'''simple docstring'''






import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_UpperCAmelCase    :     int         =       {
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

_UpperCAmelCase    :     int         =       logging.WARNING





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  os.getenv('DATASETS_VERBOSITY'   ,		__snake_case				)
     if env_level_str:
          if env_level_str in log_levels:
               return log_levels[env_level_str]
          else:
               logging.getLogger().warning(
                   F'Unknown option DATASETS_VERBOSITY={env_level_str}, '
                   F'has to be one of: { ", ".join(log_levels.keys()				) }'				)
     return _default_log_level





def  _SCREAMING_SNAKE_CASE		(      ):
     return __name__.split('.'				)[0]





def  _SCREAMING_SNAKE_CASE		(      ):
     return logging.getLogger(_get_library_name()				)





def  _SCREAMING_SNAKE_CASE		(      ):
     # Apply our default configuration to the library root logger.
     _A    		=  _get_library_root_logger()
     library_root_logger.setLevel(_get_default_logging_level()				)





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  _get_library_root_logger()
     library_root_logger.setLevel(logging.NOTSET				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[str] = None				):
     if name is None:
          _A    		=  _get_library_name()
     return logging.getLogger(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      ):
     return _get_library_root_logger().getEffectiveLevel()





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _get_library_root_logger().setLevel(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      ):
     return set_verbosity(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      ):
     return set_verbosity(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      ):
     return set_verbosity(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      ):
     return set_verbosity(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  False





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Tuple, *UpperCamelCase__      :			Union[str, Any], **UpperCamelCase__      :			Optional[int]     )  ->			Optional[Any]:  # pylint: disable=unused-argument
        _A    		=  args[0] if args else None
   def __iter__(      self      :			Optional[Any]     )  ->			int:
        return iter(self._iterator     )
   def __getattr__(      self      :			Any, UpperCamelCase__      :			Dict     )  ->			List[str]:

        def empty_fn(*UpperCamelCase__      :			str, **UpperCamelCase__      :			int     ):  # pylint: disable=unused-argument
             return

        return empty_fn
   def __enter__(      self      :			Optional[int]     )  ->			Tuple:
        return self

   def __exit__(      self      :			str, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int]     )  ->			int:
        return






_UpperCAmelCase    :     List[Any]         =       True







class lowercase_       :







   """simple docstring"""

   def __call__(      self      :			Union[str, Any], *UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			str=False, **UpperCamelCase__      :			Optional[int]     )  ->			List[Any]:
        if _tqdm_active and not disable:
             return tqdm_lib.tqdm(*UpperCamelCase__, **UpperCamelCase__     )
        else:
             return EmptyTqdm(*UpperCamelCase__, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Tuple, *UpperCamelCase__      :			Tuple, **UpperCamelCase__      :			str     )  ->			Union[str, Any]:
        _A    		=  None
        if _tqdm_active:
             return tqdm_lib.tqdm.set_lock(*UpperCamelCase__, **UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[str]:
        if _tqdm_active:
             return tqdm_lib.tqdm.get_lock()






_UpperCAmelCase    :     Any         =       _tqdm_cls()





def  _SCREAMING_SNAKE_CASE		(      ):
     global _tqdm_active
     return bool(_tqdm_active				)





def  _SCREAMING_SNAKE_CASE		(      ):
     global _tqdm_active
     _A    		=  True





def  _SCREAMING_SNAKE_CASE		(      ):
     global _tqdm_active
     _A    		=  False




'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



