import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


A_	     =   "Run commands across TPU VMs for initial setup before running `accelerate launch`."






def 							__UpperCAmelCase     (							UpperCAmelCase=None							)->							Optional[Any]:



			"""simple docstring"""







			if subparsers is not None:
						lowercase       		=		subparsers.add_parser('''tpu-config''',		description=_description							)
			else:
						lowercase       		=		argparse.ArgumentParser('''Accelerate tpu-config command''',		description=_description							)
			# Core arguments
			lowercase       		=		parser.add_argument_group(
			    '''Config Arguments''',		'''Arguments that can be configured through `accelerate config`.'''							)
			config_args.add_argument(
			    '''--config_file''',		type=_lowerCAmelCase,		default=_lowerCAmelCase,		help='''Path to the config file to use for accelerate.''',		)
			config_args.add_argument(
			    '''--tpu_name''',		default=_lowerCAmelCase,		help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''',		)
			config_args.add_argument(
			    '''--tpu_zone''',		default=_lowerCAmelCase,		help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''',		)
			lowercase       		=		parser.add_argument_group('''TPU Arguments''',		'''Arguments for options ran inside the TPU.'''							)
			pod_args.add_argument(
			    '''--use_alpha''',		action='''store_true''',		help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''',		)
			pod_args.add_argument(
			    '''--command_file''',		default=_lowerCAmelCase,		help='''The path to the file containing the commands to run on the pod on startup.''',		)
			pod_args.add_argument(
			    '''--command''',		action='''append''',		nargs='''+''',		help='''A command to run on the pod. Can be passed multiple times.''',		)
			pod_args.add_argument(
			    '''--install_accelerate''',		action='''store_true''',		help='''Whether to install accelerate on the pod. Defaults to False.''',		)
			pod_args.add_argument(
			    '''--accelerate_version''',		default='''latest''',		help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''',		)
			pod_args.add_argument(
			    '''--debug''',		action='''store_true''',		help='''If set, will print the command that would be run instead of running it.'''							)

			if subparsers is not None:
						parser.set_defaults(func=_lowerCAmelCase							)
			return parser






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



			"""simple docstring"""







			lowercase       		=		None

			# Get the default from the config file if it exists.
			if args.config_file is not None or os.path.isfile(_lowerCAmelCase							):
						lowercase       		=		load_config_from_file(args.config_file							)
						if not args.command_file and defaults.command_file is not None and not args.command:
									lowercase       		=		defaults.command_file
						if not args.command and defaults.commands is not None:
									lowercase       		=		defaults.commands
						if not args.tpu_name:
									lowercase       		=		defaults.tpu_name
						if not args.tpu_zone:
									lowercase       		=		defaults.tpu_zone
			if args.accelerate_version == "dev":
						lowercase       		=		'''git+https://github.com/huggingface/accelerate.git'''
			elif args.accelerate_version == "latest":
						lowercase       		=		'''accelerate -U'''
			elif isinstance(parse(args.accelerate_version							),		_lowerCAmelCase							):
						lowercase       		=		f'accelerate=={args.accelerate_version}'

			if not args.command_file and not args.command:
						raise ValueError('''You must specify either a command file or a command to run on the pod.'''							)

			if args.command_file:
						with open(args.command_file,		'''r'''							) as f:
									lowercase       		=		[f.read().splitlines()]

    # To turn list of lists into list of strings
			if isinstance(args.command[0],		_lowerCAmelCase							):
						lowercase       		=		[line for cmd in args.command for line in cmd]
			# Default to the shared folder and install accelerate
			lowercase       		=		['''cd /usr/share''']
			if args.install_accelerate:
						new_cmd += [f'pip install {args.accelerate_version}']
			new_cmd += args.command
			lowercase       		=		'''; '''.join(_lowerCAmelCase							)

			# Then send it to gcloud
			# Eventually try to use google-api-core to do this instead of subprocess
			lowercase       		=		['''gcloud''']
			if args.use_alpha:
						cmd += ["alpha"]
			cmd += [
			    "compute",
			    "tpus",
			    "tpu-vm",
			    "ssh",
			    args.tpu_name,
			    "--zone",
			    args.tpu_zone,
			    "--command",
			    args.command,
			    "--worker",
			    "all",
			]
			if args.debug:
						print(f'Running {" ".join(_lowerCAmelCase							)}'							)
						return
			subprocess.run(_lowerCAmelCase							)
			print('''Successfully setup pod.'''							)






def 							__UpperCAmelCase     (							)->							Any:



			"""simple docstring"""







			lowercase       		=		tpu_command_parser()
			lowercase       		=		parser.parse_args()

			tpu_command_launcher(_lowerCAmelCase							)







"""simple docstring"""
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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# lsh weights + output
						__lowerCAmelCase     =  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
						__lowerCAmelCase     =  weights[2][0][1][2]

						# Chunked Feed Forward
						if len(_lowerCAmelCase						) == 4:
												__lowerCAmelCase     =  intermediate_weights[2]

						# layernorm 2
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][1]						)
						set_param(
						    torch_block.feed_forward.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  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
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

						# word embeds
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						set_param(
						    torch_model_reformer.embeddings.word_embeddings ,    torch.tensor(_lowerCAmelCase						) ,    )

						if isinstance(weights[3] ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights						)						):
																		__lowerCAmelCase     =  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"""
																		__lowerCAmelCase     =  nn.Parameter(torch.tensor(_lowerCAmelCase						)						)

						__lowerCAmelCase     =  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						):
												__lowerCAmelCase     =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# output layer norm
						__lowerCAmelCase     =  np.asarray(weights[7][0]						)
						__lowerCAmelCase     =  np.asarray(weights[7][1]						)
						set_param(
						    torch_model_reformer.encoder.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# output embeddings
						__lowerCAmelCase     =  np.asarray(weights[9][0]						)
						__lowerCAmelCase     =  np.asarray(weights[9][1]						)
						set_param(
						    torch_model.lm_head.decoder ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  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__":
			SCREAMING_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.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
