code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_lowercase : Union[str, Any] =collections.namedtuple('''_Datasets'... | 305 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__a : Any = logging.get_logger(__name__)
class __lowercase ( lowercase__ ):
'''simple docstring'''
def __init__( self : List[Any] , ... | 637 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 0 |
"""simple docstring"""
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 ..ut... | 357 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : List[Any] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class ... | 540 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[float] ):
'''simple docstring'''
if len(__lowerCamelCase ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 f... | 664 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 0 |
# 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 ... | 146 |
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 : List[str] = {"configuration_xg... | 63 | 0 |
'''simple docstring'''
lowerCAmelCase__ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerC... | 41 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@requi... | 283 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 0 |
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... | 600 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 0 |
from collections import namedtuple
_lowerCamelCase = namedtuple('from_to', 'from_ to')
_lowerCamelCase = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1000),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.0_0454, 264.172),
"cubicyard": from_to(0.7_6455, 1.3_0795... | 6 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepi... | 63 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase_ ( yaml.SafeLoader ):
'''simple docstring'''
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
... | 568 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 0 |
def A__ ( lowercase: List[Any], lowercase: Union[str, Any] ) -> Any:
A : Tuple =0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def A__ ( lowercase: List[str], lowercase... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 0 |
def _SCREAMING_SNAKE_CASE ( ) -> int:
"""simple docstring"""
__A = [3_1, 2_8, 3_1, 3_0, 3_1, 3_0, 3_1, 3_1, 3_0, 3_1, 3_0, 3_1]
__A = 6
__A = 1
__A = 1_9_0_1
__A = 0
while year < 2_0_0_1:
day += 7
... | 637 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 0 |
"""simple docstring"""
from collections import deque
def A_ ( __lowercase ):
UpperCamelCase_ : List[Any] =len(__lowerCamelCase )
UpperCamelCase_ : List[Any] =deque()
UpperCamelCase_ : int =[False for _ in range(__lowerCamelCase )]
UpperCamelCase_ : Dict ... | 357 |
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... | 63 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case : Dict = _sym... | 540 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__magic_name__ : str =logging.get_logger(__name__)
class UpperCamelCase_ ( lowercase__ ):
"""simple docstring"""
def __init__( self : Opti... | 664 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 0 |
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
_UpperCamelCase = logging.get_logger(__name__)
class lowercase ( lowercase__ ):
'''simpl... | 146 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 0 |
'''simple docstring'''
lowerCAmelCase__ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _A ( ):
"""simple docstring"""
__lowercase = input('''Enter message: ''' )
__lowercase = input('''Enter key [alphanumeric]: ''' )
__lowercase = input('''Encrypt/Decrypt... | 41 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE :List[Any] = 1.0_5457_1817e-34 # unit of ℏ : J * s
SCREAMING_SNAKE_CASE :Any = 3e8 # unit of c : m * s^-1
... | 283 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 0 |
from __future__ import annotations
def __UpperCamelCase ( lowercase__ : str ) -> List[str]:
'''simple docstring'''
return [ord(__lowerCamelCase ) - 96 for elem in plain]
def __UpperCamelCase ( lowercase__ : list[int] ) -> Union[str,... | 600 |
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_avai... | 63 | 0 |
from __future__ import annotations
from random import random
class UpperCamelCase_ :
def __init__( self :Optional[int] , __A :int | None = None ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = value
SCREAMING_SNAKE_CASE__ ... | 6 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 0 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 568 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 0 |
import json
import pathlib
import unittest
import numpy as np
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 ImageProcessingSavingTestMixin, prepare_image_inputs
... | 305 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 0 |
class __lowercase :
'''simple docstring'''
def __init__( self : Optional[Any] ):
"""simple docstring"""
__A = {}
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
print(se... | 637 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 0 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transforme... | 357 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except Op... | 540 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 0 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
f... | 664 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
lo... | 146 |
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 : List[str] = {"configuration_xg... | 63 | 0 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ (lowercase__ ):
... | 41 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 0 |
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_ut... | 283 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 0 |
import qiskit
def __UpperCamelCase ( lowercase__ : int , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the ... | 600 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.mod... | 6 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepi... | 63 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-em... | 568 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
_lowercase : Dict =Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 0 |
from __future__ import annotations
__a : Optional[int] = list[tuple[int, int]]
__a : int = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0... | 637 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A_ ( ):
UpperCamelCase_ : int ={
"""repo_name""": ["""test_repo1""", """test_repo2""", """test_repo3"""],
"""pa... | 357 |
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... | 63 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regre... | 540 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__magic_name__ : List[str] =logging.get_logger(__... | 664 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = OrderedDict(
... | 146 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 0 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
lowerCAmelCase__ = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplificat... | 41 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 0 |
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 numpy as np
import tensorflow as tf
from transformers import TFXLMRo... | 283 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 0 |
def __UpperCamelCase ( lowercase__ : int = 1000 ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : str = 2**power
lowerCAmelCase_ : List[Any] = 0
while n:
lowerCAmelCase_ : List[str] = r + n % 1... | 600 |
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_avai... | 63 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, ... | 6 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowercase : List[str] = "scheduler_config.json"
class UpperCAmelCase_ ( ... | 568 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple ={
"configuration_blenderbot_small": [
"BLENDERBOT_SM... | 305 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :... | 637 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 0 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = input('Enter image url: ').strip()
print(F"""Downloading image from {url} ...""")
