
import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger






class 						UpperCAmelCase	(		A_				):
			A__      :	List[Any]		  =  "config.json"



			@register_to_config
			def __init__(self	:      int					,		snake_case__	:      Any=2					,		snake_case__	:      Optional[Any]=5					,		snake_case__	:      str=(2, 5)					,		snake_case__	:      List[Any]="for diffusion"					,		snake_case__	:      int=[1, 3]					,		)	->			Tuple:


										'''simple docstring'''


										pass






class 						UpperCAmelCase	(		A_				):
			A__      :	Dict		  =  "config.json"



			@register_to_config
			def __init__(self	:      Union[str, Any]					,		snake_case__	:      List[str]=2					,		snake_case__	:      Any=5					,		snake_case__	:      Optional[int]=(2, 5)					,		snake_case__	:      Dict="for diffusion"					,		snake_case__	:      Optional[int]=[1, 3]					,		)	->			Any:


										'''simple docstring'''


										pass






class 						UpperCAmelCase	(		A_				):
			A__      :	Union[str, Any]		  =  "config.json"



			@register_to_config
			def __init__(self	:      List[Any]					,		snake_case__	:      Optional[int]=2					,		snake_case__	:      int=5					,		snake_case__	:      Union[str, Any]=(2, 5)					,		snake_case__	:      Union[str, Any]="for diffusion"					,		snake_case__	:      List[str]=[1, 3]					,		snake_case__	:      Optional[Any]=[1, 3]					,		)	->			Tuple:


										'''simple docstring'''


										pass






class 						UpperCAmelCase	(		A_				):
			A__      :	Optional[int]		  =  "config.json"



			@register_to_config
			def __init__(self	:      List[str]					,		snake_case__	:      str=2					,		snake_case__	:      Tuple=5					,		snake_case__	:      Dict=(2, 5)					,		snake_case__	:      Tuple="for diffusion"					,		snake_case__	:      Tuple=[1, 5]					,		snake_case__	:      Tuple=[5, 4]					,		)	->			Any:


										'''simple docstring'''


										pass






class 						UpperCAmelCase	(		unittest.TestCase				):



			def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Tuple:


										'''simple docstring'''


										with self.assertRaises(snake_case__				):
																	ConfigMixin.load_config("dummy_path"				)



			def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Tuple:


										'''simple docstring'''


										snake_case :     Dict           =   SampleObject()
										snake_case :     Tuple           =   obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# init ignore private arguments
										snake_case :     Optional[int]           =   SampleObject(_name_or_path="lalala"				)
										snake_case :     List[Any]           =   obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# can override default
										snake_case :     int           =   SampleObject(c=6				)
										snake_case :     Union[str, Any]           =   obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == 6
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# can use positional arguments.
										snake_case :     Tuple           =   SampleObject(1					,		c=6				)
										snake_case :     Tuple           =   obj.config
										assert config["a"] == 1
										assert config["b"] == 5
										assert config["c"] == 6
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]



			def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			List[Any]:


										'''simple docstring'''


										snake_case :     Dict           =   SampleObject()
										snake_case :     str           =   obj.config

										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										with tempfile.TemporaryDirectory() as tmpdirname:
																	obj.save_config(snake_case__				)
																	snake_case :     List[Any]           =   SampleObject.from_config(SampleObject.load_config(snake_case__				)				)
																	snake_case :     Any           =   new_obj.config

										# unfreeze configs
										snake_case :     str           =   dict(snake_case__				)
										snake_case :     int           =   dict(snake_case__				)

										assert config.pop("c"				) == (2, 5)  # instantiated as tuple
										assert new_config.pop("c"				) == [2, 5]  # saved & loaded as list because of json
										config.pop("_use_default_values"				)
										assert config == new_config



			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			int:


										'''simple docstring'''


										snake_case :     Optional[int]           =   logging.get_logger("diffusers.configuration_utils"				)
										# 30 for warning
										logger.setLevel(30				)

										with CaptureLogger(snake_case__				) as cap_logger:
																	snake_case :     Dict           =   DDIMScheduler.from_pretrained(
																	    "hf-internal-testing/tiny-stable-diffusion-torch"					,		subfolder="scheduler"				)

										assert ddim.__class__ == DDIMScheduler
										# no warning should be thrown
										assert cap_logger.out == ""



			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			List[str]:


										'''simple docstring'''


										snake_case :     Dict           =   logging.get_logger("diffusers.configuration_utils"				)
										# 30 for warning
										logger.setLevel(30				)

										with CaptureLogger(snake_case__				) as cap_logger:
																	snake_case :     Tuple           =   EulerDiscreteScheduler.from_pretrained(
																	    "hf-internal-testing/tiny-stable-diffusion-torch"					,		subfolder="scheduler"				)

										assert euler.__class__ == EulerDiscreteScheduler
										# no warning should be thrown
										assert cap_logger.out == ""



			def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[Any]:


