


import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


snake_case__			 =						datasets.utils.logging.get_logger(__name__)


@dataclass
class 					lowerCAmelCase_ (       datasets.BuilderConfig):
						lowerCamelCase_         =     None
						lowerCamelCase_         =     "utf-8"
						lowerCamelCase_         =     None
						lowerCamelCase_         =     None
						lowerCamelCase_         =     True  # deprecated
						lowerCamelCase_         =     None  # deprecated
						lowerCamelCase_         =     10 << 20  # 10MB
						lowerCamelCase_         =     None






class 					lowerCAmelCase_ (       datasets.ArrowBasedBuilder):
						lowerCamelCase_         =     JsonConfig




						def  _snake_case					(	self						:   List[str]						)      ->Optional[int]:






									"""simple docstring"""






									if self.config.block_size is not None:
												logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"						)
												a__ :Any						  =    self.config.block_size
									if self.config.use_threads is not True:
												logger.warning(
												    "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."						)
									if self.config.newlines_in_values is not None:
												raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"						)
									return datasets.DatasetInfo(features=self.config.features						)




						def  _snake_case					(	self						:   Dict   ,     __A						:   int						)      ->str:






									"""simple docstring"""






									if not self.config.data_files:
												raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''						)
									a__ :List[str]						  =    dl_manager.download_and_extract(self.config.data_files						)
									if isinstance(__A   ,     (str, list, tuple)						):
												a__ :int						  =    data_files
												if isinstance(__A   ,     __A						):
															a__ :List[Any]						  =    [files]
												a__ :Dict						  =    [dl_manager.iter_files(__A						) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,     gen_kwargs={"files": files}						)]
									a__ :List[Any]						  =    []
									for split_name, files in data_files.items():
												if isinstance(__A   ,     __A						):
															a__ :List[Any]						  =    [files]
												a__ :Tuple						  =    [dl_manager.iter_files(__A						) for file in files]
												splits.append(datasets.SplitGenerator(name=__A   ,     gen_kwargs={"files": files}						)						)
									return splits




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   pa.Table						)      ->pa.Table:






									"""simple docstring"""






									if self.config.features is not None:
												# adding missing columns
												for column_name in set(self.config.features						) - set(pa_table.column_names						):
															a__ :Optional[int]						  =    self.config.features.arrow_schema.field(__A						).type
															a__ :Dict						  =    pa_table.append_column(__A   ,     pa.array([None] * len(__A						)   ,     type=__A						)						)
												# more expensive cast to support nested structures with keys in a different order
												# allows str <-> int/float or str to Audio for example
												a__ :List[Any]						  =    table_cast(__A   ,     self.config.features.arrow_schema						)
									return pa_table






						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Any						)      ->Any:






									"""simple docstring"""






									for file_idx, file in enumerate(itertools.chain.from_iterable(__A						)						):
												# If the file is one json object and if we need to look at the list of items in one specific field
												if self.config.field is not None:
															with open(__A   ,     encoding=self.config.encoding   ,     errors=self.config.encoding_errors						) as f:
																		a__ :int						  =    json.load(__A						)

															# We keep only the field we are interested in
															a__ :List[str]						  =    dataset[self.config.field]

															# We accept two format: a list of dicts or a dict of lists
															if isinstance(__A   ,     (list, tuple)						):
																		a__ :Union[str, Any]						  =    set().union(*[row.keys() for row in dataset]						)
																		a__ :int						  =    {col: [row.get(__A						) for row in dataset] for col in keys}
															else:
																		a__ :Tuple						  =    dataset
															a__ :Optional[int]						  =    pa.Table.from_pydict(__A						)
															yield file_idx, self._cast_table(__A						)

