


'''simple docstring'''
import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
  raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
  raise Exception('''requires mxnet == 1.5.0''')

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

UpperCamelCase__          = '''The Nymphenburg Palace is a beautiful palace in Munich!'''





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      str:
    UpperCAmelCase__     :						str						=							{
        '''attention_cell''': '''multi_head''',
        '''num_layers''': 4,
        '''units''': 10_24,
        '''hidden_size''': 7_68,
        '''max_length''': 5_12,
        '''num_heads''': 8,
        '''scaled''': True,
        '''dropout''': 0.1,
        '''use_residual''': True,
        '''embed_size''': 10_24,
        '''embed_dropout''': 0.1,
        '''word_embed''': None,
        '''layer_norm_eps''': 1E-5,
        '''token_type_vocab_size''': 2,
    }

    UpperCAmelCase__     :						int						=							bort_4_8_768_1024_hparams

    # Let's construct the original Bort model here
    # Taken from official BERT implementation, see:
    # https://github.com/alexa/bort/blob/master/bort/bort.py
    UpperCAmelCase__     :						Optional[Any]						=							BERTEncoder(
        attention_cell=predefined_args['''attention_cell''']    ,   num_layers=predefined_args['''num_layers''']    ,   units=predefined_args['''units''']    ,   hidden_size=predefined_args['''hidden_size''']    ,   max_length=predefined_args['''max_length''']    ,   num_heads=predefined_args['''num_heads''']    ,   scaled=predefined_args['''scaled''']    ,   dropout=predefined_args['''dropout''']    ,   output_attention=UpperCAmelCase_    ,   output_all_encodings=UpperCAmelCase_    ,   use_residual=predefined_args['''use_residual''']    ,   activation=predefined_args.get('''activation'''    ,   '''gelu'''		)    ,   layer_norm_eps=predefined_args.get('''layer_norm_eps'''    ,   UpperCAmelCase_		)    ,   )

    # Vocab information needs to be fetched first
    # It's the same as RoBERTa, so RobertaTokenizer can be used later
    UpperCAmelCase__     :						List[Any]						=							'''openwebtext_ccnews_stories_books_cased'''

    # Specify download folder to Gluonnlp's vocab
    UpperCAmelCase__     :						str						=							os.path.join(get_home_dir()    ,   '''models'''		)
    UpperCAmelCase__     :						List[str]						=							_load_vocab(UpperCAmelCase_    ,   UpperCAmelCase_    ,   UpperCAmelCase_    ,   cls=UpperCAmelCase_		)

    UpperCAmelCase__     :						str						=							nlp.model.BERTModel(
        UpperCAmelCase_    ,   len(UpperCAmelCase_		)    ,   units=predefined_args['''units''']    ,   embed_size=predefined_args['''embed_size''']    ,   embed_dropout=predefined_args['''embed_dropout''']    ,   word_embed=predefined_args['''word_embed''']    ,   use_pooler=UpperCAmelCase_    ,   use_token_type_embed=UpperCAmelCase_    ,   token_type_vocab_size=predefined_args['''token_type_vocab_size''']    ,   use_classifier=UpperCAmelCase_    ,   use_decoder=UpperCAmelCase_    ,   )

    original_bort.load_parameters(UpperCAmelCase_    ,   cast_dtype=UpperCAmelCase_    ,   ignore_extra=UpperCAmelCase_		)
    UpperCAmelCase__     :						Optional[Any]						=							original_bort._collect_params_with_prefix()

    # Build our config 🤗
    UpperCAmelCase__     :						Any						=							{
        '''architectures''': ['''BertForMaskedLM'''],
        '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_act''': '''gelu''',
        '''hidden_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_size''': predefined_args['''embed_size'''],
        '''initializer_range''': 0.0_2,
        '''intermediate_size''': predefined_args['''hidden_size'''],
        '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
        '''max_position_embeddings''': predefined_args['''max_length'''],
        '''model_type''': '''bort''',
        '''num_attention_heads''': predefined_args['''num_heads'''],
        '''num_hidden_layers''': predefined_args['''num_layers'''],
        '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
        '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
        '''vocab_size''': len(UpperCAmelCase_		),
    }

    UpperCAmelCase__     :						Tuple						=							BertConfig.from_dict(UpperCAmelCase_		)
    UpperCAmelCase__     :						Tuple						=							BertForMaskedLM(UpperCAmelCase_		)
    hf_bort_model.eval()

    # Parameter mapping table (Gluonnlp to Transformers)
    # * denotes layer index
    #
    # | Gluon Parameter                                                | Transformers Parameter
    # | -------------------------------------------------------------- | ----------------------
    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

    # Helper function to convert MXNET Arrays to PyTorch
    def to_torch(lowerCAmelCase__		) -> nn.Parameter:
        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()		)		)

    # Check param shapes and map new HF param back
    def check_and_map_params(lowerCAmelCase__    ,   lowerCAmelCase__		):
        UpperCAmelCase__     :						Optional[int]						=							hf_param.shape

