

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()
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}.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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							state_dict.pop(UpperCAmelCase__      )
					__lowerCAmelCase					=							val


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__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[:256, :]
										__lowerCAmelCase					=							in_proj_bias[:256]
										__lowerCAmelCase					=							in_proj_weight[256:512, :]
										__lowerCAmelCase					=							in_proj_bias[256:512]
										__lowerCAmelCase					=							in_proj_weight[-256:, :]
										__lowerCAmelCase					=							in_proj_bias[-256:]


def        __lowercase    (  ):

					"""simple docstring"""






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

					return im


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__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					=							250
					else:
										__lowerCAmelCase					=							91
										__lowerCAmelCase					=							'huggingface/label-files'
										__lowerCAmelCase					=							'coco-detection-id2label.json'
										__lowerCAmelCase					=							json.load(open(hf_hub_download(UpperCAmelCase__		,					UpperCAmelCase__		,					repo_type='dataset'      )		,					'r'      )      )
										__lowerCAmelCase					=							{int(UpperCAmelCase__      ): 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=UpperCAmelCase__      )

					# prepare image
					__lowerCAmelCase					=							prepare_img()
					__lowerCAmelCase					=							image_processor(images=UpperCAmelCase__		,					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'		,					UpperCAmelCase__		,					pretrained=UpperCAmelCase__      ).eval()
					__lowerCAmelCase					=							conditional_detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															__lowerCAmelCase					=							'conditional_detr.' + src
										rename_key(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase					=							rename_backbone_keys(UpperCAmelCase__      )
					# query, key and value matrices need special treatment
					read_in_q_k_v(UpperCAmelCase__		,					is_panoptic=UpperCAmelCase__      )
					# 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(UpperCAmelCase__      )
																				__lowerCAmelCase					=							val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				__lowerCAmelCase					=							state_dict.pop(UpperCAmelCase__      )
																				__lowerCAmelCase					=							val
															elif key.startswith('bbox_attention'      ) or key.startswith('mask_head'      ):
																				continue
															else:
																				__lowerCAmelCase					=							state_dict.pop(UpperCAmelCase__      )
																				__lowerCAmelCase					=							val
										else:
															if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
																				__lowerCAmelCase					=							state_dict.pop(UpperCAmelCase__      )
																				__lowerCAmelCase					=							val
    # finally, create HuggingFace model and load state dict
					__lowerCAmelCase					=							ConditionalDetrForSegmentation(UpperCAmelCase__      ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCAmelCase__      )
					model.load_state_dict(UpperCAmelCase__      )
					model.eval()
					model.push_to_hub(repo_id=UpperCAmelCase__		,					organization='DepuMeng'		,					commit_message='Add model'      )
					# verify our conversion
					__lowerCAmelCase					=							conditional_detr(UpperCAmelCase__      )
					__lowerCAmelCase					=							model(UpperCAmelCase__      )
					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(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
					model.save_pretrained(UpperCAmelCase__      )
					image_processor.save_pretrained(UpperCAmelCase__      )


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









import random


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							num - 1
					__lowerCAmelCase					=							0

					while s % 2 == 0:
										__lowerCAmelCase					=							s // 2
										t += 1

					for _ in range(5      ):
										__lowerCAmelCase					=							random.randrange(2		,					num - 1      )
										__lowerCAmelCase					=							pow(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )
										if v != 1:
															__lowerCAmelCase					=							0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									__lowerCAmelCase					=							i + 1
																									__lowerCAmelCase					=							(v**2) % num
					return True


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if num < 2:
										return False

					__lowerCAmelCase					=							[
					    2,
					    3,
					    5,
					    7,
					    11,
					    13,
					    17,
					    19,
					    23,
					    29,
					    31,
					    37,
					    41,
					    43,
					    47,
					    53,
					    59,
					    61,
					    67,
					    71,
					    73,
					    79,
					    83,
					    89,
					    97,
					    101,
					    103,
					    107,
					    109,
					    113,
					    127,
					    131,
					    137,
					    139,
					    149,
					    151,
					    157,
					    163,
					    167,
					    173,
					    179,
					    181,
					    191,
					    193,
					    197,
					    199,
					    211,
					    223,
					    227,
					    229,
					    233,
					    239,
					    241,
					    251,
					    257,
					    263,
					    269,
					    271,
					    277,
					    281,
					    283,
					    293,
					    307,
					    311,
					    313,
					    317,
					    331,
					    337,
					    347,
					    349,
					    353,
					    359,
					    367,
					    373,
					    379,
					    383,
					    389,
					    397,
					    401,
					    409,
					    419,
					    421,
					    431,
					    433,
					    439,
					    443,
					    449,
					    457,
					    461,
					    463,
					    467,
					    479,
					    487,
					    491,
					    499,
					    503,
					    509,
					    521,
					    523,
					    541,
					    547,
					    557,
					    563,
					    569,
					    571,
					    577,
					    587,
					    593,
					    599,
					    601,
					    607,
					    613,
					    617,
					    619,
					    631,
					    641,
					    643,
					    647,
					    653,
					    659,
					    661,
					    673,
					    677,
					    683,
					    691,
					    701,
					    709,
					    719,
					    727,
					    733,
					    739,
					    743,
					    751,
					    757,
					    761,
					    769,
					    773,
					    787,
					    797,
					    809,
					    811,
					    821,
					    823,
					    827,
					    829,
					    839,
					    853,
					    857,
					    859,
					    863,
					    877,
					    881,
					    883,
					    887,
					    907,
					    911,
					    919,
					    929,
					    937,
					    941,
					    947,
					    953,
					    967,
					    971,
					    977,
					    983,
					    991,
					    997,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__ = 1_024      ):

					"""simple docstring"""






					while True:
										__lowerCAmelCase					=							random.randrange(2 ** (keysize - 1)		,					2 ** (keysize)      )
										if is_prime_low_num(UpperCAmelCase__      ):
															return num


if __name__ == "__main__":
		lowerCamelCase      				=     generate_large_prime()
		print(('''Prime number:''', num))
		print(('''is_prime_low_num:''', is_prime_low_num(num)))






