


import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
    )
    from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class 			_lowercase (							_a):


       """simple docstring"""




       def __init__(   self							:			Optional[Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Tuple=13					,      __lowerCamelCase							:			List[Any]=7					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			str=True					,      __lowerCamelCase							:			List[Any]=True					,      __lowerCamelCase							:			str=True					,      __lowerCamelCase							:			str=99					,      __lowerCamelCase							:			Any=32					,      __lowerCamelCase							:			List[Any]=5					,      __lowerCamelCase							:			int=4					,      __lowerCamelCase							:			Optional[int]=37					,      __lowerCamelCase							:			str="gelu"					,      __lowerCamelCase							:			str=0.1					,      __lowerCamelCase							:			List[str]=0.1					,      __lowerCamelCase							:			List[str]=512					,      __lowerCamelCase							:			Any=16					,      __lowerCamelCase							:			int=2					,      __lowerCamelCase							:			List[Any]=0.0_2					,      __lowerCamelCase							:			List[str]=False					,      __lowerCamelCase							:			int=True					,      __lowerCamelCase							:			Union[str, Any]="None"					,      __lowerCamelCase							:			List[Any]=3					,      __lowerCamelCase							:			List[str]=4					,      __lowerCamelCase							:			Any=None					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  parent
             lowerCamelCase__		:				Any    =  batch_size
             lowerCamelCase__		:				List[Any]    =  seq_length
             lowerCamelCase__		:				str    =  is_training
             lowerCamelCase__		:				List[str]    =  use_input_mask
             lowerCamelCase__		:				List[str]    =  use_token_type_ids
             lowerCamelCase__		:				Optional[int]    =  use_labels
             lowerCamelCase__		:				Dict    =  vocab_size
             lowerCamelCase__		:				Tuple    =  hidden_size
             lowerCamelCase__		:				Tuple    =  num_hidden_layers
             lowerCamelCase__		:				List[str]    =  num_attention_heads
             lowerCamelCase__		:				Optional[int]    =  intermediate_size
             lowerCamelCase__		:				List[Any]    =  hidden_act
             lowerCamelCase__		:				Union[str, Any]    =  hidden_dropout_prob
             lowerCamelCase__		:				List[Any]    =  attention_probs_dropout_prob
             lowerCamelCase__		:				List[str]    =  max_position_embeddings
             lowerCamelCase__		:				Optional[Any]    =  type_vocab_size
             lowerCamelCase__		:				Any    =  type_sequence_label_size
             lowerCamelCase__		:				int    =  initializer_range
             lowerCamelCase__		:				Union[str, Any]    =  num_labels
             lowerCamelCase__		:				Optional[Any]    =  num_choices
             lowerCamelCase__		:				List[Any]    =  relative_attention
             lowerCamelCase__		:				List[Any]    =  position_biased_input
             lowerCamelCase__		:				str    =  pos_att_type
             lowerCamelCase__		:				Union[str, Any]    =  scope





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size       )

             lowerCamelCase__		:				int    =  None
             if self.use_input_mask:
                   lowerCamelCase__		:				Any    =  ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2       )

             lowerCamelCase__		:				Optional[int]    =  None
             if self.use_token_type_ids:
                   lowerCamelCase__		:				str    =  ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size       )

             lowerCamelCase__		:				Optional[Any]    =  None
             lowerCamelCase__		:				str    =  None
             lowerCamelCase__		:				Dict    =  None
             if self.use_labels:
                   lowerCamelCase__		:				str    =  ids_tensor([self.batch_size]					,      self.type_sequence_label_size       )
                   lowerCamelCase__		:				Dict    =  ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels       )
                   lowerCamelCase__		:				Tuple    =  ids_tensor([self.batch_size]					,      self.num_choices       )

             lowerCamelCase__		:				List[Any]    =  self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             return DebertaConfig(
                 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					,      type_vocab_size=self.type_vocab_size					,      initializer_range=self.initializer_range					,      relative_attention=self.relative_attention					,      position_biased_input=self.position_biased_input					,      pos_att_type=self.pos_att_type					,      )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.get_config()
             lowerCamelCase__		:				List[Any]    =  300
             return config





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             self.parent.assertListEqual(list(result.loss.size()       )					,      []       )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  DebertaModel(config=snake_case_       )
             model.to(snake_case_       )
             model.eval()
             lowerCamelCase__		:				Union[str, Any]    =  model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_       )[0]
             lowerCamelCase__		:				int    =  model(snake_case_					,      token_type_ids=snake_case_       )[0]
             lowerCamelCase__		:				int    =  model(snake_case_       )[0]

             self.parent.assertListEqual(list(sequence_output.size()       )					,      [self.batch_size, self.seq_length, self.hidden_size]       )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  DebertaForMaskedLM(config=snake_case_       )
             model.to(snake_case_       )
             model.eval()
             lowerCamelCase__		:				Dict    =  model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_       )

