code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import os
def _lowerCAmelCase ( ):
with open(os.path.dirname(A__ ) + '/p022_names.txt' ) as file:
lowercase__ = str(file.readlines()[0] )
lowercase__ = names.replace('"' , '' ).split(',' )
names.sort()
lowercase__ ... | 702 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__( unittest.TestCase , lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"... | 642 | 0 |
from __future__ import annotations
a__ : Tuple = [True] * 1_00_00_01
a__ : Tuple = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
a__ : Optional[int] = False
i += 1
def _lowerCAmelCase ... | 703 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : List[Any] ... | 642 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int... | 704 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCAmelCase ( A__ ):
lowercase__ = prime_factors(A__ )
if is_square_free(A__ ):
return -1 if len(A__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doc... | 642 | 0 |
import math
def _lowerCAmelCase ( A__ ):
return math.sqrt(A__ ) * math.sqrt(A__ ) == num
def _lowerCAmelCase ( A__ ):
lowercase__ = 0
lowercase__ = n
while left <= right:
lowercase__ = (left + right) // 2
if mid**2 == n:
... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[str] = logging.get_logger(__name__)
a__ : List[Any] = {
"microsoft/focalnet-tiny": "https:/... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Any = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 706 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : Dict = {
"vocab_file": "vocab.json",
"merges_fil... | 642 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _lowerCAmelCase ( A__ ):
lowercase__ = 384
lowercase... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 642 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _lowerCAmelCase ( A__ , A__ ):
lowercase__ = F'''{sampling_rate}'''
lowercase__ = '1'
lowercase__ = 'f32le'
lowercase__ = [
'ffmpe... | 708 |
import heapq
import sys
import numpy as np
a__ : Dict = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : List[str]) -> Any:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
def ... | 642 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected' , [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
({'num_shar... | 709 |
import math
import sys
def _lowerCAmelCase ( A__ ):
lowercase__ = ''
try:
with open(A__ , 'rb' ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = F'''{dat:08b}'''
r... | 642 | 0 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Union[str, Any] = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.c... | 710 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int ... | 642 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 711 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a__ : int = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and M... | 642 | 0 |
# Imports
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase : Dict=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[str]=None , lowerCAmelCase :... | 712 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...te... | 642 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : str = (DDIMParallelScheduler,)
A : Any = (("eta", 0.0), ("num_inference_step... | 713 |
def _lowerCAmelCase ( A__ , A__ , A__ ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 642 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembe... | 714 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if b == 0:
return (1, 0)
((lowercase__), (lowercase__)) = extended_euclid(A__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _lowerCAmelCase ( A__ , A__... | 642 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _lowerCAmelCase ( A__ ):
lowercase__ = {}
lowercase__ = job['started_at']
lowercase__ = job['completed_at']
lowercase__ = date_parser.parse(A__ )
lowe... | 715 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
"google/umt5-small": "https://huggingface.co/google... | 642 | 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__( lowerCamelCase ):
'''simple docs... | 716 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 642 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueError('partitions can not > number_of_bytes!' )
lowercase__ =... | 717 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase : str) -> Optional[int]:
"""simple docstring"""
lowercase__ ... | 642 | 0 |
def _lowerCAmelCase ( A__ , A__ , A__ , A__ ):
if height >= 1:
move_tower(height - 1 , A__ , A__ , A__ )
move_disk(A__ , A__ )
move_tower(height - 1 , A__ , A__ , A__ )
def _lowerCAmelCase ( A... | 718 |
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_bart import BartTokenizer
a__ ... | 642 | 0 |
from pathlib import Path
import fire
def _lowerCAmelCase ( A__ , A__ , A__ ):
lowercase__ = Path(A__ )
lowercase__ = Path(A__ )
dest_dir.mkdir(exist_ok=A__ )
for path in src_dir.iterdir():
lowercase__ = [x.rstrip() for x in list(path.open().r... | 719 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : str = (DDIMParallelScheduler,)
A : Any = (("eta", 0.0), ("num_inference_step... | 642 | 0 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_... | 720 |
import cva
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase : float , lowerCAmelCase : int) -> Dict:
"""simple docstring"""
if k in (0.04, 0.06):
lowercase__ ... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[str] = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"conv... | 721 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
... | 642 | 0 |
def _lowerCAmelCase ( A__ ):
if not isinstance(A__ , A__ ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
lowercase__ = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through e... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
tr... | 642 | 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_bart import BartTokenizer
a__ ... | 701 |
# Imports
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase : Dict=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[str]=None , lowerCAmelCase :... | 642 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Option... | 702 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__( unittest.TestCase , lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"... | 642 | 0 |
import argparse
import struct
import unittest
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase : bytes) -> None:
"""simple docstring"""
lowercase__ = data
