






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


UpperCamelCase			=      logging.get_logger(__name__)

UpperCamelCase			=      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"""),
    ]
)

UpperCamelCase			=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def     A(  snake_case_		):
       """simple docstring"""





       for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                     lowercase__:    str        =      model_type_to_module_name(snake_case_		)

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

       for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(snake_case_						,       "__name__"						,       snake_case_		) == 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.
       lowercase__:    Union[str, Any]        =      importlib.import_module("transformers"		)
       if hasattr(snake_case_						,       snake_case_		):
              return getattr(snake_case_						,       snake_case_		)

       return None







class 		_a       :





   '''simple docstring'''






   def __init__(   self)	-> Tuple:




          '''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 __lowercase    (   cls			,				UpperCAmelCase_			,				**UpperCAmelCase_)	-> Any:




          '''simple docstring'''




          lowercase__:    Optional[int]        =      kwargs.pop("config"			,				UpperCAmelCase_)
          lowercase__:    int        =      kwargs.pop("trust_remote_code"			,				UpperCAmelCase_)
          lowercase__:    List[Any]        =      True

          lowercase__:    int        =      None
          lowercase__:    Dict        =      None

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

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

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

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

                 # And check if the config contains the processor class.
                 lowercase__:    Union[str, Any]        =      getattr(UpperCAmelCase_			,				"processor_class"			,				UpperCAmelCase_)
                 if hasattr(UpperCAmelCase_			,				"auto_map") and "AutoProcessor" in config.auto_map:
                        lowercase__:    Dict        =      config.auto_map["AutoProcessor"]

          if processor_class is not None:
                 lowercase__:    Tuple        =      processor_class_from_name(UpperCAmelCase_)

          lowercase__:    Optional[int]        =      processor_auto_map is not None
          lowercase__:    Any        =      processor_class is not None or type(UpperCAmelCase_) in PROCESSOR_MAPPING
          lowercase__:    Dict        =      resolve_trust_remote_code(
              UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)

          if has_remote_code and trust_remote_code:
                 lowercase__:    Optional[int]        =      get_class_from_dynamic_module(
                     UpperCAmelCase_			,				UpperCAmelCase_			,				**UpperCAmelCase_)
                 lowercase__:    Optional[int]        =      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 __lowercase    (   UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          PROCESSOR_MAPPING.register(UpperCAmelCase_			,				UpperCAmelCase_)














"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. 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

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase			=      {
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase			=      ["""DocumentQuestionAnsweringTool"""]
  UpperCamelCase			=      ["""ImageCaptioningTool"""]
  UpperCamelCase			=      ["""ImageQuestionAnsweringTool"""]
  UpperCamelCase			=      ["""ImageSegmentationTool"""]
  UpperCamelCase			=      ["""SpeechToTextTool"""]
  UpperCamelCase			=      ["""TextClassificationTool"""]
  UpperCamelCase			=      ["""TextQuestionAnsweringTool"""]
  UpperCamelCase			=      ["""TextSummarizationTool"""]
  UpperCamelCase			=      ["""TextToSpeechTool"""]
  UpperCamelCase			=      ["""TranslationTool"""]

if TYPE_CHECKING:
  from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
  from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .document_question_answering import DocumentQuestionAnsweringTool
    from .image_captioning import ImageCaptioningTool
    from .image_question_answering import ImageQuestionAnsweringTool
    from .image_segmentation import ImageSegmentationTool
    from .speech_to_text import SpeechToTextTool
    from .text_classification import TextClassificationTool
    from .text_question_answering import TextQuestionAnsweringTool
    from .text_summarization import TextSummarizationTool
    from .text_to_speech import TextToSpeechTool
    from .translation import TranslationTool
else:
  import sys

  UpperCamelCase			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






