

from cva import destroyAllWindows, imread, imshow, waitKey







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    int	)					->					Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:     Optional[Any]     			=   img.shape[0], img.shape[1]

 def to_grayscale(SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Tuple	):
  return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

 def normalize(SCREAMING_SNAKE_CASE	:    Optional[Any]	):
  return min(SCREAMING_SNAKE_CASE					,      2_55	)

 for i in range(SCREAMING_SNAKE_CASE	):
  for j in range(SCREAMING_SNAKE_CASE	):
   __lowerCAmelCase:     Any     			=   int(to_grayscale(*img[i][j]	)	)
   __lowerCAmelCase:     List[Any]     			=   [
       normalize(SCREAMING_SNAKE_CASE	),
       normalize(greyscale + factor	),
       normalize(greyscale + 2 * factor	),
   ]

 return img


if __name__ == "__main__":
 # read original image
 _a	 =     {
     percentage: imread('''image_data/lena.jpg''', 1) for percentage in (1_0, 2_0, 3_0, 4_0)
 }

 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()








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_				:
     _lowercase    :  str          =			MarianConfig
     _lowercase    :  Optional[int]          =			{}
     _lowercase    :  Optional[Any]          =			'gelu'


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]=1_3    ,						UpperCAmelCase    :	Optional[int]=7    ,						UpperCAmelCase    :	List[str]=True    ,						UpperCAmelCase    :	Dict=False    ,						UpperCAmelCase    :	Union[str, Any]=9_9    ,						UpperCAmelCase    :	Tuple=3_2    ,						UpperCAmelCase    :	List[str]=2    ,						UpperCAmelCase    :	Dict=4    ,						UpperCAmelCase    :	Tuple=3_7    ,						UpperCAmelCase    :	Union[str, Any]=0.1    ,						UpperCAmelCase    :	Dict=0.1    ,						UpperCAmelCase    :	Any=2_0    ,						UpperCAmelCase    :	Union[str, Any]=2    ,						UpperCAmelCase    :	Any=1    ,						UpperCAmelCase    :	str=0    ,						)							->			str:
      __lowerCAmelCase:     Tuple     			=   parent
      __lowerCAmelCase:     Any     			=   batch_size
      __lowerCAmelCase:     List[Any]     			=   seq_length
      __lowerCAmelCase:     Dict     			=   is_training
      __lowerCAmelCase:     List[Any]     			=   use_labels
      __lowerCAmelCase:     List[str]     			=   vocab_size
      __lowerCAmelCase:     Optional[Any]     			=   hidden_size
      __lowerCAmelCase:     Any     			=   num_hidden_layers
      __lowerCAmelCase:     Optional[Any]     			=   num_attention_heads
      __lowerCAmelCase:     int     			=   intermediate_size

      __lowerCAmelCase:     List[Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     List[str]     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Tuple     			=   max_position_embeddings
      __lowerCAmelCase:     Tuple     			=   eos_token_id
      __lowerCAmelCase:     List[Any]     			=   pad_token_id
      __lowerCAmelCase:     str     			=   bos_token_id


     def 						UpperCAmelCase						(							self    :	str      )							->			Optional[int]:
      __lowerCAmelCase:     int     			=   ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size      )
      __lowerCAmelCase:     Optional[int]     			=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )    ,						1      )
      __lowerCAmelCase:     List[Any]     			=   tf.concat([input_ids, eos_tensor]    ,						axis=1      )

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

      __lowerCAmelCase:     Optional[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    ,						)
      __lowerCAmelCase:     Any     			=   prepare_marian_inputs_dict(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return config, inputs_dict


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	int      )							->			Union[str, Any]:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModel(config=UpperCAmelCase      ).get_decoder()
      __lowerCAmelCase:     str     			=   inputs_dict['input_ids']

      __lowerCAmelCase:     Tuple     			=   input_ids[:1, :]
      __lowerCAmelCase:     Optional[Any]     			=   inputs_dict['attention_mask'][:1, :]
      __lowerCAmelCase:     Dict     			=   inputs_dict['head_mask']
      __lowerCAmelCase:     List[Any]     			=   1

