"""simple docstring"""


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  UpperCamelCase					(				unittest.TestCase   ):







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					tempfile.mkdtemp()

            snake_case_					       =					BlipImageProcessor()
            snake_case_					       =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')
            snake_case_					       =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert')

            snake_case_					       =					InstructBlipProcessor(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)

            processor.save_pretrained(self.tmpdirname)







       def 		a_						( self,							**lowerCAmelCase__)      ->      str:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).tokenizer







       def 		a_						( self,							**lowerCAmelCase__)      ->      Dict:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).image_processor







       def 		a_						( self,							**lowerCAmelCase__)      ->      Optional[Any]:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).qformer_tokenizer







       def 		a_						( self)      ->      Dict:
            shutil.rmtree(self.tmpdirname)







       def 		a_						( self)      ->      str:
            snake_case_					       =					[np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta)]

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

            return image_inputs







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					InstructBlipProcessor(
                tokenizer=self.get_tokenizer(),							image_processor=self.get_image_processor(),							qformer_tokenizer=self.get_qformer_tokenizer(),							)
            processor.save_pretrained(self.tmpdirname)

            snake_case_					       =					self.get_tokenizer(bos_token='(BOS)',							eos_token='(EOS)')
            snake_case_					       =					self.get_image_processor(do_normalize=lowerCAmelCase__,							padding_value=1.0)

            snake_case_					       =					InstructBlipProcessor.from_pretrained(
                self.tmpdirname,							bos_token='(BOS)',							eos_token='(EOS)',							do_normalize=lowerCAmelCase__,							padding_value=1.0)

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

            self.assertEqual(processor.image_processor.to_json_string(),							image_processor_add_kwargs.to_json_string())
            self.assertIsInstance(processor.image_processor,							lowerCAmelCase__)
            self.assertIsInstance(processor.qformer_tokenizer,							lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					image_processor(lowerCAmelCase__,							return_tensors='np')
            snake_case_					       =					processor(images=lowerCAmelCase__,							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)      ->      str:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					'lower newer'

            snake_case_					       =					processor(text=lowerCAmelCase__)

            snake_case_					       =					tokenizer(lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__)
            snake_case_					       =					qformer_tokenizer(lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__)

            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)      ->      Any:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

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

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

            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(lowerCAmelCase__):
                 processor()







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

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

            snake_case_					       =					processor.batch_decode(lowerCAmelCase__)
            snake_case_					       =					tokenizer.batch_decode(lowerCAmelCase__)

            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

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

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

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




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

