





from typing import Any

import numpy as np



class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    List[str]					, UpperCamelCase					:    np.ndarray					, UpperCamelCase					:    int							):


    '''simple docstring'''





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

    _snake_case      : List[Any]														=		tableau
    _snake_case							,    _snake_case      : Optional[int]														=		tableau.shape

    # Number of decision variables x1, x2, x3...
    _snake_case      : Optional[int]														=		n_vars

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

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

    # Number of slack variables added to make inequalities into equalities
    _snake_case      : Any														=		self.n_rows - self.n_stages

    # Objectives for each stage
    _snake_case      : Optional[int]														=		['max']

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

    _snake_case      : Dict														=		['']

    # Index of current pivot row and column
    _snake_case      : Dict														=		None
    _snake_case      : Tuple														=		None

    # Does objective row only contain (non)-negative values?
    _snake_case      : Optional[int]														=		False







  @staticmethod
  def UpperCamelCase_       (					*UpperCamelCase					:    int							):


    '''simple docstring'''





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

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

    # decision | slack | artificial
    _snake_case      : Optional[int]														=		['x', 's', 'a']
    _snake_case      : Optional[Any]														=		[]
    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 UpperCamelCase_       (					self					:    Optional[int]					, UpperCamelCase					:    np.ndarray							):


    '''simple docstring'''





    _snake_case      : List[str]														=		self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    _snake_case      : List[Any]														=		(objective == 'min') - (objective == 'max')
    _snake_case      : Optional[Any]														=		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:
      _snake_case      : List[str]														=		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
    _snake_case      : str														=		slice(self.n_stages					, self.n_rows							)

    # RHS
    _snake_case      : Any														=		tableau[s, -1]

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

    # Array filled with nans
    _snake_case      : 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
    _snake_case      : str														=		np.divide(UpperCamelCase					, UpperCamelCase					, out=UpperCamelCase					, where=divisor > 0							)

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







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    np.ndarray					, UpperCamelCase					:    int					, UpperCamelCase					:    int							):


    '''simple docstring'''





    _snake_case      : Any														=		tableau[row_idx].copy()

    _snake_case      : Union[str, 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
    _snake_case      : List[Any]														=		piv_row
    return tableau







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    np.ndarray							):


    '''simple docstring'''





    self.objectives.pop()

    if not self.objectives:
      return tableau

    # Slice containing ids for artificial columns
    _snake_case      : List[str]														=		slice(-self.n_art_vars - 1					, -1							)

    # Delete the artificial variable columns
    _snake_case      : int														=		np.delete(UpperCamelCase					, UpperCamelCase					, axis=1							)

    # Delete the objective row of the first stage
    _snake_case      : List[str]														=		np.delete(UpperCamelCase					, 0					, axis=0							)

    _snake_case      : Dict														=		1
    self.n_rows -= 1
    _snake_case      : Any														=		0
    _snake_case      : Tuple														=		False
    return tableau







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    for _ in range(1_00							):
      # Completion of each stage removes an objective. If both stages
      # are complete, then no objectives are left
      if not self.objectives:
        _snake_case      : Union[str, 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							)

      _snake_case							,    _snake_case      : str														=		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
        _snake_case      : Tuple														=		self.change_stage(self.tableau							)
      else:
        _snake_case      : Union[str, Any]														=		self.pivot(self.tableau					, UpperCamelCase					, UpperCamelCase							)
    return {}







  def UpperCamelCase_       (					self					:    Dict					, UpperCamelCase					:    np.ndarray					, UpperCamelCase					:    list[str]							):


    '''simple docstring'''





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

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

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

      # If there is only one nonzero value in column, which is one
      if n_nonzero == nonzero_val == 1:
        _snake_case      : str														=		tableau[nonzero_rowidx, -1]
        _snake_case      : int														=		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(UpperCamelCase					, 0							)
    return output_dict


if __name__ == "__main__":
      import doctest

      doctest.testmod()











import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

lowerCAmelCase_        =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def 			lowerCamelCase_		(					lowerCAmelCase:		str )->						Tuple:
  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      _snake_case      : Optional[Any]														=		model_type_to_module_name(lowerCAmelCase )

      _snake_case      : Tuple														=		importlib.import_module(F""".{module_name}"""			,							'transformers.models' )
      try:
        return getattr(lowerCAmelCase			,							lowerCAmelCase )
      except AttributeError:
        continue

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(lowerCAmelCase			,							'__name__'			,							lowerCAmelCase ) == class_name:
      return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  _snake_case      : Union[str, Any]														=		importlib.import_module('transformers' )
  if hasattr(lowerCAmelCase			,							lowerCAmelCase ):
    return getattr(lowerCAmelCase			,							lowerCAmelCase )

  return None







class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    Optional[Any]							):


    '''simple docstring'''





    raise EnvironmentError(
        'AutoProcessor is designed to be instantiated '
        'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'							)







