
'''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()
UpperCAmelCase    					=   logging.get_logger(__name__)

def      A (       A_							:					Optional[Any]       ):
						# initialize config
						if "resnet-50" in model_name:
												snake_case       :     Any =    ResNetConfig.from_pretrained('''microsoft/resnet-50'''       )
						elif "resnet-101" in model_name:
												snake_case       :     Tuple =    ResNetConfig.from_pretrained('''microsoft/resnet-101'''       )
						else:
												raise ValueError('''Model name should include either resnet50 or resnet101'''       )

						snake_case       :     Optional[int] =    DetrConfig(use_timm_backbone=A_    ,							backbone_config=A_       )

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

						return config, is_panoptic

def      A (       A_							:					Dict       ):
						# here we list all keys to be renamed (original name on the left, our name on the right)
						snake_case       :     Dict =    []

						# 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      A (       A_							:					str    ,							A_							:					Optional[Any]    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[Any] =    state_dict.pop(A_       )
						snake_case       :     str =    val

def      A (       A_							:					Dict    ,							A_							:					Union[str, Any]=False       ):
						snake_case       :     Optional[int] =    ''''''
						if is_panoptic:
												snake_case       :     Union[str, 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)
												snake_case       :     Tuple =    state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
												snake_case       :     Dict =    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
												snake_case       :     Optional[Any] =    in_proj_weight[:256, :]
												snake_case       :     List[str] =    in_proj_bias[:256]
												snake_case       :     Tuple =    in_proj_weight[256:512, :]
												snake_case       :     Any =    in_proj_bias[256:512]
												snake_case       :     Optional[int] =    in_proj_weight[-256:, :]
												snake_case       :     Union[str, Any] =    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
												snake_case       :     Optional[int] =    state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
												snake_case       :     int =    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
												snake_case       :     Tuple =    in_proj_weight[:256, :]
												snake_case       :     List[Any] =    in_proj_bias[:256]
												snake_case       :     List[Any] =    in_proj_weight[256:512, :]
												snake_case       :     Dict =    in_proj_bias[256:512]
												snake_case       :     List[str] =    in_proj_weight[-256:, :]
												snake_case       :     Optional[Any] =    in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												snake_case       :     List[Any] =    state_dict.pop(
												    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
												snake_case       :     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
												snake_case       :     List[Any] =    in_proj_weight_cross_attn[:256, :]
												snake_case       :     str =    in_proj_bias_cross_attn[:256]
												snake_case       :     List[str] =    in_proj_weight_cross_attn[256:512, :]
												snake_case       :     Dict =    in_proj_bias_cross_attn[256:512]
												snake_case       :     Optional[Any] =    in_proj_weight_cross_attn[-256:, :]
												snake_case       :     int =    in_proj_bias_cross_attn[-256:]

def      A (       ):
						snake_case       :     Optional[int] =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case       :     Union[str, Any] =    Image.open(requests.get(A_    ,							stream=A_       ).raw       )

						return im

@torch.no_grad()
def      A (       A_							:					Tuple    ,							A_							:					Optional[Any]=None    ,							A_							:					str=False       ):
						snake_case       :     List[str] =    get_detr_config(A_       )

						# load original model from torch hub
						snake_case       :     Union[str, Any] =    {
						    '''detr-resnet-50''': '''detr_resnet50''',
						    '''detr-resnet-101''': '''detr_resnet101''',
						}
						logger.info(F"""Converting model {model_name}..."""       )
						snake_case       :     Union[str, Any] =    torch.hub.load('''facebookresearch/detr'''    ,							model_name_to_original_name[model_name]    ,							pretrained=A_       ).eval()
						snake_case       :     Optional[Any] =    detr.state_dict()
						# rename keys
						for src, dest in create_rename_keys(A_       ):
												if is_panoptic:
																		snake_case       :     str =    '''detr.''' + src
												rename_key(A_    ,							A_    ,							A_       )
						# query, key and value matrices need special treatment
						read_in_q_k_v(A_    ,							is_panoptic=A_       )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						snake_case       :     Optional[int] =    '''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'''       )
																		):
																								snake_case       :     List[Any] =    state_dict.pop(A_       )
																								snake_case       :     str =    val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								snake_case       :     int =    state_dict.pop(A_       )
																								snake_case       :     Union[str, Any] =    val
																		elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
																								continue
																		else:
																								snake_case       :     Dict =    state_dict.pop(A_       )
																								snake_case       :     Dict =    val
												else:
																		if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
																								snake_case       :     str =    state_dict.pop(A_       )
																								snake_case       :     int =    val

    # finally, create HuggingFace model and load state dict
						snake_case       :     Optional[int] =    DetrForSegmentation(A_       ) if is_panoptic else DetrForObjectDetection(A_       )
						model.load_state_dict(A_       )
						model.eval()

						# verify our conversion on an image
						snake_case       :     int =    '''coco_panoptic''' if is_panoptic else '''coco_detection'''
						snake_case       :     List[Any] =    DetrImageProcessor(format=A_       )

						snake_case       :     List[Any] =    processor(images=prepare_img()    ,							return_tensors='''pt'''       )
						snake_case       :     int =    encoding['''pixel_values''']

						snake_case       :     List[str] =    detr(A_       )
						snake_case       :     List[str] =    model(A_       )

						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(A_       ).mkdir(exist_ok=A_       )
												model.save_pretrained(A_       )
												processor.save_pretrained(A_       )

						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__":
						UpperCAmelCase    					=   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.")
						UpperCAmelCase    					=   parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   ["ConvBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

