



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase									=   logging.get_logger(__name__)



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Union[str, Any]:
		# initialize config
		if "resnet-50" in model_name:
				_a     :      Optional[int]        =							ResNetConfig.from_pretrained('microsoft/resnet-50'  )
		elif "resnet-101" in model_name:
				_a     :      Optional[int]        =							ResNetConfig.from_pretrained('microsoft/resnet-101'  )
		else:
				raise ValueError('Model name should include either resnet50 or resnet101'  )

		_a     :      Any        =							DetrConfig(use_timm_backbone=lowerCAmelCase_						,			backbone_config=lowerCAmelCase_  )

		# set label attributes
		_a     :      Optional[int]        =							'panoptic' in model_name
		if is_panoptic:
				_a     :      Any        =							250
		else:
				_a     :      Optional[int]        =							91
				_a     :      int        =							'huggingface/label-files'
				_a     :      Any        =							'coco-detection-id2label.json'
				_a     :      Any        =							json.load(open(hf_hub_download(lowerCAmelCase_						,			lowerCAmelCase_						,			repo_type='dataset'  )						,			'r'  )  )
				_a     :      Dict        =							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
				_a     :      List[str]        =							idalabel
				_a     :      Union[str, Any]        =							{v: k for k, v in idalabel.items()}

		return config, is_panoptic



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[Any]:
		# here we list all keys to be renamed (original name on the left, our name on the right)
		_a     :      Optional[int]        =							[]

		# stem
		# fmt: off
		rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')  )
		rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')  )
		rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')  )
		rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')  )
		rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')  )
		# stages
		for stage_idx in range(len(config.backbone_config.depths  )  ):
				for layer_idx in range(config.backbone_config.depths[stage_idx]  ):
						# shortcut
						if layer_idx == 0:
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
								    )  )
						# 3 convs
						for i in range(3  ):
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
								    )  )
								rename_keys.append(
								    (
								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
								        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
								    )  )
    # fmt: on

		for i in range(config.encoder_layers  ):
				# 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}.multihead_attn.out_proj.weight""",
				        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
				    )  )
				rename_keys.append(
				    (
				        f"""transformer.decoder.layers.{i}.multihead_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""")  )

		# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
		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'),
		    ]  )

		return rename_keys



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Union[str, Any]        =							state_dict.pop(lowerCAmelCase_  )
		_a     :      str        =							val



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      List[str]:
		_a     :      Optional[int]        =							''
		if is_panoptic:
				_a     :      List[Any]        =							'detr.'

		# first: transformer encoder
		for i in range(6  ):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				_a     :      List[str]        =							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""  )
				_a     :      str        =							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""  )
				# next, add query, keys and values (in that order) to the state dict
				_a     :      Optional[Any]        =							in_proj_weight[:256, :]
				_a     :      Any        =							in_proj_bias[:256]
				_a     :      Dict        =							in_proj_weight[256:512, :]
				_a     :      Optional[int]        =							in_proj_bias[256:512]
				_a     :      Tuple        =							in_proj_weight[-256:, :]
				_a     :      List[str]        =							in_proj_bias[-256:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6  ):
				# read in weights + bias of input projection layer of self-attention
				_a     :      Dict        =							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""  )
				_a     :      Tuple        =							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""  )
				# next, add query, keys and values (in that order) to the state dict
				_a     :      Any        =							in_proj_weight[:256, :]
				_a     :      Any        =							in_proj_bias[:256]
				_a     :      str        =							in_proj_weight[256:512, :]
				_a     :      Optional[Any]        =							in_proj_bias[256:512]
				_a     :      Any        =							in_proj_weight[-256:, :]
				_a     :      Optional[int]        =							in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				_a     :      List[Any]        =							state_dict.pop(
				    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""  )
				_a     :      Union[str, Any]        =							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""  )
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				_a     :      Union[str, Any]        =							in_proj_weight_cross_attn[:256, :]
				_a     :      Any        =							in_proj_bias_cross_attn[:256]
				_a     :      int        =							in_proj_weight_cross_attn[256:512, :]
				_a     :      Dict        =							in_proj_bias_cross_attn[256:512]
				_a     :      Union[str, Any]        =							in_proj_weight_cross_attn[-256:, :]
				_a     :      Optional[int]        =							in_proj_bias_cross_attn[-256:]



def      __lowerCamelCase     (      )			->      Optional[Any]:
		_a     :      Union[str, Any]        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a     :      str        =							Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_  ).raw  )

		return im



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=False  )			->      Dict:
		_a       ,					_a     :      Dict        =							get_detr_config(lowerCAmelCase_  )

		# load original model from torch hub
		_a     :      Union[str, Any]        =							{
		    'detr-resnet-50': 'detr_resnet50',
		    'detr-resnet-101': 'detr_resnet101',
		}
		logger.info(f"""Converting model {model_name}..."""  )
		_a     :      int        =							torch.hub.load('facebookresearch/detr'						,			model_name_to_original_name[model_name]						,			pretrained=lowerCAmelCase_  ).eval()
		_a     :      int        =							detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(lowerCAmelCase_  ):
				if is_panoptic:
						_a     :      Optional[int]        =							'detr.' + src
				rename_key(lowerCAmelCase_						,			lowerCAmelCase_						,			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
		_a     :      Dict        =							'detr.model.' if is_panoptic else 'model.'
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith('detr'  )
						    and not key.startswith('class_labels_classifier'  )
						    and not key.startswith('bbox_predictor'  )
						):
								_a     :      Optional[int]        =							state_dict.pop(lowerCAmelCase_  )
								_a     :      Optional[Any]        =							val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_a     :      int        =							state_dict.pop(lowerCAmelCase_  )
								_a     :      Dict        =							val
						elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
								continue
						else:
								_a     :      List[Any]        =							state_dict.pop(lowerCAmelCase_  )
								_a     :      Optional[int]        =							val
				else:
						if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
								_a     :      Union[str, Any]        =							state_dict.pop(lowerCAmelCase_  )
								_a     :      List[Any]        =							val

    # finally, create HuggingFace model and load state dict
		_a     :      List[str]        =							DetrForSegmentation(lowerCAmelCase_  ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_  )
		model.load_state_dict(lowerCAmelCase_  )
		model.eval()

		# verify our conversion on an image
		_a     :      str        =							'coco_panoptic' if is_panoptic else 'coco_detection'
		_a     :      Any        =							DetrImageProcessor(format=lowerCAmelCase_  )

		_a     :      Optional[Any]        =							processor(images=prepare_img()						,			return_tensors='pt'  )
		_a     :      Dict        =							encoding['pixel_values']

		_a     :      str        =							detr(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							model(lowerCAmelCase_  )

		assert torch.allclose(outputs.logits						,			original_outputs['pred_logits']						,			atol=1E-3  )
		assert torch.allclose(outputs.pred_boxes						,			original_outputs['pred_boxes']						,			atol=1E-3  )
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks						,			original_outputs['pred_masks']						,			atol=1E-4  )
		print('Looks ok!'  )

		if pytorch_dump_folder_path is not None:
				# 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_  )
				processor.save_pretrained(lowerCAmelCase_  )

		if push_to_hub:
				# Upload model and image processor to the hub
				logger.info('Uploading PyTorch model and image processor to the hub...'  )
				model.push_to_hub(f"""nielsr/{model_name}"""  )
				processor.push_to_hub(f"""nielsr/{model_name}"""  )


if __name__ == "__main__":
							__lowerCAmelCase									=   argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    default='''detr-resnet-50''',
							    type=str,
							    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
							    help='''Name of the 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.'''
							)
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
							__lowerCAmelCase									=   parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''

# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
    '''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CpmAntForCausalLM''',
							    '''CpmAntModel''',
							    '''CpmAntPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
							from .tokenization_cpmant import CpmAntTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cpmant import (
														    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CpmAntForCausalLM,
														    CpmAntModel,
														    CpmAntPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




