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






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

							_snake_case       =  DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE							, backbone_config=_SCREAMING_SNAKE_CASE				)

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

							return config, is_panoptic






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							# here we list all keys to be renamed (original name on the left, our name on the right)
							_snake_case       =  []

							# 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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  val






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
							_snake_case       =  """"""
							if is_panoptic:
														_snake_case       =  """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       =  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
														_snake_case       =  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       =  in_proj_weight[:256, :]
														_snake_case       =  in_proj_bias[:256]
														_snake_case       =  in_proj_weight[256:512, :]
														_snake_case       =  in_proj_bias[256:512]
														_snake_case       =  in_proj_weight[-256:, :]
														_snake_case       =  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
														_snake_case       =  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
														_snake_case       =  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
														_snake_case       =  in_proj_weight[:256, :]
														_snake_case       =  in_proj_bias[:256]
														_snake_case       =  in_proj_weight[256:512, :]
														_snake_case       =  in_proj_bias[256:512]
														_snake_case       =  in_proj_weight[-256:, :]
														_snake_case       =  in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														_snake_case       =  state_dict.pop(
														    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
														_snake_case       =  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
														_snake_case       =  in_proj_weight_cross_attn[:256, :]
														_snake_case       =  in_proj_bias_cross_attn[:256]
														_snake_case       =  in_proj_weight_cross_attn[256:512, :]
														_snake_case       =  in_proj_bias_cross_attn[256:512]
														_snake_case       =  in_proj_weight_cross_attn[-256:, :]
														_snake_case       =  in_proj_bias_cross_attn[-256:]






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
							_snake_case       =  Image.open(requests.get(_SCREAMING_SNAKE_CASE							, stream=_SCREAMING_SNAKE_CASE				).raw				)

							return im






@torch.no_grad()
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE=False				):
							_snake_case,							_snake_case       =  get_detr_config(_SCREAMING_SNAKE_CASE				)

							# load original model from torch hub
							_snake_case       =  {
							    """detr-resnet-50""": """detr_resnet50""",
							    """detr-resnet-101""": """detr_resnet101""",
							}
							logger.info(f"""Converting model {model_name}..."""				)
							_snake_case       =  torch.hub.load("""facebookresearch/detr"""							, model_name_to_original_name[model_name]							, pretrained=_SCREAMING_SNAKE_CASE				).eval()
							_snake_case       =  detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(_SCREAMING_SNAKE_CASE				):
														if is_panoptic:
																					_snake_case       =  """detr.""" + src
														rename_key(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
							# query, key and value matrices need special treatment
							read_in_q_k_v(_SCREAMING_SNAKE_CASE							, is_panoptic=_SCREAMING_SNAKE_CASE				)
							# 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       =  """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"""				)
																					):
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
																					elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
																												continue
																					else:
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
														else:
																					if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val

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

							# verify our conversion on an image
							_snake_case       =  """coco_panoptic""" if is_panoptic else """coco_detection"""
							_snake_case       =  DetrImageProcessor(format=_SCREAMING_SNAKE_CASE				)

							_snake_case       =  processor(images=prepare_img()							, return_tensors="""pt"""				)
							_snake_case       =  encoding["""pixel_values"""]

							_snake_case       =  detr(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  model(_SCREAMING_SNAKE_CASE				)

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

							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__":
				__lowerCAmelCase												=	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.')
				__lowerCAmelCase												=	parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .multicontrolnet import MultiControlNetModel
				from .pipeline_controlnet import StableDiffusionControlNetPipeline
				from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
				from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
				from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline