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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',... | 76 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 | 1 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = 9, 14 # noqa: F841
SCREAMING_SNAKE_CASE : List[str] = [
[0, 1, 4... | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 | 1 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
a_ = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: "Dataset Card for X" # First-lev... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( _a):
def decorator(_a):
SCREAMING_SNAKE_CASE : Dict = getattr(_a , "handle_key" , [])
handle += [key]
setattr(_a , "handle_key" , _a)
return func
return decorat... | 76 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'LongformerConfig',
... | 76 |
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
... | 76 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional imp... | 76 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 76 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 1 |
from __future__ import annotations
from collections import deque
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int , a : list[str] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : list[dict] = ... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
import json
import sys
def lowerCamelCase__ ( _a , _a):
with open(_a , encoding="utf-8") as f:
SCREAMING_SNAKE_CASE : Any = json.load(_a)
SCREAMING_SNAKE_CASE : Any = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name ... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 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_ma... | 76 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
a_ = logging.getLogger(__name__)
def low... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import B... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass | 76 | 1 |
from __future__ import annotations
import math
def lowerCamelCase__ ( _a):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number a... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
fr... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice ... | 76 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import ... | 76 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 1 |
import numpy as np
from PIL import Image
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : str = np.array(_a)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
SCREAMING_SNAKE_CASE : str = 0
SCREAMING... | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ = {'processing_layoutxlm': ['LayoutXLMProcessor']}
try:
if not is_sentencepiece_a... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'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
... | 76 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_t... | 76 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 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
... | 76 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constan... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : List[str] ) -> str:
"""simple docstr... | 76 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_... | 76 |
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
... | 76 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSav... | 76 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__nam... | 76 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
def lowerCamelCase__ ( _a = 3 , _a = 7 , _a = 1000000):
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : int = 1
for current_denominator in range(1 , limit + 1):
SCREAMING_SNAKE_CASE : int = current_denominator * numerator // de... | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 1 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , a : list[int] ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = len(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = [0] ... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
fr... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
from math import factorial
a_ = {str(digit): factorial(digit) for digit in range(10)}
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
raise TypeError("Parameter number must be int")
if number < 0:
raise ValueError("Parameter number must be greater than or equal to 0")
# ... | 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_ma... | 76 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ... | 76 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'X... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE : set[int] = set()
return any(
node not in visited and depth_first_search(_a , _a ... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
def lowerCamelCase__ ( _a , _a , _a=False):
if isinstance(_a , _a) and isinstance(_a , _a):
SCREAMING_SNAKE_CASE : List[str] = len(set_a.intersection(_a))
if alternative_union:
SCREAMING_SNAKE_CASE : Optional[int] = len(_a) + len(_a)
el... | 76 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vo... | 76 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if... | 76 | 1 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'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
... | 76 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de"... | 76 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe i... | 76 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = int(_a)
if n_element < 1:
SCREAMING_SNAKE_CASE : Optional[int] = ValueError("a should be a positive number")
raise my_error
SCREAMING_SNAKE_CASE : Union[str, Any] = [1]
SCREAMING_SNAKE_CASE ... | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = position
SCREAMING_SNAKE_CASE : Optional[int] = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
a_ = {
'micr... | 76 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
'processing_b... | 76 |
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
... | 76 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBe... | 76 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a_ = logging.get_logger(__name__)
a_ = OrderedDict(
[
# Base model mapping
('alb... | 76 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
from timeit import timeit
def lowerCamelCase__ ( _a):
if number < 0:
raise ValueError("the value of input must not be negative")
SCREAMING_SNAKE_CASE : Any = 0
while number:
number &= number - 1
result += 1
return result
def lowerCamelCase__ ( _a):
if number... | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 1 |
def lowerCamelCase__ ( _a = 4000000):
SCREAMING_SNAKE_CASE : Any = [0, 1]
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
SCREAMING_SNAKE_CASE : List[str] = 0
for j ... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
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_utils import is... | 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_ma... | 76 | 1 |
import operator as op
a_ = 'scaler.pt'
a_ = 'pytorch_model'
a_ = 'random_states'
a_ = 'optimizer'
a_ = 'scheduler'
a_ = 'pytorch_model.bin'
a_ = 'pytorch_model.bin.index.json'
a_ = 'model.safetensors'
