






import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
	from transformers.trainer import DEFAULT_CALLBACKS

	from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

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


												"""simple docstring"""

												__lowercase					:							str			=   []



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Optional[int]						,   **__a :		Optional[int]							)	->     int:


												"""simple docstring"""

												self.events.append("""on_init_end"""							)



					def 					lowerCAmelCase (     self :		int						,   __a :		Dict						,   __a :		str						,   __a :		Dict						,   **__a :		Optional[Any]							)	->     Optional[Any]:


												"""simple docstring"""

												self.events.append("""on_train_begin"""							)



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		Union[str, Any]						,   __a :		str						,   __a :		Dict						,   **__a :		Dict							)	->     Any:


												"""simple docstring"""

												self.events.append("""on_train_end"""							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		Union[str, Any]						,   __a :		Union[str, Any]						,   **__a :		List[str]							)	->     Optional[Any]:


												"""simple docstring"""

												self.events.append("""on_epoch_begin"""							)



					def 					lowerCAmelCase (     self :		str						,   __a :		int						,   __a :		Union[str, Any]						,   __a :		Optional[Any]						,   **__a :		List[str]							)	->     str:


												"""simple docstring"""

												self.events.append("""on_epoch_end"""							)



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		List[str]						,   __a :		Any						,   __a :		Optional[int]						,   **__a :		str							)	->     str:


												"""simple docstring"""

												self.events.append("""on_step_begin"""							)



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		Any						,   __a :		Tuple						,   __a :		int						,   **__a :		Optional[int]							)	->     Dict:


												"""simple docstring"""

												self.events.append("""on_step_end"""							)



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		Union[str, Any]						,   __a :		Dict						,   __a :		List[str]						,   **__a :		Union[str, Any]							)	->     Tuple:


												"""simple docstring"""

												self.events.append("""on_evaluate"""							)



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		Union[str, Any]						,   __a :		Any						,   __a :		Any						,   **__a :		Optional[Any]							)	->     Optional[int]:


												"""simple docstring"""

												self.events.append("""on_predict"""							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		List[Any]						,   __a :		Optional[int]						,   __a :		Optional[int]						,   **__a :		Optional[int]							)	->     Any:


												"""simple docstring"""

												self.events.append("""on_save"""							)



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		Union[str, Any]						,   __a :		List[str]						,   __a :		List[Any]						,   **__a :		str							)	->     str:


												"""simple docstring"""

												self.events.append("""on_log"""							)



					def 					lowerCAmelCase (     self :		Dict						,   __a :		Union[str, Any]						,   __a :		Union[str, Any]						,   __a :		List[Any]						,   **__a :		str							)	->     int:


												"""simple docstring"""

												self.events.append("""on_prediction_step"""							)







@require_torch
class 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					def 					lowerCAmelCase (     self :		int							)	->     Any:


												"""simple docstring"""

												__lowercase					:							Any			=   tempfile.mkdtemp()



					def 					lowerCAmelCase (     self :		str							)	->     int:


												"""simple docstring"""

												shutil.rmtree(self.output_dir							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		int=0						,   __a :		Optional[int]=0						,   __a :		str=64						,   __a :		Optional[Any]=64						,   __a :		List[Any]=None						,   __a :		Tuple=False						,   **__a :		str							)	->     str:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   RegressionDataset(length=__a							)
												__lowercase					:							Any			=   RegressionDataset(length=__a							)
												__lowercase					:							Union[str, Any]			=   RegressionModelConfig(a=__a						,   b=__a							)
												__lowercase					:							int			=   RegressionPreTrainedModel(__a							)

												__lowercase					:							Any			=   TrainingArguments(self.output_dir						,   disable_tqdm=__a						,   report_to=[]						,   **__a							)
												return Trainer(
												    __a						,   __a						,   train_dataset=__a						,   eval_dataset=__a						,   callbacks=__a						,   )



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Optional[Any]						,   __a :		Dict							)	->     List[Any]:


												"""simple docstring"""

												self.assertEqual(len(__a							)						,   len(__a							)							)

												# Order doesn't matter
												__lowercase					:							List[str]			=   sorted(__a						,   key=lambda __a							: cb.__name__ if isinstance(__a						,   __a							) else cb.__class__.__name__							)
												__lowercase					:							Optional[int]			=   sorted(__a						,   key=lambda __a							: cb.__name__ if isinstance(__a						,   __a							) else cb.__class__.__name__							)

												for cba, cba in zip(__a						,   __a							):
																			if isinstance(__a						,   __a							) and isinstance(__a						,   __a							):
																										self.assertEqual(__a						,   __a							)
																			elif isinstance(__a						,   __a							) and not isinstance(__a						,   __a							):
																										self.assertEqual(__a						,   cba.__class__							)
																			elif not isinstance(__a						,   __a							) and isinstance(__a						,   __a							):
																										self.assertEqual(cba.__class__						,   __a							)
																			else:
																										self.assertEqual(__a						,   __a							)



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Optional[int]							)	->     int:


												"""simple docstring"""

