code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _a ( SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
def is_in_circle(SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CAS... | 322 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE... | 322 | 1 |
class A_ :
def __init__( self : List[str] ) -> Optional[Any]:
__lowerCAmelCase: Optional[Any] = 0
__lowerCAmelCase: Optional[Any] = 0
__lowerCAmelCase: Tuple = {}
def UpperCAmelCase ( self : List[str] , U... | 322 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : ... | 322 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _a ( *SCREAMING_SNAKE_CASE : str ) -> Dict:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
_... | 322 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring... | 322 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : int ) -> Optional[int]... | 322 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.envi... | 322 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A_ ( snake_case__ ):
def __init__( self : Tuple ... | 322 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
r... | 322 | 1 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : D... | 322 |
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 A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''https://hu... | 322 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable... | 322 | 1 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring... | 322 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )... | 322 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be s... | 322 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
wh... | 322 | 1 |
from typing import Any
class A_ :
def __init__( self : Union[str, Any] , UpperCAmelCase : Any ) -> Optional[Any]:
__lowerCAmelCase: Optional[Any] = data
__lowerCAmelCase: Tuple = None
class A_ :
... | 322 |
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
_a = '''scheduler_config.json'''
class A_ ( snake_cas... | 322 | 1 |
from statistics import mean, stdev
def _a ( SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
__lowerCAmelCase: Dict = min(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(SCREAMING_SNAKE... | 322 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CAS... | 322 | 1 |
from __future__ import annotations
_a = [True] * 1_0_0_0_0_0_1
_a = 2
while i * i <= 1_0_0_0_0_0_0:
if seive[i]:
for j in range(i * i, 1_0_0_0_0_0_1, i):
_a = False
i += 1
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple... | 322 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
... | 322 | 1 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_a = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''... | 322 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CA... | 322 | 1 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CA... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CAS... | 322 | 1 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.mark.param... | 322 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be s... | 322 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_a = get_tests_dir() + ... | 322 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCR... | 322 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tenso... | 322 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_p... | 322 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = ... | 322 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 322 | 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 A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformer... | 322 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
... | 322 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every ... | 322 | 1 |
import re
def _a ( SCREAMING_SNAKE_CASE : str ) -> list:
"""simple docstring"""
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]' , str_ )]
def _a ( SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CA... | 322 | 1 |
_a = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_a = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _a ( SCREAMING_SNAKE_CASE : dict[int, list[int]] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[bool] ) -> list[int]:
... | 322 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 322 | 1 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 322 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE... | 322 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A_ ( snake_case__ , snake_case__ ):
@register_to_config
def __init__( ... | 322 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : ... | 322 | 1 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _a ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : s... | 322 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
... | 322 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.envi... | 322 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_a = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_PRETRA... | 322 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
r... | 322 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class A_ ( snake_case__ ):
# `task` is not a ClassVar since we want it t... | 322 |
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 A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 | 1 |
from manim import *
class A_ ( snake_case__ ):
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
__lowerCAmelCase: str = Rectangle(height=0.5 , width=0.5 )
__lowerCAmelCase: str = Rectangle(height=0.25 , width=0... | 322 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable... | 322 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_a = models.Sequential()
# Step 1 - Convolution
#... | 322 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )... | 322 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_a = 2_9_9_7_9_2_4_5_8
# Symbols
_a , _a , _a , _a = symbols('''ct x y z''')
def _a ( SCREAMING_SNAKE_CASE : float ) -> float:
... | 322 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
wh... | 322 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_... | 322 |
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
_a = '''scheduler_config.json'''
class A_ ( snake_cas... | 322 | 1 |
import gc
import threading
import time
import psutil
import torch
class A_ :
def __init__( self : Optional[Any] ) -> Tuple:
__lowerCAmelCase: Dict = psutil.Process()
__lowerCAmelCase: Dict = False
def UpperCAmelCase... | 322 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CAS... | 322 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a = logging.get_logger(__name__)
_a = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co... | 322 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
... | 322 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformer... | 322 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CA... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int ) -> list:
"""simple docstring"""
__lowerCAmelCase: Optional[Any] = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[0] * n for i in range(... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CAS... | 322 | 1 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from... | 322 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be s... | 322 | 1 |
import baseaa
def _a ( SCREAMING_SNAKE_CASE : str ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('utf-8' ) )
def _a ( SCREAMING_SNAKE_CASE : bytes ) -> str:
"""simple docstring"""
return bas... | 322 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCR... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> bool:
"""simple docstring"""
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def _a ( SCREAMING_SNAKE_CASE : str ) -> bool:
"""simple docstring"""
__lowerCAmelCase: ... | 322 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_p... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
__lowerCAmelCase: Tuple = sorted(string.lower() )
return len(SCREAMING_SNAKE_CA... | 322 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 322 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/... | 322 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformer... | 322 | 1 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def _a ( SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
if (
(cp >= 0x4_e_0_0 and cp <= 0... | 322 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every ... | 322 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
debug_launcher(test_scr... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CA... | 322 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
... | 322 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 322 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseC... | 322 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE... | 322 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
t... | 322 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : ... | 322 | 1 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 322 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring... | 322 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is the refere... | 322 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.envi... | 322 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _a ( SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: int = analyze_text(SCREAM... | 322 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
r... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
