





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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_    =				[]
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}.cross_attn.out_proj.weight''',
	        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
	    )
	)
	rename_keys.append(
	    (
	        F'''transformer.decoder.layers.{i}.cross_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'''))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
	)

	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:    Tuple				=  state_dict.pop(_UpperCamelCase )
				snake_case_						:    str				=  val






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												snake_case_						:    Dict				=  key.replace('''backbone.0.body''' ,				'''backbone.conv_encoder.model''' )
												snake_case_						:    str				=  value
								else:
												snake_case_						:    Union[str, Any]				=  value

				return new_state_dict






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase=False )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  ''''''
				if is_panoptic:
								snake_case_						:    Optional[int]				=  '''conditional_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)
								snake_case_						:    List[Any]				=  state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
								snake_case_						:    Tuple				=  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
								snake_case_						:    List[Any]				=  in_proj_weight[:256, :]
								snake_case_						:    List[str]				=  in_proj_bias[:256]
								snake_case_						:    Tuple				=  in_proj_weight[256:512, :]
								snake_case_						:    Union[str, Any]				=  in_proj_bias[256:512]
								snake_case_						:    Optional[Any]				=  in_proj_weight[-256:, :]
								snake_case_						:    List[Any]				=  in_proj_bias[-256:]






def      lowerCamelCase_						(				)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:    List[Any]				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:    Union[str, Any]				=  Image.open(requests.get(_UpperCamelCase ,				stream=_UpperCamelCase ).raw )

				return im






@torch.no_grad()
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								snake_case_						:    List[Any]				=  '''resnet101'''
				if "dc5" in model_name:
								snake_case_						:    List[str]				=  True
				snake_case_						:    str				=  '''panoptic''' in model_name
				if is_panoptic:
								snake_case_						:    str				=  250
				else:
								snake_case_						:    Optional[Any]				=  91
								snake_case_						:    Optional[Any]				=  '''huggingface/label-files'''
								snake_case_						:    Union[str, Any]				=  '''coco-detection-id2label.json'''
								snake_case_						:    Union[str, Any]				=  json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset''' ) ,				'''r''' ) )
								snake_case_						:    Optional[int]				=  {int(_UpperCamelCase ): v for k, v in idalabel.items()}
								snake_case_						:    Tuple				=  idalabel
								snake_case_						:    List[str]				=  {v: k for k, v in idalabel.items()}

				# load image processor
				snake_case_						:    str				=  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				snake_case_						:    Optional[Any]				=  ConditionalDetrImageProcessor(format=_UpperCamelCase )

				# prepare image
				snake_case_						:    Dict				=  prepare_img()
				snake_case_						:    Dict				=  image_processor(images=_UpperCamelCase ,				return_tensors='''pt''' )
				snake_case_						:    Union[str, Any]				=  encoding['''pixel_values''']

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

				# load original model from torch hub
				snake_case_						:    Any				=  torch.hub.load('''DeppMeng/ConditionalDETR''' ,				_UpperCamelCase ,				pretrained=_UpperCamelCase ).eval()
				snake_case_						:    Optional[Any]				=  conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												snake_case_						:    List[str]				=  '''conditional_detr.''' + src
								rename_key(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )
				snake_case_						:    int				=  rename_backbone_keys(_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
				snake_case_						:    str				=  '''conditional_detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''conditional_detr''' )
												    and not key.startswith('''class_labels_classifier''' )
												    and not key.startswith('''bbox_predictor''' )
												):
																snake_case_						:    int				=  state_dict.pop(_UpperCamelCase )
																snake_case_						:    Dict				=  val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																snake_case_						:    Optional[Any]				=  state_dict.pop(_UpperCamelCase )
																snake_case_						:    List[str]				=  val
												elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
																continue
												else:
																snake_case_						:    List[Any]				=  state_dict.pop(_UpperCamelCase )
																snake_case_						:    Tuple				=  val
								else:
												if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
																snake_case_						:    Any				=  state_dict.pop(_UpperCamelCase )
																snake_case_						:    Union[str, Any]				=  val
    # finally, create HuggingFace model and load state dict
				snake_case_						:    int				=  ConditionalDetrForSegmentation(_UpperCamelCase ) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCamelCase )
				model.load_state_dict(_UpperCamelCase )
				model.eval()
				model.push_to_hub(repo_id=_UpperCamelCase ,				organization='''DepuMeng''' ,				commit_message='''Add model''' )
				# verify our conversion
				snake_case_						:    Any				=  conditional_detr(_UpperCamelCase )
				snake_case_						:    Union[str, Any]				=  model(_UpperCamelCase )
				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(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
				model.save_pretrained(_UpperCamelCase )
				image_processor.save_pretrained(_UpperCamelCase )


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

	parser.add_argument(
	    '''--model_name''',
	    default='''conditional_detr_resnet50''',
	    type=str,
	    help='''Name of the CONDITIONAL_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.'''
	)
	lowerCAmelCase_    =				parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase_    =				datasets.logging.get_logger(__name__)


lowerCAmelCase_    =				'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

lowerCAmelCase_    =				'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

lowerCAmelCase_    =				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

lowerCAmelCase_    =				{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION     )
class 					__lowerCAmelCase       (			datasets.Metric     ):






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION					,							citation=_CITATION					,							homepage='''https://github.com/google-research/bleurt'''					,							inputs_description=_KWARGS_DESCRIPTION					,							features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''					,							id='''sequence'''    ),
							            '''references''': datasets.Value('''string'''					,							id='''sequence'''    ),
							        }    )					,							codebase_urls=['''https://github.com/google-research/bleurt''']					,							reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']					,							)






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							if self.config_name == "default":
											logger.warning(
											    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
											    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''    )
											snake_case_						:    Dict				=  '''bleurt-base-128'''

							if self.config_name.lower() in CHECKPOINT_URLS:
											snake_case_						:    Optional[int]				=  self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
											snake_case_						:    Union[str, Any]				=  self.config_name.upper()

							else:
											raise KeyError(
											    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''    )

							# download the model checkpoint specified by self.config_name and set up the scorer
							snake_case_						:    Any				=  dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
							snake_case_						:    Dict				=  score.BleurtScorer(os.path.join(__magic_name__					,							__magic_name__    )    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.scorer.score(references=__magic_name__					,							candidates=__magic_name__    )
							return {"scores": scores}
