



import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
    from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def 		UpperCamelCase	(   snake_case__       :    Union[str, Any]       ,     snake_case__       :    Tuple       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[Any]=None       ,     snake_case__       :    Tuple=None       ,     snake_case__       :    int=None       ,     snake_case__       :    List[Any]=None       ,     snake_case__       :    int=None       ,     )	->     Any:
   if attention_mask is None:
      UpperCamelCase    :  str							=	input_ids.ne(config.pad_token_id	)
   if decoder_attention_mask is None:
      UpperCamelCase    :  Dict							=	decoder_input_ids.ne(config.pad_token_id	)
   if head_mask is None:
      UpperCamelCase    :  Optional[Any]							=	torch.ones(config.encoder_layers       ,     config.encoder_attention_heads       ,     device=A__	)
   if decoder_head_mask is None:
      UpperCamelCase    :  List[Any]							=	torch.ones(config.decoder_layers       ,     config.decoder_attention_heads       ,     device=A__	)
   if cross_attn_head_mask is None:
      UpperCamelCase    :  Optional[Any]							=	torch.ones(config.decoder_layers       ,     config.decoder_attention_heads       ,     device=A__	)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=99,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_="relu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=20,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=0,			)     ->		Dict:
          UpperCamelCase    :  List[str]							=	parent
          UpperCamelCase    :  int							=	batch_size
          UpperCamelCase    :  Optional[Any]							=	seq_length
          UpperCamelCase    :  Any							=	is_training
          UpperCamelCase    :  List[str]							=	use_labels
          UpperCamelCase    :  Optional[int]							=	vocab_size
          UpperCamelCase    :  Any							=	hidden_size
          UpperCamelCase    :  str							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Any							=	intermediate_size
          UpperCamelCase    :  Any							=	hidden_act
          UpperCamelCase    :  Any							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  List[str]							=	encoder_layerdrop
          UpperCamelCase    :  List[Any]							=	decoder_layerdrop
          UpperCamelCase    :  Dict							=	max_position_embeddings
          UpperCamelCase    :  List[Any]							=	eos_token_id
          UpperCamelCase    :  str							=	pad_token_id
          UpperCamelCase    :  int							=	bos_token_id
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  int							=	ids_tensor([self.batch_size, self.seq_length],			self.vocab_size       )
          UpperCamelCase    :  List[str]							=	self.eos_token_id  # Eos Token
          UpperCamelCase    :  List[str]							=	ids_tensor([self.batch_size, self.seq_length],			self.vocab_size       )

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for M2M100 the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          UpperCamelCase    :  Union[str, Any]							=	input_ids.clamp(self.pad_token_id + 1       )
          UpperCamelCase    :  Optional[int]							=	decoder_input_ids.clamp(self.pad_token_id + 1       )

          UpperCamelCase    :  Tuple							=	self.get_config()
          UpperCamelCase    :  List[Any]							=	prepare_mam_aaa_inputs_dict(lowerCAmelCase_,			lowerCAmelCase_,			lowerCAmelCase_       )
          return config, inputs_dict
       def 					snake_case_       (			self       )     ->		int:
          return MaMaaaConfig(
              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,			encoder_layerdrop=self.encoder_layerdrop,			decoder_layerdrop=self.decoder_layerdrop,			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,			)
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  List[Any]							=	self.prepare_config_and_inputs()
          return config, inputs_dict
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		int:
          UpperCamelCase    :  Optional[Any]							=	MaMaaaModel(config=lowerCAmelCase_       ).get_decoder().to(lowerCAmelCase_       ).eval()
          UpperCamelCase    :  str							=	inputs_dict["input_ids"]
          UpperCamelCase    :  Optional[int]							=	inputs_dict["attention_mask"]
          UpperCamelCase    :  Union[str, Any]							=	inputs_dict["head_mask"]

          # first forward pass
          UpperCamelCase    :  Optional[int]							=	model(lowerCAmelCase_,			attention_mask=lowerCAmelCase_,			head_mask=lowerCAmelCase_,			use_cache=lowerCAmelCase_       )

