





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()
_snake_case  :    Tuple						    =  logging.get_logger(__name__)

def   a_    ( lowerCAmelCase_      :			Tuple	):
   # initialize config
   if "resnet-50" in model_name:
      __lowerCAmelCase								=	ResNetConfig.from_pretrained('microsoft/resnet-50'	)
   elif "resnet-101" in model_name:
      __lowerCAmelCase								=	ResNetConfig.from_pretrained('microsoft/resnet-101'	)
   else:
      raise ValueError('Model name should include either resnet50 or resnet101'	)

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

   # set label attributes
   __lowerCAmelCase								=	'panoptic' in model_name
   if is_panoptic:
      __lowerCAmelCase								=	250
   else:
      __lowerCAmelCase								=	91
      __lowerCAmelCase								=	'huggingface/label-files'
      __lowerCAmelCase								=	'coco-detection-id2label.json'
      __lowerCAmelCase								=	json.load(open(hf_hub_download(lowerCAmelCase_,	lowerCAmelCase_,	repo_type='dataset'	),	'r'	)	)
      __lowerCAmelCase								=	{int(lowerCAmelCase_	): v for k, v in idalabel.items()}
      __lowerCAmelCase								=	idalabel
      __lowerCAmelCase								=	{v: k for k, v in idalabel.items()}

   return config, is_panoptic

def   a_    ( lowerCAmelCase_      :			List[str]	):
   # here we list all keys to be renamed (original name on the left, our name on the right)
   __lowerCAmelCase								=	[]

   # 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_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Tuple	):
   __lowerCAmelCase								=	state_dict.pop(lowerCAmelCase_	)
   __lowerCAmelCase								=	val

def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			int=False	):
   __lowerCAmelCase								=	''
   if is_panoptic:
      __lowerCAmelCase								=	'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)
      __lowerCAmelCase								=	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
      __lowerCAmelCase								=	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
      __lowerCAmelCase								=	in_proj_weight[:256, :]
      __lowerCAmelCase								=	in_proj_bias[:256]
      __lowerCAmelCase								=	in_proj_weight[256:512, :]
      __lowerCAmelCase								=	in_proj_bias[256:512]
      __lowerCAmelCase								=	in_proj_weight[-256:, :]
      __lowerCAmelCase								=	in_proj_bias[-256:]
   # 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
      __lowerCAmelCase								=	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
      __lowerCAmelCase								=	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
      __lowerCAmelCase								=	in_proj_weight[:256, :]
      __lowerCAmelCase								=	in_proj_bias[:256]
      __lowerCAmelCase								=	in_proj_weight[256:512, :]
      __lowerCAmelCase								=	in_proj_bias[256:512]
      __lowerCAmelCase								=	in_proj_weight[-256:, :]
      __lowerCAmelCase								=	in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      __lowerCAmelCase								=	state_dict.pop(
          F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
      __lowerCAmelCase								=	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
      __lowerCAmelCase								=	in_proj_weight_cross_attn[:256, :]
      __lowerCAmelCase								=	in_proj_bias_cross_attn[:256]
      __lowerCAmelCase								=	in_proj_weight_cross_attn[256:512, :]
      __lowerCAmelCase								=	in_proj_bias_cross_attn[256:512]
      __lowerCAmelCase								=	in_proj_weight_cross_attn[-256:, :]
      __lowerCAmelCase								=	in_proj_bias_cross_attn[-256:]

def   a_    ( ):
   __lowerCAmelCase								=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCAmelCase								=	Image.open(requests.get(lowerCAmelCase_,	stream=lowerCAmelCase_	).raw	)

   return im

@torch.no_grad()
def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Any=None,	lowerCAmelCase_      :			str=False	):
   __lowerCAmelCase	,	__lowerCAmelCase								=	get_detr_config(lowerCAmelCase_	)

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

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

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

   __lowerCAmelCase								=	processor(images=prepare_img(),	return_tensors='pt'	)
   __lowerCAmelCase								=	encoding['pixel_values']

   __lowerCAmelCase								=	detr(lowerCAmelCase_	)
   __lowerCAmelCase								=	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__":
     _snake_case  :    List[str]						    =  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.')
     _snake_case  :    Any						    =  parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs

@pytest.mark.parametrize(
    'kwargs, expected',	[
        ({'num_shards': 0, 'max_num_jobs': 1}, []),
        ({'num_shards': 10, 'max_num_jobs': 1}, [range(10	)]),
        ({'num_shards': 10, 'max_num_jobs': 10}, [range(lowerCAmelCase_,	i + 1	) for i in range(10	)]),
        ({'num_shards': 1, 'max_num_jobs': 10}, [range(1	)]),
        ({'num_shards': 10, 'max_num_jobs': 3}, [range(0,	4	), range(4,	7	), range(7,	10	)]),
        ({'num_shards': 3, 'max_num_jobs': 10}, [range(0,	1	), range(1,	2	), range(2,	3	)]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Any	):
   __lowerCAmelCase								=	_distribute_shards(**lowerCAmelCase_	)
   assert out == expected

@pytest.mark.parametrize(
    'gen_kwargs, max_num_jobs, expected',	[
        ({'foo': 0}, 10, [{'foo': 0}]),
        ({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
        ({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
        ({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
        ({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	_split_gen_kwargs(lowerCAmelCase_,	lowerCAmelCase_	)
   assert out == expected




@pytest.mark.parametrize(
    'gen_kwargs, expected',	[
        ({'foo': 0}, 1),
        ({'shards': [0]}, 1),
        ({'shards': [0, 1, 2, 3]}, 4),
        ({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
        ({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
        ({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
    ],	)
def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Any	):
   if expected is RuntimeError:
      with pytest.raises(lowerCAmelCase_	):
         _number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
   else:
      __lowerCAmelCase								=	_number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
      assert out == expected

