

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 PoolFormerImageProcessor

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=7      ,      lowercase=3      ,      lowercase=30      ,      lowercase=400      ,      lowercase=True      ,      lowercase=None      ,      lowercase=0.9      ,      lowercase=None      ,      lowercase=True      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=[0.5, 0.5, 0.5]      ,      )  ->						str:
							'''simple docstring'''






							A__        =			size if size is not None else {"shortest_edge": 30}
							A__        =			crop_size if crop_size is not None else {"height": 30, "width": 30}
							A__        =			parent
							A__        =			batch_size
							A__        =			num_channels
							A__        =			min_resolution
							A__        =			max_resolution
							A__        =			do_resize_and_center_crop
							A__        =			size
							A__        =			crop_pct
							A__        =			crop_size
							A__        =			do_normalize
							A__        =			image_mean
							A__        =			image_std


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}

@require_torch
@require_vision
class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       PoolFormerImageProcessor if is_vision_available() else None


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			PoolFormerImageProcessingTester(self       )


			@property
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(lowercase      ,      "do_resize_and_center_crop"       )       )
							self.assertTrue(hasattr(lowercase      ,      "size"       )       )
							self.assertTrue(hasattr(lowercase      ,      "crop_pct"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_normalize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_mean"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_std"       )       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 30}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 30, "width": 30}       )

							A__        =			self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 84, "width": 84}       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      Image.Image       )

							# Test not batched input
							A__        =			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
							A__        =			image_processing(lowercase      ,      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:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      numpify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      np.ndarray       )

							# Test not batched input
							A__        =			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
							A__        =			image_processing(lowercase      ,      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       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      torchify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      torch.Tensor       )

							# Test not batched input
							A__        =			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
							A__        =			image_processing(lowercase      ,      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"],
							    )      ,      )




from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class 					a__						:
			"""simple docstring"""







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



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





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

				# Load pretrained model and tokenizer

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

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

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

				A__        =			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=SCREAMING_SNAKE_CASE_	,       decoder_config=SCREAMING_SNAKE_CASE_	,       )

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

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

				A__        =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

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

