'''simple docstring'''





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      str:


     '''simple docstring'''




     return 1.0 / (1.0 + np.exp(-_outputs			))





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   np.max(_outputs					,    axis=-1					,    keepdims=snake_case_			)
     UpperCAmelCase_           =   np.exp(_outputs - maxes			)
     return shifted_exp / shifted_exp.sum(axis=-1					,    keepdims=snake_case_			)




class        __A      ( UpperCamelCase__	):
    a__       :						List[str]          =  """sigmoid"""
    a__       :						Tuple          =  """softmax"""
    a__       :						Union[str, Any]          =  """none"""




@add_end_docstrings(
    UpperCamelCase__		,	r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """		,	)
class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  False
    a__       :						List[str]          =  ClassificationFunction.NONE



    def __init__(self    :    Dict   ,			**__a    :    Any ):
         super().__init__(**__a )

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == "tf"
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )



    def 					_lowercase						(self    :    Dict   ,			__a    :    Dict=None   ,			__a    :    Any=None   ,			__a    :    int=""   ,			**__a    :    Dict ):
         # Using "" as default argument because we're going to use `top_k=None` in user code to declare
         # "No top_k"
         UpperCAmelCase_           =   tokenizer_kwargs

         UpperCAmelCase_           =   {}
         if hasattr(self.model.config   ,			"return_all_scores" ) and return_all_scores is None:
              UpperCAmelCase_           =   self.model.config.return_all_scores

         if isinstance(__a   ,			__a ) or top_k is None:
              UpperCAmelCase_           =   top_k
              UpperCAmelCase_           =   False
         elif return_all_scores is not None:
              warnings.warn(
                  "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
                  " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."   ,			__a   ,			)
              if return_all_scores:
                   UpperCAmelCase_           =   None
              else:
                   UpperCAmelCase_           =   1

         if isinstance(__a   ,			__a ):
              UpperCAmelCase_           =   ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
              UpperCAmelCase_           =   function_to_apply
         return preprocess_params, {}, postprocess_params



    def __call__(self    :    Optional[int]   ,			*__a    :    List[Any]   ,			**__a    :    Tuple ):
         UpperCAmelCase_           =   super().__call__(*__a   ,			**__a )
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         UpperCAmelCase_           =   "top_k" not in kwargs
         if isinstance(args[0]   ,			__a ) and _legacy:
              # This pipeline is odd, and return a list when single item is run
              return [result]
         else:
              return result



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Dict   ,			**__a    :    List[str] ):
         UpperCAmelCase_           =   self.framework
         if isinstance(__a   ,			__a ):
              return self.tokenizer(**__a   ,			return_tensors=__a   ,			**__a )
         elif isinstance(__a   ,			__a ) and len(__a ) == 1 and isinstance(inputs[0]   ,			__a ) and len(inputs[0] ) == 2:
              # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
              return self.tokenizer(
                  text=inputs[0][0]   ,			text_pair=inputs[0][1]   ,			return_tensors=__a   ,			**__a )
         elif isinstance(__a   ,			__a ):
              # This is likely an invalid usage of the pipeline attempting to pass text pairs.
              raise ValueError(
                  "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
                  " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair." )
         return self.tokenizer(__a   ,			return_tensors=__a   ,			**__a )



    def 					_lowercase						(self    :    Dict   ,			__a    :    Optional[Any] ):
         return self.model(**__a )






    def 					_lowercase						(self    :    Dict   ,			__a    :    int   ,			__a    :    int=None   ,			__a    :    List[str]=1   ,			__a    :    List[str]=True ):
         # `_legacy` is used to determine if we're running the naked pipeline and in backward
         # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
         # the more natural result containing the list.
         # Default value before `set_parameters`
         if function_to_apply is None:
              if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                   UpperCAmelCase_           =   ClassificationFunction.SIGMOID
              elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                   UpperCAmelCase_           =   ClassificationFunction.SOFTMAX
              elif hasattr(self.model.config   ,			"function_to_apply" ) and function_to_apply is None:
                   UpperCAmelCase_           =   self.model.config.function_to_apply
              else:
                   UpperCAmelCase_           =   ClassificationFunction.NONE

