

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 ):







							'''simple docstring'''






							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__ ):







							'''simple docstring'''






							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__ ):







							'''simple docstring'''






							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__ ):







							'''simple docstring'''






							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__ ):







							'''simple docstring'''






							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__ ):







							'''simple docstring'''






							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__":
					_UpperCAmelCase						:  str									=			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."""
					)
					_UpperCAmelCase						:  List[str]									=			parser.parse_args()
					convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







_UpperCAmelCase						:  int									=			range(2, 20 + 1)
_UpperCAmelCase						:  Dict									=			[10**k for k in range(ks[-1] + 1)]
_UpperCAmelCase						:  dict[int, dict[int, list[list[int]]]]									=			{}





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       sum(a_i[j] for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) )
							snake_case_     							=       sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase__ )  ,				UpperCamelCase__ ) ) )

							snake_case_							,       snake_case_     							=       0, 0
							snake_case_     							=       n - i

							snake_case_     							=       memo.get(UpperCamelCase__ )

							if sub_memo is not None:
														snake_case_     							=       sub_memo.get(UpperCamelCase__ )

														if jumps is not None and len(UpperCamelCase__ ) > 0:
																					# find and make the largest jump without going over
																					snake_case_     							=       -1
																					for _k in range(len(UpperCamelCase__ ) - 1  ,				-1  ,				-1 ):
																												if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																																			snake_case_     							=       _k
																																			break

																					if max_jump >= 0:
																												snake_case_							,       snake_case_							,       snake_case_     							=       jumps[max_jump]
																												# since the difference between jumps is cached, add c
																												snake_case_     							=       diff + c
																												for j in range(min(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) ):
																																			snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																												if new_c > 0:
																																			add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

														else:
																					snake_case_     							=       []
							else:
														snake_case_     							=       {c: []}
														snake_case_     							=       sub_memo

							if dn >= max_dn or c + diff >= base[k]:
														return diff, dn

							if k > ks[0]:
														while True:
																					# keep doing smaller jumps
																					snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				k - 1  ,				i + dn  ,				UpperCamelCase__ )
																					diff += _diff
																					dn += terms_jumped

																					if dn >= max_dn or c + diff >= base[k]:
																												break
							else:
														# would be too small a jump, just compute sequential terms instead
														snake_case_							,       snake_case_     							=       compute(UpperCamelCase__  ,				UpperCamelCase__  ,				i + dn  ,				UpperCamelCase__ )
														diff += _diff
														dn += terms_jumped

							snake_case_     							=       sub_memo[c]

							# keep jumps sorted by # of terms skipped
							snake_case_     							=       0
							while j < len(UpperCamelCase__ ):
														if jumps[j][1] > dn:
																					break
														j += 1

							# cache the jump for this value digitsum(b) and c
							sub_memo[c].insert(UpperCamelCase__  ,				(diff, dn, k) )
							return (diff, dn)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							if i >= n:
														return 0, i
							if k > len(UpperCamelCase__ ):
														a_i.extend([0 for _ in range(k - len(UpperCamelCase__ ) )] )

							# note: a_i -> b * 10^k + c
							# ds_b -> digitsum(b)
							# ds_c -> digitsum(c)
							snake_case_     							=       i
							snake_case_							,       snake_case_							,       snake_case_     							=       0, 0, 0
							for j in range(len(UpperCamelCase__ ) ):
														if j >= k:
																					ds_b += a_i[j]
														else:
																					ds_c += a_i[j]

							while i < n:
														i += 1
														snake_case_     							=       ds_c + ds_b
														diff += addend
														snake_case_     							=       0
														for j in range(UpperCamelCase__ ):
																					snake_case_     							=       a_i[j] + addend
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )

																					ds_c += a_i[j]

														if addend > 0:
																					break

							if addend > 0:
														add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
							return diff, i - start_i





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ):
														snake_case_     							=       digits[j] + addend
														if s >= 10:
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																					snake_case_     							=       addend // 10 + quotient
														else:
																					snake_case_     							=       s
																					snake_case_     							=       addend // 10

														if addend == 0:
																					break

							while addend > 0:
														snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
														digits.append(UpperCamelCase__ )





def    __lowerCamelCase      (					UpperCamelCase__ = 10**15 ):







							'''simple docstring'''






							snake_case_     							=       [1]
							snake_case_     							=       1
							snake_case_     							=       0
							while True:
														snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				20  ,				i + dn  ,				UpperCamelCase__ )
														dn += terms_jumped
														if dn == n - i:
																					break

							snake_case_     							=       0
							for j in range(len(UpperCamelCase__ ) ):
														a_n += digits[j] * 10**j
							return a_n


if __name__ == "__main__":
					print(F'''{solution() = }''')