      # first forward pass
      __lowerCAmelCase:     Union[str, Any]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						head_mask=UpperCAmelCase    ,						use_cache=UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   outputs.to_tuple()

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

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

      __lowerCAmelCase:     Optional[int]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase      )[0]
      __lowerCAmelCase:     List[str]     			=   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:     Optional[int]     			=   int(ids_tensor((1,)    ,						output_from_past.shape[-1]      )      )
      __lowerCAmelCase:     Tuple     			=   output_from_no_past[:, -3:, random_slice_idx]
      __lowerCAmelCase:     Dict     			=   output_from_past[:, :, random_slice_idx]

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







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[int]=None					,      SCREAMING_SNAKE_CASE	:    Dict=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      SCREAMING_SNAKE_CASE	:    List[str]=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      )					->					Dict:

 """simple docstring"""






 if attention_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE					,      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:     List[Any]     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  __lowerCAmelCase:     Dict     			=   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      ):
     _lowercase    :  Optional[int]          =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _lowercase    :  int          =			(TFMarianMTModel,) if is_tf_available() else ()
     _lowercase    :  Optional[int]          =			(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _lowercase    :  Dict          =			True
     _lowercase    :  List[Any]          =			False
     _lowercase    :  Optional[int]          =			False


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModelTester(self      )
      __lowerCAmelCase:     Any     			=   ConfigTester(self    ,						config_class=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Any:
      self.config_tester.run_common_tests()


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     Tuple     			=   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      ):
     _lowercase    :  int          =			1_0_0_0  # show more chars for failing integration tests


     @classmethod
     def 						UpperCAmelCase						(							cls    :	Dict      )							->			None:
      __lowerCAmelCase:     int     			=   F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls


     @cached_property
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			MarianTokenizer:
      return AutoTokenizer.from_pretrained(self.model_name      )


     @property
     def 						UpperCAmelCase						(							self    :	Any      )							->			int:
      return self.tokenizer.eos_token_id


     @cached_property
     def 						UpperCAmelCase						(							self    :	int      )							->			Optional[int]:
      warnings.simplefilter('error'      )
      __lowerCAmelCase:     TFMarianMTModel     			=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
      assert isinstance(UpperCAmelCase    ,						UpperCAmelCase      )
      __lowerCAmelCase:     List[str]     			=   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 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Dict      )							->			int:
      __lowerCAmelCase:     Optional[int]     			=   self.translate_src_text(**UpperCAmelCase      )
      self.assertListEqual(self.expected_text    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Optional[int]      )							->			Optional[int]:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(self.src_text    ,						**UpperCAmelCase    ,						padding=UpperCAmelCase    ,						return_tensors='tf'      )
      __lowerCAmelCase:     Tuple     			=   self.model.generate(
          model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=1_2_8      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=UpperCAmelCase      )
      return generated_words







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


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Any      )							->			List[str]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Optional[int]          =			'en'
     _lowercase    :  Optional[Any]          =			'zh'
     _lowercase    :  Tuple          =			['My name is Wolfgang and I live in Berlin']
     _lowercase    :  List[Any]          =			['我叫沃尔夫冈 我住在柏林']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Optional[Any]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Tuple          =			'en'
     _lowercase    :  Optional[int]          =			'ROMANCE'
     _lowercase    :  Any          =			[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     _lowercase    :  Tuple          =			[
         '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 						UpperCAmelCase						(							self    :	Tuple      )							->			Dict:
      self._assert_generated_batch_equal_expected()


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[str]:
      __lowerCAmelCase:     Optional[Any]     			=   TranslationPipeline(self.model    ,						self.tokenizer    ,						framework='tf'      )
      __lowerCAmelCase:     Tuple     			=   pipeline(self.src_text      )
      self.assertEqual(self.expected_text    ,						[x['translation_text'] for x in output]      )





