






'''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()
a_					=	logging.get_logger(__name__)





def 	_a(					UpperCamelCase__			:  Optional[Any]							):




      '''simple docstring'''






      if "resnet-50" in model_name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=ResNetConfig.from_pretrained('''microsoft/resnet-50'''							)
      elif "resnet-101" in model_name:
            SCREAMING_SNAKE_CASE__       :			Dict													=ResNetConfig.from_pretrained('''microsoft/resnet-101'''							)
      else:
            raise ValueError('''Model name should include either resnet50 or resnet101'''							)

      SCREAMING_SNAKE_CASE__       :			List[str]													=DetrConfig(use_timm_backbone=UpperCamelCase__,		backbone_config=UpperCamelCase__							)

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

      return config, is_panoptic





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=[]

      # 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 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=state_dict.pop(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=val





def 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Tuple=False							):




      '''simple docstring'''






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





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__       :			List[str]													=Image.open(requests.get(UpperCamelCase__,		stream=UpperCamelCase__							).raw							)

      return im





@torch.no_grad()
def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  List[str]=None,		UpperCamelCase__			:  str=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=get_detr_config(UpperCamelCase__							)

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

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

      # verify our conversion on an image
      SCREAMING_SNAKE_CASE__       :			int													='''coco_panoptic''' if is_panoptic else '''coco_detection'''
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=DetrImageProcessor(format=UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=processor(images=prepare_img(),		return_tensors='''pt'''							)
      SCREAMING_SNAKE_CASE__       :			Dict													=encoding['''pixel_values''']

      SCREAMING_SNAKE_CASE__       :			str													=detr(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			List[str]													=model(UpperCamelCase__							)

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

      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__":
  a_					=	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.')
  a_					=	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_torch_available


a_					=	{
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MegatronBertForCausalLM',
      'MegatronBertForMaskedLM',
      'MegatronBertForMultipleChoice',
      'MegatronBertForNextSentencePrediction',
      'MegatronBertForPreTraining',
      'MegatronBertForQuestionAnswering',
      'MegatronBertForSequenceClassification',
      'MegatronBertForTokenClassification',
      'MegatronBertModel',
      'MegatronBertPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_megatron_bert import (
        MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
        MegatronBertPreTrainedModel,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)