






"""simple docstring"""
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_ :







      '''simple docstring'''

      lowerCamelCase__			:							Union[str, Any]					=      MarianConfig
      lowerCamelCase__			:							Optional[Any]					=      {}
      lowerCamelCase__			:							Any					=      'gelu'




      def __init__(self,	lowerCamelCase_,	lowerCamelCase_=1_3,	lowerCamelCase_=7,	lowerCamelCase_=True,	lowerCamelCase_=False,	lowerCamelCase_=9_9,	lowerCamelCase_=3_2,	lowerCamelCase_=2,	lowerCamelCase_=4,	lowerCamelCase_=3_7,	lowerCamelCase_=0.1,	lowerCamelCase_=0.1,	lowerCamelCase_=2_0,	lowerCamelCase_=2,	lowerCamelCase_=1,	lowerCamelCase_=0,	):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		parent
       lowerCamelCase__	:     Optional[int]								=		batch_size
       lowerCamelCase__	:     Tuple								=		seq_length
       lowerCamelCase__	:     Optional[Any]								=		is_training
       lowerCamelCase__	:     Optional[int]								=		use_labels
       lowerCamelCase__	:     Any								=		vocab_size
       lowerCamelCase__	:     List[Any]								=		hidden_size
       lowerCamelCase__	:     List[Any]								=		num_hidden_layers
       lowerCamelCase__	:     Any								=		num_attention_heads
       lowerCamelCase__	:     List[Any]								=		intermediate_size

       lowerCamelCase__	:     Optional[Any]								=		hidden_dropout_prob
       lowerCamelCase__	:     Dict								=		attention_probs_dropout_prob
       lowerCamelCase__	:     Optional[int]								=		max_position_embeddings
       lowerCamelCase__	:     int								=		eos_token_id
       lowerCamelCase__	:     Any								=		pad_token_id
       lowerCamelCase__	:     Union[str, Any]								=		bos_token_id




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		ids_tensor([self.batch_size, self.seq_length - 1],	self.vocab_size   )
       lowerCamelCase__	:     int								=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   ),	1   )
       lowerCamelCase__	:     Optional[Any]								=		tf.concat([input_ids, eos_tensor],	axis=1   )

       lowerCamelCase__	:     Any								=		ids_tensor([self.batch_size, self.seq_length],	self.vocab_size   )

       lowerCamelCase__	:     List[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,	)
       lowerCamelCase__	:     str								=		prepare_marian_inputs_dict(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   )
       return config, inputs_dict





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		TFMarianModel(config=lowerCamelCase_   ).get_decoder()
       lowerCamelCase__	:     int								=		inputs_dict['input_ids']

       lowerCamelCase__	:     Tuple								=		input_ids[:1, :]
       lowerCamelCase__	:     Any								=		inputs_dict['attention_mask'][:1, :]
       lowerCamelCase__	:     Any								=		inputs_dict['head_mask']
       lowerCamelCase__	:     str								=		1

       # first forward pass
       lowerCamelCase__	:     Tuple								=		model(lowerCamelCase_,	attention_mask=lowerCamelCase_,	head_mask=lowerCamelCase_,	use_cache=lowerCamelCase_   )

       lowerCamelCase__							,	lowerCamelCase__	:     Any								=		outputs.to_tuple()

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

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

       lowerCamelCase__	:     Union[str, Any]								=		model(lowerCamelCase_,	attention_mask=lowerCamelCase_   )[0]
       lowerCamelCase__	:     Optional[int]								=		model(lowerCamelCase_,	attention_mask=lowerCamelCase_,	past_key_values=lowerCamelCase_   )[0]

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

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

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






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase=None     ,  _lowerCamelCase=None     ,  _lowerCamelCase=None     ,  _lowerCamelCase=None     ,  _lowerCamelCase=None     ,  ):
 if attention_mask is None:
  lowerCamelCase__	:     Any								=		tf.cast(tf.math.not_equal(_lowerCamelCase     ,  config.pad_token_id							)     ,  tf.inta							)
 if decoder_attention_mask is None:
  lowerCamelCase__	:     Tuple								=		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__	:     List[str]								=		tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
 if decoder_head_mask is None:
  lowerCamelCase__	:     Optional[int]								=		tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
 if cross_attn_head_mask is None:
  lowerCamelCase__	:     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_ (      snake_case_    ,		snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							List[Any]					=      (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      lowerCamelCase__			:							Any					=      (TFMarianMTModel,) if is_tf_available() else ()
      lowerCamelCase__			:							List[str]					=      (
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      lowerCamelCase__			:							str					=      True
      lowerCamelCase__			:							List[str]					=      False
      lowerCamelCase__			:							Optional[int]					=      False




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		TFMarianModelTester(self   )
       lowerCamelCase__	:     List[str]								=		ConfigTester(self,	config_class=lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       self.config_tester.run_common_tests()





      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_   )



@require_tf
class 						a_ (      unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							List[str]					=      1_000  # show more chars for failing integration tests




      @classmethod
      def      a__				(cls   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
       return cls




      @cached_property
      def      a__				(self   ):

       '''simple docstring'''



       return AutoTokenizer.from_pretrained(self.model_name   )




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.tokenizer.eos_token_id




      @cached_property
      def      a__				(self   ):

