

'''simple docstring'''






import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MPNetForMaskedLM,
     MPNetForMultipleChoice,
     MPNetForQuestionAnswering,
     MPNetForSequenceClassification,
     MPNetForTokenClassification,
     MPNetModel,
 )






class      lowercase_      :




  """simple docstring"""




  def __init__( self  :	Optional[Any]   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Optional[int]=1_3   ,lowercase__  :	Union[str, Any]=7   ,lowercase__  :	Dict=True   ,lowercase__  :	Dict=True   ,lowercase__  :	List[str]=False   ,lowercase__  :	Optional[Any]=True   ,lowercase__  :	int=9_9   ,lowercase__  :	Tuple=6_4   ,lowercase__  :	Any=5   ,lowercase__  :	str=4   ,lowercase__  :	Tuple=6_4   ,lowercase__  :	Dict="gelu"   ,lowercase__  :	Tuple=0.1   ,lowercase__  :	Dict=0.1   ,lowercase__  :	Union[str, Any]=5_1_2   ,lowercase__  :	Union[str, Any]=1_6   ,lowercase__  :	int=2   ,lowercase__  :	int=0.0_2   ,lowercase__  :	Union[str, Any]=3   ,lowercase__  :	Dict=4   ,lowercase__  :	Union[str, Any]=None   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					seq_length
   __lowercase				    =					is_training
   __lowercase				    =					use_input_mask
   __lowercase				    =					use_token_type_ids
   __lowercase				    =					use_labels
   __lowercase				    =					vocab_size
   __lowercase				    =					hidden_size
   __lowercase				    =					num_hidden_layers
   __lowercase				    =					num_attention_heads
   __lowercase				    =					intermediate_size
   __lowercase				    =					hidden_act
   __lowercase				    =					hidden_dropout_prob
   __lowercase				    =					attention_probs_dropout_prob
   __lowercase				    =					max_position_embeddings
   __lowercase				    =					type_vocab_size
   __lowercase				    =					type_sequence_label_size
   __lowercase				    =					initializer_range
   __lowercase				    =					num_labels
   __lowercase				    =					num_choices
   __lowercase				    =					scope






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size     )

   __lowercase				    =					None
   if self.use_input_mask:
    __lowercase				    =					random_attention_mask([self.batch_size, self.seq_length]     )

   __lowercase				    =					None
   __lowercase				    =					None
   __lowercase				    =					None
   if self.use_labels:
    __lowercase				    =					ids_tensor([self.batch_size]   ,self.type_sequence_label_size     )
    __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels     )
    __lowercase				    =					ids_tensor([self.batch_size]   ,self.num_choices     )

   __lowercase				    =					self.get_config()
   return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   return MPNetConfig(
       vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,initializer_range=self.initializer_range   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	Dict   ,lowercase__  :	List[Any]   ,lowercase__  :	Optional[int]   ,lowercase__  :	List[str]   ,lowercase__  :	int   ,lowercase__  :	Optional[int]     ):
   __lowercase				    =					MPNetModel(config=__lowercase     )
   model.to(__lowercase     )
   model.eval()
   __lowercase				    =					model(__lowercase   ,__lowercase     )
   __lowercase				    =					model(__lowercase     )
   self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)     )
   self.parent.assertEqual(result.pooler_output.shape   ,(self.batch_size, self.hidden_size)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Any   ,lowercase__  :	Dict   ,lowercase__  :	Optional[int]   ,lowercase__  :	Tuple   ,lowercase__  :	Dict   ,lowercase__  :	str     ):
   __lowercase				    =					MPNetForQuestionAnswering(config=__lowercase     )
   model.to(__lowercase     )
   model.eval()
   __lowercase				    =					model(
       __lowercase   ,attention_mask=__lowercase   ,start_positions=__lowercase   ,end_positions=__lowercase   ,)
   self.parent.assertEqual(result.start_logits.shape   ,(self.batch_size, self.seq_length)     )
   self.parent.assertEqual(result.end_logits.shape   ,(self.batch_size, self.seq_length)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Tuple   ,lowercase__  :	List[str]   ,lowercase__  :	str   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Any   ,lowercase__  :	List[Any]     ):
   __lowercase				    =					self.num_labels
   __lowercase				    =					MPNetForSequenceClassification(__lowercase     )
   model.to(__lowercase     )
   model.eval()
   __lowercase				    =					model(__lowercase   ,attention_mask=__lowercase   ,labels=__lowercase     )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	str   ,lowercase__  :	Any   ,lowercase__  :	List[str]   ,lowercase__  :	int   ,lowercase__  :	str   ,lowercase__  :	Optional[Any]     ):
   __lowercase				    =					self.num_choices
   __lowercase				    =					MPNetForMultipleChoice(config=__lowercase     )
   model.to(__lowercase     )
   model.eval()
   __lowercase				    =					input_ids.unsqueeze(1     ).expand(-1   ,self.num_choices   ,-1     ).contiguous()
   __lowercase				    =					input_mask.unsqueeze(1     ).expand(-1   ,self.num_choices   ,-1     ).contiguous()
   __lowercase				    =					model(
       __lowercase   ,attention_mask=__lowercase   ,labels=__lowercase   ,)
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_choices)     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	Any   ,lowercase__  :	Optional[int]   ,lowercase__  :	List[str]   ,lowercase__  :	Optional[int]   ,lowercase__  :	int   ,lowercase__  :	Tuple     ):
   __lowercase				    =					self.num_labels
   __lowercase				    =					MPNetForTokenClassification(config=__lowercase     )
   model.to(__lowercase     )
   model.eval()
   __lowercase				    =					model(__lowercase   ,attention_mask=__lowercase   ,labels=__lowercase     )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.prepare_config_and_inputs()
   (__lowercase)				    =					config_and_inputs
   __lowercase				    =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class      lowercase_      (snake_case__							,			snake_case__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Any							     =     (
      (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE       :    Dict							     =     (
      {
          """feature-extraction""": MPNetModel,
          """fill-mask""": MPNetForMaskedLM,
          """question-answering""": MPNetForQuestionAnswering,
          """text-classification""": MPNetForSequenceClassification,
          """token-classification""": MPNetForTokenClassification,
          """zero-shot""": MPNetForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE       :    List[str]							     =     False
  SCREAMING_SNAKE_CASE       :    str							     =     True