										'''simple docstring'''


										snake_case :     Union[str, Any]           =   logging.get_logger("diffusers.configuration_utils"				)
										# 30 for warning
										logger.setLevel(30				)

										with CaptureLogger(snake_case__				) as cap_logger:
																	snake_case :     List[str]           =   EulerAncestralDiscreteScheduler.from_pretrained(
																	    "hf-internal-testing/tiny-stable-diffusion-torch"					,		subfolder="scheduler"				)

										assert euler.__class__ == EulerAncestralDiscreteScheduler
										# no warning should be thrown
										assert cap_logger.out == ""



			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Any:


										'''simple docstring'''


										snake_case :     int           =   logging.get_logger("diffusers.configuration_utils"				)
										# 30 for warning
										logger.setLevel(30				)

										with CaptureLogger(snake_case__				) as cap_logger:
																	snake_case :     List[str]           =   PNDMScheduler.from_pretrained(
																	    "hf-internal-testing/tiny-stable-diffusion-torch"					,		subfolder="scheduler"				)

										assert pndm.__class__ == PNDMScheduler
										# no warning should be thrown
										assert cap_logger.out == ""



			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			Optional[Any]:


										'''simple docstring'''


										snake_case :     Union[str, Any]           =   logging.get_logger("diffusers.configuration_utils"				)
										# 30 for warning
										logger.setLevel(30				)

										with CaptureLogger(snake_case__				) as cap_logger:
																	snake_case :     Any           =   DDPMScheduler.from_pretrained(
																	    "hf-internal-testing/tiny-stable-diffusion-torch"					,		subfolder="scheduler"					,		prediction_type="sample"					,		beta_end=8					,		)

										with CaptureLogger(snake_case__				) as cap_logger_a:
																	snake_case :     List[Any]           =   DDPMScheduler.from_pretrained("google/ddpm-celebahq-256"					,		beta_start=88				)

										assert ddpm.__class__ == DDPMScheduler
										assert ddpm.config.prediction_type == "sample"
										assert ddpm.config.beta_end == 8
										assert ddpm_a.config.beta_start == 88

										# no warning should be thrown
										assert cap_logger.out == ""
										assert cap_logger_a.out == ""



			def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[str]:


										'''simple docstring'''


										snake_case :     str           =   logging.get_logger("diffusers.configuration_utils"				)
										# 30 for warning
										logger.setLevel(30				)

										with CaptureLogger(snake_case__				) as cap_logger:
																	snake_case :     Any           =   DPMSolverMultistepScheduler.from_pretrained(
																	    "hf-internal-testing/tiny-stable-diffusion-torch"					,		subfolder="scheduler"				)

										assert dpm.__class__ == DPMSolverMultistepScheduler
										# no warning should be thrown
										assert cap_logger.out == ""



			def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			Optional[Any]:


										'''simple docstring'''


										snake_case :     Tuple           =   SampleObject()

										snake_case :     Any           =   {k: v for k, v in config.config.items() if not k.startswith("_"				)}

										# make sure that default config has all keys in `_use_default_values`
										assert set(config_dict.keys()				) == set(config.config._use_default_values				)

										with tempfile.TemporaryDirectory() as tmpdirname:
																	config.save_config(snake_case__				)

																	# now loading it with SampleObject2 should put f into `_use_default_values`
																	snake_case :     Union[str, Any]           =   SampleObjecta.from_config(snake_case__				)

																	assert "f" in config._use_default_values
																	assert config.f == [1, 3]

										# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
										# **BECAUSE** it is part of `config._use_default_values`
										snake_case :     List[Any]           =   SampleObjecta.from_config(config.config				)
										assert new_config.f == [5, 4]

										config.config._use_default_values.pop()
										snake_case :     List[Any]           =   SampleObjecta.from_config(config.config				)
										assert new_config_a.f == [1, 3]

										# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
										assert new_config_a.e == [1, 3]







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()
__lowerCamelCase							= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase							= []
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      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Any					):
       snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
       snake_case :     int           =   val


