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 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
fro... | 116 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ (UpperCamelCase_ ):
lowercase_ : Optional[Any] = "Speech2TextFeatureExtractor"
lowercase_ : int = "Speech2TextTokenizer"
def... | 615 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 7 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 0 |
_lowerCamelCase : Union[str, Any] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=... | 663 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImageP... | 549 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 0 |
"""simple docstring"""
lowerCamelCase_ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
... | 498 | import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class snake_case__ ( UpperCamelCase_ ):
A__ = ['''image_processor''', '''feature_extractor''']
A__ = '''TvltImageProcessor'''
A__ = '''TvltFeatureExtractor'''
def __init__( self : int , __a ... | 286 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( A__ : str , A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : int = get_failure_array(lowercase__ )
# 2) Step through text searching for... | 275 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Any:
lowercase : Dict = [
'encoder.version',
'decoder.version',
'model.en... | 336 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 0 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase (_snake_case ,_snake_case ,_snake_case ) -> List[Any]:
'''simple docstr... | 505 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase_ ( UpperCamelCase_ ... | 364 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 0 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase : Tuple = HfApi()
lowercase : Union[str, Any] = {}
# fmt: off
lowercase : str = torch.tensor([
... | 116 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 0 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def a_ ( __lowerCAmelCase , __lowerCAmelC... | 615 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 0 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class lowercase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict ):
super().__init__()
... | 7 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 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... | 663 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _low... | 549 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
"""simple docstring"""
def __lowerCamelCase ( a_ : str , a_ : bool = False ) -> List[str]:
if not isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE :Any = f'''Expected string as input, found {type(lowercase__ )}'''
... | 498 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Tuple = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor... | 286 | 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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 0 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
__A : Optional[int] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : Tupl... | 275 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stable... | 85 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Tenso... | 336 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 0 |
"""simple docstring"""
import math
def lowercase (_snake_case ,_snake_case ) -> int:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > ... | 505 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperC... | 364 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 0 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase = 10 , __lowercase = 22 ):
snake_case : List[Any] = range(1 , lowercase__ )
snake_case : List[Any] = range(1 , lowercase__ )
return sum(
... | 116 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 0 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models... | 615 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 0 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
f... | 7 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"ut/deta": "https://huggingface.co... | 663 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a ( metaclass=UpperCamelCase_ ):
SCREAMING_SNAKE_CASE : Dict = ["""torch""", """torchsde"""]
def __init__( self : Tuple , *__SCREAMING_SNAKE_CASE : Any ... | 549 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 0 |
"""simple docstring"""
def __lowerCamelCase ( a_ : str ) -> Tuple:
assert column_title.isupper()
__SCREAMING_SNAKE_CASE :str = 0
__SCREAMING_SNAKE_CASE :List[Any] = len(lowercase__ ) - 1
__SCREAMING_SNAKE_CASE :str ... | 498 | import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 0 |
'''simple docstring'''
import functools
from typing import Any
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : list[str] ) -> List[str]:
if not isinstance(lowercase__ ,lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be no... | 286 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( A__ : list[float] ):
'''simple docstring'''
if len(lowercase__ ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
i... | 275 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be ... | 336 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 0 |
"""simple docstring"""
_A = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_A = [
999,
... | 505 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 0 |
import random
class UpperCamelCase_ :
@staticmethod
def __a ( lowerCamelCase : str ):
lowerCamelCase_ : str = [ord(a_ ) for i in text]
lowerCamelCase_ : List[str] = []
lowerCamelCase_ : A... | 364 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 0 |
'''simple docstring'''
import requests
lowercase : Union[str, Any] = "YOUR API KEY"
def lowerCamelCase__ ( __lowercase , __lowercase = giphy_api_key ):
snake_case : Optional[int] = '+'.join(query.split() )
snake_... | 116 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 0 |
# Function to print upper half of diamond (pyramid)
def a_ ( __lowerCAmelCase ):
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... | 615 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availab... | 7 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_ut... | 663 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 549 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( a_ : list[int] ) -> Any: # This function is recursive
__SCREAMING_SNAKE_CASE :str = len(lowercase__ )
# If the array contains only one element, we return it (it's the s... | 498 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 0 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class snake_case__ ( yaml.SafeLoader ):
def A_ ( self : Any , __a : Dict ) -> Any:
'''simple docstring'''
... | 286 | 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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Any = logging.get_logger(__name__)
__a :List[str] = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre': 'https:... | 86 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 1 |
from __future__ import annotations
from typing import TypedDict
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : str
_lowerCamelCase : int
def __snake_case ( __UpperCamelCase : str ):
"""simple docs... | 86 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 1 |
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __snake_case ( ):
"""simple docstring"""
assert nand_gate(0 ,0 ) == 1
assert na... | 86 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _a ( yaml.SafeLoader ):
"""simple docstring"""
def __A ( self : Optional[Any] , UpperCAmelCase : int ):
A_ = [s... | 86 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 86 | 1 |
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [[0 for _ in range(__UpperCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
A_ = 1
for n in range(m + 1 ):
for k in range(... | 86 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 1 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable... | 86 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
def decorator(__UpperCamelCase : Union[str, Any] ):
A_ = getattr(__UpperCamelCase ,"handle_key" ,[] )
... | 86 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [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... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a :Any = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
... | 86 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 | 1 |
from collections import deque
from .hash_table import HashTable