a_ = 'model.safetensors.index.json'
a_ = '1.10.2'
a_ ... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
print(f"Vertex\tShortest Distance from vertex {src}")
for i, d in enumerate(_a):
print(f"{i}\t\t{d}")
def lowerCamelCase__ ( _a , _a , _a):
for j in range(_a):
SCREAMING_SNAKE_CASE ,SCREAMI... | 76 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 1 |
from __future__ import annotations
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any , a : int = 6 ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Node | None = None
S... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'Ro... | 76 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass | 76 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
a_ = logging.get_logger(__na... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.p... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 | 1 |
import collections
import os
import re
from pathlib import Path
a_ = 'src/transformers'
# Matches is_xxx_available()
a_ = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
a_ = re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-val... | 76 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 1 |
import math
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Optional[Any] = input("Enter message: ")
SCREAMING_SNAKE_CASE : Optional[int] = int(input(f"Enter key [2-{len(_a) - 1}]: "))
SCREAMING_SNAKE_CASE : Optional[int] = input("Encryption/Decryption... | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if... | 76 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
# See all M-CTC-T models at https://huggingface.co/models?filter=... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'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
... | 76 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memo... | 76 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 1 |
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, ran... | 76 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : int , *a : L... | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 | 1 |
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 _UpperCamelCase ( __A ):
'''simple docstr... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeni... | 76 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 1 |
def lowerCamelCase__ ( _a , _a):
return number | (1 << position)
def lowerCamelCase__ ( _a , _a):
return number & ~(1 << position)
def lowerCamelCase__ ( _a , _a):
return number ^ (1 << position)
def lowerCamelCase__ ( _a , ... | 76 |
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
... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'process... | 76 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Optional[int] ) ... | 76 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_mi... | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
),
# See all Speech2Text models at h... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Tuple = get_failure_array(_a)
# 2) Step through text searching for pattern
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = 0, 0 # index into text, patte... | 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_ma... | 76 | 1 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a_ = HUGGINGFACE_HUB_CACHE
a_ = 'config.json'
a_ = 'diffusion_pytorch_model.bin'
a_ = 'diffusion_flax_model.msgpack'
a_ = 'model.onnx'
a_ = 'diffusion_pytorch_model.safetensors'
a_ = 'we... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
import operator as op
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Union[str, Any] = lambda _a , _a: int(x / y) # noqa: E731 integer division operation
SCREAMING_SNAKE_CASE : Tuple = {
"^": op.pow,... | 76 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 1 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
a_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
from collections.abc import Callable
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : float = a
SCREAMING_SNAKE_CASE : float = b
if function(_a) == 0: # one of the a or b is a root for the function
return a
elif function(_a) == 0:
return ... | 76 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCamelCase ( __A ):
'''simple docstrin... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[Any] = f"{sampling_rate}"
SCREAMING_SNAKE_CASE : Optional[int] = "1"
SCREAMING_SNAKE_... | 76 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfi... | 76 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 1 |
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 transfor... | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if... | 76 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, 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 tran... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'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
... | 76 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a_ = logging.get_logger(__name__)
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Any = r"\w+[.]\d+"
SCREAMING_SNAKE_C... | 76 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
if b == 0:
return (1, 0)
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Dict = extended_euclid(_a , a % b)
SCREAMING_SNAKE_CASE : Tuple = a // b
return (y, x - k * y)
def ... | 76 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 | 1 |
def lowerCamelCase__ ( _a , _a):
return abs(_a) if a == 0 else greatest_common_divisor(b % a , _a)
def lowerCamelCase__ ( _a , _a):
while y: # --> when y=0 then loop will terminate and return x as final GCD.
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : ... | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, ... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 | 1 |
# 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 ap... | 76 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowerCamelCase__ ( ):
raise RuntimeError("CUDA out of memory.")
class _UpperCamelCase ( nn.Module ... | 76 |
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
... | 76 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase__ ( _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Any = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de"... | 76 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...featu... | 76 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
import os
def lowerCamelCase__ ( _a = "input.txt"):
with open(os.path.join(os.path.dirname(_a) , _a)) as input_file:
SCREAMING_SNAKE_CASE : Dict = [
[int(_a) for element in line.split(",")]
for line in input_file.readlines()
]
SCREAMING_SNAKE_CASE : T... | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 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 torch
... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
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 ... | 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_ma... | 76 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import repl... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
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