'''simple docstring'''





def     __lowerCAmelCase						(       a_       ,      a_		)      ->	list:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       word.split()

     def justify(a_       ,      a_       ,      a_		) -> str:
          SCREAMING_SNAKE_CASE					:			Any    	=       max_width - width
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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:
               SCREAMING_SNAKE_CASE					:			List[str]    	=       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					:			Optional[Any]    	=       spaces_to_insert_between_words * [
                   overall_spaces_count // spaces_to_insert_between_words
               ]
               SCREAMING_SNAKE_CASE					:			Any    	=       (
                   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
               SCREAMING_SNAKE_CASE					:			Any    	=       []
               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_		)

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
     SCREAMING_SNAKE_CASE					:			list[str]    	=       []
     SCREAMING_SNAKE_CASE					:			Union[str, 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
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [word], len(a_		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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 LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class 				UpperCAmelCase :




    '''simple docstring'''







    snake_case__							:   Dict							=						LEDConfig
    snake_case__							:   Dict							=						{}
    snake_case__							:   int							=						"gelu"






    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=False    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=2    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=20    ,							lowercase__=2    ,							lowercase__=1    ,							lowercase__=0    ,							lowercase__=4    ,							)							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       parent
         SCREAMING_SNAKE_CASE					:			str    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       seq_length
         SCREAMING_SNAKE_CASE					:			List[str]    	=       is_training
         SCREAMING_SNAKE_CASE					:			Tuple    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       intermediate_size

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Dict    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Any    	=       eos_token_id
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pad_token_id
         SCREAMING_SNAKE_CASE					:			Tuple    	=       bos_token_id
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       attention_window

         # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
         # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
         # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
         # because its local attention only attends to `self.attention_window` and one before and one after
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.attention_window + 2

         # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
         # the `test_attention_outputs` and `test_hidden_states_output` tests
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
             self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
         )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )    ,							1 )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       tf.concat([input_ids, eos_tensor]    ,							axis=1 )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.config_cls(
             vocab_size=self.vocab_size    ,							d_model=self.hidden_size    ,							encoder_layers=self.num_hidden_layers    ,							decoder_layers=self.num_hidden_layers    ,							encoder_attention_heads=self.num_attention_heads    ,							decoder_attention_heads=self.num_attention_heads    ,							encoder_ffn_dim=self.intermediate_size    ,							decoder_ffn_dim=self.intermediate_size    ,							dropout=self.hidden_dropout_prob    ,							attention_dropout=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							eos_token_ids=[2]    ,							bos_token_id=self.bos_token_id    ,							pad_token_id=self.pad_token_id    ,							decoder_start_token_id=self.pad_token_id    ,							attention_window=self.attention_window    ,							**self.config_updates    ,							)
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       prepare_led_inputs_dict(lowercase__    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       tf.concat(
             [tf.zeros_like(lowercase__ )[:, :-1], tf.ones_like(lowercase__ )[:, -1:]]    ,							axis=-1    ,							)
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       global_attention_mask
         return config, inputs_dict






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> Dict:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       TFLEDModel(config=lowercase__ ).get_decoder()
         SCREAMING_SNAKE_CASE					:			int    	=       inputs_dict['input_ids']

         SCREAMING_SNAKE_CASE					:			Tuple    	=       input_ids[:1, :]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       inputs_dict['attention_mask'][:1, :]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1

         # first forward pass
         SCREAMING_SNAKE_CASE					:			Any    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							use_cache=lowercase__ )

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         SCREAMING_SNAKE_CASE					:			str    	=       ids_tensor((self.batch_size, 3)    ,							config.vocab_size )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.cast(ids_tensor((self.batch_size, 3)    ,							2 )    ,							tf.inta )

         # append to next input_ids and
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.concat([input_ids, next_tokens]    ,							axis=-1 )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       tf.concat([attention_mask, next_attn_mask]    ,							axis=-1 )

         SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							attention_mask=lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							past_key_values=lowercase__ )[0]

         self.parent.assertEqual(next_tokens.shape[1]    ,							output_from_past.shape[1] )

         # select random slice
         SCREAMING_SNAKE_CASE					:			str    	=       int(ids_tensor((1,)    ,							output_from_past.shape[-1] ) )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       output_from_no_past[:, -3:, random_slice_idx]
         SCREAMING_SNAKE_CASE					:			Tuple    	=       output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(lowercase__    ,							lowercase__    ,							rtol=1E-3 )






def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_=None       ,      a_=None       ,      a_=None       ,      a_=None       ,      )      ->	Any:





     '''simple docstring'''





     if attention_mask is None:
          SCREAMING_SNAKE_CASE					:			int    	=       tf.cast(tf.math.not_equal(a_       ,      config.pad_token_id		)       ,      tf.inta		)
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape       ,      dtype=tf.inta		),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,      config.pad_token_id		)       ,      tf.inta		),
              ]       ,      axis=-1       ,      )
     if head_mask is None:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
         "decoder_input_ids": decoder_input_ids,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
     }






