"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_=None )      ->int:
    assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
    _lowerCamelCase	: Dict       =       nn.Parameter(__UpperCAmelCase )
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
        _lowerCamelCase	: Dict       =       nn.Parameter(__UpperCAmelCase )



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[str]:
    _lowerCamelCase	: List[Any]       =       np.asarray(weights[0] )
    _lowerCamelCase	: List[str]       =       np.asarray(weights[1] )
    _lowerCamelCase	: Tuple       =       np.asarray(weights[2] )

    set_param(
        torch_layer.self_attention.query_key						,	torch.tensor(__UpperCAmelCase ).transpose(1						,	2 ).contiguous().view(-1						,	__UpperCAmelCase )						,	)
    set_param(
        torch_layer.self_attention.value						,	torch.tensor(__UpperCAmelCase ).transpose(1						,	2 ).contiguous().view(-1						,	__UpperCAmelCase )						,	)
    set_param(
        torch_layer.output.dense						,	torch.tensor(__UpperCAmelCase ).view(-1						,	__UpperCAmelCase ).contiguous().transpose(0						,	1 )						,	)



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Tuple:
    _lowerCamelCase	: List[Any]       =       np.asarray(weights[0] )
    _lowerCamelCase	: Any       =       np.asarray(weights[1] )
    _lowerCamelCase	: Tuple       =       np.asarray(weights[2] )
    _lowerCamelCase	: Any       =       np.asarray(weights[3] )

    set_param(
        torch_layer.self_attention.query						,	torch.tensor(__UpperCAmelCase ).transpose(1						,	2 ).contiguous().view(-1						,	__UpperCAmelCase )						,	)
    set_param(
        torch_layer.self_attention.key						,	torch.tensor(__UpperCAmelCase ).transpose(1						,	2 ).contiguous().view(-1						,	__UpperCAmelCase )						,	)
    set_param(
        torch_layer.self_attention.value						,	torch.tensor(__UpperCAmelCase ).transpose(1						,	2 ).contiguous().view(-1						,	__UpperCAmelCase )						,	)
    set_param(
        torch_layer.output.dense						,	torch.tensor(__UpperCAmelCase ).view(-1						,	__UpperCAmelCase ).contiguous().transpose(0						,	1 )						,	)



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Union[str, Any]:
    _lowerCamelCase	: Tuple       =       weights[0][0][0]
    _lowerCamelCase	: List[Any]       =       np.asarray(layer_norm_a[0] )
    _lowerCamelCase	: Dict       =       np.asarray(layer_norm_a[1] )
    set_param(
        torch_block.attention.layer_norm						,	torch.tensor(__UpperCAmelCase )						,	torch.tensor(__UpperCAmelCase )						,	)

    # lsh weights + output
    _lowerCamelCase	: Optional[Any]       =       weights[0][1]
    if len(__UpperCAmelCase ) < 4:
        set_layer_weights_in_torch_lsh(__UpperCAmelCase						,	torch_block.attention						,	__UpperCAmelCase )
    else:
        set_layer_weights_in_torch_local(__UpperCAmelCase						,	torch_block.attention						,	__UpperCAmelCase )

    # intermediate weighs
    _lowerCamelCase	: Union[str, Any]       =       weights[2][0][1][2]

    # Chunked Feed Forward
    if len(__UpperCAmelCase ) == 4:
        _lowerCamelCase	: int       =       intermediate_weights[2]

    # layernorm 2
    _lowerCamelCase	: List[Any]       =       np.asarray(intermediate_weights[0][0] )
    _lowerCamelCase	: List[str]       =       np.asarray(intermediate_weights[0][1] )
    set_param(
        torch_block.feed_forward.layer_norm						,	torch.tensor(__UpperCAmelCase )						,	torch.tensor(__UpperCAmelCase )						,	)

    # intermediate dense
    _lowerCamelCase	: Optional[Any]       =       np.asarray(intermediate_weights[1][0] )
    _lowerCamelCase	: List[str]       =       np.asarray(intermediate_weights[1][1] )
    set_param(
        torch_block.feed_forward.dense.dense						,	torch.tensor(__UpperCAmelCase ).transpose(0						,	1 ).contiguous()						,	torch.tensor(__UpperCAmelCase )						,	)

    # intermediate out
    _lowerCamelCase	: int       =       np.asarray(intermediate_weights[4][0] )
    _lowerCamelCase	: Dict       =       np.asarray(intermediate_weights[4][1] )
    set_param(
        torch_block.feed_forward.output.dense						,	torch.tensor(__UpperCAmelCase ).transpose(0						,	1 ).contiguous()						,	torch.tensor(__UpperCAmelCase )						,	)



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Dict:
    _lowerCamelCase	: Optional[Any]       =       torch_model.reformer

    # word embeds
    _lowerCamelCase	: str       =       np.asarray(weights[1] )
    set_param(
        torch_model_reformer.embeddings.word_embeddings						,	torch.tensor(__UpperCAmelCase )						,	)

    if isinstance(weights[3]						,	__UpperCAmelCase ):
        _lowerCamelCase	: int       =       torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights ) ):
            _lowerCamelCase	: str       =       np.asarray(weights[3][emb_idx][0] )
            assert (
                position_embeddings.weights[emb_idx].shape == emb_weights.shape
            ), F'''{position_embeddings[emb_idx]} emb does not match'''
            _lowerCamelCase	: Optional[Any]       =       nn.Parameter(torch.tensor(__UpperCAmelCase ) )

