





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__lowercase	: Tuple       =logging.get_logger(__name__)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	,     lowercase__=False	):






   '''simple docstring'''






   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
          " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
          " instructions."	)
      raise

   if not is_sharded:
      UpperCAmelCase_           =os.path.abspath(lowercase__	)
      logger.info(F'Loading PyTorch weights from {pt_path}'	)

      UpperCAmelCase_           =torch.load(lowercase__	,     map_location="cpu"	)
      logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()	):,} parameters.'	)

      UpperCAmelCase_           =convert_pytorch_state_dict_to_flax(lowercase__	,     lowercase__	)
   else:
      # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
      UpperCAmelCase_           =convert_pytorch_sharded_state_dict_to_flax(lowercase__	,     lowercase__	)
   return flax_state_dict




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	,     ):






   '''simple docstring'''






   def is_key_or_prefix_key_in_dict(lowercase__	) -> bool:
      return len(set(lowercase__	) & {key, (model_prefix,) + key}	) > 0

   # layer norm
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("scale",)
   if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowercase__	):
      return renamed_pt_tuple_key, pt_tensor

   # batch norm layer mean
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("mean",)
   if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowercase__	):
      return renamed_pt_tuple_key, pt_tensor

   # batch norm layer var
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("var",)
   if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowercase__	):
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("embedding",)
   if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowercase__	):
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("kernel",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowercase__	):
      UpperCAmelCase_           =pt_tensor.transpose(2	,     3	,     1	,     0	)
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("kernel",)
   if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowercase__	):
      UpperCAmelCase_           =pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("weight",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   UpperCAmelCase_           =pt_tuple_key[:-1] + ("bias",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
   UpperCAmelCase_           =None
   if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
      UpperCAmelCase_           =pt_tuple_key[-2] + "_g"
   elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
      UpperCAmelCase_           =pt_tuple_key[-2] + "_v"
   if name is not None:
      UpperCAmelCase_           =pt_tuple_key[:-3] + (name,)
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           ={k: v.numpy() for k, v in pt_state_dict.items()}

   UpperCAmelCase_           =flax_model.base_model_prefix

   # use params dict if the model contains batch norm layers
   if "params" in flax_model.params:
      UpperCAmelCase_           =flax_model.params["params"]
   else:
      UpperCAmelCase_           =flax_model.params
   UpperCAmelCase_           =flatten_dict(lowercase__	)

   # add batch_stats keys,values to dict
   if "batch_stats" in flax_model.params:
      UpperCAmelCase_           =flatten_dict(flax_model.params["batch_stats"]	)
      random_flax_state_dict.update(lowercase__	)

   UpperCAmelCase_           ={}

   UpperCAmelCase_           =(model_prefix not in flax_model_params) and (
       model_prefix in {k.split("."	)[0] for k in pt_state_dict.keys()}
   )
   UpperCAmelCase_           =(model_prefix in flax_model_params) and (
       model_prefix not in {k.split("."	)[0] for k in pt_state_dict.keys()}
   )

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      UpperCAmelCase_           =tuple(pt_key.split("."	)	)

      # remove base model prefix if necessary
      UpperCAmelCase_           =pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
         UpperCAmelCase_           =pt_tuple_key[1:]

      # Correctly rename weight parameters
      UpperCAmelCase_    ,							UpperCAmelCase_           =rename_key_and_reshape_tensor(
          lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	)

      # add model prefix if necessary
      UpperCAmelCase_           =(model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
         UpperCAmelCase_           =(model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'	)

        # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
         if "mean" in flax_key[-1] or "var" in flax_key[-1]:
            UpperCAmelCase_           =jnp.asarray(lowercase__	)
            continue
         # remove num_batches_tracked key
         if "num_batches_tracked" in flax_key[-1]:
            flax_state_dict.pop(lowercase__	,     lowercase__	)
            continue

         # also add unexpected weight so that warning is thrown
         UpperCAmelCase_           =jnp.asarray(lowercase__	)

      else:
         # also add unexpected weight so that warning is thrown
         UpperCAmelCase_           =jnp.asarray(lowercase__	)

   return unflatten_dict(lowercase__	)




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   import torch

   # Load the index
   UpperCAmelCase_           ={}
   for shard_file in shard_filenames:
      # load using msgpack utils
      UpperCAmelCase_           =torch.load(lowercase__	)
      UpperCAmelCase_           ={k: v.numpy() for k, v in pt_state_dict.items()}

      UpperCAmelCase_           =flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
      if "batch_stats" in flax_model.params:
         UpperCAmelCase_           =flax_model.params["params"]

         UpperCAmelCase_           =flatten_dict(lowercase__	)
         random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]	)	)
      else:
         UpperCAmelCase_           =flax_model.params
         UpperCAmelCase_           =flatten_dict(lowercase__	)

