





"""simple docstring"""





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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			)						->Optional[int]:





  """simple docstring"""


  if attention_mask is None:
    __UpperCAmelCase    :						Any       =						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    :						List[Any]       =						torch.ones(config.encoder_layers  ,			config.encoder_attention_heads  ,			device=UpperCAmelCase_  )
  if decoder_head_mask is None:
    __UpperCAmelCase    :						Optional[Any]       =						torch.ones(config.decoder_layers  ,			config.decoder_attention_heads  ,			device=UpperCAmelCase_  )
  if cross_attn_head_mask is None:
    __UpperCAmelCase    :						Any       =						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 							snake_case							:

   '''simple docstring'''



   def __init__(							self       :      List[str]						,				__lowercase       :      List[str]						,				__lowercase       :      Optional[Any]=13						,				__lowercase       :      Tuple=7						,				__lowercase       :      str=True						,				__lowercase       :      Dict=False						,				__lowercase       :      Tuple=99						,				__lowercase       :      List[Any]=16						,				__lowercase       :      int=2						,				__lowercase       :      Optional[Any]=4						,				__lowercase       :      Tuple=4						,				__lowercase       :      Any="relu"						,				__lowercase       :      Any=0.1						,				__lowercase       :      List[Any]=0.1						,				__lowercase       :      Tuple=0.0						,				__lowercase       :      List[str]=0.0						,				__lowercase       :      Optional[int]=20						,				__lowercase       :      int=2						,				__lowercase       :      Tuple=1						,				__lowercase       :      Tuple=0						,				):


     '''simple docstring'''




     __UpperCAmelCase    :						List[str]       =						parent
     __UpperCAmelCase    :						Tuple       =						batch_size
     __UpperCAmelCase    :						Optional[int]       =						seq_length
     __UpperCAmelCase    :						Any       =						is_training
     __UpperCAmelCase    :						Optional[Any]       =						use_labels
     __UpperCAmelCase    :						int       =						vocab_size
     __UpperCAmelCase    :						List[str]       =						hidden_size
     __UpperCAmelCase    :						Dict       =						num_hidden_layers
     __UpperCAmelCase    :						Dict       =						num_attention_heads
     __UpperCAmelCase    :						Optional[Any]       =						intermediate_size
     __UpperCAmelCase    :						int       =						hidden_act
     __UpperCAmelCase    :						Dict       =						hidden_dropout_prob
     __UpperCAmelCase    :						Optional[int]       =						attention_probs_dropout_prob
     __UpperCAmelCase    :						Any       =						encoder_layerdrop
     __UpperCAmelCase    :						List[str]       =						decoder_layerdrop
     __UpperCAmelCase    :						Tuple       =						max_position_embeddings
     __UpperCAmelCase    :						Optional[Any]       =						eos_token_id
     __UpperCAmelCase    :						Tuple       =						pad_token_id
     __UpperCAmelCase    :						str       =						bos_token_id

   def       A_					(							self       :      Dict ):


     '''simple docstring'''




     __UpperCAmelCase    :						List[Any]       =						ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size )
     __UpperCAmelCase    :						Union[str, Any]       =						self.eos_token_id  # Eos Token
     __UpperCAmelCase    :						Optional[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
     __UpperCAmelCase    :						Dict       =						input_ids.clamp(self.pad_token_id + 1 )
     __UpperCAmelCase    :						Optional[Any]       =						decoder_input_ids.clamp(self.pad_token_id + 1 )

     __UpperCAmelCase    :						List[Any]       =						self.get_config()
     __UpperCAmelCase    :						Tuple       =						prepare_mam_aaa_inputs_dict(__lowercase						,				__lowercase						,				__lowercase )
     return config, inputs_dict

   def       A_					(							self       :      Any ):


     '''simple docstring'''




     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       :      Dict ):


     '''simple docstring'''




     __UpperCAmelCase    :						Optional[int]       =						self.prepare_config_and_inputs()
     return config, inputs_dict

   def       A_					(							self       :      str						,				__lowercase       :      Optional[int]						,				__lowercase       :      List[Any] ):


     '''simple docstring'''




