
'''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()
lowercase    :							Optional[int]      =					logging.get_logger(__name__)







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	# initialize config
	if "resnet-50" in model_name:
		_snake_case   						=		ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		_snake_case   						=		ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	_snake_case   						=		DetrConfig(use_timm_backbone=__A			,				backbone_config=__A	)

	# set label attributes
	_snake_case   						=		'panoptic' in model_name
	if is_panoptic:
		_snake_case   						=		250
	else:
		_snake_case   						=		91
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'coco-detection-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	return config, is_panoptic







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Tuple:
	# here we list all keys to be renamed (original name on the left, our name on the right)
	_snake_case   						=		[]

	# 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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Union[str, Any]:
	_snake_case   						=		state_dict.pop(__A	)
	_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=False	)       ->	int:
	_snake_case   						=		''
	if is_panoptic:
		_snake_case   						=		'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   						=		state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'	)
		_snake_case   						=		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   						=		in_proj_weight[:256, :]
		_snake_case   						=		in_proj_bias[:256]
		_snake_case   						=		in_proj_weight[256:512, :]
		_snake_case   						=		in_proj_bias[256:512]
		_snake_case   						=		in_proj_weight[-256:, :]
		_snake_case   						=		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   						=		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'	)
		_snake_case   						=		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   						=		in_proj_weight[:256, :]
		_snake_case   						=		in_proj_bias[:256]
		_snake_case   						=		in_proj_weight[256:512, :]
		_snake_case   						=		in_proj_bias[256:512]
		_snake_case   						=		in_proj_weight[-256:, :]
		_snake_case   						=		in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		_snake_case   						=		state_dict.pop(
		    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'	)
		_snake_case   						=		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   						=		in_proj_weight_cross_attn[:256, :]
		_snake_case   						=		in_proj_bias_cross_attn[:256]
		_snake_case   						=		in_proj_weight_cross_attn[256:512, :]
		_snake_case   						=		in_proj_bias_cross_attn[256:512]
		_snake_case   						=		in_proj_weight_cross_attn[-256:, :]
		_snake_case   						=		in_proj_bias_cross_attn[-256:]







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Optional[int]:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)

	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=None			,				__A=False	)       ->	Optional[int]:
	_snake_case						, _snake_case   						=		get_detr_config(__A	)

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

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

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

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

	_snake_case   						=		detr(__A	)
	_snake_case   						=		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__":
					lowercase    :							Optional[int]      =					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.")
					lowercase    :							Any      =					parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__snake_case    						=logging.get_logger("""transformers.models.encodec""")

__snake_case    						={
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
__snake_case    						={
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
__snake_case    						={
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
__snake_case    						={
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
__snake_case    						={
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
__snake_case    						={
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case    						={
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case    						=[]
__snake_case    						=[]





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     int	, lowerCamelCase	:     Any	, lowerCamelCase	:     List[str]				):
   for attribute in key.split('.'				):
      lowerCAmelCase     =			getattr(lowerCamelCase	, lowerCamelCase				)

   if weight_type is not None:
      lowerCAmelCase     =			getattr(lowerCamelCase	, lowerCamelCase				).shape
   else:
      lowerCAmelCase     =			hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
          f''' {value.shape} for {full_name}'''				)

   if weight_type == "weight":
      lowerCAmelCase     =			value
   elif weight_type == "weight_g":
      lowerCAmelCase     =			value
   elif weight_type == "weight_v":
      lowerCAmelCase     =			value
   elif weight_type == "bias":
      lowerCAmelCase     =			value
   elif weight_type == "running_mean":
      lowerCAmelCase     =			value
   elif weight_type == "running_var":
      lowerCAmelCase     =			value
   elif weight_type == "num_batches_tracked":
      lowerCAmelCase     =			value
   elif weight_type == "weight_ih_l0":
      lowerCAmelCase     =			value
   elif weight_type == "weight_hh_l0":
      lowerCAmelCase     =			value
   elif weight_type == "bias_ih_l0":
      lowerCAmelCase     =			value
   elif weight_type == "bias_hh_l0":
      lowerCAmelCase     =			value
   elif weight_type == "weight_ih_l1":
      lowerCAmelCase     =			value
   elif weight_type == "weight_hh_l1":
      lowerCAmelCase     =			value
   elif weight_type == "bias_ih_l1":
      lowerCAmelCase     =			value
   elif weight_type == "bias_hh_l1":
      lowerCAmelCase     =			value
   else:
      lowerCAmelCase     =			value

   logger.info(f'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''				)





