import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCamelCase   :				Optional[int]     			=						'''\
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W04-1013",
    pages = "74--81",
}
'''

_lowerCamelCase   :				str     			=						'''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''

_lowerCamelCase   :				int     			=						'''
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
        `"rougeL"`: Longest common subsequence based scoring.
        `"rougeLSum"`: rougeLsum splits text using `"\n"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric(\'rouge\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
    >>> print(results["rouge1"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results["rouge1"].mid.fmeasure)
    1.0
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,    _KWARGS_DESCRIPTION       )
class   lowercase      (       datasets.Metric       ):




							def 		__snake_case(  self				:   Dict )   ->			Dict:



												'''simple docstring'''

												return datasets.MetricInfo(
												    description=_DESCRIPTION		,							citation=_CITATION		,							inputs_description=_KWARGS_DESCRIPTION		,							features=datasets.Features(
												        {
												            "predictions": datasets.Value("string"		,							id="sequence" ),
												            "references": datasets.Value("string"		,							id="sequence" ),
												        } )		,							codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]		,							reference_urls=[
												        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
												        "https://github.com/google-research/google-research/tree/master/rouge",
												    ]		,							)





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   List[str]=True		,							_UpperCamelCase				:   Union[str, Any]=False )   ->			str:



												'''simple docstring'''

												if rouge_types is None:
																	SCREAMING_SNAKE_CASE           =    ["rouge1", "rouge2", "rougeL", "rougeLsum"]

												SCREAMING_SNAKE_CASE           =    rouge_scorer.RougeScorer(rouge_types=_UpperCamelCase		,							use_stemmer=_UpperCamelCase )
												if use_aggregator:
																	SCREAMING_SNAKE_CASE           =    scoring.BootstrapAggregator()
												else:
																	SCREAMING_SNAKE_CASE           =    []

												for ref, pred in zip(_UpperCamelCase		,							_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    scorer.score(_UpperCamelCase		,							_UpperCamelCase )
																	if use_aggregator:
																						aggregator.add_scores(_UpperCamelCase )
																	else:
																						scores.append(_UpperCamelCase )

												if use_aggregator:
																	SCREAMING_SNAKE_CASE           =    aggregator.aggregate()
												else:
																	SCREAMING_SNAKE_CASE           =    {}
																	for key in scores[0]:
																						SCREAMING_SNAKE_CASE           =    [score[key] for score in scores]

												return result





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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict					):
					SCREAMING_SNAKE_CASE           =    SwinConfig(
					    embed_dim=1_9_2 ,	depths=(2, 2, 1_8, 2) ,	num_heads=(6, 1_2, 2_4, 4_8) ,	window_size=1_2 ,	out_features=["stage2", "stage3", "stage4"] ,	)

					SCREAMING_SNAKE_CASE           =    DetaConfig(
					    backbone_config=UpperCAmelCase__ ,	num_queries=9_0_0 ,	encoder_ffn_dim=2_0_4_8 ,	decoder_ffn_dim=2_0_4_8 ,	num_feature_levels=5 ,	assign_first_stage=UpperCAmelCase__ ,	with_box_refine=UpperCAmelCase__ ,	two_stage=UpperCAmelCase__ ,	)

					# set labels
					SCREAMING_SNAKE_CASE           =    "huggingface/label-files"
					if "o365" in model_name:
										SCREAMING_SNAKE_CASE           =    3_6_6
										SCREAMING_SNAKE_CASE           =    "object365-id2label.json"
					else:
										SCREAMING_SNAKE_CASE           =    9_1
										SCREAMING_SNAKE_CASE           =    "coco-detection-id2label.json"

					SCREAMING_SNAKE_CASE           =    num_labels
					SCREAMING_SNAKE_CASE           =    json.load(open(cached_download(hf_hub_url(UpperCAmelCase__ ,	UpperCAmelCase__ ,	repo_type="dataset"					)					) ,	"r"					)					)
					SCREAMING_SNAKE_CASE           =    {int(UpperCAmelCase__					): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE           =    idalabel
					SCREAMING_SNAKE_CASE           =    {v: k for k, v in idalabel.items()}

					return config





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    []

					# stem
					# fmt: off
					rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")					)
					rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")					)
					rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")					)
					rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")					)
					# stages
					for i in range(len(config.backbone_config.depths					)					):
										for j in range(config.backbone_config.depths[i]					):
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")					)

										if i < 3:
															rename_keys.append((F"backbone.0.body.layers.{i}.downsample.reduction.weight", F"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.weight", F"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.bias", F"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")					)

					rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")					)
					rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")					)
					rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")					)
					rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")					)
					rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")					)
					rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")					)

					# 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 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    dct.pop(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    val





