






'''simple docstring'''




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       __lowercase		(    __lowercase ) ->		List[str]:

      '''simple docstring'''


      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  _A      =			model_type_to_module_name(__lowercase )

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

      for processor in PROCESSOR_MAPPING._extra_content.values():
            if getattr(__lowercase     ,						"__name__"     ,						__lowercase ) == 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.
      _A      =			importlib.import_module("transformers" )
      if hasattr(__lowercase     ,						__lowercase ):
            return getattr(__lowercase     ,						__lowercase )

      return None


class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			List[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 		lowerCAmelCase				(	cls				:			int   ,   __UpperCAmelCase				:			Dict   ,   **__UpperCAmelCase				:			Dict	):





         '''simple docstring'''





         _A      =			kwargs.pop("config"   ,   __UpperCAmelCase	)
         _A      =			kwargs.pop("trust_remote_code"   ,   __UpperCAmelCase	)
         _A      =			True

         _A      =			None
         _A      =			None

         # First, let's see if we have a preprocessor config.
         # Filter the kwargs for `get_file_from_repo`.
         _A      =			{
             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
         _A      =			get_file_from_repo(
             __UpperCAmelCase   ,   __UpperCAmelCase   ,   **__UpperCAmelCase	)
         if preprocessor_config_file is not None:
               _A  ,			_A      =			ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase   ,   **__UpperCAmelCase	)
               _A      =			config_dict.get("processor_class"   ,   __UpperCAmelCase	)
               if "AutoProcessor" in config_dict.get("auto_map"   ,   {}	):
                     _A      =			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:
               _A  ,			_A      =			FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase   ,   **__UpperCAmelCase	)
               _A      =			config_dict.get("processor_class"   ,   __UpperCAmelCase	)
               if "AutoProcessor" in config_dict.get("auto_map"   ,   {}	):
                     _A      =			config_dict["auto_map"]["AutoProcessor"]

         if processor_class is None:
               # Next, let's check whether the processor class is saved in a tokenizer
               _A      =			get_file_from_repo(
                   __UpperCAmelCase   ,   __UpperCAmelCase   ,   **__UpperCAmelCase	)
               if tokenizer_config_file is not None:
                     with open(__UpperCAmelCase   ,   encoding="utf-8"	) as reader:
                           _A      =			json.load(__UpperCAmelCase	)

                     _A      =			config_dict.get("processor_class"   ,   __UpperCAmelCase	)
                     if "AutoProcessor" in config_dict.get("auto_map"   ,   {}	):
                           _A      =			config_dict["auto_map"]["AutoProcessor"]

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

               # And check if the config contains the processor class.
               _A      =			getattr(__UpperCAmelCase   ,   "processor_class"   ,   __UpperCAmelCase	)
               if hasattr(__UpperCAmelCase   ,   "auto_map"	) and "AutoProcessor" in config.auto_map:
                     _A      =			config.auto_map["AutoProcessor"]

         if processor_class is not None:
               _A      =			processor_class_from_name(__UpperCAmelCase	)

         _A      =			processor_auto_map is not None
         _A      =			processor_class is not None or type(__UpperCAmelCase	) in PROCESSOR_MAPPING
         _A      =			resolve_trust_remote_code(
             __UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase	)

         if has_remote_code and trust_remote_code:
               _A      =			get_class_from_dynamic_module(
                   __UpperCAmelCase   ,   __UpperCAmelCase   ,   **__UpperCAmelCase	)
               _A      =			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 		lowerCAmelCase				(	__UpperCAmelCase				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         PROCESSOR_MAPPING.register(__UpperCAmelCase   ,   __UpperCAmelCase	)













'''simple docstring'''




from typing import Any

import numpy as np

def       __lowercase		(    __lowercase ) ->		bool:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(__lowercase     ,						matrix.T ) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      _A  ,			_A      =			np.linalg.eigh(__lowercase )

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0 ) )

def       __lowercase		(    __lowercase ) ->		Any:

      '''simple docstring'''


      _A      =			np.random.randn(__lowercase     ,						__lowercase )
      _A      =			np.dot(__lowercase     ,						random_matrix.T )
      assert _is_matrix_spd(__lowercase )
      return spd_matrix

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase = 1000     ,						__lowercase = 1e-8     ,						) ->		Any:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]
      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[0]
      assert _is_matrix_spd(__lowercase )

      # Initialize solution guess, residual, search direction.
      _A      =			np.zeros((np.shape(__lowercase )[0], 1) )
      _A      =			np.copy(__lowercase )
      _A      =			np.copy(__lowercase )

      # Set initial errors in solution guess and residual.
      _A      =			1e9
      _A      =			1e9
      _A      =			1e9

      # Set iteration counter to threshold number of iterations.
      _A      =			0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            _A      =			np.dot(__lowercase     ,						__lowercase )

            # The main algorithm.

            # Update search direction magnitude.
            _A      =			np.dot(ra.T     ,						__lowercase ) / np.dot(pa.T     ,						__lowercase )
            # Update solution guess.
            _A      =			xa + alpha * pa
            # Calculate new residual.
            _A      =			ra - alpha * w
            # Calculate new Krylov subspace scale.
            _A      =			np.dot(r.T     ,						__lowercase ) / np.dot(ra.T     ,						__lowercase )
            # Calculate new A conjuage search direction.
            _A      =			r + beta * pa

            # Calculate errors.
            _A      =			np.linalg.norm(r - ra )
            _A      =			np.linalg.norm(x - xa )
            _A      =			np.maximum(__lowercase     ,						__lowercase )

            # Update variables.
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x

def       __lowercase		(    ) ->		None:

      '''simple docstring'''


      _A      =			3
      _A      =			_create_spd_matrix(__lowercase )
      _A      =			np.random.randn(__lowercase     ,						1 )
      _A      =			np.dot(__lowercase     ,						__lowercase )

      # Numpy solution.
      _A      =			np.linalg.solve(__lowercase     ,						__lowercase )

      # Our implementation.
      _A      =			conjugate_gradient(__lowercase     ,						__lowercase )

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true ) <= 1e-6
      assert np.linalg.norm(x_conjugate_gradient - x_true ) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_conjugate_gradient()





