"""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


__UpperCamelCase       :    str         =		logging.get_logger(__name__)

__UpperCamelCase       :    Tuple         =		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       :    Dict         =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def   __UpperCAmelCase     (	_snake_case		:       str       ):
   for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
      if class_name in processors:
         _lowercase							     =       model_type_to_module_name(_snake_case       )

         _lowercase							     =       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							     =       importlib.import_module("transformers"       )
   if hasattr(_snake_case,				_snake_case       ):
      return getattr(_snake_case,				_snake_case       )

   return None
class   UpperCAmelCase_	:
    def __init__(	self			:       str		)						->    Tuple:
       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(_lowercase		)
    def 		_lowerCamelCase      (	cls			:       List[str]   ,						_lowercase			:       Union[str, Any]   ,						**_lowercase			:       str		)						->    Tuple:
       _lowercase							     =       kwargs.pop("config"   ,						_lowercase		)
       _lowercase							     =       kwargs.pop("trust_remote_code"   ,						_lowercase		)
       _lowercase							     =       True

       _lowercase							     =       None
       _lowercase							     =       None

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

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

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

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

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

       if processor_class is not None:
          _lowercase							     =       processor_class_from_name(_lowercase		)

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

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

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

          try:
             return AutoFeatureExtractor.from_pretrained(
                 _lowercase   ,						trust_remote_code=_lowercase   ,						**_lowercase		)
          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      (	_lowercase			:       Tuple   ,						_lowercase			:       str		)						->    int:
       PROCESSOR_MAPPING.register(_lowercase   ,						_lowercase		)
"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase       :    Union[str, Any]         =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase       :    Optional[Any]         =		[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)


def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _lowercase							     =       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase							     =       key.replace("backbone.0.body",				"backbone.conv_encoder.model"       )
         _lowercase							     =       value
      else:
         _lowercase							     =       value

   return new_state_dict


def   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int=False       ):
   _lowercase							     =       ""
   if is_panoptic:
      _lowercase							     =       "detr."

   # first: transformer encoder
   for i in range(6       ):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) to the state dict
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6       ):
      # read in weights + bias of input projection layer of self-attention
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) to the state dict
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _lowercase							     =       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      _lowercase							     =       in_proj_weight_cross_attn[:2_5_6, :]
      _lowercase							     =       in_proj_bias_cross_attn[:2_5_6]
      _lowercase							     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight_cross_attn[-2_5_6:, :]
      _lowercase							     =       in_proj_bias_cross_attn[-2_5_6:]


def   __UpperCAmelCase     (	):
   _lowercase							     =       "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowercase							     =       Image.open(requests.get(_snake_case,				stream=_snake_case       ).raw       )

   return im


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       str       ):
   _lowercase							     =       DetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      _lowercase							     =       "resnet101"
   if "dc5" in model_name:
      _lowercase							     =       True
   _lowercase							     =       "panoptic" in model_name
   if is_panoptic:
      _lowercase							     =       2_5_0
   else:
      _lowercase							     =       9_1
      _lowercase							     =       "huggingface/label-files"
      _lowercase							     =       "coco-detection-id2label.json"
      _lowercase							     =       json.load(open(hf_hub_download(_snake_case,				_snake_case,				repo_type="dataset"       ),				"r"       )       )
      _lowercase							     =       {int(_snake_case       ): v for k, v in idalabel.items()}
      _lowercase							     =       idalabel
      _lowercase							     =       {v: k for k, v in idalabel.items()}

   # load image processor
   _lowercase							     =       "coco_panoptic" if is_panoptic else "coco_detection"
   _lowercase							     =       DetrImageProcessor(format=_snake_case       )

   # prepare image
   _lowercase							     =       prepare_img()
   _lowercase							     =       image_processor(images=_snake_case,				return_tensors="pt"       )
   _lowercase							     =       encoding["pixel_values"]

   logger.info(f"""Converting model {model_name}..."""       )

   # load original model from torch hub
   _lowercase							     =       torch.hub.load("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "detr." + src
      rename_key(_snake_case,				_snake_case,				_snake_case       )
   _lowercase							     =       rename_backbone_keys(_snake_case       )
   # query, key and value matrices need special treatment
   read_in_q_k_v(_snake_case,				is_panoptic=_snake_case       )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   _lowercase							     =       "detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("detr"       )
             and not key.startswith("class_labels_classifier"       )
             and not key.startswith("bbox_predictor"       )
         ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
    # finally, create HuggingFace model and load state dict
   _lowercase							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       detr(_snake_case       )
   _lowercase							     =       model(_snake_case       )
   assert torch.allclose(outputs.logits,				original_outputs["pred_logits"],				atol=1e-4       )
   assert torch.allclose(outputs.pred_boxes,				original_outputs["pred_boxes"],				atol=1e-4       )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks,				original_outputs["pred_masks"],				atol=1e-4       )

   # Save model and image processor
   logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
   Path(_snake_case       ).mkdir(exist_ok=_snake_case       )
   model.save_pretrained(_snake_case       )
   image_processor.save_pretrained(_snake_case       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		argparse.ArgumentParser()

  parser.add_argument(
      "--model_name", default="detr_resnet50", type=str, help="Name of the DETR model you'd like to convert."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
  )
  __UpperCamelCase       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)