code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCAmelCase__ : Any = pytest.mark.integration
@pytest.mark.parame... | 714 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Any = logging.get_logger(__name__)
lowerCAmelCase__ : str = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class snake_case ( __Up... | 632 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase__ : str = logging.get... | 715 | """simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 632 | 0 |
"""simple docstring"""
import math
def a_ ( lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCamelCase )
if number < 1:
UpperCA... | 716 | """simple docstring"""
def a_ ( lowerCamelCase , lowerCamelCase ):
return x if y == 0 else greatest_common_divisor(lowerCamelCase , x % y )
def a_ ( lowerCamelCase , lowerCamelCase ):
return (x * y) // greatest_common_divisor(lowerCamelCase , lowe... | 632 | 0 |
"""simple docstring"""
lowerCAmelCase__ : Tuple = range(2, 20 + 1)
lowerCAmelCase__ : Optional[Any] = [10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__ : dict[int, dict[int, list[list[int]]]] = {}
def a_ ( lowerCamelCase , lowerCamelCase ,... | 717 | """simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def a_ ( lowerCamelCase ):
@wraps(lowerCamelCase )
def _inner_fn(*lowerCamelCase , **lowerCamelCase ):
warnings.warn(
(f'''\'{fn.__name__}\' is experimental and might ... | 632 | 0 |
"""simple docstring"""
def a_ ( lowerCamelCase ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
UpperCAmelCase__ = len(lowerCamelCase )
UpperCAmelCase__ = max(lowerCamelCase )
UpperCAmelCase__ ... | 718 | """simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowerCAmelCase__ : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__ : list[int] = [ord(l... | 632 | 0 |
from __future__ import annotations
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ , UpperCAmelCase__ = set(lowerCamelCase ), [start]
while stack:
UpperCAmelCase__ = stack.pop()
explored.add(lowerCamelCase )
# Differences from BF... | 719 | """simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_co... | 632 | 0 |
"""simple docstring"""
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_transf... | 720 | """simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a_ ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def a_ ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
lowerCAme... | 632 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : str = logging.get_logger(__name__)
lowerCAmelCase__ : ... | 721 | """simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case ( ctypes.Structure ):
"""simple docstring"""
snake_case__ = [("size... | 632 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 633 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_... | 633 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ :Tuple = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
... | 633 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 mat... | 633 |
import logging
from transformers import PretrainedConfig
lowercase__ :int = logging.getLogger(__name__)
lowercase__ :Dict = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class... | 633 | 1 |
from __future__ import annotations
from cmath import sqrt
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.''' )
lowercase = b * b - 4 * a * c... | 633 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 633 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
requ... | 633 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 633 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ :Optional[int] = logging.get_logger(__name__)
lowercase__ :Union[str, Any] = {
"shi-labs/dina... | 633 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : List[... | 633 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowercase__ :Optional[int] = 2_9979_2458
# Symbols
lowercase__ , lowercase__ , lowercase__ , lowercase__ :Dict = symbols("ct x y z")
def UpperCamelCase ( ... | 633 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 633 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 10**-10 ):
'''simple docstring'''
lowercase = a
while True:
lowe... | 633 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase :
def __init__( self ,A__ ,A__):
if len(A__) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the deg... | 633 |
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase , lowercase , lowercase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def UpperCamelCase ... | 633 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = f'{sampling_rate}'
lowercase = '''1'''
lowercase = '''f32... | 633 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.... | 633 | 1 |
from __future__ import annotations
from random import random
class lowercase :
def __init__( self ,A__ = None):
lowercase = value
lowercase = random()
lowercase = None
lowercase = None
def __repr__( ... | 633 |
from __future__ import annotations
from random import random
class lowercase :
def __init__( self ,A__ = None):
lowercase = value
lowercase = random()
lowercase = None
lowercase = None
def __repr__( ... | 633 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class lowercase :
def __init__( self ,A__):
lowercase = str(id_)
lowercase = None
lowercase = None
lowercase = []
lowercase = {} #... | 633 |
def UpperCamelCase ( lowerCAmelCase__ = 1000 ):
'''simple docstring'''
lowercase = -1
lowercase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase = (n * n - 2 * a * n) // (2 * n - 2 * a... | 633 | 1 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ :Dict = logging.get_logger(__name__)
def UpperCamelCase ( lowerCAmelCase__ , lower... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Tuple = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
... | 633 | 1 |
from math import factorial
class lowercase :
def __init__( self ,A__ ,A__):
lowercase = real
if isinstance(A__ ,A__):
lowercase = [1] * rank
else:
lowercase = rank
def __repr__( ... | 633 |
