'''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 OwlViTImageProcessor, OwlViTProcessor






@require_vision
class 		__A							(unittest.TestCase ):






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Dict												=		tempfile.mkdtemp()

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

              __UpperCAmelCase	:					int												=		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(a_   ) + "\n"   )
              with open(self.merges_file					,  "w"					,  encoding="utf-8"   ) as fp:
                     fp.write("\n".join(a_   )   )

              __UpperCAmelCase	:					Optional[int]												=		{
                  "do_resize": True,
                  "size": 20,
                  "do_center_crop": True,
                  "crop_size": 18,
                  "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],
              }
              __UpperCAmelCase	:					Any												=		os.path.join(self.tmpdirname					,  a_   )
              with open(self.image_processor_file					,  "w"					,  encoding="utf-8"   ) as fp:
                     json.dump(a_					,  a_   )






       def       _snake_case    (   self					,  **UpperCamelCase_   ):
              return CLIPTokenizer.from_pretrained(self.tmpdirname					,  pad_token="!"					,  **a_   )






       def       _snake_case    (   self					,  **UpperCamelCase_   ):
              return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,  pad_token="!"					,  **a_   )






       def       _snake_case    (   self					,  **UpperCamelCase_   ):
              return OwlViTImageProcessor.from_pretrained(self.tmpdirname					,  **a_   )






       def       _snake_case    (   self   ):
              shutil.rmtree(self.tmpdirname   )






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Dict												=		[np.random.randint(2_55					,  size=(3, 30, 4_00)					,  dtype=np.uinta   )]

              __UpperCAmelCase	:					Dict												=		[Image.fromarray(np.moveaxis(a_					,  0					,  -1   )   ) for x in image_inputs]

              return image_inputs






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Tuple												=		self.get_tokenizer()
              __UpperCAmelCase	:					str												=		self.get_rust_tokenizer()
              __UpperCAmelCase	:					str												=		self.get_image_processor()

              __UpperCAmelCase	:					int												=		OwlViTProcessor(tokenizer=a_					,  image_processor=a_   )
              processor_slow.save_pretrained(self.tmpdirname   )
              __UpperCAmelCase	:					List[str]												=		OwlViTProcessor.from_pretrained(self.tmpdirname					,  use_fast=a_   )

              __UpperCAmelCase	:					Optional[int]												=		OwlViTProcessor(tokenizer=a_					,  image_processor=a_   )
              processor_fast.save_pretrained(self.tmpdirname   )
              __UpperCAmelCase	:					Union[str, Any]												=		OwlViTProcessor.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					,  a_   )
              self.assertIsInstance(processor_fast.tokenizer					,  a_   )

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






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Dict												=		OwlViTProcessor(tokenizer=self.get_tokenizer()					,  image_processor=self.get_image_processor()   )
              processor.save_pretrained(self.tmpdirname   )

              __UpperCAmelCase	:					List[Any]												=		self.get_tokenizer(bos_token="(BOS)"					,  eos_token="(EOS)"   )
              __UpperCAmelCase	:					Tuple												=		self.get_image_processor(do_normalize=a_   )

              __UpperCAmelCase	:					str												=		OwlViTProcessor.from_pretrained(
                  self.tmpdirname					,  bos_token="(BOS)"					,  eos_token="(EOS)"					,  do_normalize=a_   )

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

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






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Dict												=		self.get_image_processor()
              __UpperCAmelCase	:					Any												=		self.get_tokenizer()

              __UpperCAmelCase	:					int												=		OwlViTProcessor(tokenizer=a_					,  image_processor=a_   )

              __UpperCAmelCase	:					Tuple												=		self.prepare_image_inputs()

              __UpperCAmelCase	:					List[str]												=		image_processor(a_					,  return_tensors="np"   )
              __UpperCAmelCase	:					Tuple												=		processor(images=a_					,  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   ):
              __UpperCAmelCase	:					int												=		self.get_image_processor()
              __UpperCAmelCase	:					List[Any]												=		self.get_tokenizer()

              __UpperCAmelCase	:					Dict												=		OwlViTProcessor(tokenizer=a_					,  image_processor=a_   )

              __UpperCAmelCase	:					Union[str, Any]												=		"lower newer"

              __UpperCAmelCase	:					List[Any]												=		processor(text=a_					,  return_tensors="np"   )

              __UpperCAmelCase	:					List[str]												=		tokenizer(a_					,  return_tensors="np"   )

              for key in encoded_tok.keys():
                     self.assertListEqual(encoded_tok[key][0].tolist()					,  encoded_processor[key][0].tolist()   )