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]					):
       snake_case :     Optional[int]           =   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     snake_case :     List[str]           =   key.replace("backbone.0.body"       ,      "backbone.conv_encoder.model"					)
                     snake_case :     str           =   value
              else:
                     snake_case :     Any           =   value

       return new_state_dict


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Dict=False					):
       snake_case :     List[str]           =   ""
       if is_panoptic:
              snake_case :     Dict           =   "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)
              snake_case :     Optional[Any]           =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     int           =   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
              snake_case :     Optional[Any]           =   in_proj_weight[:256, :]
              snake_case :     List[str]           =   in_proj_bias[:256]
              snake_case :     Tuple           =   in_proj_weight[256:512, :]
              snake_case :     Dict           =   in_proj_bias[256:512]
              snake_case :     Optional[int]           =   in_proj_weight[-256:, :]
              snake_case :     Optional[int]           =   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
              snake_case :     int           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     List[str]           =   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
              snake_case :     Dict           =   in_proj_weight[:256, :]
              snake_case :     Optional[int]           =   in_proj_bias[:256]
              snake_case :     Union[str, Any]           =   in_proj_weight[256:512, :]
              snake_case :     Optional[Any]           =   in_proj_bias[256:512]
              snake_case :     Dict           =   in_proj_weight[-256:, :]
              snake_case :     int           =   in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              snake_case :     Dict           =   state_dict.pop(
                  f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
              snake_case :     Dict           =   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
              snake_case :     Any           =   in_proj_weight_cross_attn[:256, :]
              snake_case :     Dict           =   in_proj_bias_cross_attn[:256]
              snake_case :     Any           =   in_proj_weight_cross_attn[256:512, :]
              snake_case :     Optional[int]           =   in_proj_bias_cross_attn[256:512]
              snake_case :     List[Any]           =   in_proj_weight_cross_attn[-256:, :]
              snake_case :     Optional[Any]           =   in_proj_bias_cross_attn[-256:]


def     UpperCamelCase      (					):
       snake_case :     Optional[Any]           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case :     List[str]           =   Image.open(requests.get(__lowerCamelCase       ,      stream=__lowerCamelCase					).raw					)

       return im


@torch.no_grad()
def     UpperCamelCase      (					__lowerCamelCase  :				Any       ,      __lowerCamelCase  :				List[Any]					):
       snake_case :     Tuple           =   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              snake_case :     Optional[Any]           =   "resnet101"
       if "dc5" in model_name:
              snake_case :     int           =   True
       snake_case :     List[str]           =   "panoptic" in model_name
       if is_panoptic:
              snake_case :     Dict           =   250
       else:
              snake_case :     int           =   91
              snake_case :     List[Any]           =   "huggingface/label-files"
              snake_case :     Dict           =   "coco-detection-id2label.json"
              snake_case :     Dict           =   json.load(open(hf_hub_download(__lowerCamelCase       ,      __lowerCamelCase       ,      repo_type="dataset"					)       ,      "r"					)					)
              snake_case :     Union[str, Any]           =   {int(__lowerCamelCase					): v for k, v in idalabel.items()}
              snake_case :     int           =   idalabel
              snake_case :     str           =   {v: k for k, v in idalabel.items()}

       # load image processor
       snake_case :     Tuple           =   "coco_panoptic" if is_panoptic else "coco_detection"
       snake_case :     str           =   DetrImageProcessor(format=__lowerCamelCase					)

       # prepare image
       snake_case :     Optional[int]           =   prepare_img()
       snake_case :     Optional[Any]           =   image_processor(images=__lowerCamelCase       ,      return_tensors="pt"					)
       snake_case :     Optional[int]           =   encoding["pixel_values"]

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

       # load original model from torch hub
       snake_case :     Dict           =   torch.hub.load("facebookresearch/detr"       ,      __lowerCamelCase       ,      pretrained=__lowerCamelCase					).eval()
       snake_case :     Any           =   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     snake_case :     List[str]           =   "detr." + src
              rename_key(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					)
       snake_case :     str           =   rename_backbone_keys(__lowerCamelCase					)
       # query, key and value matrices need special treatment
       read_in_q_k_v(__lowerCamelCase       ,      is_panoptic=__lowerCamelCase					)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       snake_case :     List[Any]           =   "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"					)
                     ):
                            snake_case :     Tuple           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Any           =   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            snake_case :     Dict           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[Any]           =   val
                     elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
                            continue
                     else:
                            snake_case :     Union[str, Any]           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     int           =   val
              else:
                     if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
                            snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[int]           =   val
    # finally, create HuggingFace model and load state dict
       snake_case :     Union[str, Any]           =   DetrForSegmentation(__lowerCamelCase					) if is_panoptic else DetrForObjectDetection(__lowerCamelCase					)
       model.load_state_dict(__lowerCamelCase					)
       model.eval()
       # verify our conversion
       snake_case :     List[Any]           =   detr(__lowerCamelCase					)
       snake_case :     Tuple           =   model(__lowerCamelCase					)
       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(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
       model.save_pretrained(__lowerCamelCase					)
       image_processor.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
   __lowerCamelCase							= 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."""
   )
   __lowerCamelCase							= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