      UpperCAmelCase_           =(model_prefix not in flax_model_params) and (
          model_prefix in {k.split("."	)[0] for k in pt_state_dict.keys()}
      )
      UpperCAmelCase_           =(model_prefix in flax_model_params) and (
          model_prefix not in {k.split("."	)[0] for k in pt_state_dict.keys()}
      )
      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
         UpperCAmelCase_           =tuple(pt_key.split("."	)	)

         # remove base model prefix if necessary
         UpperCAmelCase_           =pt_tuple_key[0] == model_prefix
         if load_model_with_head_into_base_model and has_base_model_prefix:
            UpperCAmelCase_           =pt_tuple_key[1:]

         # Correctly rename weight parameters
         UpperCAmelCase_    ,							UpperCAmelCase_           =rename_key_and_reshape_tensor(
             lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	)
         # add model prefix if necessary
         UpperCAmelCase_           =(model_prefix,) + flax_key in random_flax_state_dict
         if load_base_model_into_model_with_head and require_base_model_prefix:
            UpperCAmelCase_           =(model_prefix,) + flax_key

         if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
               raise ValueError(
                   F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                   F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'	)

            # add batch stats if the model contains batchnorm layers
         if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1]:
               UpperCAmelCase_           =jnp.asarray(lowercase__	)
               continue
            if "var" in flax_key[-1]:
               UpperCAmelCase_           =jnp.asarray(lowercase__	)
               continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
               flax_state_dict.pop(lowercase__	,     lowercase__	)
               continue

            # also add unexpected weight so that warning is thrown
            UpperCAmelCase_           =jnp.asarray(lowercase__	)

         else:
            # also add unexpected weight so that warning is thrown
            UpperCAmelCase_           =jnp.asarray(lowercase__	)
   return unflatten_dict(lowercase__	)




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =os.path.abspath(lowercase__	)
   logger.info(F'Loading Flax weights from {flax_checkpoint_path}'	)

   # import correct flax class
   UpperCAmelCase_           =getattr(lowercase__	,     "Flax" + model.__class__.__name__	)

   # load flax weight dict
   with open(lowercase__	,     "rb"	) as state_f:
      try:
         UpperCAmelCase_           =from_bytes(lowercase__	,     state_f.read()	)
      except UnpicklingError:
         raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '	)

   return load_flax_weights_in_pytorch_model(lowercase__	,     lowercase__	)





def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
          " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
          " instructions."	)
      raise

   # check if we have bf16 weights
   UpperCAmelCase_           =flatten_dict(jax.tree_util.tree_map(lambda lowercase__	: x.dtype == jnp.bfloataa	,     lowercase__	)	).values()
   if any(lowercase__	):
      # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
      # and bf16 is not fully supported in PT yet.
      logger.warning(
          "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
          "before loading those in PyTorch model."	)
      UpperCAmelCase_           =jax.tree_util.tree_map(
          lambda lowercase__	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params	,     lowercase__	)

   UpperCAmelCase_           =flatten_dict(lowercase__	)
   UpperCAmelCase_           =pt_model.state_dict()

   UpperCAmelCase_           =(pt_model.base_model_prefix in flax_state) and (
       pt_model.base_model_prefix not in {k.split("."	)[0] for k in pt_model_dict.keys()}
   )
   UpperCAmelCase_           =(pt_model.base_model_prefix not in flax_state) and (
       pt_model.base_model_prefix in {k.split("."	)[0] for k in pt_model_dict.keys()}
   )

   # keep track of unexpected & missing keys
   UpperCAmelCase_           =[]
   UpperCAmelCase_           =set(pt_model_dict.keys()	)

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      UpperCAmelCase_           =flax_key_tuple[0] == pt_model.base_model_prefix
      UpperCAmelCase_           =".".join((pt_model.base_model_prefix,) + flax_key_tuple	) in pt_model_dict

      # adapt flax_key to prepare for loading from/to base model only
      if load_model_with_head_into_base_model and has_base_model_prefix:
         UpperCAmelCase_           =flax_key_tuple[1:]
      elif load_base_model_into_model_with_head and require_base_model_prefix:
         UpperCAmelCase_           =(pt_model.base_model_prefix,) + flax_key_tuple

      # rename flax weights to PyTorch format
      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowercase__	) not in pt_model_dict:
         # conv layer
         UpperCAmelCase_           =flax_key_tuple[:-1] + ("weight",)
         UpperCAmelCase_           =jnp.transpose(lowercase__	,     (3, 2, 0, 1)	)
      elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase__	) not in pt_model_dict:
         # linear layer
         UpperCAmelCase_           =flax_key_tuple[:-1] + ("weight",)
         UpperCAmelCase_           =flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
         UpperCAmelCase_           =flax_key_tuple[:-1] + ("weight",)