             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  self.num_labels
             lowerCamelCase__		:				Dict    =  DebertaForSequenceClassification(snake_case_       )
             model.to(snake_case_       )
             model.eval()
             lowerCamelCase__		:				List[str]    =  model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_       )
             self.parent.assertListEqual(list(result.logits.size()       )					,      [self.batch_size, self.num_labels]       )
             self.check_loss_output(snake_case_       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.num_labels
             lowerCamelCase__		:				Tuple    =  DebertaForTokenClassification(config=snake_case_       )
             model.to(snake_case_       )
             model.eval()
             lowerCamelCase__		:				Optional[int]    =  model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)       )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  DebertaForQuestionAnswering(config=snake_case_       )
             model.to(snake_case_       )
             model.eval()
             lowerCamelCase__		:				Union[str, Any]    =  model(
                 snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      start_positions=snake_case_					,      end_positions=snake_case_					,      )
             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 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  self.prepare_config_and_inputs()
             (
                 lowerCamelCase__				
             )		:				str    =  config_and_inputs
             lowerCamelCase__		:				int    =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
             return config, inputs_dict







@require_torch
class 			_lowercase (							_a							,  _a							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(
           (
               DebertaModel,
               DebertaForMaskedLM,
               DebertaForSequenceClassification,
               DebertaForTokenClassification,
               DebertaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       A__											=			(
           {
               "feature-extraction": DebertaModel,
               "fill-mask": DebertaForMaskedLM,
               "question-answering": DebertaForQuestionAnswering,
               "text-classification": DebertaForSequenceClassification,
               "token-classification": DebertaForTokenClassification,
               "zero-shot": DebertaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       A__											=			True
       A__											=			False
       A__											=			False
       A__											=			False
       A__											=			False





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  DebertaModelTester(self       )
             lowerCamelCase__		:				List[str]    =  ConfigTester(self					,      config_class=snake_case_					,      hidden_size=37       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_model(*snake_case_       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case_       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case_       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_question_answering(*snake_case_       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_token_classification(*snake_case_       )





       @slow
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				Union[str, Any]    =  DebertaModel.from_pretrained(snake_case_       )
                   self.assertIsNotNone(snake_case_       )







@require_torch
@require_sentencepiece
@require_tokenizers
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       @unittest.skip(reason="Model not available yet"       )
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             pass





       @slow
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  DebertaModel.from_pretrained("microsoft/deberta-base"       )

             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       )
             lowerCamelCase__		:				int    =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
             with torch.no_grad():
                   lowerCamelCase__		:				Any    =  model(snake_case_					,      attention_mask=snake_case_       )[0]
             # compare the actual values for a slice.
             lowerCamelCase__		:				Optional[int]    =  torch.tensor(
                 [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]       )
             self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,      snake_case_					,      atol=1E-4       )					,      f"{output[:, 1:4, 1:4]}"       )







'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__a          =		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__a          =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      for attribute in key.split("""."""		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

      if weight_type is not None:
            snake_case__  :    List[Any]    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		).shape
      else:
            snake_case__  :    Union[str, Any]    						=      hf_pointer.shape

      assert hf_shape == value.shape, (
          f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
          f" {value.shape} for {full_name}"
      )

      if weight_type == "weight":
            snake_case__  :    int    						=      value
      elif weight_type == "weight_g":
            snake_case__  :    List[str]    						=      value
      elif weight_type == "weight_v":
            snake_case__  :    List[str]    						=      value
      elif weight_type == "bias":
            snake_case__  :    Optional[Any]    						=      value
      else:
            snake_case__  :    str    						=      value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    Dict    						=      fairseq_model.state_dict()

      snake_case__  :    List[Any]    						=      hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      snake_case__  :    Optional[int]    						=      None

      for name, value in fairseq_dict.items():
            snake_case__  :    List[Any]    						=      False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
                  snake_case__  :    Union[str, Any]    						=      True
            elif name.split("""."""		)[0] == "proj":
                  snake_case__  :    Tuple    						=      fairseq_model.proj
                  snake_case__  :    int    						=      True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                              snake_case__  :    Optional[Any]    						=      True
                              if "*" in mapped_key:
                                    snake_case__  :    Optional[int]    						=      name.split(_lowerCAmelCase		)[0].split("""."""		)[-2]
                                    snake_case__  :    Tuple    						=      mapped_key.replace("""*"""     ,   _lowerCAmelCase		)
                              if "weight_g" in name:
                                    snake_case__  :    str    						=      """weight_g"""
                              elif "weight_v" in name:
                                    snake_case__  :    int    						=      """weight_v"""
                              elif "bias" in name:
                                    snake_case__  :    Dict    						=      """bias"""
                              elif "weight" in name:
                                    snake_case__  :    Union[str, Any]    						=      """weight"""
                              else:
                                    snake_case__  :    Union[str, Any]    						=      None
                              set_recursively(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(_lowerCAmelCase		)