												# If the file has one json object per line
												else:
															with open(__A   ,     "rb"						) as f:
																		a__ :Union[str, Any]						  =    0
																		# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																		# Set a default minimum value of 16kB if the chunk size is really small
																		a__ :Dict						  =    max(self.config.chunksize // 32   ,     16 << 10						)
																		a__ :Union[str, Any]						  =    (
																		    self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
																		)
																		while True:
																					a__ :Any						  =    f.read(self.config.chunksize						)
																					if not batch:
																								break
																					# Finish current line
																					try:
																								batch += f.readline()
																					except (AttributeError, io.UnsupportedOperation):
																								batch += readline(__A						)
																					# PyArrow only accepts utf-8 encoded bytes
																					if self.config.encoding != "utf-8":
																								a__ :List[Any]						  =    batch.decode(self.config.encoding   ,     errors=__A						).encode("utf-8"						)
																					try:
																								while True:
																											try:
																														a__ :str						  =    paj.read_json(
																														    io.BytesIO(__A						)   ,     read_options=paj.ReadOptions(block_size=__A						)						)
																														break
																											except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																														if (
																														    isinstance(__A   ,     pa.ArrowInvalid						)
																														    and "straddling" not in str(__A						)
																														    or block_size > len(__A						)
																														):
																																	raise
																														else:
																																	# Increase the block size in case it was too small.
																																	# The block size will be reset for the next file.
																																	logger.debug(
																																	    F'''Batch of {len(__A						)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''						)
																																	block_size *= 2
																					except pa.ArrowInvalid as e:
																								try:
																											with open(
																											    __A   ,     encoding=self.config.encoding   ,     errors=self.config.encoding_errors						) as f:
																														a__ :Optional[int]						  =    json.load(__A						)
																								except json.JSONDecodeError:
																											logger.error(F'''Failed to read file \'{file}\' with error {type(__A						)}: {e}'''						)
																											raise e
																								# If possible, parse the file as a list of json objects and exit the loop
																								if isinstance(__A   ,     __A						):  # list is the only sequence type supported in JSON
																											try:
																														a__ :List[str]						  =    set().union(*[row.keys() for row in dataset]						)
																														a__ :Union[str, Any]						  =    {col: [row.get(__A						) for row in dataset] for col in keys}
																														a__ :Dict						  =    pa.Table.from_pydict(__A						)
																											except (pa.ArrowInvalid, AttributeError) as e:
																														logger.error(F'''Failed to read file \'{file}\' with error {type(__A						)}: {e}'''						)
																														raise ValueError(F'''Not able to read records in the JSON file at {file}.'''						) from None
																											yield file_idx, self._cast_table(__A						)
																											break
																								else:
																											logger.error(F'''Failed to read file \'{file}\' with error {type(__A						)}: {e}'''						)
																											raise ValueError(
																											    F'''Not able to read records in the JSON file at {file}. '''
																											    F'''You should probably indicate the field of the JSON file containing your records. '''
																											    F'''This JSON file contain the following fields: {str(list(dataset.keys()						)						)}. '''
																											    F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''						) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																					yield (file_idx, batch_idx), self._cast_table(__A						)
																					batch_idx += 1






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()
snake_case__			 =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__			 =						[]
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__	(      a	:   Any	,  a	:   Any	,  a	:   List[Any]				)    ->	Any:





			"""simple docstring"""
			a__ :Optional[Any]						  =    state_dict.pop(a				)
			a__ :Tuple						  =    val


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	str:





			"""simple docstring"""
			a__ :Any						  =    OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									a__ :List[Any]						  =    key.replace("backbone.0.body"	,  "backbone.conv_encoder.model"				)
									a__ :List[Any]						  =    value
						else:
									a__ :Dict						  =    value

			return new_state_dict


def 				lowerCamelCase__	(      a	:   str	,  a	:   Dict=False				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    ""
			if is_panoptic:
						a__ :str						  =    "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)
						a__ :str						  =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
						a__ :Optional[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
						a__ :int						  =    in_proj_weight[:256, :]
						a__ :List[str]						  =    in_proj_bias[:256]
						a__ :List[str]						  =    in_proj_weight[256:512, :]
						a__ :Optional[Any]						  =    in_proj_bias[256:512]
						a__ :Dict						  =    in_proj_weight[-256:, :]
						a__ :Tuple						  =    in_proj_bias[-256:]


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





			"""simple docstring"""
			a__ :str						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__ :Optional[Any]						  =    Image.open(requests.get(a	,  stream=a				).raw				)

			return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Union[str, Any]				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						a__ :str						  =    "resnet101"
			if "dc5" in model_name:
						a__ :Any						  =    True
			a__ :Optional[int]						  =    "panoptic" in model_name
			if is_panoptic:
						a__ :Union[str, Any]						  =    250
			else:
						a__ :str						  =    91
						a__ :Dict						  =    "huggingface/label-files"
						a__ :Optional[Any]						  =    "coco-detection-id2label.json"
						a__ :Dict						  =    json.load(open(hf_hub_download(a	,  a	,  repo_type="dataset"				)	,  "r"				)				)
						a__ :str						  =    {int(a				): v for k, v in idalabel.items()}
						a__ :int						  =    idalabel
						a__ :Dict						  =    {v: k for k, v in idalabel.items()}

			# load image processor
			a__ :Union[str, Any]						  =    "coco_panoptic" if is_panoptic else "coco_detection"
			a__ :Any						  =    ConditionalDetrImageProcessor(format=a				)

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

			logger.info(F'''Converting model {model_name}...'''				)

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


if __name__ == "__main__":
		snake_case__			 =						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.'''
		)
		snake_case__			 =						parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