      # adding batch stats from flax batch norm to pt
      elif "mean" in flax_key_tuple[-1]:
         UpperCAmelCase_           =flax_key_tuple[:-1] + ("running_mean",)
      elif "var" in flax_key_tuple[-1]:
         UpperCAmelCase_           =flax_key_tuple[:-1] + ("running_var",)

      if "batch_stats" in flax_state:
         UpperCAmelCase_           =".".join(flax_key_tuple[1:]	)  # Remove the params/batch_stats header
      else:
         UpperCAmelCase_           =".".join(lowercase__	)

      # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
      UpperCAmelCase_           ={}
      # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
      for key in pt_model_dict:
         UpperCAmelCase_           =key.split("."	)
         UpperCAmelCase_           =None
         if key_components[-3::2] == ["parametrizations", "original0"]:
            UpperCAmelCase_           =key_components[-2] + "_g"
         elif key_components[-3::2] == ["parametrizations", "original1"]:
            UpperCAmelCase_           =key_components[-2] + "_v"
         if name is not None:
            UpperCAmelCase_           =key_components[:-3] + [name]
            UpperCAmelCase_           =".".join(lowercase__	)
            UpperCAmelCase_           =key

      if flax_key in special_pt_names:
         UpperCAmelCase_           =special_pt_names[flax_key]

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'	)
         else:
            # add weight to pytorch dict
            UpperCAmelCase_           =np.asarray(lowercase__	) if not isinstance(lowercase__	,     np.ndarray	) else flax_tensor
            UpperCAmelCase_           =torch.from_numpy(lowercase__	)
            # remove from missing keys
            missing_keys.remove(lowercase__	)
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(lowercase__	)

   pt_model.load_state_dict(lowercase__	)

   # re-transform missing_keys to list
   UpperCAmelCase_           =list(lowercase__	)

   if len(lowercase__	) > 0:
      logger.warning(
          "Some weights of the Flax model were not used when initializing the PyTorch model"
          F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
          F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
          " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
          F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
          " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
          " FlaxBertForSequenceClassification model)."	)
   else:
      logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'	)
   if len(lowercase__	) > 0:
      logger.warning(
          F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
          F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
          " use it for predictions and inference."	)
   else:
      logger.warning(
          F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
          "If your task is similar to the task the model of the checkpoint was trained on, "
          F'you can already use {pt_model.__class__.__name__} for predictions without further training.'	)

   return pt_model







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      A__					(			__A  :						Any     ,				__A  :						Dict     ,				__A  :						Optional[int]=None			)					->Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
      __A    =nn.Parameter(__A			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
            __A    =nn.Parameter(__A			)





def      A__					(			__A  :						List[Any]     ,				__A  :						Tuple     ,				__A  :						List[Any]			)					->Dict:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)

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





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple     ,				__A  :						Optional[Any]			)					->int:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)
      __A    =np.asarray(weights[3]			)

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





def      A__					(			__A  :						int     ,				__A  :						List[str]     ,				__A  :						Optional[Any]			)					->Any:
      # layernorm 1
      __A    =weights[0][0][0]
      __A    =np.asarray(layer_norm_a[0]			)
      __A    =np.asarray(layer_norm_a[1]			)
      set_param(
          torch_block.attention.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

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

      # intermediate weighs
      __A    =weights[2][0][1][2]

      # Chunked Feed Forward
      if len(__A			) == 4:
            __A    =intermediate_weights[2]

      # layernorm 2
      __A    =np.asarray(intermediate_weights[0][0]			)
      __A    =np.asarray(intermediate_weights[0][1]			)
      set_param(
          torch_block.feed_forward.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # intermediate dense
      __A    =np.asarray(intermediate_weights[1][0]			)
      __A    =np.asarray(intermediate_weights[1][1]			)
      set_param(
          torch_block.feed_forward.dense.dense     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)

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





def      A__					(			__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						Optional[int]			)					->List[Any]:
      # reformer model
      __A    =torch_model.reformer

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

      if isinstance(weights[3]     ,				__A			):
            __A    =torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights			)			):
                  __A    =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'''
                  __A    =nn.Parameter(torch.tensor(__A			)			)

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

      # output layer norm
      __A    =np.asarray(weights[7][0]			)
      __A    =np.asarray(weights[7][1]			)
      set_param(
          torch_model_reformer.encoder.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

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





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Tuple			)					->Union[str, Any]:
      # Initialise PyTorch model
      __A    =ReformerConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      __A    =ReformerModelWithLMHead(__A			)

      with open(__A     ,				'''rb'''			) as f:
            __A    =pickle.load(__A			)['''weights''']

      set_model_weights_in_torch(__A     ,				__A     ,				config.hidden_size			)

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


if __name__ == "__main__":
   _lowerCamelCase      :					Union[str, 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.'''
   )
   _lowerCamelCase      :					Tuple					    =      parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


