"""simple docstring"""





import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:




			class  _snake_case		:







							"""simple docstring"""






							@staticmethod
							def 		_lowerCAmelCase		(      *_A							:							str      ,    **_A							:							int):



														"""simple docstring"""







														pass




@is_pipeline_test
@require_torch
@require_vision
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				a			=						MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							List[Any]      ,    _A							:							str      ,    _A							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    pipeline("""visual-question-answering"""      ,    model="""hf-internal-testing/tiny-vilt-random-vqa""")
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [
											    {
											        """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""),
											        """question""": """How many cats are there?""",
											    },
											    {
											        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
											        """question""": """How many cats are there?""",
											    },
											]
											return vqa_pipeline, examples





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							Tuple      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    vqa_pipeline(_A      ,    top_k=1)
											self.assertEqual(
											    _A      ,    [
											        [{"""score""": ANY(_A), """answer""": ANY(_A)}],
											        [{"""score""": ANY(_A), """answer""": ANY(_A)}],
											    ]      ,    )





				@require_torch
				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    pipeline("""visual-question-answering"""      ,    model="""hf-internal-testing/tiny-vilt-random-vqa""")
											_SCREAMING_SNAKE_CASE		:						str			  =    """./tests/fixtures/tests_samples/COCO/000000039769.png"""
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    """How many cats are there?"""

											_SCREAMING_SNAKE_CASE		:						Any			  =    vqa_pipeline(image=_A      ,    question="""How many cats are there?"""      ,    top_k=2)
											self.assertEqual(
											    _A      ,    [{"""score""": ANY(_A), """answer""": ANY(_A)}, {"""score""": ANY(_A), """answer""": ANY(_A)}])

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    vqa_pipeline({"""image""": image, """question""": question}      ,    top_k=2)
											self.assertEqual(
											    _A      ,    [{"""score""": ANY(_A), """answer""": ANY(_A)}, {"""score""": ANY(_A), """answer""": ANY(_A)}])





				@slow
				@require_torch
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pipeline("""visual-question-answering"""      ,    model="""dandelin/vilt-b32-finetuned-vqa""")
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """./tests/fixtures/tests_samples/COCO/000000039769.png"""
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    """How many cats are there?"""

											_SCREAMING_SNAKE_CASE		:						Dict			  =    vqa_pipeline(image=_A      ,    question=_A      ,    top_k=2)
											self.assertEqual(
											    nested_simplify(_A      ,    decimals=4)      ,    [{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}])

											_SCREAMING_SNAKE_CASE		:						Any			  =    vqa_pipeline({"""image""": image, """question""": question}      ,    top_k=2)
											self.assertEqual(
											    nested_simplify(_A      ,    decimals=4)      ,    [{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}])

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    vqa_pipeline(
											    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]      ,    top_k=2)
											self.assertEqual(
											    nested_simplify(_A      ,    decimals=4)      ,    [[{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2      ,    )





				@require_tf
				@unittest.skip("""Visual question answering not implemented in TF""")
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											pass



"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[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
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														_SCREAMING_SNAKE_CASE		:						str			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasModel(config=__SCREAMING_SNAKE_CASE  )
							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(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

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

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


if __name__ == "__main__":
			lowerCAmelCase_     =	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_     =	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,
			)


