"""simple docstring"""


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__UpperCamelCase       :    Dict         =		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def   __UpperCAmelCase     (	_snake_case		:       List[Any]       ):
   if "token" in model_name_or_path:
      return "rag_token"
   if "sequence" in model_name_or_path:
      return "rag_sequence"
   if "bart" in model_name_or_path:
      return "bart"
   return None


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       List[Any],				_snake_case		:       Optional[int]       ):
   return max(metric_fn(_snake_case,				_snake_case       ) for gt in ground_truths       )


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       str,				_snake_case		:       Optional[Any]       ):
   _lowercase							     =       [line.strip() for line in open(_snake_case,				"r"       ).readlines()]
   _lowercase							     =       []

   if args.gold_data_mode == "qa":
      _lowercase							     =       pd.read_csv(_snake_case,				sep="\t",				header=_snake_case       )
      for answer_list in data[1]:
         _lowercase							     =       ast.literal_eval(_snake_case       )
         answers.append(_snake_case       )
   else:
      _lowercase							     =       [line.strip() for line in open(_snake_case,				"r"       ).readlines()]
      _lowercase							     =       [[reference] for reference in references]

   _lowercase							     =       _lowercase							     =       _lowercase							     =       0
   for prediction, ground_truths in zip(_snake_case,				_snake_case       ):
      total += 1
      em += metric_max_over_ground_truths(_snake_case,				_snake_case,				_snake_case       )
      fa += metric_max_over_ground_truths(_snake_case,				_snake_case,				_snake_case       )

   _lowercase							     =       1_0_0.0 * em / total
   _lowercase							     =       1_0_0.0 * fa / total

   logger.info(f"""F1: {fa:.2f}"""       )
   logger.info(f"""EM: {em:.2f}"""       )


def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       List[str],				_snake_case		:       Union[str, Any]       ):
   _lowercase							     =       args.k
   _lowercase							     =       [line.strip() for line in open(_snake_case,				"r"       ).readlines()]
   _lowercase							     =       [line.strip() for line in open(_snake_case,				"r"       ).readlines()]

   _lowercase							     =       _lowercase							     =       0
   for hypo, reference in zip(_snake_case,				_snake_case       ):
      _lowercase							     =       set(hypo.split("\t"       )[:k]       )
      _lowercase							     =       set(reference.split("\t"       )       )
      total += 1
      em += len(hypo_provenance & ref_provenance       ) / k

   _lowercase							     =       1_0_0.0 * em / total
   logger.info(f"""Precision@{k}: {em: .2f}"""       )


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       str,				_snake_case		:       List[str]       ):
   def strip_title(_snake_case		:       int       ):
      if title.startswith("\""       ):
         _lowercase							     =       title[1:]
      if title.endswith("\""       ):
         _lowercase							     =       title[:-1]
      return title

   _lowercase							     =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
       _snake_case,				return_tensors="pt",				padding=_snake_case,				truncation=_snake_case,				)["input_ids"].to(args.device       )

   _lowercase							     =       rag_model.rag.question_encoder(_snake_case       )
   _lowercase							     =       question_enc_outputs[0]

   _lowercase							     =       rag_model.retriever(
       _snake_case,				question_enc_pool_output.cpu().detach().to(torch.floataa       ).numpy(),				prefix=rag_model.rag.generator.config.prefix,				n_docs=rag_model.config.n_docs,				return_tensors="pt",				)
   _lowercase							     =       rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
   _lowercase							     =       []
   for docs in all_docs:
      _lowercase							     =       [strip_title(_snake_case       ) for title in docs["title"]]
      provenance_strings.append("\t".join(_snake_case       )       )
   return provenance_strings


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       Any,				_snake_case		:       Dict       ):
   with torch.no_grad():
      _lowercase							     =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          _snake_case,				return_tensors="pt",				padding=_snake_case,				truncation=_snake_case       )

      _lowercase							     =       inputs_dict.input_ids.to(args.device       )
      _lowercase							     =       inputs_dict.attention_mask.to(args.device       )
      _lowercase							     =       rag_model.generate(  # rag_model overwrites generate
          _snake_case,				attention_mask=_snake_case,				num_beams=args.num_beams,				min_length=args.min_length,				max_length=args.max_length,				early_stopping=_snake_case,				num_return_sequences=1,				bad_words_ids=[[0, 0]],				)
      _lowercase							     =       rag_model.retriever.generator_tokenizer.batch_decode(_snake_case,				skip_special_tokens=_snake_case       )

