





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  A__    (							unittest.TestCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	str =				tempfile.mkdtemp()

          a__						:	str =				BlipImageProcessor()
          a__						:	Optional[int] =				GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')

          a__						:	int =				BlipaProcessor(lowercase					,  lowercase)

          processor.save_pretrained(self.tmpdirname)

   def 							__lowercase		(					self					,  **lowercase)			->						int:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  **lowercase)			->						Union[str, Any]:







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          shutil.rmtree(self.tmpdirname)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	List[str] =				[np.random.randint(255					,  size=(3, 30, 400)					,  dtype=np.uinta)]

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

          return image_inputs

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[Any] =				BlipaProcessor(tokenizer=self.get_tokenizer()					,  image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          a__						:	int =				self.get_tokenizer(bos_token='(BOS)'					,  eos_token='(EOS)')
          a__						:	Tuple =				self.get_image_processor(do_normalize=lowercase					,  padding_value=1.0)

          a__						:	str =				BlipaProcessor.from_pretrained(
              self.tmpdirname					,  bos_token='(BOS)'					,  eos_token='(EOS)'					,  do_normalize=lowercase					,  padding_value=1.0)

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

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

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[Any] =				self.get_image_processor()
          a__						:	List[str] =				self.get_tokenizer()

          a__						:	List[str] =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	List[str] =				self.prepare_image_inputs()

          a__						:	Tuple =				image_processor(lowercase					,  return_tensors='np')
          a__						:	Optional[int] =				processor(images=lowercase					,  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)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	List[Any] =				self.get_image_processor()
          a__						:	int =				self.get_tokenizer()

          a__						:	Tuple =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	Union[str, Any] =				'lower newer'

          a__						:	str =				processor(text=lowercase)

          a__						:	int =				tokenizer(lowercase					,  return_token_type_ids=lowercase)

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

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

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

          a__						:	Optional[int] =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	Optional[Any] =				'lower newer'
          a__						:	Optional[int] =				self.prepare_image_inputs()

          a__						:	Any =				processor(text=lowercase					,  images=lowercase)

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

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

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

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

          a__						:	Tuple =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	Union[str, Any] =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          a__						:	Dict =				processor.batch_decode(lowercase)
          a__						:	Any =				tokenizer.batch_decode(lowercase)

          self.assertListEqual(lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

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

          a__						:	Optional[Any] =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

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

          a__						:	Optional[int] =				processor(text=lowercase					,  images=lowercase)

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









from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def A_	(		A__				)  ->		Tuple:

       # A local function to see if a dot lands in the circle.
       def is_in_circle(A__		,      A__				) -> bool:
              a__						:	List[str] =				sqrt((x**2) + (y**2)				)
              # Our circle has a radius of 1, so a distance
              # greater than 1 would land outside the circle.
              return distance_from_centre <= 1

       # The proportion of guesses that landed in the circle
       a__						:	List[str] =				mean(
           int(is_in_circle(uniform(-1.0		,      1.0				)		,      uniform(-1.0		,      1.0				)				)				)
           for _ in range(A__				)				)
       # The ratio of the area for circle to square is pi/4.
       a__						:	Optional[Any] =				proportion * 4
       print(F'The estimated value of pi is {pi_estimate}'				)
       print(F'The numpy value of pi is {pi}'				)
       print(F'The total error is {abs(pi - pi_estimate				)}'				)



def A_	(		A__		,      A__		,      A__ = 0.0		,      A__ = 1.0		,      )  ->		float:
       return mean(
           function_to_integrate(uniform(A__		,      A__				)				) for _ in range(A__				)				) * (max_value - min_value)



def A_	(		A__		,      A__ = 0.0		,      A__ = 1.0				)  ->		None:

       def identity_function(A__				) -> float:
              return x

       a__						:	List[Any] =				area_under_curve_estimator(
           A__		,      A__		,      A__		,      A__				)
       a__						:	Union[str, Any] =				(max_value * max_value - min_value * min_value) / 2

       print('******************'				)
       print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {expected_value}'				)
       print(F'Total error is {abs(estimated_value - expected_value				)}'				)
       print('******************'				)



def A_	(		A__				)  ->		None:

       def function_to_integrate(A__				) -> float:
              return sqrt(4.0 - x * x				)

       a__						:	Dict =				area_under_curve_estimator(
           A__		,      A__		,      0.0		,      2.0				)

       print('******************'				)
       print('Estimating pi using area_under_curve_estimator'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {pi}'				)
       print(F'Total error is {abs(estimated_value - pi				)}'				)
       print('******************'				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


