

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()
a_     =			logging.get_logger(__name__)

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

	SCREAMING_SNAKE_CASE      :					str    =				DetrConfig(use_timm_backbone=_a							,	backbone_config=_a)

	# set label attributes
	SCREAMING_SNAKE_CASE      :					List[str]    =				"panoptic" in model_name
	if is_panoptic:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				250
	else:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				91
		SCREAMING_SNAKE_CASE      :					str    =				"huggingface/label-files"
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				"coco-detection-id2label.json"
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				json.load(open(hf_hub_download(_a							,	_a							,	repo_type="dataset")							,	"r"))
		SCREAMING_SNAKE_CASE      :					int    =				{int(_a): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE      :					List[Any]    =				idalabel
		SCREAMING_SNAKE_CASE      :					List[Any]    =				{v: k for k, v in idalabel.items()}

	return config, is_panoptic

def        lowerCamelCase__       (    _a):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[]

	# 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        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					str    =				state_dict.pop(_a)
	SCREAMING_SNAKE_CASE      :					int    =				val

def        lowerCamelCase__       (    _a							,	_a=False):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				""
	if is_panoptic:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				"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)
		SCREAMING_SNAKE_CASE      :					List[str]    =				state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
		SCREAMING_SNAKE_CASE      :					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
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				in_proj_weight[:256, :]
		SCREAMING_SNAKE_CASE      :					int    =				in_proj_bias[:256]
		SCREAMING_SNAKE_CASE      :					Tuple    =				in_proj_weight[256:512, :]
		SCREAMING_SNAKE_CASE      :					List[Any]    =				in_proj_bias[256:512]
		SCREAMING_SNAKE_CASE      :					str    =				in_proj_weight[-256:, :]
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				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
		SCREAMING_SNAKE_CASE      :					List[str]    =				state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight")
		SCREAMING_SNAKE_CASE      :					str    =				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
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				in_proj_weight[:256, :]
		SCREAMING_SNAKE_CASE      :					Dict    =				in_proj_bias[:256]
		SCREAMING_SNAKE_CASE      :					List[Any]    =				in_proj_weight[256:512, :]
		SCREAMING_SNAKE_CASE      :					Any    =				in_proj_bias[256:512]
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				in_proj_weight[-256:, :]
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				state_dict.pop(
		    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight")
		SCREAMING_SNAKE_CASE      :					int    =				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
		SCREAMING_SNAKE_CASE      :					Tuple    =				in_proj_weight_cross_attn[:256, :]
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				in_proj_bias_cross_attn[:256]
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				in_proj_weight_cross_attn[256:512, :]
		SCREAMING_SNAKE_CASE      :					Dict    =				in_proj_bias_cross_attn[256:512]
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				in_proj_weight_cross_attn[-256:, :]
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				in_proj_bias_cross_attn[-256:]

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Tuple    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				Image.open(requests.get(_a							,	stream=_a).raw)

	return im

@torch.no_grad()
def        lowerCamelCase__       (    _a							,	_a=None							,	_a=False):
	SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Optional[int]    =				get_detr_config(_a)

	# load original model from torch hub
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{
	    "detr-resnet-50": "detr_resnet50",
	    "detr-resnet-101": "detr_resnet101",
	}
	logger.info(f"Converting model {model_name}...")
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.hub.load("facebookresearch/detr"							,	model_name_to_original_name[model_name]							,	pretrained=_a).eval()
	SCREAMING_SNAKE_CASE      :					Tuple    =				detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(_a):
		if is_panoptic:
			SCREAMING_SNAKE_CASE      :					List[str]    =				"detr." + src
		rename_key(_a							,	_a							,	_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
	SCREAMING_SNAKE_CASE      :					List[Any]    =				"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")
			):
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				state_dict.pop(_a)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				state_dict.pop(_a)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				val
			elif key.startswith("bbox_attention") or key.startswith("mask_head"):
				continue
			else:
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				state_dict.pop(_a)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				val
		else:
			if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
				SCREAMING_SNAKE_CASE      :					Any    =				state_dict.pop(_a)
				SCREAMING_SNAKE_CASE      :					Any    =				val

    # finally, create HuggingFace model and load state dict
	SCREAMING_SNAKE_CASE      :					int    =				DetrForSegmentation(_a) if is_panoptic else DetrForObjectDetection(_a)
	model.load_state_dict(_a)
	model.eval()

	# verify our conversion on an image
	SCREAMING_SNAKE_CASE      :					int    =				"coco_panoptic" if is_panoptic else "coco_detection"
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				DetrImageProcessor(format=_a)

	SCREAMING_SNAKE_CASE      :					List[str]    =				processor(images=prepare_img()							,	return_tensors="pt")
	SCREAMING_SNAKE_CASE      :					Any    =				encoding["pixel_values"]

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				detr(_a)
	SCREAMING_SNAKE_CASE      :					Any    =				model(_a)

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

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



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :      List[str]												= {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= ["Speech2TextTokenizer"]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= ["Speech2TextFeatureExtractor"]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= [
     "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSpeech2TextForConditionalGeneration",
     "TFSpeech2TextModel",
     "TFSpeech2TextPreTrainedModel",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      str												= [
     "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Speech2TextForConditionalGeneration",
     "Speech2TextModel",
     "Speech2TextPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[Any]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


