






"""simple docstring"""
def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Tuple      =      word.split()

			def justify(A__			,A__			,A__		) -> str:
						UpperCAmelCase_  :				int      =      max_width - width
						UpperCAmelCase_  :				Any      =      len(A__		)
						if len(A__		) == 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:
									UpperCAmelCase_  :				Union[str, Any]      =      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]
									UpperCAmelCase_  :				Tuple      =      spaces_to_insert_between_words * [
									    overall_spaces_count // spaces_to_insert_between_words
									]
									UpperCAmelCase_  :				Dict      =      (
									    overall_spaces_count % spaces_to_insert_between_words
									)
									# distribute spaces via round robin to the left words
									for i in range(A__		):
												num_spaces_between_words_list[i] += 1
									UpperCAmelCase_  :				int      =      []
									for i in range(A__		):
												# 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(A__		)

			UpperCAmelCase_  :				int      =      []
			UpperCAmelCase_  :				list[str]      =      []
			UpperCAmelCase_  :				Optional[Any]      =      0
			for word in words:
						if width + len(A__		) + len(A__		) <= 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(A__		)
									width += len(A__		)
						else:
									# justify the line and add it to result
									answer.append(justify(A__			,A__			,A__		)		)
									# reset new line and new width
									UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      [word], len(A__		)
			UpperCAmelCase_  :				Union[str, Any]      =      max_width - width - len(A__		)
			answer.append(" ".join(A__		) + (remaining_spaces + 1) * " "		)
			return answer


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

			testmod()











"""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     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

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

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			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		)		)



