





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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=None						,	_UpperCAmelCase=None						,	_UpperCAmelCase=None						,	_UpperCAmelCase=None						,	_UpperCAmelCase=None						,	)			->     str:
    if attention_mask is None:
        lowerCamelCase__    :      int					 =		input_ids.ne(config.pad_token_id )
    if decoder_attention_mask is None:
        lowerCamelCase__    :      int					 =		decoder_input_ids.ne(config.pad_token_id )
    if head_mask is None:
        lowerCamelCase__    :      Tuple					 =		torch.ones(config.encoder_layers						,	config.encoder_attention_heads						,	device=_UpperCAmelCase )
    if decoder_head_mask is None:
        lowerCamelCase__    :      Dict					 =		torch.ones(config.decoder_layers						,	config.decoder_attention_heads						,	device=_UpperCAmelCase )
    if cross_attn_head_mask is None:
        lowerCamelCase__    :      Tuple					 =		torch.ones(config.decoder_layers						,	config.decoder_attention_heads						,	device=_UpperCAmelCase )
    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  :      Dict			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      int=13			,     UpperCAmelCase  :      Tuple=7			,     UpperCAmelCase  :      int=True			,     UpperCAmelCase  :      List[Any]=False			,     UpperCAmelCase  :      List[str]=99			,     UpperCAmelCase  :      Optional[int]=16			,     UpperCAmelCase  :      Tuple=2			,     UpperCAmelCase  :      str=4			,     UpperCAmelCase  :      Optional[int]=4			,     UpperCAmelCase  :      List[Any]="relu"			,     UpperCAmelCase  :      Dict=0.1			,     UpperCAmelCase  :      Optional[int]=0.1			,     UpperCAmelCase  :      List[Any]=0.0			,     UpperCAmelCase  :      List[str]=0.0			,     UpperCAmelCase  :      str=20			,     UpperCAmelCase  :      List[Any]=2			,     UpperCAmelCase  :      str=1			,     UpperCAmelCase  :      List[str]=0			,     )			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		parent
     lowerCamelCase__    :      Tuple					 =		batch_size
     lowerCamelCase__    :      int					 =		seq_length
     lowerCamelCase__    :      Union[str, Any]					 =		is_training
     lowerCamelCase__    :      Dict					 =		use_labels
     lowerCamelCase__    :      Optional[int]					 =		vocab_size
     lowerCamelCase__    :      Optional[Any]					 =		hidden_size
     lowerCamelCase__    :      str					 =		num_hidden_layers
     lowerCamelCase__    :      Dict					 =		num_attention_heads
     lowerCamelCase__    :      str					 =		intermediate_size
     lowerCamelCase__    :      str					 =		hidden_act
     lowerCamelCase__    :      Dict					 =		hidden_dropout_prob
     lowerCamelCase__    :      Any					 =		attention_probs_dropout_prob
     lowerCamelCase__    :      Dict					 =		encoder_layerdrop
     lowerCamelCase__    :      List[Any]					 =		decoder_layerdrop
     lowerCamelCase__    :      Any					 =		max_position_embeddings
     lowerCamelCase__    :      str					 =		eos_token_id
     lowerCamelCase__    :      int					 =		pad_token_id
     lowerCamelCase__    :      Union[str, Any]					 =		bos_token_id
 def      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      List[str]					 =		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size	)
     lowerCamelCase__    :      str					 =		self.eos_token_id  # Eos Token
     lowerCamelCase__    :      int					 =		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
     lowerCamelCase__    :      Any					 =		input_ids.clamp(self.pad_token_id + 1	)
     lowerCamelCase__    :      Dict					 =		decoder_input_ids.clamp(self.pad_token_id + 1	)

     lowerCamelCase__    :      Tuple					 =		self.get_config()
     lowerCamelCase__    :      str					 =		prepare_mam_aaa_inputs_dict(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     return config, inputs_dict
 def      A_	(      self  :      Optional[int]	)			->	Union[str, Any]:
     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      A_	(      self  :      int	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		self.prepare_config_and_inputs()
     return config, inputs_dict
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Union[str, Any]	)			->	Any:
     lowerCamelCase__    :      Any					 =		MaMaaaModel(config=UpperCAmelCase	).get_decoder().to(UpperCAmelCase	).eval()
     lowerCamelCase__    :      Tuple					 =		inputs_dict['input_ids']
     lowerCamelCase__    :      Tuple					 =		inputs_dict['attention_mask']
     lowerCamelCase__    :      str					 =		inputs_dict['head_mask']

