



"""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()
UpperCAmelCase						:		Optional[int]			= logging.get_logger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  if "resnet-50" in model_name:
    lowercase_								= ResNetConfig.from_pretrained("""microsoft/resnet-50"""							)
  elif "resnet-101" in model_name:
    lowercase_								= ResNetConfig.from_pretrained("""microsoft/resnet-101"""							)
  else:
    raise ValueError("""Model name should include either resnet50 or resnet101"""							)

  lowercase_								= DetrConfig(use_timm_backbone=__lowerCAmelCase ,  backbone_config=__lowerCAmelCase							)

  # set label attributes
  lowercase_								= """panoptic""" in model_name
  if is_panoptic:
    lowercase_								= 2_50
  else:
    lowercase_								= 91
    lowercase_								= """huggingface/label-files"""
    lowercase_								= """coco-detection-id2label.json"""
    lowercase_								= json.load(open(hf_hub_download(__lowerCAmelCase ,  __lowerCAmelCase ,  repo_type="""dataset"""							) ,  """r"""							)							)
    lowercase_								= {int(__lowerCAmelCase							): v for k, v in idalabel.items()}
    lowercase_								= idalabel
    lowercase_								= {v: k for k, v in idalabel.items()}

  return config, is_panoptic


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= []

  # 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  lowercase_								= state_dict.pop(__lowerCAmelCase							)
  lowercase_								= val


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=False							) ->						Dict:







  '''simple docstring'''


  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_56, :]
    lowercase_								= in_proj_bias[:2_56]
    lowercase_								= in_proj_weight[2_56:5_12, :]
    lowercase_								= in_proj_bias[2_56:5_12]
    lowercase_								= in_proj_weight[-2_56:, :]
    lowercase_								= in_proj_bias[-2_56:]
  # 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_56, :]
    lowercase_								= in_proj_bias[:2_56]
    lowercase_								= in_proj_weight[2_56:5_12, :]
    lowercase_								= in_proj_bias[2_56:5_12]
    lowercase_								= in_proj_weight[-2_56:, :]
    lowercase_								= in_proj_bias[-2_56:]
    # 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_56, :]
    lowercase_								= in_proj_bias_cross_attn[:2_56]
    lowercase_								= in_proj_weight_cross_attn[2_56:5_12, :]
    lowercase_								= in_proj_bias_cross_attn[2_56:5_12]
    lowercase_								= in_proj_weight_cross_attn[-2_56:, :]
    lowercase_								= in_proj_bias_cross_attn[-2_56:]


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''simple docstring'''


  lowercase_								= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase_								= Image.open(requests.get(__lowerCAmelCase ,  stream=__lowerCAmelCase							).raw							)

  return im


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=None ,  __lowerCAmelCase=False							) ->						List[Any]:







  '''simple docstring'''


  lowercase_					,    lowercase_								= get_detr_config(__lowerCAmelCase							)

  # load original model from torch hub
  lowercase_								= {
      """detr-resnet-50""": """detr_resnet50""",
      """detr-resnet-101""": """detr_resnet101""",
  }
  logger.info(F'''Converting model {model_name}...'''							)
  lowercase_								= torch.hub.load("""facebookresearch/detr""" ,  model_name_to_original_name[model_name] ,  pretrained=__lowerCAmelCase							).eval()
  lowercase_								= detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(__lowerCAmelCase							):
    if is_panoptic:
      lowercase_								= """detr.""" + src
    rename_key(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  # query, key and value matrices need special treatment
  read_in_q_k_v(__lowerCAmelCase ,  is_panoptic=__lowerCAmelCase							)
  # 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(__lowerCAmelCase							)
        lowercase_								= val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowercase_								= state_dict.pop(__lowerCAmelCase							)
        lowercase_								= val
      elif key.startswith("""bbox_attention"""							) or key.startswith("""mask_head"""							):
        continue
      else:
        lowercase_								= state_dict.pop(__lowerCAmelCase							)
        lowercase_								= val
    else:
      if not key.startswith("""class_labels_classifier"""							) and not key.startswith("""bbox_predictor"""							):
        lowercase_								= state_dict.pop(__lowerCAmelCase							)
        lowercase_								= val

    # finally, create HuggingFace model and load state dict
  lowercase_								= DetrForSegmentation(__lowerCAmelCase							) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase							)
  model.load_state_dict(__lowerCAmelCase							)
  model.eval()

  # verify our conversion on an image
  lowercase_								= """coco_panoptic""" if is_panoptic else """coco_detection"""
  lowercase_								= DetrImageProcessor(format=__lowerCAmelCase							)

  lowercase_								= processor(images=prepare_img() ,  return_tensors="""pt"""							)
  lowercase_								= encoding["""pixel_values"""]

  lowercase_								= detr(__lowerCAmelCase							)
  lowercase_								= model(__lowerCAmelCase							)

  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(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
    model.save_pretrained(__lowerCAmelCase							)
    processor.save_pretrained(__lowerCAmelCase							)

  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__":
       UpperCAmelCase						:		Any			= 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.")
       UpperCAmelCase						:		Tuple			= parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Any			= {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= ["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Dict			= ["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		int			= [
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= [
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure)