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
					for i in range(len(backbone_config.depths					)					):
										SCREAMING_SNAKE_CASE           =    num_features[i]
										for j in range(backbone_config.depths[i]					):
															# fmt: off
															# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
															SCREAMING_SNAKE_CASE           =    state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"					)
															SCREAMING_SNAKE_CASE           =    state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"					)
															# next, add query, keys and values (in that order) to the state dict
															SCREAMING_SNAKE_CASE           =    in_proj_weight[:dim, :]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[: dim]
															SCREAMING_SNAKE_CASE           =    in_proj_weight[
															    dim : dim * 2, :
															]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[
															    dim : dim * 2
															]
															SCREAMING_SNAKE_CASE           =    in_proj_weight[
															    -dim :, :
															]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[-dim :]
															# fmt: on





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							List[str]					):
					# transformer decoder self-attention layers
					SCREAMING_SNAKE_CASE           =    config.d_model
					for i in range(config.decoder_layers					):
										# read in weights + bias of input projection layer of self-attention
										SCREAMING_SNAKE_CASE           =    state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
										SCREAMING_SNAKE_CASE           =    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
										SCREAMING_SNAKE_CASE           =    in_proj_weight[:hidden_size, :]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[:hidden_size]
										SCREAMING_SNAKE_CASE           =    in_proj_weight[
										    hidden_size : hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[hidden_size : hidden_size * 2]
										SCREAMING_SNAKE_CASE           =    in_proj_weight[-hidden_size:, :]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[-hidden_size:]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE           =    Image.open(requests.get(UpperCAmelCase__ ,	stream=UpperCAmelCase__					).raw					)

					return im





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    get_deta_config(UpperCAmelCase__					)

					# load original state dict
					if model_name == "deta-swin-large":
										SCREAMING_SNAKE_CASE           =    hf_hub_download(repo_id="nielsr/deta-checkpoints" ,	filename="adet_swin_ft.pth"					)
					elif model_name == "deta-swin-large-o365":
										SCREAMING_SNAKE_CASE           =    hf_hub_download(repo_id="jozhang97/deta-swin-l-o365" ,	filename="deta_swin_pt_o365.pth"					)
					else:
										raise ValueError(F"Model name {model_name} not supported"					)

					SCREAMING_SNAKE_CASE           =    torch.load(UpperCAmelCase__ ,	map_location="cpu"					)["model"]

					# original state dict
					for name, param in state_dict.items():
										print(UpperCAmelCase__ ,	param.shape					)

					# rename keys
					SCREAMING_SNAKE_CASE           =    create_rename_keys(UpperCAmelCase__					)
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					read_in_swin_q_k_v(UpperCAmelCase__ ,	config.backbone_config					)
					read_in_decoder_q_k_v(UpperCAmelCase__ ,	UpperCAmelCase__					)

					# fix some prefixes
					for key in state_dict.copy().keys():
										if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val
										if "input_proj" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val
										if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val

    # finally, create HuggingFace model and load state dict
					SCREAMING_SNAKE_CASE           =    DetaForObjectDetection(UpperCAmelCase__					)
					model.load_state_dict(UpperCAmelCase__					)
					model.eval()

					SCREAMING_SNAKE_CASE           =    "cuda" if torch.cuda.is_available() else "cpu"
					model.to(UpperCAmelCase__					)

					# load image processor
					SCREAMING_SNAKE_CASE           =    DetaImageProcessor(format="coco_detection"					)

					# verify our conversion on image
					SCREAMING_SNAKE_CASE           =    prepare_img()
					SCREAMING_SNAKE_CASE           =    processor(images=UpperCAmelCase__ ,	return_tensors="pt"					)
					SCREAMING_SNAKE_CASE           =    encoding["pixel_values"]
					SCREAMING_SNAKE_CASE           =    model(pixel_values.to(UpperCAmelCase__					)					)

					# verify logits
					print("Logits:" ,	outputs.logits[0, :3, :3]					)
					print("Boxes:" ,	outputs.pred_boxes[0, :3, :3]					)
					if model_name == "deta-swin-large":
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]					)
					elif model_name == "deta-swin-large-o365":
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]					)
					assert torch.allclose(outputs.logits[0, :3, :3] ,	expected_logits.to(UpperCAmelCase__					) ,	atol=1e-4					)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,	expected_boxes.to(UpperCAmelCase__					) ,	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(UpperCAmelCase__					).mkdir(exist_ok=UpperCAmelCase__					)
										model.save_pretrained(UpperCAmelCase__					)
										processor.save_pretrained(UpperCAmelCase__					)

					# 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__":
							_lowerCamelCase   :				Optional[Any]     			=						argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    type=str,
							    default='''deta-swin-large''',
							    choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
							    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.'''
							)
							_lowerCamelCase   :				Any     			=						parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




