




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,
      BertTokenizerFast,
      BlipImageProcessor,
      GPTaTokenizer,
      InstructBlipProcessor,
      PreTrainedTokenizerFast,
  )







@require_vision
class 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							int:
           _A							:  Tuple    =		tempfile.mkdtemp()

           _A							:  List[str]    =		BlipImageProcessor()
           _A							:  List[Any]    =		GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""       )
           _A							:  List[str]    =		BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""       )

           _A							:  Any    =		InstructBlipProcessor(_a	,  _a	,  _a       )

           processor.save_pretrained(self.tmpdirname       )

     def       a__			(						self	,  **_a       )		->							Dict:
           return AutoProcessor.from_pretrained(self.tmpdirname	,  **_a       ).tokenizer

     def       a__			(						self	,  **_a       )		->							Union[str, Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname	,  **_a       ).image_processor

     def       a__			(						self	,  **_a       )		->							Tuple:
           return AutoProcessor.from_pretrained(self.tmpdirname	,  **_a       ).qformer_tokenizer

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

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  int    =		[np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta       )]

           _A							:  Optional[int]    =		[Image.fromarray(np.moveaxis(_a	,  0	,  -1       )       ) for x in image_inputs]

           return image_inputs

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  Union[str, Any]    =		InstructBlipProcessor(
               tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()	,  qformer_tokenizer=self.get_qformer_tokenizer()	,  )
           processor.save_pretrained(self.tmpdirname       )

           _A							:  List[Any]    =		self.get_tokenizer(bos_token="""(BOS)"""	,  eos_token="""(EOS)"""       )
           _A							:  str    =		self.get_image_processor(do_normalize=_a	,  padding_value=1.0       )

           _A							:  Dict    =		InstructBlipProcessor.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       )
           self.assertIsInstance(processor.qformer_tokenizer	,  _a       )

     def       a__			(						self       )		->							Any:
           _A							:  Tuple    =		self.get_image_processor()
           _A							:  List[str]    =		self.get_tokenizer()
           _A							:  Tuple    =		self.get_qformer_tokenizer()

           _A							:  Optional[int]    =		InstructBlipProcessor(
               tokenizer=_a	,  image_processor=_a	,  qformer_tokenizer=_a       )

           _A							:  Union[str, Any]    =		self.prepare_image_inputs()

           _A							:  str    =		image_processor(_a	,  return_tensors="""np"""       )
           _A							:  Optional[int]    =		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       a__			(						self       )		->							Any:
           _A							:  Optional[int]    =		self.get_image_processor()
           _A							:  Tuple    =		self.get_tokenizer()
           _A							:  Any    =		self.get_qformer_tokenizer()

           _A							:  str    =		InstructBlipProcessor(
               tokenizer=_a	,  image_processor=_a	,  qformer_tokenizer=_a       )

           _A							:  Union[str, Any]    =		"""lower newer"""

           _A							:  Dict    =		processor(text=_a       )

           _A							:  Optional[int]    =		tokenizer(_a	,  return_token_type_ids=_a       )
           _A							:  int    =		qformer_tokenizer(_a	,  return_token_type_ids=_a       )

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

           for key in encoded_tokens_qformer.keys():
                 self.assertListEqual(encoded_tokens_qformer[key]	,  encoded_processor["""qformer_""" + key]       )

     def       a__			(						self       )		->							Tuple:
           _A							:  Dict    =		self.get_image_processor()
           _A							:  str    =		self.get_tokenizer()
           _A							:  List[Any]    =		self.get_qformer_tokenizer()

           _A							:  str    =		InstructBlipProcessor(
               tokenizer=_a	,  image_processor=_a	,  qformer_tokenizer=_a       )

           _A							:  Any    =		"""lower newer"""
           _A							:  Union[str, Any]    =		self.prepare_image_inputs()

           _A							:  List[Any]    =		processor(text=_a	,  images=_a       )

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

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

     def       a__			(						self       )		->							str:
           _A							:  str    =		self.get_image_processor()
           _A							:  List[Any]    =		self.get_tokenizer()
           _A							:  List[Any]    =		self.get_qformer_tokenizer()

           _A							:  Dict    =		InstructBlipProcessor(
               tokenizer=_a	,  image_processor=_a	,  qformer_tokenizer=_a       )

           _A							:  List[Any]    =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A							:  List[Any]    =		processor.batch_decode(_a       )
           _A							:  List[str]    =		tokenizer.batch_decode(_a       )

           self.assertListEqual(_a	,  _a       )



     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		self.get_image_processor()
           _A							:  Union[str, Any]    =		self.get_tokenizer()
           _A							:  int    =		self.get_qformer_tokenizer()

           _A							:  Dict    =		InstructBlipProcessor(
               tokenizer=_a	,  image_processor=_a	,  qformer_tokenizer=_a       )

           _A							:  Union[str, Any]    =		"""lower newer"""
           _A							:  Any    =		self.prepare_image_inputs()

           _A							:  List[Any]    =		processor(text=_a	,  images=_a       )

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










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







def        lowerCAmelCase_			(  snake_case_  ):

      # A local function to see if a dot lands in the circle.
      def is_in_circle(snake_case_,snake_case_  ) -> 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							:  Optional[int]    =		mean(
          int(is_in_circle(uniform(-1.0,1.0  ),uniform(-1.0,1.0  )  )  )
          for _ in range(snake_case_  )  )
      # The ratio of the area for circle to square is pi/4.
      _A							:  List[str]    =		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        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_ = 0.0,snake_case_ = 1.0,):
      return mean(
          function_to_integrate(uniform(snake_case_,snake_case_  )  ) for _ in range(snake_case_  )  ) * (max_value - min_value)







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 0.0,snake_case_ = 1.0  ):

      def identity_function(snake_case_  ) -> float:
            return x

      _A							:  Any    =		area_under_curve_estimator(
          snake_case_,snake_case_,snake_case_,snake_case_  )
      _A							:  Tuple    =		(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        lowerCAmelCase_			(  snake_case_  ):

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

      _A							:  Optional[int]    =		area_under_curve_estimator(
          snake_case_,snake_case_,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()

