
import os
import string
import sys


_lowerCamelCase       :				Union[str, Any]       =			1 << 8

_lowerCamelCase       :				Dict       =			{
    '''tab''': ord('''\t'''),
    '''newline''': ord('''\r'''),
    '''esc''': 27,
    '''up''': 65 + ARROW_KEY_FLAG,
    '''down''': 66 + ARROW_KEY_FLAG,
    '''right''': 67 + ARROW_KEY_FLAG,
    '''left''': 68 + ARROW_KEY_FLAG,
    '''mod_int''': 91,
    '''undefined''': sys.maxsize,
    '''interrupt''': 3,
    '''insert''': 50,
    '''delete''': 51,
    '''pg_up''': 53,
    '''pg_down''': 54,
}

_lowerCamelCase       :				int       =			KEYMAP['''up''']
_lowerCamelCase       :				Optional[Any]       =			KEYMAP['''left''']

if sys.platform == "win32":
			_lowerCamelCase       :				Union[str, Any]       =			[]
			_lowerCamelCase       :				List[str]       =			{
			    B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
			    B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
			    B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
			    B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
			    B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
			    B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
			    B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
			    B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
			}

for i in range(10):
			_lowerCamelCase       :				Tuple       =			ord(str(i))






def        a_		(       ) ->  List[str]:
			if os.name == "nt":
						import msvcrt

						_snake_case		=			'mbcs'
						# Flush the keyboard buffer
						while msvcrt.kbhit():
									msvcrt.getch()
						if len(__lowercase ) == 0:
									# Read the keystroke
									_snake_case		=			msvcrt.getch()

									# If it is a prefix char, get second part
									if ch in (b"\x00", b"\xe0"):
												_snake_case		=			ch + msvcrt.getch()
												# Translate actual Win chars to bullet char types
												try:
															_snake_case		=			chr(WIN_KEYMAP[cha] )
															WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
															WIN_CH_BUFFER.append(__lowercase )
															if ord(__lowercase ) in (
															    KEYMAP["insert"] - 1 << 9,
															    KEYMAP["delete"] - 1 << 9,
															    KEYMAP["pg_up"] - 1 << 9,
															    KEYMAP["pg_down"] - 1 << 9,
															):
																		WIN_CH_BUFFER.append(chr(126 ) )
															_snake_case		=			chr(KEYMAP['esc'] )
												except KeyError:
															_snake_case		=			cha[1]
									else:
												_snake_case		=			ch.decode(__lowercase )
						else:
									_snake_case		=			WIN_CH_BUFFER.pop(0 )
			elif os.name == "posix":
						import termios
						import tty

						_snake_case		=			sys.stdin.fileno()
						_snake_case		=			termios.tcgetattr(__lowercase )
						try:
									tty.setraw(__lowercase )
									_snake_case		=			sys.stdin.read(1 )
						finally:
									termios.tcsetattr(__lowercase       ,					termios.TCSADRAIN       ,					__lowercase )
			return ch






def        a_		(       ) ->  Dict:
			_snake_case		=			get_raw_chars()
			if ord(__lowercase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
						return char

			elif ord(__lowercase ) == KEYMAP["esc"]:
						_snake_case		=			get_raw_chars()
						if ord(__lowercase ) == KEYMAP["mod_int"]:
									_snake_case		=			get_raw_chars()
									if ord(__lowercase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__lowercase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
												return chr(ord(__lowercase ) + ARROW_KEY_FLAG )
									else:
												return KEYMAP["undefined"]
						else:
									return get_raw_chars()

			else:
						if char in string.printable:
									return char
						else:
									return KEYMAP["undefined"]

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






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






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Tuple       ,					__lowercase   :	Any ) ->  Optional[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(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.key       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.value       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.output.dense       ,					torch.tensor(__lowercase ).view(-1       ,					__lowercase ).contiguous().transpose(0       ,					1 )       ,					)






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	List[str]       ,					__lowercase   :	Union[str, Any] ) ->  Optional[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(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

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

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

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

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

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






def        a_		(       __lowercase   :	Tuple       ,					__lowercase   :	Tuple       ,					__lowercase   :	Dict ) ->  Optional[int]:
			# 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(__lowercase )       ,					)

			if isinstance(weights[3]       ,					__lowercase ):
						_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(__lowercase ) )

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

			# 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(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

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






def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	Dict       ,					__lowercase   :	List[Any] ) ->  Optional[int]:
			# Initialise PyTorch model
			_snake_case		=			ReformerConfig.from_json_file(__lowercase )
			print(f'''Building PyTorch model from configuration: {config}''' )
			_snake_case		=			ReformerModelWithLMHead(__lowercase )

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

			set_model_weights_in_torch(__lowercase       ,					__lowercase       ,					config.hidden_size )

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


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       :				List[Any]       =			parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)