  @classmethod
  @replace_list_option_in_docstrings(UpperCamelCase							)
  def UpperCamelCase_       (					cls					:    Optional[Any]					, UpperCamelCase					:    Optional[Any]					, **UpperCamelCase					:    Dict							):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		kwargs.pop('config'					, UpperCamelCase							)
    _snake_case      : str														=		kwargs.pop('trust_remote_code'					, UpperCamelCase							)
    _snake_case      : List[Any]														=		True

    _snake_case      : str														=		None
    _snake_case      : str														=		None

    # First, let's see if we have a preprocessor config.
    # Filter the kwargs for `get_file_from_repo`.
    _snake_case      : Dict														=		{
        key: kwargs[key] for key in inspect.signature(UpperCamelCase							).parameters.keys() if key in kwargs
    }
    # Let's start by checking whether the processor class is saved in an image processor
    _snake_case      : List[Any]														=		get_file_from_repo(
        UpperCamelCase					, UpperCamelCase					, **UpperCamelCase							)
    if preprocessor_config_file is not None:
      _snake_case							,    _snake_case      : Optional[int]														=		ImageProcessingMixin.get_image_processor_dict(UpperCamelCase					, **UpperCamelCase							)
      _snake_case      : List[str]														=		config_dict.get('processor_class'					, UpperCamelCase							)
      if "AutoProcessor" in config_dict.get('auto_map'					, {}							):
        _snake_case      : Optional[Any]														=		config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
    if preprocessor_config_file is not None and processor_class is None:
      _snake_case							,    _snake_case      : Union[str, Any]														=		FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase					, **UpperCamelCase							)
      _snake_case      : int														=		config_dict.get('processor_class'					, UpperCamelCase							)
      if "AutoProcessor" in config_dict.get('auto_map'					, {}							):
        _snake_case      : str														=		config_dict['auto_map']['AutoProcessor']

    if processor_class is None:
      # Next, let's check whether the processor class is saved in a tokenizer
      _snake_case      : Union[str, Any]														=		get_file_from_repo(
          UpperCamelCase					, UpperCamelCase					, **UpperCamelCase							)
      if tokenizer_config_file is not None:
        with open(UpperCamelCase					, encoding='utf-8'							) as reader:
          _snake_case      : Optional[int]														=		json.load(UpperCamelCase							)

        _snake_case      : int														=		config_dict.get('processor_class'					, UpperCamelCase							)
        if "AutoProcessor" in config_dict.get('auto_map'					, {}							):
          _snake_case      : str														=		config_dict['auto_map']['AutoProcessor']

    if processor_class is None:
      # Otherwise, load config, if it can be loaded.
      if not isinstance(UpperCamelCase					, UpperCamelCase							):
        _snake_case      : Dict														=		AutoConfig.from_pretrained(
            UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)

      # And check if the config contains the processor class.
      _snake_case      : str														=		getattr(UpperCamelCase					, 'processor_class'					, UpperCamelCase							)
      if hasattr(UpperCamelCase					, 'auto_map'							) and "AutoProcessor" in config.auto_map:
        _snake_case      : Any														=		config.auto_map['AutoProcessor']

    if processor_class is not None:
      _snake_case      : Tuple														=		processor_class_from_name(UpperCamelCase							)

    _snake_case      : int														=		processor_auto_map is not None
    _snake_case      : Optional[int]														=		processor_class is not None or type(UpperCamelCase							) in PROCESSOR_MAPPING
    _snake_case      : Union[str, Any]														=		resolve_trust_remote_code(
        UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)

    if has_remote_code and trust_remote_code:
      _snake_case      : List[str]														=		get_class_from_dynamic_module(
          UpperCamelCase					, UpperCamelCase					, **UpperCamelCase							)
      _snake_case      : Optional[Any]														=		kwargs.pop('code_revision'					, UpperCamelCase							)
      if os.path.isdir(UpperCamelCase							):
        processor_class.register_for_auto_class()
      return processor_class.from_pretrained(
          UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
    elif processor_class is not None:
      return processor_class.from_pretrained(
          UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
    # Last try: we use the PROCESSOR_MAPPING.
    elif type(UpperCamelCase							) in PROCESSOR_MAPPING:
      return PROCESSOR_MAPPING[type(UpperCamelCase							)].from_pretrained(UpperCamelCase					, **UpperCamelCase							)

    # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
    # tokenizer.
    try:
      return AutoTokenizer.from_pretrained(
          UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
    except Exception:
      try:
        return AutoImageProcessor.from_pretrained(
            UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
      except Exception:
        pass

      try:
        return AutoFeatureExtractor.from_pretrained(
            UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
      except Exception:
        pass

    raise ValueError(
        f"""Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a """
        'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
        'the files of at least one of those processing classes.'							)







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    int					, UpperCamelCase					:    str							):


    '''simple docstring'''





    PROCESSOR_MAPPING.register(UpperCamelCase					, UpperCamelCase							)