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(url... | 357 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 0 |
import re
from filelock import FileLock
try:
import nltk
__snake_case : Optional[int] = True
except (ImportError, ModuleNotFoundError):
__snake_case : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def ... | 540 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ : Dict =logging.get_logger(__name__)
__magic_name__ : Tuple =[
["attention", "attn"],
... | 664 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 0 |
from __future__ import annotations
_UpperCamelCase = "Muhammad Umer Farooq"
_UpperCamelCase = "MIT"
_UpperCamelCase = "1.0.0"
_UpperCamelCase = "Muhammad Umer Farooq"
_UpperCamelCase = "contact@muhammadumerfarooq.me"
_UpperCamelCase = "Alpha"... | 146 |
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 : List[str] = {"configuration_xg... | 63 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not... | 41 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
SCREAMING_SNAKE_CASE :Optional[Any] = 0B1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0... | 283 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __a :
__snake_case : int
__snake_case : Node | None = None
__snake_case : Node | None = None
... | 600 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 0 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 6 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepi... | 63 | 0 |
lowercase : str = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowercase : Any = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase : ... | 568 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__a : Dict = TypeVar("T")
def _SCREAMING_SNAKE_CASE ( __lowercase : int ) -> str:
"""simple docstring"""
return (position - 1) // 2
def _SCREAMING_SNAKE... | 637 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a__ ( unittest.Test... | 357 |
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... | 63 | 0 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _UpperCAmelCase ( a__ , a__=() , a__=None , a__="no" , a__="29500"):
'''simple docstring'''
a_ : in... | 540 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__magic_name__ : Tuple =logging.g... | 664 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 0 |
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=False)... | 64 | def A__ ( snake_case_ : float , snake_case_ : float ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import do... | 64 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ac... | 64 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Any = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],... | 64 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin... | 64 | import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class _lowerCamelCase ( UpperCamelCase_ ):
# `task` is not a ClassVar since we want it to be ... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Any = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],... | 64 | import inspect
import unittest
class _lowerCamelCase ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> Any:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCamelCase_ ( self ) -> List[str]:
impo... | 64 | 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... | 64 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 64 | 1 |
import warnings
from .generation import TFGenerationMixin
class _lowerCamelCase ( UpperCamelCase_ ):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformers v5. Import... | 64 | import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
... | 64 | 1 |
def A__ ( snake_case_ : int = 1_000 ):
SCREAMING_SNAKE_CASE__: Optional[int]= -1
SCREAMING_SNAKE_CASE__: Optional[Any]= 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
SCREAMING_SNAKE_CASE__: Dict= (n * n - 2 * a * n) // (2 *... | 64 | 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=False)... | 64 | 1 |
import enum
import shutil
import sys
lowercase_ , lowercase_ : Optional[int] = shutil.get_terminal_size()
lowercase_ : Union[str, Any] = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class _lowerCamelCase ( enum.Enum ):
__a = 0
__a = 1
def ... | 64 | 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... | 64 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoToke... | 64 | import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase_ : Tuple = 3
def A__ ( snake_case_ : int ):
print('''Generating primitive root of p''' )
while True:
SCREAMING_SNAKE_CASE__: List[Any]= random.randrange(3 ... | 64 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowerCamelCase :
def __init__( self , lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__: Any= data
SCREAMING_SNAKE_CASE__: Node | None= None
class _lowerCamel... | 64 | from math import factorial
def A__ ( snake_case_ : int , snake_case_ : int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('''Please enter positiv... | 64 | 1 |
