


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()
A_														=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A_														=      []
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 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      ):







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       state_dict.pop(snake_case_      )
					SCREAMING_SNAKE_CASE_	      =       val







def 							UpperCAmelCase				(     UpperCAmelCase      ):







					'''simple docstring'''



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

					return new_state_dict







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase=False      ):







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       ''''''
					if is_panoptic:
										SCREAMING_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)
										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
										SCREAMING_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
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[:256, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[:256]
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[256:512, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[256:512]
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[-256:, :]
										SCREAMING_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
										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
										SCREAMING_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
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[:256, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[:256]
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[256:512, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[256:512]
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[-256:, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(
										    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''      )
										SCREAMING_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
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight_cross_attn[:256, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias_cross_attn[:256]
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight_cross_attn[256:512, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias_cross_attn[256:512]
										SCREAMING_SNAKE_CASE_	      =       in_proj_weight_cross_attn[-256:, :]
										SCREAMING_SNAKE_CASE_	      =       in_proj_bias_cross_attn[-256:]







def 							UpperCAmelCase				(     ):







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					SCREAMING_SNAKE_CASE_	      =       Image.open(requests.get(snake_case_				,stream=snake_case_      ).raw      )

					return im







@torch.no_grad()
def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      ):







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       DetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										SCREAMING_SNAKE_CASE_	      =       '''resnet101'''
					if "dc5" in model_name:
										SCREAMING_SNAKE_CASE_	      =       True
					SCREAMING_SNAKE_CASE_	      =       '''panoptic''' in model_name
					if is_panoptic:
										SCREAMING_SNAKE_CASE_	      =       250
					else:
										SCREAMING_SNAKE_CASE_	      =       91
										SCREAMING_SNAKE_CASE_	      =       '''huggingface/label-files'''
										SCREAMING_SNAKE_CASE_	      =       '''coco-detection-id2label.json'''
										SCREAMING_SNAKE_CASE_	      =       json.load(open(hf_hub_download(snake_case_				,snake_case_				,repo_type='''dataset'''      )				,'''r'''      )      )
										SCREAMING_SNAKE_CASE_	      =       {int(snake_case_      ): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE_	      =       idalabel
										SCREAMING_SNAKE_CASE_	      =       {v: k for k, v in idalabel.items()}

					# load image processor
					SCREAMING_SNAKE_CASE_	      =       '''coco_panoptic''' if is_panoptic else '''coco_detection'''
					SCREAMING_SNAKE_CASE_	      =       DetrImageProcessor(format=snake_case_      )

					# prepare image
					SCREAMING_SNAKE_CASE_	      =       prepare_img()
					SCREAMING_SNAKE_CASE_	      =       image_processor(images=snake_case_				,return_tensors='''pt'''      )
					SCREAMING_SNAKE_CASE_	      =       encoding['''pixel_values''']

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

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


if __name__ == "__main__":
		A_														=      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."
		)
		A_														=      parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






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,
)


A_														=      {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["MarianTokenizer"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      [
		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MarianForCausalLM",
		    "MarianModel",
		    "MarianMTModel",
		    "MarianPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_marian import MarianTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_marian import (
				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MarianForCausalLM,
				    MarianModel,
				    MarianMTModel,
				    MarianPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
		import sys

		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


