

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

__UpperCAmelCase								=			{
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__UpperCAmelCase								=			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def      snake_case_				(__A					:						Any ,       __A					:						Optional[int] ,       __A					:						str ,       __A					:						str ,       __A					:						Tuple ,       __A					:						Union[str, Any]					)      ->     Union[str, Any]:




   for attribute in key.split("""."""					):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            __lowerCAmelCase  :      List[str]    =						"""lm_head"""

      __lowerCAmelCase  :      Any    =						getattr(lowerCamelCase__ ,       lowerCamelCase__					)

   if weight_type is not None:
      __lowerCAmelCase  :      List[str]    =						getattr(lowerCamelCase__ ,       lowerCamelCase__					).shape
   else:
      __lowerCAmelCase  :      int    =						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":
      __lowerCAmelCase  :      List[str]    =						value
   elif weight_type == "weight_g":
      __lowerCAmelCase  :      Optional[Any]    =						value
   elif weight_type == "weight_v":
      __lowerCAmelCase  :      Any    =						value
   elif weight_type == "bias":
      __lowerCAmelCase  :      Any    =						value
   else:
      __lowerCAmelCase  :      List[Any]    =						value

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





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						Dict ,       __A					:						Optional[int]					)      ->     Any:
   __lowerCAmelCase  :      Optional[Any]    =						[]
   __lowerCAmelCase  :      Union[str, Any]    =						fairseq_model.state_dict()

   __lowerCAmelCase  :      List[Any]    =						hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      __lowerCAmelCase  :      Optional[Any]    =						False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__ ,       hf_model.config.feat_extract_norm == """group""" ,       )
         __lowerCAmelCase  :      List[Any]    =						True
      else:
         for key, mapped_key in MAPPING.items():
            __lowerCAmelCase  :      int    =						"""unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
               __lowerCAmelCase  :      Optional[int]    =						True
               if "*" in mapped_key:
                  __lowerCAmelCase  :      Optional[int]    =						name.split(lowerCamelCase__					)[0].split("""."""					)[-2]
                  __lowerCAmelCase  :      Union[str, Any]    =						mapped_key.replace("""*""" ,       lowerCamelCase__					)
               if "weight_g" in name:
                  __lowerCAmelCase  :      Any    =						"""weight_g"""
               elif "weight_v" in name:
                  __lowerCAmelCase  :      Union[str, Any]    =						"""weight_v"""
               elif "bias" in name:
                  __lowerCAmelCase  :      List[Any]    =						"""bias"""
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  __lowerCAmelCase  :      int    =						"""weight"""
               else:
                  __lowerCAmelCase  :      List[Any]    =						None
               set_recursively(lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__					)
            continue
      if not is_used:
         unused_weights.append(lowerCamelCase__					)

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





def      snake_case_				(__A					:						Any ,       __A					:						Dict ,       __A					:						Any ,       __A					:						List[Any] ,       __A					:						str					)      ->     Tuple:
   __lowerCAmelCase  :      str    =						full_name.split("""conv_layers."""					)[-1]
   __lowerCAmelCase  :      Dict    =						name.split("""."""					)
   __lowerCAmelCase  :      List[str]    =						int(items[0]					)
   __lowerCAmelCase  :      int    =						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.'''
         )
         __lowerCAmelCase  :      List[str]    =						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.'''
         )
         __lowerCAmelCase  :      Any    =						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."
         )
         __lowerCAmelCase  :      str    =						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.'''
         )
         __lowerCAmelCase  :      int    =						value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
   else:
      unused_weights.append(lowerCamelCase__					)





@torch.no_grad()
def      snake_case_				(__A					:						Any ,       __A					:						Dict ,       __A					:						Union[str, Any]=None ,       __A					:						List[Any]=None ,       __A					:						Union[str, Any]=True					)      ->     str:




   if config_path is not None:
      __lowerCAmelCase  :      Dict    =						UniSpeechConfig.from_pretrained(lowerCamelCase__					)
   else:
      __lowerCAmelCase  :      Dict    =						UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         __lowerCAmelCase  :      Any    =						Dictionary.load_from_json(lowerCamelCase__					)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __lowerCAmelCase  :      Dict    =						target_dict.pad_index
         __lowerCAmelCase  :      str    =						target_dict.bos_index
         __lowerCAmelCase  :      Optional[Any]    =						target_dict.eos_index
         __lowerCAmelCase  :      List[str]    =						len(target_dict.symbols					)
         __lowerCAmelCase  :      Dict    =						os.path.join(lowerCamelCase__ ,       """vocab.json"""					)
         if not os.path.isdir(lowerCamelCase__					):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase__					)					)
            return
         os.makedirs(lowerCamelCase__ ,       exist_ok=lowerCamelCase__					)
         __lowerCAmelCase  :      Optional[Any]    =						target_dict.indices