def A__ ( snake_case_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__: Optional[Any]= 0
SCREAMING_SNAKE_CASE__: int= len(snake_case_ )
for i in range(n - 1 ):
for j in range(i + 1 , snake_case_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def ... | 64 | import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeature... | 64 | 1 |
from datetime import datetime as dt
import os
from github import Github
lowercase_ : int = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def A__ ( ):
SCREAMING_SNAKE_CASE__: int= Github(os.environ... | 64 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase_ : List[Any] = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_C... | 64 | 1 |
def A__ ( snake_case_ : list ):
if len(snake_case_ ) <= 1:
return [tuple(snake_case_ )]
SCREAMING_SNAKE_CASE__: str= []
def generate(snake_case_ : int , snake_case_ : list ):
SCREAMING_SNAKE_CASE__: int= [0] * n
res.append(tuple(snake_case_ ) )
SCREAMING_SN... | 64 | import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def A__ ( ):
SCREAMING_SNAKE_CASE__: U... | 64 | 1 |
lowercase_ : str = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 64 | from __future__ import annotations
from collections import deque
class _lowerCamelCase :
def __init__( self , lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__: list[dict]= []
self.adlist.append(
{'''value''': '''''', '''next_states''': [], '''fail_state''': 0,... | 64 | 1 |
from __future__ import annotations
from math import pi
def A__ ( snake_case_ : float , snake_case_ : float , snake_case_ : float ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductanc... | 64 | import numpy as np
def A__ ( snake_case_ : str , snake_case_ : List[str] , snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : Optional[int] ):
SCREAMING_SNAKE_CASE__: List[Any]= int(np.ceil((x_end - xa) / h ) )
SCREAMING_... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase_ : List[Any] = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_C... | 64 | import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _lowerCamelCase ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE__: Tuple= get_activation('''swish''' )
self.assertIsI... | 64 | 1 |
lowercase_ : Optional[Any] = tuple[float, float, float]
lowercase_ : Dict = tuple[float, float, float]
def A__ ( snake_case_ : Pointad , snake_case_ : Pointad ):
SCREAMING_SNAKE_CASE__: Tuple= end_pointa[0] - end_pointa[0]
SCREAMING_SNAKE_CASE__: Union[s... | 64 | from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase_ : Tuple = TypeVar('T')
class _lowerCamelCase ( Generic[T] ):
def __init__( self , lowerCAmelCase , lowerCAmelCase ) -> None:
SCREAMING_SNAKE... | 64 | 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
from ..auto import CONFIG_MAPPING
lowercase_ : str = logging.get_logger(__name__)
lowercase_ : An... | 64 | # This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
Au... | 64 | 1 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A__ ( snake_case_ : List[str] , snake_case_ : Optional[Any] , snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , sna... | 64 | # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
Co... | 64 | 1 |
def A__ ( snake_case_ : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 | def A__ ( snake_case_ : int ):
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
SCREAMING_SNAKE_CASE__: List[Any]= [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE__: List[str]= 1
if upper_limit > 0:
SCREAMING_SNAKE_CASE_... | 64 | 1 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def A__ ( snake_case_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__: Dict= test_file.split(os.path.sep )
if componen... | 64 | from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
exce... | 64 | 1 |
def A__ ( snake_case_ : int = 10 , snake_case_ : int = 22 ):
SCREAMING_SNAKE_CASE__: List[str]= range(1 , snake_case_ )
SCREAMING_SNAKE_CASE__: str= range(1 , snake_case_ )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ... | 64 | import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : Optional[int] = logging.get_logger(__name__)
def A__ ( snake_case_ : List[Any] ):
SCREAMING_SNAKE_... | 64 | 1 |
import os
import pytest
from attr import dataclass
lowercase_ : Optional[Any] = 'us-east-1' # defaults region
@dataclass
class _lowerCamelCase :
__a = 42
__a = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
__a = {
"task_name": "mnli",
... | 64 | def A__ ( snake_case_ : float , snake_case_ : float ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import do... | 64 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_... | 64 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Any = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],... | 64 | 1 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _lowerCamelCase ( UpperCamelCase_ ):
@require_torch
