code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at https://h... | 60 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
from __future__ import annotations
from collections.abc import Callable
def _A ( lowerCAmelCase_ : Callable[[int | float], int | float] , lowerCAmelCase_ : int | float , lowerCAmelCase_ : int | float , lowerCAmelCase_ : int = 100 , ):
"""simple docstri... | 61 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 0 |
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, is_tpu_available
from .transformer_engine impo... | 62 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase__ ( __lowerCamelCase : int ):
__UpperCAmelCase : Optional[Any] = prime_factors(__lowerCamelCase )
if is_square_free(__lowerCamelCase ... | 63 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 0 |
lowercase_ : str = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 64 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 0 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
... | 65 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 0 |
import os
import pytest
from attr import dataclass
UpperCamelCase = "us-east-1" # defaults region
@dataclass
class lowerCAmelCase_ :
_UpperCamelCase : str
_UpperCamelCase : int = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
_UpperCamelCas... | 66 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
class A_ :
"""simple docstring"""
def __init__( self : int ,__A : List[str] ,__A : Tuple ,__A : Union[str, Any] ) -> Optional[Any]:
_lowercase = name
_lowercase ... | 67 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSch... | 68 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : List[str] = {}
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE... | 69 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
"asapp/sew-tiny-100k": "https://huggingface... | 70 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for... | 71 |
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
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json'... | 72 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 0 |
import numpy as np
import datasets
a_ : str = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof... | 73 |
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 ConfigTester
from .... | 643 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 74 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = field(default='language-modeling... | 75 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__lowercase : Optional[int] = 0
while number:
... | 76 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
A = loggi... | 77 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_: Tuple ={'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 78 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class UpperCAme... | 79 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 0 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Ac... | 80 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_snake_case : Optional[Any] = False
class a (unittest.TestCase ):
"""simple docstrin... | 81 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_p... | 82 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCAmelCase__ = logging.getLogger()
@unittest.skip("T... | 83 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp... | 84 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join #... | 85 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_to_con... | 86 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 87 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 0 |
"""simple docstring"""
def _snake_case ( __snake_case : str , __snake_case : list[str] ):
"""simple docstring"""
_lowerCamelCase : int = """"""
for word_or_phrase in separated:
if not isinstance(__snake_case , __snake_ca... | 88 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
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 _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( ... | 89 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__UpperCAmelCase = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''... | 90 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 91 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
'''simple docstring'''
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()... | 92 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 93 |
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
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('p... | 94 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 0 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 95 |
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 ConfigTester
from .... | 643 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : list[int] , __UpperCAmelCase : int ) -> bool:
__magic_name__: Optional[int] = len(__UpperCAmelCase )
__magic_name__: str = [[False] * (required_sum + 1) for _ in ra... | 96 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 0 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 97 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowercase : list, lowercase : int ) -> List[Any]:
"""simple docstring"""
if len(lowercase ) <= 1 or n <= 1:
return
insert_next(lowercase, n - 1 )
rec_insertion_sort(low... | 98 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 0 |
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.numpy as jnp
... | 99 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 0 |
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> Tuple:
SCREAMING_SNAKE_CASE__ = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE__ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
... | 100 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 0 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ : List[str] =True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ : Union[str, Any] =False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('... | 101 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 0 |
"""simple docstring"""
import qiskit
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
... | 102 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 0 |
"""simple docstring"""
snake_case = 6_5_5_2_1
def snake_case ( lowerCAmelCase_ ) -> int:
_snake_case = 1
_snake_case = 0
for plain_chr in plain_text:
_snake_case = (a + ord(lowerCAmelCase_ )) % MOD_ADLER
_snake_case = ... | 103 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = ... | 104 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLay... | 105 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 0 |
def lowerCamelCase_ ( lowerCAmelCase__ : list[int] ) -> int:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(lowerCAmelCase__ , (list, tuple) ) or not all(
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) for numb... | 106 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 0 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencod... | 107 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__a: Any = TypeVar('''T''')
class SCREAMING_SNAKE_CASE__ ( Generic[T] ):
'''simple docstring'''
_lowerCamelCase = 42 # Cache store of keys
_lowerCamelCase ... | 108 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __a ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Any ):
'''simple docstring... | 109 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVPr... | 147 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
