





'''simple docstring'''







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCAmelCase_					:    Any      	=			data_utils.TransfoXLTokenizer
lowerCAmelCase_					:    str      	=			data_utils.TransfoXLCorpus
lowerCAmelCase_					:    Union[str, Any]      	=			data_utils
lowerCAmelCase_					:    List[str]      	=			data_utils






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict    ,							lowercase						:      Dict						)	->	Tuple:
				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(lowercase    ,							"rb"						) as fp:
												_a								= pickle.load(lowercase    ,							encoding="latin1"						)
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								_a								= pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
								print(F'Save vocabulary to {pytorch_vocab_dump_path}'						)
								_a								= corpus.vocab.__dict__
								torch.save(lowercase    ,							lowercase						)

								_a								= corpus.__dict__
								corpus_dict_no_vocab.pop("vocab"    ,							lowercase						)
								_a								= pytorch_dump_folder_path + "/" + CORPUS_NAME
								print(F'Save dataset to {pytorch_dataset_dump_path}'						)
								torch.save(lowercase    ,							lowercase						)

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								_a								= os.path.abspath(lowercase						)
								_a								= os.path.abspath(lowercase						)

								print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'						)
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												_a								= TransfoXLConfig()
								else:
												_a								= TransfoXLConfig.from_json_file(lowercase						)
								print(F'Building PyTorch model from configuration: {config}'						)
								_a								= TransfoXLLMHeadModel(lowercase						)

								_a								= load_tf_weights_in_transfo_xl(lowercase    ,							lowercase    ,							lowercase						)
								# Save pytorch-model
								_a								= os.path.join(lowercase    ,							lowercase						)
								_a								= os.path.join(lowercase    ,							lowercase						)
								print(F'Save PyTorch model to {os.path.abspath(lowercase						)}'						)
								torch.save(model.state_dict()    ,							lowercase						)
								print(F'Save configuration file to {os.path.abspath(lowercase						)}'						)
								with open(lowercase    ,							"w"    ,							encoding="utf-8"						) as f:
												f.write(config.to_json_string()						)


if __name__ == "__main__":
					lowerCAmelCase_					:    str      	=			argparse.ArgumentParser()
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default=None,
					    type=str,
					    required=True,
					    help='Path to the folder to store the PyTorch model or dataset/vocab.',
					)
					parser.add_argument(
					    '--tf_checkpoint_path',
					    default='',
					    type=str,
					    help='An optional path to a TensorFlow checkpoint path to be converted.',
					)
					parser.add_argument(
					    '--transfo_xl_config_file',
					    default='',
					    type=str,
					    help=(
					        'An optional config json file corresponding to the pre-trained BERT model. \n'
					        'This specifies the model architecture.'
					    ),
					)
					parser.add_argument(
					    '--transfo_xl_dataset_file',
					    default='',
					    type=str,
					    help='An optional dataset file to be converted in a vocabulary.',
					)
					lowerCAmelCase_					:    Optional[Any]      	=			parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)







'''simple docstring'''







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	str:
				for param, grad_param in zip(model_a.parameters()    ,							model_b.parameters()						):
								if not param.requires_grad:
												continue
								if not did_step:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad    ,							grad_param.grad						) is False
												), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
								else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad    ,							grad_param.grad						) is True
												), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'






def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      int    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]    ,							lowercase						:      int=True						)	->	Any:
				model.train()
				_a								= model(lowercase						)
				_a								= F.mse_loss(lowercase    ,							target.to(output.device						)						)
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowercase						)






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      Tuple=False						)	->	List[str]:
				set_seed(42						)
				_a								= RegressionModel()
				_a								= deepcopy(lowercase						)
				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				model.to(accelerator.device						)
				if sched:
								_a								= AdamW(params=model.parameters()    ,							lr=1E-3						)
								_a								= AdamW(params=ddp_model.parameters()    ,							lr=1E-3						)
								_a								= LambdaLR(lowercase    ,							lr_lambda=lambda lowercase						: epoch**0.65						)
								_a								= LambdaLR(lowercase    ,							lr_lambda=lambda lowercase						: epoch**0.65						)
				# Make a copy of `model`
				if sched:
								_a	,					_a	,					_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
				else:
								_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase						)
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader






