


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   lowerCamelCase(				a__		,a__		,a__=None		,a__=None		,a__=None		,a__=None		,a__=None		,a__=None		,):
				if attention_mask is None:
								_SCREAMING_SNAKE_CASE     					=np.where(input_ids != config.pad_token_id		,1		,0)
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE     					=np.where(decoder_input_ids != config.pad_token_id		,1		,0)
				if head_mask is None:
								_SCREAMING_SNAKE_CASE     					=np.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE     					=np.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								_SCREAMING_SNAKE_CASE     					=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   A__							:




						def __init__(    self   : str			,      _a   : str			,      _a   : Any=13			,      _a   : str=7			,      _a   : Union[str, Any]=True			,      _a   : List[str]=False			,      _a   : Tuple=99			,      _a   : List[str]=16			,      _a   : Any=2			,      _a   : Optional[Any]=4			,      _a   : List[Any]=4			,      _a   : str="gelu"			,      _a   : List[Any]=0.1			,      _a   : str=0.1			,      _a   : Optional[int]=32			,      _a   : List[Any]=2			,      _a   : Optional[Any]=1			,      _a   : Optional[Any]=0			,      _a   : str=0.02			,      )							->       Optional[int]:


										"""simple docstring"""





										_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_act
										_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
										_SCREAMING_SNAKE_CASE     					=initializer_range




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,      self.vocab_size					)			,      3			,      self.vocab_size					)
										_SCREAMING_SNAKE_CASE     					=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,      dtype=np.intaa					))			,      -1					)

										_SCREAMING_SNAKE_CASE     					=shift_tokens_right(_a			,      1			,      2					)

										_SCREAMING_SNAKE_CASE     					=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=_a			,      )
										_SCREAMING_SNAKE_CASE     					=prepare_marian_inputs_dict(_a			,      _a			,      _a					)
										return config, inputs_dict




						def 							__UpperCamelCase			(    self   : Any					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.prepare_config_and_inputs()
										return config, inputs_dict




						def 							__UpperCamelCase			(    self   : Any			,      _a   : Optional[int]			,      _a   : Optional[int]			,      _a   : Any					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=20
										_SCREAMING_SNAKE_CASE     					=model_class_name(_a					)

										_SCREAMING_SNAKE_CASE     					=model.encode(inputs_dict['''input_ids''']					)

										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=(
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										_SCREAMING_SNAKE_CASE     					=model.init_cache(decoder_input_ids.shape[0]			,      _a			,      _a					)
										_SCREAMING_SNAKE_CASE     					=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,      dtype='''i4'''					)

										_SCREAMING_SNAKE_CASE     					=jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]			,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,      )
										_SCREAMING_SNAKE_CASE     					=model.decode(
										    decoder_input_ids[:, :-1]			,      _a			,      decoder_attention_mask=_a			,      past_key_values=_a			,      decoder_position_ids=_a			,      )

										_SCREAMING_SNAKE_CASE     					=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,      dtype='''i4'''					)
										_SCREAMING_SNAKE_CASE     					=model.decode(
										    decoder_input_ids[:, -1:]			,      _a			,      decoder_attention_mask=_a			,      past_key_values=outputs_cache.past_key_values			,      decoder_position_ids=_a			,      )

										_SCREAMING_SNAKE_CASE     					=model.decode(_a			,      _a					)

										_SCREAMING_SNAKE_CASE     					=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 							__UpperCamelCase			(    self   : Tuple			,      _a   : Optional[int]			,      _a   : int			,      _a   : Optional[int]					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=20
										_SCREAMING_SNAKE_CASE     					=model_class_name(_a					)

										_SCREAMING_SNAKE_CASE     					=model.encode(inputs_dict['''input_ids''']					)

										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=(
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										_SCREAMING_SNAKE_CASE     					=jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
										    ]			,      axis=-1			,      )

										_SCREAMING_SNAKE_CASE     					=model.init_cache(decoder_input_ids.shape[0]			,      _a			,      _a					)
										_SCREAMING_SNAKE_CASE     					=jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]			,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,      )

