

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()
__a  :Dict										=       logging.get_logger(__name__)

__a  :List[Any]										=       {
    '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',
}
__a  :Optional[Any]										=       [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def 	__snake_case		(      __UpperCamelCase     :      str							,__UpperCamelCase     :      List[str]							,__UpperCamelCase     :      Union[str, Any]							,__UpperCamelCase     :      str							,__UpperCamelCase     :      Any							,__UpperCamelCase     :      Any  ):



      """simple docstring"""

      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
                        A_		   =							"lm_head"

            A_		   =							getattr(lowerCAmelCase__							,lowerCAmelCase__  )

      if weight_type is not None:
            A_		   =							getattr(lowerCAmelCase__							,lowerCAmelCase__  ).shape
      else:
            A_		   =							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":
            A_		   =							value
      elif weight_type == "weight_g":
            A_		   =							value
      elif weight_type == "weight_v":
            A_		   =							value
      elif weight_type == "bias":
            A_		   =							value
      else:
            A_		   =							value

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

def 	__snake_case		(      __UpperCamelCase     :      List[str]							,__UpperCamelCase     :      Any							,__UpperCamelCase     :      Tuple  ):



      """simple docstring"""

      A_		   =							[]
      A_		   =							fairseq_model.state_dict()

      A_		   =							hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            A_		   =							False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,hf_model.config.feat_extract_norm == "group"							,)
                  A_		   =							True
            else:
                  for key, mapped_key in MAPPING.items():
                        A_		   =							"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]:
                              A_		   =							True
                              if "*" in mapped_key:
                                    A_		   =							name.split(lowerCAmelCase__  )[0].split("."  )[-2]
                                    A_		   =							mapped_key.replace("*"							,lowerCAmelCase__  )
                              if "weight_g" in name:
                                    A_		   =							"weight_g"
                              elif "weight_v" in name:
                                    A_		   =							"weight_v"
                              elif "bias" in name:
                                    A_		   =							"bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    A_		   =							"weight"
                              else:
                                    A_		   =							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		(      __UpperCamelCase     :      Union[str, Any]							,__UpperCamelCase     :      int							,__UpperCamelCase     :      str							,__UpperCamelCase     :      List[Any]							,__UpperCamelCase     :      int  ):



      """simple docstring"""

      A_		   =							full_name.split("conv_layers."  )[-1]
      A_		   =							name.split("."  )
      A_		   =							int(items[0]  )
      A_		   =							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.'''
                  )
                  A_		   =							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.'''
                  )
                  A_		   =							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."
                  )
                  A_		   =							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.'''
                  )
                  A_		   =							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		(      __UpperCamelCase     :      List[Any]							,__UpperCamelCase     :      Optional[Any]							,__UpperCamelCase     :      Optional[int]=None							,__UpperCamelCase     :      List[str]=None							,__UpperCamelCase     :      Dict=True  ):



      """simple docstring"""

      if config_path is not None:
            A_		   =							UniSpeechConfig.from_pretrained(lowerCAmelCase__  )
      else:
            A_		   =							UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  A_		   =							Dictionary.load_from_json(lowerCAmelCase__  )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  A_		   =							target_dict.pad_index
                  A_		   =							target_dict.bos_index
                  A_		   =							target_dict.eos_index
                  A_		   =							len(target_dict.symbols  )
                  A_		   =							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__  )
                  A_		   =							target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  A_		   =							42
                  A_		   =							43
                  with open(lowerCAmelCase__							,"w"							,encoding="utf-8"  ) as vocab_handle:
                        json.dump(lowerCAmelCase__							,lowerCAmelCase__  )
                  A_		   =							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__							,)
                  A_		   =							True if config.feat_extract_norm == "layer" else False
                  A_		   =							WavaVecaFeatureExtractor(
                      feature_size=1							,sampling_rate=1_6000							,padding_value=0							,do_normalize=lowerCAmelCase__							,return_attention_mask=lowerCAmelCase__							,)
                  A_		   =							WavaVecaProcessor(feature_extractor=lowerCAmelCase__							,tokenizer=lowerCAmelCase__  )
                  processor.save_pretrained(lowerCAmelCase__  )

            A_		   =							UniSpeechForCTC(lowerCAmelCase__  )
      else:
            A_		   =							UniSpeechForPreTraining(lowerCAmelCase__  )

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

      A_		   =							model[0].eval()

      recursively_load_weights(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__  )

      hf_unispeech.save_pretrained(lowerCAmelCase__  )


if __name__ == "__main__":
      __a  :Optional[int]										=       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'
      )
      __a  :Tuple										=       parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



"""simple docstring"""




import warnings

from ..trainer import Trainer
from ..utils import logging


lowercase_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a=None							, **_a      ):
				warnings.warn(
				    '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
				    '''instead.'''							, _a							, )
				super().__init__(args=_a							, **_a      )






