


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       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=None )						->	Dict:


						'''simple docstring'''






						assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
						__snake_case				=		nn.Parameter(_lowerCAmelCase )
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
												__snake_case				=		nn.Parameter(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''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(_lowerCAmelCase ).transpose(1      ,	2 ).contiguous().view(-1      ,	_lowerCAmelCase )      ,	)
						set_param(
						    torch_layer.self_attention.value      ,	torch.tensor(_lowerCAmelCase ).transpose(1      ,	2 ).contiguous().view(-1      ,	_lowerCAmelCase )      ,	)
						set_param(
						    torch_layer.output.dense      ,	torch.tensor(_lowerCAmelCase ).view(-1      ,	_lowerCAmelCase ).contiguous().transpose(0      ,	1 )      ,	)





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Optional[Any]:


						'''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(_lowerCAmelCase ).transpose(1      ,	2 ).contiguous().view(-1      ,	_lowerCAmelCase )      ,	)
						set_param(
						    torch_layer.self_attention.key      ,	torch.tensor(_lowerCAmelCase ).transpose(1      ,	2 ).contiguous().view(-1      ,	_lowerCAmelCase )      ,	)
						set_param(
						    torch_layer.self_attention.value      ,	torch.tensor(_lowerCAmelCase ).transpose(1      ,	2 ).contiguous().view(-1      ,	_lowerCAmelCase )      ,	)
						set_param(
						    torch_layer.output.dense      ,	torch.tensor(_lowerCAmelCase ).view(-1      ,	_lowerCAmelCase ).contiguous().transpose(0      ,	1 )      ,	)





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	int:


						'''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(_lowerCAmelCase )      ,	torch.tensor(_lowerCAmelCase )      ,	)

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

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

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

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

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''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(_lowerCAmelCase )      ,	)

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

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

						# 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(_lowerCAmelCase )      ,	torch.tensor(_lowerCAmelCase )      ,	)

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		ReformerConfig.from_json_file(_lowerCAmelCase )
						print(F'''Building PyTorch model from configuration: {config}''' )
						__snake_case				=		ReformerModelWithLMHead(_lowerCAmelCase )

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

						set_model_weights_in_torch(_lowerCAmelCase      ,	_lowerCAmelCase      ,	config.hidden_size )

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


if __name__ == "__main__":
	A							:					Optional[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.'
	)
	A							:					Dict   						=     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)









import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class     UpperCamelCase:


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	__snake_case				=		deepcopy(SCREAMING_SNAKE_CASE						)
											elif os.path.exists(SCREAMING_SNAKE_CASE						):
																	with io.open(SCREAMING_SNAKE_CASE     ,    "r"     ,    encoding="utf-8"						) as f:
																							__snake_case				=		json.load(SCREAMING_SNAKE_CASE						)
											else:
																	try:
																							__snake_case				=		baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE						).decode("utf-8"						)
																							__snake_case				=		json.loads(SCREAMING_SNAKE_CASE						)
																	except (UnicodeDecodeError, AttributeError, ValueError):
																							raise ValueError(
																							    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''						)

											__snake_case				=		config

											self.set_stage_and_offload()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.get_value("zero_optimization.stage"     ,    -1						)

											# offload
											__snake_case				=		False
											if self.is_zeroa() or self.is_zeroa():
																	__snake_case				=		set(["cpu", "nvme"]						)
																	__snake_case				=		set(
																	    [
																	        self.get_value("zero_optimization.offload_optimizer.device"						),
																	        self.get_value("zero_optimization.offload_param.device"						),
																	    ]						)
																	if len(offload_devices & offload_devices_valid						) > 0:
																							__snake_case				=		True


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											__snake_case				=		nodes.pop()
											for node in nodes:
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							return None, ds_key

											return config, ds_key


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Tuple=None						)			->	List[str]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.find_config_node(SCREAMING_SNAKE_CASE						)
											if config is None:
																	return default
											return config.get(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Tuple=False						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											for node in nodes:
																	__snake_case				=		config
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							if must_exist:
																													raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''						)
																							else:
																													return

        # if found remove it
											if parent_config is not None:
																	parent_config.pop(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else bool(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else not bool(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											return self._stage == 2


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											return self._stage == 3






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											return self._offload






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		engine






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	List[str]:




											'''simple docstring'''

											self.engine.backward(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											# Deepspeed's `engine.step` performs the following operations:
											# - gradient accumulation check
											# - gradient clipping
											# - optimizer step
											# - zero grad
											# - checking overflow
											# - lr_scheduler step (only if engine.lr_scheduler is not None)
											self.engine.step()
											# and this plugin overrides the above calls with no-ops when Accelerate runs under
											# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
											# training loop that works transparently under many training regimes.






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    device_placement=SCREAMING_SNAKE_CASE     ,    scaler=SCREAMING_SNAKE_CASE						)
											__snake_case				=		hasattr(self.optimizer     ,    "overflow"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	str:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Any:




											'''simple docstring'''

											if self.__has_overflow__:
																	return self.optimizer.overflow
											return False






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.001     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		params
											__snake_case				=		lr
											__snake_case				=		weight_decay
											__snake_case				=		kwargs






class     UpperCamelCase:


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict=None     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Tuple						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		optimizer
											__snake_case				=		total_num_steps
											__snake_case				=		warmup_num_steps
											__snake_case				=		kwargs





