


'''simple docstring'''

import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_UpperCAmelCase				: Optional[int]							    =				collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_UpperCAmelCase				: Optional[Any]							    =				"""https://storage.googleapis.com/cvdf-datasets/mnist/"""

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				numpy.dtype(numpy.uintaa).newbyteorder('''>''')
						return numpy.frombuffer(bytestream.read(4),				dtype=lowerCamelCase)[0]

@deprecated(lowerCamelCase,				'''Please use tf.data to implement this functionality.''')
def 							__magic_name__(						lowerCamelCase):
						print('''Extracting''',				f.name)
						with gzip.GzipFile(fileobj=lowerCamelCase) as bytestream:
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												if magic != 2_0_5_1:
																		raise ValueError(
																		    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name))
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												__lowerCAmelCase							       =				bytestream.read(rows * cols * num_images)
												__lowerCAmelCase							       =				numpy.frombuffer(lowerCamelCase,				dtype=numpy.uinta)
												__lowerCAmelCase							       =				data.reshape(lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				1)
												return data

@deprecated(lowerCamelCase,				'''Please use tf.one_hot on tensors.''')
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				labels_dense.shape[0]
						__lowerCAmelCase							       =				numpy.arange(lowerCamelCase) * num_classes
						__lowerCAmelCase							       =				numpy.zeros((num_labels, num_classes))
						__lowerCAmelCase							       =				1
						return labels_one_hot

@deprecated(lowerCamelCase,				'''Please use tf.data to implement this functionality.''')
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False,				lowerCamelCase=1_0):
						print('''Extracting''',				f.name)
						with gzip.GzipFile(fileobj=lowerCamelCase) as bytestream:
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												if magic != 2_0_4_9:
																		raise ValueError(
																		    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name))
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												__lowerCAmelCase							       =				bytestream.read(lowerCamelCase)
												__lowerCAmelCase							       =				numpy.frombuffer(lowerCamelCase,				dtype=numpy.uinta)
												if one_hot:
																		return _dense_to_one_hot(lowerCamelCase,				lowerCamelCase)
												return labels


class       a__				:







				"""simple docstring"""



				@deprecated(
				    __lowercase       ,			'''Please use alternatives such as official/mnist/_DataSet.py'''
				    ''' from tensorflow/models.'''       ,			)
				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase=False       ,			__lowercase=False       ,			__lowercase=dtypes.floataa       ,			__lowercase=True       ,			__lowercase=None       ,			):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				random_seed.get_seed(__lowercase		)
										# If op level seed is not set, use whatever graph level seed is returned
										numpy.random.seed(seeda if seed is None else seeda		)
										__lowerCAmelCase							       =				dtypes.as_dtype(__lowercase		).base_dtype
										if dtype not in (dtypes.uinta, dtypes.floataa):
																raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype		)
										if fake_data:
																__lowerCAmelCase							       =				1_00_00
																__lowerCAmelCase							       =				one_hot
										else:
																assert (
																    images.shape[0] == labels.shape[0]
																), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
																__lowerCAmelCase							       =				images.shape[0]

																# Convert shape from [num examples, rows, columns, depth]
																# to [num examples, rows*columns] (assuming depth == 1)
																if reshape:
																						assert images.shape[3] == 1
																						__lowerCAmelCase							       =				images.reshape(
																						    images.shape[0]       ,			images.shape[1] * images.shape[2]		)
																if dtype == dtypes.floataa:
																						# Convert from [0, 255] -> [0.0, 1.0].
																						__lowerCAmelCase							       =				images.astype(numpy.floataa		)
																						__lowerCAmelCase							       =				numpy.multiply(__lowercase       ,			1.0 / 2_5_5.0		)
										__lowerCAmelCase							       =				images
										__lowerCAmelCase							       =				labels
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0




				@property
				def 					_snake_case  (self		):
										return self._images




				@property
				def 					_snake_case  (self		):
										return self._labels




				@property
				def 					_snake_case  (self		):
										return self._num_examples




				@property
				def 					_snake_case  (self		):
										return self._epochs_completed




