

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()
a		=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a		=[]
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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Tuple 	=			state_dict.pop(__UpperCAmelCase      )
	__lowerCamelCase    :    Any 	=			val






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    List[Any] 	=			OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			__lowerCamelCase    :    Optional[int] 	=			key.replace('backbone.0.body'     ,			'backbone.conv_encoder.model'      )
			__lowerCamelCase    :    List[Any] 	=			value
		else:
			__lowerCamelCase    :    List[str] 	=			value

	return new_state_dict






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					Tuple:
	__lowerCamelCase    :    Any 	=			''''''
	if is_panoptic:
		__lowerCamelCase    :    Union[str, Any] 	=			'''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)
		__lowerCamelCase    :    Dict 	=			state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"      )
		__lowerCamelCase    :    Optional[int] 	=			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    :    List[Any] 	=			in_proj_weight[:2_5_6, :]
		__lowerCamelCase    :    List[str] 	=			in_proj_bias[:2_5_6]
		__lowerCamelCase    :    Dict 	=			in_proj_weight[2_5_6:5_1_2, :]
		__lowerCamelCase    :    Union[str, Any] 	=			in_proj_bias[2_5_6:5_1_2]
		__lowerCamelCase    :    Dict 	=			in_proj_weight[-2_5_6:, :]
		__lowerCamelCase    :    List[Any] 	=			in_proj_bias[-2_5_6:]






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    Tuple 	=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__lowerCamelCase    :    Optional[int] 	=			Image.open(requests.get(__UpperCAmelCase     ,			stream=__UpperCAmelCase      ).raw      )

	return im






@torch.no_grad()
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    Tuple 	=			ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		__lowerCamelCase    :    str 	=			'''resnet101'''
	if "dc5" in model_name:
		__lowerCamelCase    :    Dict 	=			True
	__lowerCamelCase    :    Dict 	=			'''panoptic''' in model_name
	if is_panoptic:
		__lowerCamelCase    :    Optional[int] 	=			2_5_0
	else:
		__lowerCamelCase    :    int 	=			9_1
		__lowerCamelCase    :    Union[str, Any] 	=			'''huggingface/label-files'''
		__lowerCamelCase    :    int 	=			'''coco-detection-id2label.json'''
		__lowerCamelCase    :    str 	=			json.load(open(hf_hub_download(__UpperCAmelCase     ,			__UpperCAmelCase     ,			repo_type='dataset'      )     ,			'r'      )      )
		__lowerCamelCase    :    Any 	=			{int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
		__lowerCamelCase    :    List[str] 	=			idalabel
		__lowerCamelCase    :    Optional[int] 	=			{v: k for k, v in idalabel.items()}

	# load image processor
	__lowerCamelCase    :    str 	=			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
	__lowerCamelCase    :    Any 	=			ConditionalDetrImageProcessor(format=__UpperCAmelCase      )

	# prepare image
	__lowerCamelCase    :    List[Any] 	=			prepare_img()
	__lowerCamelCase    :    Optional[int] 	=			image_processor(images=__UpperCAmelCase     ,			return_tensors='pt'      )
	__lowerCamelCase    :    Tuple 	=			encoding['''pixel_values''']

	logger.info(F"Converting model {model_name}..."      )

	# load original model from torch hub
	__lowerCamelCase    :    Dict 	=			torch.hub.load('DeppMeng/ConditionalDETR'     ,			__UpperCAmelCase     ,			pretrained=__UpperCAmelCase      ).eval()
	__lowerCamelCase    :    Optional[int] 	=			conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			__lowerCamelCase    :    int 	=			'''conditional_detr.''' + src
		rename_key(__UpperCAmelCase     ,			__UpperCAmelCase     ,			__UpperCAmelCase      )
	__lowerCamelCase    :    Optional[Any] 	=			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
	__lowerCamelCase    :    Dict 	=			'''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'      )
			):
				__lowerCamelCase    :    str 	=			state_dict.pop(__UpperCAmelCase      )
				__lowerCamelCase    :    Union[str, Any] 	=			val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__lowerCamelCase    :    str 	=			state_dict.pop(__UpperCAmelCase      )
				__lowerCamelCase    :    Optional[int] 	=			val
			elif key.startswith('bbox_attention'      ) or key.startswith('mask_head'      ):
				continue
			else:
				__lowerCamelCase    :    Any 	=			state_dict.pop(__UpperCAmelCase      )
				__lowerCamelCase    :    List[Any] 	=			val
		else:
			if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
				__lowerCamelCase    :    str 	=			state_dict.pop(__UpperCAmelCase      )
				__lowerCamelCase    :    Dict 	=			val
    # finally, create HuggingFace model and load state dict
	__lowerCamelCase    :    List[str] 	=			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
	__lowerCamelCase    :    str 	=			conditional_detr(__UpperCAmelCase      )
	__lowerCamelCase    :    Tuple 	=			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__":
					a		=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."""
					)
					a		=parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