from numpy import exp, pi, sqrt
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 633 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowercase__ :Optional[Any] = logging.getLogger(__name... | 633 |
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
from... | 633 | 1 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 10**-10 ):
'''simple docstring'''
lowercase = a
while True:
lowe... | 633 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla... | 633 | 1 |
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__ :List[Any] = logging.get_logger(__name__)
lowercase__ :str = {
"fac... | 633 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 633 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import... | 633 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 633 | 1 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 633 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowerc... | 633 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Dict = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
],
}
try... | 633 |
class lowercase :
def __init__( self ,A__):
lowercase = val
lowercase = None
lowercase = None
def A__ ( self ,A__):
if self.val:
if val < self.val:
if self.left ... | 633 | 1 |
lowercase__ :List[str] = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring... | 633 |
import os
def UpperCamelCase ( lowerCAmelCase__ = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase__ ) , lowerCAmelCase__ ) ) as input_file:
lowercase = [
[int(lowerCAmelCase__ ) for element in line.split(''',''' )]
... | 633 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :List[str] = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 633 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if len(lowerCAmelCase__ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All ... | 633 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 633 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_... | 633 | 1 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowercase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ :Tuple = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ = 1000 ):
'''simple docstring'''
lowercase = -1
lowercase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase = (n * n - 2 * a * n) // (2 * n - 2 * a... | 633 |
import logging
from transformers import PretrainedConfig
lowercase__ :int = logging.getLogger(__name__)
lowercase__ :Dict = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class... | 633 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase__ :List[Any] = logging.getLogger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def __init__( ... | 633 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 633 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vi... | 633 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 633 | 1 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 633 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : List[... | 633 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 633 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = {}
lowercase = job['''started_at''']
lowercase = job['''completed_at''']
lowercase ... | 633 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 10**-10 ):
'''simple docstring'''
lowercase = a
while True:
lowe... | 633 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''... | 633 |
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase , lowercase , lowercase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def UpperCamelCase ... | 633 | 1 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils impo... | 633 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.... | 633 | 1 |
from torch import nn
class lowercase ( nn.Module ):
def __init__( self ,A__ ,A__):
super().__init__()
lowercase = class_size
lowercase = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.ml... | 633 |
from __future__ import annotations
from random import random
class lowercase :
def __init__( self ,A__ = None):
lowercase = value
lowercase = random()
lowercase = None
lowercase = None
def __repr__( ... | 633 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 633 |
def UpperCamelCase ( lowerCAmelCase__ = 1000 ):
'''simple docstring'''
lowercase = -1
lowercase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase = (n * n - 2 * a * n) // (2 * n - 2 * a... | 633 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase__ :str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__ :list[int] = [ord(letter) for letter in string.ascii_lowercase]... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Tuple = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
... | 633 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def A__ ( self ,A__):
with open(A__ ,encoding='''utf-8''') as input_file:
lowercase = ... | 633 |
from numpy import exp, pi, sqrt
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 633 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowercase__ :int = "\\n\n"
lowercase__ :Optional[int] = "\nPerplexity (PPL) is one of the most common metrics for... | 633 |
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
from... | 633 | 1 |
lowercase__ :int = {str(digit): digit**5 for digit in range(10)}
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase__ ) )
def UpperCamelCase ( ):
'''simple docstring'''
return ... | 633 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla... | 633 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_... | 633 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 633 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( low... | 633 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 633 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase , lowercase , lowercase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def UpperCamelCase ... | 633 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowerc... | 633 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available... | 633 |
