"""simple docstring"""




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	int			=    0
					UpperCamelCase_ :	bool			=    False
					UpperCamelCase_ :	float			=    3.0

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							self.assertDictEqual(MockClass().to_kwargs()  ,						{}     )
							self.assertDictEqual(MockClass(a=2     ).to_kwargs()  ,						{"""a""": 2}     )
							self.assertDictEqual(MockClass(a=2  ,						b=snake_case__     ).to_kwargs()  ,						{"""a""": 2, """b""": True}     )
							self.assertDictEqual(MockClass(a=2  ,						c=2.25     ).to_kwargs()  ,						{"""a""": 2, """c""": 2.25}     )




					@require_cuda
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							lowerCAmelCase__        =   GradScalerKwargs(init_scale=1024  ,						growth_factor=2     )
							AcceleratorState._reset_state()
							lowerCAmelCase__        =   Accelerator(mixed_precision="""fp16"""  ,						kwargs_handlers=[scaler_handler]     )
							print(accelerator.use_fpaa     )
							lowerCAmelCase__        =   accelerator.scaler

							# Check the kwargs have been applied
							self.assertEqual(scaler._init_scale  ,						1024.0     )
							self.assertEqual(scaler._growth_factor  ,						2.0     )

							# Check the other values are at the default
							self.assertEqual(scaler._backoff_factor  ,						0.5     )
							self.assertEqual(scaler._growth_interval  ,						2000     )
							self.assertEqual(scaler._enabled  ,						snake_case__     )




					@require_multi_gpu
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   ["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__     )]
							execute_subprocess_async(snake_case__  ,						env=os.environ.copy()     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, Any]													=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
			__lowerCAmelCase    :							Tuple													=						Accelerator(kwargs_handlers=[ddp_scaler])
			__lowerCAmelCase    :							Optional[Any]													=						torch.nn.Linear(1_00, 2_00)
			__lowerCAmelCase    :							Optional[int]													=						accelerator.prepare(model)

			# Check the values changed in kwargs
			__lowerCAmelCase    :							str													=						""
			__lowerCAmelCase    :							Optional[Any]													=						model.bucket_bytes_cap // (10_24 * 10_24)
			if observed_bucket_cap_map != 15:
						error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
			if model.find_unused_parameters is not True:
						error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

			# Check the values of the defaults
			if model.dim != 0:
						error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
			if model.broadcast_buffers is not True:
						error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
			if model.gradient_as_bucket_view is not False:
						error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

			# Raise error at the end to make sure we don't stop at the first failure.
			if len(error_msg) > 0:
						raise ValueError(error_msg)





"""simple docstring"""




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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__        =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   True
				# hparam_utils.py hparams
				lowerCAmelCase__        =   0.66_46_94
				lowerCAmelCase__        =   0.20_79_51
				lowerCAmelCase__        =   0.12_11_94
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   False
				lowerCAmelCase__        =   0.0_35_25_13

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   False
				# hparam_utils.py hparams
				lowerCAmelCase__        =   36.45_19
				lowerCAmelCase__        =   0.90_34_21
				lowerCAmelCase__        =   2_22.0_88
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   0.76_31_41

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   TapasModel(config=lowerCamelCase__     )
		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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

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

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

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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						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,
			)




