


"""simple docstring"""


import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__lowerCamelCase      :List[Any]	    =		logging.get_logger('transformers.models.encodec')

__lowerCamelCase      :Optional[Any]	    =		{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
__lowerCamelCase      :Any	    =		{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
__lowerCamelCase      :int	    =		{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
__lowerCamelCase      :List[str]	    =		{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
__lowerCamelCase      :Any	    =		{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
__lowerCamelCase      :Optional[Any]	    =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase      :str	    =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase      :Optional[int]	    =		[]
__lowerCamelCase      :str	    =		[]




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						str    )   ->      Tuple:
      for attribute in key.split("""."""    ):
            lowerCamelCase					:					Tuple        =	getattr(UpperCamelCase__			,				UpperCamelCase__    )

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

      if hf_shape != value.shape:
            raise ValueError(
                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					:					Dict        =	value
      elif weight_type == "weight_g":
            lowerCamelCase					:					str        =	value
      elif weight_type == "weight_v":
            lowerCamelCase					:					Any        =	value
      elif weight_type == "bias":
            lowerCamelCase					:					Union[str, Any]        =	value
      elif weight_type == "running_mean":
            lowerCamelCase					:					int        =	value
      elif weight_type == "running_var":
            lowerCamelCase					:					int        =	value
      elif weight_type == "num_batches_tracked":
            lowerCamelCase					:					List[Any]        =	value
      elif weight_type == "weight_ih_l0":
            lowerCamelCase					:					Tuple        =	value
      elif weight_type == "weight_hh_l0":
            lowerCamelCase					:					Optional[int]        =	value
      elif weight_type == "bias_ih_l0":
            lowerCamelCase					:					Any        =	value
      elif weight_type == "bias_hh_l0":
            lowerCamelCase					:					Tuple        =	value
      elif weight_type == "weight_ih_l1":
            lowerCamelCase					:					int        =	value
      elif weight_type == "weight_hh_l1":
            lowerCamelCase					:					Optional[int]        =	value
      elif weight_type == "bias_ih_l1":
            lowerCamelCase					:					Optional[int]        =	value
      elif weight_type == "bias_hh_l1":
            lowerCamelCase					:					List[Any]        =	value
      else:
            lowerCamelCase					:					Tuple        =	value

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




def   snake_case		(     UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Tuple    )   ->      Optional[int]:
      for key in ignore_keys:
            if key.endswith(""".*"""    ):
                  if name.startswith(key[:-1]    ):
                        return True
            elif ".*." in key:
                  lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	key.split(""".*."""    )
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False




def   snake_case		(     UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						List[Any]    )   ->      List[Any]:
      lowerCamelCase					:					Dict        =	[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowerCamelCase					:					int        =	MAPPING_24K
      elif model_name == "encodec_48khz":
            lowerCamelCase					:					Union[str, Any]        =	MAPPING_48K
      else:
            raise ValueError(F'Unsupported model: {model_name}'    )

      for name, value in orig_dict.items():
            if should_ignore(UpperCamelCase__			,				UpperCamelCase__    ):
                  logger.info(F'{name} was ignored'    )
                  continue

            lowerCamelCase					:					Union[str, Any]        =	False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowerCamelCase     ,			lowerCamelCase					:					str        =	key.split(""".*."""    )
                        if prefix in name and suffix in name:
                              lowerCamelCase					:					List[str]        =	suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith("""embed"""    ) and name.endswith("""embed_avg"""    ):
                              continue

                        lowerCamelCase					:					List[str]        =	True
                        if "*" in mapped_key:
                              lowerCamelCase					:					str        =	name.split(UpperCamelCase__    )[0].split("""."""    )[-2]
                              lowerCamelCase					:					Union[str, Any]        =	mapped_key.replace("""*"""			,				UpperCamelCase__    )
                        if "weight_g" in name:
                              lowerCamelCase					:					Dict        =	"""weight_g"""
                        elif "weight_v" in name:
                              lowerCamelCase					:					Tuple        =	"""weight_v"""
                        elif "weight_ih_l0" in name:
                              lowerCamelCase					:					Union[str, Any]        =	"""weight_ih_l0"""
                        elif "weight_hh_l0" in name:
                              lowerCamelCase					:					Tuple        =	"""weight_hh_l0"""
                        elif "bias_ih_l0" in name:
                              lowerCamelCase					:					List[Any]        =	"""bias_ih_l0"""
                        elif "bias_hh_l0" in name:
                              lowerCamelCase					:					str        =	"""bias_hh_l0"""
                        elif "weight_ih_l1" in name:
                              lowerCamelCase					:					Any        =	"""weight_ih_l1"""
                        elif "weight_hh_l1" in name:
                              lowerCamelCase					:					List[str]        =	"""weight_hh_l1"""
                        elif "bias_ih_l1" in name:
                              lowerCamelCase					:					Dict        =	"""bias_ih_l1"""
                        elif "bias_hh_l1" in name:
                              lowerCamelCase					:					Dict        =	"""bias_hh_l1"""
                        elif "bias" in name:
                              lowerCamelCase					:					Optional[int]        =	"""bias"""
                        elif "weight" in name:
                              lowerCamelCase					:					str        =	"""weight"""
                        elif "running_mean" in name:
                              lowerCamelCase					:					List[str]        =	"""running_mean"""
                        elif "running_var" in name:
                              lowerCamelCase					:					Any        =	"""running_var"""
                        elif "num_batches_tracked" in name:
                              lowerCamelCase					:					Tuple        =	"""num_batches_tracked"""
                        else:
                              lowerCamelCase					:					List[str]        =	None
                        set_recursively(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
                  continue
            if not is_used:
                  unused_weights.append(UpperCamelCase__    )

