






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


snake_case__   :						List[Any]													=   getLogger(__name__)


snake_case__   :						Optional[Any]													=   """cuda""" if torch.cuda.is_available() else """cpu"""






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

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

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






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






def      _snake_case     (__lowercase=True):
   UpperCamelCase_						=  argparse.ArgumentParser()
   parser.add_argument('model_name'     ,   type=__lowercase     ,   help='like facebook/bart-large-cnn,t5-base, etc.')
   parser.add_argument('input_path'     ,   type=__lowercase     ,   help='like cnn_dm/test.source')
   parser.add_argument('save_path'     ,   type=__lowercase     ,   help='where to save summaries')
   parser.add_argument('--reference_path'     ,   type=__lowercase     ,   required=__lowercase     ,   help='like cnn_dm/test.target')
   parser.add_argument('--score_path'     ,   type=__lowercase     ,   required=__lowercase     ,   default='metrics.json'     ,   help='where to save metrics')
   parser.add_argument('--device'     ,   type=__lowercase     ,   required=__lowercase     ,   default=__lowercase     ,   help='cuda, cuda:1, cpu etc.')
   parser.add_argument(
       '--prefix'     ,   type=__lowercase     ,   required=__lowercase     ,   default=__lowercase     ,   help='will be added to the begininng of src examples')
   parser.add_argument('--task'     ,   type=__lowercase     ,   default='summarization'     ,   help='used for task_specific_params + metrics')
   parser.add_argument('--bs'     ,   type=__lowercase     ,   default=8     ,   required=__lowercase     ,   help='batch size')
   parser.add_argument(
       '--n_obs'     ,   type=__lowercase     ,   default=-1     ,   required=__lowercase     ,   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=__lowercase     ,   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
   UpperCamelCase_		,   UpperCamelCase_						=  parser.parse_known_args()
   UpperCamelCase_						=  parse_numeric_n_bool_cl_kwargs(__lowercase)
   if parsed_args and verbose:
      print(f"""parsed the following generate kwargs: {parsed_args}""")
   UpperCamelCase_						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
   if args.n_obs > 0:
      UpperCamelCase_						=  examples[: args.n_obs]
   Path(args.save_path).parent.mkdir(exist_ok=__lowercase)

   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')

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

   if args.reference_path is None:
      return {}

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

   if args.dump_args:
      scores.update(__lowercase)
   if args.info:
      UpperCamelCase_						=  args.info

   if verbose:
      print(__lowercase)

   if args.score_path is not None:
      json.dump(__lowercase     ,   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)






'''simple docstring'''

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()
A_     :						Dict    							=logging.get_logger(__name__)



def snake_case_      (					__snake_case  :							Any)			->					Tuple:
   # 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 snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   # 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 snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple	,   __snake_case  :							Optional[Any])			->					List[str]:
   lowerCAmelCase_			=      state_dict.pop(__snake_case)
   lowerCAmelCase_			=      val



def snake_case_      (					__snake_case  :							List[Any]	,   __snake_case  :							Optional[int]=False)			->					str:
   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 snake_case_      (					)			->					str:
   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 snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							List[Any]=None	,   __snake_case  :							Any=False)			->					Optional[Any]:
   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__":
 A_     :						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.''')
 A_     :						Optional[int]    							=parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