def UpperCamelCase_ ( self ) -> Any:
# this tes... | 64 | import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class _lowerCamelCase ( UpperCamelCase_ ):
# `task` is not a ClassVar since we want it to be ... | 64 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
lowercase_ : Optional[Any] = TypeVar('T')
class _lowerCamelCase ( Generic[T] ):
def __init__( self , lowerCAmelCase ) -> None:
SCREAMING_SNAKE_CASE__: int= data
SCREAMING_SNAKE_CASE__: Any= ... | 64 | import inspect
import unittest
class _lowerCamelCase ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> Any:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCamelCase_ ( self ) -> List[str]:
impo... | 64 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[int] = logging.get_logger(__name__)
lowercase_ : Optional[Any] = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT MAE... | 64 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 64 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_con... | 64 | import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
... | 64 | 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 imp... | 64 | 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=False)... | 64 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowercase_ : List[Any] = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def A__ ( snake_case_ : str = "mumbai" ):
SCREAMING_SNAKE_CASE__: List[str]... | 64 | 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... | 64 | 1 |
from ..utils import DummyObject, requires_backends
class _lowerCamelCase ( metaclass=UpperCamelCase_ ):
__a = ["torch", "transformers", "onnx"]
def __init__( self , *lowerCAmelCase , **lowerCAmelCase ) -> int:
requires_backends(self , ['''torch''', '''transformer... | 64 | import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase_ : Tuple = 3
def A__ ( snake_case_ : int ):
print('''Generating primitive root of p''' )
while True:
SCREAMING_SNAKE_CASE__: List[Any]= random.randrange(3 ... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ : Optional[int] = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise ... | 64 | from math import factorial
def A__ ( snake_case_ : int , snake_case_ : int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('''Please enter positiv... | 64 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( snake_case_ : Tuple , snake_case_ : str , snake_case_ : Tuple ):
# Ini... | 64 | import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeature... | 64 | 1 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowercase_ : Tuple = {
'n_samples': 6_4,
'horizon': 3_2,
'num_inference_steps': 2_0,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_st... | 64 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase_ : List[Any] = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_C... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ : Optional[int] = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecode... | 64 | import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def A__ ( ):
SCREAMING_SNAKE_CASE__: U... | 64 | 1 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowercase_ : Tuple = ... | 64 | from __future__ import annotations
from collections import deque
class _lowerCamelCase :
def __init__( self , lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__: list[dict]= []
self.adlist.append(
{'''value''': '''''', '''next_states''': [], '''fail_state''': 0,... | 64 | 1 |
def A__ ( snake_case_ : list[int] , snake_case_ : list[int] , snake_case_ : int ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case_ ) )
def A__ ( snake_case_ : list[list[int]] ... | 64 | import numpy as np
def A__ ( snake_case_ : str , snake_case_ : List[str] , snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : Optional[int] ):
SCREAMING_SNAKE_CASE__: List[Any]= int(np.ceil((x_end - xa) / h ) )
SCREAMING_... | 64 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ch... | 64 | import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _lowerCamelCase ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE__: Tuple= get_activation('''swish''' )
self.assertIsI... | 64 | 1 |
from PIL import Image
def A__ ( snake_case_ : Image ):
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: List[Any]= image.size
SCREAMING_SNAKE_CASE__: Dict= 0
SCREAMING_SNAKE_CASE__: str= image.load()
for i in range(snake_case_ ):
for j in range(snake_case_ ):
SCREAMING_SN... | 64 | from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase_ : Tuple = TypeVar('T')
class _lowerCamelCase ( Generic[T] ):
def __init__( self , lowerCAmelCase , lowerCAmelCase ) -> None:
SCREAMING_SNAKE... | 64 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
class _lowerCamelCase ( UpperCamelCase_ ):
def __init__... | 64 | # This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
Au... | 64 | 1 |
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