





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   :
				__A            = MarianConfig
				__A            = {}
				__A            = """gelu"""


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_=13      ,       UpperCAmelCase_=7      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=False      ,       UpperCAmelCase_=99      ,       UpperCAmelCase_=32      ,       UpperCAmelCase_=2      ,       UpperCAmelCase_=4      ,       UpperCAmelCase_=37      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=20      ,       UpperCAmelCase_=2      ,       UpperCAmelCase_=1      ,       UpperCAmelCase_=0      ,       ):
								lowerCamelCase		       =parent
								lowerCamelCase		       =batch_size
								lowerCamelCase		       =seq_length
								lowerCamelCase		       =is_training
								lowerCamelCase		       =use_labels
								lowerCamelCase		       =vocab_size
								lowerCamelCase		       =hidden_size
								lowerCamelCase		       =num_hidden_layers
								lowerCamelCase		       =num_attention_heads
								lowerCamelCase		       =intermediate_size

								lowerCamelCase		       =hidden_dropout_prob
								lowerCamelCase		       =attention_probs_dropout_prob
								lowerCamelCase		       =max_position_embeddings
								lowerCamelCase		       =eos_token_id
								lowerCamelCase		       =pad_token_id
								lowerCamelCase		       =bos_token_id


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

								lowerCamelCase		       =ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

								lowerCamelCase		       =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      ,       )
								lowerCamelCase		       =prepare_marian_inputs_dict(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							)
								return config, inputs_dict


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =TFMarianModel(config=UpperCAmelCase_							).get_decoder()
								lowerCamelCase		       =inputs_dict["""input_ids"""]

								lowerCamelCase		       =input_ids[:1, :]
								lowerCamelCase		       =inputs_dict["""attention_mask"""][:1, :]
								lowerCamelCase		       =inputs_dict["""head_mask"""]
								lowerCamelCase		       =1

								# first forward pass
								lowerCamelCase		       =model(UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_      ,       head_mask=UpperCAmelCase_      ,       use_cache=UpperCAmelCase_							)

								lowerCamelCase       ,     lowerCamelCase		       =outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase		       =ids_tensor((self.batch_size, 3)      ,       config.vocab_size							)
								lowerCamelCase		       =tf.cast(ids_tensor((self.batch_size, 3)      ,       2							)      ,       tf.inta							)

								# append to next input_ids and
								lowerCamelCase		       =tf.concat([input_ids, next_tokens]      ,       axis=-1							)
								lowerCamelCase		       =tf.concat([attention_mask, next_attn_mask]      ,       axis=-1							)

								lowerCamelCase		       =model(UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_							)[0]
								lowerCamelCase		       =model(UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_      ,       past_key_values=UpperCAmelCase_							)[0]

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

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

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





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=None	,					_UpperCAmelCase=None	,					_UpperCAmelCase=None	,					_UpperCAmelCase=None	,					_UpperCAmelCase=None	,					)  ->     Any:
				if attention_mask is None:
								lowerCamelCase		       =tf.cast(tf.math.not_equal(_UpperCAmelCase	,					config.pad_token_id )	,					tf.inta )
				if decoder_attention_mask is None:
								lowerCamelCase		       =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:
								lowerCamelCase		       =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
				if decoder_head_mask is None:
								lowerCamelCase		       =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
				if cross_attn_head_mask is None:
								lowerCamelCase		       =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   (  a			,    a			,    unittest.TestCase			):
				__A            = (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
				__A            = (TFMarianMTModel,) if is_tf_available() else ()
				__A            = (
				    {
				        """conversational""": TFMarianMTModel,
				        """feature-extraction""": TFMarianModel,
				        """summarization""": TFMarianMTModel,
				        """text2text-generation""": TFMarianMTModel,
				        """translation""": TFMarianMTModel,
				    }
				    if is_tf_available()
				    else {}
				)
				__A            = True
				__A            = False
				__A            = False


				def _snake_case				(						self							):
								lowerCamelCase		       =TFMarianModelTester(self							)
								lowerCamelCase		       =ConfigTester(self      ,       config_class=UpperCAmelCase_							)


				def _snake_case				(						self							):
								self.config_tester.run_common_tests()


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_							)




@require_tf
class 					__A   (  unittest.TestCase			):
				__A            = 10_00  # show more chars for failing integration tests


				@classmethod
				def _snake_case				(						cls							):
								lowerCamelCase		       =f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls


				@cached_property
				def _snake_case				(						self							):
								return AutoTokenizer.from_pretrained(self.model_name							)