# Initialize hash values
... | 703 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : List[Any] ... | 642 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTok... | 704 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCAmelCase ( A__ ):
lowercase__ = prime_factors(A__ )
if is_square_free(A__ ):
return -1 if len(A__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doc... | 642 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import ... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[str] = logging.get_logger(__name__)
a__ : List[Any] = {
"microsoft/focalnet-tiny": "https:/... | 642 | 0 |
import functools
def _lowerCAmelCase ( A__ , A__ ):
lowercase__ = len(A__ )
lowercase__ = len(A__ )
@functools.cache
def min_distance(A__ , A__ ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= ... | 706 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : Dict = {
"vocab_file": "vocab.json",
"merges_fil... | 642 | 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 ( A__ , A__ , A__ ):
lowercase__ = 1.5
lowercase__ = int(factor * n... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 642 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_avai... | 708 |
import heapq
import sys
import numpy as np
a__ : Dict = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : List[str]) -> Any:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
def ... | 642 | 0 |
from __future__ import annotations
import math
def _lowerCAmelCase ( A__ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 709 |
import math
import sys
def _lowerCAmelCase ( A__ ):
lowercase__ = ''
try:
with open(A__ , 'rb' ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = F'''{dat:08b}'''
r... | 642 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (... | 710 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int ... | 642 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ : Union[str, Any] = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderC... | 711 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a__ : int = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and M... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : int = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:... | 712 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...te... | 642 | 0 |
def _lowerCAmelCase ( A__ ):
lowercase__ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase__ = set()
return any(
node not in visited and depth_first_search(A__ , A__ , A__ , A__ )
for no... | 713 |
def _lowerCAmelCase ( A__ , A__ , A__ ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 642 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( A__ ):
lowercase__ = [True] * limit
lowercase__ = False
lowercase__ = False
lowercase__ = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
lowercase__ = i * 2
... | 714 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if b == 0:
return (1, 0)
((lowercase__), (lowercase__)) = extended_euclid(A__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _lowerCAmelCase ( A__ , A__... | 642 | 0 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is... | 715 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
"google/umt5-small": "https://huggingface.co/google... | 642 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCAmelCase__:
'''simple docstring'''
@property
def UpperCAmelCase ... | 716 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 642 | 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__ : int = logging.get_logger(__name__)
a__ : Tuple = {
"google/mobilenet_v... | 717 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase : str) -> Optional[int]:
"""simple docstring"""
lowercase__ ... | 642 | 0 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
dep... | 718 |
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_bart import BartTokenizer
a__ ... | 642 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[str] = logging.get_logger(__name__)
a__ : List[Any] = {
"microsoft/focalnet-tiny": "https:/... | 719 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : str = (DDIMParallelScheduler,)
A : Any = (("eta", 0.0), ("num_inference_step... | 642 | 0 |
def _lowerCAmelCase ( A__ , A__ ):
while second != 0:
lowercase__ = first & second
first ^= second
lowercase__ = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ : int = ... | 720 |
import cva
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase : float , lowerCAmelCase : int) -> Dict:
"""simple docstring"""
if k in (0.04, 0.06):
lowercase__ ... | 642 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( A__ , A__ , A__ ):
# Initialise PyTorch model
lowercase__ = RemBertConfig... | 721 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
... | 642 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
tr... | 642 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRCont... | 701 |
# Imports
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase : Dict=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[str]=None , lowerCAmelCase :... | 642 | 0 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
a__ : Optional[int] = ""
a__ : Union[str, Any] = ""
a__ : str = ""
a__ : Optional[Any] = ""
def _lowerCAmelCase ( A__ ):
# authorize twitter, initialize tweepy
l... | 702 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__( unittest.TestCase , lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"... | 642 | 0 |
def _lowerCAmelCase ( A__ ):
lowercase__ = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
lowercase__ = hex_num[0] == '-'
if is_negative:
lowercase__ = hex_num[1:]
try:
lowercase__ ... | 703 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : List[Any] ... | 642 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, Pr... | 704 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCAmelCase ( A__ ):
lowercase__ = prime_factors(A__ )
if is_square_free(A__ ):
return -1 if len(A__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doc... | 642 | 0 |
def _lowerCAmelCase ( A__ , A__ , A__ ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[str] = logging.get_logger(__name__)
a__ : List[Any] = {
"microsoft/focalnet-tiny": "https:/... | 642 | 0 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import ... | 706 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : Dict = {
"vocab_file": "vocab.json",
"merges_fil... | 642 | 0 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : List[Any] ... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 642 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
a__ : str = input("Enter image url: ").strip()
print(F'''Downloading image from {url} ...''')
