
"""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


__A 		= logging.get_logger(__name__)

__A 		= 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'),
    ]
)

__A 		= _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)



def 					_lowerCamelCase(__UpperCamelCase )       ->				List[Any]:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_lowerCAmelCase			=model_type_to_module_name(__UpperCamelCase )

			_lowerCAmelCase			=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.
	_lowerCAmelCase			=importlib.import_module("""transformers""" )
	if hasattr(__UpperCamelCase					,				__UpperCamelCase ):
		return getattr(__UpperCamelCase					,				__UpperCamelCase )

	return None







class 	lowerCamelCase__      :





			'''simple docstring'''



			def __init__(							self						)				->  Any:
				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  ,  __UpperCAmelCase  ,  **__UpperCAmelCase						)				->  Dict:
				_lowerCAmelCase			=kwargs.pop("""config"""  ,  __UpperCAmelCase						)
				_lowerCAmelCase			=kwargs.pop("""trust_remote_code"""  ,  __UpperCAmelCase						)
				_lowerCAmelCase			=True

				_lowerCAmelCase			=None
				_lowerCAmelCase			=None

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

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

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

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

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

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

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

				if has_remote_code and trust_remote_code:
					_lowerCAmelCase			=get_class_from_dynamic_module(
					    __UpperCAmelCase  ,  __UpperCAmelCase  ,  **__UpperCAmelCase						)
					_lowerCAmelCase			=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  ,  __UpperCAmelCase						)				->  Tuple:
				PROCESSOR_MAPPING.register(__UpperCAmelCase  ,  __UpperCAmelCase						)





"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvNextV2ForImageClassification',
			    'ConvNextV2Model',
			    'ConvNextV2PreTrainedModel',
			    'ConvNextV2Backbone',
			]


if TYPE_CHECKING:
			from .configuration_convnextva import (
			    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConvNextVaConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnextva import (
						    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextVaBackbone,
						    ConvNextVaForImageClassification,
						    ConvNextVaModel,
						    ConvNextVaPreTrainedModel,
						)

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



