



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:
							__magic_name__       					=      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    lowerCAmelCase__				(			unittest.TestCase       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							int      =       tempfile.mkdtemp()

							lowerCamelCase_			:							List[Any]      =       PixaStructImageProcessor()
							lowerCamelCase_			:							Tuple      =       TaTokenizer.from_pretrained("t5-small"     )

							lowerCamelCase_			:							Any      =       PixaStructProcessor(a_				,							a_     )

							processor.save_pretrained(self.tmpdirname     )




						def 					_UpperCamelCase			(   self				,							**a_     ):
							return AutoProcessor.from_pretrained(self.tmpdirname				,							**a_     ).tokenizer




						def 					_UpperCamelCase			(   self				,							**a_     ):
							return AutoProcessor.from_pretrained(self.tmpdirname				,							**a_     ).image_processor




						def 					_UpperCamelCase			(   self     ):
							shutil.rmtree(self.tmpdirname     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta     )]

							lowerCamelCase_			:							Union[str, Any]      =       [Image.fromarray(np.moveaxis(a_				,							0				,							-1     )     ) for x in image_inputs]

							return image_inputs




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       PixaStructProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor()     )
							processor.save_pretrained(self.tmpdirname     )

							lowerCamelCase_			:							Any      =       self.get_tokenizer(bos_token="(BOS)"				,							eos_token="(EOS)"     )
							lowerCamelCase_			:							int      =       self.get_image_processor(do_normalize=a_				,							padding_value=1.0     )

							lowerCamelCase_			:							List[Any]      =       PixaStructProcessor.from_pretrained(
							    self.tmpdirname				,							bos_token="(BOS)"				,							eos_token="(EOS)"				,							do_normalize=a_				,							padding_value=1.0     )

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

							self.assertEqual(processor.image_processor.to_json_string()				,							image_processor_add_kwargs.to_json_string()     )
							self.assertIsInstance(processor.image_processor				,							a_     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[Any]      =       self.get_image_processor()
							lowerCamelCase_			:							Optional[int]      =       self.get_tokenizer()

							lowerCamelCase_			:							List[str]      =       PixaStructProcessor(tokenizer=a_				,							image_processor=a_     )

							lowerCamelCase_			:							Union[str, Any]      =       self.prepare_image_inputs()

							lowerCamelCase_			:							int      =       image_processor(a_				,							return_tensors="np"     )
							lowerCamelCase_			:							Any      =       processor(images=a_				,							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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       self.get_image_processor()
							lowerCamelCase_			:							Optional[Any]      =       self.get_tokenizer()

							lowerCamelCase_			:							int      =       PixaStructProcessor(tokenizer=a_				,							image_processor=a_     )

							lowerCamelCase_			:							Tuple      =       "lower newer"

							lowerCamelCase_			:							Dict      =       processor(text=a_     )

							lowerCamelCase_			:							Union[str, Any]      =       tokenizer(a_				,							return_token_type_ids=a_				,							add_special_tokens=a_     )

							for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]				,							encoded_processor[key]     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Tuple      =       self.get_image_processor()
							lowerCamelCase_			:							List[str]      =       self.get_tokenizer()

							lowerCamelCase_			:							Tuple      =       PixaStructProcessor(tokenizer=a_				,							image_processor=a_     )

							lowerCamelCase_			:							List[Any]      =       "lower newer"
							lowerCamelCase_			:							str      =       self.prepare_image_inputs()

							lowerCamelCase_			:							List[Any]      =       processor(text=a_				,							images=a_     )

							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(a_     ):
								processor()




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[Any]      =       self.get_image_processor()
							lowerCamelCase_			:							Optional[Any]      =       self.get_tokenizer()

							lowerCamelCase_			:							str      =       PixaStructProcessor(tokenizer=a_				,							image_processor=a_     )

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

							lowerCamelCase_			:							Dict      =       processor(text=a_				,							images=a_     )

							lowerCamelCase_			:							Dict      =       [512, 1024, 2048, 4096]
							lowerCamelCase_			:							Tuple      =       [770, 770, 770, 770]
							# with text
							for i, max_patch in enumerate(a_     ):
								lowerCamelCase_			:							List[Any]      =       processor(text=a_				,							images=a_				,							max_patches=a_     )
								self.assertEqual(inputs["flattened_patches"][0].shape[0]				,							a_     )
								self.assertEqual(inputs["flattened_patches"][0].shape[1]				,							expected_hidden_size[i]     )

							# without text input
							for i, max_patch in enumerate(a_     ):
								lowerCamelCase_			:							int      =       processor(images=a_				,							max_patches=a_     )
								self.assertEqual(inputs["flattened_patches"][0].shape[0]				,							a_     )
								self.assertEqual(inputs["flattened_patches"][0].shape[1]				,							expected_hidden_size[i]     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[Any]      =       self.get_image_processor()
							lowerCamelCase_			:							Any      =       self.get_tokenizer()

							lowerCamelCase_			:							Optional[Any]      =       PixaStructProcessor(tokenizer=a_				,							image_processor=a_     )

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

							lowerCamelCase_			:							Union[str, Any]      =       processor.batch_decode(a_     )
							lowerCamelCase_			:							Any      =       tokenizer.batch_decode(a_     )

							self.assertListEqual(a_				,							a_     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       self.get_image_processor()
							lowerCamelCase_			:							List[Any]      =       self.get_tokenizer()

							lowerCamelCase_			:							int      =       PixaStructProcessor(tokenizer=a_				,							image_processor=a_     )

							lowerCamelCase_			:							List[str]      =       "lower newer"
							lowerCamelCase_			:							Any      =       self.prepare_image_inputs()

							lowerCamelCase_			:							Dict      =       processor(text=a_				,							images=a_     )

							# 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"]     )

							lowerCamelCase_			:							str      =       processor(text=a_     )

							# 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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       0
	for ele in arr:
		total_sum += product_sum(lowerCAmelCase_      ,					depth + 1) if isinstance(lowerCAmelCase_      ,					lowerCAmelCase_) else ele
	return total_sum * depth







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return product_sum(lowerCAmelCase_      ,					1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

