



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


UpperCAmelCase__     = logging.get_logger(__name__)

# General docstring
UpperCAmelCase__     = "ResNetConfig"

# Base docstring
UpperCAmelCase__     = "microsoft/resnet-50"
UpperCAmelCase__     = [1, 2048, 7, 7]

# Image classification docstring
UpperCAmelCase__     = "microsoft/resnet-50"
UpperCAmelCase__     = "tiger cat"

UpperCAmelCase__     = [
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class     __lowerCAmelCase					(    nn.Module		):



			def __init__(		self     :						List[Any]				,	A     :						int				,	A     :						int				,	A     :						int = 3				,	A     :						int = 1				,	A     :						str = "relu")     ->					List[str]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     nn.Convad(
						    A				,	A				,	kernel_size=A				,	stride=A				,	padding=kernel_size // 2				,	bias=A)
						_UpperCAmelCase	 =     nn.BatchNormad(A)
						_UpperCAmelCase	 =     ACTaFN[activation] if activation is not None else nn.Identity()



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Tensor)     ->					Tensor:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.convolution(A)
						_UpperCAmelCase	 =     self.normalization(A)
						_UpperCAmelCase	 =     self.activation(A)
						return hidden_state







class     __lowerCAmelCase					(    nn.Module		):



			def __init__(		self     :						Dict				,	A     :						ResNetConfig)     ->					Optional[int]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     ResNetConvLayer(
						    config.num_channels				,	config.embedding_size				,	kernel_size=7				,	stride=2				,	activation=config.hidden_act)
						_UpperCAmelCase	 =     nn.MaxPoolad(kernel_size=3				,	stride=2				,	padding=1)
						_UpperCAmelCase	 =     config.num_channels



			def 					_lowerCamelCase (		self     :						str				,	A     :						Tensor)     ->					Tensor:



						"""simple docstring"""






						_UpperCAmelCase	 =     pixel_values.shape[1]
						if num_channels != self.num_channels:
									raise ValueError(
									    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.')
						_UpperCAmelCase	 =     self.embedder(A)
						_UpperCAmelCase	 =     self.pooler(A)
						return embedding







class     __lowerCAmelCase					(    nn.Module		):



			def __init__(		self     :						int				,	A     :						int				,	A     :						int				,	A     :						int = 2)     ->					Optional[int]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     nn.Convad(A				,	A				,	kernel_size=1				,	stride=A				,	bias=A)
						_UpperCAmelCase	 =     nn.BatchNormad(A)



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Tensor)     ->					Tensor:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.convolution(A)
						_UpperCAmelCase	 =     self.normalization(A)
						return hidden_state







class     __lowerCAmelCase					(    nn.Module		):



			def __init__(		self     :						Tuple				,	A     :						int				,	A     :						int				,	A     :						int = 1				,	A     :						str = "relu")     ->					List[str]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     in_channels != out_channels or stride != 1
						_UpperCAmelCase	 =     (
						    ResNetShortCut(A				,	A				,	stride=A) if should_apply_shortcut else nn.Identity()
						)
						_UpperCAmelCase	 =     nn.Sequential(
						    ResNetConvLayer(A				,	A				,	stride=A)				,	ResNetConvLayer(A				,	A				,	activation=A)				,	)
						_UpperCAmelCase	 =     ACTaFN[activation]



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						Any)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     hidden_state
						_UpperCAmelCase	 =     self.layer(A)
						_UpperCAmelCase	 =     self.shortcut(A)
						hidden_state += residual
						_UpperCAmelCase	 =     self.activation(A)
						return hidden_state







class     __lowerCAmelCase					(    nn.Module		):



			def __init__(		self     :						str				,	A     :						int				,	A     :						int				,	A     :						int = 1				,	A     :						str = "relu"				,	A     :						int = 4)     ->					Union[str, Any]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     in_channels != out_channels or stride != 1
						_UpperCAmelCase	 =     out_channels // reduction
						_UpperCAmelCase	 =     (
						    ResNetShortCut(A				,	A				,	stride=A) if should_apply_shortcut else nn.Identity()
						)
						_UpperCAmelCase	 =     nn.Sequential(
						    ResNetConvLayer(A				,	A				,	kernel_size=1)				,	ResNetConvLayer(A				,	A				,	stride=A)				,	ResNetConvLayer(A				,	A				,	kernel_size=1				,	activation=A)				,	)
						_UpperCAmelCase	 =     ACTaFN[activation]