_a ... | 322 |
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 A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 | 1 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
r... | 322 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable... | 322 | 1 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCR... | 322 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )... | 322 | 1 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration,... | 322 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
wh... | 322 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers im... | 322 |
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
_a = '''scheduler_config.json'''
class A_ ( snake_cas... | 322 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql ... | 322 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CAS... | 322 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_a = {
'''co... | 322 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
... | 322 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
... | 322 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CA... | 322 | 1 |
_a = 6_5_5_2_1
def _a ( SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
__lowerCAmelCase: List[str] = 1
__lowerCAmelCase: Optional[int] = 0
for plain_chr in plain_text:
__lowerCAmelCase: Optional[int] = (a + ... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CAS... | 322 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_a = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[str] , UpperCAmelCase : T ) -> List[Any]:
__lowerCA... | 322 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be s... | 322 | 1 |
from math import factorial
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('Please enter positive integers for n and k where n >= k' )
return factorial(SCREAMING_SNA... | 322 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCR... | 322 | 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
_a = logging.get_logger(__name__)
_a = {
'''facebook/deit-base-distil... | 322 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_p... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : int = 4_00_00_00 ) -> int:
"""simple docstring"""
__lowerCAmelCase: str = []
__lowerCAmelCase , __lowerCAmelCase: Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(SCREAMING_SNAKE_CASE )
__l... | 322 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 322 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_... | 322 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformer... | 322 | 1 |
from manim import *
class A_ ( snake_case__ ):
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
__lowerCAmelCase: int = Rectangle(height=0.5 , width=0.5 )
__lowerCAmelCase: int = Rectangle(height=0.46 ... | 322 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every ... | 322 | 1 |
import collections
import os
import re
from pathlib import Path
_a = '''src/transformers'''
# Matches is_xxx_available()
_a = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
_a = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CA... | 322 | 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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 322 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CA... | 322 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE... | 322 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 322 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : ... | 322 | 1 |
# Algorithm for the pigeonhole sorting
def _a ( SCREAMING_SNAKE_CASE : List[str] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase: str = min(SCREAMING_SNAKE_CASE ) # min() finds the minimum value
__lowerCAmelCase: List[Any] = max(SC... | 322 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring... | 322 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
... | 322 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.envi... | 322 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}
t... | 322 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
r... | 322 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_a = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] A... | 322 |
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 A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 | 1 |
from collections.abc import Sequence
def _a ( SCREAMING_SNAKE_CASE : Sequence[float] , SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(SCREAMING_SNAKE_CASE ) )
def _a ( SCREA... | 322 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable... | 322 | 1 |
import os
def _a ( ) -> str:
"""simple docstring"""
with open(os.path.dirname(SCREAMING_SNAKE_CASE ) + '/grid.txt' ) as f:
__lowerCAmelCase: int = [] # noqa: E741
for _ in range(20 ):
l.append([int(SCREAMING_SNAKE_CASE ) for x in f.readline().spl... | 322 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )... | 322 | 1 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vi... | 322 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
wh... | 322 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_a = logging.get_logger(__name__)
class A_ ( snake_ca... | 322 |
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
_a = '''scheduler_config.json'''
class A_ ( snake_cas... | 322 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
'''Jukebo... | 322 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CAS... | 322 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : set , SCREAMING_SNAKE_CASE : set , SCREAMING_SNAKE_CASE : dict , SCREAMING_S... | 322 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : str ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = int(SCREAMING_SNAKE_CASE )
# Initialize Result
__lowerCAmelCase: int = []
# Traverse through al... | 322 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CA... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : list[list[int]] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : set ) -> int:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = len(SCREAMING_S... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CAS... | 322 | 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_retribert import RetriBertTokenizer
_a = logging.get_logger(__name__)
_a = {''... | 322 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be s... | 322 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 322 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCR... | 322 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLMRobertaXLOnnxConfig... | 322 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_p... | 322 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 322 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 322 | 1 |
import math
def _a ( ) -> None:
"""simple docstring"""
__lowerCAmelCase: int = input('Enter message: ' )
__lowerCAmelCase: Dict = int(input(f'''Enter key [2-{len(SCREAMING_SNAKE_CASE ) - 1}]: ''' ) )
__lowerCAmelCase: Optional[Any] = ... | 322 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformer... | 322 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/co... | 322 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every ... | 322 | 1 |
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _a (... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CA... | 322 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Autoen... | 322 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 322 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
... | 322 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE... | 322 | 1 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]: # noqa: E741
"""simple docstring"""
... | 322 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : ... | 322 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
re... | 322 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring... | 322 | 1 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_a = transforms.Comp... | 322 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.envi... | 322 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaske... | 322 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
r... | 322 | 1 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int | str] ) -> None:
"""simple docstring"""
create_state_space_tree(SCREAMING_SNAKE_CASE , [] , 0 , [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )
def _... | 322 |
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 A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 | 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():
i... | 322 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable... | 322 | 1 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )... | 322 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def _a ( SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
return quad(SCREAMING_SNAKE_CASE , 0 , SCREAMING_... | 322 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
wh... | 322 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
f... | 322 |
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
_a = '''scheduler_config.json'''
class A_ ( snake_cas... | 322 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_p... | 322 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CAS... | 322 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig'... | 322 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
... | 322 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.