code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class __snake_case ( A__ ):
... | 388 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
def __init__( self ... | 414 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__a : int = 0
__a : List[str] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0,... | 414 | 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 transformers.utils impor... | 37 | from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False)... | 64 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.k... | 420 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
... | 420 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_snake_case = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def sna... | 510 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atten... | 510 | 1 |
from maths.prime_factors import prime_factors
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
lowerCamelCase = F"""Input value of [number={number}] must be an integer"""
rais... | 711 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a_ : Optional[int] = (3, 9, -1_1, 0, 7, 5, 1, -1)
a_ : str = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class lowerCamelCase__ :
"""simple d... | 484 | 0 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: float , lowerCAmelCase: float ) -> float:
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __... | 300 |
from statistics import mean, stdev
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: list , lowerCAmelCase: int = 3 ) -> list:
_UpperCAmelCase : Tuple = min(lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = max(lowerCAmelCase )
# normalize data
retur... | 300 | 1 |
'''simple docstring'''
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version im... | 712 |
'''simple docstring'''
def _lowerCAmelCase( UpperCAmelCase_ : str ) -> int:
assert column_title.isupper()
lowerCAmelCase__ = 0
lowerCAmelCase__ = len(UpperCAmelCase_ ) - 1
lowerCAmelCase__ = 0
while index >= 0:
... | 211 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : dict[int, int] = {}
UpperCAmelCase__ : Optional[int] = 2
while True:
Upp... | 65 |
"""simple docstring"""
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 i... | 118 | 0 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.... | 319 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
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_con... | 319 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class _A ( unittest.TestCase):
def UpperCAmelCase ( self ):
... | 511 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import ... | 511 | 1 |
'''simple docstring'''
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
)
| 427 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( a , a ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(a ):
print(f'{i}\t\t{d}' )
def lowerCamelCase__ ( a , a ,... | 427 | 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_mobilebert import MobileBertTokenizer
_snake_case : Dict = logging.get_logger(__name__)
_s... | 53 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
... | 649 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase_ = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
excep... | 710 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfor... | 510 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
SCREAMING_SNAKE_CASE__ = HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
# Load c... | 47 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"configuration_mbart": ["MBART_PR... | 256 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase = False ) -> bool:
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # ... | 719 | """simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None ) -> str:
'''simple docstring''... | 132 | 0 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.ut... | 90 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _SCREAMING_SNAKE_CASE( A ):
@staticmethod
@abstractmethod
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> str:
"... | 498 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProce... | 57 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 57 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _SCREAMING_SNAKE_CASE ( UpperCamelCase : list[list[int]] , UpperCamelCase : lis... | 574 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINE... | 574 | 1 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __magic_name__ ( _lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
... | 63 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : str = 0
__... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Optional[int] = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependenc... | 108 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_token... | 247 | 0 |
from ....utils import logging
__snake_case :Any = logging.get_logger(__name__)
class _A ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : Optiona... | 60 |
__snake_case :str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# ... | 60 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
A__: Union[str, Any] = logging.get_logger(__name__)
class A__ ( UpperCAmelCase__ ):
def __init__( self... | 694 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
A__: Union[str, Any] = logging.get_logger('''transformers.models.speecht5''')
def SCR... | 694 | 1 |
import warnings
from functools import wraps
from typing import Callable
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
@wraps(lowerCAmelCase__ )
def _inner_fn(*lowerCAmelCase__ , **lowerCAmelCase__ ):
warnings.warn(
(f"""'{fn.__n... | 587 | def UpperCamelCase_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.... | 587 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_SNAKE_CASE__ ( ... | 581 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.... | 581 | 1 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils... | 710 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( UpperCamelCase__ = "laptop" ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = F'''https://www.amazon.in/l... | 168 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import... | 251 | '''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 251 | 1 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase (... | 87 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def a__ ( a : float , a : float , a : bool = False ):