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




@torch.no_grad()
def   snake_case		(     UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						Optional[int]=None			,				UpperCamelCase__       :						int=None			,				)   ->      Optional[int]:
      if config_path is not None:
            lowerCamelCase					:					Any        =	EncodecConfig.from_pretrained(UpperCamelCase__    )
      else:
            lowerCamelCase					:					List[str]        =	EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowerCamelCase					:					Optional[int]        =	[8, 5, 4, 4]
            lowerCamelCase					:					List[Any]        =	[2.2]
            lowerCamelCase					:					List[str]        =	64
            lowerCamelCase					:					Optional[Any]        =	32000
            lowerCamelCase					:					List[str]        =	2048
            lowerCamelCase					:					Dict        =	False
            lowerCamelCase					:					List[Any]        =	False
            lowerCamelCase					:					Any        =	False
      elif model_name == "encodec_48khz":
            lowerCamelCase					:					Dict        =	[8, 5, 4, 2]
            lowerCamelCase					:					Optional[Any]        =	[3.0, 6.0, 1_2.0, 2_4.0]
            lowerCamelCase					:					Optional[int]        =	48000
            lowerCamelCase					:					str        =	2
            lowerCamelCase					:					Tuple        =	False
            lowerCamelCase					:					Union[str, Any]        =	"""time_group_norm"""
            lowerCamelCase					:					Optional[Any]        =	True
            lowerCamelCase					:					Dict        =	1.0
            lowerCamelCase					:					str        =	0.0_1
      else:
            raise ValueError(F'Unknown model name: {model_name}'    )

      lowerCamelCase					:					Optional[Any]        =	EncodecModel(UpperCamelCase__    )

      lowerCamelCase					:					Optional[int]        =	EncodecFeatureExtractor(
          feature_size=config.audio_channels			,				sampling_rate=config.sampling_rate			,				chunk_length_s=config.chunk_length_s			,				overlap=config.overlap			,				)
      feature_extractor.save_pretrained(UpperCamelCase__    )

      lowerCamelCase					:					Any        =	torch.load(UpperCamelCase__    )
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            lowerCamelCase					:					Optional[Any]        =	original_checkpoint["""best_state"""]
      recursively_load_weights(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      model.save_pretrained(UpperCamelCase__    )

      if repo_id:
            print("""Pushing to the hub..."""    )
            feature_extractor.push_to_hub(UpperCamelCase__    )
            model.push_to_hub(UpperCamelCase__    )


if __name__ == "__main__":
    __lowerCamelCase      :List[Any]	    =		argparse.ArgumentParser()
    parser.add_argument(
        '--model',
        default='encodec_24khz',
        type=str,
        help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
    )
    parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
    )

    __lowerCamelCase      :Optional[int]	    =		parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )








"""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   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Optional[Any]=None    )   ->      Tuple:
      # set parameter of one layer
      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					:					Any        =	nn.Parameter(UpperCamelCase__    )




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Tuple    )   ->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Dict        =	np.asarray(weights[0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[str]        =	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   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Any    )   ->      List[Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Tuple        =	np.asarray(weights[0]    )
      lowerCamelCase					:					Any        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[2]    )
      lowerCamelCase					:					List[str]        =	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   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Any    )   ->      Optional[Any]:
      # layernorm 1
      lowerCamelCase					:					str        =	weights[0][0][0]
      lowerCamelCase					:					Optional[int]        =	np.asarray(layer_norm_a[0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(layer_norm_a[1]    )
      set_param(
          torch_block.attention.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # lsh weights + output
      lowerCamelCase					:					List[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					:					int        =	weights[2][0][1][2]

      # Chunked Feed Forward
      if len(UpperCamelCase__    ) == 4:
            lowerCamelCase					:					Dict        =	intermediate_weights[2]

      # layernorm 2
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[0][0]    )
      lowerCamelCase					:					Tuple        =	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					:					Union[str, Any]        =	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					:					Optional[int]        =	np.asarray(intermediate_weights[4][0]    )
      lowerCamelCase					:					List[Any]        =	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   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						int    )   ->      List[Any]:
      # reformer model
      lowerCamelCase					:					List[Any]        =	torch_model.reformer

      # word embeds
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(weights[1]    )
      set_param(
          torch_model_reformer.embeddings.word_embeddings			,				torch.tensor(UpperCamelCase__    )			,				)

      if isinstance(weights[3]			,				UpperCamelCase__    ):
            lowerCamelCase					:					Union[str, Any]        =	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					:					Dict        =	nn.Parameter(torch.tensor(UpperCamelCase__    )    )

      lowerCamelCase					:					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					:					Dict        =	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      # output layer norm
      lowerCamelCase					:					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					:					List[Any]        =	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   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						str    )   ->      Optional[int]:
      # Initialise PyTorch model
      lowerCamelCase					:					Union[str, Any]        =	ReformerConfig.from_json_file(UpperCamelCase__    )
      print(F'Building PyTorch model from configuration: {config}'    )
      lowerCamelCase					:					str        =	ReformerModelWithLMHead(UpperCamelCase__    )

      with open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					str        =	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__":
    __lowerCamelCase      :Dict	    =		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.'
    )
    __lowerCamelCase      :Optional[int]	    =		parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




