

'''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 							_snake_case (       unittest.TestCase						):
			@slow
			def     __UpperCamelCase						( self			:      Union[str, Any]							):
					SCREAMING_SNAKE_CASE:str						      =					TFCamembertModel.from_pretrained("jplu/tf-camembert-base"							)

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

					SCREAMING_SNAKE_CASE:int						      =					model(lowercase__							)['''last_hidden_state''']
					SCREAMING_SNAKE_CASE:List[Any]						      =					tf.TensorShape((1, 10, 768)							)
					self.assertEqual(output.shape     ,lowercase__							)
					# compare the actual values for a slice.
					SCREAMING_SNAKE_CASE:int						      =					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							)							)









'''simple docstring'''

def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

  def justify(snake_case      ,       snake_case      ,       snake_case ) -> str:
    SCREAMING_SNAKE_CASE:str						      =					max_width - width
    SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
    if len(snake_case ) == 1:
      # if there is only word in line
      # just insert overall_spaces_count for the remainder of line
      return line[0] + " " * overall_spaces_count
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					words_count - 1
      # num_spaces_between_words_list[i] : tells you to insert
      # num_spaces_between_words_list[i] spaces
      # after word on line[i]
      SCREAMING_SNAKE_CASE:List[Any]						      =					spaces_to_insert_between_words * [
          overall_spaces_count // spaces_to_insert_between_words
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					(
          overall_spaces_count % spaces_to_insert_between_words
      )
      # distribute spaces via round robin to the left words
      for i in range(snake_case ):
        num_spaces_between_words_list[i] += 1
      SCREAMING_SNAKE_CASE:str						      =					[]
      for i in range(snake_case ):
        # add the word
        aligned_words_list.append(line[i] )
        # add the spaces to insert
        aligned_words_list.append(num_spaces_between_words_list[i] * " " )
      # just add the last word to the sentence
      aligned_words_list.append(line[-1] )
      # join the aligned words list to form a justified line
      return "".join(snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= max_width:
      # keep adding words until we can fill out max_width
      # width = sum of length of all words (without overall_spaces_count)
      # len(word) = length of current word
      # len(line) = number of overall_spaces_count to insert between words
      line.append(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()






