
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402







class        lowerCAmelCase__      (   unittest.TestCase	):

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


								'''simple docstring'''

								_UpperCamelCase						    =	0

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""				)
								self.assertIsInstance(a       ,							a				)

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


								'''simple docstring'''

								with tempfile.TemporaryDirectory() as tmpdirname:
										_UpperCamelCase						    =	Path(a				) / """preprocessor_config.json"""
										_UpperCamelCase						    =	Path(a				) / """config.json"""
										json.dump(
										    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}       ,							open(a       ,							"""w"""				)       ,							)
										json.dump({"""model_type""": """clip"""}       ,							open(a       ,							"""w"""				)				)

										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a				)
										self.assertIsInstance(a       ,							a				)

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


								'''simple docstring'''

								with tempfile.TemporaryDirectory() as tmpdirname:
										_UpperCamelCase						    =	Path(a				) / """preprocessor_config.json"""
										_UpperCamelCase						    =	Path(a				) / """config.json"""
										json.dump(
										    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}       ,							open(a       ,							"""w"""				)       ,							)
										json.dump({"""model_type""": """clip"""}       ,							open(a       ,							"""w"""				)				)

										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a				)
										self.assertIsInstance(a       ,							a				)

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


								'''simple docstring'''

								with tempfile.TemporaryDirectory() as tmpdirname:
										_UpperCamelCase						    =	CLIPConfig()

										# Create a dummy config file with image_proceesor_type
										_UpperCamelCase						    =	Path(a				) / """preprocessor_config.json"""
										_UpperCamelCase						    =	Path(a				) / """config.json"""
										json.dump(
										    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}       ,							open(a       ,							"""w"""				)       ,							)
										json.dump({"""model_type""": """clip"""}       ,							open(a       ,							"""w"""				)				)

										# remove image_processor_type to make sure config.json alone is enough to load image processor locally
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a				).to_dict()

										config_dict.pop("""image_processor_type"""				)
										_UpperCamelCase						    =	CLIPImageProcessor(**a				)

										# save in new folder
										model_config.save_pretrained(a				)
										config.save_pretrained(a				)

										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a				)

										# make sure private variable is not incorrectly saved
										_UpperCamelCase						    =	json.loads(config.to_json_string()				)
										self.assertTrue("""_processor_class""" not in dict_as_saved				)

								self.assertIsInstance(a       ,							a				)

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								with tempfile.TemporaryDirectory() as tmpdirname:
										_UpperCamelCase						    =	Path(a				) / """preprocessor_config.json"""
										json.dump(
										    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}       ,							open(a       ,							"""w"""				)       ,							)

										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a				)
										self.assertIsInstance(a       ,							a				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								with self.assertRaisesRegex(
								    a       ,							"""clip-base is not a local folder and is not a valid model identifier"""				):
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""clip-base"""				)

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


								'''simple docstring'''

								with self.assertRaisesRegex(
								    a       ,							r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a       ,							revision="""aaaaaa"""				)

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								with self.assertRaisesRegex(
								    a       ,							"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""       ,							):
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""				)

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								with self.assertRaises(a				):
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""				)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(a				):
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(
										    """hf-internal-testing/test_dynamic_image_processor"""       ,							trust_remote_code=a				)

								_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(
								    """hf-internal-testing/test_dynamic_image_processor"""       ,							trust_remote_code=a				)
								self.assertEqual(image_processor.__class__.__name__       ,							"""NewImageProcessor"""				)

								# Test image processor can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
										image_processor.save_pretrained(a				)
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a       ,							trust_remote_code=a				)
								self.assertEqual(reloaded_image_processor.__class__.__name__       ,							"""NewImageProcessor"""				)

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


								'''simple docstring'''

								try:
										AutoConfig.register("""custom"""       ,							a				)
										AutoImageProcessor.register(a       ,							a				)
										# Trying to register something existing in the Transformers library will raise an error
										with self.assertRaises(a				):
												AutoImageProcessor.register(a       ,							a				)

										with tempfile.TemporaryDirectory() as tmpdirname:
												_UpperCamelCase						    =	Path(a				) / """preprocessor_config.json"""
												_UpperCamelCase						    =	Path(a				) / """config.json"""
												json.dump(
												    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}       ,							open(a       ,							"""w"""				)       ,							)
												json.dump({"""model_type""": """clip"""}       ,							open(a       ,							"""w"""				)				)

												_UpperCamelCase						    =	CustomImageProcessor.from_pretrained(a				)

										# Now that the config is registered, it can be used as any other config with the auto-API
										with tempfile.TemporaryDirectory() as tmp_dir:
												image_processor.save_pretrained(a				)
												_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(a				)
												self.assertIsInstance(a       ,							a				)

								finally:
										if "custom" in CONFIG_MAPPING._extra_content:
												del CONFIG_MAPPING._extra_content["custom"]
										if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
												del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								class        lowerCAmelCase__      (   __lowercase	):
														UpperCamelCase_							: Dict	=		True

								try:
										AutoConfig.register("""custom"""       ,							a				)
										AutoImageProcessor.register(a       ,							a				)
										# If remote code is not set, the default is to use local
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""				)
										self.assertEqual(image_processor.__class__.__name__       ,							"""NewImageProcessor"""				)
										self.assertTrue(image_processor.is_local				)

										# If remote code is disabled, we load the local one.
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(
										    """hf-internal-testing/test_dynamic_image_processor"""       ,							trust_remote_code=a				)
										self.assertEqual(image_processor.__class__.__name__       ,							"""NewImageProcessor"""				)
										self.assertTrue(image_processor.is_local				)

										# If remote is enabled, we load from the Hub
										_UpperCamelCase						    =	AutoImageProcessor.from_pretrained(
										    """hf-internal-testing/test_dynamic_image_processor"""       ,							trust_remote_code=a				)
										self.assertEqual(image_processor.__class__.__name__       ,							"""NewImageProcessor"""				)
										self.assertTrue(not hasattr(a       ,							"""is_local"""				)				)

								finally:
										if "custom" in CONFIG_MAPPING._extra_content:
												del CONFIG_MAPPING._extra_content["custom"]
										if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
												del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





from __future__ import annotations


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	len(lowerCAmelCase )

		# If row is equal to the size of the board it means there are a queen in each row in
		# the current board (possible_board)
		if row == n:
				# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
				# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
				boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board] )
				return

		# We iterate each column in the row to find all possible results in each row
		for col in range(lowerCAmelCase ):
				# We apply that we learned previously. First we check that in the current board
				# (possible_board) there are not other same value because if there is it means
				# that there are a collision in vertical. Then we apply the two formulas we
				# learned before:
				#
				# 45º: y - x = b or 45: row - col = b
				# 135º: y + x = b or row + col = b.
				#
				# And we verify if the results of this two formulas not exist in their variables
				# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
				#
				# If any or these are True it means there is a collision so we continue to the
				# next value in the for loop.
				if (
				    col in possible_board
				    or row - col in diagonal_right_collisions
				    or row + col in diagonal_left_collisions
				):
						continue

				# If it is False we call dfs function again and we update the inputs
				depth_first_search(
				    [*possible_board, col]						,  [*diagonal_right_collisions, row - col]						,  [*diagonal_left_collisions, row + col]						,  lowerCAmelCase						,  lowerCAmelCase						,  )


def 							__A(lowerCAmelCase )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		depth_first_search([]						,  []						,  []						,  lowerCAmelCase						,  lowerCAmelCase )

		# Print all the boards
		for board in boards:
				for column in board:
						print(lowerCAmelCase )
				print("""""" )

		print(len(lowerCAmelCase )						,  """solutions were found.""" )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



