





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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				tempfile.mkdtemp()

				UpperCAmelCase   		=				BlipImageProcessor()
				UpperCAmelCase   		=				BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''		)

				UpperCAmelCase   		=				BlipProcessor(_A							,					_A		)

				processor.save_pretrained(self.tmpdirname		)
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				return AutoProcessor.from_pretrained(self.tmpdirname							,					**_A		).tokenizer
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				return AutoProcessor.from_pretrained(self.tmpdirname							,					**_A		).image_processor
	def      _lowercase				(    self		):






				'''simple docstring'''







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






				'''simple docstring'''







				UpperCAmelCase   		=				[np.random.randint(2_5_5							,					size=(3, 3_0, 4_0_0)							,					dtype=np.uinta		)]

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

				return image_inputs
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				BlipProcessor(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=_A							,					padding_value=1.0		)

				UpperCAmelCase   		=				BlipProcessor.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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				image_processor(_A							,					return_tensors='''np'''		)
				UpperCAmelCase   		=				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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''lower newer'''

				UpperCAmelCase   		=				processor(text=_A		)

				UpperCAmelCase   		=				tokenizer(_A							,					return_token_type_ids=_A		)

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






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''lower newer'''
				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				processor(text=_A							,					images=_A		)

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

				# test if it raises when no input is passed
				with pytest.raises(_A		):
							processor()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipProcessor(tokenizer=_A							,					image_processor=_A		)

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

				UpperCAmelCase   		=				processor.batch_decode(_A		)
				UpperCAmelCase   		=				tokenizer.batch_decode(_A		)

				self.assertListEqual(_A							,					_A		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''lower newer'''
				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				processor(text=_A							,					images=_A		)

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










from __future__ import annotations

from collections.abc import Callable





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = 100			,			)  ->				float:







			'''simple docstring'''

			UpperCAmelCase   		=				x_start
			UpperCAmelCase   		=				fnc(UpperCamelCase__ )
			UpperCAmelCase   		=				0.0
			for _ in range(UpperCamelCase__ ):
						# Approximates small segments of curve as linear and solve
						# for trapezoidal area
						UpperCAmelCase   		=				(x_end - x_start) / steps + xa
						UpperCAmelCase   		=				fnc(UpperCamelCase__ )
						area += abs(fxa + fxa ) * (xa - xa) / 2
						# Increment step
						UpperCAmelCase   		=				xa
						UpperCAmelCase   		=				fxa
			return area


if __name__ == "__main__":





				def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				str:







							'''simple docstring'''

							return x**3 + x**2

				print("f(x) = x^3 + x^2")
				print("The area between the curve, x = -5, x = 5 and the x axis is:")
				__A				:    List[Any]       =   10
				while i <= 100_000:
								print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
								i *= 10



