

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment





def a(lowercase__			, lowercase__=()			, lowercase__=None			, lowercase__="no"			, lowercase__="29500"	):





	'''simple docstring'''
	snake_case_      		= False
	snake_case_      		= False
	if any(key.startswith('KAGGLE'	) for key in os.environ.keys()	):
		snake_case_      		= True
	elif "IPython" in sys.modules:
		snake_case_      		= 'google.colab' in str(sys.modules['IPython'].get_ipython()	)

	try:
		snake_case_      		= PrecisionType(mixed_precision.lower()	)
	except ValueError:
		raise ValueError(
		    f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""	)

	if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'			, lowercase__	) is not None):
		# TPU launch
		import torch_xla.distributed.xla_multiprocessing as xmp

		if len(AcceleratorState._shared_state	) > 0:
			raise ValueError(
			    'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
			    'your training function. Restart your notebook and make sure no cells initializes an '
			    '`Accelerator`.'	)
		if num_processes is None:
			snake_case_      		= 8

		snake_case_      		= PrepareForLaunch(lowercase__			, distributed_type='TPU'	)
		print(f"""Launching a training on {num_processes} TPU cores."""	)
		xmp.spawn(lowercase__			, args=lowercase__			, nprocs=lowercase__			, start_method='fork'	)
	elif in_colab:
		# No need for a distributed launch otherwise as it's either CPU or one GPU.
		if torch.cuda.is_available():
			print('Launching training on one GPU.'	)
		else:
			print('Launching training on one CPU.'	)
		function(*lowercase__	)
	else:
		if num_processes is None:
			raise ValueError(
			    'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'	)

		if num_processes > 1:
			# Multi-GPU launch
			from torch.multiprocessing import start_processes
			from torch.multiprocessing.spawn import ProcessRaisedException

			if len(AcceleratorState._shared_state	) > 0:
				raise ValueError(
				    'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
				    'inside your training function. Restart your notebook and make sure no cells initializes an '
				    '`Accelerator`.'	)

			if torch.cuda.is_initialized():
				raise ValueError(
				    'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
				    'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
				    'function.'	)

			# torch.distributed will expect a few environment variable to be here. We set the ones common to each
			# process here (the other ones will be set be the launcher).
			with patch_environment(
			    world_size=lowercase__			, master_addr='127.0.01'			, master_port=lowercase__			, mixed_precision=lowercase__	):
				snake_case_      		= PrepareForLaunch(lowercase__			, distributed_type='MULTI_GPU'	)
				print(f"""Launching training on {num_processes} GPUs."""	)
				try:
					start_processes(lowercase__			, args=lowercase__			, nprocs=lowercase__			, start_method='fork'	)
				except ProcessRaisedException as e:
					if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
						raise RuntimeError(
						    'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
						    'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
						    'Please review your imports and test them when running the `notebook_launcher()` to identify '
						    'which one is problematic.'	) from e

		else:
			# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
			if is_mps_available():
				snake_case_      		= '1'
				print('Launching training on MPS.'	)
			elif torch.cuda.is_available():
				print('Launching training on one GPU.'	)
			else:
				print('Launching training on CPU.'	)
			function(*lowercase__	)






def a(lowercase__			, lowercase__=()			, lowercase__=2	):





	'''simple docstring'''
	from torch.multiprocessing import start_processes

	with tempfile.NamedTemporaryFile() as tmp_file:
		# torch.distributed will expect a few environment variable to be here. We set the ones common to each
		# process here (the other ones will be set be the launcher).
		with patch_environment(
		    world_size=lowercase__			, master_addr='127.0.01'			, master_port='29500'			, accelerate_mixed_precision='no'			, accelerate_debug_rdv_file=tmp_file.name			, accelerate_use_cpu='yes'			, ):
			snake_case_      		= PrepareForLaunch(lowercase__			, debug=lowercase__	)
			start_processes(lowercase__			, args=lowercase__			, nprocs=lowercase__			, start_method='fork'	)



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_      		= TapasConfig.from_json_file(lowercase__	)
	# set absolute/relative position embeddings parameter
	snake_case_      		= reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= True
		# hparam_utils.py hparams
		snake_case_      		= 0.66_4694
		snake_case_      		= 0.20_7951
		snake_case_      		= 0.12_1194
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= False
		snake_case_      		= 0.035_2513

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= False
		# hparam_utils.py hparams
		snake_case_      		= 36.4519
		snake_case_      		= 0.90_3421
		snake_case_      		= 222.088
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= 0.76_3141

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "TABFACT":
		snake_case_      		= TapasForSequenceClassification(config=lowercase__	)
	elif task == "MLM":
		snake_case_      		= TapasForMaskedLM(config=lowercase__	)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_      		= TapasModel(config=lowercase__	)
	else:
		raise ValueError(f"""Task {task} not supported."""	)

	print(f"""Building PyTorch model from configuration: {config}"""	)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(lowercase__			, lowercase__			, lowercase__	)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
	model.save_pretrained(lowercase__	)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""	)
	snake_case_      		= TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'			, model_max_length=512	)
	tokenizer.save_pretrained(lowercase__	)

	print('Used relative position embeddings:'			, model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
		)
		parser.add_argument(
		    '--reset_position_index_per_cell',
		    default=False,
		    action='store_true',
		    help='Whether to use relative position embeddings or not. Defaults to True.',
		)
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--tapas_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained TAPAS 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											=     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