     # first forward pass
     lowerCamelCase__    :      Dict					 =		model(UpperCAmelCase			,     attention_mask=UpperCAmelCase			,     head_mask=UpperCAmelCase			,     use_cache=UpperCAmelCase	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		outputs.to_tuple()

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

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

     lowerCamelCase__    :      Tuple					 =		model(UpperCAmelCase			,     attention_mask=UpperCAmelCase	)['last_hidden_state']
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase			,     attention_mask=UpperCAmelCase			,     past_key_values=UpperCAmelCase	)[
         'last_hidden_state'
     ]

     # select random slice
     lowerCamelCase__    :      int					 =		ids_tensor((1,)			,     output_from_past.shape[-1]	).item()
     lowerCamelCase__    :      str					 =		output_from_no_past[:, -3:, random_slice_idx].detach()
     lowerCamelCase__    :      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(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-2	)	)







 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      str	)			->	List[str]:
     lowerCamelCase__    :      Optional[int]					 =		MaMaaaModel(config=UpperCAmelCase	).to(UpperCAmelCase	).eval()
     lowerCamelCase__    :      Optional[int]					 =		model(**UpperCAmelCase	)

     lowerCamelCase__    :      Dict					 =		outputs.encoder_last_hidden_state
     lowerCamelCase__    :      int					 =		outputs.last_hidden_state

     with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase__    :      Optional[int]					 =		model.get_encoder()
         encoder.save_pretrained(UpperCAmelCase	)
         lowerCamelCase__    :      List[Any]					 =		MaMaaaEncoder.from_pretrained(UpperCAmelCase	).to(UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		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:
         lowerCamelCase__    :      List[Any]					 =		model.get_decoder()
         decoder.save_pretrained(UpperCAmelCase	)
         lowerCamelCase__    :      Any					 =		MaMaaaDecoder.from_pretrained(UpperCAmelCase	).to(UpperCAmelCase	)

     lowerCamelCase__    :      Any					 =		decoder(
         input_ids=inputs_dict['decoder_input_ids']			,     attention_mask=inputs_dict['decoder_attention_mask']			,     encoder_hidden_states=UpperCAmelCase			,     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							(					__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(
     (
         MaMaaaModel,
         MaMaaaForConditionalGeneration,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__													=		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
 UpperCAmelCase__													=		(
     {
         """conversational""": MaMaaaForConditionalGeneration,
         """feature-extraction""": MaMaaaModel,
         """summarization""": MaMaaaForConditionalGeneration,
         """text2text-generation""": MaMaaaForConditionalGeneration,
         """translation""": MaMaaaForConditionalGeneration,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      List[str]	)			->	Any:
     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      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaModelTester(self	)
     lowerCamelCase__    :      Dict					 =		ConfigTester(self			,     config_class=UpperCAmelCase	)
 def      A_	(      self  :      int	)			->	List[Any]:
     self.config_tester.run_common_tests()
 def      A_	(      self  :      Optional[int]	)			->	Union[str, Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         lowerCamelCase__    :      Any					 =		model_class(UpperCAmelCase	)

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

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
         lowerCamelCase__    :      Union[str, Any]					 =		model_class(UpperCAmelCase	)
         model.to(UpperCAmelCase	)
         model.eval()

         lowerCamelCase__    :      List[str]					 =		copy.deepcopy(self._prepare_for_class(UpperCAmelCase			,     UpperCAmelCase	)	)

         if not self.is_encoder_decoder:
             lowerCamelCase__    :      Tuple					 =		inputs['input_ids']
             del inputs["input_ids"]
         else:
             lowerCamelCase__    :      List[str]					 =		inputs['input_ids']
             lowerCamelCase__    :      List[Any]					 =		inputs.get('decoder_input_ids'			,     UpperCAmelCase	)
             del inputs["input_ids"]
             inputs.pop('decoder_input_ids'			,     UpperCAmelCase	)

         lowerCamelCase__    :      List[str]					 =		model.get_input_embeddings()
         if not self.is_encoder_decoder:
             lowerCamelCase__    :      List[Any]					 =		wte(UpperCAmelCase	)
         else:
             lowerCamelCase__    :      int					 =		wte(UpperCAmelCase	)
             lowerCamelCase__    :      Optional[Any]					 =		wte(UpperCAmelCase	)

