






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowerCamelCase			:					Dict         =	'''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowerCamelCase			:					List[str]         =	'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

lowerCamelCase			:					Any         =	'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''



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


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           if version.parse(scb.__version__	) < version.parse("1.4.12"	):
                raise ImportWarning(
                    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
                    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."	)
           return datasets.MetricInfo(
               description=_DESCRIPTION					,						citation=_CITATION					,						homepage="http://www.cs.umd.edu/~snover/tercom/"					,						inputs_description=_KWARGS_DESCRIPTION					,						features=datasets.Features(
                   {
                       "predictions": datasets.Value("string"					,						id="sequence"	),
                       "references": datasets.Sequence(datasets.Value("string"					,						id="sequence"	)					,						id="references"	),
                   }	)					,						codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]					,						reference_urls=[
                   "https://github.com/jhclark/tercom",
               ]					,						)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						)->	List[str]:
           __lowerCAmelCase								=					len(references[0]	)
           if any(len(__UpperCamelCase	) != references_per_prediction for refs in references	):
                raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
           __lowerCAmelCase								=					[[refs[i] for refs in references] for i in range(__UpperCamelCase	)]

           __lowerCAmelCase								=					TER(
               normalized=__UpperCamelCase					,						no_punct=__UpperCamelCase					,						asian_support=__UpperCamelCase					,						case_sensitive=__UpperCamelCase					,						)
           __lowerCAmelCase								=					sb_ter.corpus_score(__UpperCamelCase					,						__UpperCamelCase	)

           return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}














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





def       __lowerCAmelCase      (						__snake_case       ):
     # initialize config
     if "resnet-50" in model_name:
          __lowerCAmelCase								=					ResNetConfig.from_pretrained("microsoft/resnet-50"       )
     elif "resnet-101" in model_name:
          __lowerCAmelCase								=					ResNetConfig.from_pretrained("microsoft/resnet-101"       )
     else:
          raise ValueError("Model name should include either resnet50 or resnet101"       )

     __lowerCAmelCase								=					DetrConfig(use_timm_backbone=__snake_case					, backbone_config=__snake_case       )

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

     return config, is_panoptic





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

     # 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      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					state_dict.pop(__snake_case       )
     __lowerCAmelCase								=					val





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





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

     return im





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case=None					, __snake_case=False       ):
     __lowerCAmelCase			, __lowerCAmelCase								=					get_detr_config(__snake_case       )

     # load original model from torch hub
     __lowerCAmelCase								=					{
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(F"""Converting model {model_name}..."""       )
     __lowerCAmelCase								=					torch.hub.load("facebookresearch/detr"					, model_name_to_original_name[model_name]					, pretrained=__snake_case       ).eval()
     __lowerCAmelCase								=					detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(__snake_case       ):
          if is_panoptic:
               __lowerCAmelCase								=					"detr." + src
          rename_key(__snake_case					, __snake_case					, __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
     __lowerCAmelCase								=					"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"       )
               ):
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
               elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
                    continue
               else:
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
          else:
               if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val

    # finally, create HuggingFace model and load state dict
     __lowerCAmelCase								=					DetrForSegmentation(__snake_case       ) if is_panoptic else DetrForObjectDetection(__snake_case       )
     model.load_state_dict(__snake_case       )
     model.eval()

     # verify our conversion on an image
     __lowerCAmelCase								=					"coco_panoptic" if is_panoptic else "coco_detection"
     __lowerCAmelCase								=					DetrImageProcessor(format=__snake_case       )

     __lowerCAmelCase								=					processor(images=prepare_img()					, return_tensors="pt"       )
     __lowerCAmelCase								=					encoding["pixel_values"]

     __lowerCAmelCase								=					detr(__snake_case       )
     __lowerCAmelCase								=					model(__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(__snake_case       ).mkdir(exist_ok=__snake_case       )
          model.save_pretrained(__snake_case       )
          processor.save_pretrained(__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__":
 lowerCamelCase			:					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.''')
 lowerCamelCase			:					int         =	parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






