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 |
|---|---|---|---|---|
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 643 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 1 |
from torch import nn
def a__ ( snake_case__ : str ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported activation function: {act_fn}''' )
| 643 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a__ ( snake_case__ : bool = True , *snake_case__ : List[Any] , **snake_case__ : Optional[Any] ):
if not is_tqdm_avai... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester... | 643 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : List[Any] = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor... | 643 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CA... | 643 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVe... | 643 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 1 |
import enum
import shutil
import sys
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = shutil.get_terminal_size()
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class _SCREAMING_SNAKE_CASE ( enum.... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config... | 643 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 1 |
from numpy import exp, pi, sqrt
def a__ ( snake_case__ : List[str] , snake_case__ : float = 0.0 , snake_case__ : float = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import docte... | 643 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 1 |
from __future__ import annotations
def a__ ( snake_case__ : list[float] ):
_UpperCAmelCase : Dict = 0.00
_UpperCAmelCase : Dict = 0
for resistor in resistors:
if resistor <= 0:
_UpperCAmelCase : Optional[int] = f'''Resistor a... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeli... | 643 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 1 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def a__ ( snake_case__ : str ... | 643 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
SCREAMING_SNAKE_CASE__ : Tuple = 'http://www.mocksi... | 643 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'C... | 643 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 1 |
from __future__ import annotations
def a__ ( snake_case__ : int , snake_case__ : int ):
_UpperCAmelCase : list[list[int]] = []
create_all_state(1 , snake_case__ , snake_case__ , [] , snake_case__ )
return result
def ... | 643 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 1 |
import os
import numpy
import onnx
def a__ ( snake_case__ : Optional[Any] , snake_case__ : str ):
_UpperCAmelCase : List[Any] = a.name
_UpperCAmelCase : Union[str, Any] = b.name
_UpperCAmelCase : List[Any] = """"""
... | 643 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 1 |
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
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE ( A ):
... | 643 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( snake_case__ : List[str] , snake_case__ : Optional[int] , snake_ca... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a__ ( snake_case__ : str ):
if "cls_token" in name:
_UpperCAmelCase : Dict = name.replace("""cls_token""" , ... | 643 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 643 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 1 |
import math
def a__ ( snake_case__ : int ):
return math.sqrt(snake_case__ ) * math.sqrt(snake_case__ ) == num
def a__ ( snake_case__ : int ):
_UpperCAmelCase : List[Any] = 0
_UpperCAmelCase : List[str] = n
while left ... | 643 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 1 |
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
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 1 |
import torch
from transformers import AutoModel
class _SCREAMING_SNAKE_CASE ( torch.nn.Module ):
def __init__( self , A_="sayef/fsner-bert-base-uncased" ):
super(A_ , self ).__init__()
_UpperCAmelCase : Optional[Any] = AutoModel.from... | 643 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __snake_case( self ):
_UpperCAmelCase : Dict = 10
... | 643 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 1 |
import math
import tensorflow as tf
from packaging import version
def a__ ( snake_case__ : Tuple ):
_UpperCAmelCase : Tuple = tf.convert_to_tensor(snake_case__ )
_UpperCAmelCase : Optional[Any] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ... | 643 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since t... | 643 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class _SCREAMING_SNAKE_CASE :
def __init__( self , A_ , A_ ):
if len(A_ ) != degree + 1:
raise ValueError(
"""The number of coefficients should be equal to the degree... | 643 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 1 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import c... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 1 |
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__ : Any = logging.get_lo... | 643 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 1 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.be... | 643 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def a__ ( snake_case__ : Union[dict, list, tuple, torch.Tensor] ):
_UpperCAme... | 643 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 1 |
from __future__ import annotations
def a__ ( snake_case__ : int = 4 ):
_UpperCAmelCase : Union[str, Any] = abs(snake_case__ ) or 4
return [[1 + x + y * row_size for x in range(snake_case__ )] for y in range(snake_case__ )]
def a__ ( snake_case__ ... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 1 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.... | 643 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForm... | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
def _A ( _lowercase = 1_00_00_00 ) -> int:
"""simple docstring"""
__UpperCamelCase = set(range(3 , _lowercase , 2 ) )
primes.add(2 )
for p in range(3 , _lowercase , 2 ):
if p not in primes:
continue
primes.differen... | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFA... | 2 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : List[str] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( sna... | 3 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable... | 4 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/re... | 5 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
) | 6 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerca... | 7 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase__ : int = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase__ : List[str] = (4, 6, 2, 0, 8, 10, 3, -2)
@datac... | 8 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 0 |
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE__ = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 9 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _snake_case ( __snake_case ):