    _lowerCamelCase	: Optional[int]       =       weights[5]
    assert len(torch_model_reformer.encoder.layers ) * 4 == len(
        __UpperCAmelCase ), "HF and trax model do not have the same number of layers"
    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
        _lowerCamelCase	: str       =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(__UpperCAmelCase						,	__UpperCAmelCase						,	__UpperCAmelCase )

    # output layer norm
    _lowerCamelCase	: Union[str, Any]       =       np.asarray(weights[7][0] )
    _lowerCamelCase	: List[str]       =       np.asarray(weights[7][1] )
    set_param(
        torch_model_reformer.encoder.layer_norm						,	torch.tensor(__UpperCAmelCase )						,	torch.tensor(__UpperCAmelCase )						,	)

    # output embeddings
    _lowerCamelCase	: int       =       np.asarray(weights[9][0] )
    _lowerCamelCase	: Optional[int]       =       np.asarray(weights[9][1] )
    set_param(
        torch_model.lm_head.decoder						,	torch.tensor(__UpperCAmelCase ).transpose(0						,	1 ).contiguous()						,	torch.tensor(__UpperCAmelCase )						,	)



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[str]:
    _lowerCamelCase	: List[Any]       =       ReformerConfig.from_json_file(__UpperCAmelCase )
    print(F'''Building PyTorch model from configuration: {config}''' )
    _lowerCamelCase	: int       =       ReformerModelWithLMHead(__UpperCAmelCase )

    with open(__UpperCAmelCase						,	'''rb''' ) as f:
        _lowerCamelCase	: Union[str, Any]       =       pickle.load(__UpperCAmelCase )['''weights''']

    set_model_weights_in_torch(__UpperCAmelCase						,	__UpperCAmelCase						,	config.hidden_size )

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}''' )
    torch.save(model.state_dict()						,	__UpperCAmelCase )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__   :      Any							=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained Reformer model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE__   :      Optional[Any]							=parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   for attribute in key.split("""."""   ):
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   )

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

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							fairseq_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __SCREAMING_SNAKE_CASE   				=							False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						hf_model.config.feat_extract_norm == """group"""				,						)
         __SCREAMING_SNAKE_CASE   				=							True
      else:
         for key, mapped_key in MAPPING.items():
            __SCREAMING_SNAKE_CASE   				=							"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

            if key in name or (key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0] and not is_finetuned):
               __SCREAMING_SNAKE_CASE   				=							True
               if "*" in mapped_key:
                  __SCREAMING_SNAKE_CASE   				=							name.split(__UpperCAmelCase   )[0].split("""."""   )[-2]
                  __SCREAMING_SNAKE_CASE   				=							mapped_key.replace("""*"""				,						__UpperCAmelCase   )
               if "weight_g" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_g"""
               elif "weight_v" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_v"""
               elif "weight" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight"""
               elif "bias" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""bias"""
               else:
                  __SCREAMING_SNAKE_CASE   				=							None
               set_recursively(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
            continue
      if not is_used:
         unused_weights.append(__UpperCAmelCase   )

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							full_name.split("""conv_layers."""   )[-1]
   __SCREAMING_SNAKE_CASE   				=							name.split("""."""   )
   __SCREAMING_SNAKE_CASE   				=							int(items[0]   )
   __SCREAMING_SNAKE_CASE   				=							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."""
         )
         __SCREAMING_SNAKE_CASE   				=							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."""
         )
         __SCREAMING_SNAKE_CASE   				=							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."
         )
         __SCREAMING_SNAKE_CASE   				=							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."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(__UpperCAmelCase   )

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=True   )    ->       Tuple:

   '''simple docstring'''







   if config_path is not None:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig.from_pretrained(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig()

   if is_finetuned:
      if dict_path:
         __SCREAMING_SNAKE_CASE   				=							Dictionary.load(__UpperCAmelCase   )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __SCREAMING_SNAKE_CASE   				=							target_dict.pad_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.bos_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.eos_index
         __SCREAMING_SNAKE_CASE   				=							len(target_dict.symbols   )
         __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						"""vocab.json"""   )
         if not os.path.isdir(__UpperCAmelCase   ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCAmelCase   )   )
            return
         os.makedirs(__UpperCAmelCase				,						exist_ok=__UpperCAmelCase   )
         with open(__UpperCAmelCase				,						"""w"""				,						encoding="""utf-8"""   ) as vocab_handle:
            json.dump(target_dict.indices				,						__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							WavaVecaCTCTokenizer(
             __UpperCAmelCase				,						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=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							True if config.feat_extract_norm == """layer""" else False
         __SCREAMING_SNAKE_CASE   				=							WavaVecaFeatureExtractor(
             feature_size=1				,						sampling_rate=16000				,						padding_value=0				,						do_normalize=__UpperCAmelCase				,						return_attention_mask=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase				,						tokenizer=__UpperCAmelCase   )
         processor.save_pretrained(__UpperCAmelCase   )

      __SCREAMING_SNAKE_CASE   				=							HubertForCTC(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertModel(__UpperCAmelCase   )

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

   __SCREAMING_SNAKE_CASE   				=							model[0].eval()

   recursively_load_weights(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   hf_wavavec.save_pretrained(__UpperCAmelCase   )


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("--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   	=    parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