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Optional[Any]				):
   for key in ignore_keys:
      if key.endswith('.*'				):
         if name.startswith(key[:-1]				):
            return True
      elif ".*." in key:
         lowerCAmelCase     ,  lowerCAmelCase     =			key.split('.*.'				)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Any	, lowerCamelCase	:     str				):
   lowerCAmelCase     =			[]

   if model_name == "encodec_24khz" or "encodec_32khz":
      lowerCAmelCase     =			MAPPING_24K
   elif model_name == "encodec_48khz":
      lowerCAmelCase     =			MAPPING_48K
   else:
      raise ValueError(f'''Unsupported model: {model_name}'''				)

   for name, value in orig_dict.items():
      if should_ignore(lowerCamelCase	, lowerCamelCase				):
         logger.info(f'''{name} was ignored'''				)
         continue

      lowerCAmelCase     =			False
      for key, mapped_key in MAPPING.items():
         if "*" in key:
            lowerCAmelCase     ,  lowerCAmelCase     =			key.split('.*.'				)
            if prefix in name and suffix in name:
               lowerCAmelCase     =			suffix

         if key in name:
            # HACK otherwise .embed gets initialized with .embed_avg too
            if key.endswith('embed'				) and name.endswith('embed_avg'				):
               continue

            lowerCAmelCase     =			True
            if "*" in mapped_key:
               lowerCAmelCase     =			name.split(lowerCamelCase				)[0].split('.'				)[-2]
               lowerCAmelCase     =			mapped_key.replace('*'	, lowerCamelCase				)
            if "weight_g" in name:
               lowerCAmelCase     =			'weight_g'
            elif "weight_v" in name:
               lowerCAmelCase     =			'weight_v'
            elif "weight_ih_l0" in name:
               lowerCAmelCase     =			'weight_ih_l0'
            elif "weight_hh_l0" in name:
               lowerCAmelCase     =			'weight_hh_l0'
            elif "bias_ih_l0" in name:
               lowerCAmelCase     =			'bias_ih_l0'
            elif "bias_hh_l0" in name:
               lowerCAmelCase     =			'bias_hh_l0'
            elif "weight_ih_l1" in name:
               lowerCAmelCase     =			'weight_ih_l1'
            elif "weight_hh_l1" in name:
               lowerCAmelCase     =			'weight_hh_l1'
            elif "bias_ih_l1" in name:
               lowerCAmelCase     =			'bias_ih_l1'
            elif "bias_hh_l1" in name:
               lowerCAmelCase     =			'bias_hh_l1'
            elif "bias" in name:
               lowerCAmelCase     =			'bias'
            elif "weight" in name:
               lowerCAmelCase     =			'weight'
            elif "running_mean" in name:
               lowerCAmelCase     =			'running_mean'
            elif "running_var" in name:
               lowerCAmelCase     =			'running_var'
            elif "num_batches_tracked" in name:
               lowerCAmelCase     =			'num_batches_tracked'
            else:
               lowerCAmelCase     =			None
            set_recursively(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
         continue
      if not is_used:
         unused_weights.append(lowerCamelCase				)

   logger.warning(f'''Unused weights: {unused_weights}'''				)





@torch.no_grad()
def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     str	, lowerCamelCase	:     Dict=None	, lowerCamelCase	:     Union[str, Any]=None	, ):
   if config_path is not None:
      lowerCAmelCase     =			EncodecConfig.from_pretrained(lowerCamelCase				)
   else:
      lowerCAmelCase     =			EncodecConfig()

   if model_name == "encodec_24khz":
      pass  # config is already correct
   elif model_name == "encodec_32khz":
      lowerCAmelCase     =			[8, 5, 4, 4]
      lowerCAmelCase     =			[2.2]
      lowerCAmelCase     =			64
      lowerCAmelCase     =			32000
      lowerCAmelCase     =			2048
      lowerCAmelCase     =			False
      lowerCAmelCase     =			False
      lowerCAmelCase     =			False
   elif model_name == "encodec_48khz":
      lowerCAmelCase     =			[8, 5, 4, 2]
      lowerCAmelCase     =			[3.0, 6.0, 12.0, 24.0]
      lowerCAmelCase     =			48000
      lowerCAmelCase     =			2
      lowerCAmelCase     =			False
      lowerCAmelCase     =			'time_group_norm'
      lowerCAmelCase     =			True
      lowerCAmelCase     =			1.0
      lowerCAmelCase     =			0.01
   else:
      raise ValueError(f'''Unknown model name: {model_name}'''				)

   lowerCAmelCase     =			EncodecModel(lowerCamelCase				)

   lowerCAmelCase     =			EncodecFeatureExtractor(
       feature_size=config.audio_channels	, sampling_rate=config.sampling_rate	, chunk_length_s=config.chunk_length_s	, overlap=config.overlap	, )
   feature_extractor.save_pretrained(lowerCamelCase				)

   lowerCAmelCase     =			torch.load(lowerCamelCase				)
   if "best_state" in original_checkpoint:
      # we might have a training state saved, in which case discard the yaml results and just retain the weights
      lowerCAmelCase     =			original_checkpoint['best_state']
   recursively_load_weights(lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   model.save_pretrained(lowerCamelCase				)

   if repo_id:
      print('Pushing to the hub...'				)
      feature_extractor.push_to_hub(lowerCamelCase				)
      model.push_to_hub(lowerCamelCase				)


if __name__ == "__main__":
       __snake_case    						=argparse.ArgumentParser()
       parser.add_argument(
           """--model""",
           default="""encodec_24khz""",
           type=str,
           help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
       )
       parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
       parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
       parser.add_argument(
           """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
       )

       __snake_case    						=parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )