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







 def      A_	(      self  :      Dict	)			->	List[str]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__    :      Dict					 =		input_dict['input_ids']
     lowerCamelCase__    :      List[str]					 =		input_ids.ne(1	).to(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		MaMaaaForConditionalGeneration(UpperCAmelCase	).eval().to(UpperCAmelCase	)
     if torch_device == "cuda":
         model.half()
     model.generate(UpperCAmelCase			,     attention_mask=UpperCAmelCase	)
     model.generate(num_beams=4			,     do_sample=UpperCAmelCase			,     early_stopping=UpperCAmelCase			,     num_return_sequences=3	)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Optional[Any]:
    return torch.tensor(_UpperCAmelCase						,	dtype=torch.long						,	device=_UpperCAmelCase )


_UpperCAmelCase				:       Union[str, Any]	   =				1E-4


@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class    lowerCAmelCase							(					unittest.TestCase			):
 @cached_property
 def      A_	(      self  :      Dict	)			->	Optional[int]:
     return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'	)
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      Optional[int]					 =		MaMaaaModel.from_pretrained('facebook/m2m100_418M'	).to(UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]	)
     lowerCamelCase__    :      Any					 =		_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]	)
     lowerCamelCase__    :      Optional[int]					 =		prepare_mam_aaa_inputs_dict(model.config			,     UpperCAmelCase			,     UpperCAmelCase	)
     with torch.no_grad():
         lowerCamelCase__    :      Tuple					 =		model(**UpperCAmelCase	)[0]
     lowerCamelCase__    :      Tuple					 =		torch.Size((1, 11, 1024)	)
     self.assertEqual(output.shape			,     UpperCAmelCase	)
     # change to expected output here
     lowerCamelCase__    :      int					 =		torch.tensor(
         [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]			,     device=UpperCAmelCase	)
     self.assertTrue(torch.allclose(output[:, :3, :3]			,     UpperCAmelCase			,     atol=UpperCAmelCase	)	)
 def      A_	(      self  :      List[str]	)			->	Dict:
     lowerCamelCase__    :      List[Any]					 =		MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'	).to(UpperCAmelCase	)

     # change to intended input
     lowerCamelCase__    :      Union[str, Any]					 =		_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]	)
     lowerCamelCase__    :      Tuple					 =		_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]	)
     lowerCamelCase__    :      Optional[int]					 =		prepare_mam_aaa_inputs_dict(model.config			,     UpperCAmelCase			,     UpperCAmelCase	)
     with torch.no_grad():
         lowerCamelCase__    :      str					 =		model(**UpperCAmelCase	)[0]
     lowerCamelCase__    :      Dict					 =		torch.Size((1, 11, model.config.vocab_size)	)
     self.assertEqual(output.shape			,     UpperCAmelCase	)
     # change to expected output here
     lowerCamelCase__    :      Optional[int]					 =		torch.tensor(
         [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]			,     device=UpperCAmelCase	)
     self.assertTrue(torch.allclose(output[:, :3, :3]			,     UpperCAmelCase			,     atol=UpperCAmelCase	)	)







 def      A_	(      self  :      int	)			->	str:
     lowerCamelCase__    :      List[str]					 =		MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'	).to(UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'			,     src_lang='fr'			,     tgt_lang='en'	)

     lowerCamelCase__    :      Dict					 =		[
         '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
     lowerCamelCase__    :      List[str]					 =		tokenizer(UpperCAmelCase			,     padding=UpperCAmelCase			,     return_tensors='pt'	)

     lowerCamelCase__    :      Tuple					 =		model.generate(
         input_ids=dct['input_ids'].to(UpperCAmelCase	)			,     attention_mask=dct['attention_mask'].to(UpperCAmelCase	)			,     num_beams=5			,     forced_bos_token_id=tokenizer.get_lang_id('en'	)			,     )

     lowerCamelCase__    :      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.',
     ]

     lowerCamelCase__    :      Tuple					 =		tokenizer.batch_decode(
         hypotheses_batch.tolist()			,     clean_up_tokenization_spaces=UpperCAmelCase			,     skip_special_tokens=UpperCAmelCase	)
     assert generated == expected_en









import warnings

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





