




'''simple docstring'''







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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase							:		List[Any]										=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase							:		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}.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'),
    ]
)






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







		_snake_case			 = state_dict.pop(lowercase_						)
		_snake_case			 = val






def 	_lowercase	(			lowercase_						):



		'''simple docstring'''







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

		return new_state_dict






def 	_lowercase	(			lowercase_		,							lowercase_=False						):



		'''simple docstring'''







		_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 	_lowercase	(			):



		'''simple docstring'''







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

		return im






@torch.no_grad()
def 	_lowercase	(			lowercase_		,							lowercase_						):



		'''simple docstring'''







		_snake_case			 = DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				_snake_case			 = "resnet101"
		if "dc5" in model_name:
				_snake_case			 = True
		_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(lowercase_		,							lowercase_		,							repo_type="dataset"						)		,							"r"						)						)
				_snake_case			 = {int(lowercase_						): v for k, v in idalabel.items()}
				_snake_case			 = idalabel
				_snake_case			 = {v: k for k, v in idalabel.items()}

		# load image processor
		_snake_case			 = "coco_panoptic" if is_panoptic else "coco_detection"
		_snake_case			 = DetrImageProcessor(format=lowercase_						)

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

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

		# load original model from torch hub
		_snake_case			 = torch.hub.load("facebookresearch/detr"		,							lowercase_		,							pretrained=lowercase_						).eval()
		_snake_case			 = detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						_snake_case			 = "detr." + src
				rename_key(lowercase_		,							lowercase_		,							lowercase_						)
		_snake_case			 = rename_backbone_keys(lowercase_						)
		# query, key and value matrices need special treatment
		read_in_q_k_v(lowercase_		,							is_panoptic=lowercase_						)
		# 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(lowercase_						)
								_snake_case			 = val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_snake_case			 = state_dict.pop(lowercase_						)
								_snake_case			 = val
						elif key.startswith("bbox_attention"						) or key.startswith("mask_head"						):
								continue
						else:
								_snake_case			 = state_dict.pop(lowercase_						)
								_snake_case			 = val
				else:
						if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
								_snake_case			 = state_dict.pop(lowercase_						)
								_snake_case			 = val
    # finally, create HuggingFace model and load state dict
		_snake_case			 = DetrForSegmentation(lowercase_						) if is_panoptic else DetrForObjectDetection(lowercase_						)
		model.load_state_dict(lowercase_						)
		model.eval()
		# verify our conversion
		_snake_case			 = detr(lowercase_						)
		_snake_case			 = model(lowercase_						)
		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(lowercase_						).mkdir(exist_ok=lowercase_						)
		model.save_pretrained(lowercase_						)
		image_processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
						_lowerCamelCase							:		int										=						argparse.ArgumentParser()

						parser.add_argument(
						    '--model_name', default='detr_resnet50', type=str, 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.'
						)
						_lowerCamelCase							:		Union[str, Any]										=						parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase							:		Any										=						{'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:		Optional[Any]										=						['PegasusTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:		Optional[Any]										=						['PegasusTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:		List[str]										=						[
						    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'PegasusForCausalLM',
						    'PegasusForConditionalGeneration',
						    'PegasusModel',
						    'PegasusPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:		Union[str, Any]										=						[
						    'TFPegasusForConditionalGeneration',
						    'TFPegasusModel',
						    'TFPegasusPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:		List[Any]										=						[
						    'FlaxPegasusForConditionalGeneration',
						    'FlaxPegasusModel',
						    'FlaxPegasusPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus import PegasusTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus_fast import PegasusTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus import (
												    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusForCausalLM,
												    PegasusForConditionalGeneration,
												    PegasusModel,
												    PegasusPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_pegasus import (
												    FlaxPegasusForConditionalGeneration,
												    FlaxPegasusModel,
												    FlaxPegasusPreTrainedModel,
												)

else:
						import sys

						_lowerCamelCase							:		List[Any]										=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