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						Dict)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     hidden_state
						_UpperCAmelCase	 =     self.layer(A)
						_UpperCAmelCase	 =     self.shortcut(A)
						hidden_state += residual
						_UpperCAmelCase	 =     self.activation(A)
						return hidden_state







class     __lowerCAmelCase					(    nn.Module		):



			def __init__(		self     :						Dict				,	A     :						ResNetConfig				,	A     :						int				,	A     :						int				,	A     :						int = 2				,	A     :						int = 2				,	)     ->					Optional[int]:



						"""simple docstring"""






						super().__init__()

						_UpperCAmelCase	 =     ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

						_UpperCAmelCase	 =     nn.Sequential(
						    # downsampling is done in the first layer with stride of 2
						    layer(A				,	A				,	stride=A				,	activation=config.hidden_act)				,	*[layer(A				,	A				,	activation=config.hidden_act) for _ in range(depth - 1)]				,	)



			def 					_lowerCamelCase (		self     :						str				,	A     :						Tensor)     ->					Tensor:



						"""simple docstring"""






						_UpperCAmelCase	 =     input
						for layer in self.layers:
									_UpperCAmelCase	 =     layer(A)
						return hidden_state







class     __lowerCAmelCase					(    nn.Module		):



			def __init__(		self     :						Any				,	A     :						ResNetConfig)     ->					Dict:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     nn.ModuleList([])
						# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
						self.stages.append(
						    ResNetStage(
						        A				,	config.embedding_size				,	config.hidden_sizes[0]				,	stride=2 if config.downsample_in_first_stage else 1				,	depth=config.depths[0]				,	))
						_UpperCAmelCase	 =     zip(config.hidden_sizes				,	config.hidden_sizes[1:])
						for (in_channels, out_channels), depth in zip(A				,	config.depths[1:]):
									self.stages.append(ResNetStage(A				,	A				,	A				,	depth=A))



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						Tensor				,	A     :						bool = False				,	A     :						bool = True)     ->					BaseModelOutputWithNoAttention:



						"""simple docstring"""






						_UpperCAmelCase	 =     () if output_hidden_states else None

						for stage_module in self.stages:
									if output_hidden_states:
												_UpperCAmelCase	 =     hidden_states + (hidden_state,)

									_UpperCAmelCase	 =     stage_module(A)

						if output_hidden_states:
									_UpperCAmelCase	 =     hidden_states + (hidden_state,)

						if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None)

						return BaseModelOutputWithNoAttention(
						    last_hidden_state=A				,	hidden_states=A				,	)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ResNetConfig
			UpperCamelCase           =  '''resnet'''
			UpperCamelCase           =  '''pixel_values'''
			UpperCamelCase           =  True



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int)     ->					str:



						"""simple docstring"""






						if isinstance(A				,	nn.Convad):
									nn.init.kaiming_normal_(module.weight				,	mode='fan_out'				,	nonlinearity='relu')
						elif isinstance(A				,	(nn.BatchNormad, nn.GroupNorm)):
									nn.init.constant_(module.weight				,	1)
									nn.init.constant_(module.bias				,	0)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						List[Any]				,	A     :						Union[str, Any]=False)     ->					str:



						"""simple docstring"""






						if isinstance(A				,	A):
									_UpperCAmelCase	 =     value





UpperCAmelCase__     = r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

UpperCAmelCase__     = r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"







@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''       ,		A       ,		)
class     __lowerCAmelCase					(    A		):



			def __init__(		self     :						List[Any]				,	A     :						Any)     ->					str:



						"""simple docstring"""






						super().__init__(A)
						_UpperCAmelCase	 =     config
						_UpperCAmelCase	 =     ResNetEmbeddings(A)
						_UpperCAmelCase	 =     ResNetEncoder(A)
						_UpperCAmelCase	 =     nn.AdaptiveAvgPoolad((1, 1))
						# Initialize weights and apply final processing
						self.post_init()



			@add_start_docstrings_to_model_forward(A)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC				,	output_type=A				,	config_class=_CONFIG_FOR_DOC				,	modality='vision'				,	expected_output=_EXPECTED_OUTPUT_SHAPE				,	)
			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Tensor				,	A     :						Optional[bool] = None				,	A     :						Optional[bool] = None)     ->					BaseModelOutputWithPoolingAndNoAttention:



						"""simple docstring"""






						_UpperCAmelCase	 =     (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						_UpperCAmelCase	 =     return_dict if return_dict is not None else self.config.use_return_dict