def lowerCamelCase__ ( a : list[int] , a : list[int] , a : int ) -> Union[str, Any]:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case__ ) )
def lowe... | 395 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_A : Union[str, Any] = logging.get_logger(__... | 427 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path ... | 556 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 579 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase: int ={
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_... | 607 |
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
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from .... | 494 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
__A = JukeboxTokenizer
__A = {
'''artist''': '''Zac Brown Band''',
'''genres''... | 547 |
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 ConfigTester
from .... | 643 | 0 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : float ):
'''simple docstring'''
return 10 - x * x
def lowercase ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ):
'''simple... | 602 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 0 |
from __future__ import annotations
def a__ (__lowercase :list[float] , __lowercase :str ) -> Dict:
print(f"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(snake_case__ ):
print(f"""{i}\t\t{d}""" )
def a__ (__lowercase ... | 206 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRA... | 586 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'Conv... | 147 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase__ ( a ... | 395 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 0 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
_A : List[str] = logging.get_logger(__name__)
_A : Any = R'\n Args:\n inp... | 427 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 0 |
from PIL import Image
def lowerCAmelCase_ (lowerCAmelCase__: Image , lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(lowerCAmelCase__: int )... | 556 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 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... | 579 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase: Dict =HfApi()
lowerCAmelCase: Dict ={}
# fmt: off
lowerCAmelCase: List[str] =torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.... | 607 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
'''simple docstring'''
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_... | 494 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 547 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 0 |
"""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"):
__A : str = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilin... | 602 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a__ (__lowercase :... | 206 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 0 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Dict:
lowercase__: Tuple = tf.convert_to_tensor(snake_case__ )
lowercase__: Optional[Any] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.... | 586 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 0 |
def _lowercase ( a__ : Optional[int] , a__ : Optional[Any] , a__ : List[Any] , a__ : Optional[int] , a__ : Dict , a__ : Dict ) -> List[str]:
"""simple docstring"""
if index == r:
for j in range(snake_case__ ... | 147 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCamelCase__ ( a : Tuple , a : str ) -> Dict:
"""simple docstring"""
a__ :int = int(snake_case__ )
assert noofclusters < len(snake_case__ )
# Find out the ... | 395 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[Any] = ["""image_... | 427 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor... | 556 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
SCREAMING_SNAKE_CASE = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_st... | 579 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
"""simple docstring"""
def __snake_case ( __A ) -> Optional[Any]:
return "".join(chr(ord(snake_case__ ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 607 |
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
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 494 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 0 |
def lowerCAmelCase__ ( a__ = 50 ) ->str:
'''simple docstring'''
_UpperCamelCase = [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] ... | 547 |
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 ConfigTester
from .... | 643 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _a ( metaclass=lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self : Optional[int] , *__UpperCamelC... | 602 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 0 |
from math import pow, sqrt
def a__ (*__lowercase :float ) -> int:
_A : Any = len(snake_case__ ) > 0 and all(value > 0.0 for value in values )
return result
def a__ (__lowercase :float , __lowercase :float ) -> Tuple:
return (
... | 206 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 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
__A = logging.get_logger(__name__)
__A ... | 586 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 0 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils... | 147 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 395 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def UpperCamelCase_ ( snake_case_ : list[Any] ) -> int:
'''simple docstring'''
create_state_space_tree(snake_case__ , [] , 0 )
def UpperCamelCase_ ( snake_case... | 427 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 0 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
a : Any = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
... | 556 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase ( lowercase__ ):
... | 579 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
"""simple docstring"""
lowerCAmelCase: Tuple ={
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def __snake_case ( __A ,__A ,__A ) -> str:
lowerc... | 607 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def __UpperCAmelCase ( a_: List[Any], a_: List[Any] ):
_UpperCAmelCase : int = k_size //... | 494 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_... | 547 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 0 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : bool = False ):
'''simple docstring'''
_UpperCAmelCase = {i: [] for i in rang... | 602 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaF... | 206 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 0 |
"""simple docstring"""
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_attentio... | 586 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _lowercase ( a__ : Any , a__ : Union[str, Any] , a__ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = AutoConfig.... | 147 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class lowerCAmelCase_ ( _a):
... | 395 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def a ( self : List[str] ) -> Tuple:
__lowerCAmelCase = [10, 20, 30, 40, 50, 60]
... | 427 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 556 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
from random import randint, random
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = 5 , ) -> Any:
UpperCAmelCase_ ... | 579 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
"""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_tes... | 607 |
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
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
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