


"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 snake_case__		:  List[str]     					=					'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import MarianTokenizer
 from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def   _snake_case    (    _snake_case					:      Dict			, _snake_case					:      Tuple			, _snake_case					:      int=None			, _snake_case					:      Dict=None			, _snake_case					:      List[str]=None			, _snake_case					:      Optional[int]=None			, _snake_case					:      Any=None			, _snake_case					:      Dict=None			, ):
      if attention_mask is None:
            lowerCAmelCase       :						Any							= np.where(input_ids != config.pad_token_id			, 1			, 0      )
      if decoder_attention_mask is None:
            lowerCAmelCase       :						List[str]							= np.where(decoder_input_ids != config.pad_token_id			, 1			, 0      )
      if head_mask is None:
            lowerCAmelCase       :						Optional[int]							= np.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            lowerCAmelCase       :						List[Any]							= np.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            lowerCAmelCase       :						List[str]							= np.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": attention_mask,
      }



class 		snake_case_:






     def __init__(						self	:       str       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       int=1_3       ,					UpperCamelCase_	:       Optional[Any]=7       ,					UpperCamelCase_	:       List[Any]=True       ,					UpperCamelCase_	:       Optional[Any]=False       ,					UpperCamelCase_	:       int=9_9       ,					UpperCamelCase_	:       str=1_6       ,					UpperCamelCase_	:       Optional[int]=2       ,					UpperCamelCase_	:       Optional[Any]=4       ,					UpperCamelCase_	:       str=4       ,					UpperCamelCase_	:       str="gelu"       ,					UpperCamelCase_	:       Tuple=0.1       ,					UpperCamelCase_	:       Union[str, Any]=0.1       ,					UpperCamelCase_	:       Tuple=3_2       ,					UpperCamelCase_	:       Union[str, Any]=2       ,					UpperCamelCase_	:       Optional[int]=1       ,					UpperCamelCase_	:       List[str]=0       ,					UpperCamelCase_	:       List[str]=0.02       ,					):
           lowerCAmelCase       :						Any							= parent
           lowerCAmelCase       :						Tuple							= batch_size
           lowerCAmelCase       :						Optional[Any]							= seq_length
           lowerCAmelCase       :						Tuple							= is_training
           lowerCAmelCase       :						Dict							= use_labels
           lowerCAmelCase       :						str							= vocab_size
           lowerCAmelCase       :						Union[str, Any]							= hidden_size
           lowerCAmelCase       :						Optional[Any]							= num_hidden_layers
           lowerCAmelCase       :						Union[str, Any]							= num_attention_heads
           lowerCAmelCase       :						Optional[Any]							= intermediate_size
           lowerCAmelCase       :						str							= hidden_act
           lowerCAmelCase       :						Optional[int]							= hidden_dropout_prob
           lowerCAmelCase       :						str							= attention_probs_dropout_prob
           lowerCAmelCase       :						int							= max_position_embeddings
           lowerCAmelCase       :						List[Any]							= eos_token_id
           lowerCAmelCase       :						int							= pad_token_id
           lowerCAmelCase       :						Union[str, Any]							= bos_token_id
           lowerCAmelCase       :						Optional[Any]							= initializer_range






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase       :						Dict							= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,					self.vocab_size					)       ,					3       ,					self.vocab_size					)
           lowerCAmelCase       :						int							= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,					dtype=np.intaa					))       ,					-1					)

           lowerCAmelCase       :						str							= shift_tokens_right(UpperCamelCase_       ,					1       ,					2					)

           lowerCAmelCase       :						int							= MarianConfig(
               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_id=self.eos_token_id       ,					bos_token_id=self.bos_token_id       ,					pad_token_id=self.pad_token_id       ,					initializer_range=self.initializer_range       ,					use_cache=UpperCamelCase_       ,					)
           lowerCAmelCase       :						Tuple							= prepare_marian_inputs_dict(UpperCamelCase_       ,					UpperCamelCase_       ,					UpperCamelCase_					)
           return config, inputs_dict






