import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


a_					      = logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
a_					      = direct_transformers_import(Path(__file__).parent)


a_					      = {
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}





class 	_lowercase		(      snake_case_ ):
	lowercase         =     ['feature_extractor', 'tokenizer']
	# Names need to be attr_class for attr in attributes
	lowercase         =     None
	lowercase         =     None
	lowercase         =     None

	def __init__(				self			:							Optional[Any]     ,   *snake_case			:							int     ,   **snake_case			:							Union[str, Any]    )     ->		Union[str, Any]:

		"""simple docstring"""





		for key in kwargs:
			if key not in self.attributes:
				raise TypeError(f"Unexpected keyword argument {key}."    )
		for arg, attribute_name in zip(snake_case     ,   self.attributes    ):
			if attribute_name in kwargs:
				raise TypeError(f"Got multiple values for argument {attribute_name}."    )
			else:
				UpperCamelCase_      :		Any				=      arg

		if len(snake_case    ) != len(self.attributes    ):
			raise ValueError(
			    f"This processor requires {len(self.attributes    )} arguments: {', '.join(self.attributes    )}. Got "
			    f"{len(snake_case    )} arguments instead."    )

		# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
		for attribute_name, arg in kwargs.items():
			UpperCamelCase_      :		int				=      getattr(self     ,   f"{attribute_name}_class"    )
			# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
			UpperCamelCase_      :		Optional[Any]				=      AUTO_TO_BASE_CLASS_MAPPING.get(snake_case     ,   snake_case    )
			if isinstance(snake_case     ,   snake_case    ):
				UpperCamelCase_      :		Optional[int]				=      tuple(getattr(snake_case     ,   snake_case    ) for n in class_name if n is not None    )
			else:
				UpperCamelCase_      :		Dict				=      getattr(snake_case     ,   snake_case    )

			if not isinstance(snake_case     ,   snake_case    ):
				raise ValueError(
				    f"Received a {type(snake_case    ).__name__} for argument {attribute_name}, but a {class_name} was expected."    )

			setattr(self     ,   snake_case     ,   snake_case    )

	def __repr__(				self			:							int    )     ->		Any:

		"""simple docstring"""





		UpperCamelCase_      :		int				=      [f"- {name}: {repr(getattr(self     ,   snake_case    )    )}" for name in self.attributes]
		UpperCamelCase_      :		Union[str, Any]				=      '\n'.join(snake_case    )
		return f"{self.__class__.__name__}:\n{attributes_repr}"

	def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							Any     ,   snake_case			:							bool = False     ,   **snake_case			:							str    )     ->		int:

		"""simple docstring"""





		os.makedirs(snake_case     ,   exist_ok=snake_case    )

		if push_to_hub:
			UpperCamelCase_      :		Optional[int]				=      kwargs.pop('commit_message'     ,   snake_case    )
			UpperCamelCase_      :		Tuple				=      kwargs.pop('repo_id'     ,   save_directory.split(os.path.sep    )[-1]    )
			UpperCamelCase_      :		int				=      self._create_repo(snake_case     ,   **snake_case    )
			UpperCamelCase_      :		Optional[int]				=      self._get_files_timestamps(snake_case    )
		# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
		# loaded from the Hub.
		if self._auto_class is not None:
			UpperCamelCase_      :		Tuple				=      [getattr(self     ,   snake_case    ) for attribute_name in self.attributes]
			UpperCamelCase_      :		Any				=      [(a.init_kwargs if isinstance(snake_case     ,   snake_case    ) else a) for a in attrs]
			custom_object_save(self     ,   snake_case     ,   config=snake_case    )

		for attribute_name in self.attributes:
			UpperCamelCase_      :		List[Any]				=      getattr(self     ,   snake_case    )
			# Include the processor class in the attribute config so this processor can then be reloaded with the
			# `AutoProcessor` API.
			if hasattr(snake_case     ,   '_set_processor_class'    ):
				attribute._set_processor_class(self.__class__.__name__    )
			attribute.save_pretrained(snake_case    )

		if self._auto_class is not None:
			# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
			for attribute_name in self.attributes:
				UpperCamelCase_      :		Dict				=      getattr(self     ,   snake_case    )
				if isinstance(snake_case     ,   snake_case    ):
					del attribute.init_kwargs["auto_map"]

		if push_to_hub:
			self._upload_modified_files(
			    snake_case     ,   snake_case     ,   snake_case     ,   commit_message=snake_case     ,   token=kwargs.get('use_auth_token'    )     ,   )

	@classmethod
	def 		SCREAMING_SNAKE_CASE__     (				cls			:							Optional[int]     ,   snake_case			:							Union[str, os.PathLike]     ,   snake_case			:							Optional[Union[str, os.PathLike]] = None     ,   snake_case			:							bool = False     ,   snake_case			:							bool = False     ,   snake_case			:							Optional[Union[str, bool]] = None     ,   snake_case			:							str = "main"     ,   **snake_case			:							int     ,   )     ->		Optional[Any]:

		"""simple docstring"""





