

# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowercase_             =     logging.get_logger(__name__)

lowercase_             =     {}
lowercase_             =     {}
lowercase_             =     {}
def   a   (    A__			:     type   ,     A__			:     Optional[str]   ,     A__			:     Optional[List[str]] = None   ,     )					->   Dict:





      """simple docstring"""



      _lowercase			 =aliases if aliases is not None else []
      if format_type in _FORMAT_TYPES:
            logger.warning(
                F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''  )
      _lowercase			 =formatter_cls
      for alias in set(aliases + [format_type]  ):
            if alias in _FORMAT_TYPES_ALIASES:
                  logger.warning(
                      F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''  )
            _lowercase			 =format_type
def   a   (    A__			:     Exception   ,     A__			:     Optional[str]   ,     A__			:     Optional[List[str]] = None  )					->   str:





      """simple docstring"""



      _lowercase			 =aliases if aliases is not None else []
      for alias in set(aliases + [format_type]  ):
            _lowercase			 =unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
  from .torch_formatter import TorchFormatter

  _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
  lowercase_             =     ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
  _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
  from .tf_formatter import TFFormatter

  _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
  lowercase_             =     ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
  _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
  from .jax_formatter import JaxFormatter

  _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
  lowercase_             =     ValueError('JAX needs to be installed to be able to return JAX arrays.')
  _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def   a   (    A__			:     Optional[str]  )					->   Optional[str]:





      """simple docstring"""



      if format_type in _FORMAT_TYPES_ALIASES:
            return _FORMAT_TYPES_ALIASES[format_type]
      else:
            return format_type







def   a   (    A__			:     Optional[str]   ,     **A__			:     Optional[Any]  )					->   Formatter:





      """simple docstring"""



      _lowercase			 =get_format_type_from_alias(A__  )
      if format_type in _FORMAT_TYPES:
            return _FORMAT_TYPES[format_type](**A__  )
      if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
            raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
      else:
            raise ValueError(
                F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None  )}, but got \'{format_type}\''''  )





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 				__lowerCAmelCase  (      unittest.TestCase							):




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

        _lowercase			 =tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]							,     dtype=tf.intaa							,     )  # J'aime le camembert !"

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        _lowercase			 =tf.TensorShape((1, 10, 768)      )
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice.
        _lowercase			 =tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]							,     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      )      )