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCase : Dict , **UpperCAmelCase : Optional[int] ):
super().__in... | 86 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__a :int = '\\n\n'
__a :Any = '\nPerplexity (PPL) is one of the most common metrics for evaluating langua... | 86 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFas... | 86 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExt... | 86 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __snake_case ( __UpperCamelCase : dict ,__UpperCamelCase : str ,__UpperCamelCase : set ,__UpperCamelCase : set ,__UpperCamelCase : dict ,__UpperCamelCase : dict ,__UpperCamelCase : PriorityQueue ,__U... | 86 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneCon... | 86 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 | 1 |
from typing import Any
import numpy as np
def __snake_case ( __UpperCamelCase : np.ndarray ):
"""simple docstring"""
return np.array_equal(__UpperCamelCase ,matrix.conjugate().T )
def __snake_case ( __UpperCamelCase : np.ndarray ,__UpperCamelCase : np... | 86 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 86 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __snake_case ( __UpperCamelCase : ndarray ):
"""simple docstring"""
return np.dot(__UpperCamelCase ,__UpperCamelCase )
class _a :
"""s... | 86 |
# 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
if... | 86 | 1 |
from __future__ import annotations
__a :str = '#'
class _a :
"""simple docstring"""
def __init__( self : int ):
A_ = {}
def __A ( self : int , UpperCAmelCase : str ):
A_ ... | 86 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
... | 86 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __snake_case ( ):
... | 86 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 1 |
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise TypeError("Input value must be a 'int' t... | 86 |
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Dict ):
A_ = None
A_ = None
A_ ... | 86 | 1 |
def __snake_case ( __UpperCamelCase : list[int] ):
"""simple docstring"""
A_ = []
if len(__UpperCamelCase ) == 1:
return [nums.copy()]
for _ in range(len(__UpperCamelCase ) ):
A_ = nums.pop(0 )
A... | 86 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :int = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
... | 86 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__a :List[Any] = 'src/transformers'
# This is to make sure ... | 86 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 1 |
import re
from filelock import FileLock
try:
import nltk
__a :List[Any] = True
except (ImportError, ModuleNotFoundError):
__a :List[str] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def ... | 86 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__a :Any = ''
__a :int = ''
__a :str = ''
__a :Optional[Any] = 1 # (0 is vertical, 1 is horizontal)
def __snake_case ( ):
"""simple docstring"""
... | 86 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 1 |
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if collection == []:
return []
# get some information about the collection
A_ = len(__UpperCamelCase )
A_ = max(__UpperCamelCase )
A_ ... | 86 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 86 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __snake_case ( __UpperCamelCase : Optional[int] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
A_ ... | 86 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 1 |
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 a... | 86 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 1 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__a :Any = logging.get_logger(__name__)
__a :Tuple = 'T5Config'
class _a ( snake_case_ ):
"""simp... | 86 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [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... | 86 | 1 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 86 | 1 |
from typing import List
import numpy as np
def __snake_case ( __UpperCamelCase : dict ):
"""simple docstring"""
A_ = {key: len(__UpperCamelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCamelCase ,__UpperCamelCase )}
if len(set(li... | 86 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a :Optional[int] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
i... | 86 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a :Union[str, Any] = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViT... | 86 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 86 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExt... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :Optional[Any] = {
'configuration_bert'... | 86 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available(... | 86 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
__a :List[str] = logging.get_logger(__name__)
__a :Dict = {name: getattr(transformers, name + 'Fast') for name in... | 86 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Tuple = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokenization_m2m_100': ['... | 86 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 |
# 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
if... | 86 | 1 |
from copy import deepcopy
class _a :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase : list[int] | None = None , UpperCAmelCase : int | None = None ):
if arr is None and size is not None:
... | 86 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
... | 86 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
... | 86 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 1 |
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(__UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 86 |
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Dict ):
A_ = None
A_ = None
A_ ... | 86 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = (IPNDMScheduler,)
_lowerCamelCase : List[st... | 86 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils im... | 86 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 1 |
__a :List[str] = 6_5521
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = 1
A_ = 0
for plain_chr in plain_text:
A_ = (a + ord(__UpperCamelCase )) % MOD_ADLER
A_ = ... | 86 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__a :str = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and ... | 86 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 1 |
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ):
"""simple docstring"""
A_ = len(__UpperCamelCase )
A_ = len(__UpperCamelCase )
A_ = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A_ ... | 86 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 1 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 86 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if ... | 86 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 1 |
from __future__ import annotations
from typing import Any
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : float = 0 ):
A_ ... | 86 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__a :List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 86 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [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... | 86 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :List[str] = logging.get_logger(__name__)
__a :List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
)... | 86 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 86 | 1 |
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 import load_imag... | 86 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 | 1 |
from PIL import Image
def __snake_case ( __UpperCamelCase : Image ,__UpperCamelCase : float ):
"""simple docstring"""
def brightness(__UpperCamelCase : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
rais... | 86 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 1 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [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... | 86 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a :Any = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise Option... | 86 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExt... | 86 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import... | 86 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,**__UpperCamelCase : str ):
"""simple docstring"""
A_ = AutoConfig.from_pretrained(__UpperCamelCase ,*... | 86 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 | 1 |
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