@require_tf
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   str							=						(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    snake_case__							:   List[str]							=						(TFLEDForConditionalGeneration,) if is_tf_available() else ()
    snake_case__							:   Union[str, Any]							=						(
        {
            "conversational": TFLEDForConditionalGeneration,
            "feature-extraction": TFLEDModel,
            "summarization": TFLEDForConditionalGeneration,
            "text2text-generation": TFLEDForConditionalGeneration,
            "translation": TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    snake_case__							:   List[Any]							=						True
    snake_case__							:   Tuple							=						False
    snake_case__							:   Dict							=						False
    snake_case__							:   int							=						False






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       TFLEDModelTester(self )
         SCREAMING_SNAKE_CASE					:			Dict    	=       ConfigTester(self    ,							config_class=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tf.zeros_like(inputs_dict['attention_mask'] )
         SCREAMING_SNAKE_CASE					:			str    	=       2
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.where(
             tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices    ,							1    ,							inputs_dict['global_attention_mask']    ,							)

         SCREAMING_SNAKE_CASE					:			Dict    	=       True
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.seq_length
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.encoder_seq_length

         def check_decoder_attentions_output(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       outputs.decoder_attentions
              self.assertEqual(len(lowercase__ )    ,							self.model_tester.num_hidden_layers )
              self.assertListEqual(
                  list(decoder_attentions[0].shape[-3:] )    ,							[self.model_tester.num_attention_heads, seq_length, seq_length]    ,							)

         def check_encoder_attentions_output(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [t.numpy() for t in outputs.encoder_attentions]
              SCREAMING_SNAKE_CASE					:			Tuple    	=       [t.numpy() for t in outputs.encoder_global_attentions]
              self.assertEqual(len(lowercase__ )    ,							self.model_tester.num_hidden_layers )
              self.assertEqual(len(lowercase__ )    ,							self.model_tester.num_hidden_layers )
              self.assertListEqual(
                  list(attentions[0].shape[-3:] )    ,							[self.model_tester.num_attention_heads, seq_length, seq_length]    ,							)
              self.assertListEqual(
                  list(global_attentions[0].shape[-3:] )    ,							[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    ,							)

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       True
              SCREAMING_SNAKE_CASE					:			Tuple    	=       False
              SCREAMING_SNAKE_CASE					:			str    	=       False
              SCREAMING_SNAKE_CASE					:			Any    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )
              SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )
              self.assertEqual(config.output_hidden_states    ,							lowercase__ )
              check_encoder_attentions_output(lowercase__ )

              if self.is_encoder_decoder:
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model_class(lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )
                   self.assertEqual(config.output_hidden_states    ,							lowercase__ )
                   check_decoder_attentions_output(lowercase__ )

              # Check that output attentions can also be changed via the config
              del inputs_dict["output_attentions"]
              SCREAMING_SNAKE_CASE					:			Dict    	=       True
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			List[str]    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )
              self.assertEqual(config.output_hidden_states    ,							lowercase__ )
              check_encoder_attentions_output(lowercase__ )

              # Check attention is always last and order is fine
              SCREAMING_SNAKE_CASE					:			str    	=       True
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       True
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(self._prepare_for_class(lowercase__    ,							lowercase__ ) )

              self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    ,							len(lowercase__ ) )
              self.assertEqual(model.config.output_hidden_states    ,							lowercase__ )
              check_encoder_attentions_output(lowercase__ )






    @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
    def       _UpperCamelCase    (       self )							-> Tuple:
         pass






    def       _UpperCamelCase    (       self )							-> Tuple:
         # TODO: Head-masking not yet implement
         pass






def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     return tf.constant(a_       ,      dtype=tf.intaa		)


_lowerCAmelCase  :Union[str, Any]  				=    1E-4






@slow
@require_tf
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led

         # change to intended input here
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			Any    	=       prepare_led_inputs_dict(model.config    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(**lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			str    	=       (1, 1_024, 768)
         self.assertEqual(output.shape    ,							lowercase__ )
         # change to expected output here
         SCREAMING_SNAKE_CASE					:			str    	=       tf.convert_to_tensor(
             [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]    ,							)
         tf.debugging.assert_near(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-3 )






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			str    	=       TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )

         # change to intended input here
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			str    	=       _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       prepare_led_inputs_dict(model.config    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       model(**lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (1, 1_024, model.config.vocab_size)
         self.assertEqual(output.shape    ,							lowercase__ )
         # change to expected output here
         SCREAMING_SNAKE_CASE					:			str    	=       tf.convert_to_tensor(
             [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]    ,							)
         tf.debugging.assert_near(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-3    ,							rtol=1E-3 )