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					MPNetModelTester(self     )
   __lowercase				    =					ConfigTester(self   ,config_class=__lowercase   ,hidden_size=3_7     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   self.config_tester.run_common_tests()






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mpnet_model(*__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase     )






@require_torch
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					MPNetModel.from_pretrained('''microsoft/mpnet-base'''     )
   __lowercase				    =					torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
   __lowercase				    =					model(__lowercase     )[0]
   __lowercase				    =					torch.Size((1, 1_1, 7_6_8)     )
   self.assertEqual(output.shape   ,__lowercase     )
   __lowercase				    =					torch.tensor(
       [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]     )
   # compare the actual values for a slice.
   self.assertTrue(torch.allclose(output[:, :3, :3]   ,__lowercase   ,atol=1e-4     )     )








"""simple docstring"""





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase					      =   logging.get_logger(__name__)
logging.set_verbosity_info()



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if "xprophetnet" in prophetnet_checkpoint_path:
														__lowerCamelCase   :   Optional[Any]       =XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =XLMProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =ProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''self_attn''': '''ngram_self_attn''',
							    '''cross_attn''': '''encoder_attn''',
							    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
							    '''feed_forward_layer_norm''': '''final_layer_norm''',
							    '''feed_forward''': '''''',
							    '''intermediate''': '''fc1''',
							    '''output''': '''fc2''',
							    '''key_proj''': '''k_proj''',
							    '''query_proj''': '''q_proj''',
							    '''value_proj''': '''v_proj''',
							    '''word_embeddings''': '''embed_tokens''',
							    '''embeddings_layer_norm''': '''emb_layer_norm''',
							    '''relative_pos_embeddings''': '''relative_linear''',
							    '''ngram_embeddings''': '''ngram_input_embed''',
							    '''position_embeddings''': '''embed_positions''',
							}

							for key in loading_info["missing_keys"]:
														__lowerCamelCase   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

														__lowerCamelCase   :   Optional[Any]       =False
														for attribute in attributes:
																					if attribute in mapping:
																												__lowerCamelCase   :   Optional[Any]       =mapping[attribute]
																												if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
																																			__lowerCamelCase   :   Any       =attribute
																					elif hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   Any       =attribute

																					if attribute == "weight":
																												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																												__lowerCamelCase   :   str       =old_model.weight
																												logger.info(F'{attribute} is initialized.' )
																												__lowerCamelCase   :   Any       =True
																												break
																					elif attribute == "bias":
																												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																												__lowerCamelCase   :   Union[str, Any]       =old_model.bias
																												logger.info(F'{attribute} is initialized' )
																												__lowerCamelCase   :   str       =True
																												break
																					elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE     ,    '''in_proj_weight''' ):
																												__lowerCamelCase   :   int       =old_model.in_proj_weight.shape[0] // 3
																												__lowerCamelCase   :   Union[str, Any]       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
																												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																												if attribute == "query_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
																																			__lowerCamelCase   :   str       =nn.Parameter(old_model.in_proj_bias[:embed_dim] )

																												elif attribute == "key_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
																																			__lowerCamelCase   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   Dict       =True
																												break
																					elif attribute == "position_embeddings":
																												assert (
																												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																												), "Hidden size has to match"
																												assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

																					if attribute.isdigit():
																												__lowerCamelCase   :   List[str]       =model[int(SCREAMING_SNAKE_CASE )]
																												__lowerCamelCase   :   Optional[Any]       =old_model[int(SCREAMING_SNAKE_CASE )]
																					else:
																												__lowerCamelCase   :   int       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

																												if old_attribute == "":
																																			__lowerCamelCase   :   Dict       =old_model
																												else:
																																			if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}' )
																																			__lowerCamelCase   :   Tuple       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

														if not is_key_init:
																					raise ValueError(F'{key} was not correctly initialized!' )

							print(F'Saving model to {pytorch_dump_folder_path}' )
							prophet.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							_UpperCamelCase					      =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							_UpperCamelCase					      =   parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




