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 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class a :
def __init__( self : List[str] , lowercase_ : list[tuple[float, float]] ):
snake_case_ = list_of_points
# Degree dete... | 640 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DD... | 640 |
'''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... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import T... | 640 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 1 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=1024, __UpperCAmelCase=1024, __UpperCA... | 640 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class a ( _lowerCamelCase ):
pass
class a :
def __init__( self : List[Any] , lowercase_ : Any ):
snake_case_ = data
snake_case_ ... | 640 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
a : List[Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class a :
snake... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Union[str, Any] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_... | 640 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
return "".join(sorted(__UpperCAmelCase ) )
def __magic_name__ ... | 640 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 1 |
'''simple docstring'''
from string import ascii_uppercase
a : int = {char: i for i, char in enumerate(ascii_uppercase)}
a : Dict = dict(enumerate(ascii_uppercase))
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> str:
'''s... | 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 |
'''simple docstring'''
import json
import sys
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
with open(__UpperCAmelCase, encoding='''utf-8''' ) as f:
snake_case_ = json.load(__UpperCAmelCase ... | 640 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = ... | 640 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
snake_case_ = 1
snake_case_ = 2
while i * i <= n:
snake_case_ = 0
while n % i == 0:
n //= i
... | 640 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_v... | 640 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 640 | 1 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class a ( unittest.TestCase ):
def A_ ( self : Optional[int] ):
snake_case_ = [10, 20, 30, 40, 50, 60]
snake_case_ = [2, 4, 6, 8, 10, 12]
... | 640 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, t... | 640 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = os.path.join(args.t... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a ( nn.Module ):
def __init__( self : Any , lowercase_ : int = 16 , lowercase_ : int = 88 , ... | 640 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 1 |
def __lowercase ( snake_case ):
"""simple docstring"""
if not head:
return True
# split the list to two parts
__magic_name__ , __magic_name__ :List[str] = head.next, head
while fast and fast.next:
__magic_name__ :str = fast.next.next
... | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner impo... | 1 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 100 ) -> int:
_A = set()
_A = 0
_A = n + 1 # maximum limit
for a in range(2 , _snake_case ):
for b in range(2 , _snake_case ):
_A = a**b # calculates the c... | 2 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fro... | 3 |
'''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... | 640 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils ... | 4 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
... | 5 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.... | 6 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
"""simple docstring"""
a = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingfac... | 7 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
lowercase__ : Any = []
def _lowerCAmelCase ( __snake_case : list[list[int]] , __snake_case : int , __snake_case : int ) -> bool:
for i in range(len(__... | 8 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 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,
t... | 9 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
_lowerCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_lowerCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _snake_case ( __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = True
_UpperCamelCase = []
for ... | 10 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase )... | 11 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
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_diffus... | 12 |
'''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 | 0 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
A__ : int = HfArgumentParser(InitializationArguments)
A__ : Dict = parser.parse_args()
# Load codeparrot tok... | 13 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMa... | 15 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 16 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeni... | 17 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 640 | 0 |
'''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 SPIECE_UNDERLINE, logging
_SCREAMING_SNAKE_CASE = ... | 18 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
"""simple docstring"""
from typing import Any
class _UpperCAmelCase:
def __init__( self , __a) -> List[str]:
'''simple docstring'''
_UpperCamelCase = data
_UpperCamelCase = None
class ... | 19 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 20 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 21 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
'''simple docstring'''
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
fr... | 22 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
# Function to print upper half of diamond (pyramid)
def _snake_case (__lowercase):
for i in range(0 , __lowercase):
for _ in range(0 , n - i - 1): # printing spaces
print(' ' , end='')
for _ in range(0 , i + 1): # printing stars
... | 23 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _UpperCamelCase (_lowerCamelCase : Optional[Any] , _lowerCamelCase : Any , _lowerCamelCase : Optional[int] , _lowerCamelCase ... | 24 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 25 |
'''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... | 640 | 0 |
'''simple docstring'''
import math
__UpperCamelCase = 10
__UpperCamelCase = 7
__UpperCamelCase = BALLS_PER_COLOUR * NUM_COLOURS
def _a ( _lowerCamelCase = 20 ) -> str:
"""simple docstring"""
__snake_case : ... | 26 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
import sys
__A : Union[str, Any] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504... | 27 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCamelCase_ = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that develo... | 28 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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
#
# Unl... | 29 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
import sys