"""simple docstring"""
if radian_mode:
return [magnitu... | 87 | 1 |
_snake_case = 65521
def lowerCAmelCase_ ( snake_case_ ):
_A : Optional[int] = 1
_A : Tuple = 0
for plain_chr in plain_text:
_A : Union[str, Any] = (a + ord(_lowercase )) % MOD_ADLER
_A : Dict = (b + a) % MOD_ADLER
... | 307 | import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = OmegaConf.load(_lowercase )
SCREAMING_SNAKE_... | 248 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase :Dict = {
'configuration_chinese_clip': [
'CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ChineseCLIPConfig',
'Chin... | 704 |
import math
import sys
import cva
import numpy as np
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
_a = math.sqrt(_UpperCamelCase )
_a = 1 / (sigma * math.sqrt(2 * ... | 346 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ... | 57 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _A ( __snake_case :BertModel , __snake_case :str , __snake_case :str ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE ... | 693 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :List[Any] = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 705 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : List[... | 633 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if... | 466 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __low... | 377 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
UpperCamelCase_ : Any = logging.get_lo... | 482 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : int ) -> List[Any]:
"""simple docstring"""
A_ = {}
... | 482 | 1 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def snake_case ( lowerCAmelCase_ ) -> int:
_snake_case = prime_factors(lowerCAmelCase_ )
if is_square_free(lowerCAmelCase_ ):
return -1 if l... | 103 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 0 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase :str = lo... | 686 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
lowerCamelCase :int = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER... | 686 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
fr... | 44 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _lowerCAmelCase : Optional[Any] ):
... | 44 | 1 |
def snake_case (UpperCamelCase : list[int] ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
lowerCamelCase__ = sum(UpperCamelCase ) / len(UpperCamelCase ) # Calculate the average
... | 235 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availab... | 235 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = input("Enter image url: ").strip()
print(f'Downloading image from {url} ...')
SCREAMING_SNAKE_CASE__ = BeautifulSoup(requests.get... | 532 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_roformer": ["ROFORMER_PRET... | 532 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowerCamelCase__ = list[list[float | int]]
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : int = len(_UpperCamelCase )
__lowerCAmelCase : Matr... | 549 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCamelCase__ = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not... | 549 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, ... | 103 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 1 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
return getitem, k
def lowerCAmelCase_ ( __A : Any... | 692 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : PriorityQueue , __A : dict , __... | 692 | 1 |
'''simple docstring'''
import os
import sys
import unittest
UpperCamelCase_ = 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_dummies # noqa: E402
from check_dummies import create_dummy_f... | 28 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class... | 28 | 1 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
UpperCamelCase_ = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Ima... | 718 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
ra... | 599 | 0 |
"""simple docstring"""
lowercase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def A_ ( lowercase ) -> bytes:
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
UpperCAmelCase_ : Optional[i... | 470 |
"""simple docstring"""
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , a_ : int )-> str:
"""simple docstring"""
UpperCAmelCase_ : Any = n
UpperCAmelCase_ : str = [None] * self.n
... | 470 | 1 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> tuple[float, list[float]]:
lowerCamelCase : List[str] =list(range(len(SCREAMING_SNAKE_CASE_ ) ) )
lowerCamelCase : List[s... | 262 |
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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logge... | 262 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.uti... | 306 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requi... | 306 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase=False ) -> Optional[int]:
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) and isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : List[Any] = len(set_a.intersection(SCREA... | 715 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase : List[str] = 3
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
print("""Generating primitive root of p""" )
while True:
snake_case : O... | 684 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase :Tuple = logging.get_logger(__name__)
__lowerCamelCase :Any = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-larg... | 222 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def snake_case ( ) -> Generator[int, None, None]:
lowerCamelCase : dict[int, int] = {}
lowerCamelCase : str = 2
while True:
lowerCamelCase ... | 222 | 1 |
from __future__ import annotations
def _a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> float:
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0' )
