






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 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Tuple         =   tempfile.mkdtemp()

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

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

					snake_case__      :Optional[Any]         =   {
					    "do_resize": True,
					    "size": 20,
					    "do_center_crop": True,
					    "crop_size": 18,
					    "do_normalize": True,
					    "image_mean": [0.48145466, 0.4578275, 0.40821073],
					    "image_std": [0.26862954, 0.26130258, 0.27577711],
					}
					snake_case__      :int         =   os.path.join(self.tmpdirname					,UpperCamelCase       )
					with open(self.image_processor_file					,"w"					,encoding="utf-8"       ) as fp:
									json.dump(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    Optional[Any]:
					return CLIPTokenizer.from_pretrained(self.tmpdirname					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    List[Any]:
					return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    List[str]:
					return CLIPImageProcessor.from_pretrained(self.tmpdirname					,**UpperCamelCase       )






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






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Optional[int]         =   [np.random.randint(255					,size=(3, 30, 400)					,dtype=np.uinta       )]

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

					return image_inputs






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :str         =   self.get_tokenizer()
					snake_case__      :Union[str, Any]         =   self.get_rust_tokenizer()
					snake_case__      :Union[str, Any]         =   self.get_image_processor()

					snake_case__      :Any         =   CLIPProcessor(tokenizer=UpperCamelCase					,image_processor=UpperCamelCase       )
					processor_slow.save_pretrained(self.tmpdirname       )
					snake_case__      :Optional[int]         =   CLIPProcessor.from_pretrained(self.tmpdirname					,use_fast=UpperCamelCase       )

					snake_case__      :Optional[int]         =   CLIPProcessor(tokenizer=UpperCamelCase					,image_processor=UpperCamelCase       )
					processor_fast.save_pretrained(self.tmpdirname       )
					snake_case__      :str         =   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    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Optional[int]         =   CLIPProcessor(tokenizer=self.get_tokenizer()					,image_processor=self.get_image_processor()       )
					processor.save_pretrained(self.tmpdirname       )

					snake_case__      :Dict         =   self.get_tokenizer(bos_token="(BOS)"					,eos_token="(EOS)"       )
					snake_case__      :Optional[Any]         =   self.get_image_processor(do_normalize=UpperCamelCase					,padding_value=1.0       )

					snake_case__      :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    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Any         =   self.get_image_processor()
					snake_case__      :Dict         =   self.get_tokenizer()

					snake_case__      :List[str]         =   CLIPProcessor(tokenizer=UpperCamelCase					,image_processor=UpperCamelCase       )

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

					snake_case__      :Optional[Any]         =   image_processor(UpperCamelCase					,return_tensors="np"       )
					snake_case__      :Dict         =   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    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Tuple         =   self.get_image_processor()
					snake_case__      :int         =   self.get_tokenizer()

					snake_case__      :Dict         =   CLIPProcessor(tokenizer=UpperCamelCase					,image_processor=UpperCamelCase       )

					snake_case__      :Optional[int]         =   "lower newer"

					snake_case__      :List[str]         =   processor(text=UpperCamelCase       )

					snake_case__      :Optional[Any]         =   tokenizer(UpperCamelCase       )

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






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Tuple         =   self.get_image_processor()
					snake_case__      :Optional[Any]         =   self.get_tokenizer()

					snake_case__      :List[Any]         =   CLIPProcessor(tokenizer=UpperCamelCase					,image_processor=UpperCamelCase       )

					snake_case__      :str         =   "lower newer"
					snake_case__      :Dict         =   self.prepare_image_inputs()

					snake_case__      :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    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :int         =   self.get_image_processor()
					snake_case__      :Optional[Any]         =   self.get_tokenizer()

					snake_case__      :Any         =   CLIPProcessor(tokenizer=UpperCamelCase					,image_processor=UpperCamelCase       )

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

					snake_case__      :Any         =   processor.batch_decode(UpperCamelCase       )
					snake_case__      :Dict         =   tokenizer.batch_decode(UpperCamelCase       )

					self.assertListEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Any         =   self.get_image_processor()
					snake_case__      :Any         =   self.get_tokenizer()

					snake_case__      :Dict         =   CLIPProcessor(tokenizer=UpperCamelCase					,image_processor=UpperCamelCase       )

					snake_case__      :Dict         =   "lower newer"
					snake_case__      :Any         =   self.prepare_image_inputs()

					snake_case__      :str         =   processor(text=UpperCamelCase					,images=UpperCamelCase       )

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







from __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


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

				if len(__snake_case      ) == 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	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

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





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()