				@property
				def _snake_case				(						self							):
								return self.tokenizer.eos_token_id


				@cached_property
				def _snake_case				(						self							):
								warnings.simplefilter("""error"""							)
								lowerCamelCase		       =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
								assert isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							)
								lowerCamelCase		       =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 _snake_case				(						self      ,       **UpperCAmelCase_							):
								lowerCamelCase		       =self.translate_src_text(**UpperCAmelCase_							)
								self.assertListEqual(self.expected_text      ,       UpperCAmelCase_							)


				def _snake_case				(						self      ,       **UpperCAmelCase_							):
								lowerCamelCase		       =self.tokenizer(self.src_text      ,       **UpperCAmelCase_      ,       padding=UpperCAmelCase_      ,       return_tensors="""tf"""							)
								lowerCamelCase		       =self.model.generate(
								    model_inputs.input_ids      ,       attention_mask=model_inputs.attention_mask      ,       num_beams=2      ,       max_length=128							)
								lowerCamelCase		       =self.tokenizer.batch_decode(generated_ids.numpy()      ,       skip_special_tokens=UpperCAmelCase_							)
								return generated_words




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


				@unittest.skip("""Skipping until #12647 is resolved."""							)
				@slow
				def _snake_case				(						self							):
								self._assert_generated_batch_equal_expected()




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


				@unittest.skip("""Skipping until #12647 is resolved."""							)
				@slow
				def _snake_case				(						self							):
								self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class 					__A   (  a			):
				__A            = """en"""
				__A            = """ROMANCE"""
				__A            = [
				    """>>fr<< Don't spend so much time watching TV.""",
				    """>>pt<< Your message has been sent.""",
				    """>>es<< He's two years older than me.""",
				]
				__A            = [
				    """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 _snake_case				(						self							):
								self._assert_generated_batch_equal_expected()


				@unittest.skip("""Skipping until #12647 is resolved."""							)
				@slow
				def _snake_case				(						self							):
								lowerCamelCase		       =TranslationPipeline(self.model      ,       self.tokenizer      ,       framework="""tf"""							)
								lowerCamelCase		       =pipeline(self.src_text							)
								self.assertEqual(self.expected_text      ,       [x["""translation_text"""] for x in output]							)












def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     int:
				print("""\nVertex Distance""" )
				for i in range(_UpperCAmelCase ):
								if dist[i] != float("""inf""" ):
												print(_UpperCAmelCase	,					"""\t"""	,					int(dist[i] )	,					end="""\t""" )
								else:
												print(_UpperCAmelCase	,					"""\t"""	,					"""INF"""	,					end="""\t""" )
								print()





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Dict:
				lowerCamelCase		       =float("""inf""" )
				lowerCamelCase		       =-1
				for i in range(_UpperCAmelCase ):
								if (not vset[i]) and mdist[i] < min_val:
												lowerCamelCase		       =i
												lowerCamelCase		       =mdist[i]
				return min_ind





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Optional[int]:
				lowerCamelCase		       =[float("""inf""" ) for _ in range(_UpperCAmelCase )]
				lowerCamelCase		       =[False for _ in range(_UpperCAmelCase )]
				lowerCamelCase		       =0.0

				for _ in range(v - 1 ):
								lowerCamelCase		       =min_dist(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )
								lowerCamelCase		       =True

								for i in range(_UpperCAmelCase ):
												if (
												    (not vset[i])
												    and graph[u][i] != float("""inf""" )
												    and mdist[u] + graph[u][i] < mdist[i]
												):
																lowerCamelCase		       =mdist[u] + graph[u][i]

				print_dist(_UpperCAmelCase	,					_UpperCAmelCase )


if __name__ == "__main__":
							UpperCAmelCase__  :	Tuple     =int(input('''Enter number of vertices: ''').strip())
							UpperCAmelCase__  :	str     =int(input('''Enter number of edges: ''').strip())

							UpperCAmelCase__  :	str     =[[float('''inf''') for i in range(V)] for j in range(V)]

							for i in range(V):
														UpperCAmelCase__  :	Dict     =0.0

							for i in range(E):
														print('''\nEdge ''', i + 1)
														UpperCAmelCase__  :	int     =int(input('''Enter source:''').strip())
														UpperCAmelCase__  :	str     =int(input('''Enter destination:''').strip())
														UpperCAmelCase__  :	List[Any]     =float(input('''Enter weight:''').strip())
														UpperCAmelCase__  :	str     =weight

							UpperCAmelCase__  :	Tuple     =int(input('''\nEnter shortest path source:''').strip())
							dijkstra(graph, V, gsrc)