        UpperCAmelCase__     :						List[Any]						=							to_torch(params[gluon_param]		)
        UpperCAmelCase__     :						Optional[int]						=							gluon_param.shape

        assert (
            shape_hf == shape_gluon
        ), F"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

        return gluon_param

    UpperCAmelCase__     :						List[str]						=							check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight    ,   '''word_embed.0.weight'''		)
    UpperCAmelCase__     :						Dict						=							check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight    ,   '''encoder.position_weight'''		)
    UpperCAmelCase__     :						Optional[Any]						=							check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias    ,   '''encoder.layer_norm.beta'''		)
    UpperCAmelCase__     :						Union[str, Any]						=							check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.weight    ,   '''encoder.layer_norm.gamma'''		)

    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
    UpperCAmelCase__     :						Union[str, Any]						=							torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data		)

    for i in range(hf_bort_config.num_hidden_layers		):
        UpperCAmelCase__     :						str						=							hf_bort_model.bert.encoder.layer[i]

        # self attention
        UpperCAmelCase__     :						Optional[int]						=							layer.attention.self

        UpperCAmelCase__     :						List[Any]						=							check_and_map_params(
            self_attn.key.bias.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""		)

        UpperCAmelCase__     :						Any						=							check_and_map_params(
            self_attn.key.weight.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""		)
        UpperCAmelCase__     :						Dict						=							check_and_map_params(
            self_attn.query.bias.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""		)
        UpperCAmelCase__     :						str						=							check_and_map_params(
            self_attn.query.weight.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""		)
        UpperCAmelCase__     :						Tuple						=							check_and_map_params(
            self_attn.value.bias.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""		)
        UpperCAmelCase__     :						Dict						=							check_and_map_params(
            self_attn.value.weight.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""		)

        # self attention output
        UpperCAmelCase__     :						Dict						=							layer.attention.output

        UpperCAmelCase__     :						str						=							check_and_map_params(
            self_output.dense.bias    ,   F"""encoder.transformer_cells.{i}.proj.bias"""		)
        UpperCAmelCase__     :						Dict						=							check_and_map_params(
            self_output.dense.weight    ,   F"""encoder.transformer_cells.{i}.proj.weight"""		)
        UpperCAmelCase__     :						List[Any]						=							check_and_map_params(
            self_output.LayerNorm.bias    ,   F"""encoder.transformer_cells.{i}.layer_norm.beta"""		)
        UpperCAmelCase__     :						int						=							check_and_map_params(
            self_output.LayerNorm.weight    ,   F"""encoder.transformer_cells.{i}.layer_norm.gamma"""		)

        # intermediate
        UpperCAmelCase__     :						Any						=							layer.intermediate

        UpperCAmelCase__     :						Dict						=							check_and_map_params(
            intermediate.dense.bias    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""		)
        UpperCAmelCase__     :						List[str]						=							check_and_map_params(
            intermediate.dense.weight    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""		)

        # output
        UpperCAmelCase__     :						List[Any]						=							layer.output

        UpperCAmelCase__     :						Optional[int]						=							check_and_map_params(
            bert_output.dense.bias    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""		)
        UpperCAmelCase__     :						Optional[Any]						=							check_and_map_params(
            bert_output.dense.weight    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""		)
        UpperCAmelCase__     :						Any						=							check_and_map_params(
            bert_output.LayerNorm.bias    ,   F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""		)
        UpperCAmelCase__     :						int						=							check_and_map_params(
            bert_output.LayerNorm.weight    ,   F"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""		)

    # Save space and energy 🎄
    hf_bort_model.half()

    # Compare output of both models
    UpperCAmelCase__     :						Optional[Any]						=							RobertaTokenizer.from_pretrained('''roberta-base'''		)

    UpperCAmelCase__     :						Optional[int]						=							tokenizer.encode_plus(UpperCAmelCase_		)['''input_ids''']

    # Get gluon output
    UpperCAmelCase__     :						Optional[int]						=							mx.nd.array([input_ids]		)
    UpperCAmelCase__     :						Tuple						=							original_bort(inputs=UpperCAmelCase_    ,   token_types=[]		)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(UpperCAmelCase_		)
    UpperCAmelCase__     :						int						=							BertModel.from_pretrained(UpperCAmelCase_		)
    hf_bort_model.eval()

    UpperCAmelCase__     :						int						=							tokenizer.encode_plus(UpperCAmelCase_    ,   return_tensors='''pt'''		)
    UpperCAmelCase__     :						Union[str, Any]						=							hf_bort_model(**UpperCAmelCase_		)[0]

    UpperCAmelCase__     :						List[str]						=							output_gluon[0].asnumpy()
    UpperCAmelCase__     :						Dict						=							output_hf[0].detach().numpy()

    UpperCAmelCase__     :						Any						=							np.max(np.abs(hf_layer - gluon_layer		)		).item()
    UpperCAmelCase__     :						Optional[int]						=							np.allclose(UpperCAmelCase_    ,   UpperCAmelCase_    ,   atol=1E-3		)

    if success:
        print('''✔️ Both model do output the same tensors'''		)
    else:
        print('''❌ Both model do **NOT** output the same tensors'''		)
        print('''Absolute difference is:'''    ,   UpperCAmelCase_		)


if __name__ == "__main__":
  UpperCamelCase__          = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  UpperCamelCase__          = parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







'''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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# lsh weights + output
	snake_case__	       =   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
	snake_case__	       =   weights[2][0][1][2]

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

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

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	snake_case__	       =   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
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

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

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

	snake_case__	       =   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							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

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

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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   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__":
			a__					:			Optional[int]          =     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.'''
			)
			a__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



