


"""simple docstring"""






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()
_lowerCamelCase  =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase  =	[]
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 __lowercase  (   lowerCamelCase_	:   Dict    ,       lowerCamelCase_	:   int    ,       lowerCamelCase_	:   Optional[int]	):
				SCREAMING_SNAKE_CASE__  				=					state_dict.pop(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					val


def __lowercase  (   lowerCamelCase_	:   Any	):
				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 __lowercase  (   lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   List[str]=False	):
				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 __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					"http://images.cocodataset.org/val2017/000000039769.jpg"
				SCREAMING_SNAKE_CASE__  				=					Image.open(requests.get(lowerCamelCase_    ,       stream=lowerCamelCase_	).raw	)

				return im


@torch.no_grad()
def __lowercase  (   lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   Optional[int]	):
				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(lowerCamelCase_    ,       lowerCamelCase_    ,       repo_type="dataset"	)    ,       "r"	)	)
								SCREAMING_SNAKE_CASE__  				=					{int(lowerCamelCase_	): 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=lowerCamelCase_	)

				# prepare image
				SCREAMING_SNAKE_CASE__  				=					prepare_img()
				SCREAMING_SNAKE_CASE__  				=					image_processor(images=lowerCamelCase_    ,       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"    ,       lowerCamelCase_    ,       pretrained=lowerCamelCase_	).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(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					rename_backbone_keys(lowerCamelCase_	)
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowerCamelCase_    ,       is_panoptic=lowerCamelCase_	)
				# 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(lowerCamelCase_	)
																SCREAMING_SNAKE_CASE__  				=					val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																SCREAMING_SNAKE_CASE__  				=					state_dict.pop(lowerCamelCase_	)
																SCREAMING_SNAKE_CASE__  				=					val
												elif key.startswith("bbox_attention"	) or key.startswith("mask_head"	):
																continue
												else:
																SCREAMING_SNAKE_CASE__  				=					state_dict.pop(lowerCamelCase_	)
																SCREAMING_SNAKE_CASE__  				=					val
								else:
												if not key.startswith("class_labels_classifier"	) and not key.startswith("bbox_predictor"	):
																SCREAMING_SNAKE_CASE__  				=					state_dict.pop(lowerCamelCase_	)
																SCREAMING_SNAKE_CASE__  				=					val
    # finally, create HuggingFace model and load state dict
				SCREAMING_SNAKE_CASE__  				=					DetrForSegmentation(lowerCamelCase_	) if is_panoptic else DetrForObjectDetection(lowerCamelCase_	)
				model.load_state_dict(lowerCamelCase_	)
				model.eval()
				# verify our conversion
				SCREAMING_SNAKE_CASE__  				=					detr(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					model(lowerCamelCase_	)
				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(lowerCamelCase_	).mkdir(exist_ok=lowerCamelCase_	)
				model.save_pretrained(lowerCamelCase_	)
				image_processor.save_pretrained(lowerCamelCase_	)


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




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
