
from dataclasses import dataclass, field
from typing import Optional

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







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



def 		lowercase_    (     )							->							Any:
    """simple docstring"""

    snake_case				  =      HfArgumentParser((ModelArguments,)	)
    ((snake_case )  ,							 )				  =      parser.parse_args_into_dataclasses()

    # Load pretrained model and tokenizer

    # Use explicit specified encoder config
    if model_args.encoder_config_name:
        snake_case				  =      AutoConfig.from_pretrained(model_args.encoder_config_name	)
    # Use pretrained encoder model's config
    else:
        snake_case				  =      AutoConfig.from_pretrained(model_args.encoder_model_name_or_path	)

    # Use explicit specified decoder config
    if model_args.decoder_config_name:
        snake_case				  =      AutoConfig.from_pretrained(model_args.decoder_config_name	)
    # Use pretrained decoder model's config
    else:
        snake_case				  =      AutoConfig.from_pretrained(model_args.decoder_model_name_or_path	)

    # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
    snake_case				  =      True
    snake_case				  =      True

    snake_case				  =      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=UpperCamelCase__				,	decoder_config=UpperCamelCase__				,	)

    # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
    snake_case				  =      decoder_config.decoder_start_token_id
    snake_case				  =      decoder_config.pad_token_id
    if decoder_start_token_id is None:
        snake_case				  =      decoder_config.bos_token_id
    if pad_token_id is None:
        snake_case				  =      decoder_config.eos_token_id

    # This is necessary to make Flax's generate() work
    snake_case				  =      decoder_config.eos_token_id
    snake_case				  =      decoder_start_token_id
    snake_case				  =      pad_token_id

    snake_case				  =      AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path	)

    snake_case				  =      AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path	)
    snake_case				  =      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()



import unittest

import numpy as np

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

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Optional[int]       ,					_A   :					Dict       ,					_A   :					Optional[int]=7       ,					_A   :					Union[str, Any]=3       ,					_A   :					Tuple=1_8       ,					_A   :					str=3_0       ,					_A   :					Union[str, Any]=4_0_0       ,					_A   :					Optional[Any]=True       ,					_A   :					Union[str, Any]=None       ,					_A   :					str=True       ,					_A   :					Optional[int]=None       ,					)		->		int:
         snake_case				  =      size if size is not None else {"shortest_edge": 2_0}
         snake_case				  =      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_center_crop
         snake_case				  =      crop_size





     def 	UpperCAmelCase(self   :					str     )		->		int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[int]     =	MobileNetVaImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         snake_case				  =      MobileNetVaImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )
         self.assertTrue(hasattr(_A       ,					"do_center_crop"     )     )
         self.assertTrue(hasattr(_A       ,					"crop_size"     )     )





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 2_0}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 1_8, "width": 1_8}     )

         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 4_2}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 8_4, "width": 8_4}     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         pass





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					numpify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					np.ndarray     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					List[str]     )		->		List[str]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					torchify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					torch.Tensor     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

