



import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
			import torch

			from transformers import AutoModelForImageClassification

if is_vision_available():
			from transformers import AutoImageProcessor

@require_torch
@require_vision
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'		)
													__SCREAMING_SNAKE_CASE									= AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'		)
													model.to(_A		)

													from datasets import load_dataset

													__SCREAMING_SNAKE_CASE									= load_dataset('nielsr/rvlcdip-demo'		)

													__SCREAMING_SNAKE_CASE									= dataset['train'][0]['image'].convert('RGB'		)

													__SCREAMING_SNAKE_CASE									= image_processor(_A				,							return_tensors='pt'		).to(_A		)

													# forward pass
													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(**_A		)
																				__SCREAMING_SNAKE_CASE									= outputs.logits

													__SCREAMING_SNAKE_CASE									= torch.Size((1, 16)		)
													self.assertEqual(logits.shape				,							_A		)

													__SCREAMING_SNAKE_CASE									= torch.tensor(
													    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]				,							device=_A				,							dtype=torch.float				,							)
													self.assertTrue(torch.allclose(logits[0, :3]				,							_A				,							atol=1e-4		)		)











from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




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







def      __lowercase       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments,)       )
							((__SCREAMING_SNAKE_CASE)							,	)									= parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

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

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

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

							__SCREAMING_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=a__							,       decoder_config=a__							,       )

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

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

							__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
							__SCREAMING_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()






