code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils im... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 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.m... | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
from collections.abc import Sequence
from queue import Queue
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase=None , __lowercase=None ) -> Dict:
lowerCAm... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class snake_case__( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase , __... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeMo... | 713 |
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 : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pi... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict =logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] ={
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/reso... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """Visio... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Optional[Any] ={"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """P... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase ( lowerCAmelCase_ )-> Dict:
def decorator(lowerCAmelCase_ ):
lowerCAmelCase_ : Optional[Any] = getattr(lowercase__ , '''handle_key''' , [] )
handle += [key]
setattr(lower... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class snake_case__:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
def lowercase_ ( self ) -> int:
... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : str = value
lowerCAmelCase_ : List[str] ... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
import argparse
import os
import re
_UpperCAmelCase : Optional[int] ="""src/transformers"""
# Pattern that looks at the indentation in a line.
_UpperCAmelCase : List[Any] =re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_UpperCAmelCase : Optional[int] =r... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class snake_case__( unittest.TestCase,... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
import argparse
import os
import re
import packaging.version
_UpperCAmelCase : Optional[int] ="""examples/"""
_UpperCAmelCase : Any ={
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCA... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 0 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testi... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case__( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
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
_UpperCAmelCase =logging.get_logger(__name__)
_UpperCAmelCase ={
"""microsoft/beit-base-patch16... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Union[str, Any] = set()
# edges = list of graph's edges
lowerCAmelCase_ : Union[str, Any] = get_edges(lowercase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node... | 707 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torc... | 619 | 0 |
import string
from math import logaa
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
lowerCAmelCase_ : int = document.translate(
str.maketrans('''''' , '''''' , string.punctuation ) ).replace('''\n''' , '''''' )
lowerCAmelCase_ : Optional[int] ... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_UpperCAmelCase : List[Any] ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
from __future__ import annotations
from typing import Any
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
if not postfix_notation:
return 0
lowerCAmelCase_ : Any = {"""+""", """-""", """*""", """/"""}
lowerCAmelCase_ : list[Any] = []
for token in po... | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...te... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class snake_case__... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
# using dfs for finding eulerian path traversal
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None )-> Dict:
lowerCAmelCase_ : int = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
lowerCAm... | 713 |
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 : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_UpperCAmelCase : List[str] =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_UpperCAmelCase : Union[str, Any] =[file fo... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_UpperCAmelCase : Optional[int] =pd.read_csv("""sample_data.csv""", header=None)
_UpperCA... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_UpperCAmelCase : Any ='''__DUMMY_TRANSFORMERS_USER__'''
_UpperCAmelCase : Optional[int] ='''Dummy User'''
_UpperCAmelCase : str ='''hf_h... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Any ={
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase ( lowerCAmelCase_ )-> Dict:
lowerCAmelCase_ : Optional[int] = tf.convert_to_tensor(_lowercase )
lowerCAmelCase_ : str = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) )... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case__:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 42 # [batch_size x 3]
SCREAMING_SNAKE_CASE__ : Any = 42... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
_UpperCAmelCase : int =tuple[float, float, float]
_UpperCAmelCase : Optional[Any] =tuple[float, float, float]
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Vectorad:
lowerCAmelCase_ : List[Any] = end_pointa[0] - end_pointa[0]
lowerCAmelCase_ : Union[str... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCAmelC... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : str ={
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig""",
"""PoolFo... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowerCAmelCase_ : Union[str, Any] = 1
lowerCAmelCase_ : str = 1
while repunit:
lowerCAmelCase_ : Union[str, Any] = (10 * repunit + 1) % di... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsM... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase ) -> List[str]:
lowerCAmel... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 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 OptionalDepen... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
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... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__( __a, unittest.TestCase ):
... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_UpperCAmelCase : int =False
class snake_case__( unittest.TestCase ... | 707 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torc... | 619 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> tuple:
lowerCAmelCase_ : Union[str, Any] = namedtuple('''result''' , '''name value''' )
if (voltage, curr... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
import numpy as np
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
return np.where(vector > 0 , A_ , (alpha * (np.exp(A_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
import requests
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> None:
lowerCAmelCase_ : List[Any] = {'''Content-Type''': '''application/json'''}