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase,   lowerCAmelCase       :						Dict							= self.prepare_config_and_inputs()
           return config, inputs_dict






     def   lowerCamelCase__  (						self	:       Any       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       int					):
           lowerCAmelCase       :						Optional[int]							= 2_0
           lowerCAmelCase       :						List[Any]							= model_class_name(UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= model.encode(inputs_dict['''input_ids''']					)

           lowerCAmelCase,   lowerCAmelCase       :						Optional[Any]							= (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           lowerCAmelCase       :						List[str]							= model.init_cache(decoder_input_ids.shape[0]       ,					UpperCamelCase_       ,					UpperCamelCase_					)
           lowerCAmelCase       :						Optional[int]							= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,					dtype='''i4'''					)

           lowerCAmelCase       :						Dict							= jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,					)
           lowerCAmelCase       :						int							= model.decode(
               decoder_input_ids[:, :-1]       ,					UpperCamelCase_       ,					decoder_attention_mask=UpperCamelCase_       ,					past_key_values=UpperCamelCase_       ,					decoder_position_ids=UpperCamelCase_       ,					)

           lowerCAmelCase       :						str							= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,					dtype='''i4'''					)
           lowerCAmelCase       :						Optional[int]							= model.decode(
               decoder_input_ids[:, -1:]       ,					UpperCamelCase_       ,					decoder_attention_mask=UpperCamelCase_       ,					past_key_values=outputs_cache.past_key_values       ,					decoder_position_ids=UpperCamelCase_       ,					)

           lowerCAmelCase       :						Optional[Any]							= model.decode(UpperCamelCase_       ,					UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
           self.parent.assertTrue(diff < 1E-3       ,					msg=F'''Max diff is {diff}'''					)






     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       List[Any]					):
           lowerCAmelCase       :						List[Any]							= 2_0
           lowerCAmelCase       :						Optional[int]							= model_class_name(UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= model.encode(inputs_dict['''input_ids''']					)

           lowerCAmelCase,   lowerCAmelCase       :						List[Any]							= (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           lowerCAmelCase       :						List[Any]							= jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
               ]       ,					axis=-1       ,					)

           lowerCAmelCase       :						Any							= model.init_cache(decoder_input_ids.shape[0]       ,					UpperCamelCase_       ,					UpperCamelCase_					)
           lowerCAmelCase       :						List[str]							= jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,					)

           lowerCAmelCase       :						Optional[int]							= model.decode(
               decoder_input_ids[:, :-1]       ,					UpperCamelCase_       ,					decoder_attention_mask=UpperCamelCase_       ,					past_key_values=UpperCamelCase_       ,					decoder_position_ids=UpperCamelCase_       ,					)
           lowerCAmelCase       :						Union[str, Any]							= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,					dtype='''i4'''					)
           lowerCAmelCase       :						int							= model.decode(
               decoder_input_ids[:, -1:]       ,					UpperCamelCase_       ,					past_key_values=outputs_cache.past_key_values       ,					decoder_attention_mask=UpperCamelCase_       ,					decoder_position_ids=UpperCamelCase_       ,					)

           lowerCAmelCase       :						List[str]							= model.decode(UpperCamelCase_       ,					UpperCamelCase_       ,					decoder_attention_mask=UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
           self.parent.assertTrue(diff < 1E-3       ,					msg=F'''Max diff is {diff}'''					)



@require_flax
class 		snake_case_(							a__				,       unittest.TestCase				,       a__      ):
     __UpperCamelCase	=       True
     __UpperCamelCase	=       (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     __UpperCamelCase	=       (FlaxMarianMTModel,) if is_flax_available() else ()






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase       :						Optional[Any]							= FlaxMarianModelTester(self					)






     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						Dict							= self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCamelCase_       ,					UpperCamelCase_       ,					UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase,   lowerCAmelCase       :						List[str]							= self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_       ,					UpperCamelCase_       ,					UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase,   lowerCAmelCase       :						List[Any]							= self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__					):
                       lowerCAmelCase       :						Optional[Any]							= self._prepare_for_class(UpperCamelCase_       ,					UpperCamelCase_					)
                       lowerCAmelCase       :						List[Any]							= model_class(UpperCamelCase_					)

                       @jax.jit
                       def encode_jitted(UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Dict=None       ,					**UpperCamelCase_	:       Optional[Any]					):
                             return model.encode(input_ids=UpperCamelCase_       ,					attention_mask=UpperCamelCase_					)

                       with self.subTest('''JIT Enabled'''					):
                             lowerCAmelCase       :						Optional[Any]							= encode_jitted(**UpperCamelCase_					).to_tuple()

                       with self.subTest('''JIT Disabled'''					):
                             with jax.disable_jit():
                                   lowerCAmelCase       :						List[Any]							= encode_jitted(**UpperCamelCase_					).to_tuple()

                       self.assertEqual(len(UpperCamelCase_					)       ,					len(UpperCamelCase_					)					)
                       for jitted_output, output in zip(UpperCamelCase_       ,					UpperCamelCase_					):
                             self.assertEqual(jitted_output.shape       ,					output.shape					)






