
from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.mpnet.modeling_tf_mpnet import (
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetModel,
  )






class     __a			:







       def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=13							,lowerCamelCase_=7							,lowerCamelCase_=True							,lowerCamelCase_=True							,lowerCamelCase_=False							,lowerCamelCase_=True							,lowerCamelCase_=99							,lowerCamelCase_=64							,lowerCamelCase_=2							,lowerCamelCase_=4							,lowerCamelCase_=64							,lowerCamelCase_="gelu"							,lowerCamelCase_=0.1							,lowerCamelCase_=0.1							,lowerCamelCase_=512							,lowerCamelCase_=16							,lowerCamelCase_=2							,lowerCamelCase_=0.0_2							,lowerCamelCase_=3							,lowerCamelCase_=4							,lowerCamelCase_=None							,):



          '''simple docstring'''

          _UpperCAmelCase   	=					parent
          _UpperCAmelCase   	=					batch_size
          _UpperCAmelCase   	=					seq_length
          _UpperCAmelCase   	=					is_training
          _UpperCAmelCase   	=					use_input_mask
          _UpperCAmelCase   	=					use_token_type_ids
          _UpperCAmelCase   	=					use_labels
          _UpperCAmelCase   	=					vocab_size
          _UpperCAmelCase   	=					hidden_size
          _UpperCAmelCase   	=					num_hidden_layers
          _UpperCAmelCase   	=					num_attention_heads
          _UpperCAmelCase   	=					intermediate_size
          _UpperCAmelCase   	=					hidden_act
          _UpperCAmelCase   	=					hidden_dropout_prob
          _UpperCAmelCase   	=					attention_probs_dropout_prob
          _UpperCAmelCase   	=					max_position_embeddings
          _UpperCAmelCase   	=					type_vocab_size
          _UpperCAmelCase   	=					type_sequence_label_size
          _UpperCAmelCase   	=					initializer_range
          _UpperCAmelCase   	=					num_labels
          _UpperCAmelCase   	=					num_choices
          _UpperCAmelCase   	=					scope







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size		)

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

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

          _UpperCAmelCase   	=					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							,)
          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					TFMPNetModel(config=_A		)
          _UpperCAmelCase   	=					{'input_ids': input_ids, 'attention_mask': input_mask}
          _UpperCAmelCase   	=					model(_A		)
          _UpperCAmelCase   	=					[input_ids, input_mask]
          _UpperCAmelCase   	=					model(_A		)
          self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					TFMPNetForMaskedLM(config=_A		)
          _UpperCAmelCase   	=					{'input_ids': input_ids, 'attention_mask': input_mask}
          _UpperCAmelCase   	=					model(_A		)
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.vocab_size)		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					TFMPNetForQuestionAnswering(config=_A		)
          _UpperCAmelCase   	=					{
              'input_ids': input_ids,
              'attention_mask': input_mask,
          }
          _UpperCAmelCase   	=					model(_A		)
          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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.num_labels
          _UpperCAmelCase   	=					TFMPNetForSequenceClassification(_A		)
          _UpperCAmelCase   	=					{'input_ids': input_ids, 'attention_mask': input_mask}
          _UpperCAmelCase   	=					model(_A		)
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.num_choices
          _UpperCAmelCase   	=					TFMPNetForMultipleChoice(_A		)
          _UpperCAmelCase   	=					tf.tile(tf.expand_dims(_A							,1		)							,(1, self.num_choices, 1)		)
          _UpperCAmelCase   	=					tf.tile(tf.expand_dims(_A							,1		)							,(1, self.num_choices, 1)		)
          _UpperCAmelCase   	=					{
              'input_ids': multiple_choice_inputs_ids,
              'attention_mask': multiple_choice_input_mask,
          }
          _UpperCAmelCase   	=					model(_A		)
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_choices)		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.num_labels
          _UpperCAmelCase   	=					TFMPNetForTokenClassification(_A		)
          _UpperCAmelCase   	=					{'input_ids': input_ids, 'attention_mask': input_mask}
          _UpperCAmelCase   	=					model(_A		)
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.num_labels)		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.prepare_config_and_inputs()
          (_UpperCAmelCase)   	=					config_and_inputs
          _UpperCAmelCase   	=					{'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict






@require_tf
class     __a			(					lowercase__				,	lowercase__				,	unittest.TestCase			):
       _lowerCamelCase							:							str       =			(
           (
               TFMPNetForMaskedLM,
               TFMPNetForMultipleChoice,
               TFMPNetForQuestionAnswering,
               TFMPNetForSequenceClassification,
               TFMPNetForTokenClassification,
               TFMPNetModel,
           )
           if is_tf_available()
           else ()
       )
       _lowerCamelCase							:							Union[str, Any]       =			(
           {
               """feature-extraction""": TFMPNetModel,
               """fill-mask""": TFMPNetForMaskedLM,
               """question-answering""": TFMPNetForQuestionAnswering,
               """text-classification""": TFMPNetForSequenceClassification,
               """token-classification""": TFMPNetForTokenClassification,
               """zero-shot""": TFMPNetForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _lowerCamelCase							:							Dict       =			False
       _lowerCamelCase							:							Any       =			False







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					TFMPNetModelTester(self		)
          _UpperCAmelCase   	=					ConfigTester(self							,config_class=_A							,hidden_size=37		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          self.config_tester.run_common_tests()







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_model(*_A		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_masked_lm(*_A		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_question_answering(*_A		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_sequence_classification(*_A		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_multiple_choice(*_A		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_token_classification(*_A		)







       @slow
       def    _snake_case			(					self		):



          '''simple docstring'''

          for model_name in ["microsoft/mpnet-base"]:
             _UpperCAmelCase   	=					TFMPNetModel.from_pretrained(_A		)
             self.assertIsNotNone(_A		)






@require_tf
class     __a			(					unittest.TestCase			):







       @slow
       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					TFMPNetModel.from_pretrained("microsoft/mpnet-base"		)
          _UpperCAmelCase   	=					tf.constant([[0, 1, 2, 3, 4, 5]]		)
          _UpperCAmelCase   	=					model(_A		)[0]

          _UpperCAmelCase   	=					[1, 6, 768]
          self.assertEqual(output.shape							,_A		)

          _UpperCAmelCase   	=					tf.constant(
              [
                  [
                      [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
                      [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
                      [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
                  ]
              ]		)
          tf.debugging.assert_near(output[:, :3, :3]							,_A							,atol=1e-4		)



"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
   if "model" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
   if "norm1" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
   if "norm2" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
   if "norm" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
   if "transformer" in orig_key:
      _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
      _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
   if "mha.attn" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
   if "mha" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
   if "W_q" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
   if "W_k" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
   if "W_v" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
   if "ff.0" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
   if "ff.2" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
   if "ff" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
   if "mlm_class" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
   if "mlm" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
   if "backbone.backbone.encoders" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
   if "cls" not in orig_key:
      _UpperCAmelCase   	=					"mra." + orig_key

   return orig_key
def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
   for key in orig_state_dict.copy().keys():
      _UpperCAmelCase   	=					orig_state_dict.pop(a_				)

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         _UpperCAmelCase   	=					val

   _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
   _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2

   return orig_state_dict
def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
   _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
   _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
   _UpperCAmelCase   	=					MraForMaskedLM(a_				)

   _UpperCAmelCase   	=					convert_checkpoint_helper(config.max_position_embeddings    ,  a_				)

   print(model.load_state_dict(a_				)				)
   model.eval()
   model.save_pretrained(a_				)

   print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The json file for Mra model config.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

