

'''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 CLIPSegProcessor, ViTImageProcessor




@require_vision
class     a			(				unittest.TestCase       ):






      """simple docstring"""




      def       __magic_name__							(							self					:      Any  ):



        '''simple docstring'''



        snake_case__						:       List[str]       						=  tempfile.mkdtemp()

        # fmt: off
        snake_case__						:       Any       						=  ['''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
        snake_case__						:       List[str]       						=  dict(zip(snake_case_			,			range(len(snake_case_  )  )  )  )
        snake_case__						:       int       						=  ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        snake_case__						:       Optional[int]       						=  {'''unk_token''': '''<unk>'''}

        snake_case__						:       Union[str, Any]       						=  os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']  )
        snake_case__						:       str       						=  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(snake_case_  ) + '''\n'''  )
        with open(self.merges_file			,			'''w'''			,			encoding='''utf-8'''  ) as fp:
          fp.write('''\n'''.join(snake_case_  )  )

        snake_case__						:       str       						=  {
            '''do_resize''': True,
            '''size''': 2_0,
            '''do_center_crop''': True,
            '''crop_size''': 1_8,
            '''do_normalize''': True,
            '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
        }
        snake_case__						:       List[Any]       						=  os.path.join(self.tmpdirname			,			snake_case_  )
        with open(self.image_processor_file			,			'''w'''			,			encoding='''utf-8'''  ) as fp:
          json.dump(snake_case_			,			snake_case_  )







      def       __magic_name__							(							self					:      Dict			,			**snake_case_					:      List[str]  ):



        '''simple docstring'''



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







      def       __magic_name__							(							self					:      List[Any]			,			**snake_case_					:      str  ):



        '''simple docstring'''



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







      def       __magic_name__							(							self					:      Union[str, Any]			,			**snake_case_					:      Union[str, Any]  ):



        '''simple docstring'''



        return ViTImageProcessor.from_pretrained(self.tmpdirname			,			**snake_case_  )







      def       __magic_name__							(							self					:      int  ):



        '''simple docstring'''



        shutil.rmtree(self.tmpdirname  )







      def       __magic_name__							(							self					:      Optional[Any]  ):



        '''simple docstring'''



        snake_case__						:       Union[str, Any]       						=  [np.random.randint(2_5_5			,			size=(3, 3_0, 4_0_0)			,			dtype=np.uinta  )]

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

        return image_inputs







      def       __magic_name__							(							self					:      Optional[int]  ):



        '''simple docstring'''



        snake_case__						:       List[str]       						=  self.get_tokenizer()
        snake_case__						:       str       						=  self.get_rust_tokenizer()
        snake_case__						:       Tuple       						=  self.get_image_processor()

        snake_case__						:       str       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )
        processor_slow.save_pretrained(self.tmpdirname  )
        snake_case__						:       List[str]       						=  CLIPSegProcessor.from_pretrained(self.tmpdirname			,			use_fast=snake_case_  )

        snake_case__						:       List[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )
        processor_fast.save_pretrained(self.tmpdirname  )
        snake_case__						:       Tuple       						=  CLIPSegProcessor.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			,			snake_case_  )
        self.assertIsInstance(processor_fast.tokenizer			,			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			,			snake_case_  )
        self.assertIsInstance(processor_fast.image_processor			,			snake_case_  )







      def       __magic_name__							(							self					:      Union[str, Any]  ):



        '''simple docstring'''



        snake_case__						:       Dict       						=  CLIPSegProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()  )
        processor.save_pretrained(self.tmpdirname  )

        snake_case__						:       Tuple       						=  self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''  )
        snake_case__						:       Union[str, Any]       						=  self.get_image_processor(do_normalize=snake_case_			,			padding_value=1.0  )

        snake_case__						:       int       						=  CLIPSegProcessor.from_pretrained(
            self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=snake_case_			,			padding_value=1.0  )

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

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







      def       __magic_name__							(							self					:      Any  ):



        '''simple docstring'''



        snake_case__						:       Dict       						=  self.get_image_processor()
        snake_case__						:       str       						=  self.get_tokenizer()

        snake_case__						:       Tuple       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

        snake_case__						:       List[Any]       						=  self.prepare_image_inputs()

        snake_case__						:       Any       						=  image_processor(snake_case_			,			return_tensors='''np'''  )
        snake_case__						:       Optional[int]       						=  processor(images=snake_case_			,			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       __magic_name__							(							self					:      Tuple  ):



        '''simple docstring'''



        snake_case__						:       List[str]       						=  self.get_image_processor()
        snake_case__						:       Tuple       						=  self.get_tokenizer()

        snake_case__						:       Optional[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

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

        snake_case__						:       str       						=  processor(text=snake_case_  )

        snake_case__						:       List[str]       						=  tokenizer(snake_case_  )

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







      def       __magic_name__							(							self					:      List[str]  ):



        '''simple docstring'''



        snake_case__						:       Tuple       						=  self.get_image_processor()
        snake_case__						:       List[Any]       						=  self.get_tokenizer()

        snake_case__						:       List[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

        snake_case__						:       List[Any]       						=  '''lower newer'''
        snake_case__						:       int       						=  self.prepare_image_inputs()

        snake_case__						:       Tuple       						=  processor(text=snake_case_			,			images=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(snake_case_  ):
          processor()







      def       __magic_name__							(							self					:      List[str]  ):



        '''simple docstring'''



        snake_case__						:       Dict       						=  self.get_image_processor()
        snake_case__						:       Any       						=  self.get_tokenizer()

        snake_case__						:       List[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

        snake_case__						:       Optional[Any]       						=  self.prepare_image_inputs()
        snake_case__						:       str       						=  self.prepare_image_inputs()

        snake_case__						:       List[str]       						=  processor(images=snake_case_			,			visual_prompt=snake_case_  )

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

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







      def       __magic_name__							(							self					:      List[Any]  ):



        '''simple docstring'''



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

        snake_case__						:       Optional[int]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

        snake_case__						:       str       						=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case__						:       str       						=  processor.batch_decode(snake_case_  )
        snake_case__						:       Union[str, Any]       						=  tokenizer.batch_decode(snake_case_  )

        self.assertListEqual(snake_case_			,			snake_case_  )






'''simple docstring'''


def 	_a       (				__lowerCAmelCase		:      float   ,							__lowerCAmelCase		:      float   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		if principal <= 0:
				raise Exception('''Principal borrowed must be > 0'''     )
		if rate_per_annum < 0:
				raise Exception('''Rate of interest must be >= 0'''     )
		if years_to_repay <= 0 or not isinstance(__lowerCAmelCase   ,							__lowerCAmelCase     ):
				raise Exception('''Years to repay must be an integer > 0'''     )

		# Yearly rate is divided by 12 to get monthly rate
		snake_case__						:       int       						=  rate_per_annum / 12

		# Years to repay is multiplied by 12 to get number of payments as payment is monthly
		snake_case__						:       Tuple       						=  years_to_repay * 12

		return (
		    principal
		    * rate_per_month
		    * (1 + rate_per_month) ** number_of_payments
		    / ((1 + rate_per_month) ** number_of_payments - 1)
		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