# A local function to see if a dot lands in the circle.
def is_in_circle(__snake_case , __snake_case ) -> bool:
_UpperCamelCas... | 10 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 0 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in rang... | 11 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def UpperCamelCase ( ... | 12 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCAmelCase__ ( UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] ) -> List[str]:
__lowerCamelCase : str = int(UpperCA... | 13 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 0 |
from 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__ = {
'''microsoft/b... | 14 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
A : Optional[Any] = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', '... | 15 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sched... | 16 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import ... | 17 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __a(SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_SNAKE_CASE_ : list[int] , ... | 18 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 0 |
"""simple docstring"""
import 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
_a = lo... | 19 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 0 |
import warnings
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
_lowerCAmelCase: Optional[int] = logging.get_logger(__name__)
_lowerCAmelC... | 20 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 21 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 0 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not u... | 22 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ():
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT):
... | 23 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 24 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowerCamelCase__ ( _a):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().... | 25 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def _a ( _lowerCamelCase , _lowerCamelCase ) -> str:
"""simple docstring"""
__snake_case : str = int(_lowerCamelC... | 26 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
from collections import defaultdict
class lowerCamelCase:
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ ):
_A = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
... | 27 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_l... | 28 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_int... | 29 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 0 |
import 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 lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAme... | 30 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 0 |
import unittest
import numpy as np
import requests
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_image_inputs
if is_torch_available(... | 31 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase... | 32 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 33 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'nielsr/canine-s': 2048,
}
# Unicode de... | 34 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 0 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a ( A__ ) -> List[Any]:
'''simple docstring'''
return 1 / (1 + np.exp(-z ))
def ... | 35 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 0 |
import os
import sys
__lowercase : Union[str, Any] = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceCl... | 36 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 0 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.pa... | 37 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 38 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 39 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 0 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self ) -> Tuple:
UpperCamelCase : Any = ... | 40 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 0 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
c... | 41 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://huggingface.co/ucl... | 42 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 0 |
import 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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import float... | 43 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
... | 44 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCamelCase = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def A ( l... | 45 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 0 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : str = first_str.lower().strip()
_lowerCamelCase : Any = second_str.low... | 46 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTok... | 47 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
'''simple docstring'''
def A ( UpperCamelCase_ : int ) -> str:
'''simple docstring'''
lowerCAmelCase__ = int(UpperCamelCase_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(UpperCamelCase_ )
lowerCAmelCase__ ,lowerCAmelCase__... | 48 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowercase : str = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from... | 49 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 0 |
'''simple docstring'''
import 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,
EulerAncestralDisc... | 50 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
requir... | 51 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __A ( a_ :np.ndarray) -> np.ndarray:
__a , __a , __a : Dict = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7... | 52 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_confi... | 53 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[Any] =logging.get_logger(__name__)
__lowercase : Optional[int] ={"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class A ( __lowercase ... | 54 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 0 |
def UpperCAmelCase ( a_ = 1_0 , a_ = 1_0_0_0 , a_ = True ) -> int:
"""simple docstring"""
assert (
isinstance(a_ , a_ )
and isinstance(a_ , a_ )
and isinstance(a_ , a_ )
), "Invalid type of value(s) specified to functi... | 55 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 0 |
'''simple docstring'''
import os
def _a () -> List[str]:
"""simple docstring"""
with open(os.path.dirname(lowercase__ ) + '/p022_names.txt' ) as file:
__snake_case = str(file.readlines()[0] )
__snake_case = names.replace... | 56 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 0 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
A_ : Optional[Any] = False
try... | 57 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 0 |
"""simple docstring"""
import 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 ( __UpperCamelCase :... | 58 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 0 |
from math import pow
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , ) -> tuple[int, int]:
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution... | 59 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
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