												__lowercase					:							List[str]			=   ["""on_init_end""", """on_train_begin"""]
												__lowercase					:							Tuple			=   0
												__lowercase					:							int			=   len(trainer.get_eval_dataloader()							)
												__lowercase					:							List[Any]			=   ["""on_prediction_step"""] * len(trainer.get_eval_dataloader()							) + ["""on_log""", """on_evaluate"""]
												for _ in range(trainer.state.num_train_epochs							):
																			expected_events.append("""on_epoch_begin"""							)
																			for _ in range(__a							):
																										step += 1
																										expected_events += ["on_step_begin", "on_step_end"]
																										if step % trainer.args.logging_steps == 0:
																																	expected_events.append("""on_log"""							)
																										if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
																																	expected_events += evaluation_events.copy()
																										if step % trainer.args.save_steps == 0:
																																	expected_events.append("""on_save"""							)
																			expected_events.append("""on_epoch_end"""							)
																			if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
																										expected_events += evaluation_events.copy()
												expected_events += ["on_log", "on_train_end"]
												return expected_events



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


												"""simple docstring"""

												__lowercase					:							Dict			=   self.get_trainer()
												__lowercase					:							Optional[int]			=   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)

												# Callbacks passed at init are added to the default callbacks
												__lowercase					:							Optional[Any]			=   self.get_trainer(callbacks=[MyTestTrainerCallback]							)
												expected_callbacks.append(__a							)
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)

												# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
												__lowercase					:							Any			=   self.get_trainer(disable_tqdm=__a							)
												__lowercase					:							Any			=   DEFAULT_CALLBACKS.copy() + [PrinterCallback]
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)



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


												"""simple docstring"""

												__lowercase					:							int			=   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
												__lowercase					:							int			=   self.get_trainer()

												# We can add, pop, or remove by class name
												trainer.remove_callback(__a							)
												expected_callbacks.remove(__a							)
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)

												__lowercase					:							str			=   self.get_trainer()
												__lowercase					:							List[Any]			=   trainer.pop_callback(__a							)
												self.assertEqual(cb.__class__						,   __a							)
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)

												trainer.add_callback(__a							)
												expected_callbacks.insert(0						,   __a							)
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)

												# We can also add, pop, or remove by instance
												__lowercase					:							Dict			=   self.get_trainer()
												__lowercase					:							Tuple			=   trainer.callback_handler.callbacks[0]
												trainer.remove_callback(__a							)
												expected_callbacks.remove(__a							)
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)

												__lowercase					:							Union[str, Any]			=   self.get_trainer()
												__lowercase					:							str			=   trainer.callback_handler.callbacks[0]
												__lowercase					:							List[str]			=   trainer.pop_callback(__a							)
												self.assertEqual(__a						,   __a							)
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)

												trainer.add_callback(__a							)
												expected_callbacks.insert(0						,   __a							)
												self.check_callbacks_equality(trainer.callback_handler.callbacks						,   __a							)



					def 					lowerCAmelCase (     self :		Any							)	->     List[Any]:


												"""simple docstring"""

												import warnings

												# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
												warnings.simplefilter(action="""ignore"""						,   category=__a							)

												__lowercase					:							Optional[Any]			=   self.get_trainer(callbacks=[MyTestTrainerCallback]							)
												trainer.train()
												__lowercase					:							List[str]			=   trainer.callback_handler.callbacks[-2].events
												self.assertEqual(__a						,   self.get_expected_events(__a							)							)

												# Independent log/save/eval
												__lowercase					:							Optional[int]			=   self.get_trainer(callbacks=[MyTestTrainerCallback]						,   logging_steps=5							)
												trainer.train()
												__lowercase					:							Tuple			=   trainer.callback_handler.callbacks[-2].events
												self.assertEqual(__a						,   self.get_expected_events(__a							)							)

												__lowercase					:							Optional[int]			=   self.get_trainer(callbacks=[MyTestTrainerCallback]						,   save_steps=5							)
												trainer.train()
												__lowercase					:							Optional[int]			=   trainer.callback_handler.callbacks[-2].events
												self.assertEqual(__a						,   self.get_expected_events(__a							)							)

												__lowercase					:							Any			=   self.get_trainer(callbacks=[MyTestTrainerCallback]						,   eval_steps=5						,   evaluation_strategy="""steps"""							)
												trainer.train()
												__lowercase					:							Union[str, Any]			=   trainer.callback_handler.callbacks[-2].events
												self.assertEqual(__a						,   self.get_expected_events(__a							)							)

												__lowercase					:							Union[str, Any]			=   self.get_trainer(callbacks=[MyTestTrainerCallback]						,   evaluation_strategy="""epoch"""							)
												trainer.train()
												__lowercase					:							str			=   trainer.callback_handler.callbacks[-2].events
												self.assertEqual(__a						,   self.get_expected_events(__a							)							)

												# A bit of everything
												__lowercase					:							Optional[int]			=   self.get_trainer(
												    callbacks=[MyTestTrainerCallback]						,   logging_steps=3						,   save_steps=10						,   eval_steps=5						,   evaluation_strategy="""steps"""						,   )
												trainer.train()
												__lowercase					:							Tuple			=   trainer.callback_handler.callbacks[-2].events
												self.assertEqual(__a						,   self.get_expected_events(__a							)							)

												# warning should be emitted for duplicated callbacks
												with patch("""transformers.trainer_callback.logger.warning"""							) as warn_mock:
																			__lowercase					:							List[str]			=   self.get_trainer(
																			    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]						,   )
																			assert str(__a							) in warn_mock.call_args[0][0]







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 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# 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
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   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}"					)
							__lowercase					:							Any			=   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[Any]        =				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       :       int        =				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,
	)