




'''simple docstring'''






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:
				_lowerCAmelCase				     = 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  A		(			unittest.TestCase		):




				'''simple docstring'''





				def 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      int									=       tempfile.mkdtemp()

							__UpperCamelCase				:      Tuple									=       PixaStructImageProcessor()
							__UpperCamelCase				:      Any									=       TaTokenizer.from_pretrained("t5-small"      )

							__UpperCamelCase				:      int									=       PixaStructProcessor(UpperCamelCase_			,  UpperCamelCase_      )

							processor.save_pretrained(self.tmpdirname      )
				def 	a_	(self			,  **_UpperCAmelCase      )     ->	str:
							return AutoProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase_      ).tokenizer
				def 	a_	(self			,  **_UpperCAmelCase      )     ->	Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase_      ).image_processor
				def 	a_	(self      )     ->	Dict:
							shutil.rmtree(self.tmpdirname      )
				def 	a_	(self      )     ->	Any:
							__UpperCamelCase				:      Union[str, Any]									=       [np.random.randint(2_5_5			,  size=(3, 3_0, 4_0_0)			,  dtype=np.uinta      )]

							__UpperCamelCase				:      int									=       [Image.fromarray(np.moveaxis(UpperCamelCase_			,  0			,  -1      )      ) for x in image_inputs]

							return image_inputs
				def 	a_	(self      )     ->	List[str]:
							__UpperCamelCase				:      Optional[Any]									=       PixaStructProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

							__UpperCamelCase				:      int									=       self.get_tokenizer(bos_token="(BOS)"			,  eos_token="(EOS)"      )
							__UpperCamelCase				:      Dict									=       self.get_image_processor(do_normalize=UpperCamelCase_			,  padding_value=1.0      )

							__UpperCamelCase				:      int									=       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 	a_	(self      )     ->	int:
							__UpperCamelCase				:      List[Any]									=       self.get_image_processor()
							__UpperCamelCase				:      List[str]									=       self.get_tokenizer()

							__UpperCamelCase				:      Optional[int]									=       PixaStructProcessor(tokenizer=UpperCamelCase_			,  image_processor=UpperCamelCase_      )

							__UpperCamelCase				:      Optional[Any]									=       self.prepare_image_inputs()

							__UpperCamelCase				:      str									=       image_processor(UpperCamelCase_			,  return_tensors="np"      )
							__UpperCamelCase				:      Tuple									=       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 	a_	(self      )     ->	Any:
							__UpperCamelCase				:      List[Any]									=       self.get_image_processor()
							__UpperCamelCase				:      Any									=       self.get_tokenizer()

							__UpperCamelCase				:      Dict									=       PixaStructProcessor(tokenizer=UpperCamelCase_			,  image_processor=UpperCamelCase_      )

							__UpperCamelCase				:      Tuple									=       "lower newer"

							__UpperCamelCase				:      List[str]									=       processor(text=UpperCamelCase_      )

							__UpperCamelCase				:      Union[str, Any]									=       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 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      str									=       self.get_image_processor()
							__UpperCamelCase				:      Any									=       self.get_tokenizer()

							__UpperCamelCase				:      Optional[Any]									=       PixaStructProcessor(tokenizer=UpperCamelCase_			,  image_processor=UpperCamelCase_      )

							__UpperCamelCase				:      int									=       "lower newer"
							__UpperCamelCase				:      Tuple									=       self.prepare_image_inputs()

							__UpperCamelCase				:      int									=       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 	a_	(self      )     ->	List[Any]:
							__UpperCamelCase				:      List[Any]									=       self.get_image_processor()
							__UpperCamelCase				:      int									=       self.get_tokenizer()

							__UpperCamelCase				:      int									=       PixaStructProcessor(tokenizer=UpperCamelCase_			,  image_processor=UpperCamelCase_      )

							__UpperCamelCase				:      str									=       "lower newer"
							__UpperCamelCase				:      Union[str, Any]									=       self.prepare_image_inputs()

							__UpperCamelCase				:      Dict									=       processor(text=UpperCamelCase_			,  images=UpperCamelCase_      )

							__UpperCamelCase				:      Tuple									=       [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
							__UpperCamelCase				:      Optional[int]									=       [7_7_0, 7_7_0, 7_7_0, 7_7_0]
							# with text
							for i, max_patch in enumerate(UpperCamelCase_      ):
										__UpperCamelCase				:      Optional[Any]									=       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_      ):
										__UpperCamelCase				:      List[str]									=       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 	a_	(self      )     ->	str:
							__UpperCamelCase				:      List[str]									=       self.get_image_processor()
							__UpperCamelCase				:      Tuple									=       self.get_tokenizer()

							__UpperCamelCase				:      Tuple									=       PixaStructProcessor(tokenizer=UpperCamelCase_			,  image_processor=UpperCamelCase_      )

							__UpperCamelCase				:      Optional[int]									=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							__UpperCamelCase				:      Tuple									=       processor.batch_decode(UpperCamelCase_      )
							__UpperCamelCase				:      str									=       tokenizer.batch_decode(UpperCamelCase_      )

							self.assertListEqual(UpperCamelCase_			,  UpperCamelCase_      )





				def 	a_	(self      )     ->	Dict:
							__UpperCamelCase				:      Dict									=       self.get_image_processor()
							__UpperCamelCase				:      Union[str, Any]									=       self.get_tokenizer()

							__UpperCamelCase				:      Union[str, Any]									=       PixaStructProcessor(tokenizer=UpperCamelCase_			,  image_processor=UpperCamelCase_      )

							__UpperCamelCase				:      Optional[Any]									=       "lower newer"
							__UpperCamelCase				:      Tuple									=       self.prepare_image_inputs()

							__UpperCamelCase				:      Dict									=       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"]      )

							__UpperCamelCase				:      Tuple									=       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"]      )









'''simple docstring'''






import math
import os

import cva
import numpy as np

_lowerCAmelCase				     = 255.0

def       __lowerCAmelCase (   snake_case__ ,				snake_case__						):
			__UpperCamelCase				:      Dict									=       np.mean((original - contrast) ** 2						)
			if mse == 0:
						return 100

			return 20 * math.logaa(PIXEL_MAX / math.sqrt(snake_case__						)						)

def       __lowerCAmelCase (   ):
			__UpperCamelCase				:      Tuple									=       os.path.dirname(os.path.realpath(snake_case__						)						)
			# Loading images (original image and compressed image)
			__UpperCamelCase				:      Tuple									=       cva.imread(os.path.join(snake_case__ ,				"image_data/original_image.png"						)						)
			__UpperCamelCase				:      List[Any]									=       cva.imread(os.path.join(snake_case__ ,				"image_data/compressed_image.png"						) ,				1						)

			__UpperCamelCase				:      Dict									=       cva.imread(os.path.join(snake_case__ ,				"image_data/PSNR-example-base.png"						)						)
			__UpperCamelCase				:      Optional[Any]									=       cva.imread(
			    os.path.join(snake_case__ ,				"image_data/PSNR-example-comp-10.jpg"						) ,				1						)

			# Value expected: 29.73dB
			print("-- First Test --"						)
			print(F"PSNR value is {peak_signal_to_noise_ratio(snake_case__ ,				snake_case__						)} dB"						)

			# # Value expected: 31.53dB (Wikipedia Example)
			print("\n-- Second Test --"						)
			print(F"PSNR value is {peak_signal_to_noise_ratio(snake_case__ ,				snake_case__						)} dB"						)


if __name__ == "__main__":
				main()