         UpperCAmelCase_           =   model_outputs["logits"][0]
         UpperCAmelCase_           =   outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
              UpperCAmelCase_           =   sigmoid(__a )
         elif function_to_apply == ClassificationFunction.SOFTMAX:
              UpperCAmelCase_           =   softmax(__a )
         elif function_to_apply == ClassificationFunction.NONE:
              UpperCAmelCase_           =   outputs
         else:
              raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""" )

         if top_k == 1 and _legacy:
              return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         UpperCAmelCase_           =   [
             {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(__a )
         ]
         if not _legacy:
              dict_scores.sort(key=lambda __a : x["score"]   ,			reverse=__a )
              if top_k is not None:
                   UpperCAmelCase_           =   dict_scores[:top_k]
         return dict_scores





'''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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE_:     Tuple  =[]
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}.cross_attn.out_proj.weight",
            f"decoder.layers.{i}.encoder_attn.out_proj.weight",
        )
    )
    rename_keys.append(
        (
            f"transformer.decoder.layers.{i}.cross_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"))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight")
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight")
    )

    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias")
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias")
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Any					,    snake_case_       : Optional[int]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   state_dict.pop(snake_case_			)
     UpperCAmelCase_           =   val





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCAmelCase_           =   key.replace("backbone.0.body"					,    "backbone.conv_encoder.model"			)
               UpperCAmelCase_           =   value
          else:
               UpperCAmelCase_           =   value

     return new_state_dict





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : Dict=False			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   ""
     if is_panoptic:
          UpperCAmelCase_           =   "conditional_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)
          UpperCAmelCase_           =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
          UpperCAmelCase_           =   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
          UpperCAmelCase_           =   in_proj_weight[:2_56, :]
          UpperCAmelCase_           =   in_proj_bias[:2_56]
          UpperCAmelCase_           =   in_proj_weight[2_56:5_12, :]
          UpperCAmelCase_           =   in_proj_bias[2_56:5_12]
          UpperCAmelCase_           =   in_proj_weight[-2_56:, :]
          UpperCAmelCase_           =   in_proj_bias[-2_56:]





def 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




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

     return im





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : Dict			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          UpperCAmelCase_           =   "resnet101"
     if "dc5" in model_name:
          UpperCAmelCase_           =   True
     UpperCAmelCase_           =   "panoptic" in model_name
     if is_panoptic:
          UpperCAmelCase_           =   2_50
     else:
          UpperCAmelCase_           =   91
          UpperCAmelCase_           =   "huggingface/label-files"
          UpperCAmelCase_           =   "coco-detection-id2label.json"
          UpperCAmelCase_           =   json.load(open(hf_hub_download(snake_case_					,    snake_case_					,    repo_type="dataset"			)					,    "r"			)			)
          UpperCAmelCase_           =   {int(snake_case_			): v for k, v in idalabel.items()}
          UpperCAmelCase_           =   idalabel
          UpperCAmelCase_           =   {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCAmelCase_           =   "coco_panoptic" if is_panoptic else "coco_detection"
     UpperCAmelCase_           =   ConditionalDetrImageProcessor(format=snake_case_			)

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

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

     # load original model from torch hub
     UpperCAmelCase_           =   torch.hub.load("DeppMeng/ConditionalDETR"					,    snake_case_					,    pretrained=snake_case_			).eval()
     UpperCAmelCase_           =   conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               UpperCAmelCase_           =   "conditional_detr." + src
          rename_key(snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   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
     UpperCAmelCase_           =   "conditional_detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("conditional_detr"			)
                   and not key.startswith("class_labels_classifier"			)
                   and not key.startswith("bbox_predictor"			)
               ):
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
               elif key.startswith("bbox_attention"			) or key.startswith("mask_head"			):
                    continue
               else:
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
          else:
               if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_           =   ConditionalDetrForSegmentation(snake_case_			) if is_panoptic else ConditionalDetrForObjectDetection(snake_case_			)
     model.load_state_dict(snake_case_			)
     model.eval()
     model.push_to_hub(repo_id=snake_case_					,    organization="DepuMeng"					,    commit_message="Add model"			)
     # verify our conversion
     UpperCAmelCase_           =   conditional_detr(snake_case_			)
     UpperCAmelCase_           =   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__":
    SCREAMING_SNAKE_CASE_:     List[str]  =argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        default='conditional_detr_resnet50',
        type=str,
        help='Name of the CONDITIONAL_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.'
    )
    SCREAMING_SNAKE_CASE_:     int  =parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




