



'''simple docstring'''




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
							from ...feature_extraction_utils import FeatureExtractionMixin
							from ...tokenization_utils_base import PreTrainedTokenizerBase
							from ...utils import TensorType

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

a     :    Dict      						=					{
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
a     :    Dict      						=					[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a     :    Optional[Any]      						=					[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]

class     UpperCamelCase_				(							__SCREAMING_SNAKE_CASE    ):
	lowercase    =				'whisper'
	lowercase    =				['past_key_values']
	lowercase    =				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





	def __init__(						self	,	A=51865	,	A=80	,	A=6	,	A=4	,	A=6	,	A=4	,	A=1536	,	A=1536	,	A=0.0	,	A=0.0	,	A=50257	,	A=True	,	A=True	,	A="gelu"	,	A=256	,	A=0.0	,	A=0.0	,	A=0.0	,	A=0.0_2	,	A=False	,	A=1500	,	A=448	,	A=50256	,	A=50256	,	A=50256	,	A=None	,	A=[220, 50256]	,	A=False	,	A=256	,	A=False	,	A=0.0_5	,	A=10	,	A=2	,	A=0.0	,	A=10	,	A=0	,	A=7	,	**A	,	)    ->    Any:
								UpperCAmelCase  :	Union[str, Any]							  =	vocab_size
								UpperCAmelCase  :	List[str]							  =	num_mel_bins
								UpperCAmelCase  :	Tuple							  =	d_model
								UpperCAmelCase  :	List[str]							  =	encoder_layers
								UpperCAmelCase  :	List[Any]							  =	encoder_attention_heads
								UpperCAmelCase  :	Dict							  =	decoder_layers
								UpperCAmelCase  :	Any							  =	decoder_attention_heads
								UpperCAmelCase  :	Union[str, Any]							  =	decoder_ffn_dim
								UpperCAmelCase  :	Optional[Any]							  =	encoder_ffn_dim
								UpperCAmelCase  :	Union[str, Any]							  =	dropout
								UpperCAmelCase  :	Tuple							  =	attention_dropout
								UpperCAmelCase  :	Tuple							  =	activation_dropout
								UpperCAmelCase  :	str							  =	activation_function
								UpperCAmelCase  :	Union[str, Any]							  =	init_std
								UpperCAmelCase  :	Optional[Any]							  =	encoder_layerdrop
								UpperCAmelCase  :	List[Any]							  =	decoder_layerdrop
								UpperCAmelCase  :	Tuple							  =	use_cache
								UpperCAmelCase  :	Optional[Any]							  =	encoder_layers
								UpperCAmelCase  :	List[str]							  =	scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase  :	Optional[int]							  =	max_source_positions
								UpperCAmelCase  :	str							  =	max_target_positions

								# Audio Classification-specific parameters. Feel free to ignore for other classes.
								UpperCAmelCase  :	Tuple							  =	classifier_proj_size
								UpperCAmelCase  :	Tuple							  =	use_weighted_layer_sum

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								UpperCAmelCase  :	Any							  =	apply_spec_augment
								UpperCAmelCase  :	Optional[int]							  =	mask_time_prob
								UpperCAmelCase  :	Optional[int]							  =	mask_time_length
								UpperCAmelCase  :	Optional[int]							  =	mask_time_min_masks
								UpperCAmelCase  :	Optional[Any]							  =	mask_feature_prob
								UpperCAmelCase  :	Optional[Any]							  =	mask_feature_length
								UpperCAmelCase  :	Any							  =	mask_feature_min_masks

								UpperCAmelCase  :	List[str]							  =	median_filter_width

								super().__init__(
								    pad_token_id=_a	,	bos_token_id=_a	,	eos_token_id=_a	,	is_encoder_decoder=_a	,	decoder_start_token_id=_a	,	suppress_tokens=_a	,	begin_suppress_tokens=_a	,	**_a	,	)




class     UpperCamelCase_				(							__SCREAMING_SNAKE_CASE    ):





	@property
	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Optional[int]							  =	OrderedDict(
								    [
								        ("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
								    ]					)
								if self.use_past:
															UpperCAmelCase  :	Optional[int]							  =	{0: """batch"""}
								else:
															UpperCAmelCase  :	Optional[Any]							  =	{0: """batch""", 1: """decoder_sequence"""}

								if self.use_past:
															self.fill_with_past_key_values_(_a	,	direction="""inputs"""					)

								return common_inputs





	def 				_lowercase(						self	,	A	,	A = -1	,	A = -1	,	A = False	,	A = None	,	A = 22050	,	A = 5.0	,	A = 220	,	)    ->    Tuple:
								UpperCAmelCase  :	int							  =	OrderedDict()
								UpperCAmelCase  :	Dict							  =	OnnxConfig.generate_dummy_inputs(
								    self	,	preprocessor=preprocessor.feature_extractor	,	batch_size=_a	,	framework=_a	,	sampling_rate=_a	,	time_duration=_a	,	frequency=_a	,	)
								UpperCAmelCase  :	int							  =	encoder_inputs["""input_features"""].shape[2]
								UpperCAmelCase  :	Union[str, Any]							  =	encoder_sequence_length // 2 if self.use_past else seq_length

								UpperCAmelCase  :	int							  =	super().generate_dummy_inputs(
								    preprocessor.tokenizer	,	_a	,	_a	,	_a	,	_a					)

								UpperCAmelCase  :	Optional[Any]							  =	encoder_inputs.pop("""input_features"""					)
								UpperCAmelCase  :	Dict							  =	decoder_inputs.pop("""decoder_input_ids"""					)

								if "past_key_values" in decoder_inputs:
															UpperCAmelCase  :	List[Any]							  =	decoder_inputs.pop("""past_key_values"""					)

								return dummy_inputs






	@property
	def 				_lowercase(						self					)    ->    List[Any]:
								return 1e-3






'''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()
a     :    List[Any]      						=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a     :    List[str]      						=					[]
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						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	List[str]							  =	state_dict.pop(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	val



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	Union[str, Any]							  =	OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCAmelCase  :	List[str]							  =	key.replace("""backbone.0.body"""			,       """backbone.conv_encoder.model"""	)
																					UpperCAmelCase  :	Dict							  =	value
														else:
																					UpperCAmelCase  :	List[Any]							  =	value

							return new_state_dict



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    Optional[int]:
							UpperCAmelCase  :	Dict							  =	""""""
							if is_panoptic:
														UpperCAmelCase  :	Tuple							  =	"""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  :	List[Any]							  =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
														UpperCAmelCase  :	List[Any]							  =	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  :	Dict							  =	in_proj_weight[:2_5_6, :]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[:2_5_6]
														UpperCAmelCase  :	List[Any]							  =	in_proj_weight[2_5_6:5_1_2, :]
														UpperCAmelCase  :	Tuple							  =	in_proj_bias[2_5_6:5_1_2]
														UpperCAmelCase  :	List[str]							  =	in_proj_weight[-2_5_6:, :]
														UpperCAmelCase  :	List[str]							  =	in_proj_bias[-2_5_6:]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	List[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)

							return im



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

							# load image processor
							UpperCAmelCase  :	List[str]							  =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrImageProcessor(format=_lowercase	)

							# prepare image
							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()
							UpperCAmelCase  :	Dict							  =	image_processor(images=_lowercase			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	List[Any]							  =	encoding["""pixel_values"""]

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

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


if __name__ == "__main__":
							a     :    Tuple      						=					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."""
							)
							a     :    Optional[Any]      						=					parser.parse_args()
							convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

