




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()
UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# initialize config
				if "resnet-50" in model_name:
								__magic_name__	:	Union[str, Any]								=ResNetConfig.from_pretrained("""microsoft/resnet-50"""   )
				elif "resnet-101" in model_name:
								__magic_name__	:	Optional[int]								=ResNetConfig.from_pretrained("""microsoft/resnet-101"""   )
				else:
								raise ValueError("""Model name should include either resnet50 or resnet101"""   )

				__magic_name__	:	Tuple								=DetrConfig(use_timm_backbone=__SCREAMING_SNAKE_CASE	,     backbone_config=__SCREAMING_SNAKE_CASE   )

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

				return config, is_panoptic




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				__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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=state_dict.pop(__SCREAMING_SNAKE_CASE   )
				__magic_name__	:	List[str]								=val




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




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Tuple								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__	:	str								=Image.open(requests.get(__SCREAMING_SNAKE_CASE	,     stream=__SCREAMING_SNAKE_CASE   ).raw   )

				return im




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	str								=get_detr_config(__SCREAMING_SNAKE_CASE   )

				# 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__	:	Tuple								=torch.hub.load("""facebookresearch/detr"""	,     model_name_to_original_name[model_name]	,     pretrained=__SCREAMING_SNAKE_CASE   ).eval()
				__magic_name__	:	Tuple								=detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(__SCREAMING_SNAKE_CASE   ):
								if is_panoptic:
												__magic_name__	:	Optional[Any]								="""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
				__magic_name__	:	Union[str, 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"""   )
												):
																__magic_name__	:	Union[str, Any]								=state_dict.pop(__SCREAMING_SNAKE_CASE   )
																__magic_name__	:	str								=val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																__magic_name__	:	List[Any]								=state_dict.pop(__SCREAMING_SNAKE_CASE   )
																__magic_name__	:	Union[str, Any]								=val
												elif key.startswith("""bbox_attention"""   ) or key.startswith("""mask_head"""   ):
																continue
												else:
																__magic_name__	:	Any								=state_dict.pop(__SCREAMING_SNAKE_CASE   )
																__magic_name__	:	Tuple								=val
								else:
												if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
																__magic_name__	:	Dict								=state_dict.pop(__SCREAMING_SNAKE_CASE   )
																__magic_name__	:	Any								=val

    # finally, create HuggingFace model and load state dict
				__magic_name__	:	str								=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
				__magic_name__	:	Dict								="""coco_panoptic""" if is_panoptic else """coco_detection"""
				__magic_name__	:	List[str]								=DetrImageProcessor(format=__SCREAMING_SNAKE_CASE   )

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

				__magic_name__	:	int								=detr(__SCREAMING_SNAKE_CASE   )
				__magic_name__	:	Dict								=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__":
							UpperCAmelCase_		:				Union[str, Any]									=     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.")
							UpperCAmelCase_		:				Any									=     parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""Speech2TextFeatureExtractor"""
						UpperCamelCase     						=					"""Speech2TextTokenizer"""

						def __init__(		self   :Any    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										super().__init__(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=self.feature_extractor
										__magic_name__	:	Any								=False

						def __call__(		self   :Tuple    , *__snake_case   :List[str]    , **__snake_case   :List[str] ):
										'''simple docstring'''

										if self._in_target_context_manager:
														return self.current_processor(*__snake_case    , **__snake_case )

										if "raw_speech" in kwargs:
														warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
														__magic_name__	:	Tuple								=kwargs.pop("""raw_speech""" )
										else:
														__magic_name__	:	List[Any]								=kwargs.pop("""audio"""    , __snake_case )
										__magic_name__	:	Union[str, Any]								=kwargs.pop("""sampling_rate"""    , __snake_case )
										__magic_name__	:	Dict								=kwargs.pop("""text"""    , __snake_case )
										if len(__snake_case ) > 0:
														__magic_name__	:	Union[str, Any]								=args[0]
														__magic_name__	:	int								=args[1:]

										if audio is None and text is None:
														raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )

										if audio is not None:
														__magic_name__	:	List[str]								=self.feature_extractor(__snake_case    , *__snake_case    , sampling_rate=__snake_case    , **__snake_case )
										if text is not None:
														__magic_name__	:	Any								=self.tokenizer(__snake_case    , **__snake_case )

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														__magic_name__	:	List[str]								=encodings["""input_ids"""]
														return inputs

						def     A__						(		self   :List[Any]    , *__snake_case   :str    , **__snake_case   :List[str] ):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*__snake_case    , **__snake_case )

						def     A__						(		self   :int    , *__snake_case   :int    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										return self.tokenizer.decode(*__snake_case    , **__snake_case )


						@contextmanager
						def     A__						(		self   :int ):
										'''simple docstring'''

										warnings.warn(
										    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
										    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
										    """your audio inputs, or in a separate call.""" )
										__magic_name__	:	int								=True
										__magic_name__	:	List[Any]								=self.tokenizer
										yield
										__magic_name__	:	List[Any]								=self.feature_extractor
										__magic_name__	:	str								=False