      logger.warning(f"Unused weights: {unused_weights}"		)

      return proj_weight





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    int    						=      full_name.split("""conv_layers."""		)[-1]
      snake_case__  :    Dict    						=      name.split("""."""		)
      snake_case__  :    Any    						=      int(items[0]		)
      snake_case__  :    Optional[Any]    						=      int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  snake_case__  :    str    						=      value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  snake_case__  :    Union[str, Any]    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__						,				snake_case__  :    str    						=      emb.weight.shape
      snake_case__  :    List[str]    						=      nn.Linear(_lowerCAmelCase     ,   _lowerCAmelCase     ,   bias=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      emb.weight.data
      return lin_layer





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      with open(_lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8"""		) as f:
            snake_case__  :    int    						=      f.readlines()
            snake_case__  :    List[Any]    						=      [line.split(""" """		)[0] for line in lines]

      snake_case__  :    Union[str, Any]    						=      len(_lowerCAmelCase		)

      snake_case__  :    Any    						=      {
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

      vocab_dict.update(dict(zip(_lowerCAmelCase     ,   range(4     ,   num_words + 4		)		)		)		)
      return vocab_dict





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   )		-> int:
      snake_case__  :    Optional[Any]    						=      WavaVecaConfig.from_pretrained(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SpeechaTextaConfig.from_pretrained(
          _lowerCAmelCase     ,   vocab_size=_lowerCAmelCase     ,   decoder_layers=_lowerCAmelCase     ,   do_stable_layer_norm=_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      WavaVecaFeatureExtractor(
          feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=_lowerCAmelCase     ,   return_attention_mask=_lowerCAmelCase     ,   )

      snake_case__						,				snake_case__						,				snake_case__  :    Union[str, Any]    						=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]     ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
      snake_case__  :    Tuple    						=      model[0].eval()

      # set weights for wav2vec2 encoder
      snake_case__  :    Optional[Any]    						=      WavaVecaModel(_lowerCAmelCase		)
      snake_case__  :    Dict    						=      recursively_load_weights_wavaveca(model.encoder     ,   _lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      SpeechaTextaForCausalLM(_lowerCAmelCase		)
      snake_case__						,				snake_case__  :    Tuple    						=      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,   strict=_lowerCAmelCase		)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""		)
      snake_case__  :    Tuple    						=      nn.Parameter(model.decoder.embed_out.detach()		)

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"		)
      logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"		)

      snake_case__  :    List[Any]    						=      SpeechEncoderDecoderModel(encoder=_lowerCAmelCase     ,   decoder=_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      False

      # add projection layer
      snake_case__  :    Union[str, Any]    						=      nn.Parameter(projection_layer.weight		)
      snake_case__  :    int    						=      nn.Parameter(projection_layer.bias		)

      snake_case__  :    Tuple    						=      create_vocab_dict(_lowerCAmelCase		)

      with open(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)     ,   """w"""		) as fp:
            json.dump(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Tuple    						=      SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)		)
      tokenizer.save_pretrained(_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      hf_wavavec.config.to_dict()
      snake_case__  :    Tuple    						=      tokenizer.pad_token_id
      snake_case__  :    Optional[Any]    						=      tokenizer.bos_token_id
      snake_case__  :    int    						=      tokenizer.eos_token_id
      snake_case__  :    str    						=      """speech_to_text_2"""
      snake_case__  :    List[Any]    						=      """wav2vec2"""

      snake_case__  :    List[str]    						=      SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase		)

      hf_wavavec.save_pretrained(_lowerCAmelCase		)
      feature_extractor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
       parser.add_argument(
           "--encoder_config_path",
           default="facebook/wav2vec2-large-lv60",
           type=str,
           help="Path to hf encoder wav2vec2 checkpoint config",
       )
       parser.add_argument(
           "--decoder_config_path",
           default="facebook/s2t-small-mustc-en-fr-st",
           type=str,
           help="Path to hf decoder s2t checkpoint config",
       )
       parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       __a          =		parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           vocab_size=args.vocab_size,
           num_decoder_layers=args.num_decoder_layers,
       )
