'''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 				lowercase    :
   lowerCamelCase_            =MarianConfig
   lowerCamelCase_            ={}
   lowerCamelCase_            ='gelu'


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						List[Any]=13 ,		__lowerCAmelCase					:						List[Any]=7 ,		__lowerCAmelCase					:						Union[str, Any]=True ,		__lowerCAmelCase					:						str=False ,		__lowerCAmelCase					:						Optional[int]=99 ,		__lowerCAmelCase					:						Dict=32 ,		__lowerCAmelCase					:						List[Any]=2 ,		__lowerCAmelCase					:						Dict=4 ,		__lowerCAmelCase					:						List[Any]=37 ,		__lowerCAmelCase					:						str=0.1 ,		__lowerCAmelCase					:						int=0.1 ,		__lowerCAmelCase					:						Union[str, Any]=20 ,		__lowerCAmelCase					:						Any=2 ,		__lowerCAmelCase					:						Optional[int]=1 ,		__lowerCAmelCase					:						int=0 ,		)   ->       List[Any]:
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   seq_length
         lowercase_     			=   is_training
         lowercase_     			=   use_labels
         lowercase_     			=   vocab_size
         lowercase_     			=   hidden_size
         lowercase_     			=   num_hidden_layers
         lowercase_     			=   num_attention_heads
         lowercase_     			=   intermediate_size

         lowercase_     			=   hidden_dropout_prob
         lowercase_     			=   attention_probs_dropout_prob
         lowercase_     			=   max_position_embeddings
         lowercase_     			=   eos_token_id
         lowercase_     			=   pad_token_id
         lowercase_     			=   bos_token_id


   def __UpperCAmelCase							( self					:						List[Any])   ->       Dict:
         lowercase_     			=   ids_tensor([self.batch_size, self.seq_length - 1] ,		self.vocab_size)
         lowercase_     			=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) ,		1)
         lowercase_     			=   tf.concat([input_ids, eos_tensor] ,		axis=1)

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

         lowercase_     			=   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 ,		)
         lowercase_     			=   prepare_marian_inputs_dict(_lowercase ,		_lowercase ,		_lowercase)
         return config, inputs_dict


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Tuple)   ->       Dict:
         lowercase_     			=   TFMarianModel(config=_lowercase).get_decoder()
         lowercase_     			=   inputs_dict["""input_ids"""]

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

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

         lowercase_     			=   outputs.to_tuple()

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

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

         lowercase_     			=   model(_lowercase ,		attention_mask=_lowercase)[0]
         lowercase_     			=   model(_lowercase ,		attention_mask=_lowercase ,		past_key_values=_lowercase)[0]

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

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

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




def __a			(						__lowerCamelCase      :    Dict			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    Dict			,		__lowerCamelCase      :    Any=None			,		__lowerCamelCase      :    Union[str, Any]=None			,		__lowerCamelCase      :    Union[str, Any]=None			,		__lowerCamelCase      :    int=None			,		__lowerCamelCase      :    Tuple=None			,		)						->				Optional[int]:
      '''simple docstring'''


      if attention_mask is None:
            lowercase_     			=   tf.cast(tf.math.not_equal(_a			,		config.pad_token_id					)			,		tf.inta					)
      if decoder_attention_mask is None:
            lowercase_     			=   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:
            lowercase_     			=   tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
      if decoder_head_mask is None:
            lowercase_     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
      if cross_attn_head_mask is None:
            lowercase_     			=   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 				lowercase    (    __A			,					__A			,					unittest.TestCase			):
   lowerCamelCase_            =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   lowerCamelCase_            =(TFMarianMTModel,) if is_tf_available() else ()
   lowerCamelCase_            =(
       {
           'conversational': TFMarianMTModel,
           'feature-extraction': TFMarianModel,
           'summarization': TFMarianMTModel,
           'text2text-generation': TFMarianMTModel,
           'translation': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   lowerCamelCase_            =True
   lowerCamelCase_            =False
   lowerCamelCase_            =False


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Dict:
         lowercase_     			=   TFMarianModelTester(self)
         lowercase_     			=   ConfigTester(self ,		config_class=_lowercase)


   def __UpperCAmelCase							( self					:						Dict)   ->       Any:
         self.config_tester.run_common_tests()


   def __UpperCAmelCase							( self					:						List[Any])   ->       List[str]:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*_lowercase)





@require_tf
class 				lowercase    (    unittest.TestCase			):
   lowerCamelCase_            =1_0_0_0  # show more chars for failing integration tests


   @classmethod
   def __UpperCAmelCase							( cls					:						Dict)   ->       Any:
         lowercase_     			=   F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
         return cls


   @cached_property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return AutoTokenizer.from_pretrained(self.model_name)


   @property
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       int:
         return self.tokenizer.eos_token_id


   @cached_property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Union[str, Any]:
         warnings.simplefilter("error")
         lowercase_     			=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
         assert isinstance(_lowercase ,		_lowercase)
         lowercase_     			=   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 __UpperCAmelCase							( self					:						Tuple ,		**__lowerCAmelCase					:						Tuple)   ->       Any:
         lowercase_     			=   self.translate_src_text(**_lowercase)
         self.assertListEqual(self.expected_text ,		_lowercase)


   def __UpperCAmelCase							( self					:						Optional[Any] ,		**__lowerCAmelCase					:						Dict)   ->       List[Any]:
         lowercase_     			=   self.tokenizer(self.src_text ,		**_lowercase ,		padding=_lowercase ,		return_tensors="tf")
         lowercase_     			=   self.model.generate(
             model_inputs.input_ids ,		attention_mask=model_inputs.attention_mask ,		num_beams=2 ,		max_length=128)
         lowercase_     			=   self.tokenizer.batch_decode(generated_ids.numpy() ,		skip_special_tokens=_lowercase)
         return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowercase    (    __A			):
   lowerCamelCase_            ='mt'
   lowerCamelCase_            ='en'
   lowerCamelCase_            =['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
   lowerCamelCase_            =['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					:						str)   ->       str:
         self._assert_generated_batch_equal_expected()





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


   @unittest.skip("Skipping until #12647 is resolved.")
   @slow
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Any:
         self._assert_generated_batch_equal_expected()





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


   @unittest.skip("Skipping until #12647 is resolved.")
   @slow
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Optional[Any]:
         lowercase_     			=   TranslationPipeline(self.model ,		self.tokenizer ,		framework="tf")
         lowercase_     			=   pipeline(self.src_text)
         self.assertEqual(self.expected_text ,		[x["translation_text"] for x in output])













def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							Dict			 =		0
     for plain_chr in plain_text:
          lowerCAmelCase_       :							List[Any]			 =		(
              ord(_a      ) + (hash_value << 6) + (hash_value << 16) - hash_value
          )
     return hash_value
