






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 						__UpperCAmelCase			(     a_							,     a_							,     a_=None):
							# 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 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							# 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 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							# 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 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							# 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 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							# 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 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							# 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__":
	lowercase  							=					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."
	)
	lowercase  							=					parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							return (torch.arange(state.num_processes		) + 1.0 + (state.num_processes * state.process_index)).to(state.device		)



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Any							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		gather(_lowercase		)
							assert gathered_tensor.tolist() == list(range(1	,				state.num_processes**2 + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Any							     =		[state.process_index]
							UpperCAmelCase     :		Union[str, Any]							     =		gather_object(_lowercase		)
							assert len(_lowercase		) == state.num_processes, F'''{gathered_obj}, {len(_lowercase		)} != {state.num_processes}'''
							assert gathered_obj == list(range(state.num_processes		)		), F'''{gathered_obj} != {list(range(state.num_processes		)		)}'''



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Optional[int]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		List[str]							     =		broadcast(_lowercase		)
							assert broadcasted_tensor.shape == torch.Size([state.num_processes]		)
							assert broadcasted_tensor.tolist() == list(range(1	,				state.num_processes + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							# We need to pad the tensor with one more element if we are the main process
							# to ensure that we can pad
							if state.is_main_process:
														UpperCAmelCase     :		Optional[Any]							     =		torch.arange(state.num_processes + 1		).to(state.device		)
							else:
														UpperCAmelCase     :		Tuple							     =		torch.arange(state.num_processes		).to(state.device		)
							UpperCAmelCase     :		Optional[Any]							     =		pad_across_processes(_lowercase		)
							assert padded_tensor.shape == torch.Size([state.num_processes + 1]		)
							if not state.is_main_process:
														assert padded_tensor.tolist() == list(range(0	,				state.num_processes		)		) + [0]



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Optional[Any]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		reduce(_lowercase	,				"""sum"""		)
							UpperCAmelCase     :		Optional[Any]							     =		torch.tensor([4.0, 6]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Tuple							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[int]							     =		reduce(_lowercase	,				"""mean"""		)
							UpperCAmelCase     :		str							     =		torch.tensor([2.0, 3]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							# For xla_spawn (TPUs)
							main()



def       __lowerCamelCase  (					)      ->			int:
							UpperCAmelCase     :		List[Any]							     =		PartialState()
							state.print(F'''State: {state}'''		)
							state.print("""testing gather"""		)
							test_gather(_lowercase		)
							state.print("""testing gather_object"""		)
							test_gather_object(_lowercase		)
							state.print("""testing broadcast"""		)
							test_broadcast(_lowercase		)
							state.print("""testing pad_across_processes"""		)
							test_pad_across_processes(_lowercase		)
							state.print("""testing reduce_sum"""		)
							test_reduce_sum(_lowercase		)
							state.print("""testing reduce_mean"""		)
							test_reduce_mean(_lowercase		)


if __name__ == "__main__":
							main()

