






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()
__UpperCamelCase   :   Any      =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase   :   Union[str, Any]      =    []
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 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	state_dict.pop(A__       )
	__lowercase   							=	val





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			__lowercase   							=	key.replace("""backbone.0.body"""					,	"""backbone.conv_encoder.model"""       )
			__lowercase   							=	value
		else:
			__lowercase   							=	value

	return new_state_dict





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase=False       ):




	'''simple docstring'''







	__lowercase   							=	""""""
	if is_panoptic:
		__lowercase   							=	"""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)
		__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[:256, :]
		__lowercase   							=	in_proj_bias[:256]
		__lowercase   							=	in_proj_weight[256:512, :]
		__lowercase   							=	in_proj_bias[256:512]
		__lowercase   							=	in_proj_weight[-256:, :]
		__lowercase   							=	in_proj_bias[-256:]





def 					snake_case	(  ):




	'''simple docstring'''







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

	return im





@torch.no_grad()
def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		__lowercase   							=	"""resnet101"""
	if "dc5" in model_name:
		__lowercase   							=	True
	__lowercase   							=	"""panoptic""" in model_name
	if is_panoptic:
		__lowercase   							=	250
	else:
		__lowercase   							=	91
		__lowercase   							=	"""huggingface/label-files"""
		__lowercase   							=	"""coco-detection-id2label.json"""
		__lowercase   							=	json.load(open(hf_hub_download(A__					,	A__					,	repo_type="""dataset"""       )					,	"""r"""       )       )
		__lowercase   							=	{int(A__       ): v for k, v in idalabel.items()}
		__lowercase   							=	idalabel
		__lowercase   							=	{v: k for k, v in idalabel.items()}

	# load image processor
	__lowercase   							=	"""coco_panoptic""" if is_panoptic else """coco_detection"""
	__lowercase   							=	ConditionalDetrImageProcessor(format=A__       )

	# prepare image
	__lowercase   							=	prepare_img()
	__lowercase   							=	image_processor(images=A__					,	return_tensors="""pt"""       )
	__lowercase   							=	encoding["""pixel_values"""]

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

	# load original model from torch hub
	__lowercase   							=	torch.hub.load("""DeppMeng/ConditionalDETR"""					,	A__					,	pretrained=A__       ).eval()
	__lowercase   							=	conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			__lowercase   							=	"""conditional_detr.""" + src
		rename_key(A__					,	A__					,	A__       )
	__lowercase   							=	rename_backbone_keys(A__       )
	# query, key and value matrices need special treatment
	read_in_q_k_v(A__					,	is_panoptic=A__       )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	__lowercase   							=	"""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"""       )
			):
				__lowercase   							=	state_dict.pop(A__       )
				__lowercase   							=	val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__lowercase   							=	state_dict.pop(A__       )
				__lowercase   							=	val
			elif key.startswith("""bbox_attention"""       ) or key.startswith("""mask_head"""       ):
				continue
			else:
				__lowercase   							=	state_dict.pop(A__       )
				__lowercase   							=	val
		else:
			if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
				__lowercase   							=	state_dict.pop(A__       )
				__lowercase   							=	val
    # finally, create HuggingFace model and load state dict
	__lowercase   							=	ConditionalDetrForSegmentation(A__       ) if is_panoptic else ConditionalDetrForObjectDetection(A__       )
	model.load_state_dict(A__       )
	model.eval()
	model.push_to_hub(repo_id=A__					,	organization="""DepuMeng"""					,	commit_message="""Add model"""       )
	# verify our conversion
	__lowercase   							=	conditional_detr(A__       )
	__lowercase   							=	model(A__       )
	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(A__       ).mkdir(exist_ok=A__       )
	model.save_pretrained(A__       )
	image_processor.save_pretrained(A__       )


if __name__ == "__main__":
						__UpperCamelCase   :   Optional[Any]      =    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."""
						)
						__UpperCamelCase   :   str      =    parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


