"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   tempfile.mkdtemp()

							# fmt: off
							lowerCAmelCase__        =   ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
							# fmt: on
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

							lowerCAmelCase__        =   {
							    """do_resize""": True,
							    """size""": {"""height""": 18, """width""": 18},
							    """do_normalize""": True,
							    """image_mean""": [0.5, 0.5, 0.5],
							    """image_std""": [0.5, 0.5, 0.5],
							}
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						snake_case__     )
							with open(self.image_processor_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as fp:
									json.dump(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						**snake_case__						:     str     ):
							return BertTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						**snake_case__						:     Dict     ):
							return ViTImageProcessor.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							shutil.rmtree(self.tmpdirname     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta     )]

							lowerCAmelCase__        =   [Image.fromarray(np.moveaxis(snake_case__  ,						0  ,						-1     )     ) for x in image_inputs]

							return image_inputs




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.get_tokenizer()
							lowerCAmelCase__        =   self.get_image_processor()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							processor.save_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

							self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer.get_vocab()     )
							self.assertIsInstance(processor.tokenizer  ,						(BertTokenizer, BertTokenizerFast)     )

							self.assertEqual(processor.image_processor.to_json_string()  ,						image_processor.to_json_string()     )
							self.assertIsInstance(processor.image_processor  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor()     )
							processor.save_pretrained(self.tmpdirname     )

							lowerCAmelCase__        =   self.get_tokenizer(bos_token="""(BOS)"""  ,						eos_token="""(EOS)"""     )
							lowerCAmelCase__        =   self.get_image_processor(do_normalize=snake_case__  ,						padding_value=1.0     )

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor.from_pretrained(
							    self.tmpdirname  ,						bos_token="""(BOS)"""  ,						eos_token="""(EOS)"""  ,						do_normalize=snake_case__  ,						padding_value=1.0     )

							self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer_add_kwargs.get_vocab()     )
							self.assertIsInstance(processor.tokenizer  ,						(BertTokenizer, BertTokenizerFast)     )

							self.assertEqual(processor.image_processor.to_json_string()  ,						image_processor_add_kwargs.to_json_string()     )
							self.assertIsInstance(processor.image_processor  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   self.prepare_image_inputs()

							lowerCAmelCase__        =   image_processor(snake_case__  ,						return_tensors="""np"""     )
							lowerCAmelCase__        =   processor(images=snake_case__  ,						return_tensors="""np"""     )

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   """lower newer"""

							lowerCAmelCase__        =   processor(text=snake_case__     )

							lowerCAmelCase__        =   tokenizer(snake_case__     )

							for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   """lower newer"""
							lowerCAmelCase__        =   self.prepare_image_inputs()

							lowerCAmelCase__        =   processor(text=snake_case__  ,						images=snake_case__     )

							self.assertListEqual(list(inputs.keys()     )  ,						["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

							# test if it raises when no input is passed
							with self.assertRaises(snake_case__     ):
									processor()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCAmelCase__        =   processor.batch_decode(snake_case__     )
							lowerCAmelCase__        =   tokenizer.batch_decode(snake_case__     )

							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   """lower newer"""
							lowerCAmelCase__        =   self.prepare_image_inputs()

							lowerCAmelCase__        =   processor(text=snake_case__  ,						images=snake_case__     )

							self.assertListEqual(list(inputs.keys()     )  ,						processor.model_input_names     )





"""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,
			)