a__ : Tuple = BeautifulSoup(requests.get(url).content, "html.parser")
... | 708 |
import heapq
import sys
import numpy as np
a__ : Dict = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : List[str]) -> Any:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
def ... | 642 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a__ : List[str] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _lowerCAmelCase ( A__ , A_... | 709 |
import math
import sys
def _lowerCAmelCase ( A__ ):
lowercase__ = ''
try:
with open(A__ , 'rb' ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = F'''{dat:08b}'''
r... | 642 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _lowerCAmelCase ( A__ , A__=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return ".".jo... | 710 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int ... | 642 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resol... | 711 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a__ : int = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and M... | 642 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 712 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...te... | 642 | 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 required by ... | 713 |
def _lowerCAmelCase ( A__ , A__ , A__ ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 642 | 0 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 714 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if b == 0:
return (1, 0)
((lowercase__), (lowercase__)) = extended_euclid(A__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _lowerCAmelCase ( A__ , A__... | 642 | 0 |
import heapq
import sys
import numpy as np
a__ = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : List[str]) -> Any:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
def UpperCAmelCa... | 715 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
"google/umt5-small": "https://huggingface.co/google... | 642 | 0 |
def _lowerCAmelCase ( A__ ):
if isinstance(A__ , A__ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(A__ , A__ ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if num == 0:
... | 716 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 642 | 0 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from .... | 717 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase : str) -> Optional[int]:
"""simple docstring"""
lowercase__ ... | 642 | 0 |
from math import ceil
def _lowerCAmelCase ( A__ , A__ ):
lowercase__ = list(range(0 , A__ ) )
lowercase__ = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
lowercase__ = []
for i in device_map_blocks:
... | 718 |
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_bart import BartTokenizer
a__ ... | 642 | 0 |
from __future__ import annotations
a__ : str = tuple[int, int, int]
a__ : int = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
a__ : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection -----------... | 719 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : str = (DDIMParallelScheduler,)
A : Any = (("eta", 0.0), ("num_inference_step... | 642 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : List[Any] = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/goog... | 720 |
import cva
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase : float , lowerCAmelCase : int) -> Dict:
"""simple docstring"""
if k in (0.04, 0.06):
lowercase__ ... | 642 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _lowerCAmelCase ( ):
lowercase__ = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
'path': ['test_1.py', 'test_2.py', 'un... | 721 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfig",
"TableTransformerOnnx... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
tr... | 642 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
def __init__( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
self.test()
def UpperCAmelCase ( ... | 701 |
# Imports
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase : Dict=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[str]=None , lowerCAmelCase :... | 642 | 0 |
'''simple docstring'''
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 trans... | 702 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__( unittest.TestCase , lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"... | 642 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wav... | 703 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : List[Any] ... | 642 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if b == 0:
return (1, 0)
((lowercase__), (lowercase__)) = extended_euclid(A__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _lowerCAmelCase ( A__ , A_... | 704 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCAmelCase ( A__ ):
lowercase__ = prime_factors(A__ )
if is_square_free(A__ ):
return -1 if len(A__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doc... | 642 | 0 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def _lowerCAmelCase ( A__ ... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[str] = logging.get_logger(__name__)
a__ : List[Any] = {
"microsoft/focalnet-tiny": "https:/... | 642 | 0 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowerCAmelCase ( A__ = "laptop" ):
lowercase__ = F'''https://www.amazon.in/laptop/s?k={product}'''
lowercase__ = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_6... | 706 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : Dict = {
"vocab_file": "vocab.json",
"merges_fil... | 642 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a__ : Dict = datasets.logging.get_logger(__name__)
a__ : Tuple = "\\n@InProceedings{moosavi2019minimum,\n author = { ... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 642 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
a__ : Union[str, Any] = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[int] , lowerCAmelCase : set[int] , lowerCAmelCase : Mapping... | 708 |
import heapq
import sys
import numpy as np
a__ : Dict = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : List[str]) -> Any:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
def ... | 642 | 0 |
import requests
def _lowerCAmelCase ( A__ , A__ ):
lowercase__ = {'Content-Type': 'application/json'}
lowercase__ = requests.post(A__ , json={'text': message_body} , headers=A__ )
if response.status_code != 200:
lowercase__ = (
... | 709 |
import math
import sys
def _lowerCAmelCase ( A__ ):
lowercase__ = ''
try:
with open(A__ , 'rb' ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = F'''{dat:08b}'''
