'''simple docstring'''






import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Dict:
  # initialize config
  if "resnet-50" in model_name:
    __UpperCAmelCase:					str		= ResNetConfig.from_pretrained("""microsoft/resnet-50"""     )
  elif "resnet-101" in model_name:
    __UpperCAmelCase:					Dict		= ResNetConfig.from_pretrained("""microsoft/resnet-101"""     )
  else:
    raise ValueError("""Model name should include either resnet50 or resnet101"""     )

  __UpperCAmelCase:					Optional[Any]		= DetrConfig(use_timm_backbone=_lowercase   ,    backbone_config=_lowercase     )

  # set label attributes
  __UpperCAmelCase:					Optional[Any]		= """panoptic""" in model_name
  if is_panoptic:
    __UpperCAmelCase:					List[Any]		= 2_5_0
  else:
    __UpperCAmelCase:					str		= 9_1
    __UpperCAmelCase:					Optional[int]		= """huggingface/label-files"""
    __UpperCAmelCase:					str		= """coco-detection-id2label.json"""
    __UpperCAmelCase:					str		= json.load(open(hf_hub_download(_lowercase   ,    _lowercase   ,    repo_type="""dataset"""     )   ,    """r"""     )     )
    __UpperCAmelCase:					str		= {int(_lowercase     ): v for k, v in idalabel.items()}
    __UpperCAmelCase:					Union[str, Any]		= idalabel
    __UpperCAmelCase:					Any		= {v: k for k, v in idalabel.items()}

  return config, is_panoptic






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	List[Any]:
  # here we list all keys to be renamed (original name on the left, our name on the right)
  __UpperCAmelCase:					Tuple		= []

  # stem
  # fmt: off
  rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")     )
  rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")     )
  rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")     )
  rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")     )
  rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")     )
  # stages
  for stage_idx in range(len(config.backbone_config.depths     )     ):
    for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
            )     )
      # 3 convs
      for i in range(3     ):
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
            )     )
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
            )     )
    # fmt: on

  for i in range(config.encoder_layers     ):
    # 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"""),
      ]     )

  return rename_keys






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				List[Any]   ,    _lowercase    :				int     )							->	Any:
  __UpperCAmelCase:					Tuple		= state_dict.pop(_lowercase     )
  __UpperCAmelCase:					str		= val






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Optional[int]=False     )							->	List[str]:
  __UpperCAmelCase:					Dict		= """"""
  if is_panoptic:
    __UpperCAmelCase:					List[str]		= """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)
    __UpperCAmelCase:					Tuple		= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''     )
    __UpperCAmelCase:					Optional[Any]		= 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
    __UpperCAmelCase:					Optional[Any]		= in_proj_weight[:2_5_6, :]
    __UpperCAmelCase:					Any		= in_proj_bias[:2_5_6]
    __UpperCAmelCase:					str		= in_proj_weight[2_5_6:5_1_2, :]
    __UpperCAmelCase:					str		= in_proj_bias[2_5_6:5_1_2]
    __UpperCAmelCase:					Optional[Any]		= in_proj_weight[-2_5_6:, :]
    __UpperCAmelCase:					List[Any]		= 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
    __UpperCAmelCase:					Tuple		= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
    __UpperCAmelCase:					Any		= 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
    __UpperCAmelCase:					Dict		= in_proj_weight[:2_5_6, :]
    __UpperCAmelCase:					List[Any]		= in_proj_bias[:2_5_6]
    __UpperCAmelCase:					Any		= in_proj_weight[2_5_6:5_1_2, :]
    __UpperCAmelCase:					Tuple		= in_proj_bias[2_5_6:5_1_2]
    __UpperCAmelCase:					Optional[Any]		= in_proj_weight[-2_5_6:, :]
    __UpperCAmelCase:					Optional[Any]		= in_proj_bias[-2_5_6:]
    # read in weights + bias of input projection layer of cross-attention
    __UpperCAmelCase:					int		= state_dict.pop(
        F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''     )
    __UpperCAmelCase:					Optional[int]		= 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
    __UpperCAmelCase:					Optional[Any]		= in_proj_weight_cross_attn[:2_5_6, :]
    __UpperCAmelCase:					Union[str, Any]		= in_proj_bias_cross_attn[:2_5_6]
    __UpperCAmelCase:					str		= in_proj_weight_cross_attn[2_5_6:5_1_2, :]
    __UpperCAmelCase:					Union[str, Any]		= in_proj_bias_cross_attn[2_5_6:5_1_2]
    __UpperCAmelCase:					Optional[Any]		= in_proj_weight_cross_attn[-2_5_6:, :]
    __UpperCAmelCase:					Union[str, Any]		= in_proj_bias_cross_attn[-2_5_6:]