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						List[Any]							= self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__					):
                       lowerCAmelCase       :						List[str]							= model_class(UpperCamelCase_					)
                       lowerCAmelCase       :						str							= model.encode(inputs_dict['''input_ids''']       ,					inputs_dict['''attention_mask''']					)

                       lowerCAmelCase       :						str							= {
                           '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                           '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                           '''encoder_outputs''': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Optional[Any]					):
                             return model.decode(
                                 decoder_input_ids=UpperCamelCase_       ,					decoder_attention_mask=UpperCamelCase_       ,					encoder_outputs=UpperCamelCase_       ,					)

                       with self.subTest('''JIT Enabled'''					):
                             lowerCAmelCase       :						Union[str, Any]							= decode_jitted(**UpperCamelCase_					).to_tuple()

                       with self.subTest('''JIT Disabled'''					):
                             with jax.disable_jit():
                                   lowerCAmelCase       :						List[Any]							= decode_jitted(**UpperCamelCase_					).to_tuple()

                       self.assertEqual(len(UpperCamelCase_					)       ,					len(UpperCamelCase_					)					)
                       for jitted_output, output in zip(UpperCamelCase_       ,					UpperCamelCase_					):
                             self.assertEqual(jitted_output.shape       ,					output.shape					)






     @slow
     def   lowerCamelCase__  (						self	:       List[str]					):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase       :						List[Any]							= model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''					)
                 # FlaxMarianForSequenceClassification expects eos token in input_ids
                 lowerCAmelCase       :						str							= np.ones((1, 1)					) * model.config.eos_token_id
                 lowerCAmelCase       :						List[Any]							= model(UpperCamelCase_					)
                 self.assertIsNotNone(UpperCamelCase_					)



@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_(							unittest.TestCase      ):
     __UpperCamelCase	=       None
     __UpperCamelCase	=       None






     @classmethod
     def   lowerCamelCase__  (						cls	:       Union[str, Any]					):
           lowerCAmelCase       :						int							= F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
           return cls






     @cached_property
     def   lowerCamelCase__  (						self	:       int					):
           return MarianTokenizer.from_pretrained(self.model_name					)






     @property
     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           return self.tokenizer.eos_token_id






     @cached_property
     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           lowerCAmelCase       :						FlaxMarianMTModel							= FlaxMarianMTModel.from_pretrained(self.model_name					)
           self.assertEqual(model.config.decoder_start_token_id       ,					model.config.pad_token_id					)
           return model






     def   lowerCamelCase__  (						self	:       Any       ,					**UpperCamelCase_	:       Any					):
           lowerCAmelCase       :						List[str]							= self.translate_src_text(**UpperCamelCase_					)
           self.assertListEqual(self.expected_text       ,					UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Dict       ,					**UpperCamelCase_	:       str					):
           lowerCAmelCase       :						Dict							= self.tokenizer(self.src_text       ,					padding=UpperCamelCase_       ,					return_tensors='''np'''       ,					**UpperCamelCase_					)
           lowerCAmelCase       :						Union[str, Any]							= self.model.generate(
               model_inputs.input_ids       ,					attention_mask=model_inputs.attention_mask       ,					num_beams=2       ,					max_length=1_2_8       ,					).sequences
           lowerCAmelCase       :						Any							= self.tokenizer.batch_decode(UpperCamelCase_       ,					skip_special_tokens=UpperCamelCase_					)
           return generated_words



@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''en'''
     __UpperCamelCase	=       '''fr'''
     __UpperCamelCase	=       [
         '''I am a small frog.''',
         '''Now I can forget the 100 words of german that I know.''',
     ]
     __UpperCamelCase	=       [
         '''Je suis une petite grenouille.''',
         '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
     ]






     @slow
     def   lowerCamelCase__  (						self	:       str					):
           self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''fr'''
     __UpperCamelCase	=       '''en'''
     __UpperCamelCase	=       [
         '''Donnez moi le micro.''',
         '''Tom et Mary étaient assis à une table.''',  # Accents
     ]
     __UpperCamelCase	=       [
         '''Give me the microphone.''',
         '''Tom and Mary were sitting at a table.''',
     ]






     @slow
     def   lowerCamelCase__  (						self	:       List[str]					):
           self._assert_generated_batch_equal_expected()



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






     @slow
     def   lowerCamelCase__  (						self	:       Any					):
           self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''en'''
     __UpperCamelCase	=       '''de'''
     __UpperCamelCase	=       [
         '''I am a small frog.''',
         '''Now I can forget the 100 words of german that I know.''',
         '''Tom asked his teacher for advice.''',
         '''That\'s how I would do it.''',
         '''Tom really admired Mary\'s courage.''',
         '''Turn around and close your eyes.''',
     ]
     __UpperCamelCase	=       [
         '''Ich bin ein kleiner Frosch.''',
         '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
         '''Tom bat seinen Lehrer um Rat.''',
         '''So würde ich das machen.''',
         '''Tom bewunderte Marias Mut wirklich.''',
         '''Drehen Sie sich um und schließen Sie die Augen.''',
     ]






