code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import qiskit
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Dict:
A: Tuple = qiskit.Aer.get_backend('''aer_simulator''' )
A: Any = qiskit.QuantumCircuit(4 , 2 )
# encode ... | 319 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 0 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors=... | 177 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
... | 219 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''nielsr/canine-s''': 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
_A = 1_114_112
# Below: Constan... | 278 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case ={
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
... | 4 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Optional[int] = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config... | 33 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 0 |
import heapq
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Any:
__lowerCamelCase : str = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq work... | 73 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 | 0 |
from jiwer import compute_measures
import datasets
_SCREAMING_SNAKE_CASE = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluatio... | 180 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 0 |
import torch
from diffusers import StableDiffusionPipeline
lowercase__ :Tuple = "path-to-your-trained-model"
lowercase__ :List[str] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
lowercase__ :str = "A photo of sks dog in a bucket"
lowercase__ :Tuple =... | 101 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""face... | 296 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_lowercase : List[str] = False
_lowercase : Tuple = True
_lowercase : Any = F... | 332 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.tes... | 224 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase ) -> List[Any]:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def SCREAMING_SNAKE_CASE( __lowercase ) -> Union[str, Any]:
A: List[str] = 0
A: ... | 319 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 177 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 0 |
import math
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple ) -> str:
"""simple docstring"""
return math.sqrt(_A ) * math.sqrt(_A ) == num
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[Any] ) -> int:
... | 219 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def a_ ( lowerCamelCase : Any , lowerCamelCase : Dict , lowerCamelCase : List[Any] = 1 / sqrt(2 ) ):
lowerCAmelCase = tau * frequency / samplerate
... | 4 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : str ):
lowercase_ : Any = 1
lowercase_ : int = 2
while i * i <= n:
lowercase_ : Tuple = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *=... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 0 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTe... | 73 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 0 |
def snake_case ( snake_case__ :Dict , snake_case__ :List[Any] , snake_case__ :List[str] , snake_case__ :Optional[Any]) -> Any:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 180 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_A = '''scheduler_config.json'''
class A ( __UpperCAmelCase ):
__snake_case = 1
__snake_case = 2
... | 278 | 0 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 101 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 0 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCamelCase__ ( pl.LightningModule ):
'''simple docstring'''
def __init__( self : List[str]... | 296 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_... | 332 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 0 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : int = 6008_5147_5143 ) -> Tuple:
"""simple docstring"""
try:
lowerCAmelCase_ : Any = int(_A )
except (TypeError, ValueError):
raise TypeError('Parameter n mu... | 224 |
from functools import lru_cache
@lru_cache
def __UpperCamelCase ( _A ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 278 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except O... | 319 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Union[str, Any]:
return " ".join(
''''''.join(word[::-1] ) if len(_A ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words... | 177 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
__lowerCamelCase : List[str] = {
'''BridgeTower/bridgetower-base''': '''https://huggi... | 219 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''nielsr/canine-s''': 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
_A = 1_114_112
# Below: Constan... | 278 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a_ ( lowerCamelCase : Tuple , lowerCamelCase : ... | 4 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageC... | 33 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 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 requ... | 73 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 | 0 |
def snake_case ( snake_case__ :Union[str, Any]) -> List[Any]:
_A = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_A = set()
return any(
node not in visited and depth_first_search(_A , _A , _A , _A... | 180 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 0 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = get_failure_array(_A )
# 2) Step through text searching for pattern
lowercase , lowercase = 0, 0 # index into text, p... | 101 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_ = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
SCREAMING_SNAKE_CASE_ = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> ... | 296 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
im... | 332 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 0 |
"""simple docstring"""
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class UpperCamelCase__ ( __UpperCAmelCase, unittest.TestCase ):
... | 224 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,... | 319 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 0 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__A = 4
__A = 3
class UpperCAmelCase (__UpperCAmelCase ):
"""si... | 177 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@r... | 219 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def a_ ( lowerCamelCase : Optional[int] , lowerCamelCase : Dict , lowerCamelCase : Optional[Any] ):
lowerCAmelCase = ... | 4 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 0 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCAmelCase :
def __init__( self : Tuple , A : Any ) -> List[str]:
lowercase_ : Optional[int] ... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 73 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 0 |
def snake_case ( snake_case__ :Dict = 1_000_000) -> Optional[Any]:
_A = set(range(3 , _A , 2))
primes.add(2)
for p in range(3 , _A , 2):
if p not in primes:
continue
primes.difference_update(set(range(p * p , ... | 180 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_A = '''scheduler_config.json'''
class A ( __UpperCAmelCase ):
__snake_case = 1
__snake_case = 2
... | 278 | 0 |
from math import ceil
def UpperCamelCase ( lowerCAmelCase__ = 1001 ):
'''simple docstring'''
lowercase = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
lowercase = 2 * i + 1
lowercase = 2 * i
lowercase = to... | 101 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""nielsr/canine-s""": 2_0_4_8,
}
# Unicode... | 296 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 0 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_s... | 332 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image... | 224 |
from functools import lru_cache
@lru_cache
def __UpperCamelCase ( _A ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 278 | 0 |
'''simple docstring'''
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> st... | 319 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
lowercase__: Any = len(_A )
lowercase__: Tuple = len(_A )
lowercase__: List[Any] = (
first_str_length if first_str_length > second_str_length else sec... | 177 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 0 |
import math
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[Any] ) -> int:
"""simple docstring"""
if not isinstance(_A , _A ):
SCREAMING_SNAKE_CASE__ = f"""Input value of [number={number}] must be an integer"""
raise Type... | 219 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''nielsr/canine-s''': 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
_A = 1_114_112
# Below: Constan... | 278 | 0 |
'''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/... | 4 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : Dict = 1_0_0_0_0_0_0 ):
lowercase_ : Any = 1
lowercase_ : Any = 1
lowercase_ : str = {1: 1}
for inputa in range(2 , _A ):
lowercase_ : Optional[int] ... | 33 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 0 |
a ={
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.2.0""",
"""cookiecutter""": """cookiecutter==1.7.3""",
... | 73 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