						_UpperCAmelCase	 =     self.embedder(A)

						_UpperCAmelCase	 =     self.encoder(
						    A				,	output_hidden_states=A				,	return_dict=A)

						_UpperCAmelCase	 =     encoder_outputs[0]

						_UpperCAmelCase	 =     self.pooler(A)

						if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

						return BaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=A				,	pooler_output=A				,	hidden_states=encoder_outputs.hidden_states				,	)







@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''       ,		A       ,		)
class     __lowerCAmelCase					(    A		):



			def __init__(		self     :						Any				,	A     :						Any)     ->					int:



						"""simple docstring"""






						super().__init__(A)
						_UpperCAmelCase	 =     config.num_labels
						_UpperCAmelCase	 =     ResNetModel(A)
						# classification head
						_UpperCAmelCase	 =     nn.Sequential(
						    nn.Flatten()				,	nn.Linear(config.hidden_sizes[-1]				,	config.num_labels) if config.num_labels > 0 else nn.Identity()				,	)
						# initialize weights and apply final processing
						self.post_init()



			@add_start_docstrings_to_model_forward(A)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT				,	output_type=A				,	config_class=_CONFIG_FOR_DOC				,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,	)
			def 					_lowerCamelCase (		self     :						Any				,	A     :						Optional[torch.FloatTensor] = None				,	A     :						Optional[torch.LongTensor] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[bool] = None				,	)     ->					ImageClassifierOutputWithNoAttention:



						"""simple docstring"""






						_UpperCAmelCase	 =     return_dict if return_dict is not None else self.config.use_return_dict

						_UpperCAmelCase	 =     self.resnet(A				,	output_hidden_states=A				,	return_dict=A)

						_UpperCAmelCase	 =     outputs.pooler_output if return_dict else outputs[1]

						_UpperCAmelCase	 =     self.classifier(A)

						_UpperCAmelCase	 =     None

						if labels is not None:
									if self.config.problem_type is None:
												if self.num_labels == 1:
															_UpperCAmelCase	 =     'regression'
												elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															_UpperCAmelCase	 =     'single_label_classification'
												else:
															_UpperCAmelCase	 =     'multi_label_classification'
									if self.config.problem_type == "regression":
												_UpperCAmelCase	 =     MSELoss()
												if self.num_labels == 1:
															_UpperCAmelCase	 =     loss_fct(logits.squeeze()				,	labels.squeeze())
												else:
															_UpperCAmelCase	 =     loss_fct(A				,	A)
									elif self.config.problem_type == "single_label_classification":
												_UpperCAmelCase	 =     CrossEntropyLoss()
												_UpperCAmelCase	 =     loss_fct(logits.view(-1				,	self.num_labels)				,	labels.view(-1))
									elif self.config.problem_type == "multi_label_classification":
												_UpperCAmelCase	 =     BCEWithLogitsLoss()
												_UpperCAmelCase	 =     loss_fct(A				,	A)

						if not return_dict:
									_UpperCAmelCase	 =     (logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

						return ImageClassifierOutputWithNoAttention(loss=A				,	logits=A				,	hidden_states=outputs.hidden_states)







@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''       ,		A       ,		)
class     __lowerCAmelCase					(    A       ,		A		):



			def __init__(		self     :						Dict				,	A     :						List[str])     ->					Any:



						"""simple docstring"""






						super().__init__(A)
						super()._init_backbone(A)

						_UpperCAmelCase	 =     [config.embedding_size] + config.hidden_sizes
						_UpperCAmelCase	 =     ResNetEmbeddings(A)
						_UpperCAmelCase	 =     ResNetEncoder(A)

						# initialize weights and apply final processing
						self.post_init()



			@add_start_docstrings_to_model_forward(A)
			@replace_return_docstrings(output_type=A				,	config_class=_CONFIG_FOR_DOC)
			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Tensor				,	A     :						Optional[bool] = None				,	A     :						Optional[bool] = None)     ->					BackboneOutput:



						"""simple docstring"""






						_UpperCAmelCase	 =     return_dict if return_dict is not None else self.config.use_return_dict
						_UpperCAmelCase	 =     (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)

						_UpperCAmelCase	 =     self.embedder(A)

						_UpperCAmelCase	 =     self.encoder(A				,	output_hidden_states=A				,	return_dict=A)