      if args.print_predictions:
         for q, a in zip(_snake_case,				_snake_case       ):
            logger.info("Q: {} - A: {}".format(_snake_case,				_snake_case       )       )

      return answers


def   __UpperCAmelCase     (	):
   _lowercase							     =       argparse.ArgumentParser()
   parser.add_argument(
       "--model_type",				choices=["rag_sequence", "rag_token", "bart"],				type=_snake_case,				help=(
           "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
           " model_name_or_path"
       ),				)
   parser.add_argument(
       "--index_name",				default=_snake_case,				choices=["exact", "compressed", "legacy"],				type=_snake_case,				help="RAG model retriever type",				)
   parser.add_argument(
       "--index_path",				default=_snake_case,				type=_snake_case,				help="Path to the retrieval index",				)
   parser.add_argument("--n_docs",				default=5,				type=_snake_case,				help="Number of retrieved docs"       )
   parser.add_argument(
       "--model_name_or_path",				default=_snake_case,				type=_snake_case,				required=_snake_case,				help="Path to pretrained checkpoints or model identifier from huggingface.co/models",				)
   parser.add_argument(
       "--eval_mode",				choices=["e2e", "retrieval"],				default="e2e",				type=_snake_case,				help=(
           "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
           " precision@k."
       ),				)
   parser.add_argument("--k",				default=1,				type=_snake_case,				help="k for the precision@k calculation"       )
   parser.add_argument(
       "--evaluation_set",				default=_snake_case,				type=_snake_case,				required=_snake_case,				help="Path to a file containing evaluation samples",				)
   parser.add_argument(
       "--gold_data_path",				default=_snake_case,				type=_snake_case,				required=_snake_case,				help="Path to a tab-separated file with gold samples",				)
   parser.add_argument(
       "--gold_data_mode",				default="qa",				type=_snake_case,				choices=["qa", "ans"],				help=(
           "Format of the gold data file"
           "qa - a single line in the following format: question [tab] answer_list"
           "ans - a single line of the gold file contains the expected answer string"
       ),				)
   parser.add_argument(
       "--predictions_path",				type=_snake_case,				default="predictions.txt",				help="Name of the predictions file, to be stored in the checkpoints directory",				)
   parser.add_argument(
       "--eval_all_checkpoints",				action="store_true",				help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",				)
   parser.add_argument(
       "--eval_batch_size",				default=8,				type=_snake_case,				help="Batch size per GPU/CPU for evaluation.",				)
   parser.add_argument(
       "--recalculate",				help="Recalculate predictions even if the prediction file exists",				action="store_true",				)
   parser.add_argument(
       "--num_beams",				default=4,				type=_snake_case,				help="Number of beams to be used when generating answers",				)
   parser.add_argument("--min_length",				default=1,				type=_snake_case,				help="Min length of the generated answers"       )
   parser.add_argument("--max_length",				default=5_0,				type=_snake_case,				help="Max length of the generated answers"       )

   parser.add_argument(
       "--print_predictions",				action="store_true",				help="If True, prints predictions while evaluating.",				)
   parser.add_argument(
       "--print_docs",				action="store_true",				help="If True, prints docs retried while generating.",				)
   _lowercase							     =       parser.parse_args()
   _lowercase							     =       torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
   return args


def   __UpperCAmelCase     (	_snake_case		:       List[Any]       ):
   _lowercase							     =       {}
   if args.model_type is None:
      _lowercase							     =       infer_model_type(args.model_name_or_path       )
      assert args.model_type is not None
   if args.model_type.startswith("rag"       ):
      _lowercase							     =       RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
      _lowercase							     =       args.n_docs
      if args.index_name is not None:
         _lowercase							     =       args.index_name
      if args.index_path is not None:
         _lowercase							     =       args.index_path
   else:
      _lowercase							     =       BartForConditionalGeneration

   _lowercase							     =       (
       [f.path for f in os.scandir(args.model_name_or_path       ) if f.is_dir()]
       if args.eval_all_checkpoints
       else [args.model_name_or_path]
   )

   logger.info("Evaluate the following checkpoints: %s",				_snake_case       )

