




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   :						Tuple				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	Tuple				):





					'''simple docstring'''






					if "resnet-50" in model_name:
										__magic_name__							:	int    							=							ResNetConfig.from_pretrained('''microsoft/resnet-50'''				)
					elif "resnet-101" in model_name:
										__magic_name__							:	Dict    							=							ResNetConfig.from_pretrained('''microsoft/resnet-101'''				)
					else:
										raise ValueError('''Model name should include either resnet50 or resnet101'''				)

					__magic_name__							:	Union[str, Any]    							=							DetrConfig(use_timm_backbone=__A							,backbone_config=__A				)

					# set label attributes
					__magic_name__							:	Tuple    							=							'''panoptic''' in model_name
					if is_panoptic:
										__magic_name__							:	Optional[Any]    							=							2_5_0
					else:
										__magic_name__							:	int    							=							9_1
										__magic_name__							:	List[Any]    							=							'''huggingface/label-files'''
										__magic_name__							:	Any    							=							'''coco-detection-id2label.json'''
										__magic_name__							:	Dict    							=							json.load(open(hf_hub_download(__A							,__A							,repo_type='''dataset'''				)							,'''r'''				)				)
										__magic_name__							:	int    							=							{int(__A				): v for k, v in idalabel.items()}
										__magic_name__							:	List[Any]    							=							idalabel
										__magic_name__							:	List[Any]    							=							{v: k for k, v in idalabel.items()}

					return config, is_panoptic







def 		lowercase__   (		__A:	int				):





					'''simple docstring'''






					__magic_name__							:	List[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 		lowercase__   (		__A:	List[str]							,__A:	str							,__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							state_dict.pop(__A				)
					__magic_name__							:	Tuple    							=							val







def 		lowercase__   (		__A:	List[str]							,__A:	Union[str, Any]=False				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							''''''
					if is_panoptic:
										__magic_name__							:	List[Any]    							=							'''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)
										__magic_name__							:	Dict    							=							state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
										__magic_name__							:	str    							=							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
										__magic_name__							:	Optional[int]    							=							in_proj_weight[:2_5_6, :]
										__magic_name__							:	List[Any]    							=							in_proj_bias[:2_5_6]
										__magic_name__							:	int    							=							in_proj_weight[2_5_6:5_1_2, :]
										__magic_name__							:	str    							=							in_proj_bias[2_5_6:5_1_2]
										__magic_name__							:	Dict    							=							in_proj_weight[-2_5_6:, :]
										__magic_name__							:	Tuple    							=							in_proj_bias[-2_5_6:]
					# 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
										__magic_name__							:	Dict    							=							state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
										__magic_name__							:	List[Any]    							=							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
										__magic_name__							:	str    							=							in_proj_weight[:2_5_6, :]
										__magic_name__							:	Optional[Any]    							=							in_proj_bias[:2_5_6]
										__magic_name__							:	Optional[int]    							=							in_proj_weight[2_5_6:5_1_2, :]
										__magic_name__							:	str    							=							in_proj_bias[2_5_6:5_1_2]
										__magic_name__							:	Union[str, Any]    							=							in_proj_weight[-2_5_6:, :]
										__magic_name__							:	Dict    							=							in_proj_bias[-2_5_6:]
										# read in weights + bias of input projection layer of cross-attention
										__magic_name__							:	Optional[int]    							=							state_dict.pop(
										    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
										__magic_name__							:	List[str]    							=							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
										__magic_name__							:	List[str]    							=							in_proj_weight_cross_attn[:2_5_6, :]
										__magic_name__							:	Optional[Any]    							=							in_proj_bias_cross_attn[:2_5_6]
										__magic_name__							:	Tuple    							=							in_proj_weight_cross_attn[2_5_6:5_1_2, :]
										__magic_name__							:	Tuple    							=							in_proj_bias_cross_attn[2_5_6:5_1_2]
										__magic_name__							:	List[Any]    							=							in_proj_weight_cross_attn[-2_5_6:, :]
										__magic_name__							:	List[str]    							=							in_proj_bias_cross_attn[-2_5_6:]







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__							:	Dict    							=							Image.open(requests.get(__A							,stream=__A				).raw				)

					return im







@torch.no_grad()
def 		lowercase__   (		__A:	Optional[int]							,__A:	Optional[Any]=None							,__A:	Dict=False				):





					'''simple docstring'''






					__magic_name__							,    __magic_name__							:	Tuple    							=							get_detr_config(__A				)

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

    # finally, create HuggingFace model and load state dict
					__magic_name__							:	Any    							=							DetrForSegmentation(__A				) if is_panoptic else DetrForObjectDetection(__A				)
					model.load_state_dict(__A				)
					model.eval()

					# verify our conversion on an image
					__magic_name__							:	Optional[Any]    							=							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
					__magic_name__							:	Optional[Any]    							=							DetrImageProcessor(format=__A				)

					__magic_name__							:	Tuple    							=							processor(images=prepare_img()							,return_tensors='''pt'''				)
					__magic_name__							:	Union[str, Any]    							=							encoding['''pixel_values''']

					__magic_name__							:	Tuple    							=							detr(__A				)
					__magic_name__							:	Optional[int]    							=							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__":
	__lowerCamelCase   :						List[str]				=			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   :						List[str]				=			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,
)


__lowerCamelCase   :						List[Any]				=			{
    '''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:
	__lowerCamelCase   :						Any				=			['''Speech2TextTokenizer''']

try:
	if not is_speech_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			['''Speech2TextFeatureExtractor''']

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''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

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