class lowercase :
def __init__( self ,A__):
lowercase = val
lowercase = None
lowercase = None
def A__ ( self ,A__):
if self.val:
if val < self.val:
if self.left ... | 633 | 1 |
from math import factorial, radians
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ = 18 , lowerCAmelCase__ = 10 ):
'''simple docstring'''
lowercase = angle_in_degrees - ((angle_in_degrees // 3_60.0) * 3_60.0)
# Converting from degrees to radians
lowercase ... | 633 |
import os
def UpperCamelCase ( lowerCAmelCase__ = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase__ ) , lowerCAmelCase__ ) ) as input_file:
lowercase = [
[int(lowerCAmelCase__ ) for element in line.split(''',''' )]
... | 633 | 1 |
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,
... | 633 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if len(lowerCAmelCase__ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All ... | 633 | 1 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator... | 633 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_... | 633 | 1 |
import unittest
from transformers import DonutProcessor
lowercase__ :Optional[int] = "naver-clova-ix/donut-base"
class lowercase ( unittest.TestCase ):
def A__ ( self):
lowercase = DonutProcessor.from_pretrained(A__)
def A__ (... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ :Tuple = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
... | 633 | 1 |
from __future__ import annotations
lowercase__ :Optional[Any] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class lowercase :
def __init__( self ,A__ ,A__... | 633 |
import logging
from transformers import PretrainedConfig
lowercase__ :int = logging.getLogger(__name__)
lowercase__ :Dict = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class... | 633 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : Dict ... | 633 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 633 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase ( unittest.TestCase ):
lowercase_ : Any =inspect.getfile(ac... | 633 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 633 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( SCREAMING_S... | 633 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : List[... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'{price_plus_tax(100, 0.25) = }')
print(F'{price_plus_tax(125.50, 0.05) = }')
| 633 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImag... | 633 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 10**-10 ):
'''simple docstring'''
lowercase = a
while True:
lowe... | 633 | 1 |
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueErr... | 633 |
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase , lowercase , lowercase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def UpperCamelCase ... | 633 | 1 |
from math import sqrt
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not pr... | 633 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.... | 633 | 1 |
class lowercase :
def __init__( self ,A__):
lowercase = val
lowercase = None
lowercase = None
def A__ ( self ,A__):
if self.val:
if val < self.val:
if self.left ... | 633 |
from __future__ import annotations
from random import random
class lowercase :
def __init__( self ,A__ = None):
lowercase = value
lowercase = random()
lowercase = None
lowercase = None
def __repr__( ... | 633 | 1 |
import sys
lowercase__ :Union[str, Any] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6689664895044524452316173185... | 633 |
def UpperCamelCase ( lowerCAmelCase__ = 1000 ):
'''simple docstring'''
lowercase = -1
lowercase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase = (n * n - 2 * a * n) // (2 * n - 2 * a... | 633 | 1 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Tuple = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
... | 633 | 1 |
import collections
import os
import re
from pathlib import Path
lowercase__ :Optional[int] = "src/transformers"
# Matches is_xxx_available()
lowercase__ :Tuple = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowercase__ :int = re.compile... | 633 |
from numpy import exp, pi, sqrt
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 633 | 1 |
lowercase__ :Union[str, Any] = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/... | 633 |
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
from... | 633 | 1 |
from jiwer import compute_measures
import datasets
lowercase__ :List[str] = "\\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 evaluation me... | 633 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla... | 633 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
... | 633 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31... | 633 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 633 | 1 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
... | 633 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowerc... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_... | 633 |
class lowercase :
def __init__( self ,A__):
lowercase = val
lowercase = None
lowercase = None
def A__ ( self ,A__):
if self.val:
if val < self.val:
if self.left ... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
lowercase = sum(lowerCAmelCase__ ) / len(lowerCAmelCase__ ) # Calculate the average
return sum(abs(x - average ) f... | 633 |
import os
def UpperCamelCase ( lowerCAmelCase__ = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase__ ) , lowerCAmelCase__ ) ) as input_file:
lowercase = [
[int(lowerCAmelCase__ ) for element in line.split(''',''' )]
... | 633 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ :int = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": [... | 633 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if len(lowerCAmelCase__ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All ... | 633 | 1 |
class lowercase : # Public class to implement a graph
def __init__( self ,A__ ,A__ ,A__):
lowercase = row
