




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





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

    UpperCAmelCase     =	DetrConfig(use_timm_backbone=_a					,     backbone_config=_a  )

    # set label attributes
    UpperCAmelCase     =	'''panoptic''' in model_name
    if is_panoptic:
        UpperCAmelCase     =	2_5_0
    else:
        UpperCAmelCase     =	9_1
        UpperCAmelCase     =	'''huggingface/label-files'''
        UpperCAmelCase     =	'''coco-detection-id2label.json'''
        UpperCAmelCase     =	json.load(open(hf_hub_download(_a					,     _a					,     repo_type='''dataset'''  )					,     '''r'''  )  )
        UpperCAmelCase     =	{int(_a  ): v for k, v in idalabel.items()}
        UpperCAmelCase     =	idalabel
        UpperCAmelCase     =	{v: k for k, v in idalabel.items()}

    return config, is_panoptic





def 		snake_case_							(_a			:							Union[str, Any]  ):
    # here we list all keys to be renamed (original name on the left, our name on the right)
    UpperCAmelCase     =	[]

    # 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 		snake_case_							(_a			:							Optional[int]					,     _a			:							Tuple					,     _a			:							Optional[Any]  ):
    UpperCAmelCase     =	state_dict.pop(_a  )
    UpperCAmelCase     =	val





def 		snake_case_							(_a			:							Optional[int]					,     _a			:							str=False  ):
    UpperCAmelCase     =	''''''
    if is_panoptic:
        UpperCAmelCase     =	'''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)
        UpperCAmelCase     =	state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"  )
        UpperCAmelCase     =	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
        UpperCAmelCase     =	in_proj_weight[:2_5_6, :]
        UpperCAmelCase     =	in_proj_bias[:2_5_6]
        UpperCAmelCase     =	in_proj_weight[2_5_6:5_1_2, :]
        UpperCAmelCase     =	in_proj_bias[2_5_6:5_1_2]
        UpperCAmelCase     =	in_proj_weight[-2_5_6:, :]
        UpperCAmelCase     =	in_proj_bias[-2_5_6:]
    # 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
        UpperCAmelCase     =	state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"  )
        UpperCAmelCase     =	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
        UpperCAmelCase     =	in_proj_weight[:2_5_6, :]
        UpperCAmelCase     =	in_proj_bias[:2_5_6]
        UpperCAmelCase     =	in_proj_weight[2_5_6:5_1_2, :]
        UpperCAmelCase     =	in_proj_bias[2_5_6:5_1_2]
        UpperCAmelCase     =	in_proj_weight[-2_5_6:, :]
        UpperCAmelCase     =	in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        UpperCAmelCase     =	state_dict.pop(
            F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"  )
        UpperCAmelCase     =	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
        UpperCAmelCase     =	in_proj_weight_cross_attn[:2_5_6, :]
        UpperCAmelCase     =	in_proj_bias_cross_attn[:2_5_6]
        UpperCAmelCase     =	in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        UpperCAmelCase     =	in_proj_bias_cross_attn[2_5_6:5_1_2]
        UpperCAmelCase     =	in_proj_weight_cross_attn[-2_5_6:, :]
        UpperCAmelCase     =	in_proj_bias_cross_attn[-2_5_6:]





def 		snake_case_							():
    UpperCAmelCase     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase     =	Image.open(requests.get(_a					,     stream=_a  ).raw  )

    return im





@torch.no_grad()
def 		snake_case_							(_a			:							Dict					,     _a			:							Dict=None					,     _a			:							Optional[int]=False  ):
    UpperCAmelCase ,  UpperCAmelCase     =	get_detr_config(_a  )

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

    # finally, create HuggingFace model and load state dict
    UpperCAmelCase     =	DetrForSegmentation(_a  ) if is_panoptic else DetrForObjectDetection(_a  )
    model.load_state_dict(_a  )
    model.eval()

    # verify our conversion on an image
    UpperCAmelCase     =	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCAmelCase     =	DetrImageProcessor(format=_a  )

    UpperCAmelCase     =	processor(images=prepare_img()					,     return_tensors='''pt'''  )
    UpperCAmelCase     =	encoding['''pixel_values''']

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










'''simple docstring'''







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=logging.get_logger(__name__)

A									={
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class     _a						(			__a				):
       __a       :  Union[str, Any]            =      """encodec"""
       def __init__(			self		:			Optional[int]     ,       lowercase		:			Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,       lowercase		:			str=24_000     ,       lowercase		:			int=1     ,       lowercase		:			int=False     ,       lowercase		:			List[str]=None     ,       lowercase		:			Tuple=None     ,       lowercase		:			int=128     ,       lowercase		:			List[Any]=32     ,       lowercase		:			Optional[int]=1     ,       lowercase		:			Optional[int]=[8, 5, 4, 2]     ,       lowercase		:			Union[str, Any]="weight_norm"     ,       lowercase		:			Optional[int]=7     ,       lowercase		:			Tuple=7     ,       lowercase		:			Tuple=3     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Tuple="reflect"     ,       lowercase		:			Any=2     ,       lowercase		:			List[str]=2     ,       lowercase		:			str=1.0     ,       lowercase		:			List[str]=1_024     ,       lowercase		:			Optional[Any]=None     ,       lowercase		:			Any=True     ,       **lowercase		:			Optional[Any]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	target_bandwidths
           UpperCAmelCase     =	sampling_rate
           UpperCAmelCase     =	audio_channels
           UpperCAmelCase     =	normalize
           UpperCAmelCase     =	chunk_length_s
           UpperCAmelCase     =	overlap
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_filters
           UpperCAmelCase     =	num_residual_layers
           UpperCAmelCase     =	upsampling_ratios
           UpperCAmelCase     =	norm_type
           UpperCAmelCase     =	kernel_size
           UpperCAmelCase     =	last_kernel_size
           UpperCAmelCase     =	residual_kernel_size
           UpperCAmelCase     =	dilation_growth_rate
           UpperCAmelCase     =	use_causal_conv
           UpperCAmelCase     =	pad_mode
           UpperCAmelCase     =	compress
           UpperCAmelCase     =	num_lstm_layers
           UpperCAmelCase     =	trim_right_ratio
           UpperCAmelCase     =	codebook_size
           UpperCAmelCase     =	codebook_dim if codebook_dim is not None else hidden_size
           UpperCAmelCase     =	use_conv_shortcut

           if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"       )

           super().__init__(**lowercase       )
       @property
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           if self.chunk_length_s is None:
               return None
           else:
               return int(self.chunk_length_s * self.sampling_rate       )
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           if self.chunk_length_s is None or self.overlap is None:
               return None
           else:
               return max(1     ,       int((1.0 - self.overlap) * self.chunk_length       )       )
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	np.prod(self.upsampling_ratios       )
           return math.ceil(self.sampling_rate / hop_length       )







       @property
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )




