



"""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()
__SCREAMING_SNAKE_CASE	:    Any     				=   logging.get_logger(__name__)

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



def 							lowerCAmelCase_( lowercase_  :				Union[str, Any]				)						->							str:
				_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 							lowerCAmelCase_( lowercase_  :				int    , lowercase_  :				str=False				)						->							List[Any]:
				_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_56, :]
								_lowerCamelCase   	=					in_proj_bias[:2_56]
								_lowerCamelCase   	=					in_proj_weight[2_56:5_12, :]
								_lowerCamelCase   	=					in_proj_bias[2_56:5_12]
								_lowerCamelCase   	=					in_proj_weight[-2_56:, :]
								_lowerCamelCase   	=					in_proj_bias[-2_56:]



def 							lowerCAmelCase_( )						->							int:
				_lowerCamelCase   	=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_lowerCamelCase   	=					Image.open(requests.get(lowercase_    , stream=lowercase_				).raw				)

				return im



@torch.no_grad()
def 							lowerCAmelCase_( lowercase_  :				Tuple    , lowercase_  :				str				)						->							Any:
				_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_50
				else:
								_lowerCamelCase   	=					91
								_lowerCamelCase   	=					'''huggingface/label-files'''
								_lowerCamelCase   	=					'''coco-detection-id2label.json'''
								_lowerCamelCase   	=					json.load(open(hf_hub_download(lowercase_    , lowercase_    , repo_type='''dataset'''				)    , '''r'''				)				)
								_lowerCamelCase   	=					{int(lowercase_				): 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=lowercase_				)

				# prepare image
				_lowerCamelCase   	=					prepare_img()
				_lowerCamelCase   	=					image_processor(images=lowercase_    , 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'''    , lowercase_    , pretrained=lowercase_				).eval()
				_lowerCamelCase   	=					conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												_lowerCamelCase   	=					'''conditional_detr.''' + src
								rename_key(lowercase_    , lowercase_    , lowercase_				)
				_lowerCamelCase   	=					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
				_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(lowercase_				)
																_lowerCamelCase   	=					val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_lowerCamelCase   	=					state_dict.pop(lowercase_				)
																_lowerCamelCase   	=					val
												elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
																continue
												else:
																_lowerCamelCase   	=					state_dict.pop(lowercase_				)
																_lowerCamelCase   	=					val
								else:
												if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
																_lowerCamelCase   	=					state_dict.pop(lowercase_				)
																_lowerCamelCase   	=					val
								# finally, create HuggingFace model and load state dict
				_lowerCamelCase   	=					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
				_lowerCamelCase   	=					conditional_detr(lowercase_				)
				_lowerCamelCase   	=					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__":
			__SCREAMING_SNAKE_CASE	:    List[Any]     				=   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.'''
			)
			__SCREAMING_SNAKE_CASE	:    Optional[int]     				=   parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Dict  				=				CustomTokenizer
    pass