lowercase = col
lowercase = graph
def A__ ( self ,A__ ,A__ ,A__):
return (
... | 633 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_... | 633 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = AutoConfig.from_pretrained(lowerCAmelCase__ )
lowe... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ :Tuple = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
... | 633 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ :str = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig... | 633 |
import logging
from transformers import PretrainedConfig
lowercase__ :int = logging.getLogger(__name__)
lowercase__ :Dict = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class... | 633 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ :int = logging.get_logger(__name__)
def UpperCa... | 633 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 633 | 1 |
class lowercase ( SCREAMING_SNAKE_CASE__ ):
pass
class lowercase ( SCREAMING_SNAKE_CASE__ ):
pass
class lowercase :
def __init__( self):
lowercase = [
[],
[],
[],
]
... | 633 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 633 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase__ :List[str] = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 633 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : List[... | 633 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
lowercase__ :Dict = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-c... | 633 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.uti... | 633 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 10**-10 ):
'''simple docstring'''
lowercase = a
while True:
lowe... | 633 | 1 |
import colorsys
from PIL import Image # type: ignore
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = x
lowercase = y
for step in range(lowerCAmelCase__ ): # noqa: B007
lowercase = ... | 633 |
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase , lowercase , lowercase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def UpperCamelCase ... | 633 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla... | 633 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCAmelCase__ ) )
def UpperCamelCase ( lowerCAmelCase__... | 633 |
from __future__ import annotations
from random import random
class lowercase :
def __init__( self ,A__ = None):
lowercase = value
lowercase = random()
lowercase = None
lowercase = None
def __repr__( ... | 633 | 1 |
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,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 633 |
def UpperCamelCase ( lowerCAmelCase__ = 1000 ):
'''simple docstring'''
lowercase = -1
lowercase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase = (n * n - 2 * a * n) // (2 * n - 2 * a... | 633 | 1 |
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
lowercase__ :Optional[Any] = logging.get_logger(__name__)
lowercase_... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Tuple = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
... | 633 | 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
from... | 633 |
from numpy import exp, pi, sqrt
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 633 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowercase ( SCREA... | 633 |
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
from... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError('''Input value must be an \'int\' type''' )
lowercase = 0
while number:
position += 1
number >>= 1
retu... | 633 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla... | 633 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=Fals... | 633 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 633 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def wrapper(*lowerCAmelCase__ , **lowerCAmelCase__ ):
lowercase = ... | 633 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 633 | 1 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
raise ValueErro... | 633 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowerc... | 633 | 1 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 |
class lowercase :
def __init__( self ,A__):
lowercase = val
lowercase = None
lowercase = None
def A__ ( self ,A__):
if self.val:
if val < self.val:
if self.left ... | 633 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = os.path.join(args.tf_model_dir , '''parameters.json''' )
lowercase =... | 633 |
import os
def UpperCamelCase ( lowerCAmelCase__ = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase__ ) , lowerCAmelCase__ ) ) as input_file:
lowercase = [
[int(lowerCAmelCase__ ) for element in line.split(''',''' )]
... | 633 | 1 |
import os
def UpperCamelCase ( lowerCAmelCase__ = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase__ ) , lowerCAmelCase__ ) ) as input_file:
lowercase = [
[int(lowerCAmelCase__ ) for element in line.split(''',''' )]
... | 633 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if len(lowerCAmelCase__ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All ... | 633 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = {
'''en''': '''Machine learning is great, isn\'t it?'''... | 633 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_... | 633 | 1 |
from collections.abc import Callable
class lowercase :
def __init__( self ,A__ = None):
# Stores actual heap items.
lowercase = []
# Stores indexes of each item for supporting updates and deletion.
lowercase = {}
#... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ :Tuple = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
... | 633 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 633 |
import logging
from transformers import PretrainedConfig
lowercase__ :int = logging.getLogger(__name__)
lowercase__ :Dict = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class... | 633 | 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
fr... | 633 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 633 | 1 |
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