lowerCAmelCase_ : Optional[int] = requests.post(__snake_case , json={'''text''': mes... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_a... | 713 |
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 : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClas... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowerCAmelCase_ : Tuple = mf_knapsack(i - 1 , a__ , a__ , a__ )
else:
... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class snake_case__( __lowerCAmelCase ):
'''simple docstring'''
def lowercase_ ( self ) -> List[str]:
return [
{"co... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
lowerCAmelCase_ : Dict = args.log_output... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
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_vision_available
... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : int ={
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class snake_case__( ... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
from __future__ import annotations
import requests
_UpperCAmelCase : List[str] =set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Dict =logging.get_logger(__name__)
_UpperCAmelCase : List[Any] ={
"""google/bigbird-roberta-base""": "... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lo... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : str ={
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvB... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : Union[str, Any] ={
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_to... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 0 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_UpperCAmelCase : Dict =logging.get_logger(__name__)
class snake_case__( snake_case__ ):
'''simple docstring'''
def __init__( self ... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_UpperCAmelCase =HUGGINGFACE_HUB_CACHE
_UpperCAmelCase ='config.json'
_UpperCAmelCase ='diffusion_pytorch_model.bin'
_UpperCAmelCase ='diffusion_flax_model.msgpack'
_UpperCAmelCase ='model.onnx'
_UpperCAmelC... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] =logging.get_logger(__name__)
# TODO Update this
_UpperCAmelCase : Tuple ={
"facebook/esm-1b": "https://hugg... | 707 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torc... | 619 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_UpperCAmelCase : int ={
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
"""convert... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
f... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
from manim import *
class snake_case__( a__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Tuple = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : Tupl... | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tran... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ver... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = None )-> list[list[str]]:
lowerCAmelCase_ : Optional[Any] = word_bank or []
# create a table
lowerCAmelCase_ : int = len(a_ ) + 1
lowerCAmelCase_ : list[list[list[s... | 713 |
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 : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[Any]:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class snake_case__:
'''simple docstring'''
def __init__( self ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : int ... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> list:
if n_term == "":
return []
lowerCAmelCase_ : Dict = []
for temp in range(int(_UpperCamelCase ) ):
series.append(f"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
_UpperC... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase ( lowerCAmelCase_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
ret... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
Auto... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
from PIL import Image
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
def brightness(lowerCAmelCase_ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] =logging.get_logger(__name__)
_UpperCAmelCase : str ={
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class snake_ca... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
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 utils import ... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ = 100 )-> int:
lowerCAmelCase_ : Dict = set()
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : str = n + 1 # maximum limit
for a in range(2 , lowerCAmelCase_ ):
for b in range(2 , lowerCAmelCase_ ):... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_UpperCAmelCase : List[Any] =logging.get_logger(__name__... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird imp... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 0 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from trans... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
lowerCAmelCase_ : List[str] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCAmelCase ( lowerCAmelCase_ = 100 )-> int:
lowerCAmelCase_ : int = 1
lowerCAmelCase_ : ... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import C... | 707 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torc... | 619 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCAmelCase_ , lowerCAmelCase_ ) -> bool:
... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
import math
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : int = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowerCAmelCase_ )
def lowerCAmelCase ( lowerCAmelCase_ = 1 / 12_345 )-> int:
low... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
_UpperCAmelCase : List[Any] =logging.getLogger(__name__)
if __name__ == "__main__":
... | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : List[Any] = len(__lowercase )
lowerCAmelCase_ : Optional[Any] = [0] * len_array
if len_array > ... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
from __future__ import annotations
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> Any:
lowerCAmelCase_ : Union[str, Any] = TypeError(
'''Matrices must be forme... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ = 50 )-> int:
lowerCAmelCase_ : List[str] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length]... | 713 |
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 : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def lowercase_ ( __lowercase ) -> Optional[int]:
raise NotImpl... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__lowercase , **__lowercase ) -> Dict:
super().__init__(*__lowercase , **... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] ={
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.j... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_UpperCAmelCase : Dict ="""src/transformers"""
_UpperCAmelCase : int ="""d... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , l... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : List[Any] =get_tests_dir("""fixtures/spiece.mod... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
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