



from __future__ import annotations

from collections.abc import MutableSequence






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						MutableSequence[float]					)      ->			None:
							if len(__UpperCAmelCase					) != degree + 1:
													raise ValueError(
													    """The number of coefficients should be equal to the degree + 1."""					)

							UpperCAmelCase_=       list(__UpperCAmelCase					)
							UpperCAmelCase_=       degree




	def __add__(     self     :						Any						,  __UpperCAmelCase     :						Polynomial					)      ->			Polynomial:

							if self.degree > polynomial_a.degree:
													UpperCAmelCase_=       self.coefficients[:]
													for i in range(polynomial_a.degree + 1					):
																			coefficients[i] += polynomial_a.coefficients[i]
													return Polynomial(self.degree						,  __UpperCAmelCase					)
							else:
													UpperCAmelCase_=       polynomial_a.coefficients[:]
													for i in range(self.degree + 1					):
																			coefficients[i] += self.coefficients[i]
													return Polynomial(polynomial_a.degree						,  __UpperCAmelCase					)




	def __sub__(     self     :						Tuple						,  __UpperCAmelCase     :						Polynomial					)      ->			Polynomial:
							return self + polynomial_a * Polynomial(0						,  [-1]					)




	def __neg__(     self     :						List[str]					)      ->			Polynomial:
							return Polynomial(self.degree						,  [-c for c in self.coefficients]					)




	def __mul__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Polynomial					)      ->			Polynomial:
							UpperCAmelCase_=       [0] * (self.degree + polynomial_a.degree + 1)
							for i in range(self.degree + 1					):
													for j in range(polynomial_a.degree + 1					):
																			coefficients[i + j] += (
																			    self.coefficients[i] * polynomial_a.coefficients[j]
																			)

							return Polynomial(self.degree + polynomial_a.degree						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						int | float					)      ->			int | float:
							UpperCAmelCase_=       0
							for i in range(self.degree + 1					):
													result += self.coefficients[i] * (substitution**i)
							return result




	def __str__(     self     :						Dict					)      ->			str:
							UpperCAmelCase_=       """"""
							for i in range(self.degree						,  -1						,  -1					):
													if self.coefficients[i] == 0:
																			continue
													elif self.coefficients[i] > 0:
																			if polynomial:
																									polynomial += " + "
													else:
																			polynomial += " - "

													if i == 0:
																			polynomial += str(abs(self.coefficients[i]					)					)
													elif i == 1:
																			polynomial += str(abs(self.coefficients[i]					)					) + "x"
													else:
																			polynomial += str(abs(self.coefficients[i]					)					) + "x^" + str(__UpperCAmelCase					)

							return polynomial




	def __repr__(     self     :						Dict					)      ->			str:
							return self.__str__()




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Polynomial:
							UpperCAmelCase_=       [0] * self.degree
							for i in range(self.degree					):
													UpperCAmelCase_=       self.coefficients[i + 1] * (i + 1)
							return Polynomial(self.degree - 1						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						int | float = 0					)      ->			Polynomial:
							UpperCAmelCase_=       [0] * (self.degree + 2)
							UpperCAmelCase_=       constant
							for i in range(self.degree + 1					):
													UpperCAmelCase_=       self.coefficients[i] / (i + 1)
							return Polynomial(self.degree + 1						,  __UpperCAmelCase					)




	def __eq__(     self     :						List[Any]						,  __UpperCAmelCase     :						object					)      ->			bool:
							if not isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
													return False

							if self.degree != polynomial_a.degree:
													return False

							for i in range(self.degree + 1					):
													if self.coefficients[i] != polynomial_a.coefficients[i]:
																			return False

							return True




	def __ne__(     self     :						List[str]						,  __UpperCAmelCase     :						object					)      ->			bool:
							return not self.__eq__(__UpperCAmelCase					)









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

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

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

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

							UpperCAmelCase_=       BlipaProcessor.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     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       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]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

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

							self.assertListEqual(list(inputs.keys()					)						,  ["""pixel_values""", """input_ids""", """attention_mask"""]					)

							# test if it raises when no input is passed
							with pytest.raises(__UpperCAmelCase					):
													processor()




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

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

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

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

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