										_SCREAMING_SNAKE_CASE     					=model.decode(
										    decoder_input_ids[:, :-1]			,      _a			,      decoder_attention_mask=_a			,      past_key_values=_a			,      decoder_position_ids=_a			,      )
										_SCREAMING_SNAKE_CASE     					=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,      dtype='''i4'''					)
										_SCREAMING_SNAKE_CASE     					=model.decode(
										    decoder_input_ids[:, -1:]			,      _a			,      past_key_values=outputs_cache.past_key_values			,      decoder_attention_mask=_a			,      decoder_position_ids=_a			,      )

										_SCREAMING_SNAKE_CASE     					=model.decode(_a			,      _a			,      decoder_attention_mask=_a					)

										_SCREAMING_SNAKE_CASE     					=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   A__							(						UpperCamelCase__				,       unittest.TestCase				,       UpperCamelCase__					):
						UpperCAmelCase     =  True
						UpperCAmelCase     =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						UpperCAmelCase     =  (FlaxMarianMTModel,) if is_flax_available() else ()




						def 							__UpperCamelCase			(    self   : str					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=FlaxMarianModelTester(self					)




						def 							__UpperCamelCase			(    self   : List[str]					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(_a			,      _a			,      _a					)




						def 							__UpperCamelCase			(    self   : str					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=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(_a			,      _a			,      _a					)




						def 							__UpperCamelCase			(    self   : Optional[Any]					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__					):
																		_SCREAMING_SNAKE_CASE     					=self._prepare_for_class(_a			,      _a					)
																		_SCREAMING_SNAKE_CASE     					=model_class(_a					)

																		@jax.jit
																		def encode_jitted(_a   : List[Any]			,      _a   : List[Any]=None			,      **_a   : Tuple					):
																						return model.encode(input_ids=_a			,      attention_mask=_a					)

																		with self.subTest('''JIT Enabled'''					):
																						_SCREAMING_SNAKE_CASE     					=encode_jitted(**_a					).to_tuple()

																		with self.subTest('''JIT Disabled'''					):
																						with jax.disable_jit():
																										_SCREAMING_SNAKE_CASE     					=encode_jitted(**_a					).to_tuple()

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




						def 							__UpperCamelCase			(    self   : int					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__					):
																		_SCREAMING_SNAKE_CASE     					=model_class(_a					)
																		_SCREAMING_SNAKE_CASE     					=model.encode(inputs_dict['''input_ids''']			,      inputs_dict['''attention_mask''']					)

																		_SCREAMING_SNAKE_CASE     					={
																		    '''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(_a   : List[str]			,      _a   : List[Any]			,      _a   : Any					):
																						return model.decode(
																						    decoder_input_ids=_a			,      decoder_attention_mask=_a			,      encoder_outputs=_a			,      )

																		with self.subTest('''JIT Enabled'''					):
																						_SCREAMING_SNAKE_CASE     					=decode_jitted(**_a					).to_tuple()

																		with self.subTest('''JIT Disabled'''					):
																						with jax.disable_jit():
																										_SCREAMING_SNAKE_CASE     					=decode_jitted(**_a					).to_tuple()

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




						@slow
						def 							__UpperCamelCase			(    self   : Dict					)							->       List[Any]:


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE     					=model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''					)
														# FlaxMarianForSequenceClassification expects eos token in input_ids
														_SCREAMING_SNAKE_CASE     					=np.ones((1, 1)					) * model.config.eos_token_id
														_SCREAMING_SNAKE_CASE     					=model(_a					)
														self.assertIsNotNone(_a					)

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						unittest.TestCase					):
						UpperCAmelCase     =  None
						UpperCAmelCase     =  None




						@classmethod
						def 							__UpperCamelCase			(    cls   : Dict					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
										return cls




						@cached_property
						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       str:


										"""simple docstring"""





										return MarianTokenizer.from_pretrained(self.model_name					)




						@property
						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       int:


