
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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,
)


a_											=						logging.get_logger(__name__)

a_											=						OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

a_											=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            snake_case_ :    int     			=						model_type_to_module_name(__SCREAMING_SNAKE_CASE       )

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

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(__SCREAMING_SNAKE_CASE,		"__name__",		__SCREAMING_SNAKE_CASE       ) == class_name:
            return extractor

    # 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_ :    List[Any]     			=						importlib.import_module("transformers"       )
    if hasattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
        return getattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    return None

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		__SCREAMING_SNAKE_CASE = False,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		**__SCREAMING_SNAKE_CASE,		):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						get_file_from_repo(
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		cache_dir=__SCREAMING_SNAKE_CASE,		force_download=__SCREAMING_SNAKE_CASE,		resume_download=__SCREAMING_SNAKE_CASE,		proxies=__SCREAMING_SNAKE_CASE,		use_auth_token=__SCREAMING_SNAKE_CASE,		revision=__SCREAMING_SNAKE_CASE,		local_files_only=__SCREAMING_SNAKE_CASE,		)
    if resolved_config_file is None:
        logger.info(
            "Could not locate the image processor configuration file, will try to use the model config instead."       )
        return {}

    with open(__SCREAMING_SNAKE_CASE,		encoding="utf-8"       ) as reader:
        return json.load(__SCREAMING_SNAKE_CASE       )






class    UpperCAmelCase_				:





 def __init__(    self):
     raise EnvironmentError(
         "AutoImageProcessor is designed to be instantiated "
         "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.")





 @classmethod
 @replace_list_option_in_docstrings(lowercase_)
 def        snake_case__		(    cls ,       lowercase_ ,       **lowercase_):
     snake_case_ :    Optional[int]     			=						kwargs.pop("config" ,       lowercase_)
     snake_case_ :    Union[str, Any]     			=						kwargs.pop("trust_remote_code" ,       lowercase_)
     snake_case_ :    str     			=						True

     snake_case_			, snake_case_ :    str     			=						ImageProcessingMixin.get_image_processor_dict(lowercase_ ,       **lowercase_)
     snake_case_ :    Union[str, Any]     			=						config_dict.get("image_processor_type" ,       lowercase_)
     snake_case_ :    Union[str, Any]     			=						None
     if "AutoImageProcessor" in config_dict.get("auto_map" ,       {}):
         snake_case_ :    Optional[Any]     			=						config_dict["auto_map"]["AutoImageProcessor"]

     # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
     # and if so, infer the image processor class from there.
     if image_processor_class is None and image_processor_auto_map is None:
         snake_case_ :    str     			=						config_dict.pop("feature_extractor_type" ,       lowercase_)
         if feature_extractor_class is not None:
             logger.warning(
                 "Could not find image processor class in the image processor config or the model config. Loading"
                 " based on pattern matching with the model's feature extractor configuration.")
             snake_case_ :    Dict     			=						feature_extractor_class.replace("FeatureExtractor" ,       "ImageProcessor")
         if "AutoFeatureExtractor" in config_dict.get("auto_map" ,       {}):
             snake_case_ :    Optional[int]     			=						config_dict["auto_map"]["AutoFeatureExtractor"]
             snake_case_ :    str     			=						feature_extractor_auto_map.replace("FeatureExtractor" ,       "ImageProcessor")
             logger.warning(
                 "Could not find image processor auto map in the image processor config or the model config."
                 " Loading based on pattern matching with the model's feature extractor configuration.")

        # If we don't find the image processor class in the image processor config, let's try the model config.
     if image_processor_class is None and image_processor_auto_map is None:
         if not isinstance(lowercase_ ,       lowercase_):
             snake_case_ :    int     			=						AutoConfig.from_pretrained(lowercase_ ,       **lowercase_)
         # It could be in `config.image_processor_type``
         snake_case_ :    Union[str, Any]     			=						getattr(lowercase_ ,       "image_processor_type" ,       lowercase_)
         if hasattr(lowercase_ ,       "auto_map") and "AutoImageProcessor" in config.auto_map:
             snake_case_ :    List[Any]     			=						config.auto_map["AutoImageProcessor"]

     if image_processor_class is not None:
         snake_case_ :    List[str]     			=						image_processor_class_from_name(lowercase_)

     snake_case_ :    Dict     			=						image_processor_auto_map is not None
     snake_case_ :    Any     			=						image_processor_class is not None or type(lowercase_) in IMAGE_PROCESSOR_MAPPING
     snake_case_ :    Tuple     			=						resolve_trust_remote_code(
         lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_)

     if has_remote_code and trust_remote_code:
         snake_case_ :    List[str]     			=						get_class_from_dynamic_module(
             lowercase_ ,       lowercase_ ,       **lowercase_)
         snake_case_ :    Tuple     			=						kwargs.pop("code_revision" ,       lowercase_)
         if os.path.isdir(lowercase_):
             image_processor_class.register_for_auto_class()
         return image_processor_class.from_dict(lowercase_ ,       **lowercase_)
     elif image_processor_class is not None:
         return image_processor_class.from_dict(lowercase_ ,       **lowercase_)
     # Last try: we use the IMAGE_PROCESSOR_MAPPING.
     elif type(lowercase_) in IMAGE_PROCESSOR_MAPPING:
         snake_case_ :    Tuple     			=						IMAGE_PROCESSOR_MAPPING[type(lowercase_)]
         return image_processor_class.from_dict(lowercase_ ,       **lowercase_)

     raise ValueError(
         F'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
         F'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
         F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}')





 @staticmethod
 def        snake_case__		(    lowercase_ ,       lowercase_):
     IMAGE_PROCESSOR_MAPPING.register(lowercase_ ,       lowercase_)






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