def  _lowerCamelCase				( lowercase						:      Optional[Any]						)	->	Optional[int]:
				# Test when on a single CPU or GPU that the context manager does nothing
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowercase						):
																step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								else:
												# Sync grads
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								for param, ddp_param in zip(model.parameters()    ,							ddp_model.parameters()						):
												if not param.requires_grad:
																continue
												assert torch.allclose(
												    param.grad    ,							ddp_param.grad						), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      Tuple						)	->	Tuple:
				# Test on distributed setup that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowercase						):
																step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								else:
												# Sync grads
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()    ,							ddp_model.parameters()						):
												if not param.requires_grad:
																continue
												if iteration % 2 == 0:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is False
																), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
												else:
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is True
																), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      List[Any]=False    ,							lowercase						:      Optional[int]=False						)	->	Any:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_a	,					_a								= batch.values()
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(lowercase						):
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()    ,							ddp_model.parameters()						):
												if not param.requires_grad:
																continue
												if ((iteration + 1) % 2 == 0) or (iteration == len(lowercase						) - 1):
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is True
																), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
												else:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is False
																), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]
				GradientState._reset_state()






def  _lowerCamelCase				( lowercase						:      int=False    ,							lowercase						:      int=False						)	->	Dict:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a	,					_a	,					_a	,					_a	,					_a								= get_training_setup(lowercase    ,							lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_a	,					_a								= batch.values()
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								model.train()
								ddp_model.train()
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								opt.step()

								if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase						)):
												if split_batches:
																sched.step()
												else:
																for _ in range(accelerator.num_processes						):
																				sched.step()
								opt.zero_grad()
								# Perform gradient accumulation under wrapper
								with accelerator.accumulate(lowercase						):
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
												ddp_opt.step()
												ddp_sched.step()
												ddp_opt.zero_grad()

								# Learning rates should be the same
								assert (
								    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
								), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
								_a								= (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase						))
								if accelerator.num_processes > 1:
												check_model_parameters(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
				GradientState._reset_state()






def  _lowerCamelCase				( )	->	Any:
				_a								= Accelerator()

				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a								= RegressionDataset(length=96						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase						)
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(lowercase						):
								assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase						)
								if iteration < len(lowercase						) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(lowercase						):
																				assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase						)
																				if batch_num < len(lowercase						) - 1:
																								assert not accelerator.gradient_state.end_of_dataloader
																				else:
																								assert accelerator.gradient_state.end_of_dataloader
								else:
												assert accelerator.gradient_state.end_of_dataloader
				assert accelerator.gradient_state.active_dataloader is None






def  _lowerCamelCase				( )	->	Optional[Any]:
				_a								= Accelerator()
				_a								= accelerator.state
				if state.local_process_index == 0:
								print("**Test `accumulate` gradient accumulation with dataloader break**"						)
				test_dataloader_break()
				if state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print("**Test NOOP `no_sync` context manager**"						)
								test_noop_sync(lowercase						)
				if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
								if state.local_process_index == 0:
												print("**Test Distributed `no_sync` context manager**"						)
								test_distributed_sync(lowercase						)
				if state.distributed_type == DistributedType.MULTI_GPU:
								for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
																if state.local_process_index == 0:
																				print(
																				    "**Test `accumulate` gradient accumulation, "    ,							F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    ,							)
																test_gradient_accumulation(lowercase    ,							lowercase						)

    # Currently will break on torch 2.0 +, need to investigate why
				if is_torch_version("<"    ,							"2.0"						) or state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print(
												    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "    ,							"`split_batches=False`, `dispatch_batches=False`**"    ,							)
								test_gradient_accumulation_with_opt_and_scheduler()
								if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																for dispatch_batches in [True, False]:
																				if not split_batch and not dispatch_batches:
																								continue
																				if state.local_process_index == 0:
																								print(
																								    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "    ,							F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    ,							)
																				test_gradient_accumulation_with_opt_and_scheduler(lowercase    ,							lowercase						)






def  _lowerCamelCase				( lowercase						:      Any						)	->	Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()