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate must... | 144 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resol... | 144 | 1 |
'''simple docstring'''
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, Lis... | 640 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ... | 720 |
from PIL import Image
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = image.size
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = image.load()
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = ... | 597 | 0 |
import numpy as np
class lowerCamelCase_ :
def __init__( self : Dict ):
'''simple docstring'''
a = (0, 0)
a = None
a = 0
a = 0
... | 387 |
import numpy as np
class lowerCamelCase_ :
def __init__( self : Dict ):
'''simple docstring'''
a = (0, 0)
a = None
a = 0
a = 0
... | 387 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
"""configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 534 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divi... | 534 | 1 |
'''simple docstring'''
import os
import sys
__UpperCAmelCase = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuest... | 90 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : str = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google... | 316 | # 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 appl... | 316 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 397 |
from math import factorial
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> float:
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes... | 397 | 1 |
'''simple docstring'''
import torch
from torch import nn
class __lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , a__ , a__ , a__ , a__ , a__=1 , a__=False ):
super().__init__()
... | 564 |
'''simple docstring'''
def __A ( _SCREAMING_SNAKE_CASE : int = 1_0_0_0 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 564 | 1 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase__ : Optional[int] = 6_3_7_8_1_3_7.0
UpperCAmelCase__ : Any = 6_3_5_6_7_5_2.3_1_4_2_4_5
UpperCAmelCase__ : List[str] = 6_37_81_37
def A... | 48 |
'''simple docstring'''
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 ... | 296 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin ... | 467 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __SCREA... | 467 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtract... | 415 | import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,... | 415 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe imp... | 164 | import math
def a_ (_lowerCAmelCase : list , _lowerCAmelCase : int = 0 , _lowerCAmelCase : int = 0 )-> list:
snake_case: List[str] = end or len(_lowerCAmelCase )
for i in range(_lowerCAmelCase , _lowerCAmelCase ):
snake_case: Union[s... | 164 | 1 |
'''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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
V... | 588 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray , lowercase_ : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase_ , ... | 588 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 701 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase... | 361 | 0 |
import inspect
import unittest
from transformers import BitConfig
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_backbone_common import BackboneTesterMixi... | 117 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early... | 718 |
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = int(__magic_name__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(__magic_name__ )
UpperCAmelCase_, UpperCAmelCase_ = divmod(__magic_name__ , 2 )
return b... | 407 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_SC... | 369 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoToke... | 647 | 0 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
UpperCamelCase__ = (
'''This metric will be remove... | 312 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(lowerCAmelCase__ , lowerCA... | 312 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 135 |
'''simple docstring'''
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 logging
__SCREAMING_SNAKE_CASE : Optional[Any... | 135 | 1 |
'''simple docstring'''
import sys
from collections import defaultdict
class A :
def __init__( self : Any ) -> Dict:
"""simple docstring"""
_a = []
def __lowerCAmelCase (... | 704 |
'''simple docstring'''
import math
import unittest
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
assert isinstance(UpperCamelCase , UpperCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
... | 377 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_... | 427 | '''simple docstring'''
import argparse
import os
import re
_A : str = '''src/transformers'''
# Pattern that looks at the indentation in a line.
_A : List[str] = re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
_A : List[str] = ... | 427 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def a_ ( lowerCamelCase , lowerCamelCase = 2 , lowerCamelCase = 1 , lowerCamelCase = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
... | 711 | """simple docstring"""
import random
class snake_case :
"""simple docstring"""
@staticmethod
def __lowerCAmelCase ( lowerCamelCase__ : str ):
UpperCAmelCase__ = [ord(lowerCamelCase__ ) for i in text]
UpperCAmelCase__ = []
... | 632 | 0 |
def UpperCamelCase( ):
for n in range(1 ,1000000 ):
yield n * (n + 1) // 2
def UpperCamelCase( __UpperCamelCase : Union[str, Any] ):
lowerCAmelCase_ : Optional[Any] = 1
lowerCAmelCase_ : List[Any] = 2
while i * i <= n:
lowerCAmelCas... | 171 |
import fire
from utils import calculate_rouge, save_json
def UpperCamelCase( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : str=None ,**__UpperCamelCase : Optional[Any] ):
lowerCAmelCase_ : int = [x.strip() for x in open(__UpperCa... | 171 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_lowerCAmelCase : Tuple = logging.getLogger(... | 646 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCAmelCase : List[str] = logging.get_logger('transformers.models.speecht5')