						_UpperCAmelCase	 =     outputs.hidden_states

						_UpperCAmelCase	 =     ()
						for idx, stage in enumerate(self.stage_names):
									if stage in self.out_features:
												feature_maps += (hidden_states[idx],)

						if not return_dict:
									_UpperCAmelCase	 =     (feature_maps,)
									if output_hidden_states:
												output += (outputs.hidden_states,)
									return output

						return BackboneOutput(
						    feature_maps=A				,	hidden_states=outputs.hidden_states if output_hidden_states else None				,	attentions=A				,	)





import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


UpperCAmelCase__     = "https://openaipublic.azureedge.net/jukebox/models/"
UpperCAmelCase__     = {
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}





def 							A			(  _UpperCAmelCase       :     List[str]					)       ->				Tuple:







			'''simple docstring'''
			if key.endswith('.model.1.bias'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.1.bias'							,     '.conv1d_1.bias'					)
			elif key.endswith('.model.1.weight'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.1.weight'							,     '.conv1d_1.weight'					)
			elif key.endswith('.model.3.bias'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.3.bias'							,     '.conv1d_2.bias'					)
			elif key.endswith('.model.3.weight'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.3.weight'							,     '.conv1d_2.weight'					)

			if "conditioner_blocks.0." in key:
						_UpperCAmelCase	 =     key.replace('conditioner_blocks.0'							,     'conditioner_blocks'					)

			if "prime_prior" in key:
						_UpperCAmelCase	 =     key.replace('prime_prior'							,     'encoder'					)

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						_UpperCAmelCase	 =     key.replace('.emb.'							,     '.'					)

			if key.endswith('k'					):  # replace vqvae.X.k with vqvae.X.codebook
						return key.replace('.k'							,     '.codebook'					)
			if "y_emb." in key:
						return key.replace('y_emb.'							,     'metadata_embedding.'					)

			if "x_emb.emb." in key:
						_UpperCAmelCase	 =     key.replace('0.x_emb.emb'							,     'embed_tokens'					)

			if "prime_state_ln" in key:
						return key.replace('prime_state_ln'							,     'encoder.final_layer_norm'					)
			if ".ln" in key:
						return key.replace('.ln'							,     '.layer_norm'					)
			if "_ln" in key:
						return key.replace('_ln'							,     '_layer_norm'					)

			if "prime_state_proj" in key:
						return key.replace('prime_state_proj'							,     'encoder.proj_in'					)
			if "prime_x_out" in key:
						return key.replace('prime_x_out'							,     'encoder.lm_head'					)
			if "prior.x_out" in key:
						return key.replace('x_out'							,     'fc_proj_out'					)
			if "x_emb" in key:
						return key.replace('x_emb'							,     'embed_tokens'					)

			return key





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			import re

			_UpperCAmelCase	 =     re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(
			    R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'					)

			_UpperCAmelCase	 =     re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(
			    R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'					)

			_UpperCAmelCase	 =     re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(
			    R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'					)

			for original_key, value in state_dict.items():
						# rename vqvae.encoder keys
						if re_encoder_block_conv_in.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_encoder_block_conv_in.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[2]					) * 2 + int(groups[3]					)
									_UpperCAmelCase	 =     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
									_UpperCAmelCase	 =     re_encoder_block_conv_in.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						elif re_encoder_block_resnet.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_encoder_block_resnet.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[2]					) * 2 + int(groups[3]					)
									_UpperCAmelCase	 =     {'1': 1, '3': 2}[groups[-2]]
									_UpperCAmelCase	 =     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
									_UpperCAmelCase	 =     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
									_UpperCAmelCase	 =     prefix + resnet_block
									_UpperCAmelCase	 =     re_encoder_block_resnet.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						elif re_encoder_block_proj_out.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_encoder_block_proj_out.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
									_UpperCAmelCase	 =     re_encoder_block_proj_out.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						# rename vqvae.decoder keys
						elif re_decoder_block_conv_out.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_decoder_block_conv_out.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[2]					) * 2 + int(groups[3]					) - 2
									_UpperCAmelCase	 =     F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
									_UpperCAmelCase	 =     re_decoder_block_conv_out.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						elif re_decoder_block_resnet.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_decoder_block_resnet.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[2]					) * 2 + int(groups[3]					) - 2
									_UpperCAmelCase	 =     {'1': 1, '3': 2}[groups[-2]]
									_UpperCAmelCase	 =     F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
									_UpperCAmelCase	 =     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
									_UpperCAmelCase	 =     prefix + resnet_block
									_UpperCAmelCase	 =     re_decoder_block_resnet.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						elif re_decoder_block_proj_in.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_decoder_block_proj_in.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
									_UpperCAmelCase	 =     re_decoder_block_proj_in.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						# rename prior cond.model to upsampler.upsample_block and resnet
						elif re_prior_cond_conv_out.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_prior_cond_conv_out.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[1]					) * 2 + int(groups[2]					) - 2
									_UpperCAmelCase	 =     F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
									_UpperCAmelCase	 =     re_prior_cond_conv_out.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						elif re_prior_cond_resnet.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_prior_cond_resnet.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[1]					) * 2 + int(groups[2]					) - 2
									_UpperCAmelCase	 =     {'1': 1, '3': 2}[groups[-2]]
									_UpperCAmelCase	 =     F"conditioner_blocks.upsampler.upsample_block.{block_index}."
									_UpperCAmelCase	 =     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
									_UpperCAmelCase	 =     prefix + resnet_block
									_UpperCAmelCase	 =     re_prior_cond_resnet.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						elif re_prior_cond_proj_in.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_prior_cond_proj_in.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
									_UpperCAmelCase	 =     re_prior_cond_proj_in.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

