

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 _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       tempfile.mkdtemp()

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

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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	)	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       {
				    "do_resize": True,
				    "size": 20,
				    "do_center_crop": True,
				    "crop_size": 18,
				    "do_normalize": True,
				    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
				    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				SCREAMING_SNAKE_CASE   :      Any				     =       os.path.join(self.tmpdirname      ,      a	)
				with open(self.image_processor_file      ,      "w"      ,      encoding="utf-8"	) as fp:
					json.dump(a      ,      a	)
			def 							__UpperCamelCase (			self							: int      ,      **a							: Any	)							->       Optional[int]:

				"""simple docstring"""

				return CLIPTokenizer.from_pretrained(self.tmpdirname      ,      pad_token="!"      ,      **a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      **a							: Tuple	)							->       List[str]:

				"""simple docstring"""

				return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,      pad_token="!"      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      **a							: List[str]	)							->       str:

				"""simple docstring"""

				return OwlViTImageProcessor.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				shutil.rmtree(self.tmpdirname	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       str:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [Image.fromarray(np.moveaxis(a      ,      0      ,      -1	)	) for x in image_inputs]

				return image_inputs
			def 							__UpperCamelCase (			self							: Tuple	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       self.get_tokenizer()
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_rust_tokenizer()
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_image_processor()

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       OwlViTProcessor(tokenizer=a      ,      image_processor=a	)
				processor_slow.save_pretrained(self.tmpdirname	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       OwlViTProcessor.from_pretrained(self.tmpdirname      ,      use_fast=a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       OwlViTProcessor(tokenizer=a      ,      image_processor=a	)
				processor_fast.save_pretrained(self.tmpdirname	)
				SCREAMING_SNAKE_CASE   :      Optional[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 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       OwlViTProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_tokenizer(bos_token="(BOS)"      ,      eos_token="(EOS)"	)
				SCREAMING_SNAKE_CASE   :      int				     =       self.get_image_processor(do_normalize=a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       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 							__UpperCamelCase (			self							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       OwlViTProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       image_processor(a      ,      return_tensors="np"	)
				SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: str	)							->       Any:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Tuple				     =       OwlViTProcessor(tokenizer=a      ,      image_processor=a	)

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

				SCREAMING_SNAKE_CASE   :      List[str]				     =       processor(text=a      ,      return_tensors="np"	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tokenizer(a      ,      return_tensors="np"	)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key][0].tolist()      ,      encoded_processor[key][0].tolist()	)
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      int				     =       OwlViTProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      Any				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      Union[str, 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 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "google/owlvit-base-patch32"
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       OwlViTProcessor.from_pretrained(a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       ["cat", "nasa badge"]
				SCREAMING_SNAKE_CASE   :      Dict				     =       processor(text=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       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 							__UpperCamelCase (			self							: List[Any]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       "google/owlvit-base-patch32"
				SCREAMING_SNAKE_CASE   :      Dict				     =       OwlViTProcessor.from_pretrained(a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [["cat", "nasa badge"], ["person"]]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       processor(text=a	)

				SCREAMING_SNAKE_CASE   :      str				     =       16
				SCREAMING_SNAKE_CASE   :      Dict				     =       len(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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 							__UpperCamelCase (			self							: Dict	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       "google/owlvit-base-patch32"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       OwlViTProcessor.from_pretrained(a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       ["cat", "nasa badge"]
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       processor(text=a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       16
				SCREAMING_SNAKE_CASE   :      List[str]				     =       inputs["input_ids"]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				    [4_9406, 6841, 1_1301, 4_9407, 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 							__UpperCamelCase (			self							: int	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      str				     =       OwlViTProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.prepare_image_inputs()
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       OwlViTProcessor(tokenizer=a      ,      image_processor=a	)

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

				SCREAMING_SNAKE_CASE   :      str				     =       processor.batch_decode(a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       tokenizer.batch_decode(a	)

				self.assertListEqual(a      ,      a	)


import numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    (-1, -1),
				    (-1, 0),
				    (-1, 1),
				    (0, -1),
				    (0, 1),
				    (1, -1),
				    (1, 0),
				    (1, 1),
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for n in neughbour_cord:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       current_x + n[0]
					SCREAMING_SNAKE_CASE   :      int				     =       current_y + n[1]
					if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
						SCREAMING_SNAKE_CASE   :      int				     =       Cell()
						SCREAMING_SNAKE_CASE   :      str				     =       (x, y)
						SCREAMING_SNAKE_CASE   :      Any				     =       cell
						neighbours.append(a	)
				return neighbours

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

	while _open:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.argmin([n.f for n in _open])
		SCREAMING_SNAKE_CASE   :      List[str]				     =       _open[min_f]
		_closed.append(_open.pop(_a))
		if current == goal:
			break
		for n in world.get_neigbours(_a):
			for c in _closed:
				if c == n:
					continue
			SCREAMING_SNAKE_CASE   :      Tuple				     =       current.g + 1
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       n.position
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       goal.position
			SCREAMING_SNAKE_CASE   :      Any				     =       (ya - ya) ** 2 + (xa - xa) ** 2
			SCREAMING_SNAKE_CASE   :      List[str]				     =       n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


if __name__ == "__main__":
			a_						     =    Gridworld()
			# Start position and goal
			a_						     =    Cell()
			a_						     =    (0, 0)
			a_						     =    Cell()
			a_						     =    (4, 4)
			print(F'''path from {start.position} to {goal.position}''')
			a_						     =    astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						a_						     =    1
			print(world.w)