





from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel

@require_tf
class 				A    :
      _SCREAMING_SNAKE_CASE					=							MarianConfig
      _SCREAMING_SNAKE_CASE					=							{}
      _SCREAMING_SNAKE_CASE					=							"""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     ,  )      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						parent
             _snake_case      :							int     				=						batch_size
             _snake_case      :							int     				=						seq_length
             _snake_case      :							Any     				=						is_training
             _snake_case      :							Dict     				=						use_labels
             _snake_case      :							List[Any]     				=						vocab_size
             _snake_case      :							Any     				=						hidden_size
             _snake_case      :							int     				=						num_hidden_layers
             _snake_case      :							Optional[Any]     				=						num_attention_heads
             _snake_case      :							str     				=						intermediate_size

             _snake_case      :							Tuple     				=						hidden_dropout_prob
             _snake_case      :							List[Any]     				=						attention_probs_dropout_prob
             _snake_case      :							Dict     				=						max_position_embeddings
             _snake_case      :							Union[str, Any]     				=						eos_token_id
             _snake_case      :							List[str]     				=						pad_token_id
             _snake_case      :							List[str]     				=						bos_token_id



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size					)
             _snake_case      :							List[str]     				=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)     ,  1					)
             _snake_case      :							str     				=						tf.concat([input_ids, eos_tensor]     ,  axis=1					)

             _snake_case      :							Tuple     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

             _snake_case      :							str     				=						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     ,  **self.config_updates     ,  )
             _snake_case      :							Optional[Any]     				=						prepare_marian_inputs_dict(lowercase_     ,  lowercase_     ,  lowercase_					)
             return config, inputs_dict



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Any     				=						TFMarianModel(config=lowercase_					).get_decoder()
             _snake_case      :							int     				=						inputs_dict['''input_ids''']

             _snake_case      :							Optional[Any]     				=						input_ids[:1, :]
             _snake_case      :							int     				=						inputs_dict['''attention_mask'''][:1, :]
             _snake_case      :							Tuple     				=						inputs_dict['''head_mask''']
             _snake_case      :							List[Any]     				=						1

             # first forward pass
             _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_     ,  head_mask=lowercase_     ,  use_cache=lowercase_					)

             _snake_case				,    _snake_case      :							Optional[int]     				=						outputs.to_tuple()

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

             # append to next input_ids and
             _snake_case      :							Dict     				=						tf.concat([input_ids, next_tokens]     ,  axis=-1					)
             _snake_case      :							Optional[int]     				=						tf.concat([attention_mask, next_attn_mask]     ,  axis=-1					)

             _snake_case      :							List[Any]     				=						model(lowercase_     ,  attention_mask=lowercase_					)[0]
             _snake_case      :							str     				=						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
             _snake_case      :							int     				=						int(ids_tensor((1,)     ,  output_from_past.shape[-1]					)					)
             _snake_case      :							Union[str, Any]     				=						output_from_no_past[:, -3:, random_slice_idx]
             _snake_case      :							str     				=						output_from_past[:, :, random_slice_idx]

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




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=None      ,		lowercase_=None      ,		lowercase_=None      ,		lowercase_=None      ,		)			->						Optional[int]:
       if attention_mask is None:
              _snake_case      :							str     				=						tf.cast(tf.math.not_equal(lowercase_      ,		config.pad_token_id  )      ,		tf.inta  )
       if decoder_attention_mask is None:
              _snake_case      :							Any     				=						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:
              _snake_case      :							List[Any]     				=						tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
       if decoder_head_mask is None:
              _snake_case      :							Union[str, Any]     				=						tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
       if cross_attn_head_mask is None:
              _snake_case      :							List[Any]     				=						tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }

@require_tf
class 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE					=							(TFMarianMTModel,) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE					=							(
          {
              """conversational""": TFMarianMTModel,
              """feature-extraction""": TFMarianModel,
              """summarization""": TFMarianMTModel,
              """text2text-generation""": TFMarianMTModel,
              """translation""": TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE					=							True
      _SCREAMING_SNAKE_CASE					=							False
      _SCREAMING_SNAKE_CASE					=							False



      def    __a (					self					)      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						TFMarianModelTester(self					)
             _snake_case      :							List[Any]     				=						ConfigTester(self     ,  config_class=lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             self.config_tester.run_common_tests()



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*lowercase_					)


@require_tf
class 				A    (unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							1_000  # show more chars for failing integration tests



      @classmethod
      def    __a (					cls					)      ->   None:



             '''simple docstring'''

             _snake_case      :							str     				=						F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
             return cls



      @cached_property
      def    __a (					self					)      ->   MarianTokenizer:



             '''simple docstring'''

             return AutoTokenizer.from_pretrained(self.model_name					)



      @property
      def    __a (					self					)      ->   int:



             '''simple docstring'''

             return self.tokenizer.eos_token_id



      @cached_property
      def    __a (					self					)      ->   List[str]:



             '''simple docstring'''

             warnings.simplefilter('''error'''					)
             _snake_case      :							TFMarianMTModel     				=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
             assert isinstance(lowercase_     ,  lowercase_					)
             _snake_case      :							Dict     				=						model.config
             self.assertListEqual(c.bad_words_ids     ,  [[c.pad_token_id]]					)
             self.assertEqual(c.max_length     ,  512					)
             self.assertEqual(c.decoder_start_token_id     ,  c.pad_token_id					)
             return model



      def    __a (					self     ,  **lowercase_					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Dict     				=						self.translate_src_text(**lowercase_					)
             self.assertListEqual(self.expected_text     ,  lowercase_					)



      def    __a (					self     ,  **lowercase_					)      ->   int:



             '''simple docstring'''

             _snake_case      :							Any     				=						self.tokenizer(self.src_text     ,  **lowercase_     ,  padding=lowercase_     ,  return_tensors='''tf'''					)
             _snake_case      :							str     				=						self.model.generate(
                 model_inputs.input_ids     ,  attention_mask=model_inputs.attention_mask     ,  num_beams=2     ,  max_length=128					)
             _snake_case      :							str     				=						self.tokenizer.batch_decode(generated_ids.numpy()     ,  skip_special_tokens=lowercase_					)
             return generated_words


@require_sentencepiece
@require_tokenizers
@require_tf
class 				A    (__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							"""mt"""
      _SCREAMING_SNAKE_CASE					=							"""en"""
      _SCREAMING_SNAKE_CASE					=							["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
      _SCREAMING_SNAKE_CASE					=							["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]



      @unittest.skip('''Skipping until #12647 is resolved.'''					)
      @slow
      def    __a (					self					)      ->   str:



             '''simple docstring'''

             self._assert_generated_batch_equal_expected()


@require_sentencepiece
@require_tokenizers
@require_tf
class 				A    (__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							"""en"""
      _SCREAMING_SNAKE_CASE					=							"""zh"""
      _SCREAMING_SNAKE_CASE					=							["""My name is Wolfgang and I live in Berlin"""]
      _SCREAMING_SNAKE_CASE					=							["""我叫沃尔夫冈 我住在柏林"""]



      @unittest.skip('''Skipping until #12647 is resolved.'''					)
      @slow
      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             self._assert_generated_batch_equal_expected()


@require_sentencepiece
@require_tokenizers
@require_tf
class 				A    (__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							"""en"""
      _SCREAMING_SNAKE_CASE					=							"""ROMANCE"""
      _SCREAMING_SNAKE_CASE					=							[
          """>>fr<< Don't spend so much time watching TV.""",
          """>>pt<< Your message has been sent.""",
          """>>es<< He's two years older than me.""",
      ]
      _SCREAMING_SNAKE_CASE					=							[
          """Ne passez pas autant de temps à regarder la télé.""",
          """A sua mensagem foi enviada.""",
          """Es dos años más viejo que yo.""",
      ]



      @unittest.skip('''Skipping until #12647 is resolved.'''					)
      @slow
      def    __a (					self					)      ->   Union[str, Any]:



             '''simple docstring'''

             self._assert_generated_batch_equal_expected()



      @unittest.skip('''Skipping until #12647 is resolved.'''					)
      @slow
      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Dict     				=						TranslationPipeline(self.model     ,  self.tokenizer     ,  framework='''tf'''					)
             _snake_case      :							Optional[Any]     				=						pipeline(self.src_text					)
             self.assertEqual(self.expected_text     ,  [x['''translation_text'''] for x in output]					)













from __future__ import annotations

class 				A    :



      def __init__(					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						vertices
             _snake_case      :							int     				=						[[0] * vertices for _ in range(lowercase_					)]



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             print('''Vertex \t Distance from Source'''					)
             for vertex in range(self.vertices					):
                    print(lowercase_     ,  '''\t\t'''     ,  distances_from_source[vertex]					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						1E7
             _snake_case      :							Tuple     				=						0

             # Search not nearest vertex not in the shortest path tree
             for vertex in range(self.vertices					):
                    if distances_from_source[vertex] < minimum and visited[vertex] is False:
                           _snake_case      :							Dict     				=						distances_from_source[vertex]
                           _snake_case      :							int     				=						vertex
             return min_index



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						[int(1E7					)] * self.vertices  # distances from the source
             _snake_case      :							List[Any]     				=						0
             _snake_case      :							List[str]     				=						[False] * self.vertices

             for _ in range(self.vertices					):
                    _snake_case      :							List[Any]     				=						self.minimum_distance(lowercase_     ,  lowercase_					)
                    _snake_case      :							List[str]     				=						True

                    # Update dist value of the adjacent vertices
                    # of the picked vertex only if the current
                    # distance is greater than new distance and
                    # the vertex in not in the shortest path tree
                    for v in range(self.vertices					):
                           if (
                               self.graph[u][v] > 0
                               and visited[v] is False
                               and distances[v] > distances[u] + self.graph[u][v]
                           ):
                                  _snake_case      :							Any     				=						distances[u] + self.graph[u][v]

             self.print_solution(lowercase_					)


if __name__ == "__main__":
   lowerCAmelCase_   =   Graph(9)
   lowerCAmelCase_   =   [
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
       [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
       [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)






