase__ = generate_large_matrix()
lowerCamelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A(__a: list[list[int]] ):
assert all(row == sorted(__a , reverse=__a ) for row in grid )
assert all(list(__a ) == sorted(__a , reverse=__a ) for col in zip(*__a ) )
def A(__a: list[int] ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(__a ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowerCAmelCase_ = (left + right) // 2
lowerCAmelCase_ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowerCAmelCase_ = mid + 1
else:
lowerCAmelCase_ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__a )
def A(__a: list[list[int]] ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(grid[0] )
for i in range(len(__a ) ):
lowerCAmelCase_ = find_negative_index(grid[i][:bound] )
total += bound
return (len(__a ) * len(grid[0] )) - total
def A(__a: list[list[int]] ):
return len([number for row in grid for number in row if number < 0] )
def A(__a: list[list[int]] ):
lowerCAmelCase_ = 0
for row in grid:
for i, number in enumerate(__a ):
if number < 0:
total += len(__a ) - i
break
return total
def A():
from timeit import timeit
print("Running benchmarks" )
lowerCAmelCase_ = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowerCAmelCase_ = timeit(F"{func}(grid=grid)" , setup=__a , number=500 )
print(F"{func}() took {time:0.4f} seconds" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 22 | 0 |
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