          UpperCamelCase    :  Optional[Any]							=	outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          UpperCamelCase    :  Tuple							=	ids_tensor((self.batch_size, 3),			config.vocab_size       )
          UpperCamelCase    :  str							=	ids_tensor((self.batch_size, 3),			2       )

          # append to next input_ids and
          UpperCamelCase    :  Any							=	torch.cat([input_ids, next_tokens],			dim=-1       )
          UpperCamelCase    :  Tuple							=	torch.cat([attention_mask, next_attn_mask],			dim=-1       )

          UpperCamelCase    :  List[str]							=	model(lowerCAmelCase_,			attention_mask=lowerCAmelCase_       )["last_hidden_state"]
          UpperCamelCase    :  Any							=	model(lowerCAmelCase_,			attention_mask=lowerCAmelCase_,			past_key_values=lowerCAmelCase_       )[
              "last_hidden_state"
          ]

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

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(lowerCAmelCase_,			lowerCAmelCase_,			atol=1e-2       )       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		str:
          UpperCamelCase    :  str							=	MaMaaaModel(config=lowerCAmelCase_       ).to(lowerCAmelCase_       ).eval()
          UpperCamelCase    :  int							=	model(**lowerCAmelCase_       )

          UpperCamelCase    :  Dict							=	outputs.encoder_last_hidden_state
          UpperCamelCase    :  Union[str, Any]							=	outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  Union[str, Any]							=	model.get_encoder()
             encoder.save_pretrained(lowerCAmelCase_       )
             UpperCamelCase    :  List[str]							=	MaMaaaEncoder.from_pretrained(lowerCAmelCase_       ).to(lowerCAmelCase_       )

          UpperCamelCase    :  List[str]							=	encoder(inputs_dict['input_ids'],			attention_mask=inputs_dict['attention_mask']       )[
              0
          ]

          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  Dict							=	model.get_decoder()
             decoder.save_pretrained(lowerCAmelCase_       )
             UpperCamelCase    :  Dict							=	MaMaaaDecoder.from_pretrained(lowerCAmelCase_       ).to(lowerCAmelCase_       )

          UpperCamelCase    :  Dict							=	decoder(
              input_ids=inputs_dict['decoder_input_ids'],			attention_mask=inputs_dict['decoder_attention_mask'],			encoder_hidden_states=lowerCAmelCase_,			encoder_attention_mask=inputs_dict['attention_mask'],			)[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3       )






@require_torch
class 				lowerCAmelCase_		(  __A       ,    __A       ,    __A       ,    unittest.TestCase							):
       UpperCAmelCase__				:					List[str]        =  (
           (
               MaMaaaModel,
               MaMaaaForConditionalGeneration,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase__				:					int        =  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
       UpperCAmelCase__				:					List[Any]        =  (
           {
               "conversational": MaMaaaForConditionalGeneration,
               "feature-extraction": MaMaaaModel,
               "summarization": MaMaaaForConditionalGeneration,
               "text2text-generation": MaMaaaForConditionalGeneration,
               "translation": MaMaaaForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       UpperCAmelCase__				:					List[str]        =  True
       UpperCAmelCase__				:					Dict        =  True
       UpperCAmelCase__				:					Optional[Any]        =  False
       UpperCAmelCase__				:					int        =  False
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		str:
          if pipeline_test_casse_name == "TranslationPipelineTests":
             # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
             # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
             return True

          return False
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  List[str]							=	MaMaaaModelTester(self       )
          UpperCamelCase    :  Dict							=	ConfigTester(self,			config_class=lowerCAmelCase_       )
       def 					snake_case_       (			self       )     ->		int:
          self.config_tester.run_common_tests()
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Any							=	self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             UpperCamelCase    :  List[str]							=	model_class(lowerCAmelCase_       )

             with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(lowerCAmelCase_       )
                UpperCamelCase    :  Tuple							=	model_class.from_pretrained(lowerCAmelCase_,			output_loading_info=lowerCAmelCase_       )
             self.assertEqual(info['missing_keys'],			[]       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase_       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Union[str, Any]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
             UpperCamelCase    :  Dict							=	model_class(lowerCAmelCase_       )
             model.to(lowerCAmelCase_       )
             model.eval()

