





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__    (							snake_case_	:  List[str]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Tuple=None       )      ->				Optional[int]:
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   __snake_case							       =   nn.Parameter(snake_case_       )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      __snake_case							       =   nn.Parameter(snake_case_       )






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Dict       )      ->				Dict:
   # 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(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.value       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.output.dense       ,      torch.tensor(snake_case_       ).view(-1       ,      snake_case_       ).contiguous().transpose(0       ,      1       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Dict       )      ->				Union[str, 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(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.key       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.value       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.output.dense       ,      torch.tensor(snake_case_       ).view(-1       ,      snake_case_       ).contiguous().transpose(0       ,      1       )       ,      )






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  int       ,      snake_case_	:  Optional[Any]       )      ->				List[Any]:
   # 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(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

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

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

   # Chunked Feed Forward
   if len(snake_case_       ) == 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(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # 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(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )

   # 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(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Union[str, Any]       )      ->				str:
   # 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(snake_case_       )       ,      )

   if isinstance(weights[3]       ,      snake_case_       ):
      __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(snake_case_       )       )

   __snake_case							       =   weights[5]
   assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
       snake_case_       ), "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(snake_case_       ,      snake_case_       ,      snake_case_       )

   # 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(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # 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(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  Tuple       )      ->				Tuple:
   # Initialise PyTorch model
   __snake_case							       =   ReformerConfig.from_json_file(snake_case_       )
   print(f"""Building PyTorch model from configuration: {config}"""       )
   __snake_case							       =   ReformerModelWithLMHead(snake_case_       )

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

   set_model_weights_in_torch(snake_case_       ,      snake_case_       ,      config.hidden_size       )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
   torch.save(model.state_dict()       ,      snake_case_       )


if __name__ == "__main__":
    snake_case_				=			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.'
    )
    snake_case_				=			parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






import math
import sys



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	''''''
				try:
								with open(__lowerCAmelCase  ,  '''rb'''      ) as binary_file:
												snake_case__	:      int     		=	binary_file.read()
								for dat in data:
												snake_case__	:      Any     		=	f"""{dat:08b}"""
												result += curr_byte
								return result
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	{'''0''': '''0''', '''1''': '''1'''}
				snake_case__		, snake_case__	:      List[Any]     		=	'''''', ''''''
				snake_case__	:      Tuple     		=	len(__lowerCAmelCase      )

				for i in range(len(__lowerCAmelCase      )      ):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								snake_case__	:      Tuple     		=	lexicon[curr_string]
								result += last_match_id
								snake_case__	:      Any     		=	last_match_id + '''0'''

								if math.loga(__lowerCAmelCase      ).is_integer():
												snake_case__	:      Tuple     		=	{}
												for curr_key in list(__lowerCAmelCase      ):
																snake_case__	:      Union[str, Any]     		=	lexicon.pop(__lowerCAmelCase      )
												snake_case__	:      Optional[Any]     		=	new_lex

								snake_case__	:      Tuple     		=	last_match_id + '''1'''
								index += 1
								snake_case__	:      Dict     		=	''''''
				return result



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Dict     		=	8
				try:
								with open(__lowerCAmelCase  ,  '''wb'''      ) as opened_file:
												snake_case__	:      Union[str, Any]     		=	[
												    to_write[i : i + byte_length]
												    for i in range(0  ,  len(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
												]

												if len(result_byte_array[-1]      ) % byte_length == 0:
																result_byte_array.append('''10000000'''      )
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]      ) - 1
																)

												for elem in result_byte_array[:-1]:
																opened_file.write(int(__lowerCAmelCase  ,  2      ).to_bytes(1  ,  byteorder='''big'''      )      )
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Any     		=	0
				for letter in data_bits:
								if letter == "1":
												break
								counter += 1

				snake_case__	:      Optional[int]     		=	data_bits[counter:]
				snake_case__	:      int     		=	data_bits[counter + 1 :]
				return data_bits



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	read_file_binary(__lowerCAmelCase      )
				snake_case__	:      List[str]     		=	remove_prefix(__lowerCAmelCase      )
				snake_case__	:      Any     		=	decompress_data(__lowerCAmelCase      )
				write_file_binary(__lowerCAmelCase  ,  __lowerCAmelCase      )


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])