def __UpperCamelCase ( _A : Any... | 646 | 1 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ ... | 48 |
'''simple docstring'''
def A ( UpperCamelCase_ : str , UpperCamelCase_ : int ) -> list:
'''simple docstring'''
lowerCAmelCase__ = word.split()
def justify(UpperCamelCase_ : list , UpperCamelCase_ : int , UpperCamelCa... | 48 | 1 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
f... | 50 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name_... | 50 | 1 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self : Tuple ):
__UpperCAmelCase = ''''''
__UpperCAmelCase = ''''''
__UpperCAmelCase = []... | 49 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 49 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
... | 716 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : List[str] = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/mai... | 651 | 0 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.mo... | 653 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclas... | 653 | 1 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.meta... | 317 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_fu... | 317 | 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 lowerCAmelCase_ ( __lowercase, __lowercase ):
@register_to_config
def __init__( self ... | 10 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor,... | 536 | 0 |
import json
import sys
def __lowercase( __snake_case : Tuple ,__snake_case : Optional[int] ) -> Tuple:
with open(UpperCAmelCase__ ,encoding='utf-8' ) as f:
__snake_case = json.load(UpperCAmelCase__ )
__snake_case = [... | 710 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_an... | 345 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : Dict = [
'encoder.version',
'decoder.version',
... | 476 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowercase : Tuple = 0b1011001111101100100100000111101... | 476 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase : Optional[Any] = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 643 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : List[An... | 643 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vi... | 233 | """simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class a ... | 277 | 0 |
import pytest
import datasets
# Import fixture modules as plugins
UpperCamelCase = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
# Mark tests as "unit" by default if... | 152 |
from functools import reduce
UpperCamelCase = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66... | 152 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase__ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'... | 75 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
return base * power(UpperCamelCase__ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of exponent using recursion...")
... | 546 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet i... | 711 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase () -> tuple[list[int], int]:
lowercase :Any = [randint(-1000 , 1000) for i in range(10)]
... | 475 | 0 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__magic_name__ = pytest.mark.integration
@pytest.mark.parametrize('pa... | 232 | """simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common... | 232 | 1 |
"""simple docstring"""
import os
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ = "input.txt" ):
with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE_ ), SCREAMING_SNAKE_CASE_ ) ) as input_file:
SCREAMING_SNAKE_CASE = [
[int(SCREAMING_S... | 406 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING... | 406 | 1 |
'''simple docstring'''
import cva
import numpy as np
class _A :
def __init__( self : Any , __magic_name__ : float , __magic_name__ : int ) -> Optional[int]:
"""simple docstring"""
if k in (0.04, 0.06):
__snake_c... | 26 |
def __a ( SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
__UpperCAmelCase ... | 303 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def __UpperCAmelCase ( __magic_name__ = 150_0000 )-> int:
"""simple docstring"""
snake_case_ : defaultdict = defaultdict(__magic_name__ )
snake_case_ : Tuple ... | 711 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
__lowerCamelCase : str = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 a... | 656 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> Dict:
... | 514 |
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
i... | 514 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import... | 152 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPega... | 152 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :dict ):
__UpperCAmelCase = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__UpperCAmelCase = set()
return any(
node not in visited and depth_first_search(snake_cas... | 49 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.vers... | 675 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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_con... | 390 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"camembert-base": "https://huggingface.co/camembert-base/r... | 390 | 1 |
"""simple docstring"""
from collections import deque
def lowercase_ ( __UpperCAmelCase ) -> int:
lowerCAmelCase__ : Optional[int] = len(__UpperCAmelCase )
lowerCAmelCase__ : int = deque()
lowerCAmelCase__ : Optional[int] = [False ... | 299 |
"""simple docstring"""
from __future__ import annotations
import queue
class _lowerCamelCase :
def __init__( self : Optional[int] , UpperCamelCase : List[Any] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, A... | 299 | 1 |
# 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 ( snake_case__ ... | 608 |
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 lowerCamelCase ( __snake_case ):
"""simple docstring"""
lo... | 608 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( _A ):
_a : UNetaDModel
... | 385 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
... | 385 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP'''... | 717 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ... | 219 | 0 |
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