
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_  =	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     _SCREAMING_SNAKE_CASE							(		unittest.TestCase							):





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  Optional[Any]   =				tempfile.mkdtemp()

								A     :  Optional[int]   =				PixaStructImageProcessor()
								A     :  List[str]   =				TaTokenizer.from_pretrained('''t5-small'''							)

								A     :  str   =				PixaStructProcessor(snake_case_					,      snake_case_							)

								processor.save_pretrained(self.tmpdirname							)





				def        _UpperCAmelCase						(		self :		Optional[int]					,      **snake_case_ :		List[str]							):


								"""simple docstring"""

								return AutoProcessor.from_pretrained(self.tmpdirname					,      **snake_case_							).tokenizer





				def        _UpperCAmelCase						(		self :		List[str]					,      **snake_case_ :		str							):


								"""simple docstring"""

								return AutoProcessor.from_pretrained(self.tmpdirname					,      **snake_case_							).image_processor





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								shutil.rmtree(self.tmpdirname							)





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A     :  Tuple   =				[np.random.randint(255					,      size=(3, 30, 400)					,      dtype=np.uinta							)]

								A     :  Dict   =				[Image.fromarray(np.moveaxis(snake_case_					,      0					,      -1							)							) for x in image_inputs]

								return image_inputs





				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  Any   =				PixaStructProcessor(tokenizer=self.get_tokenizer()					,      image_processor=self.get_image_processor()							)
								processor.save_pretrained(self.tmpdirname							)

								A     :  Tuple   =				self.get_tokenizer(bos_token='''(BOS)'''					,      eos_token='''(EOS)'''							)
								A     :  Optional[Any]   =				self.get_image_processor(do_normalize=snake_case_					,      padding_value=1.0							)

								A     :  Optional[Any]   =				PixaStructProcessor.from_pretrained(
								    self.tmpdirname					,      bos_token='''(BOS)'''					,      eos_token='''(EOS)'''					,      do_normalize=snake_case_					,      padding_value=1.0							)

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

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





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A     :  Dict   =				self.get_image_processor()
								A     :  int   =				self.get_tokenizer()

								A     :  Optional[int]   =				PixaStructProcessor(tokenizer=snake_case_					,      image_processor=snake_case_							)

								A     :  Optional[int]   =				self.prepare_image_inputs()

								A     :  int   =				image_processor(snake_case_					,      return_tensors='''np'''							)
								A     :  Any   =				processor(images=snake_case_					,      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 :		Tuple							):


								"""simple docstring"""

								A     :  Optional[Any]   =				self.get_image_processor()
								A     :  int   =				self.get_tokenizer()

								A     :  Any   =				PixaStructProcessor(tokenizer=snake_case_					,      image_processor=snake_case_							)

								A     :  str   =				'''lower newer'''

								A     :  Optional[Any]   =				processor(text=snake_case_							)

								A     :  Optional[Any]   =				tokenizer(snake_case_					,      return_token_type_ids=snake_case_					,      add_special_tokens=snake_case_							)

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





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  List[Any]   =				self.get_image_processor()
								A     :  Dict   =				self.get_tokenizer()

								A     :  Any   =				PixaStructProcessor(tokenizer=snake_case_					,      image_processor=snake_case_							)

								A     :  int   =				'''lower newer'''
								A     :  Union[str, Any]   =				self.prepare_image_inputs()

								A     :  str   =				processor(text=snake_case_					,      images=snake_case_							)

								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(snake_case_							):
												processor()





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				self.get_image_processor()
								A     :  Union[str, Any]   =				self.get_tokenizer()

								A     :  str   =				PixaStructProcessor(tokenizer=snake_case_					,      image_processor=snake_case_							)

								A     :  str   =				'''lower newer'''
								A     :  str   =				self.prepare_image_inputs()

								A     :  Union[str, Any]   =				processor(text=snake_case_					,      images=snake_case_							)

								A     :  int   =				[512, 1024, 2048, 4096]
								A     :  List[str]   =				[770, 770, 770, 770]
								# with text
								for i, max_patch in enumerate(snake_case_							):
												A     :  int   =				processor(text=snake_case_					,      images=snake_case_					,      max_patches=snake_case_							)
												self.assertEqual(inputs['''flattened_patches'''][0].shape[0]					,      snake_case_							)
												self.assertEqual(inputs['''flattened_patches'''][0].shape[1]					,      expected_hidden_size[i]							)

								# without text input
								for i, max_patch in enumerate(snake_case_							):
												A     :  List[str]   =				processor(images=snake_case_					,      max_patches=snake_case_							)
												self.assertEqual(inputs['''flattened_patches'''][0].shape[0]					,      snake_case_							)
												self.assertEqual(inputs['''flattened_patches'''][0].shape[1]					,      expected_hidden_size[i]							)





				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  Tuple   =				self.get_image_processor()
								A     :  Optional[int]   =				self.get_tokenizer()

								A     :  Union[str, Any]   =				PixaStructProcessor(tokenizer=snake_case_					,      image_processor=snake_case_							)

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

								A     :  Tuple   =				processor.batch_decode(snake_case_							)
								A     :  Optional[int]   =				tokenizer.batch_decode(snake_case_							)

								self.assertListEqual(snake_case_					,      snake_case_							)






				def        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								A     :  Tuple   =				self.get_image_processor()
								A     :  Optional[int]   =				self.get_tokenizer()

								A     :  Optional[int]   =				PixaStructProcessor(tokenizer=snake_case_					,      image_processor=snake_case_							)

								A     :  str   =				'''lower newer'''
								A     :  int   =				self.prepare_image_inputs()

								A     :  Any   =				processor(text=snake_case_					,      images=snake_case_							)

								# 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''']							)

								A     :  int   =				processor(text=snake_case_							)

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

UpperCamelCase_  =	{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

UpperCamelCase_  =	{1_45: 1_45}

UpperCamelCase_  =	{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}





def 							_lowerCamelCase			(					lowerCamelCase_:  int		):



				'''simple docstring'''







				if n in CACHE_SUM_DIGIT_FACTORIALS:
								return CACHE_SUM_DIGIT_FACTORIALS[n]
				A     :  Tuple   =				sum(DIGIT_FACTORIALS[let] for let in str(lowerCamelCase_		)		)
				A     :  Dict   =				ret
				return ret





def 							_lowerCamelCase			(					lowerCamelCase_:  int				,	lowerCamelCase_:  set | None = None		):



				'''simple docstring'''







				A     :  Optional[int]   =				previous or set()
				if n in CHAIN_LENGTH_CACHE:
								return CHAIN_LENGTH_CACHE[n]
				A     :  Dict   =				sum_digit_factorials(lowerCamelCase_		)
				if next_number in previous:
								A     :  Optional[int]   =				0
								return 0
				else:
								previous.add(lowerCamelCase_		)
								A     :  str   =				1 + chain_length(lowerCamelCase_				,	lowerCamelCase_		)
								A     :  Any   =				ret
								return ret





def 							_lowerCamelCase			(					lowerCamelCase_:  int = 60				,	lowerCamelCase_:  int = 100_0000		):



				'''simple docstring'''







				return sum(1 for i in range(1				,	lowerCamelCase_		) if chain_length(lowerCamelCase_		) == num_terms		)


if __name__ == "__main__":
					print(F'''{solution() = }''')