r... | 642 | 0 |
def _lowerCAmelCase ( ):
for n in range(1 , 1_000_000 ):
yield n * (n + 1) // 2
def _lowerCAmelCase ( A__ ):
lowercase__ = 1
lowercase__ = 2
while i * i <= n:
lowercase__ = 0
while n % i == 0:
n //=... | 710 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int ... | 642 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__( lowerCamelCase , unittest.TestCase ):
'''simple d... | 711 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a__ : int = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and M... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 712 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...te... | 642 | 0 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _lowerCAmelCase ( A__ ):
lowercase__ = [
... | 713 |
def _lowerCAmelCase ( A__ , A__ , A__ ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 642 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.ut... | 714 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if b == 0:
return (1, 0)
((lowercase__), (lowercase__)) = extended_euclid(A__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _lowerCAmelCase ( A__ , A__... | 642 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase__( tf.keras.optimizers.schedules.LearningRateSchedule ):... | 715 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
"google/umt5-small": "https://huggingface.co/google... | 642 | 0 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_... | 716 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 642 | 0 |
import heapq
def _lowerCAmelCase ( A__ ):
lowercase__ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to bui... | 717 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase : str) -> Optional[int]:
"""simple docstring"""
lowercase__ ... | 642 | 0 |
from itertools import product
def _lowerCAmelCase ( A__ , A__ ):
lowercase__ = sides_number
lowercase__ = max_face_number * dice_number
lowercase__ = [0] * (max_total + 1)
lowercase__ = 1
lowercase__ = range(A__ , max_face_number + 1 )... | 718 |
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_bart import BartTokenizer
a__ ... | 642 | 0 |
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_pipel... | 719 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : str = (DDIMParallelScheduler,)
A : Any = (("eta", 0.0), ("num_inference_step... | 642 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 720 |
import cva
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase : float , lowerCAmelCase : int) -> Dict:
"""simple docstring"""
if k in (0.04, 0.06):
lowercase__ ... | 642 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixi... | 721 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDepe... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
tr... | 642 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__( unittest.TestCase , lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"... | 701 |
# Imports
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase : Dict=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[str]=None , lowerCAmelCase :... | 642 | 0 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
a__ : Optional[Any] = "\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 classificati... | 702 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__( unittest.TestCase , lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"... | 642 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : Dict = logging.get_logger(__name__)
a__ : Union[str, Any] = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/reso... | 703 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : List[Any] ... | 642 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
a__ : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
def _lowerCAmelCase ( A__ )... | 704 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCAmelCase ( A__ ):
lowercase__ = prime_factors(A__ )
if is_square_free(A__ ):
return -1 if len(A__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doc... | 642 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a__ : List[str] = logging.get_logger(__name__)
def _lowerCAmelCase ( A__ ):
if isinstance(A__ , np.ndarray ):
return list(tensor.shape )
lowercase_... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[str] = logging.get_logger(__name__)
a__ : List[Any] = {
"microsoft/focalnet-tiny": "https:/... | 642 | 0 |
import fire
from utils import calculate_rouge, save_json
def _lowerCAmelCase ( A__ , A__ , A__=None , **A__ ):
lowercase__ = [x.strip() for x in open(A__ ).readlines()]
lowercase__ = [x.strip() for x in open(A__ ).readlines()][: len(A__ )]
lowercase__ = ... | 706 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : Dict = {
"vocab_file": "vocab.json",
"merges_fil... | 642 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 642 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Tuple , lowerCAmelCase : int) -> None:
"""simple docstring"""
lowercase__ = value
lowercase__ =... | 708 |
import heapq
import sys
import numpy as np
a__ : Dict = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : List[str]) -> Any:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
def ... | 642 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/efficientformer-l1-300/r... | 709 |
import math
import sys
def _lowerCAmelCase ( A__ ):
lowercase__ = ''
try:
with open(A__ , 'rb' ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = F'''{dat:08b}'''
r... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a__ : Dict = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:
... | 710 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int ... | 642 | 0 |
'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
a__ : Dict = "scheduler_config.json"
class Uppe... | 711 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a__ : int = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and M... | 642 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
a__ : List[Any] = {
"facebook/maskformer-swin-base-ade": (
"https:/... | 712 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...te... | 642 | 0 |
import math
import sys
import cva
import numpy as np
def _lowerCAmelCase ( A__ , A__ ):
# For applying gaussian function for each element in matrix.
lowercase__ = math.sqrt(A__ )
lowercase__ = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigm... | 713 |
def _lowerCAmelCase ( A__ , A__ , A__ ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 642 | 0 |
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