     __UpperCAmelCase    :						List[Any]       =						MaMaaaModel(config=__lowercase ).get_decoder().to(__lowercase ).eval()
     __UpperCAmelCase    :						Optional[Any]       =						inputs_dict['''input_ids''']
     __UpperCAmelCase    :						Dict       =						inputs_dict['''attention_mask''']
     __UpperCAmelCase    :						List[Any]       =						inputs_dict['''head_mask''']

     # first forward pass
     __UpperCAmelCase    :						Dict       =						model(__lowercase						,				attention_mask=__lowercase						,				head_mask=__lowercase						,				use_cache=__lowercase )

     __UpperCAmelCase    :						Optional[int]       =						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    :						Any       =						ids_tensor((self.batch_size, 3)						,				2 )

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

     __UpperCAmelCase    :						Dict       =						model(__lowercase						,				attention_mask=__lowercase )['''last_hidden_state''']
     __UpperCAmelCase    :						List[Any]       =						model(__lowercase						,				attention_mask=__lowercase						,				past_key_values=__lowercase )[
         '''last_hidden_state'''
     ]

     # select random slice
     __UpperCAmelCase    :						Any       =						ids_tensor((1,)						,				output_from_past.shape[-1] ).item()
     __UpperCAmelCase    :						Tuple       =						output_from_no_past[:, -3:, random_slice_idx].detach()
     __UpperCAmelCase    :						List[str]       =						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(__lowercase						,				__lowercase						,				atol=1e-2 ) )







   def       A_					(							self       :      int						,				__lowercase       :      Dict						,				__lowercase       :      Optional[Any] ):


     '''simple docstring'''




     __UpperCAmelCase    :						Tuple       =						MaMaaaModel(config=__lowercase ).to(__lowercase ).eval()
     __UpperCAmelCase    :						Optional[int]       =						model(**__lowercase )

     __UpperCAmelCase    :						Optional[int]       =						outputs.encoder_last_hidden_state
     __UpperCAmelCase    :						Tuple       =						outputs.last_hidden_state

     with tempfile.TemporaryDirectory() as tmpdirname:
       __UpperCAmelCase    :						Union[str, Any]       =						model.get_encoder()
       encoder.save_pretrained(__lowercase )
       __UpperCAmelCase    :						Optional[Any]       =						MaMaaaEncoder.from_pretrained(__lowercase ).to(__lowercase )

     __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    :						Optional[int]       =						model.get_decoder()
       decoder.save_pretrained(__lowercase )
       __UpperCAmelCase    :						Any       =						MaMaaaDecoder.from_pretrained(__lowercase ).to(__lowercase )

     __UpperCAmelCase    :						str       =						decoder(
         input_ids=inputs_dict['''decoder_input_ids''']						,				attention_mask=inputs_dict['''decoder_attention_mask''']						,				encoder_hidden_states=__lowercase						,				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 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase ):

   '''simple docstring'''



   _A     :       Dict         =       (
       (
           MaMaaaModel,
           MaMaaaForConditionalGeneration,
       )
       if is_torch_available()
       else ()
   )
   _A     :       Tuple         =       (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
   _A     :       Tuple         =       (
       {
           'conversational': MaMaaaForConditionalGeneration,
           'feature-extraction': MaMaaaModel,
           'summarization': MaMaaaForConditionalGeneration,
           'text2text-generation': MaMaaaForConditionalGeneration,
           'translation': MaMaaaForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   _A     :       Union[str, Any]         =       True
   _A     :       int         =       True
   _A     :       Tuple         =       False
   _A     :       Tuple         =       False

   def       A_					(							self       :      List[str]						,				__lowercase       :      List[str]						,				__lowercase       :      Any						,				__lowercase       :      List[str]						,				__lowercase       :      str						,				__lowercase       :      List[Any] ):


     '''simple docstring'''




     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 ):


     '''simple docstring'''




     __UpperCAmelCase    :						str       =						MaMaaaModelTester(self )
     __UpperCAmelCase    :						List[str]       =						ConfigTester(self						,				config_class=__lowercase )

   def       A_					(							self       :      Union[str, Any] ):


     '''simple docstring'''




     self.config_tester.run_common_tests()

   def       A_					(							self       :      Optional[Any] ):


     '''simple docstring'''