										"""simple docstring"""





										return self.tokenizer.eos_token_id




						@cached_property
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=FlaxMarianMTModel.from_pretrained(self.model_name					)
										self.assertEqual(model.config.decoder_start_token_id			,      model.config.pad_token_id					)
										return model




						def 							__UpperCamelCase			(    self   : Tuple			,      **_a   : List[Any]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.translate_src_text(**_a					)
										self.assertListEqual(self.expected_text			,      _a					)




						def 							__UpperCamelCase			(    self   : Union[str, Any]			,      **_a   : int					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.tokenizer(self.src_text			,      padding=_a			,      return_tensors='''np'''			,      **_a					)
										_SCREAMING_SNAKE_CASE     					=self.model.generate(
										    model_inputs.input_ids			,      attention_mask=model_inputs.attention_mask			,      num_beams=2			,      max_length=128			,      ).sequences
										_SCREAMING_SNAKE_CASE     					=self.tokenizer.batch_decode(_a			,      skip_special_tokens=_a					)
										return generated_words

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						UpperCamelCase__					):
						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 							__UpperCamelCase			(    self   : Optional[Any]					)							->       int:


										"""simple docstring"""





										self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						UpperCamelCase__					):
						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 							__UpperCamelCase			(    self   : str					)							->       Tuple:


										"""simple docstring"""





										self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						UpperCamelCase__					):
						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 							__UpperCamelCase			(    self   : Tuple					)							->       Union[str, Any]:


										"""simple docstring"""





										self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						UpperCamelCase__					):
						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 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       Any:


										"""simple docstring"""





										self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						UpperCamelCase__					):
						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 							__UpperCamelCase			(    self   : Tuple					)							->       List[str]:


										"""simple docstring"""





										self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						UpperCamelCase__					):
						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 							__UpperCamelCase			(    self   : int					)							->       int:


										"""simple docstring"""





										self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class   A__							(						UpperCamelCase__					):
						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 							__UpperCamelCase			(    self   : Optional[Any]					)							->       Tuple:


										"""simple docstring"""





										self._assert_generated_batch_equal_expected()



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


snake_case_  :     Tuple							  =							logging.get_logger(__name__)

snake_case_  :     int							  =							{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class   A__							(						UpperCamelCase__					):
						UpperCAmelCase     =  "plbart"
						UpperCAmelCase     =  ["past_key_values"]
						UpperCAmelCase     =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




						def __init__(    self   : Union[str, Any]			,      _a   : List[str]=5_0005			,      _a   : List[str]=1024			,      _a   : int=6			,      _a   : Union[str, Any]=3072			,      _a   : List[str]=12			,      _a   : int=6			,      _a   : Optional[int]=3072			,      _a   : Any=12			,      _a   : Any=0.0			,      _a   : Optional[Any]=0.0			,      _a   : List[Any]=True			,      _a   : Any=True			,      _a   : Any="gelu"			,      _a   : int=768			,      _a   : int=0.1			,      _a   : int=0.1			,      _a   : List[str]=0.0			,      _a   : Optional[int]=0.02			,      _a   : Dict=0.0			,      _a   : List[str]=True			,      _a   : List[Any]=1			,      _a   : Tuple=0			,      _a   : Tuple=2			,      _a   : Optional[int]=2			,      **_a   : Optional[int]			,      )							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=vocab_size
										_SCREAMING_SNAKE_CASE     					=max_position_embeddings
										_SCREAMING_SNAKE_CASE     					=d_model
										_SCREAMING_SNAKE_CASE     					=encoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=encoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=decoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=decoder_layers
										_SCREAMING_SNAKE_CASE     					=decoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=dropout
										_SCREAMING_SNAKE_CASE     					=attention_dropout
										_SCREAMING_SNAKE_CASE     					=activation_dropout
										_SCREAMING_SNAKE_CASE     					=activation_function
										_SCREAMING_SNAKE_CASE     					=init_std
										_SCREAMING_SNAKE_CASE     					=encoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=decoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=classifier_dropout
										_SCREAMING_SNAKE_CASE     					=use_cache
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=_a			,      bos_token_id=_a			,      eos_token_id=_a			,      is_encoder_decoder=_a			,      forced_eos_token_id=_a			,      **_a			,      )

class   A__							(						UpperCamelCase__					):




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										return OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
										    ]					)




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.use_past:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)
										else:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)