             UpperCamelCase    :  Optional[Any]							=	copy.deepcopy(self._prepare_for_class(lowerCAmelCase_,			lowerCAmelCase_       )       )

             if not self.is_encoder_decoder:
                UpperCamelCase    :  Optional[int]							=	inputs["input_ids"]
                del inputs["input_ids"]
             else:
                UpperCamelCase    :  Dict							=	inputs["input_ids"]
                UpperCamelCase    :  Dict							=	inputs.get('decoder_input_ids',			lowerCAmelCase_       )
                del inputs["input_ids"]
                inputs.pop('decoder_input_ids',			lowerCAmelCase_       )

             UpperCamelCase    :  Optional[int]							=	model.get_input_embeddings()
             if not self.is_encoder_decoder:
                UpperCamelCase    :  str							=	wte(lowerCAmelCase_       )
             else:
                UpperCamelCase    :  Optional[int]							=	wte(lowerCAmelCase_       )
                UpperCamelCase    :  Dict							=	wte(lowerCAmelCase_       )

             with torch.no_grad():
                model(**lowerCAmelCase_       )[0]






       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  int							=	self.model_tester.prepare_config_and_inputs()
          UpperCamelCase    :  List[str]							=	input_dict["input_ids"]
          UpperCamelCase    :  Optional[Any]							=	input_ids.ne(1       ).to(lowerCAmelCase_       )
          UpperCamelCase    :  Optional[Any]							=	MaMaaaForConditionalGeneration(lowerCAmelCase_       ).eval().to(lowerCAmelCase_       )
          if torch_device == "cuda":
             model.half()
          model.generate(lowerCAmelCase_,			attention_mask=lowerCAmelCase_       )
          model.generate(num_beams=4,			do_sample=lowerCAmelCase_,			early_stopping=lowerCAmelCase_,			num_return_sequences=3       )






def 		UpperCamelCase	(   snake_case__       :    Optional[int]	)	->     Optional[int]:
   return torch.tensor(A__       ,     dtype=torch.long       ,     device=A__	)


__UpperCAmelCase          =       1e-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Dict:
          return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Union[str, Any]							=	MaMaaaModel.from_pretrained('facebook/m2m100_418M'       ).to(lowerCAmelCase_       )
          UpperCamelCase    :  Optional[Any]							=	_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
          UpperCamelCase    :  Optional[int]							=	_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
          UpperCamelCase    :  str							=	prepare_mam_aaa_inputs_dict(model.config,			lowerCAmelCase_,			lowerCAmelCase_       )
          with torch.no_grad():
             UpperCamelCase    :  List[str]							=	model(**lowerCAmelCase_       )[0]
          UpperCamelCase    :  int							=	torch.Size((1, 11, 1024)       )
          self.assertEqual(output.shape,			lowerCAmelCase_       )
          # change to expected output here
          UpperCamelCase    :  int							=	torch.tensor(
              [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]],			device=lowerCAmelCase_       )
          self.assertTrue(torch.allclose(output[:, :3, :3],			lowerCAmelCase_,			atol=lowerCAmelCase_       )       )
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  str							=	MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'       ).to(lowerCAmelCase_       )

          # change to intended input
          UpperCamelCase    :  int							=	_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
          UpperCamelCase    :  Union[str, Any]							=	_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
          UpperCamelCase    :  Optional[Any]							=	prepare_mam_aaa_inputs_dict(model.config,			lowerCAmelCase_,			lowerCAmelCase_       )
          with torch.no_grad():
             UpperCamelCase    :  Tuple							=	model(**lowerCAmelCase_       )[0]
          UpperCamelCase    :  Union[str, Any]							=	torch.Size((1, 11, model.config.vocab_size)       )
          self.assertEqual(output.shape,			lowerCAmelCase_       )
          # change to expected output here
          UpperCamelCase    :  Union[str, Any]							=	torch.tensor(
              [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]],			device=lowerCAmelCase_       )
          self.assertTrue(torch.allclose(output[:, :3, :3],			lowerCAmelCase_,			atol=lowerCAmelCase_       )       )






       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[int]							=	MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'       ).to(lowerCAmelCase_       )
          UpperCamelCase    :  Tuple							=	MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M',			src_lang='fr',			tgt_lang='en'       )