     @slow
     def   lowerCamelCase__  (						self	:       Any					):
           self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''en'''
     __UpperCamelCase	=       '''zh'''
     __UpperCamelCase	=       ['''My name is Wolfgang and I live in Berlin''']
     __UpperCamelCase	=       ['''我叫沃尔夫冈 我住在柏林''']






     @slow
     def   lowerCamelCase__  (						self	:       List[Any]					):
           self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''ru'''
     __UpperCamelCase	=       '''fr'''
     __UpperCamelCase	=       ['''Он показал мне рукопись своей новой пьесы.''']
     __UpperCamelCase	=       ['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']






     @slow
     def   lowerCamelCase__  (						self	:       List[str]					):
           self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''en'''
     __UpperCamelCase	=       '''ROMANCE'''
     __UpperCamelCase	=       [
         '''>>fr<< Don\'t spend so much time watching TV.''',
         '''>>pt<< Your message has been sent.''',
         '''>>es<< He\'s two years older than me.''',
     ]
     __UpperCamelCase	=       [
         '''Ne passez pas autant de temps à regarder la télé.''',
         '''A sua mensagem foi enviada.''',
         '''Es dos años más viejo que yo.''',
     ]






     @slow
     def   lowerCamelCase__  (						self	:       Dict					):
           self._assert_generated_batch_equal_expected()






"""simple docstring"""





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__		:  Optional[Any]     					=					logging.get_logger(__name__)

snake_case__		:  str     					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''prophetnet'''
     __UpperCamelCase	=       ['''past_key_values''']
     __UpperCamelCase	=       {
         '''num_attention_heads''': '''num_encoder_attention_heads''',
     }






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[Union[str, Callable]] = "gelu"       ,					UpperCamelCase_	:       Optional[int] = 3_0_5_2_2       ,					UpperCamelCase_	:       Optional[int] = 1_0_2_4       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[int] = 5_1_2       ,					UpperCamelCase_	:       Optional[float] = 0.02       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 2       ,					UpperCamelCase_	:       Optional[int] = 3_2       ,					UpperCamelCase_	:       Optional[int] = 1_2_8       ,					UpperCamelCase_	:       Optional[bool] = False       ,					UpperCamelCase_	:       Optional[float] = 0.0       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 1       ,					UpperCamelCase_	:       Optional[int] = 2       ,					**UpperCamelCase_	:       List[Any]       ,					):
           lowerCAmelCase       :						Union[str, Any]							= vocab_size
           lowerCAmelCase       :						Any							= hidden_size
           lowerCAmelCase       :						Tuple							= encoder_ffn_dim
           lowerCAmelCase       :						str							= num_encoder_layers
           lowerCAmelCase       :						int							= num_encoder_attention_heads
           lowerCAmelCase       :						Optional[Any]							= decoder_ffn_dim
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_layers
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_attention_heads
           lowerCAmelCase       :						List[Any]							= max_position_embeddings
           lowerCAmelCase       :						Dict							= init_std  # Normal(0, this parameter)
           lowerCAmelCase       :						Optional[int]							= activation_function

           # parameters for prophetnet
           lowerCAmelCase       :						Tuple							= ngram
           lowerCAmelCase       :						List[Any]							= num_buckets
           lowerCAmelCase       :						Optional[Any]							= relative_max_distance
           lowerCAmelCase       :						Dict							= disable_ngram_loss
           lowerCAmelCase       :						Optional[int]							= eps

           # 3 Types of Dropout
           lowerCAmelCase       :						Any							= attention_dropout
           lowerCAmelCase       :						List[Any]							= activation_dropout
           lowerCAmelCase       :						Tuple							= dropout

           lowerCAmelCase       :						Optional[int]							= use_cache

           super().__init__(
               pad_token_id=UpperCamelCase_       ,					bos_token_id=UpperCamelCase_       ,					eos_token_id=UpperCamelCase_       ,					is_encoder_decoder=UpperCamelCase_       ,					add_cross_attention=UpperCamelCase_       ,					decoder_start_token_id=UpperCamelCase_       ,					**UpperCamelCase_       ,					)






     @property
     def   lowerCamelCase__  (						self	:       str					):
           return self.num_encoder_layers + self.num_decoder_layers






     @num_hidden_layers.setter
     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       int					):
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.'''					)