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Optional[int]												=		self.get_image_processor()
              __UpperCAmelCase	:					str												=		self.get_tokenizer()

              __UpperCAmelCase	:					Optional[Any]												=		OwlViTProcessor(tokenizer=a_					,  image_processor=a_   )

              __UpperCAmelCase	:					Union[str, Any]												=		"lower newer"
              __UpperCAmelCase	:					List[str]												=		self.prepare_image_inputs()

              __UpperCAmelCase	:					Any												=		processor(text=a_					,  images=a_   )

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

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






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					int												=		"google/owlvit-base-patch32"
              __UpperCAmelCase	:					Optional[Any]												=		OwlViTProcessor.from_pretrained(a_   )

              __UpperCAmelCase	:					Optional[Any]												=		["cat", "nasa badge"]
              __UpperCAmelCase	:					Dict												=		processor(text=a_   )

              __UpperCAmelCase	:					Tuple												=		16
              self.assertListEqual(list(inputs.keys()   )					,  ["input_ids", "attention_mask"]   )
              self.assertEqual(inputs["input_ids"].shape					,  (2, seq_length)   )

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






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Any												=		"google/owlvit-base-patch32"
              __UpperCAmelCase	:					Optional[int]												=		OwlViTProcessor.from_pretrained(a_   )

              __UpperCAmelCase	:					int												=		[["cat", "nasa badge"], ["person"]]
              __UpperCAmelCase	:					List[str]												=		processor(text=a_   )

              __UpperCAmelCase	:					str												=		16
              __UpperCAmelCase	:					Optional[int]												=		len(a_   )
              __UpperCAmelCase	:					List[Any]												=		max([len(a_   ) for texts in input_texts]   )

              self.assertListEqual(list(inputs.keys()   )					,  ["input_ids", "attention_mask"]   )
              self.assertEqual(inputs["input_ids"].shape					,  (batch_size * num_max_text_queries, seq_length)   )

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






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Optional[Any]												=		"google/owlvit-base-patch32"
              __UpperCAmelCase	:					List[str]												=		OwlViTProcessor.from_pretrained(a_   )

              __UpperCAmelCase	:					Optional[Any]												=		["cat", "nasa badge"]
              __UpperCAmelCase	:					List[Any]												=		processor(text=a_   )

              __UpperCAmelCase	:					Optional[Any]												=		16
              __UpperCAmelCase	:					Optional[int]												=		inputs["input_ids"]
              __UpperCAmelCase	:					int												=		[
                  [4_94_06, 23_68, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [4_94_06, 68_41, 1_13_01, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              ]

              self.assertListEqual(list(inputs.keys()   )					,  ["input_ids", "attention_mask"]   )
              self.assertEqual(inputs["input_ids"].shape					,  (2, seq_length)   )
              self.assertListEqual(list(input_ids[0]   )					,  predicted_ids[0]   )
              self.assertListEqual(list(input_ids[1]   )					,  predicted_ids[1]   )






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Union[str, Any]												=		self.get_image_processor()
              __UpperCAmelCase	:					Tuple												=		self.get_tokenizer()

              __UpperCAmelCase	:					Optional[int]												=		OwlViTProcessor(tokenizer=a_					,  image_processor=a_   )

              __UpperCAmelCase	:					Optional[int]												=		self.prepare_image_inputs()
              __UpperCAmelCase	:					str												=		self.prepare_image_inputs()

              __UpperCAmelCase	:					Dict												=		processor(images=a_					,  query_images=a_   )

              self.assertListEqual(list(inputs.keys()   )					,  ["query_pixel_values", "pixel_values"]   )

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







       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Optional[int]												=		self.get_image_processor()
              __UpperCAmelCase	:					Tuple												=		self.get_tokenizer()

              __UpperCAmelCase	:					Any												=		OwlViTProcessor(tokenizer=a_					,  image_processor=a_   )

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

              __UpperCAmelCase	:					List[Any]												=		processor.batch_decode(a_   )
              __UpperCAmelCase	:					Tuple												=		tokenizer.batch_decode(a_   )

              self.assertListEqual(a_					,  a_   )











import random
from typing import Any







def     UpperCAmelCase_		(					snake_case__					)	->     list[Any]:



				"""simple docstring"""





				for _ in range(len(snake_case__					)					):
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  data[b], data[a]
				return data


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   [0, 1, 2, 3, 4, 5, 6, 7]
	_lowerCAmelCase	:   Optional[Any]    =   ["python", "says", "hello", "!"]
	print("Fisher-Yates Shuffle:")
	print("List", integers, strings)
	print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
