

"""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:
						A_							:     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 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= tempfile.mkdtemp()

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

						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(__UpperCAmelCase      ,			__UpperCAmelCase    )

						processor.save_pretrained(self.tmpdirname    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any      ,			**__UpperCAmelCase			:  str    )				->      Dict:
						return AutoProcessor.from_pretrained(self.tmpdirname      ,			**__UpperCAmelCase    ).tokenizer

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			**__UpperCAmelCase			:  str    )				->      str:
						return AutoProcessor.from_pretrained(self.tmpdirname      ,			**__UpperCAmelCase    ).image_processor

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      List[str]:
						shutil.rmtree(self.tmpdirname    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= [np.random.randint(2_5_5      ,			size=(3, 3_0, 4_0_0)      ,			dtype=np.uinta    )]

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

						return image_inputs

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(tokenizer=self.get_tokenizer()      ,			image_processor=self.get_image_processor()    )
						processor.save_pretrained(self.tmpdirname    )

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

						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__									= image_processor(__UpperCAmelCase      ,			return_tensors="""np"""    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= """lower newer"""

						SCREAMING_SNAKE_CASE__									= processor(text=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

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

						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

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

						SCREAMING_SNAKE_CASE__									= processor(text=__UpperCAmelCase      ,			images=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
						SCREAMING_SNAKE_CASE__									= [7_7_0, 7_7_0, 7_7_0, 7_7_0]
						# with text
						for i, max_patch in enumerate(__UpperCAmelCase    ):
									SCREAMING_SNAKE_CASE__									= 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    ):
									SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

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

						SCREAMING_SNAKE_CASE__									= processor.batch_decode(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= tokenizer.batch_decode(__UpperCAmelCase    )

						self.assertListEqual(__UpperCAmelCase      ,			__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= PixaStructProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

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

						SCREAMING_SNAKE_CASE__									= 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"""]    )

						SCREAMING_SNAKE_CASE__									= 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"""




class 				lowerCamelCase  :

			def __init__(   self			:  Dict    )				->      None:
						SCREAMING_SNAKE_CASE__									= [0, 1]





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  int    )				->      list:
						if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(__UpperCAmelCase    ):
												self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
						return self.sequence[:index]

def 		A    (       ):




			'''simple docstring'''


			print(
			    """Fibonacci Series Using Dynamic Programming\n"""						,			"""Enter the index of the Fibonacci number you want to calculate """						,			"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""						,			sep=""""""						,			)
			SCREAMING_SNAKE_CASE__									= Fibonacci()

			while True:
						SCREAMING_SNAKE_CASE__									= input(""">> """      )
						if prompt in {"exit", "quit"}:
									break

						try:
									SCREAMING_SNAKE_CASE__									= int(snake_case__      )
						except ValueError:
									print("""Enter a number or 'exit'"""      )
									continue

						print(fibonacci.get(snake_case__      )      )


if __name__ == "__main__":
						main()