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = len(_lowercase )
UpperCAmelCase_ : Tuple = [[0 for x in range(_lowercase )] for x in range(_lowercase )]
UpperCAmelCase_ : Union[str, Any] = [[... | 30 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 31 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( A_... | 32 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : List[Any] = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLI... | 33 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ = ['''image_processor''', '''tokenizer''']
A_ = '''AutoIm... | 34 |
'''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 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
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 i... | 35 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
from __future__ import annotations
import math
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = size
# approximate the overall size of segment tree with given value
snake_ca... | 36 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import ... | 37 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : Any = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at h... | 38 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowerCAmelCase_ = datasets.logging.get_logger(__name__)
lowerCAmelCase_ = '''\
@InProceedings{moosavi2019minim... | 39 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 640 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase_ :
def __init__( self ) -> str:
UpperCamelCase : List[Any] = {}
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> ... | 40 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
... | 41 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"facebook/deit-base-distilled-patch... | 42 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
return quad(SCREAMING_SNAKE_CASE , 0 , SCREAMING_SNAKE_CASE , args=(SCRE... | 43 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any]=None ):
"""simple docstring"""... | 44 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self :Any , *lowerCamelCase__ ... | 45 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 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
_lowerCAmelCase : List[str] = logging.get_lo... | 46 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
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
SCREAMING_SNAKE_CASE__ = (
'''This metric will be removed ... | 47 |
'''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... | 640 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
UpperCAmelCase__ : List[str] = logging.get_logger(__name__)
UpperCAmelCase__ : Optional[int] = {
"Intel/dpt-large": "https://huggingface.co/... | 48 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowercase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
... | 49 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 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,
)
UpperCamelCase : List[Any] = pytest.mark.integration
@pytest.mark... | 50 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __snake_case ( SCREAMING_SNAKE_CASE_ : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
UpperCAmelCase = Decimal
# Check if the pr... | 51 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (DDPMScheduler,)
... | 52 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snak... | 53 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
... | 54 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {name: getattr(tran... | 55 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImage... | 56 |
'''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 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 57 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:... | 58 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils im... | 59 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
def lowerCamelCase_ ( _UpperCamelCase = 1_000 ) -> int:
"""simple docstring"""
snake_case_ : Tuple = 3
snake_case_ : List[Any] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
... | 60 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
from __future__ import annotations
import math
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all e... | 61 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 640 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
d... | 62 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transforme... | 63 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def A__ ( snake_case_ : Any ):
for param in module.parameters():
SCREAMING_SNAKE_CASE__: str= False
def A__ ( ):
SCREAMING_SNAKE_CASE__: Optional[Any]= '''cuda''' if torch.cuda.is_available()... | 64 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( __lowerCamelCase ):
snake_case_ = (UnCLIPScheduler,)
def __lowercase ( self : Optional[i... | 65 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase_ ( __snake_case , unittest.TestCase ):
_U... | 66 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter... | 67 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
ge... | 68 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
... | 69 |
'''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... | 640 | 0 |
# flake8: noqa
# Lint as: python3
lowerCamelCase : Any = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disabl... | 70 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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... | 71 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : List[Any] = {
'''configuration_cl... | 72 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _snake_case :
def __init__(... | 73 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
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_... | 74 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 0 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class lowerCamelCase_ :
def __init__( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : ... | 75 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase = 2 , __UpperCamelCase = 1 , __UpperCamelCase = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm... | 76 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 77 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelera... | 78 |
'''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 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_... | 79 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
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