
"""simple docstring"""


import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {'''vocab_file''': '''vocab.json'''}

_lowercase							      = {
    '''vocab_file''': {
        '''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
    }
}

_lowercase							      = {'''mgp-str''': 27}







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Any					=			VOCAB_FILES_NAMES
						_lowerCamelCase:			Optional[Any]					=			PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:			Optional[Any]					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


						def __init__(       self   :					Optional[Any]     ,A_   :					str     ,A_   :					List[Any]="[GO]"     ,A_   :					Any="[GO]"     ,A_   :					Union[str, Any]="[s]"     ,A_   :					Dict="[GO]"     ,**A_   :					Any					)   ->      str:
							super().__init__(
							    unk_token=A_     ,bos_token=A_     ,eos_token=A_     ,pad_token=A_     ,**A_     ,)

							with open(A_     ,encoding='utf-8'					) as vocab_handle:
								A        =      json.load(A_					)
							A        =      {v: k for k, v in self.vocab.items()}


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Optional[Any]:
							return len(self.vocab					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      str:
							return dict(self.vocab     ,**self.added_tokens_encoder					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,A_   :					List[Any]					)   ->      Union[str, Any]:
							A        =      []
							for s in text:
								char_tokens.extend(A_					)
							return char_tokens


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					List[Any]					)   ->      List[Any]:
							return self.vocab.get(A_     ,self.vocab.get(self.unk_token					)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int     ,A_   :					Optional[int]					)   ->      str:
							return self.decoder.get(A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					str     ,A_   :					Optional[str] = None					)   ->      Tuple[str]:
							if not os.path.isdir(A_					):
								logger.error('Vocabulary path ({}) should be a directory'.format(A_					)					)
								return
							A        =      os.path.join(
							    A_     ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

							with open(A_     ,'w'     ,encoding='utf-8'					) as f:
								f.write(json.dumps(self.vocab     ,indent=2     ,sort_keys=A_     ,ensure_ascii=A_					) + '\n'					)

							return (vocab_file,)

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 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      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      )      )




