

"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel






@require_tf
class 	UpperCAmelCase__		:






  """simple docstring"""


  a									= MBartConfig
  a									= {}
  a									= '''gelu'''






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






  def 				lowercase_		(							self						:		Optional[int] )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   ids_tensor([self.batch_size, self.seq_length - 1]     ,       self.vocab_size )
       SCREAMING_SNAKE_CASE__   =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )     ,       1 )
       SCREAMING_SNAKE_CASE__   =   tf.concat([input_ids, eos_tensor]     ,       axis=1 )

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

       SCREAMING_SNAKE_CASE__   =   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     ,       )
       SCREAMING_SNAKE_CASE__   =   prepare_mbart_inputs_dict(lowercase_     ,       lowercase_     ,       lowercase_ )
       return config, inputs_dict







  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		Tuple )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   TFMBartModel(config=lowercase_ ).get_decoder()
       SCREAMING_SNAKE_CASE__   =   inputs_dict['''input_ids''']

       SCREAMING_SNAKE_CASE__   =   input_ids[:1, :]
       SCREAMING_SNAKE_CASE__   =   inputs_dict['''attention_mask'''][:1, :]
       SCREAMING_SNAKE_CASE__   =   inputs_dict['''head_mask''']
       SCREAMING_SNAKE_CASE__   =   1

       # first forward pass
       SCREAMING_SNAKE_CASE__   =   model(lowercase_     ,       attention_mask=lowercase_     ,       head_mask=lowercase_     ,       use_cache=lowercase_ )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   outputs.to_tuple()
       SCREAMING_SNAKE_CASE__   =   past_key_values[1]



def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A=None				,		_A=None				,		_A=None				,		_A=None				,		_A=None				,		):





     '''simple docstring'''




     if attention_mask is None:
          SCREAMING_SNAKE_CASE__   =   tf.cast(tf.math.not_equal(a_				,		config.pad_token_id    )				,		tf.inta    )
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE__   =   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__   =   tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
     if decoder_head_mask is None:
          SCREAMING_SNAKE_CASE__   =   tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
     if cross_attn_head_mask is None:
          SCREAMING_SNAKE_CASE__   =   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 	UpperCAmelCase__		(	a_						,					a_						,					unittest.TestCase	):






  """simple docstring"""


  a									= (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  a									= (TFMBartForConditionalGeneration,) if is_tf_available() else ()
  a									= (
      {
          '''conversational''': TFMBartForConditionalGeneration,
          '''feature-extraction''': TFMBartModel,
          '''summarization''': TFMBartForConditionalGeneration,
          '''text2text-generation''': TFMBartForConditionalGeneration,
          '''translation''': TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  a									= True
  a									= False
  a									= False






  def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		int )		->							Any:

       if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
            # Exception encountered when calling layer '...'
            return True

       return False






  def 				lowercase_		(							self						:		Optional[Any] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   TFMBartModelTester(self )
       SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=lowercase_ )






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







  def 				lowercase_		(							self						:		int )		->							int:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*lowercase_ )






@require_sentencepiece
@require_tokenizers
@require_tf
class 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  a									= [
      ''' UN Chief Says There Is No Military Solution in Syria''',
  ]
  a									= [
      '''Şeful ONU declară că nu există o soluţie militară în Siria''',
  ]
  a									= '''facebook/mbart-large-en-ro'''






  @cached_property
  def 				lowercase_		(							self						:		int )		->							List[Any]:
       return AutoTokenizer.from_pretrained(self.model_name )






  @cached_property
  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
       return model






  def 				lowercase_		(							self						:		str     ,       **__lowerCamelCase						:		Tuple )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   self.translate_src_text(**lowercase_ )
       self.assertListEqual(self.expected_text     ,       lowercase_ )






  def 				lowercase_		(							self						:		Any     ,       **__lowerCamelCase						:		int )		->							str:
       SCREAMING_SNAKE_CASE__   =   self.tokenizer(self.src_text     ,       **lowercase_     ,       return_tensors='''tf''' )
       SCREAMING_SNAKE_CASE__   =   self.model.generate(
           model_inputs.input_ids     ,       attention_mask=model_inputs.attention_mask     ,       num_beams=2 )
       SCREAMING_SNAKE_CASE__   =   self.tokenizer.batch_decode(lowercase_     ,       skip_special_tokens=lowercase_ )
       return generated_words







  @slow
  def 				lowercase_		(							self						:		Dict )		->							Dict:
       self._assert_generated_batch_equal_expected()





import doctest
from collections import deque

import numpy as np






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		List[Any] )		->							None:
       SCREAMING_SNAKE_CASE__   =   [2, 1, 2, -1]
       SCREAMING_SNAKE_CASE__   =   [1, 2, 3, 4]







  def 				lowercase_		(							self						:		Optional[int] )		->							list[float]:
       SCREAMING_SNAKE_CASE__   =   len(self.first_signal )
       SCREAMING_SNAKE_CASE__   =   len(self.second_signal )

       SCREAMING_SNAKE_CASE__   =   max(__lowerCamelCase     ,       __lowerCamelCase )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__   =   [[0] * max_length for i in range(__lowerCamelCase )]

       # fills the smaller signal with zeros to make both signals of same length
       if length_first_signal < length_second_signal:
            self.first_signal += [0] * (max_length - length_first_signal)
       elif length_first_signal > length_second_signal:
            self.second_signal += [0] * (max_length - length_second_signal)
       for i in range(__lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   deque(self.second_signal )
            rotated_signal.rotate(__lowerCamelCase )
            for j, item in enumerate(__lowerCamelCase ):
                 matrix[i][j] += item

        # multiply the matrix with the first signal
       SCREAMING_SNAKE_CASE__   =   np.matmul(np.transpose(__lowerCamelCase )     ,       np.transpose(self.first_signal ) )

       # rounding-off to two decimal places
       return [round(__lowerCamelCase     ,       2 ) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()


