
"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			tempfile.mkdtemp()

         # fmt: off
         a__		=			['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         a__		=			dict(zip(__SCREAMING_SNAKE_CASE     ,				range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
         a__		=			['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
         a__		=			{'unk_token': '<unk>'}

         a__		=			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES['vocab_file']   )
         a__		=			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES['merges_file']   )
         with open(self.vocab_file     ,				'w'     ,				encoding='utf-8'   ) as fp:
             fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + '\n'   )
         with open(self.merges_file     ,				'w'     ,				encoding='utf-8'   ) as fp:
             fp.write('\n'.join(__SCREAMING_SNAKE_CASE   )   )

         a__		=			{
             'do_resize': True,
             'size': 2_0,
             'do_center_crop': True,
             'crop_size': 1_8,
             'do_normalize': True,
             'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         a__		=			os.path.join(self.tmpdirname     ,				__SCREAMING_SNAKE_CASE   )
         with open(self.image_processor_file     ,				'w'     ,				encoding='utf-8'   ) as fp:
             json.dump(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         return CLIPTokenizer.from_pretrained(self.tmpdirname     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     List[str]:







         """simple docstring"""



         return CLIPImageProcessor.from_pretrained(self.tmpdirname     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         shutil.rmtree(self.tmpdirname   )



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



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

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

         return image_inputs



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.get_tokenizer()
         a__		=			self.get_rust_tokenizer()
         a__		=			self.get_image_processor()

         a__		=			CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				image_processor=__SCREAMING_SNAKE_CASE   )
         processor_slow.save_pretrained(self.tmpdirname   )
         a__		=			CLIPProcessor.from_pretrained(self.tmpdirname     ,				use_fast=__SCREAMING_SNAKE_CASE   )

         a__		=			CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				image_processor=__SCREAMING_SNAKE_CASE   )
         processor_fast.save_pretrained(self.tmpdirname   )
         a__		=			CLIPProcessor.from_pretrained(self.tmpdirname   )

         self.assertEqual(processor_slow.tokenizer.get_vocab()     ,				tokenizer_slow.get_vocab()   )
         self.assertEqual(processor_fast.tokenizer.get_vocab()     ,				tokenizer_fast.get_vocab()   )
         self.assertEqual(tokenizer_slow.get_vocab()     ,				tokenizer_fast.get_vocab()   )
         self.assertIsInstance(processor_slow.tokenizer     ,				__SCREAMING_SNAKE_CASE   )
         self.assertIsInstance(processor_fast.tokenizer     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual(processor_slow.image_processor.to_json_string()     ,				image_processor.to_json_string()   )
         self.assertEqual(processor_fast.image_processor.to_json_string()     ,				image_processor.to_json_string()   )
         self.assertIsInstance(processor_slow.image_processor     ,				__SCREAMING_SNAKE_CASE   )
         self.assertIsInstance(processor_fast.image_processor     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			CLIPProcessor(tokenizer=self.get_tokenizer()     ,				image_processor=self.get_image_processor()   )
         processor.save_pretrained(self.tmpdirname   )

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

         a__		=			CLIPProcessor.from_pretrained(
             self.tmpdirname     ,				bos_token='(BOS)'     ,				eos_token='(EOS)'     ,				do_normalize=__SCREAMING_SNAKE_CASE     ,				padding_value=1.0   )

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

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



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         a__		=			self.get_image_processor()
         a__		=			self.get_tokenizer()

         a__		=			CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				image_processor=__SCREAMING_SNAKE_CASE   )

         a__		=			self.prepare_image_inputs()

         a__		=			image_processor(__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )
         a__		=			processor(images=__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )

         for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()     ,				input_processor[key].sum()     ,				delta=1e-2   )



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.get_image_processor()
         a__		=			self.get_tokenizer()

         a__		=			CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				image_processor=__SCREAMING_SNAKE_CASE   )

         a__		=			'lower newer'

         a__		=			processor(text=__SCREAMING_SNAKE_CASE   )

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )

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



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.get_image_processor()
         a__		=			self.get_tokenizer()

         a__		=			CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				image_processor=__SCREAMING_SNAKE_CASE   )

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

         a__		=			processor(text=__SCREAMING_SNAKE_CASE     ,				images=__SCREAMING_SNAKE_CASE   )

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

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



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







         """simple docstring"""



         a__		=			self.get_image_processor()
         a__		=			self.get_tokenizer()

         a__		=			CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				image_processor=__SCREAMING_SNAKE_CASE   )

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

         a__		=			processor.batch_decode(__SCREAMING_SNAKE_CASE   )
         a__		=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         a__		=			self.get_image_processor()
         a__		=			self.get_tokenizer()

         a__		=			CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				image_processor=__SCREAMING_SNAKE_CASE   )

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

         a__		=			processor(text=__SCREAMING_SNAKE_CASE     ,				images=__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(list(inputs.keys()   )     ,				processor.model_input_names   )




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		)     ->						float:
    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
    a		:       Any     						=       0.3

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


