



"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase:  List[str]			     =				logging.get_logger(__name__)


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: Union[str, Any]     =   DetaConfig(
		    num_queries=900   ,				encoder_ffn_dim=2048   ,				decoder_ffn_dim=2048   ,				num_feature_levels=5   ,				assign_first_stage=snake_case_   ,				with_box_refine=snake_case_   ,				two_stage=snake_case_   ,				)

		# set labels
		_lowercase				: Optional[Any]     =   91
		_lowercase				: List[Any]     =   '''huggingface/label-files'''
		_lowercase				: Optional[int]     =   '''coco-detection-id2label.json'''
		_lowercase				: Any     =   json.load(open(cached_download(hf_hub_url(snake_case_   ,				snake_case_   ,				repo_type="""dataset"""      )      )   ,				"""r"""      )      )
		_lowercase				: str     =   {int(snake_case_      ): v for k, v in idalabel.items()}
		_lowercase				: Dict     =   idalabel
		_lowercase				: Tuple     =   {v: k for k, v in idalabel.items()}

		return config


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: str     =   []

		# stem
		# fmt: off
		rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")      )
		rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")      )
		rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")      )
		rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")      )
		rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
								    )      )
    # transformer encoder
		for i in range(config.encoder_layers      ):
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")      )
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")      )

		# transformer decoder
		for i in range(config.decoder_layers      ):
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")      )
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")      )

		# fmt: on

		return rename_keys


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Tuple     =   dct.pop(snake_case_      )
		_lowercase				: Tuple     =   val


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Dict     =   config.d_model
		for i in range(config.decoder_layers      ):
				# read in weights + bias of input projection layer of self-attention
				_lowercase				: List[str]     =   state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""      )
				_lowercase				: List[str]     =   state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""      )
				# next, add query, keys and values (in that order) to the state dict
				_lowercase				: Union[str, Any]     =   in_proj_weight[:hidden_size, :]
				_lowercase				: str     =   in_proj_bias[:hidden_size]
				_lowercase				: List[str]     =   in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				_lowercase				: Union[str, Any]     =   in_proj_bias[hidden_size : hidden_size * 2]
				_lowercase				: List[Any]     =   in_proj_weight[-hidden_size:, :]
				_lowercase				: Tuple     =   in_proj_bias[-hidden_size:]


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: Optional[Any]     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		_lowercase				: List[str]     =   Image.open(requests.get(snake_case_   ,				stream=snake_case_      ).raw      )

		return im


@torch.no_grad()
def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Any     =   get_deta_config()

		# load original state dict
		if model_name == "deta-resnet-50":
				_lowercase				: List[str]     =   '''adet_checkpoint0011.pth'''
		elif model_name == "deta-resnet-50-24-epochs":
				_lowercase				: Optional[Any]     =   '''adet_2x_checkpoint0023.pth'''
		else:
				raise ValueError(F"""Model name {model_name} not supported"""      )
		_lowercase				: Dict     =   hf_hub_download(repo_id="""nielsr/deta-checkpoints"""   ,				filename=snake_case_      )
		_lowercase				: Tuple     =   torch.load(snake_case_   ,				map_location="""cpu"""      )['''model''']

		# rename keys
		_lowercase				: Optional[int]     =   create_rename_keys(snake_case_      )
		for src, dest in rename_keys:
				rename_key(snake_case_   ,				snake_case_   ,				snake_case_      )
		read_in_decoder_q_k_v(snake_case_   ,				snake_case_      )

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						_lowercase				: Union[str, Any]     =   state_dict.pop(snake_case_      )
						_lowercase				: Tuple     =   val
				if "input_proj" in key:
						_lowercase				: Any     =   state_dict.pop(snake_case_      )
						_lowercase				: Optional[Any]     =   val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						_lowercase				: List[Any]     =   state_dict.pop(snake_case_      )
						_lowercase				: Union[str, Any]     =   val

    # finally, create HuggingFace model and load state dict
		_lowercase				: Any     =   DetaForObjectDetection(snake_case_      )
		model.load_state_dict(snake_case_      )
		model.eval()

		_lowercase				: Optional[Any]     =   '''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(snake_case_      )

		# load image processor
		_lowercase				: Tuple     =   DetaImageProcessor(format="""coco_detection"""      )

		# verify our conversion on image
		_lowercase				: Dict     =   prepare_img()
		_lowercase				: List[Any]     =   processor(images=snake_case_   ,				return_tensors="""pt"""      )
		_lowercase				: Dict     =   encoding['''pixel_values''']
		_lowercase				: int     =   model(pixel_values.to(snake_case_      )      )

		# verify logits
		if model_name == "deta-resnet-50":
				_lowercase				: Any     =   torch.tensor(
				    [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]      )
				_lowercase				: int     =   torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]      )
		elif model_name == "deta-resnet-50-24-epochs":
				_lowercase				: int     =   torch.tensor(
				    [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]      )
				_lowercase				: Optional[int]     =   torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]      )

		assert torch.allclose(outputs.logits[0, :3, :3]   ,				expected_logits.to(snake_case_      )   ,				atol=1E-4      )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,				expected_boxes.to(snake_case_      )   ,				atol=1E-4      )
		print("""Everything ok!"""      )

		if pytorch_dump_folder_path:
				# Save model and processor
				logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""      )
				Path(snake_case_      ).mkdir(exist_ok=snake_case_      )
				model.save_pretrained(snake_case_      )
				processor.save_pretrained(snake_case_      )

		# Push to hub
		if push_to_hub:
				print("""Pushing model and processor to hub..."""      )
				model.push_to_hub(F"""jozhang97/{model_name}"""      )
				processor.push_to_hub(F"""jozhang97/{model_name}"""      )


if __name__ == "__main__":
		UpperCAmelCase:  Optional[Any]			     =				argparse.ArgumentParser()

		parser.add_argument(
		    """--model_name""",
		    type=str,
		    default="""deta-resnet-50""",
		    choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
		    help="""Name of the 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 or not to push the converted model to the 🤗 hub."""
		)
		UpperCAmelCase:  Tuple			     =				parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''






import warnings

from .generation import FlaxGenerationMixin

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		# warning at import time
		warnings.warn(
		    'Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'     ,		__A     ,		)