     __UpperCAmelCase    :						List[Any]       =						self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       __UpperCAmelCase    :						int       =						model_class(__lowercase )

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(__lowercase )
         __UpperCAmelCase    :						Optional[int]       =						model_class.from_pretrained(__lowercase						,				output_loading_info=__lowercase )
       self.assertEqual(info['''missing_keys''']						,				[] )

   def       A_					(							self       :      int ):


     '''simple docstring'''




     __UpperCAmelCase    :						List[Any]       =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowercase )

   def       A_					(							self       :      str ):


     '''simple docstring'''




     __UpperCAmelCase    :						int       =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*__lowercase )

   def       A_					(							self       :      Union[str, Any] ):


     '''simple docstring'''




     __UpperCAmelCase    :						Tuple       =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
       __UpperCAmelCase    :						Tuple       =						model_class(__lowercase )
       model.to(__lowercase )
       model.eval()

       __UpperCAmelCase    :						str       =						copy.deepcopy(self._prepare_for_class(__lowercase						,				__lowercase ) )

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

       __UpperCAmelCase    :						Union[str, Any]       =						model.get_input_embeddings()
       if not self.is_encoder_decoder:
         __UpperCAmelCase    :						int       =						wte(__lowercase )
       else:
         __UpperCAmelCase    :						Union[str, Any]       =						wte(__lowercase )
         __UpperCAmelCase    :						Optional[Any]       =						wte(__lowercase )

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







   def       A_					(							self       :      Optional[Any] ):


     '''simple docstring'''




     __UpperCAmelCase    :						Any       =						self.model_tester.prepare_config_and_inputs()
     __UpperCAmelCase    :						int       =						input_dict['''input_ids''']
     __UpperCAmelCase    :						List[Any]       =						input_ids.ne(1 ).to(__lowercase )
     __UpperCAmelCase    :						Optional[Any]       =						MaMaaaForConditionalGeneration(__lowercase ).eval().to(__lowercase )
     if torch_device == "cuda":
       model.half()
     model.generate(__lowercase						,				attention_mask=__lowercase )
     model.generate(num_beams=4						,				do_sample=__lowercase						,				early_stopping=__lowercase						,				num_return_sequences=3 )


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  return torch.tensor(UpperCAmelCase_  ,			dtype=torch.long  ,			device=UpperCAmelCase_  )


lowercase__				:Dict				   =       1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 							snake_case							(			unittest.TestCase ):

   '''simple docstring'''



   @cached_property
   def       A_					(							self       :      Tuple ):


     '''simple docstring'''




     return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''' )

   def       A_					(							self       :      List[Any] ):


     '''simple docstring'''




     __UpperCAmelCase    :						Dict       =						MaMaaaModel.from_pretrained('''facebook/m2m100_418M''' ).to(__lowercase )
     __UpperCAmelCase    :						Dict       =						_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
     __UpperCAmelCase    :						Optional[Any]       =						_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
     __UpperCAmelCase    :						str       =						prepare_mam_aaa_inputs_dict(model.config						,				__lowercase						,				__lowercase )
     with torch.no_grad():
       __UpperCAmelCase    :						Optional[int]       =						model(**__lowercase )[0]
     __UpperCAmelCase    :						str       =						torch.Size((1, 11, 1_024) )
     self.assertEqual(output.shape						,				__lowercase )
     # change to expected output here
     __UpperCAmelCase    :						str       =						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=__lowercase )
     self.assertTrue(torch.allclose(output[:, :3, :3]						,				__lowercase						,				atol=__lowercase ) )

   def       A_					(							self       :      Optional[int] ):


     '''simple docstring'''




     __UpperCAmelCase    :						int       =						MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''' ).to(__lowercase )

     # change to intended input
     __UpperCAmelCase    :						Any       =						_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
     __UpperCAmelCase    :						Union[str, Any]       =						_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
     __UpperCAmelCase    :						Optional[Any]       =						prepare_mam_aaa_inputs_dict(model.config						,				__lowercase						,				__lowercase )
     with torch.no_grad():
       __UpperCAmelCase    :						List[Any]       =						model(**__lowercase )[0]
     __UpperCAmelCase    :						Optional[int]       =						torch.Size((1, 11, model.config.vocab_size) )
     self.assertEqual(output.shape						,				__lowercase )
     # change to expected output here
     __UpperCAmelCase    :						Union[str, Any]       =						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=__lowercase )
     self.assertTrue(torch.allclose(output[:, :3, :3]						,				__lowercase						,				atol=__lowercase ) )







   def       A_					(							self       :      int ):


