

import pytest

import datasets


# Import fixture modules as plugins
_UpperCamelCase 			=		["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]







def 		_lowercase (     lowercase__   ,	lowercase__						):
      # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
      for item in items:
            if any(marker in item.keywords for marker in ['''integration''', '''unit''']						):
                  continue
            item.add_marker(pytest.mark.unit						)







def 		_lowercase (     lowercase__						):
      config.addinivalue_line('''markers'''   ,	'''torchaudio_latest: mark test to run with torchaudio>=0.12'''						)







@pytest.fixture(autouse=lowercase__						)
def 		_lowercase (     lowercase__   ,	lowercase__						):
      # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
      __lowerCAmelCase					:						int					=					tmp_path_factory.getbasetemp() / '''cache'''
      __lowerCAmelCase					:						Optional[Any]					=					test_hf_cache_home / '''datasets'''
      __lowerCAmelCase					:						Tuple					=					test_hf_cache_home / '''metrics'''
      __lowerCAmelCase					:						Optional[int]					=					test_hf_cache_home / '''modules'''
      monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''   ,	str(lowercase__						)						)
      monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''   ,	str(lowercase__						)						)
      monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''   ,	str(lowercase__						)						)
      __lowerCAmelCase					:						List[str]					=					test_hf_datasets_cache / '''downloads'''
      monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''   ,	str(lowercase__						)						)
      __lowerCAmelCase					:						List[str]					=					test_hf_datasets_cache / '''downloads''' / '''extracted'''
      monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''   ,	str(lowercase__						)						)







@pytest.fixture(autouse=lowercase__   ,	scope='''session'''						)
def 		_lowercase (     ):
      datasets.disable_progress_bar()







@pytest.fixture(autouse=lowercase__						)
def 		_lowercase (     lowercase__						):
      # don't take tests into account when counting downloads
      monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''   ,	lowercase__						)







@pytest.fixture
def 		_lowercase (     lowercase__						):
      # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
      # To be removed once SQLAlchemy 2.0 supported
      monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''   ,	lowercase__						)





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
            return True
      __lowerCAmelCase					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							padding=self.padding      ,							max_length=self.max_length      ,							pad_to_multiple_of=self.pad_to_multiple_of      ,							return_tensors='''pt''' if labels is None else None      ,							)

       if labels is None:
             return batch

       __lowerCAmelCase					:						Dict					=					torch.tensor(batch['''entity_ids''']					).shape[1]
       __lowerCAmelCase					:						Optional[int]					=					self.tokenizer.padding_side
       if padding_side == "right":
             __lowerCAmelCase					:						Any					=					[
                 list(A_					) + [self.label_pad_token_id] * (sequence_length - len(A_					)) for label in labels
             ]
       else:
             __lowerCAmelCase					:						Optional[int]					=					[
                 [self.label_pad_token_id] * (sequence_length - len(A_					)) + list(A_					) for label in labels
             ]

       __lowerCAmelCase					:						Tuple					=					[feature['''ner_tags'''] for feature in features]
       __lowerCAmelCase					:						List[Any]					=					padding_tensor(A_      ,							-1      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					[feature['''original_entity_spans'''] for feature in features]
       __lowerCAmelCase					:						Any					=					padding_tensor(A_      ,							(-1, -1)      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[Any]					=					{k: torch.tensor(A_      ,							dtype=torch.intaa					) for k, v in batch.items()}

       return batch


