

'''simple docstring'''





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        A  (	unittest.TestCase ):





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										_a        =   0





						def __lowerCAmelCase   (	self			)			->							Dict:
										_a        =   AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"			)
										self.assertIsInstance(snake_case_	,    snake_case_			)





						def __lowerCAmelCase   (	self			)			->							List[Any]:

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

														_a        =   AutoImageProcessor.from_pretrained(snake_case_			)
														self.assertIsInstance(snake_case_	,    snake_case_			)





						def __lowerCAmelCase   (	self			)			->							Tuple:

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

														_a        =   AutoImageProcessor.from_pretrained(snake_case_			)
														self.assertIsInstance(snake_case_	,    snake_case_			)





						def __lowerCAmelCase   (	self			)			->							Tuple:

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

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

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

														config_dict.pop("image_processor_type"			)
														_a        =   CLIPImageProcessor(**snake_case_			)

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

														_a        =   AutoImageProcessor.from_pretrained(snake_case_			)

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

										self.assertIsInstance(snake_case_	,    snake_case_			)





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

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

														_a        =   AutoImageProcessor.from_pretrained(snake_case_			)
														self.assertIsInstance(snake_case_	,    snake_case_			)





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

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





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

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





						def __lowerCAmelCase   (	self			)			->							Any:

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





						def __lowerCAmelCase   (	self			)			->							Any:

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

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

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





						def __lowerCAmelCase   (	self			)			->							int:

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

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

																		_a        =   CustomImageProcessor.from_pretrained(snake_case_			)

														# 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(snake_case_			)
																		_a        =   AutoImageProcessor.from_pretrained(snake_case_			)
																		self.assertIsInstance(snake_case_	,    snake_case_			)

										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 __lowerCAmelCase   (	self			)			->							Optional[Any]:
										class        A  (	_snake_case ):
																__UpperCAmelCase				:	int   =   True

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

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












'''simple docstring'''

from __future__ import annotations







class __a			:





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      list[list[int]]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Matrices must be formed from a list of zero or more lists containing at """
          """least one and the same number of values, each of which must be of type """
          """int or float."""     )
      if len(lowerCamelCase     ) != 0:
         __SCREAMING_SNAKE_CASE   				=							len(rows[0]     )
         if cols == 0:
            raise error
         for row in rows:
            if len(lowerCamelCase     ) != cols:
               raise error
            for value in row:
               if not isinstance(lowerCamelCase					,(int, float)     ):
                  raise error
         __SCREAMING_SNAKE_CASE   				=							rows
      else:
         __SCREAMING_SNAKE_CASE   				=							[]





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return [[row[i] for row in self.rows] for i in range(len(self.rows[0]     )     )]





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return len(self.rows     )





   @property
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      return len(self.rows[0]     )





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return (self.num_rows, self.num_columns)





   @property
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return self.order[0] == self.order[1]





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [0 if column_num != row_num else 1 for column_num in range(self.num_rows     )]
          for row_num in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if not self.is_square:
         return 0
      if self.order == (0, 0):
         return 1
      if self.order == (1, 1):
         return int(self.rows[0][0]     )
      if self.order == (2, 2):
         return int(
             (self.rows[0][0] * self.rows[1][1])
             - (self.rows[0][1] * self.rows[1][0])     )
      else:
         return sum(
             self.rows[0][column] * self.cofactors().rows[0][column]
             for column in range(self.num_columns     )     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return bool(self.determinant()     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [
              self.rows[other_row][other_column]
              for other_column in range(self.num_columns     )
              if other_column != column
          ]
          for other_row in range(self.num_rows     )
          if other_row != row
      ]
      return Matrix(lowerCamelCase     ).determinant()





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if (row + column) % 2 == 0:
         return self.get_minor(lowerCamelCase					,lowerCamelCase     )
      return -1 * self.get_minor(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return Matrix(
          [
              [self.get_minor(lowerCamelCase					,lowerCamelCase     ) for column in range(self.num_columns     )]
              for row in range(self.num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return Matrix(
          [
              [
                  self.minors().rows[row][column]
                  if (row + column) % 2 == 0
                  else self.minors().rows[row][column] * -1
                  for column in range(self.minors().num_columns     )
              ]
              for row in range(self.minors().num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [self.cofactors().rows[column][row] for column in range(self.num_columns     )]
          for row in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.determinant()
      if not determinant:
         raise TypeError("""Only matrices with a non-zero determinant have an inverse"""     )
      return self.adjugate() * (1 / determinant)





   def __repr__(       self					:      List[Any]     ):

      '''simple docstring'''

      return str(self.rows     )





   def __str__(       self					:      List[str]     ):

      '''simple docstring'''

      if self.num_rows == 0:
         return "[]"
      if self.num_rows == 1:
         return "[[" + ". ".join(str(self.rows[0]     )     ) + "]]"
      return (
          "["
          + "\n ".join(
              [
                  """[""" + """. """.join([str(lowerCamelCase     ) for value in row]     ) + """.]"""
                  for row in self.rows
              ]     )
          + "]"
      )





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError("""Row must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in row:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_columns:
         raise ValueError(
             """Row must be equal in length to the other rows in the matrix"""     )
      if position is None:
         self.rows.append(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							self.rows[0:position] + [row] + self.rows[position:]





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Column must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in column:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_rows:
         raise ValueError(
             """Column must be equal in length to the other columns in the matrix"""     )
      if position is None:
         __SCREAMING_SNAKE_CASE   				=							[self.rows[i] + [column[i]] for i in range(self.num_rows     )]
      else:
         __SCREAMING_SNAKE_CASE   				=							[
             self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
             for i in range(self.num_rows     )
         ]





   def __eq__(       self					:      int					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         return NotImplemented
      return self.rows == other.rows





   def __ne__(       self					:      Any					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      return not self == other





   def __neg__(       self					:      Any     ):

      '''simple docstring'''

      return self * -1





   def __add__(       self					:      List[Any]					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Addition requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __sub__(       self					:      Any					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Subtraction requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __mul__(       self					:      Any					,lowerCamelCase					:      Matrix | int | float     ):

      '''simple docstring'''

      if isinstance(lowerCamelCase					,(int, float)     ):
         return Matrix(
             [[int(element * other     ) for element in row] for row in self.rows]     )
      elif isinstance(lowerCamelCase					,lowerCamelCase     ):
         if self.num_columns != other.num_rows:
            raise ValueError(
                """The number of columns in the first matrix must """
                """be equal to the number of rows in the second"""     )
         return Matrix(
             [
                 [Matrix.dot_product(lowerCamelCase					,lowerCamelCase     ) for column in other.columns()]
                 for row in self.rows
             ]     )
      else:
         raise TypeError(
             """A Matrix can only be multiplied by an int, float, or another matrix"""     )





   def __pow__(       self					:      Optional[int]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise TypeError("""A Matrix can only be raised to the power of an int"""     )
      if not self.is_square:
         raise ValueError("""Only square matrices can be raised to a power"""     )
      if other == 0:
         return self.identity()
      if other < 0:
         if self.is_invertable():
            return self.inverse() ** (-other)
         raise ValueError(
             """Only invertable matrices can be raised to a negative power"""     )
      __SCREAMING_SNAKE_CASE   				=							self
      for _ in range(other - 1     ):
         result *= self
      return result





   @classmethod
   def   UpperCAmelCase__  (       cls					:      str					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      list[int]     ):

      '''simple docstring'''

      return sum(row[i] * column[i] for i in range(len(lowerCamelCase     )     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