f... | 180 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowercase ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( ... | 101 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE_ = TypeVar("""T""")
SCREAMING_SNAKE_CASE_ = TypeVar("""U""")
class UpperCamelCase__ ( Generic[T, U] ):
'''simple docstr... | 296 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 0 |
"""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/LI... | 332 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 0 |
"""simple docstring"""
import os
from collections.abc import Iterator
def UpperCamelCase_ ( lowerCAmelCase__ : Union[str, Any] = "." ) -> List[str]:
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(_A ):
lowerCAmelCas... | 224 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAme... | 319 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_visio... | 177 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 0 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers imp... | 219 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 0 |
'''simple docstring'''
from statistics import mean
import numpy as np
def a_ ( lowerCamelCase : Dict , lowerCamelCase : Any , lowerCamelCase : int , lowerCamelCase : List[Any] ):
lowerCAmelCase = 0
# Number of processes finished
lowerCAmelCase =... | 4 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 0 |
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_ ... | 279 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowerCAmelCase_ = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
... | 279 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
snake_case_ : List[str] = int(_UpperCamelCase )
snake_case_ : int = i... | 279 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( _a, unittest.TestCase ):
... | 279 | 1 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowerCAmelCase_ = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
... | 279 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> tuple[float, list[float]]:
"""simple docstring"""
snake_case_ : Dict = list(range(len(_UpperCamelCase ) ) )
snake_case_ : Dict ... | 279 | 1 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelC... | 279 |
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 torc... | 279 | 1 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch... | 279 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
excep... | 279 | 1 |
import os
import string
import sys
lowerCAmelCase_ = 1 << 8
lowerCAmelCase_ = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
'''right''': 6_7 + ARROW_KEY_FLAG,
'''left''': 6_8 + ... | 279 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning t... | 279 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 279 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, type=str, required=... | 279 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# U... | 279 |
from math import factorial
lowerCAmelCase_ = {str(digit): factorial(digit) for digit in range(1_0)}
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''Parameter ... | 279 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
),
}
... | 279 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = ''''''
lowerCamelCase_ : str = (
... | 279 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''only integers accepted as input''' )
else:
snake_case_ : Tuple = str(abs(_UpperCamelCase ) )
... | 279 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_... | 279 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT... | 279 |
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_u... | 279 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : Dict = HfArgumentParser(_UpperCamelCase )
snake_case_ : List[Any] = parser.parse_... | 279 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowerCAmelCase_ = logging.getLogger(__name__)
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[str] ... | 279 | 1 |
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 torc... | 279 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 279 | 1 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCAmelCase_ = HUGGINGFACE_HUB_CACHE
lowerCAmelCase_ = '''config.json'''
lowerCAmelCase_ = '''diffusion_pytorch_model.bin'''
lowerCAmelCase_ = '''diffusion_flax_model.msgpack'''
lowerCAmelCase_ ... | 279 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 279 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_ut... | 279 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d3... | 279 | 1 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
... | 279 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6... | 279 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase_ ( _UpperCamelCase ) -> tuple:
... | 279 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class ... | 279 | 1 |
from math import loga
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''Input value... | 279 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impor... | 279 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requir... | 279 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if dist[i][j] !... | 279 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d3... | 279 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_... | 279 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
excep... | 279 |
lowerCAmelCase_ = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_... | 279 | 1 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 279 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowerCAmelCase_ = datasets.logging.get_logger(__name__)
lowerCAmelCase_ = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Se... | 279 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def lowerCamelCase_ ( *_UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase=True , _UpperCamelCase=2 ) -> Any:
"""simple docstring"""
from .. import _... | 279 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowerCAmelCase_ = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
... | 279 | 1 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6... | 279 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( _a, unittest.TestCase ):
... | 279 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_ten... | 279 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> tuple[float, list[float]]:
"""simple docstring"""
snake_case_ : Dict = list(range(len(_UpperCamelCase ) ) )
snake_case_ : Dict ... | 279 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
A... | 279 |
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 torc... | 279 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator,... | 279 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
excep... | 279 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_... | 279 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning t... | 279 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __lowerCAmelCase ( nn.Module ):
lowerCamelCase_ : int
lowerCamelCase_ : int
lowerCamel... | 279 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, type=str, required=... | 279 | 1 |
import math
def lowerCamelCase_ ( _UpperCamelCase ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of ... | 279 |
from math import factorial
lowerCAmelCase_ = {str(digit): factorial(digit) for digit in range(1_0)}
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''Parameter ... | 279 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class __lowerCAmelCas... | 279 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = ''''''
lowerCamelCase_ : str = (
... | 279 | 1 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from tr... | 279 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_... | 279 | 1 |
import functools
from typing import Any
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or len(_UpperCamelCase ) == 0:
raise ValueError('''the string should be not... | 279 |
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_u... | 279 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if dist[i][j] !... | 279 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowerCAmelCase_ = logging.getLogger(__name__)
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[str] ... | 279 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowerCAmelCase_ = datasets.logging.get_logger(__name__)
lowerCAmelCase_ = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Se... | 279 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 279 | 1 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@require_torch
... | 279 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 279 | 1 |
import os
import sys
import unittest
lowerCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get... | 279 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d3... | 279 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6... | 279 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils im... | 279 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class ... | 279 | 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:/... | 279 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impor... | 279 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState... | 279 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if dist[i][j] !... | 279 | 1 |
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