





"""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_	:		str         =   logging.get_logger(__name__)

lowerCamelCase_	:		int         =   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_	:		Any         =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      A_    :			int									=    model_type_to_module_name(_UpperCAmelCase   )

      A_    :			Optional[int]									=    importlib.import_module(f""".{module_name}"""					,   'transformers.models'   )
      try:
        return getattr(_UpperCAmelCase					,   _UpperCAmelCase   )
      except AttributeError:
        continue

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(_UpperCAmelCase					,   '__name__'					,   _UpperCAmelCase   ) == 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_    :			Any									=    importlib.import_module('transformers'   )
  if hasattr(_UpperCAmelCase					,   _UpperCAmelCase   ):
    return getattr(_UpperCAmelCase					,   _UpperCAmelCase   )

  return None





class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self    ):

        """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(snake_case_    )
      def    lowerCamelCase_     (	cls   , snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    kwargs.pop('config'   , snake_case_    )
        A_    :			Tuple									=    kwargs.pop('trust_remote_code'   , snake_case_    )
        A_    :			Optional[Any]									=    True

        A_    :			Optional[Any]									=    None
        A_    :			Optional[int]									=    None

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

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

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

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

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

        if processor_class is not None:
          A_    :			List[Any]									=    processor_class_from_name(snake_case_    )

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

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

        # 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(
              snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
        except Exception:
          try:
            return AutoImageProcessor.from_pretrained(
                snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
          except Exception:
            pass

          try:
            return AutoFeatureExtractor.from_pretrained(
                snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
          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_     (	snake_case_   , snake_case_    ):

        """simple docstring"""

        PROCESSOR_MAPPING.register(snake_case_   , snake_case_    )



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import TvltImageProcessor






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=10			,  __lowerCamelCase=10			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   []
 for i in range(image_processor_tester.num_frames							):
  video.append(np.random.randint(255			,  size=(image_processor_tester.num_channels, width, height)			,  dtype=np.uinta							)							)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  snake_case :			Optional[int]							=   [Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for frame in video]

 if torchify:
  snake_case :			int							=   [torch.from_numpy(__lowerCamelCase							) for frame in video]

 return video






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Optional[Any]:


 '''simple docstring'''
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 snake_case :			Any							=   []
 for i in range(image_processor_tester.batch_size							):
  if equal_resolution:
   snake_case :			Optional[int]							=   image_processor_tester.max_resolution
  else:
   snake_case	,       snake_case :			str							=   np.random.choice(
       np.arange(image_processor_tester.min_resolution			,  image_processor_tester.max_resolution							)			,  2							)
   snake_case :			Any							=   prepare_video(
       image_processor_tester=__lowerCamelCase			,  width=__lowerCamelCase			,  height=__lowerCamelCase			,  numpify=__lowerCamelCase			,  torchify=__lowerCamelCase			,  )
  video_inputs.append(__lowerCamelCase							)

 return video_inputs

class   lowerCamelCase						(	unittest.TestCase  ):

       def __init__(      self						,    a__						,    a__=7						,    a__=3						,    a__=4						,    a__=18						,    a__=30						,    a__=4_00						,    a__=True						,    a__=None						,    a__=True						,    a__=[0.5, 0.5, 0.5]						,    a__=[0.5, 0.5, 0.5]						,    a__=True						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Tuple							=   size if size is not None else {'''shortest_edge''': 18}
        snake_case :			List[str]							=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        snake_case :			Optional[Any]							=   parent
        snake_case :			List[Any]							=   batch_size
        snake_case :			Optional[int]							=   num_channels
        snake_case :			Union[str, Any]							=   num_frames
        snake_case :			List[Any]							=   image_size
        snake_case :			Tuple							=   min_resolution
        snake_case :			List[Any]							=   max_resolution
        snake_case :			Tuple							=   do_resize
        snake_case :			List[Any]							=   size
        snake_case :			List[Any]							=   do_normalize
        snake_case :			List[Any]							=   image_mean
        snake_case :			List[str]							=   image_std
        snake_case :			Tuple							=   do_center_crop
        snake_case :			str							=   crop_size



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   TvltImageProcessor if is_vision_available() else None

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   TvltImageProcessorTester(self       )

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(a__						,    '''image_mean'''       )       )
        self.assertTrue(hasattr(a__						,    '''image_std'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_normalize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_resize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_center_crop'''       )       )
        self.assertTrue(hasattr(a__						,    '''size'''       )       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL videos
        snake_case :			List[str]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    Image.Image       )

        # Test not batched input
        snake_case :			int							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Optional[Any]							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        snake_case :			Union[str, Any]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    numpify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    np.ndarray       )

        # Test not batched input
        snake_case :			List[str]							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Tuple							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        snake_case :			str							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    torchify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    torch.Tensor       )

        # Test not batched input
        snake_case :			Tuple							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Optional[int]							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )