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=False       ,			__lowercase=True		):
										if fake_data:
																__lowerCAmelCase							       =				[1] * 7_84
																__lowerCAmelCase							       =				[1] + [0] * 9 if self.one_hot else 0
																return (
																    [fake_image for _ in range(__lowercase		)],
																    [fake_label for _ in range(__lowercase		)],
																)
										__lowerCAmelCase							       =				self._index_in_epoch
										# Shuffle for the first epoch
										if self._epochs_completed == 0 and start == 0 and shuffle:
																__lowerCAmelCase							       =				numpy.arange(self._num_examples		)
																numpy.random.shuffle(__lowercase		)
																__lowerCAmelCase							       =				self.images[perma]
																__lowerCAmelCase							       =				self.labels[perma]
										# Go to the next epoch
										if start + batch_size > self._num_examples:
																# Finished epoch
																self._epochs_completed += 1
																# Get the rest examples in this epoch
																__lowerCAmelCase							       =				self._num_examples - start
																__lowerCAmelCase							       =				self._images[start : self._num_examples]
																__lowerCAmelCase							       =				self._labels[start : self._num_examples]
																# Shuffle the data
																if shuffle:
																						__lowerCAmelCase							       =				numpy.arange(self._num_examples		)
																						numpy.random.shuffle(__lowercase		)
																						__lowerCAmelCase							       =				self.images[perm]
																						__lowerCAmelCase							       =				self.labels[perm]
																# Start next epoch
																__lowerCAmelCase							       =				0
																__lowerCAmelCase							       =				batch_size - rest_num_examples
																__lowerCAmelCase							       =				self._index_in_epoch
																__lowerCAmelCase							       =				self._images[start:end]
																__lowerCAmelCase							       =				self._labels[start:end]
																return (
																    numpy.concatenate((images_rest_part, images_new_part)       ,			axis=0		),
																    numpy.concatenate((labels_rest_part, labels_new_part)       ,			axis=0		),
																)
										else:
																self._index_in_epoch += batch_size
																__lowerCAmelCase							       =				self._index_in_epoch
																return self._images[start:end], self._labels[start:end]

@deprecated(lowerCamelCase,				'''Please write your own downloading logic.''')
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						if not gfile.Exists(lowerCamelCase):
												gfile.MakeDirs(lowerCamelCase)
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				lowerCamelCase)
						if not gfile.Exists(lowerCamelCase):
												urllib.request.urlretrieve(lowerCamelCase,				lowerCamelCase)  # noqa: S310
												with gfile.GFile(lowerCamelCase) as f:
																		__lowerCAmelCase							       =				f.size()
												print('''Successfully downloaded''',				lowerCamelCase,				lowerCamelCase,				'''bytes.''')
						return filepath




@deprecated(
    lowerCamelCase,				'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''')
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False,				lowerCamelCase=False,				lowerCamelCase=dtypes.floataa,				lowerCamelCase=True,				lowerCamelCase=5_0_0_0,				lowerCamelCase=None,				lowerCamelCase=DEFAULT_SOURCE_URL,				):
						if fake_data:

												def fake():
																		return _DataSet(
																		    [],				[],				fake_data=lowerCamelCase,				one_hot=lowerCamelCase,				dtype=lowerCamelCase,				seed=lowerCamelCase)

												__lowerCAmelCase							       =				fake()
												__lowerCAmelCase							       =				fake()
												__lowerCAmelCase							       =				fake()
												return _Datasets(train=lowerCamelCase,				validation=lowerCamelCase,				test=lowerCamelCase)

						if not source_url:  # empty string check
												__lowerCAmelCase							       =				DEFAULT_SOURCE_URL

						__lowerCAmelCase							       =				'''train-images-idx3-ubyte.gz'''
						__lowerCAmelCase							       =				'''train-labels-idx1-ubyte.gz'''
						__lowerCAmelCase							       =				'''t10k-images-idx3-ubyte.gz'''
						__lowerCAmelCase							       =				'''t10k-labels-idx1-ubyte.gz'''

						__lowerCAmelCase							       =				_maybe_download(
						    lowerCamelCase,				lowerCamelCase,				source_url + train_images_file)
						with gfile.Open(lowerCamelCase,				'''rb''') as f:
												__lowerCAmelCase							       =				_extract_images(lowerCamelCase)

						__lowerCAmelCase							       =				_maybe_download(
						    lowerCamelCase,				lowerCamelCase,				source_url + train_labels_file)
						with gfile.Open(lowerCamelCase,				'''rb''') as f:
												__lowerCAmelCase							       =				_extract_labels(lowerCamelCase,				one_hot=lowerCamelCase)

						__lowerCAmelCase							       =				_maybe_download(
						    lowerCamelCase,				lowerCamelCase,				source_url + test_images_file)
						with gfile.Open(lowerCamelCase,				'''rb''') as f:
												__lowerCAmelCase							       =				_extract_images(lowerCamelCase)

						__lowerCAmelCase							       =				_maybe_download(
						    lowerCamelCase,				lowerCamelCase,				source_url + test_labels_file)
						with gfile.Open(lowerCamelCase,				'''rb''') as f:
												__lowerCAmelCase							       =				_extract_labels(lowerCamelCase,				one_hot=lowerCamelCase)

						if not 0 <= validation_size <= len(lowerCamelCase):
												__lowerCAmelCase							       =				(
												    '''Validation size should be between 0 and '''
												    F"""{len(lowerCamelCase)}. Received: {validation_size}."""
												)
												raise ValueError(lowerCamelCase)

						__lowerCAmelCase							       =				train_images[:validation_size]
						__lowerCAmelCase							       =				train_labels[:validation_size]
						__lowerCAmelCase							       =				train_images[validation_size:]
						__lowerCAmelCase							       =				train_labels[validation_size:]

						__lowerCAmelCase							       =				{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

						__lowerCAmelCase							       =				_DataSet(lowerCamelCase,				lowerCamelCase,				**lowerCamelCase)
						__lowerCAmelCase							       =				_DataSet(lowerCamelCase,				lowerCamelCase,				**lowerCamelCase)
						__lowerCAmelCase							       =				_DataSet(lowerCamelCase,				lowerCamelCase,				**lowerCamelCase)

						return _Datasets(train=lowerCamelCase,				validation=lowerCamelCase,				test=lowerCamelCase)





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

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

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		__lowerCAmelCase							       =				key.replace('''backbone.0.body''',				'''backbone.conv_encoder.model''')
																		__lowerCAmelCase							       =				value
												else:
																		__lowerCAmelCase							       =				value

						return new_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				''''''
						if is_panoptic:
												__lowerCAmelCase							       =				'''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)
												__lowerCAmelCase							       =				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""")
												__lowerCAmelCase							       =				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
												__lowerCAmelCase							       =				in_proj_weight[:2_5_6, :]
												__lowerCAmelCase							       =				in_proj_bias[:2_5_6]
												__lowerCAmelCase							       =				in_proj_weight[2_5_6:5_1_2, :]
												__lowerCAmelCase							       =				in_proj_bias[2_5_6:5_1_2]
												__lowerCAmelCase							       =				in_proj_weight[-2_5_6:, :]
												__lowerCAmelCase							       =				in_proj_bias[-2_5_6:]

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__lowerCAmelCase							       =				Image.open(requests.get(lowerCamelCase,				stream=lowerCamelCase).raw)

						return im

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

						# load image processor
						__lowerCAmelCase							       =				'''coco_panoptic''' if is_panoptic else '''coco_detection'''
						__lowerCAmelCase							       =				ConditionalDetrImageProcessor(format=lowerCamelCase)

						# prepare image
						__lowerCAmelCase							       =				prepare_img()
						__lowerCAmelCase							       =				image_processor(images=lowerCamelCase,				return_tensors='''pt''')
						__lowerCAmelCase							       =				encoding['''pixel_values''']

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

						# load original model from torch hub
						__lowerCAmelCase							       =				torch.hub.load('''DeppMeng/ConditionalDETR''',				lowerCamelCase,				pretrained=lowerCamelCase).eval()
						__lowerCAmelCase							       =				conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		__lowerCAmelCase							       =				'''conditional_detr.''' + src
												rename_key(lowerCamelCase,				lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				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
						__lowerCAmelCase							       =				'''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''')
																		):
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
																		elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
																								continue
																		else:
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
												else:
																		if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
    # finally, create HuggingFace model and load state dict
						__lowerCAmelCase							       =				ConditionalDetrForSegmentation(lowerCamelCase) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase)
						model.load_state_dict(lowerCamelCase)
						model.eval()
						model.push_to_hub(repo_id=lowerCamelCase,				organization='''DepuMeng''',				commit_message='''Add model''')
						# verify our conversion
						__lowerCAmelCase							       =				conditional_detr(lowerCamelCase)
						__lowerCAmelCase							       =				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__":
	_UpperCAmelCase				: 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."""
	)
	_UpperCAmelCase				: Any							    =				parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