   _lowercase							     =       get_scores if args.eval_mode == "e2e" else get_precision_at_k
   _lowercase							     =       evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

   for checkpoint in checkpoints:
      if os.path.exists(args.predictions_path       ) and (not args.recalculate):
         logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path       )       )
         score_fn(_snake_case,				args.predictions_path,				args.gold_data_path       )
         continue

      logger.info("***** Running evaluation for {} *****".format(_snake_case       )       )
      logger.info("  Batch size = %d",				args.eval_batch_size       )
      logger.info("  Predictions will be stored under {}".format(args.predictions_path       )       )

      if args.model_type.startswith("rag"       ):
         _lowercase							     =       RagRetriever.from_pretrained(_snake_case,				**_snake_case       )
         _lowercase							     =       model_class.from_pretrained(_snake_case,				retriever=_snake_case,				**_snake_case       )
         model.retriever.init_retrieval()
      else:
         _lowercase							     =       model_class.from_pretrained(_snake_case,				**_snake_case       )
      model.to(args.device       )

      with open(args.evaluation_set,				"r"       ) as eval_file, open(args.predictions_path,				"w"       ) as preds_file:
         _lowercase							     =       []
         for line in tqdm(_snake_case       ):
            questions.append(line.strip()       )
            if len(_snake_case       ) == args.eval_batch_size:
               _lowercase							     =       evaluate_batch_fn(_snake_case,				_snake_case,				_snake_case       )
               preds_file.write("\n".join(_snake_case       ) + "\n"       )
               preds_file.flush()
               _lowercase							     =       []
         if len(_snake_case       ) > 0:
            _lowercase							     =       evaluate_batch_fn(_snake_case,				_snake_case,				_snake_case       )
            preds_file.write("\n".join(_snake_case       )       )
            preds_file.flush()

         score_fn(_snake_case,				args.predictions_path,				args.gold_data_path       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		get_args()
  main(args)
"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase       :    Union[str, Any]         =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase       :    Optional[Any]         =		[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)


def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _lowercase							     =       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase							     =       key.replace("backbone.0.body",				"backbone.conv_encoder.model"       )
         _lowercase							     =       value
      else:
         _lowercase							     =       value

   return new_state_dict


def   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int=False       ):
   _lowercase							     =       ""
   if is_panoptic:
      _lowercase							     =       "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)
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
   # 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
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _lowercase							     =       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight_cross_attn[:2_5_6, :]
      _lowercase							     =       in_proj_bias_cross_attn[:2_5_6]
      _lowercase							     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight_cross_attn[-2_5_6:, :]
      _lowercase							     =       in_proj_bias_cross_attn[-2_5_6:]


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

   return im


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       str       ):
   _lowercase							     =       DetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      _lowercase							     =       "resnet101"
   if "dc5" in model_name:
      _lowercase							     =       True
   _lowercase							     =       "panoptic" in model_name
   if is_panoptic:
      _lowercase							     =       2_5_0
   else:
      _lowercase							     =       9_1
      _lowercase							     =       "huggingface/label-files"
      _lowercase							     =       "coco-detection-id2label.json"
      _lowercase							     =       json.load(open(hf_hub_download(_snake_case,				_snake_case,				repo_type="dataset"       ),				"r"       )       )
      _lowercase							     =       {int(_snake_case       ): v for k, v in idalabel.items()}
      _lowercase							     =       idalabel
      _lowercase							     =       {v: k for k, v in idalabel.items()}

   # load image processor
   _lowercase							     =       "coco_panoptic" if is_panoptic else "coco_detection"
   _lowercase							     =       DetrImageProcessor(format=_snake_case       )

   # prepare image
   _lowercase							     =       prepare_img()
   _lowercase							     =       image_processor(images=_snake_case,				return_tensors="pt"       )
   _lowercase							     =       encoding["pixel_values"]

   logger.info(f"""Converting model {model_name}..."""       )

   # load original model from torch hub
   _lowercase							     =       torch.hub.load("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "detr." + src
      rename_key(_snake_case,				_snake_case,				_snake_case       )
   _lowercase							     =       rename_backbone_keys(_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
   _lowercase							     =       "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"       )
         ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
    # finally, create HuggingFace model and load state dict
   _lowercase							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       detr(_snake_case       )
   _lowercase							     =       model(_snake_case       )
   assert torch.allclose(outputs.logits,				original_outputs["pred_logits"],				atol=1e-4       )
   assert torch.allclose(outputs.pred_boxes,				original_outputs["pred_boxes"],				atol=1e-4       )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks,				original_outputs["pred_masks"],				atol=1e-4       )

   # Save model and image processor
   logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
   Path(_snake_case       ).mkdir(exist_ok=_snake_case       )
   model.save_pretrained(_snake_case       )
   image_processor.save_pretrained(_snake_case       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		argparse.ArgumentParser()

  parser.add_argument(
      "--model_name", default="detr_resnet50", type=str, help="Name of the DETR model you'd like to convert."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
  )
  __UpperCamelCase       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)