         # fairseq has the <pad> and <s> switched
         __lowerCAmelCase  :      int    =						4_2
         __lowerCAmelCase  :      int    =						4_3
         with open(lowerCamelCase__ ,       """w""" ,       encoding="""utf-8"""					) as vocab_handle:
            json.dump(lowerCamelCase__ ,       lowerCamelCase__					)
         __lowerCAmelCase  :      List[Any]    =						WavaVecaPhonemeCTCTokenizer(
             lowerCamelCase__ ,       unk_token=target_dict.unk_word ,       pad_token=target_dict.pad_word ,       bos_token=target_dict.bos_word ,       eos_token=target_dict.eos_word ,       word_delimiter_token="""|""" ,       do_lower_case=lowerCamelCase__ ,       )
         __lowerCAmelCase  :      Dict    =						True if config.feat_extract_norm == """layer""" else False
         __lowerCAmelCase  :      List[str]    =						WavaVecaFeatureExtractor(
             feature_size=1 ,       sampling_rate=1_6_0_0_0 ,       padding_value=0 ,       do_normalize=lowerCamelCase__ ,       return_attention_mask=lowerCamelCase__ ,       )
         __lowerCAmelCase  :      Any    =						WavaVecaProcessor(feature_extractor=lowerCamelCase__ ,       tokenizer=lowerCamelCase__					)
         processor.save_pretrained(lowerCamelCase__					)

      __lowerCAmelCase  :      str    =						UniSpeechForCTC(lowerCamelCase__					)
   else:
      __lowerCAmelCase  :      Union[str, Any]    =						UniSpeechForPreTraining(lowerCamelCase__					)

   if is_finetuned:
      __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path] ,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					), """w2v_path""": checkpoint_path}					)
   else:
      __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      str    =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

   __lowerCAmelCase  :      List[Any]    =						model[0].eval()

   recursively_load_weights(lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__					)

   hf_unispeech.save_pretrained(lowerCamelCase__					)


if __name__ == "__main__":
   __UpperCAmelCase								=			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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
   )
   __UpperCAmelCase								=			parser.parse_args()
   convert_unispeech_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   str=13	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=99	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Optional[Any]=37	,							lowerCAmelCase			:   str="gelu"	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   Any=5_12	,							lowerCAmelCase			:   Optional[Any]=16	,							lowerCAmelCase			:   Dict=2	,							lowerCAmelCase			:   Tuple=0.02	,							lowerCAmelCase			:   Optional[int]=4	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						parent
     __lowerCAmelCase  :      Dict    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      Union[str, Any]    =						is_training
     __lowerCAmelCase  :      List[Any]    =						use_attention_mask
     __lowerCAmelCase  :      List[Any]    =						use_token_type_ids
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      Any    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      Optional[int]    =						num_attention_heads
     __lowerCAmelCase  :      Dict    =						intermediate_size
     __lowerCAmelCase  :      Tuple    =						hidden_act
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      Any    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Union[str, Any]    =						max_position_embeddings
     __lowerCAmelCase  :      int    =						type_vocab_size
     __lowerCAmelCase  :      Tuple    =						type_sequence_label_size
     __lowerCAmelCase  :      int    =						initializer_range
     __lowerCAmelCase  :      Optional[int]    =						num_choices







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						None
     if self.use_attention_mask:
        __lowerCAmelCase  :      Optional[int]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      Union[str, Any]    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[str]    =						BertConfig(
         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	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							)

     return config, input_ids, token_type_ids, attention_mask







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						config_and_inputs

     __lowerCAmelCase  :      Any    =						True
     __lowerCAmelCase  :      str    =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
     __lowerCAmelCase  :      int    =						ids_tensor([self.batch_size, self.seq_length]	,							vocab_size=2   )

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      int										=True

  lowerCamelCase   :      Any										=(
      (
          FlaxBertModel,
          FlaxBertForPreTraining,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForQuestionAnswering,
          FlaxBertForNextSentencePrediction,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						FlaxBertModelTester(self   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						FlaxBertModel.from_pretrained("""bert-base-cased"""   )
     __lowerCAmelCase  :      str    =						model(np.ones((1, 1)   )   )
     self.assertIsNotNone(lowerCAmelCase   )


