






import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase			:					List[str]         =	getLogger(__name__)


lowerCamelCase			:					Union[str, Any]         =	'''cuda''' if torch.cuda.is_available() else '''cpu'''





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case = 8					, __snake_case = DEFAULT_DEVICE					, __snake_case=False					, __snake_case="summarization"					, __snake_case=None					, **__snake_case					, ):
     __lowerCAmelCase								=					Path(__snake_case       ).open("w"					, encoding="utf-8"       )
     __lowerCAmelCase								=					str(__snake_case       )
     __lowerCAmelCase								=					AutoModelForSeqaSeqLM.from_pretrained(__snake_case       ).to(__snake_case       )
     if fpaa:
          __lowerCAmelCase								=					model.half()

     __lowerCAmelCase								=					AutoTokenizer.from_pretrained(__snake_case       )
     logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""       )  # if this is wrong, check config.model_type.

     __lowerCAmelCase								=					time.time()
     # update config with task specific params
     use_task_specific_params(__snake_case					, __snake_case       )
     if prefix is None:
          __lowerCAmelCase								=					prefix or getattr(model.config					, "prefix"					, ""       ) or ""
     for examples_chunk in tqdm(list(chunks(__snake_case					, __snake_case       )       )       ):
          __lowerCAmelCase								=					[prefix + text for text in examples_chunk]
          __lowerCAmelCase								=					tokenizer(__snake_case					, return_tensors="pt"					, truncation=__snake_case					, padding="longest"       ).to(__snake_case       )
          __lowerCAmelCase								=					model.generate(
              input_ids=batch.input_ids					, attention_mask=batch.attention_mask					, **__snake_case					, )
          __lowerCAmelCase								=					tokenizer.batch_decode(__snake_case					, skip_special_tokens=__snake_case					, clean_up_tokenization_spaces=__snake_case       )
          for hypothesis in dec:
               fout.write(hypothesis + "\n"       )
               fout.flush()
     fout.close()
     __lowerCAmelCase								=					int(time.time() - start_time       )  # seconds
     __lowerCAmelCase								=					len(__snake_case       )
     return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					, 4       )}





def       __lowerCAmelCase      (						):
     return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"       )





def       __lowerCAmelCase      (						__snake_case=True       ):
     __lowerCAmelCase								=					argparse.ArgumentParser()
     parser.add_argument("model_name"					, type=__snake_case					, help="like facebook/bart-large-cnn,t5-base, etc."       )
     parser.add_argument("input_path"					, type=__snake_case					, help="like cnn_dm/test.source"       )
     parser.add_argument("save_path"					, type=__snake_case					, help="where to save summaries"       )
     parser.add_argument("--reference_path"					, type=__snake_case					, required=__snake_case					, help="like cnn_dm/test.target"       )
     parser.add_argument("--score_path"					, type=__snake_case					, required=__snake_case					, default="metrics.json"					, help="where to save metrics"       )
     parser.add_argument("--device"					, type=__snake_case					, required=__snake_case					, default=__snake_case					, help="cuda, cuda:1, cpu etc."       )
     parser.add_argument(
         "--prefix"					, type=__snake_case					, required=__snake_case					, default=__snake_case					, help="will be added to the begininng of src examples"       )
     parser.add_argument("--task"					, type=__snake_case					, default="summarization"					, help="used for task_specific_params + metrics"       )
     parser.add_argument("--bs"					, type=__snake_case					, default=8					, required=__snake_case					, help="batch size"       )
     parser.add_argument(
         "--n_obs"					, type=__snake_case					, default=-1					, required=__snake_case					, help="How many observations. Defaults to all."       )
     parser.add_argument("--fp16"					, action="store_true"       )
     parser.add_argument("--dump-args"					, action="store_true"					, help="print the custom hparams with the results"       )
     parser.add_argument(
         "--info"					, nargs="?"					, type=__snake_case					, const=datetime_now()					, help=(
             "use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g."
             " lang=en-ru. If no value is passed, the current datetime string will be used."
         )					, )
     # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
     __lowerCAmelCase			, __lowerCAmelCase								=					parser.parse_known_args()
     __lowerCAmelCase								=					parse_numeric_n_bool_cl_kwargs(__snake_case       )
     if parsed_args and verbose:
          print(F"""parsed the following generate kwargs: {parsed_args}"""       )
     __lowerCAmelCase								=					[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path       ).readlines()]
     if args.n_obs > 0:
          __lowerCAmelCase								=					examples[: args.n_obs]
     Path(args.save_path       ).parent.mkdir(exist_ok=__snake_case       )

     if args.reference_path is None and Path(args.score_path       ).exists():
          warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""       )

     if args.device == "cpu" and args.fpaa:
          # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
          raise ValueError("Can\'t mix --fp16 and --device cpu"       )

     __lowerCAmelCase								=					generate_summaries_or_translations(
         __snake_case					, args.save_path					, args.model_name					, batch_size=args.bs					, device=args.device					, fpaa=args.fpaa					, task=args.task					, prefix=args.prefix					, **__snake_case					, )

     if args.reference_path is None:
          return {}

     # Compute scores
     __lowerCAmelCase								=					calculate_bleu if "translation" in args.task else calculate_rouge
     __lowerCAmelCase								=					[x.rstrip() for x in open(args.save_path       ).readlines()]
     __lowerCAmelCase								=					[x.rstrip() for x in open(args.reference_path       ).readlines()][: len(__snake_case       )]
     __lowerCAmelCase								=					score_fn(__snake_case					, __snake_case       )
     scores.update(__snake_case       )

     if args.dump_args:
          scores.update(__snake_case       )
     if args.info:
          __lowerCAmelCase								=					args.info

     if verbose:
          print(__snake_case       )

     if args.score_path is not None:
          json.dump(__snake_case					, open(args.score_path					, "w"       )       )

     return scores


if __name__ == "__main__":
    # Usage for MT:
    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
    run_generate(verbose=True)














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)






