import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()



def 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case	)  ->   Union[str, Any]:
		__snake_case		       =     ConvBertConfig.from_json_file(__snake_case	)
		__snake_case		       =     ConvBertModel(__snake_case	)

		__snake_case		       =     load_tf_weights_in_convbert(__snake_case						,						__snake_case						,						__snake_case	)
		model.save_pretrained(__snake_case	)

		__snake_case		       =     TFConvBertModel.from_pretrained(__snake_case						,						from_pt=__snake_case	)
		tf_model.save_pretrained(__snake_case	)


if __name__ == "__main__":
		lowerCamelCase__           =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT 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_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		lowerCamelCase__           =				False

if is_vision_available():
		from PIL import Image

		from transformers import (
		    AutoProcessor,
		    PixaStructImageProcessor,
		    PixaStructProcessor,
		    PreTrainedTokenizerFast,
		    TaTokenizer,
		)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,      )
@require_vision
@require_torch
class 					UpperCamelCase	(     unittest.TestCase      ):




					def 							UpperCamelCase_						( self :					Optional[int]						):


							"""simple docstring"""




							__snake_case		       =     tempfile.mkdtemp()

							__snake_case		       =     PixaStructImageProcessor()
							__snake_case		       =     TaTokenizer.from_pretrained("t5-small"						)

							__snake_case		       =     PixaStructProcessor(_lowerCAmelCase      ,_lowerCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)




					def 							UpperCamelCase_						( self :					Dict      ,**_lowerCAmelCase :					List[str]						):


							"""simple docstring"""




							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).tokenizer




					def 							UpperCamelCase_						( self :					Any      ,**_lowerCAmelCase :					List[Any]						):


							"""simple docstring"""




							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).image_processor




					def 							UpperCamelCase_						( self :					Optional[int]						):


							"""simple docstring"""




							shutil.rmtree(self.tmpdirname						)




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							__snake_case		       =     [np.random.randint(255      ,size=(3, 30, 400)      ,dtype=np.uinta						)]

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

							return image_inputs




					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




							__snake_case		       =     PixaStructProcessor(tokenizer=self.get_tokenizer()      ,image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

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

							__snake_case		       =     PixaStructProcessor.from_pretrained(
							    self.tmpdirname      ,bos_token="(BOS)"      ,eos_token="(EOS)"      ,do_normalize=_lowerCAmelCase      ,padding_value=1.0						)

							self.assertEqual(processor.tokenizer.get_vocab()      ,tokenizer_add_kwargs.get_vocab()						)
							self.assertIsInstance(processor.tokenizer      ,_lowerCAmelCase						)

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




					def 							UpperCamelCase_						( self :					Optional[int]						):


							"""simple docstring"""




							__snake_case		       =     self.get_image_processor()
							__snake_case		       =     self.get_tokenizer()

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

							__snake_case		       =     self.prepare_image_inputs()

							__snake_case		       =     image_processor(_lowerCAmelCase      ,return_tensors="np"						)
							__snake_case		       =     processor(images=_lowerCAmelCase      ,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 							UpperCamelCase_						( self :					List[str]						):


							"""simple docstring"""




							__snake_case		       =     self.get_image_processor()
							__snake_case		       =     self.get_tokenizer()

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

							__snake_case		       =     "lower newer"

							__snake_case		       =     processor(text=_lowerCAmelCase						)

							__snake_case		       =     tokenizer(_lowerCAmelCase      ,return_token_type_ids=_lowerCAmelCase      ,add_special_tokens=_lowerCAmelCase						)

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




					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




							__snake_case		       =     self.get_image_processor()
							__snake_case		       =     self.get_tokenizer()

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

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

							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)

							self.assertListEqual(
							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)

							# test if it raises when no input is passed
							with pytest.raises(_lowerCAmelCase						):
									processor()




					def 							UpperCamelCase_						( self :					str						):


							"""simple docstring"""




							__snake_case		       =     self.get_image_processor()
							__snake_case		       =     self.get_tokenizer()

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

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

							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)

							__snake_case		       =     [512, 1_024, 2_048, 4_096]
							__snake_case		       =     [770, 770, 770, 770]
							# with text
							for i, max_patch in enumerate(_lowerCAmelCase						):
									__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)

							# without text input
							for i, max_patch in enumerate(_lowerCAmelCase						):
									__snake_case		       =     processor(images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)




					def 							UpperCamelCase_						( self :					Union[str, Any]						):


							"""simple docstring"""




							__snake_case		       =     self.get_image_processor()
							__snake_case		       =     self.get_tokenizer()

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

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

							__snake_case		       =     processor.batch_decode(_lowerCAmelCase						)
							__snake_case		       =     tokenizer.batch_decode(_lowerCAmelCase						)

							self.assertListEqual(_lowerCAmelCase      ,_lowerCAmelCase						)







					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




							__snake_case		       =     self.get_image_processor()
							__snake_case		       =     self.get_tokenizer()

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

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

							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(
							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)

							__snake_case		       =     processor(text=_lowerCAmelCase						)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(list(inputs.keys()						)      ,["input_ids", "attention_mask"]						)





