

'''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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class   lowerCAmelCase     (      unittest.TestCase   ):







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=tempfile.mkdtemp()

            __lowercase					=CLIPImageProcessor()
            __lowercase					=BertTokenizer.from_pretrained(
                'hf-internal-testing/tiny-random-BertModel'						,      model_input_names=['input_ids', 'attention_mask']      )

            __lowercase					=GitProcessor(__lowercase						,      __lowercase      )

            processor.save_pretrained(self.tmpdirname      )







       def 			snake_case   (							self			: int						,      **__lowercase			: str      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Optional[Any]						,      **__lowercase			: int      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            shutil.rmtree(self.tmpdirname      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=[np.random.randint(255						,      size=(3, 30, 400)						,      dtype=np.uinta      )]

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

            return image_inputs







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=GitProcessor(tokenizer=self.get_tokenizer()						,      image_processor=self.get_image_processor()      )
            processor.save_pretrained(self.tmpdirname      )

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

            __lowercase					=GitProcessor.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 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=self.get_image_processor()
            __lowercase					=self.get_tokenizer()

            __lowercase					=GitProcessor(tokenizer=__lowercase						,      image_processor=__lowercase      )

            __lowercase					=self.prepare_image_inputs()

            __lowercase					=image_processor(__lowercase						,      return_tensors='np'      )
            __lowercase					=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 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.get_image_processor()
            __lowercase					=self.get_tokenizer()

            __lowercase					=GitProcessor(tokenizer=__lowercase						,      image_processor=__lowercase      )

            __lowercase					='lower newer'

            __lowercase					=processor(text=__lowercase      )

            __lowercase					=tokenizer(__lowercase						,      return_token_type_ids=__lowercase      )

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







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=self.get_image_processor()
            __lowercase					=self.get_tokenizer()

            __lowercase					=GitProcessor(tokenizer=__lowercase						,      image_processor=__lowercase      )

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

            __lowercase					=processor(text=__lowercase						,      images=__lowercase      )

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

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







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            __lowercase					=self.get_image_processor()
            __lowercase					=self.get_tokenizer()

            __lowercase					=GitProcessor(tokenizer=__lowercase						,      image_processor=__lowercase      )

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

            __lowercase					=processor.batch_decode(__lowercase      )
            __lowercase					=tokenizer.batch_decode(__lowercase      )

            self.assertListEqual(__lowercase						,      __lowercase      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=self.get_image_processor()
            __lowercase					=self.get_tokenizer()

            __lowercase					=GitProcessor(tokenizer=__lowercase						,      image_processor=__lowercase      )

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

            __lowercase					=processor(text=__lowercase						,      images=__lowercase      )

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





'''simple docstring'''

from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCAmelCase				   =  6.6_7_4_3E-1_1  # unit of G : m^3 * kg^-1 * s^-2

def 				__UpperCamelCase							(	lowercase__						:  float,					lowercase__						:  float,					lowercase__						:  float,					lowercase__						:  float ):






     '''simple docstring'''





     __lowercase					=mass_a * mass_a

     if (force, mass_a, mass_a, distance).count(0 ) != 1:
          raise ValueError('One and only one argument must be 0' )
     if force < 0:
          raise ValueError('Gravitational force can not be negative' )
     if distance < 0:
          raise ValueError('Distance can not be negative' )
     if mass_a < 0 or mass_a < 0:
          raise ValueError('Mass can not be negative' )
     if force == 0:
          __lowercase					=GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
          return {"force": force}
     elif mass_a == 0:
          __lowercase					=(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
          return {"mass_1": mass_a}
     elif mass_a == 0:
          __lowercase					=(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
          return {"mass_2": mass_a}
     elif distance == 0:
          __lowercase					=(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
          return {"distance": distance}
     raise ValueError('One and only one argument must be 0' )


# Run doctest
if __name__ == "__main__":
       import doctest

       doctest.testmod()


