





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 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Tuple							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   tempfile.mkdtemp()

										# fmt: off
										_UpperCamelCase      : Union[str, 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
										_UpperCamelCase      : int         =   dict(zip(UpperCamelCase__,			range(len(UpperCamelCase__							)							)							)							)
										_UpperCamelCase      : str         =   ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
										_UpperCamelCase      : Tuple         =   {"unk_token": "<unk>"}

										_UpperCamelCase      : str         =   os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['''vocab_file''']							)
										_UpperCamelCase      : Optional[int]         =   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(UpperCamelCase__							) + '''\n'''							)
										with open(self.merges_file,			'''w''',			encoding='''utf-8'''							) as fp:
																fp.write('''\n'''.join(UpperCamelCase__							)							)

										_UpperCamelCase      : List[str]         =   {
										    "do_resize": True,
										    "size": 2_0,
										    "do_center_crop": True,
										    "crop_size": 1_8,
										    "do_normalize": True,
										    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
										}
										_UpperCamelCase      : List[str]         =   os.path.join(self.tmpdirname,			UpperCamelCase__							)
										with open(self.image_processor_file,			'''w''',			encoding='''utf-8'''							) as fp:
																json.dump(UpperCamelCase__,			UpperCamelCase__							)






				def     snake_case     (			self		:   int,			**lowerCAmelCase__		:   Tuple							)     ->				List[Any]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   int,			**lowerCAmelCase__		:   int							)     ->				Dict:




										'''simple docstring'''



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






				def     snake_case     (			self		:   str,			**lowerCAmelCase__		:   List[Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



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






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




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname							)






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




										'''simple docstring'''



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

										_UpperCamelCase      : Any         =   [Image.fromarray(np.moveaxis(UpperCamelCase__,			0,			-1							)							) for x in image_inputs]

										return image_inputs






				def     snake_case     (			self		:   int							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_tokenizer()
										_UpperCamelCase      : List[Any]         =   self.get_rust_tokenizer()
										_UpperCamelCase      : Union[str, Any]         =   self.get_image_processor()

										_UpperCamelCase      : Tuple         =   CLIPProcessor(tokenizer=UpperCamelCase__,			image_processor=UpperCamelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : Optional[Any]         =   CLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=UpperCamelCase__							)

										_UpperCamelCase      : List[Any]         =   CLIPProcessor(tokenizer=UpperCamelCase__,			image_processor=UpperCamelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : Dict         =   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,			UpperCamelCase__							)
										self.assertIsInstance(processor_fast.tokenizer,			UpperCamelCase__							)

										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,			UpperCamelCase__							)
										self.assertIsInstance(processor_fast.image_processor,			UpperCamelCase__							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   CLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

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

										_UpperCamelCase      : Optional[Any]         =   CLIPProcessor.from_pretrained(
										    self.tmpdirname,			bos_token='''(BOS)''',			eos_token='''(EOS)''',			do_normalize=UpperCamelCase__,			padding_value=1.0							)

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

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






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




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Dict         =   self.get_tokenizer()

										_UpperCamelCase      : Any         =   CLIPProcessor(tokenizer=UpperCamelCase__,			image_processor=UpperCamelCase__							)

										_UpperCamelCase      : int         =   self.prepare_image_inputs()

										_UpperCamelCase      : Tuple         =   image_processor(UpperCamelCase__,			return_tensors='''np'''							)
										_UpperCamelCase      : Union[str, Any]         =   processor(images=UpperCamelCase__,			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     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.get_image_processor()
										_UpperCamelCase      : str         =   self.get_tokenizer()

										_UpperCamelCase      : List[str]         =   CLIPProcessor(tokenizer=UpperCamelCase__,			image_processor=UpperCamelCase__							)

										_UpperCamelCase      : str         =   "lower newer"

										_UpperCamelCase      : Optional[int]         =   processor(text=UpperCamelCase__							)

										_UpperCamelCase      : int         =   tokenizer(UpperCamelCase__							)

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






				def     snake_case     (			self		:   Dict							)     ->				Optional[int]:




										'''simple docstring'''



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

										_UpperCamelCase      : Any         =   CLIPProcessor(tokenizer=UpperCamelCase__,			image_processor=UpperCamelCase__							)

										_UpperCamelCase      : int         =   "lower newer"
										_UpperCamelCase      : str         =   self.prepare_image_inputs()

										_UpperCamelCase      : int         =   processor(text=UpperCamelCase__,			images=UpperCamelCase__							)

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

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






				def     snake_case     (			self		:   Tuple							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.get_image_processor()
										_UpperCamelCase      : Dict         =   self.get_tokenizer()

										_UpperCamelCase      : Dict         =   CLIPProcessor(tokenizer=UpperCamelCase__,			image_processor=UpperCamelCase__							)

										_UpperCamelCase      : Optional[int]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase      : Optional[Any]         =   processor.batch_decode(UpperCamelCase__							)
										_UpperCamelCase      : int         =   tokenizer.batch_decode(UpperCamelCase__							)

										self.assertListEqual(UpperCamelCase__,			UpperCamelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_image_processor()
										_UpperCamelCase      : Union[str, Any]         =   self.get_tokenizer()

										_UpperCamelCase      : List[str]         =   CLIPProcessor(tokenizer=UpperCamelCase__,			image_processor=UpperCamelCase__							)

										_UpperCamelCase      : List[Any]         =   "lower newer"
										_UpperCamelCase      : Optional[int]         =   self.prepare_image_inputs()

										_UpperCamelCase      : str         =   processor(text=UpperCamelCase__,			images=UpperCamelCase__							)

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










"""simple docstring"""




from __future__ import annotations

import math





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):

						if depth < 0:
												raise ValueError('''Depth cannot be less than 0'''       )

						if len(_lowercase       ) == 0:
												raise ValueError('''Scores cannot be empty'''       )

						if depth == height:
												return scores[node_index]

						if is_max:
												return max(
												    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)

						return min(
						    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)





def 	a_    (							):
						_UpperCamelCase      : Dict         =   [90, 23, 6, 33, 21, 65, 123, 3_4423]
						_UpperCamelCase      : int         =   math.log(len(_lowercase       )	,						2       )
						print('''Optimal value : '''	,						end=''''''       )
						print(minimax(0	,						0	,						_lowercase	,						_lowercase	,						_lowercase       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()



