
'''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:
		UpperCAmelCase__				:	Tuple					=						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      ):





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							tempfile.mkdtemp()

				lowerCAmelCase__									=							PixaStructImageProcessor()
				lowerCAmelCase__									=							TaTokenizer.from_pretrained("t5-small"							)

				lowerCAmelCase__									=							PixaStructProcessor(__magic_name__ ,							__magic_name__							)

				processor.save_pretrained(self.tmpdirname							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							**__magic_name__     :    str							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							**__magic_name__     :    int							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				shutil.rmtree(self.tmpdirname							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

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

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

				return image_inputs





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

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

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

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

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

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

				lowerCAmelCase__									=							self.prepare_image_inputs()

				lowerCAmelCase__									=							image_processor(__magic_name__ ,							return_tensors="np"							)
				lowerCAmelCase__									=							processor(images=__magic_name__ ,							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 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

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

				lowerCAmelCase__									=							"lower newer"

				lowerCAmelCase__									=							processor(text=__magic_name__							)

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

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

				lowerCAmelCase__									=							"lower newer"
				lowerCAmelCase__									=							self.prepare_image_inputs()

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

				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(__magic_name__							):
							processor()





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


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

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

				lowerCAmelCase__									=							"lower newer"
				lowerCAmelCase__									=							self.prepare_image_inputs()

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

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

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

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

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

				self.assertListEqual(__magic_name__ ,							__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

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

				lowerCAmelCase__									=							"lower newer"
				lowerCAmelCase__									=							self.prepare_image_inputs()

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

				# 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__									=							processor(text=__magic_name__							)

				# 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







def 					A       ( UpperCamelCase_   :       int )   ->    list:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive integers have prime factors" )
			lowerCAmelCase__									=							[]
			while n % 2 == 0:
						pf.append(2 )
						lowerCAmelCase__									=							int(n / 2 )
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						while n % i == 0:
									pf.append(UpperCamelCase_ )
									lowerCAmelCase__									=							int(n / i )
			if n > 2:
						pf.append(UpperCamelCase_ )
			return pf







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			div *= temp
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						div *= temp
			if n > 1:
						div *= 2
			return div







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			if temp > 1:
						s *= (2**temp - 1) / (2 - 1)
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						if temp > 1:
									s *= (i**temp - 1) / (i - 1)
			return int(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__									=							n
			for x in set(prime_factors(UpperCamelCase_ ) ):
						s *= (x - 1) / x
			return int(UpperCamelCase_ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