						# keep original key
						else:
									_UpperCAmelCase	 =     original_key

						_UpperCAmelCase	 =     replace_key(_UpperCAmelCase					)

						if F"{key_prefix}.{key}" not in model_state_dict or key is None:
									print(F"failed converting {original_key} to {key}, does not match"					)

						# handle missmatched shape
						elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
									_UpperCAmelCase	 =     model_state_dict[F"{key_prefix}.{key}"]
									print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"					)
									_UpperCAmelCase	 =     original_key

						_UpperCAmelCase	 =     original_key
						_UpperCAmelCase	 =     value

			return new_dict





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     List[str]=None							,     _UpperCAmelCase       :     Dict=None					)       ->				Dict:







			'''simple docstring'''
			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'					)[-1]}"					):
									_UpperCAmelCase	 =     requests.get(F"{PREFIX}{file}"							,     allow_redirects=_UpperCAmelCase					)
									os.makedirs(F"{pytorch_dump_folder_path}/"							,     exist_ok=_UpperCAmelCase					)
									open(F"{pytorch_dump_folder_path}/{file.split('/'					)[-1]}"							,     'wb'					).write(r.content					)

			_UpperCAmelCase	 =     MODEL_MAPPING[model_name.split('/'					)[-1]]

			_UpperCAmelCase	 =     JukeboxConfig.from_pretrained(_UpperCAmelCase					)
			_UpperCAmelCase	 =     JukeboxModel(_UpperCAmelCase					)

			_UpperCAmelCase	 =     []
			_UpperCAmelCase	 =     {}
			for i, dict_name in enumerate(_UpperCAmelCase					):
						_UpperCAmelCase	 =     torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'					)[-1]}"					)['model']

						_UpperCAmelCase	 =     {}
						for k in old_dic.keys():
									if k.endswith('.b'					):
												_UpperCAmelCase	 =     old_dic[k]
									elif k.endswith('.w'					):
												_UpperCAmelCase	 =     old_dic[k]
									elif "level_2" not in dict_name and "cond.model." in k:
												_UpperCAmelCase	 =     old_dic[k]
									else:
												_UpperCAmelCase	 =     old_dic[k]

						_UpperCAmelCase	 =     'vqvae' if i == 0 else F"priors.{3 - i}"
						_UpperCAmelCase	 =     fix_jukebox_keys(_UpperCAmelCase							,     model.state_dict()							,     _UpperCAmelCase							,     _UpperCAmelCase					)
						weight_dict.append(_UpperCAmelCase					)

			_UpperCAmelCase	 =     weight_dict.pop(0					)
			model.vqvae.load_state_dict(_UpperCAmelCase					)
			for i in range(len(_UpperCAmelCase					)					):
						model.priors[i].load_state_dict(weight_dict[2 - i]					)

			Path(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
			with open(F"{pytorch_dump_folder_path}/mapping.json"							,     'w'					) as txtfile:
						json.dump(_UpperCAmelCase							,     _UpperCAmelCase					)

			print(F"Saving model {model_name} to {pytorch_dump_folder_path}"					)
			model.save_pretrained(_UpperCAmelCase					)

			return weight_dict


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="jukebox-5b-lyrics",
							    type=str,
							    help="Name of the model you'd like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default="jukebox-5b-lyrics-converted",
							    type=str,
							    help="Path to the output PyTorch model directory.",
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
