



'''simple docstring'''

import unittest

import numpy as np
import requests

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

							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 PixaStructImageProcessor

class      __magic_name__						(   unittest.TestCase       ):







							def __init__(							self :	str     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	int=7     ,_UpperCAmelCase :	int=3     ,_UpperCAmelCase :	List[str]=18     ,_UpperCAmelCase :	str=30     ,_UpperCAmelCase :	Tuple=400     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	List[Any]=True     ,_UpperCAmelCase :	Any=True     ,_UpperCAmelCase :	Optional[Any]=None     ,):
									_a					:				int				=							size if size is not None else {'height': 20, 'width': 20}
									_a					:				List[Any]				=							parent
									_a					:				List[Any]				=							batch_size
									_a					:				List[Any]				=							num_channels
									_a					:				str				=							image_size
									_a					:				Optional[Any]				=							min_resolution
									_a					:				str				=							max_resolution
									_a					:				List[Any]				=							size
									_a					:				int				=							do_normalize
									_a					:				Any				=							do_convert_rgb
									_a					:				Tuple				=							[512, 1024, 2048, 4096]
									_a					:				Tuple				=							patch_size if patch_size is not None else {'height': 16, 'width': 16}







							def 							__lowercase      (							self :	List[Any]		):
									return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				List[str]				=							'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
									_a					:				Any				=							Image.open(requests.get(_UpperCAmelCase     ,stream=_UpperCAmelCase		).raw		).convert('RGB'		)
									return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,						)
@require_torch
@require_vision
class      __magic_name__						(   _UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[Any]           = PixaStructImageProcessor if is_vision_available() else None







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Dict				=							PixaStructImageProcessingTester(self		)







							@property
							def 							__lowercase      (							self :	Dict		):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 							__lowercase      (							self :	str		):
									_a					:				int				=							self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_UpperCAmelCase     ,'do_normalize'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase     ,'do_convert_rgb'		)		)







							def 							__lowercase      (							self :	Dict		):
									_a					:				List[Any]				=							self.image_processor_tester.prepare_dummy_image()

									_a					:				Optional[int]				=							self.image_processing_class(**self.image_processor_dict		)
									_a					:				List[str]				=							2048

									_a					:				Dict				=							image_processor(_UpperCAmelCase     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		)
									self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,torch.tensor(0.06_06		)     ,atol=1E-3     ,rtol=1E-3		)		)







							def 							__lowercase      (							self :	Dict		):
									# Initialize image_processor
									_a					:				List[str]				=							self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_a					:				Union[str, Any]				=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase     ,Image.Image		)

									# Test not batched input
									_a					:				Dict				=							(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_a					:				Union[str, Any]				=							image_processor(
											    image_inputs[0]     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

											# Test batched
											_a					:				Union[str, Any]				=							image_processor(
											    _UpperCAmelCase     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,)







							def 							__lowercase      (							self :	int		):
									# Initialize image_processor
									_a					:				Optional[int]				=							self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_a					:				int				=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase     ,Image.Image		)

									# Test not batched input
									_a					:				Dict				=							(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									_a					:				List[str]				=							True

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											with self.assertRaises(_UpperCAmelCase		):
													_a					:				str				=							image_processor(
													    image_inputs[0]     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches

											_a					:				Union[str, Any]				=							'Hello'

											_a					:				str				=							image_processor(
											    image_inputs[0]     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase     ,header_text=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

											# Test batched
											_a					:				Union[str, Any]				=							image_processor(
											    _UpperCAmelCase     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase     ,header_text=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,)







							def 							__lowercase      (							self :	int		):
									# Initialize image_processor
									_a					:				Dict				=							self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									_a					:				Dict				=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCAmelCase     ,numpify=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase     ,np.ndarray		)

									_a					:				Any				=							(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_a					:				Dict				=							image_processor(
											    image_inputs[0]     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

											# Test batched
											_a					:				str				=							image_processor(
											    _UpperCAmelCase     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,)







							def 							__lowercase      (							self :	Union[str, Any]		):
									# Initialize image_processor
									_a					:				Optional[int]				=							self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									_a					:				List[Any]				=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCAmelCase     ,torchify=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase     ,torch.Tensor		)

									# Test not batched input
									_a					:				Union[str, Any]				=							(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_a					:				Optional[int]				=							image_processor(
											    image_inputs[0]     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

											# Test batched
											_a					:				Union[str, Any]				=							image_processor(
											    _UpperCAmelCase     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,						)
@require_torch
@require_vision
class      __magic_name__						(   _UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = PixaStructImageProcessor if is_vision_available() else None







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Tuple				=							PixaStructImageProcessingTester(self     ,num_channels=4		)
									_a					:				Tuple				=							3







							@property
							def 							__lowercase      (							self :	Dict		):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				List[Any]				=							self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_UpperCAmelCase     ,'do_normalize'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase     ,'do_convert_rgb'		)		)







							def 							__lowercase      (							self :	Tuple		):
									# Initialize image_processor
									_a					:				Any				=							self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_a					:				Any				=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase     ,Image.Image		)

									# Test not batched input
									_a					:				int				=							(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * (self.image_processor_tester.num_channels - 1)
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_a					:				Optional[int]				=							image_processor(
											    image_inputs[0]     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

											# Test batched
											_a					:				Optional[Any]				=							image_processor(
											    _UpperCAmelCase     ,return_tensors='pt'     ,max_patches=_UpperCAmelCase		).flattened_patches
											self.assertEqual(
											    encoded_images.shape     ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,)









'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   str           = field(
							    metadata={'help': 'The output directory where the model will be written.'}      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The encoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train an encoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The decoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train a decoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}       )
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}       )



def 					__lowerCamelCase					(				)      ->				Union[str, Any]:
		_a					:				Any				=							HfArgumentParser((ModelArguments,)  )
		((_a)	, )					:				Dict				=							parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_config_name  )
		# Use pretrained encoder model's config
		else:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

		# Use explicit specified decoder config
		if model_args.decoder_config_name:
				_a					:				List[str]				=							AutoConfig.from_pretrained(model_args.decoder_config_name  )
		# Use pretrained decoder model's config
		else:
				_a					:				Optional[int]				=							AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

		# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
		_a					:				List[Any]				=							True
		_a					:				int				=							True

		_a					:				Any				=							FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
		    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,      decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,      encoder_config=lowerCAmelCase_						,      decoder_config=lowerCAmelCase_						,      )

		# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
		_a					:				List[str]				=							decoder_config.decoder_start_token_id
		_a					:				Optional[int]				=							decoder_config.pad_token_id
		if decoder_start_token_id is None:
				_a					:				Tuple				=							decoder_config.bos_token_id
		if pad_token_id is None:
				_a					:				List[Any]				=							decoder_config.eos_token_id

		# This is necessary to make Flax's generate() work
		_a					:				Any				=							decoder_config.eos_token_id
		_a					:				Tuple				=							decoder_start_token_id
		_a					:				Any				=							pad_token_id

		_a					:				Dict				=							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

		_a					:				Dict				=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
		_a					:				int				=							tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

		model.save_pretrained(model_args.output_dir  )
		image_processor.save_pretrained(model_args.output_dir  )
		tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
							main()




