






"""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 		snake_case    :
    __magic_name__       	=	MarianConfig
    __magic_name__       	=	{}
    __magic_name__       	=	'''gelu'''





    def __init__(				self      :   Optional[int]							,						A      :   Tuple							,						A      :   Dict=1_3							,						A      :   Union[str, Any]=7							,						A      :   List[Any]=True							,						A      :   int=False							,						A      :   Any=9_9							,						A      :   Dict=3_2							,						A      :   Any=2							,						A      :   Dict=4							,						A      :   Union[str, Any]=3_7							,						A      :   List[Any]=0.1							,						A      :   List[str]=0.1							,						A      :   str=2_0							,						A      :   str=2							,						A      :   Tuple=1							,						A      :   str=0							,						):


      '''simple docstring'''




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

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





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




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

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

      a	:     int							=  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							,						)
      a	:     Any							=  prepare_marian_inputs_dict(A							,						A							,						A     )
      return config, inputs_dict





    def 							lowerCamelCase__ (				self      :   Any							,						A      :   List[Any]							,						A      :   int     ):


      '''simple docstring'''




      a	:     List[Any]							=  TFMarianModel(config=A     ).get_decoder()
      a	:     List[Any]							=  inputs_dict['input_ids']

      a	:     Union[str, Any]							=  input_ids[:1, :]
      a	:     Optional[int]							=  inputs_dict['attention_mask'][:1, :]
      a	:     List[str]							=  inputs_dict['head_mask']
      a	:     List[str]							=  1

      # first forward pass
      a	:     str							=  model(A							,						attention_mask=A							,						head_mask=A							,						use_cache=A     )

      a,	a	:     str							=  outputs.to_tuple()

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

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

      a	:     Tuple							=  model(A							,						attention_mask=A     )[0]
      a	:     Optional[int]							=  model(A							,						attention_mask=A							,						past_key_values=A     )[0]

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

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

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




def      snake_case    (A_ :Tuple      ,     A_ :str      ,     A_ :Optional[Any]      ,     A_ :Optional[Any]=None      ,     A_ :Optional[int]=None      ,     A_ :int=None      ,     A_ :Optional[int]=None      ,     A_ :Dict=None      ,     ):







  '''simple docstring'''

  if attention_mask is None:
    a	:     Optional[int]							=  tf.cast(tf.math.not_equal(A_      ,     config.pad_token_id )      ,     tf.inta )
  if decoder_attention_mask is None:
    a	:     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:
    a	:     List[str]							=  tf.ones((config.encoder_layers, config.encoder_attention_heads) )
  if decoder_head_mask is None:
    a	:     Union[str, Any]							=  tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  if cross_attn_head_mask is None:
    a	:     Optional[int]							=  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 		snake_case    (    UpperCAmelCase  ,					UpperCAmelCase  ,					unittest.TestCase			):
    __magic_name__       	=	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
    __magic_name__       	=	(TFMarianMTModel,) if is_tf_available() else ()
    __magic_name__       	=	(
        {
            '''conversational''': TFMarianMTModel,
            '''feature-extraction''': TFMarianModel,
            '''summarization''': TFMarianMTModel,
            '''text2text-generation''': TFMarianMTModel,
            '''translation''': TFMarianMTModel,
        }
        if is_tf_available()
        else {}
    )
    __magic_name__       	=	True
    __magic_name__       	=	False
    __magic_name__       	=	False





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




      a	:     Any							=  TFMarianModelTester(self     )
      a	:     Optional[int]							=  ConfigTester(self							,						config_class=A     )





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      self.config_tester.run_common_tests()





    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      a	:     Dict							=  self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*A     )






@require_tf
class 		snake_case    (    unittest.TestCase			):
    __magic_name__       	=	1000  # show more chars for failing integration tests





    @classmethod
    def 							lowerCamelCase__ (				cls      :   List[Any]     ):


      '''simple docstring'''




      a	:     List[Any]							=  F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls





    @cached_property
    def 							lowerCamelCase__ (				self      :   Optional[Any]     ):


      '''simple docstring'''




      return AutoTokenizer.from_pretrained(self.model_name     )





    @property
    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      return self.tokenizer.eos_token_id





    @cached_property
    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      warnings.simplefilter('error'     )
      a	:     TFMarianMTModel							=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
      assert isinstance(A							,						A     )
      a	:     Any							=  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 							lowerCamelCase__ (				self      :   Union[str, Any]							,						**A      :   Any     ):


      '''simple docstring'''




      a	:     Union[str, Any]							=  self.translate_src_text(**A     )
      self.assertListEqual(self.expected_text							,						A     )





    def 							lowerCamelCase__ (				self      :   Dict							,						**A      :   Optional[int]     ):


      '''simple docstring'''




      a	:     Optional[Any]							=  self.tokenizer(self.src_text							,						**A							,						padding=A							,						return_tensors='tf'     )
      a	:     Optional[int]							=  self.model.generate(
          model_inputs.input_ids							,						attention_mask=model_inputs.attention_mask							,						num_beams=2							,						max_length=1_2_8     )
      a	:     Dict							=  self.tokenizer.batch_decode(generated_ids.numpy()							,						skip_special_tokens=A     )
      return generated_words






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





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


      '''simple docstring'''




      self._assert_generated_batch_equal_expected()






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





    @unittest.skip('Skipping until #12647 is resolved.'     )
    @slow
    def 							lowerCamelCase__ (				self      :   Optional[Any]     ):


      '''simple docstring'''




      self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''en'''
    __magic_name__       	=	'''ROMANCE'''
    __magic_name__       	=	[
        '''>>fr<< Don\'t spend so much time watching TV.''',
        '''>>pt<< Your message has been sent.''',
        '''>>es<< He\'s two years older than me.''',
    ]
    __magic_name__       	=	[
        '''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 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      self._assert_generated_batch_equal_expected()





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


      '''simple docstring'''




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












"""simple docstring"""


from cva import destroyAllWindows, imread, imshow, waitKey




def      snake_case    (A_ :Optional[int]      ,     A_ :int ):







  '''simple docstring'''

  a,	a	:     Tuple							=  img.shape[0], img.shape[1]

  def to_grayscale(A_ :Tuple      ,     A_ :List[str]      ,     A_ :Union[str, Any] ):
    return 0.2126 * red + 0.587 * green + 0.114 * blue

  def normalize(A_ :Union[str, Any] ):
    return min(A_      ,     2_5_5 )

  for i in range(A_ ):
    for j in range(A_ ):
      a	:     Union[str, Any]							=  int(to_grayscale(*img[i][j] ) )
      a	:     Any							=  [
          normalize(A_ ),
          normalize(greyscale + factor ),
          normalize(greyscale + 2 * factor ),
      ]

  return img


if __name__ == "__main__":
   # read original image
   _UpperCamelCase							:       List[str]       					=		{
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()




