


"""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 CLIPImageProcessor, CLIPProcessor







@require_vision
class _lowerCAmelCase							( unittest.TestCase      ):







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       tempfile.mkdtemp()

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

       snake_case       : str    =       os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]		)
       snake_case       : List[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(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       : str    =       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__		)      ->						List[str]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						int:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       shutil.rmtree(self.tmpdirname		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




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

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

       return image_inputs







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




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

       snake_case       : Dict    =       CLIPProcessor(tokenizer=UpperCamelCase__		,      image_processor=UpperCamelCase__		)
       processor_slow.save_pretrained(self.tmpdirname		)
       snake_case       : Tuple    =       CLIPProcessor.from_pretrained(self.tmpdirname		,      use_fast=UpperCamelCase__		)

       snake_case       : Tuple    =       CLIPProcessor(tokenizer=UpperCamelCase__		,      image_processor=UpperCamelCase__		)
       processor_fast.save_pretrained(self.tmpdirname		)
       snake_case       : Union[str, Any]    =       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		)      ->						Dict:

       '''simple docstring'''




       snake_case       : str    =       CLIPProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()		)
       processor.save_pretrained(self.tmpdirname		)

       snake_case       : Optional[int]    =       self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"		)
       snake_case       : List[str]    =       self.get_image_processor(do_normalize=UpperCamelCase__		,      padding_value=1.0		)

       snake_case       : Union[str, 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		)      ->						int:

       '''simple docstring'''




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

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

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

       snake_case       : List[str]    =       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		)      ->						Optional[Any]:

       '''simple docstring'''




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

       snake_case       : Optional[int]    =       CLIPProcessor(tokenizer=UpperCamelCase__		,      image_processor=UpperCamelCase__		)

       snake_case       : Tuple    =       "lower newer"

       snake_case       : List[Any]    =       processor(text=UpperCamelCase__		)

       snake_case       : Tuple    =       tokenizer(UpperCamelCase__		)

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







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




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

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

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

       snake_case       : str    =       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		)      ->						int:

       '''simple docstring'''




       snake_case       : str    =       self.get_image_processor()
       snake_case       : str    =       self.get_tokenizer()

       snake_case       : List[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       : List[str]    =       processor.batch_decode(UpperCamelCase__		)
       snake_case       : Optional[int]    =       tokenizer.batch_decode(UpperCamelCase__		)

       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




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

       snake_case       : Optional[Any]    =       CLIPProcessor(tokenizer=UpperCamelCase__		,      image_processor=UpperCamelCase__		)

       snake_case       : Any    =       "lower newer"
       snake_case       : Optional[Any]    =       self.prepare_image_inputs()

       snake_case       : Optional[int]    =       processor(text=UpperCamelCase__		,      images=UpperCamelCase__		)

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








"""simple docstring"""

from __future__ import annotations

_UpperCamelCase		     =    []



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						bool:
 for i in range(len(lowercase__   )   ):
  if board[row][i] == 1:
   return False
 for i in range(len(lowercase__   )   ):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					-1					,					-1   )   ):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					len(lowercase__   )   )   ):
  if board[i][j] == 1:
   return False
 return True



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						bool:
 if row >= len(lowercase__   ):
  solution.append(lowercase__   )
  printboard(lowercase__   )
  print()
  return True
 for i in range(len(lowercase__   )   ):
  if is_safe(lowercase__					,					lowercase__					,					lowercase__   ):
   lowerCAmelCase__   :				List[Any] 			=				1
   solve(lowercase__					,					row + 1   )
   lowerCAmelCase__   :				Dict 			=				0
 return False



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						None:
 for i in range(len(lowercase__   )   ):
  for j in range(len(lowercase__   )   ):
   if board[i][j] == 1:
    print("Q"					,					end=" "   )
   else:
    print("."					,					end=" "   )
  print()


# n=int(input("The no. of queens"))
_UpperCamelCase		     =    8
_UpperCamelCase		     =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))