          UpperCamelCase    :  str							=	[
              "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
              "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
              "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
              " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
              " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
          ]

          # The below article tests that we don't add any hypotheses outside of the top n_beams
          UpperCamelCase    :  int							=	tokenizer(lowerCAmelCase_,			padding=lowerCAmelCase_,			return_tensors='pt'       )

          UpperCamelCase    :  List[Any]							=	model.generate(
              input_ids=dct['input_ids'].to(lowerCAmelCase_       ),			attention_mask=dct['attention_mask'].to(lowerCAmelCase_       ),			num_beams=5,			forced_bos_token_id=tokenizer.get_lang_id('en'       ),			)

          UpperCamelCase    :  List[str]							=	[
              "The NSA case highlights the total absence of intelligence debate",
              "I think there are two levels of response from the French government.",
              "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
              " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
              " communications in France.",
          ]

          UpperCamelCase    :  List[str]							=	tokenizer.batch_decode(
              hypotheses_batch.tolist(),			clean_up_tokenization_spaces=lowerCAmelCase_,			skip_special_tokens=lowerCAmelCase_       )
          assert generated == expected_en











"""simple docstring"""
from typing import Mapping

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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''t5'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: List[Any]=32_128	,	lowerCAmelCase_					: Tuple=512	,	lowerCAmelCase_					: Optional[int]=64	,	lowerCAmelCase_					: List[str]=2_048	,	lowerCAmelCase_					: Tuple=6	,	lowerCAmelCase_					: Dict=None	,	lowerCAmelCase_					: str=8	,	lowerCAmelCase_					: Optional[int]=32	,	lowerCAmelCase_					: Dict=128	,	lowerCAmelCase_					: Optional[Any]=0.1	,	lowerCAmelCase_					: str=1e-6	,	lowerCAmelCase_					: Dict=1.0	,	lowerCAmelCase_					: str="relu"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: Any=0	,	lowerCAmelCase_					: Tuple=1	,	**lowerCAmelCase_					: Optional[int]	,	)						->			int:
				UpperCAmelCase_     : int			     =					vocab_size
				UpperCAmelCase_     : Optional[Any]			     =					d_model
				UpperCAmelCase_     : str			     =					d_kv
				UpperCAmelCase_     : Any			     =					d_ff
				UpperCAmelCase_     : int			     =					num_layers
				UpperCAmelCase_     : Union[str, Any]			     =					(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase_     : Optional[Any]			     =					num_heads
				UpperCAmelCase_     : Any			     =					relative_attention_num_buckets
				UpperCAmelCase_     : Optional[Any]			     =					relative_attention_max_distance
				UpperCAmelCase_     : Optional[Any]			     =					dropout_rate
				UpperCAmelCase_     : Tuple			     =					layer_norm_epsilon
				UpperCAmelCase_     : int			     =					initializer_factor
				UpperCAmelCase_     : int			     =					feed_forward_proj
				UpperCAmelCase_     : str			     =					use_cache

				UpperCAmelCase_     : Tuple			     =					self.feed_forward_proj.split("-"						)
				UpperCAmelCase_     : List[Any]			     =					act_info[-1]
				UpperCAmelCase_     : Optional[int]			     =					act_info[0] == "gated"

				if len(lowerCAmelCase_						) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_						) > 2:
							raise ValueError(
							    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
							    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
							    "'gated-gelu' or 'relu'"						)

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
							UpperCAmelCase_     : int			     =					"gelu_new"

				super().__init__(
				    pad_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_	,	)







class 			UpperCamelCase_       (__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_     : Any			     =					{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							UpperCAmelCase_     : List[Any]			     =					"past_encoder_sequence + sequence"
							UpperCAmelCase_     : Union[str, Any]			     =					{0: "batch"}
							UpperCAmelCase_     : Optional[Any]			     =					{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							UpperCAmelCase_     : List[Any]			     =					{0: "batch", 1: "decoder_sequence"}
							UpperCAmelCase_     : Tuple			     =					{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase_	,	direction="inputs"						)

				return common_inputs





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			int:
				return 13



