


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:
					UpperCamelCase :				Optional[int]						    =       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     __snake_case							(      unittest.TestCase):
					def   a (	self					:	int     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			tempfile.mkdtemp()

											__magic_name__ 		=			PixaStructImageProcessor()
											__magic_name__ 		=			TaTokenizer.from_pretrained('t5-small'     )

											__magic_name__ 		=			PixaStructProcessor(_lowerCAmelCase  ,			_lowerCAmelCase     )

											processor.save_pretrained(self.tmpdirname     )
					def   a (	self					:	Any  ,			**_lowerCAmelCase					:	Optional[int]     )       ->				Any:



											'''simple docstring'''





											return AutoProcessor.from_pretrained(self.tmpdirname  ,			**_lowerCAmelCase     ).tokenizer
					def   a (	self					:	Any  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				int:



											'''simple docstring'''





											return AutoProcessor.from_pretrained(self.tmpdirname  ,			**_lowerCAmelCase     ).image_processor
					def   a (	self					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											shutil.rmtree(self.tmpdirname     )
					def   a (	self					:	Dict     )       ->				Dict:



											'''simple docstring'''





											__magic_name__ 		=			[np.random.randint(2_5_5  ,			size=(3, 3_0, 4_0_0)  ,			dtype=np.uinta     )]

											__magic_name__ 		=			[Image.fromarray(np.moveaxis(_lowerCAmelCase  ,			0  ,			-1     )     ) for x in image_inputs]

											return image_inputs
					def   a (	self					:	Optional[Any]     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			PixaStructProcessor(tokenizer=self.get_tokenizer()  ,			image_processor=self.get_image_processor()     )
											processor.save_pretrained(self.tmpdirname     )

											__magic_name__ 		=			self.get_tokenizer(bos_token='(BOS)'  ,			eos_token='(EOS)'     )
											__magic_name__ 		=			self.get_image_processor(do_normalize=_lowerCAmelCase  ,			padding_value=1.0     )

											__magic_name__ 		=			PixaStructProcessor.from_pretrained(
											    self.tmpdirname  ,			bos_token='(BOS)'  ,			eos_token='(EOS)'  ,			do_normalize=_lowerCAmelCase  ,			padding_value=1.0     )

											self.assertEqual(processor.tokenizer.get_vocab()  ,			tokenizer_add_kwargs.get_vocab()     )
											self.assertIsInstance(processor.tokenizer  ,			_lowerCAmelCase     )

											self.assertEqual(processor.image_processor.to_json_string()  ,			image_processor_add_kwargs.to_json_string()     )
											self.assertIsInstance(processor.image_processor  ,			_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]     )       ->				List[str]:



											'''simple docstring'''





											__magic_name__ 		=			self.get_image_processor()
											__magic_name__ 		=			self.get_tokenizer()

											__magic_name__ 		=			PixaStructProcessor(tokenizer=_lowerCAmelCase  ,			image_processor=_lowerCAmelCase     )

											__magic_name__ 		=			self.prepare_image_inputs()

											__magic_name__ 		=			image_processor(_lowerCAmelCase  ,			return_tensors='np'     )
											__magic_name__ 		=			processor(images=_lowerCAmelCase  ,			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					:	Optional[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.get_image_processor()
											__magic_name__ 		=			self.get_tokenizer()

											__magic_name__ 		=			PixaStructProcessor(tokenizer=_lowerCAmelCase  ,			image_processor=_lowerCAmelCase     )

											__magic_name__ 		=			'lower newer'

											__magic_name__ 		=			processor(text=_lowerCAmelCase     )

											__magic_name__ 		=			tokenizer(_lowerCAmelCase  ,			return_token_type_ids=_lowerCAmelCase  ,			add_special_tokens=_lowerCAmelCase     )

											for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]  ,			encoded_processor[key]     )
					def   a (	self					:	Tuple     )       ->				Dict:



											'''simple docstring'''





											__magic_name__ 		=			self.get_image_processor()
											__magic_name__ 		=			self.get_tokenizer()

											__magic_name__ 		=			PixaStructProcessor(tokenizer=_lowerCAmelCase  ,			image_processor=_lowerCAmelCase     )

											__magic_name__ 		=			'lower newer'
											__magic_name__ 		=			self.prepare_image_inputs()

											__magic_name__ 		=			processor(text=_lowerCAmelCase  ,			images=_lowerCAmelCase     )

											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(_lowerCAmelCase     ):
																	processor()
					def   a (	self					:	str     )       ->				int:



											'''simple docstring'''





											__magic_name__ 		=			self.get_image_processor()
											__magic_name__ 		=			self.get_tokenizer()

											__magic_name__ 		=			PixaStructProcessor(tokenizer=_lowerCAmelCase  ,			image_processor=_lowerCAmelCase     )

											__magic_name__ 		=			'lower newer'
											__magic_name__ 		=			self.prepare_image_inputs()

											__magic_name__ 		=			processor(text=_lowerCAmelCase  ,			images=_lowerCAmelCase     )

											__magic_name__ 		=			[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
											__magic_name__ 		=			[7_7_0, 7_7_0, 7_7_0, 7_7_0]
											# with text
											for i, max_patch in enumerate(_lowerCAmelCase     ):
																	__magic_name__ 		=			processor(text=_lowerCAmelCase  ,			images=_lowerCAmelCase  ,			max_patches=_lowerCAmelCase     )
																	self.assertEqual(inputs['flattened_patches'][0].shape[0]  ,			_lowerCAmelCase     )
																	self.assertEqual(inputs['flattened_patches'][0].shape[1]  ,			expected_hidden_size[i]     )

											# without text input
											for i, max_patch in enumerate(_lowerCAmelCase     ):
																	__magic_name__ 		=			processor(images=_lowerCAmelCase  ,			max_patches=_lowerCAmelCase     )
																	self.assertEqual(inputs['flattened_patches'][0].shape[0]  ,			_lowerCAmelCase     )
																	self.assertEqual(inputs['flattened_patches'][0].shape[1]  ,			expected_hidden_size[i]     )
					def   a (	self					:	Union[str, Any]     )       ->				Optional[Any]:



											'''simple docstring'''





											__magic_name__ 		=			self.get_image_processor()
											__magic_name__ 		=			self.get_tokenizer()

											__magic_name__ 		=			PixaStructProcessor(tokenizer=_lowerCAmelCase  ,			image_processor=_lowerCAmelCase     )

											__magic_name__ 		=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											__magic_name__ 		=			processor.batch_decode(_lowerCAmelCase     )
											__magic_name__ 		=			tokenizer.batch_decode(_lowerCAmelCase     )

											self.assertListEqual(_lowerCAmelCase  ,			_lowerCAmelCase     )







					def   a (	self					:	List[str]     )       ->				str:



											'''simple docstring'''





											__magic_name__ 		=			self.get_image_processor()
											__magic_name__ 		=			self.get_tokenizer()

											__magic_name__ 		=			PixaStructProcessor(tokenizer=_lowerCAmelCase  ,			image_processor=_lowerCAmelCase     )

											__magic_name__ 		=			'lower newer'
											__magic_name__ 		=			self.prepare_image_inputs()

											__magic_name__ 		=			processor(text=_lowerCAmelCase  ,			images=_lowerCAmelCase     )

											# 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']     )

											__magic_name__ 		=			processor(text=_lowerCAmelCase     )

											# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
											self.assertListEqual(list(inputs.keys()     )  ,			['input_ids', 'attention_mask']     )





def 							UpperCamelCase_  (				__snake_case			:						int     )->      bool:




						'''simple docstring'''
						if not isinstance(__snake_case	,			__snake_case     ):
												__magic_name__ 		=			F'''Input value of [number={number}] must be an integer'''
												raise TypeError(__snake_case     )
						if number < 0 or number % 2 == 1:
												return False
						__magic_name__ 		=			int(number**0.5     )
						return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