		UpperCamelCase_      :		int				=      cache_dir
		UpperCamelCase_      :		Any				=      force_download
		UpperCamelCase_      :		Tuple				=      local_files_only
		UpperCamelCase_      :		str				=      revision

		UpperCamelCase_      :		Any				=      kwargs.pop('use_auth_token'     ,   snake_case    )
		if use_auth_token is not None:
			warnings.warn(
			    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'     ,   snake_case    )
			if token is not None:
				raise ValueError(
				    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'    )
			UpperCamelCase_      :		Optional[int]				=      use_auth_token

		if token is not None:
			# change to `token` in a follow-up PR
			UpperCamelCase_      :		Optional[Any]				=      token

		UpperCamelCase_      :		List[Any]				=      cls._get_arguments_from_pretrained(snake_case     ,   **snake_case    )
		return cls(*snake_case    )

	@classmethod
	def 		SCREAMING_SNAKE_CASE__     (				cls			:							Dict     ,   snake_case			:							str="AutoProcessor"    )     ->		Union[str, Any]:

		"""simple docstring"""





		if not isinstance(snake_case     ,   snake_case    ):
			UpperCamelCase_      :		Dict				=      auto_class.__name__

		import transformers.models.auto as auto_module

		if not hasattr(snake_case     ,   snake_case    ):
			raise ValueError(f"{auto_class} is not a valid auto class."    )

		UpperCamelCase_      :		Optional[int]				=      auto_class

	@classmethod
	def 		SCREAMING_SNAKE_CASE__     (				cls			:							Optional[int]     ,   snake_case			:							Tuple     ,   **snake_case			:							List[str]    )     ->		Tuple:

		"""simple docstring"""





		UpperCamelCase_      :		Union[str, Any]				=      []
		for attribute_name in cls.attributes:
			UpperCamelCase_      :		Union[str, Any]				=      getattr(cls     ,   f"{attribute_name}_class"    )
			if isinstance(snake_case     ,   snake_case    ):
				UpperCamelCase_      :		Any				=      tuple(getattr(snake_case     ,   snake_case    ) if n is not None else None for n in class_name    )
				UpperCamelCase_      :		Optional[Any]				=      kwargs.get('use_fast'     ,   snake_case    )
				if use_fast and classes[1] is not None:
					UpperCamelCase_      :		Dict				=      classes[1]
				else:
					UpperCamelCase_      :		List[Any]				=      classes[0]
			else:
				UpperCamelCase_      :		List[Any]				=      getattr(snake_case     ,   snake_case    )

			args.append(attribute_class.from_pretrained(snake_case     ,   **snake_case    )    )
		return args


	@property
	def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		Optional[int]:

		"""simple docstring"""





		UpperCamelCase_      :		str				=      getattr(self     ,   self.attributes[0]    )
		return getattr(snake_case     ,   'model_input_names'     ,   snake_case    )
a_					      = copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
			a_					      = ProcessorMixin.push_to_hub.__doc__.format(
			    object='processor', object_class='AutoProcessor', object_files='processor files'
			)




from typing import Any

import numpy as np





class 	_lowercase		:

 def __init__(				self			:							Dict     ,   snake_case			:							np.ndarray     ,   snake_case			:							int    )     ->		None:

  """simple docstring"""





  if np.any(tableau[:, -1]     ,   where=tableau[:, -1] < 0    ):
   raise ValueError('RHS must be > 0'    )

  UpperCamelCase_      :		List[str]				=      tableau
  UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      tableau.shape

  # Number of decision variables x1, x2, x3...
  UpperCamelCase_      :		Dict				=      n_vars

  # Number of artificial variables to be minimised
  UpperCamelCase_      :		str				=      len(np.where(tableau[self.n_vars : -1] == -1    )[0]    )

  # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  UpperCamelCase_      :		int				=      (self.n_art_vars > 0) + 1

  # Number of slack variables added to make inequalities into equalities
  UpperCamelCase_      :		Union[str, Any]				=      self.n_rows - self.n_stages

  # Objectives for each stage
  UpperCamelCase_      :		Dict				=      ['max']

  # In two stage simplex, first minimise then maximise
  if self.n_art_vars:
   self.objectives.append('min'    )

  UpperCamelCase_      :		str				=      ['']

  # Index of current pivot row and column
  UpperCamelCase_      :		str				=      None
  UpperCamelCase_      :		Optional[int]				=      None

  # Does objective row only contain (non)-negative values?
  UpperCamelCase_      :		Any				=      False

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				*snake_case			:							int    )     ->		list[str]:

  """simple docstring"""





  if len(snake_case    ) != 3:
   raise ValueError('Must provide n_vars, n_slack, and n_art_vars'    )

  if not all(x >= 0 and isinstance(snake_case     ,   snake_case    ) for x in args    ):
   raise ValueError('All arguments must be non-negative integers'    )

  # decision | slack | artificial
  UpperCamelCase_      :		Tuple				=      ['x', 's', 'a']
  UpperCamelCase_      :		Optional[int]				=      []
  for i in range(3    ):
   for j in range(args[i]    ):
    titles.append(string_starts[i] + str(j + 1    )    )
  titles.append('RHS'    )
  return titles

