


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:
			snake_case__     							= 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     snake_case__  (      unittest.TestCase  ):
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      tempfile.mkdtemp()

										__magic_name__				:							Optional[Any]				=      PixaStructImageProcessor()
										__magic_name__				:							Dict				=      TaTokenizer.from_pretrained('t5-small'      )

										__magic_name__				:							Tuple				=      PixaStructProcessor(_UpperCAmelCase     ,      _UpperCAmelCase      )

										processor.save_pretrained(self.tmpdirname      )
				def  UpperCamelCase_						(  self     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										return AutoProcessor.from_pretrained(self.tmpdirname     ,      **_UpperCAmelCase      ).tokenizer
				def  UpperCamelCase_						(  self     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										return AutoProcessor.from_pretrained(self.tmpdirname     ,      **_UpperCAmelCase      ).image_processor
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										shutil.rmtree(self.tmpdirname      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      [np.random.randint(255     ,      size=(3, 30, 400)     ,      dtype=np.uinta      )]

										__magic_name__				:							List[str]				=      [Image.fromarray(np.moveaxis(_UpperCAmelCase     ,      0     ,      -1      )      ) for x in image_inputs]

										return image_inputs
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      PixaStructProcessor(tokenizer=self.get_tokenizer()     ,      image_processor=self.get_image_processor()      )
										processor.save_pretrained(self.tmpdirname      )

										__magic_name__				:							str				=      self.get_tokenizer(bos_token='(BOS)'     ,      eos_token='(EOS)'      )
										__magic_name__				:							List[str]				=      self.get_image_processor(do_normalize=_UpperCAmelCase     ,      padding_value=1.0      )

										__magic_name__				:							List[Any]				=      PixaStructProcessor.from_pretrained(
										    self.tmpdirname     ,      bos_token='(BOS)'     ,      eos_token='(EOS)'     ,      do_normalize=_UpperCAmelCase     ,      padding_value=1.0      )

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

										self.assertEqual(processor.image_processor.to_json_string()     ,      image_processor_add_kwargs.to_json_string()      )
										self.assertIsInstance(processor.image_processor     ,      _UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.get_image_processor()
										__magic_name__				:							Dict				=      self.get_tokenizer()

										__magic_name__				:							str				=      PixaStructProcessor(tokenizer=_UpperCAmelCase     ,      image_processor=_UpperCAmelCase      )

										__magic_name__				:							List[str]				=      self.prepare_image_inputs()

										__magic_name__				:							Tuple				=      image_processor(_UpperCAmelCase     ,      return_tensors='np'      )
										__magic_name__				:							Union[str, Any]				=      processor(images=_UpperCAmelCase     ,      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      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      self.get_image_processor()
										__magic_name__				:							Optional[int]				=      self.get_tokenizer()

										__magic_name__				:							int				=      PixaStructProcessor(tokenizer=_UpperCAmelCase     ,      image_processor=_UpperCAmelCase      )

										__magic_name__				:							Optional[int]				=      'lower newer'

										__magic_name__				:							int				=      processor(text=_UpperCAmelCase      )

										__magic_name__				:							int				=      tokenizer(_UpperCAmelCase     ,      return_token_type_ids=_UpperCAmelCase     ,      add_special_tokens=_UpperCAmelCase      )

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




										"""simple docstring"""




										__magic_name__				:							Dict				=      self.get_image_processor()
										__magic_name__				:							str				=      self.get_tokenizer()

										__magic_name__				:							Optional[int]				=      PixaStructProcessor(tokenizer=_UpperCAmelCase     ,      image_processor=_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      'lower newer'
										__magic_name__				:							Union[str, Any]				=      self.prepare_image_inputs()

										__magic_name__				:							List[Any]				=      processor(text=_UpperCAmelCase     ,      images=_UpperCAmelCase      )

										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(_UpperCAmelCase      ):
																processor()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      self.get_image_processor()
										__magic_name__				:							str				=      self.get_tokenizer()

										__magic_name__				:							List[Any]				=      PixaStructProcessor(tokenizer=_UpperCAmelCase     ,      image_processor=_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      'lower newer'
										__magic_name__				:							int				=      self.prepare_image_inputs()

										__magic_name__				:							str				=      processor(text=_UpperCAmelCase     ,      images=_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      [512, 1024, 2048, 4096]
										__magic_name__				:							Optional[int]				=      [770, 770, 770, 770]
										# with text
										for i, max_patch in enumerate(_UpperCAmelCase      ):
																__magic_name__				:							Tuple				=      processor(text=_UpperCAmelCase     ,      images=_UpperCAmelCase     ,      max_patches=_UpperCAmelCase      )
																self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,      _UpperCAmelCase      )
																self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,      expected_hidden_size[i]      )

										# without text input
										for i, max_patch in enumerate(_UpperCAmelCase      ):
																__magic_name__				:							Union[str, Any]				=      processor(images=_UpperCAmelCase     ,      max_patches=_UpperCAmelCase      )
																self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,      _UpperCAmelCase      )
																self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,      expected_hidden_size[i]      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      self.get_image_processor()
										__magic_name__				:							Dict				=      self.get_tokenizer()

										__magic_name__				:							Optional[Any]				=      PixaStructProcessor(tokenizer=_UpperCAmelCase     ,      image_processor=_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__magic_name__				:							Tuple				=      processor.batch_decode(_UpperCAmelCase      )
										__magic_name__				:							Tuple				=      tokenizer.batch_decode(_UpperCAmelCase      )

										self.assertListEqual(_UpperCAmelCase     ,      _UpperCAmelCase      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.get_image_processor()
										__magic_name__				:							List[str]				=      self.get_tokenizer()

										__magic_name__				:							List[str]				=      PixaStructProcessor(tokenizer=_UpperCAmelCase     ,      image_processor=_UpperCAmelCase      )

										__magic_name__				:							str				=      'lower newer'
										__magic_name__				:							Any				=      self.prepare_image_inputs()

										__magic_name__				:							int				=      processor(text=_UpperCAmelCase     ,      images=_UpperCAmelCase      )

										# 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']      )

										__magic_name__				:							List[str]				=      processor(text=_UpperCAmelCase      )

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




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      BertGenerationDecoder(lowercase__				)
						print(f"Building PyTorch model from configuration: {config}"				)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    lowercase__      ,						lowercase__      ,						model_class='bert'      ,						is_encoder_named_decoder=lowercase__      ,						is_encoder=lowercase__      ,						)

						# Save pytorch-model
						print(f"Save PyTorch model and config to {pytorch_dump_path}"				)
						model.save_pretrained(lowercase__				)


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)