def  UpperCamelCase__					(					)							->	str:
  __UpperCAmelCase:					Optional[Any]		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase:					Tuple		= Image.open(requests.get(_lowercase   ,    stream=_lowercase     ).raw     )

  return im






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				Any=None   ,    _lowercase    :				int=False     )							->	Optional[Any]:
  __UpperCAmelCase, __UpperCAmelCase:					Union[str, Any]		= get_detr_config(_lowercase     )

  # load original model from torch hub
  __UpperCAmelCase:					List[Any]		= {
      """detr-resnet-50""": """detr_resnet50""",
      """detr-resnet-101""": """detr_resnet101""",
  }
  logger.info(F'''Converting model {model_name}...'''     )
  __UpperCAmelCase:					Union[str, Any]		= torch.hub.load("""facebookresearch/detr"""   ,    model_name_to_original_name[model_name]   ,    pretrained=_lowercase     ).eval()
  __UpperCAmelCase:					str		= detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(_lowercase     ):
    if is_panoptic:
      __UpperCAmelCase:					Union[str, Any]		= """detr.""" + src
    rename_key(_lowercase   ,    _lowercase   ,    _lowercase     )
  # query, key and value matrices need special treatment
  read_in_q_k_v(_lowercase   ,    is_panoptic=_lowercase     )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  __UpperCAmelCase:					Union[str, Any]		= """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"""     )
      ):
        __UpperCAmelCase:					List[str]		= state_dict.pop(_lowercase     )
        __UpperCAmelCase:					Union[str, Any]		= val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        __UpperCAmelCase:					str		= state_dict.pop(_lowercase     )
        __UpperCAmelCase:					List[str]		= val
      elif key.startswith("""bbox_attention"""     ) or key.startswith("""mask_head"""     ):
        continue
      else:
        __UpperCAmelCase:					Optional[Any]		= state_dict.pop(_lowercase     )
        __UpperCAmelCase:					int		= val
    else:
      if not key.startswith("""class_labels_classifier"""     ) and not key.startswith("""bbox_predictor"""     ):
        __UpperCAmelCase:					Optional[Any]		= state_dict.pop(_lowercase     )
        __UpperCAmelCase:					str		= val

    # finally, create HuggingFace model and load state dict
  __UpperCAmelCase:					Optional[Any]		= DetrForSegmentation(_lowercase     ) if is_panoptic else DetrForObjectDetection(_lowercase     )
  model.load_state_dict(_lowercase     )
  model.eval()

  # verify our conversion on an image
  __UpperCAmelCase:					Optional[int]		= """coco_panoptic""" if is_panoptic else """coco_detection"""
  __UpperCAmelCase:					Any		= DetrImageProcessor(format=_lowercase     )

  __UpperCAmelCase:					Optional[int]		= processor(images=prepare_img()   ,    return_tensors="""pt"""     )
  __UpperCAmelCase:					str		= encoding["""pixel_values"""]

  __UpperCAmelCase:					Dict		= detr(_lowercase     )
  __UpperCAmelCase:					str		= model(_lowercase     )

  assert torch.allclose(outputs.logits   ,    original_outputs["""pred_logits"""]   ,    atol=1E-3     )
  assert torch.allclose(outputs.pred_boxes   ,    original_outputs["""pred_boxes"""]   ,    atol=1E-3     )
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks   ,    original_outputs["""pred_masks"""]   ,    atol=1E-4     )
  print("""Looks ok!"""     )

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''     )
    Path(_lowercase     ).mkdir(exist_ok=_lowercase     )
    model.save_pretrained(_lowercase     )
    processor.save_pretrained(_lowercase     )

  if push_to_hub:
    # Upload model and image processor to the hub
    logger.info("""Uploading PyTorch model and image processor to the hub..."""     )
    model.push_to_hub(F'''nielsr/{model_name}'''     )
    processor.push_to_hub(F'''nielsr/{model_name}'''     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        default='detr-resnet-50',
        type=str,
        choices=['detr-resnet-50', 'detr-resnet-101'],
        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.'
    )
    parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


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