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray    )     ->		tuple[Any, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      self.objectives[-1]

  # Find entries of highest magnitude in objective rows
  UpperCamelCase_      :		str				=      (objective == 'min') - (objective == 'max')
  UpperCamelCase_      :		str				=      np.argmax(sign * tableau[0, : self.n_vars]    )

  # Choice is only valid if below 0 for maximise, and above for minimise
  if sign * self.tableau[0, col_idx] <= 0:
   UpperCamelCase_      :		Tuple				=      True
   return 0, 0

  # Pivot row is chosen as having the lowest quotient when elements of
  # the pivot column divide the right-hand side

  # Slice excluding the objective rows
  UpperCamelCase_      :		Dict				=      slice(self.n_stages     ,   self.n_rows    )

  # RHS
  UpperCamelCase_      :		str				=      tableau[s, -1]

  # Elements of pivot column within slice
  UpperCamelCase_      :		List[Any]				=      tableau[s, col_idx]

  # Array filled with nans
  UpperCamelCase_      :		Optional[int]				=      np.full(self.n_rows - self.n_stages     ,   np.nan    )

  # If element in pivot column is greater than zeron_stages, return
  # quotient or nan otherwise
  UpperCamelCase_      :		int				=      np.divide(snake_case     ,   snake_case     ,   out=snake_case     ,   where=divisor > 0    )

  # Arg of minimum quotient excluding the nan values. n_stages is added
  # to compensate for earlier exclusion of objective columns
  UpperCamelCase_      :		int				=      np.nanargmin(snake_case    ) + self.n_stages
  return row_idx, col_idx

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray     ,   snake_case			:							int     ,   snake_case			:							int    )     ->		np.ndarray:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      tableau[row_idx].copy()

  UpperCamelCase_      :		List[Any]				=      piv_row[col_idx]

  # Entry becomes 1
  piv_row *= 1 / piv_val

  # Variable in pivot column becomes basic, ie the only non-zero entry
  for idx, coeff in enumerate(tableau[:, col_idx]    ):
   tableau[idx] += -coeff * piv_row
  UpperCamelCase_      :		Any				=      piv_row
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							np.ndarray    )     ->		np.ndarray:

  """simple docstring"""





  self.objectives.pop()

  if not self.objectives:
   return tableau

  # Slice containing ids for artificial columns
  UpperCamelCase_      :		int				=      slice(-self.n_art_vars - 1     ,   -1    )

  # Delete the artificial variable columns
  UpperCamelCase_      :		Any				=      np.delete(snake_case     ,   snake_case     ,   axis=1    )

  # Delete the objective row of the first stage
  UpperCamelCase_      :		Dict				=      np.delete(snake_case     ,   0     ,   axis=0    )

  UpperCamelCase_      :		List[Any]				=      1
  self.n_rows -= 1
  UpperCamelCase_      :		int				=      0
  UpperCamelCase_      :		Optional[int]				=      False
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		dict[Any, Any]:

  """simple docstring"""





  for _ in range(1_0_0    ):
   # Completion of each stage removes an objective. If both stages
   # are complete, then no objectives are left
   if not self.objectives:
    UpperCamelCase_      :		Any				=      self.generate_col_titles(
        self.n_vars     ,   self.n_slack     ,   self.n_art_vars    )

    # Find the values of each variable at optimal solution
    return self.interpret_tableau(self.tableau     ,   self.col_titles    )

   UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      self.find_pivot(self.tableau    )

   # If there are no more negative values in objective row
   if self.stop_iter:
    # Delete artificial variable columns and rows. Update attributes
    UpperCamelCase_      :		Union[str, Any]				=      self.change_stage(self.tableau    )
   else:
    UpperCamelCase_      :		Union[str, Any]				=      self.pivot(self.tableau     ,   snake_case     ,   snake_case    )
  return {}


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							np.ndarray     ,   snake_case			:							list[str]    )     ->		dict[str, float]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      {'P': abs(tableau[0, -1]    )}

  for i in range(self.n_vars    ):
   # Gives ids of nonzero entries in the ith column
   UpperCamelCase_      :		Dict				=      np.nonzero(tableau[:, i]    )
   UpperCamelCase_      :		Union[str, Any]				=      len(nonzero[0]    )

   # First entry in the nonzero ids
   UpperCamelCase_      :		Optional[int]				=      nonzero[0][0]
   UpperCamelCase_      :		Union[str, Any]				=      tableau[nonzero_rowidx, i]

   # If there is only one nonzero value in column, which is one
   if n_nonzero == nonzero_val == 1:
    UpperCamelCase_      :		Optional[int]				=      tableau[nonzero_rowidx, -1]
    UpperCamelCase_      :		Union[str, Any]				=      rhs_val

        # Check for basic variables
  for title in col_titles:
   # Don't add RHS or slack variables to output dict
   if title[0] not in "R-s-a":
    output_dict.setdefault(snake_case     ,   0    )
  return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()