       '''simple docstring'''



       warnings.simplefilter('error'   )
       lowerCamelCase__	:     TFMarianMTModel								=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
       assert isinstance(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     Optional[int]								=		model.config
       self.assertListEqual(c.bad_words_ids,	[[c.pad_token_id]]   )
       self.assertEqual(c.max_length,	5_1_2   )
       self.assertEqual(c.decoder_start_token_id,	c.pad_token_id   )
       return model




      def      a__				(self,	**lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.translate_src_text(**lowerCamelCase_   )
       self.assertListEqual(self.expected_text,	lowerCamelCase_   )





      def      a__				(self,	**lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		self.tokenizer(self.src_text,	**lowerCamelCase_,	padding=lowerCamelCase_,	return_tensors='tf'   )
       lowerCamelCase__	:     List[Any]								=		self.model.generate(
           model_inputs.input_ids,	attention_mask=model_inputs.attention_mask,	num_beams=2,	max_length=1_2_8   )
       lowerCamelCase__	:     Tuple								=		self.tokenizer.batch_decode(generated_ids.numpy(),	skip_special_tokens=lowerCamelCase_   )
       return generated_words



@require_sentencepiece
@require_tokenizers
@require_tf
class 						a_ (      snake_case_					):







      '''simple docstring'''

      lowerCamelCase__			:							Dict					=      'mt'
      lowerCamelCase__			:							int					=      'en'
      lowerCamelCase__			:							List[str]					=      ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      lowerCamelCase__			:							Dict					=      ['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   ):

       '''simple docstring'''



       self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class 						a_ (      snake_case_					):







      '''simple docstring'''

      lowerCamelCase__			:							Dict					=      'en'
      lowerCamelCase__			:							Any					=      'zh'
      lowerCamelCase__			:							Optional[int]					=      ['My name is Wolfgang and I live in Berlin']
      lowerCamelCase__			:							Dict					=      ['我叫沃尔夫冈 我住在柏林']




      @unittest.skip('Skipping until #12647 is resolved.'   )
      @slow
      def      a__				(self   ):

       '''simple docstring'''



       self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class 						a_ (      snake_case_					):







      '''simple docstring'''

      lowerCamelCase__			:							int					=      'en'
      lowerCamelCase__			:							str					=      'ROMANCE'
      lowerCamelCase__			:							Optional[Any]					=      [
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      lowerCamelCase__			:							List[Any]					=      [
          '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   ):

       '''simple docstring'''



       self._assert_generated_batch_equal_expected()





      @unittest.skip('Skipping until #12647 is resolved.'   )
      @slow
      def      a__				(self   ):

       '''simple docstring'''



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









"""simple docstring"""
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  ):
 lowerCamelCase__	:     Any								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     List[Any]								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     int								=		len(_lowerCamelCase							)
 lowerCamelCase__	:     Optional[int]								=		len(_lowerCamelCase							)

 lowerCamelCase__	:     Tuple								=		[
     [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]
 lowerCamelCase__	:     Dict								=		[
     ['0' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]

 for i in range(1     ,  len_source_seq + 1							):
  lowerCamelCase__	:     Optional[int]								=		i * delete_cost
  lowerCamelCase__	:     List[str]								=		f'''D{source_seq[i - 1]:c}'''

 for i in range(1     ,  len_destination_seq + 1							):
  lowerCamelCase__	:     int								=		i * insert_cost
  lowerCamelCase__	:     str								=		f'''I{destination_seq[i - 1]:c}'''

 for i in range(1     ,  len_source_seq + 1							):
  for j in range(1     ,  len_destination_seq + 1							):
   if source_seq[i - 1] == destination_seq[j - 1]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j - 1] + copy_cost
    lowerCamelCase__	:     str								=		f'''C{source_seq[i - 1]:c}'''
   else:
    lowerCamelCase__	:     Tuple								=		costs[i - 1][j - 1] + replace_cost
    lowerCamelCase__	:     List[str]								=		f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

   if costs[i - 1][j] + delete_cost < costs[i][j]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j] + delete_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''D{source_seq[i - 1]:c}'''

   if costs[i][j - 1] + insert_cost < costs[i][j]:
    lowerCamelCase__	:     Optional[Any]								=		costs[i][j - 1] + insert_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''I{destination_seq[j - 1]:c}'''

 return costs, ops






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if i == 0 and j == 0:
  return []
 else:
  if ops[i][j][0] == "C" or ops[i][j][0] == "R":
   lowerCamelCase__	:     List[str]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq
  elif ops[i][j][0] == "D":
   lowerCamelCase__	:     Union[str, Any]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  _lowerCamelCase							)
   seq.append(ops[i][j]							)
   return seq
  else:
   lowerCamelCase__	:     Tuple								=		assemble_transformation(_lowerCamelCase     ,  _lowerCamelCase     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq


if __name__ == "__main__":
      A_,			A_     :					List[str]							=     compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

      A_     :					str							=     len(operations)
      A_     :					Dict							=     len(operations[0])
      A_     :					Any							=     assemble_transformation(operations, m - 1, n - 1)

      A_     :					int							=     list("Python")
      A_     :					Optional[Any]							=     0
      A_     :					Tuple							=     0

      with open("min_cost.txt", "w") as file:
            for op in sequence:
                  print("".join(string))

                  if op[0] == "C":
                        file.write("%-16s" % "Copy %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost -= 1
                  elif op[0] == "R":
                        A_     :					str							=     op[2]

                        file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
                        file.write("\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 1
                  elif op[0] == "D":
                        string.pop(i)

                        file.write("%-16s" % "Delete %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2
                  else:
                        string.insert(i, op[1])

                        file.write("%-16s" % "Insert %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2

                  i += 1

            print("".join(string))
            print("Cost: ", cost)

            file.write("\r\nMinimum cost: " + str(cost))