     '''simple docstring'''




     __UpperCAmelCase    :						List[str]       =						MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''' ).to(__lowercase )
     __UpperCAmelCase    :						Tuple       =						MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''						,				src_lang='''fr'''						,				tgt_lang='''en''' )

     __UpperCAmelCase    :						Optional[Any]       =						[
         '''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    :						Tuple       =						tokenizer(__lowercase						,				padding=__lowercase						,				return_tensors='''pt''' )

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

     __UpperCAmelCase    :						Any       =						[
         '''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    :						Union[str, Any]       =						tokenizer.batch_decode(
         hypotheses_batch.tolist()						,				clean_up_tokenization_spaces=__lowercase						,				skip_special_tokens=__lowercase )
     assert generated == expected_en






"""simple docstring"""





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


lowercase__				:int				   =       logging.get_logger(__name__)

lowercase__				:List[str]				   =       {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       'gpt_neox'

      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Any=50_432						,				__lowercase       :      int=6_144						,				__lowercase       :      List[str]=44						,				__lowercase       :      List[Any]=64						,				__lowercase       :      int=24_576						,				__lowercase       :      List[str]="gelu"						,				__lowercase       :      Optional[int]=0.2_5						,				__lowercase       :      Any=10_000						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      List[str]=0.0						,				__lowercase       :      Union[str, Any]=0.1						,				__lowercase       :      Optional[int]=2_048						,				__lowercase       :      Union[str, Any]=0.0_2						,				__lowercase       :      Optional[int]=1e-5						,				__lowercase       :      str=True						,				__lowercase       :      str=0						,				__lowercase       :      Tuple=2						,				__lowercase       :      Tuple=False						,				__lowercase       :      int=True						,				__lowercase       :      Any=None						,				**__lowercase       :      List[str]						,				):


        '''simple docstring'''




        super().__init__(bos_token_id=__lowercase						,				eos_token_id=__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						int       =						vocab_size
        __UpperCAmelCase    :						Union[str, Any]       =						max_position_embeddings
        __UpperCAmelCase    :						List[str]       =						hidden_size
        __UpperCAmelCase    :						Tuple       =						num_hidden_layers
        __UpperCAmelCase    :						Any       =						num_attention_heads
        __UpperCAmelCase    :						Union[str, Any]       =						intermediate_size
        __UpperCAmelCase    :						str       =						hidden_act
        __UpperCAmelCase    :						str       =						rotary_pct
        __UpperCAmelCase    :						str       =						rotary_emb_base
        __UpperCAmelCase    :						Dict       =						attention_dropout
        __UpperCAmelCase    :						str       =						hidden_dropout
        __UpperCAmelCase    :						Any       =						classifier_dropout
        __UpperCAmelCase    :						Tuple       =						initializer_range
        __UpperCAmelCase    :						Any       =						layer_norm_eps
        __UpperCAmelCase    :						Optional[int]       =						use_cache
        __UpperCAmelCase    :						List[Any]       =						tie_word_embeddings
        __UpperCAmelCase    :						Any       =						use_parallel_residual
        __UpperCAmelCase    :						List[str]       =						rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
          raise ValueError(
              '''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )







      def       A_					(							self       :      int ):


        '''simple docstring'''




        if self.rope_scaling is None:
          return

        if not isinstance(self.rope_scaling						,				__lowercase ) or len(self.rope_scaling ) != 2:
          raise ValueError(
              '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
              f'''got {self.rope_scaling}''' )
        __UpperCAmelCase    :						Union[str, Any]       =						self.rope_scaling.get('''type'''						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.rope_scaling.get('''factor'''						,				__lowercase )
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
          raise ValueError(
              f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
        if rope_scaling_factor is None or not isinstance(__lowercase						,				__lowercase ) or rope_scaling_factor <= 1.0:
          raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )