


from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


SCREAMING_SNAKE_CASE__					:     List[str]       =     [
    'openmmlab/upernet-convnext-tiny',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     'UperNetConfig'


class 		_SCREAMING_SNAKE_CASE     (						nn.Module     ):


     def __init__(  self							,					A_							,					A_							,					A_							,					A_ = 0							,					A_ = False							,					A_ = 1							,					):
       super().__init__()
       _UpperCAmelCase		:							int							=     nn.Convad(
           in_channels=A_							,					out_channels=A_							,					kernel_size=A_							,					padding=A_							,					bias=A_							,					dilation=A_							,					)
       _UpperCAmelCase		:							List[Any]							=     nn.BatchNormad(A_    )
       _UpperCAmelCase		:							Optional[int]							=     nn.ReLU()




     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							str							=     self.conv(A_    )
       _UpperCAmelCase		:							Union[str, Any]							=     self.batch_norm(A_    )
       _UpperCAmelCase		:							Dict							=     self.activation(A_    )

       return output


class 		_SCREAMING_SNAKE_CASE     (						nn.Module     ):


     def __init__(  self							,					A_							,					A_							,					A_    ):
       super().__init__()
       _UpperCAmelCase		:							str							=     [
           nn.AdaptiveAvgPoolad(A_    ),
           UperNetConvModule(A_							,					A_							,					kernel_size=1    ),
       ]
       for i, layer in enumerate(self.layers    ):
         self.add_module(str(A_    )							,					A_    )




     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Optional[int]							=     input
       for layer in self.layers:
         _UpperCAmelCase		:							Optional[Any]							=     layer(A_    )
       return hidden_state


class 		_SCREAMING_SNAKE_CASE     (						nn.Module     ):


     def __init__(  self							,					A_							,					A_							,					A_							,					A_    ):
       super().__init__()
       _UpperCAmelCase		:							Optional[Any]							=     pool_scales
       _UpperCAmelCase		:							Optional[int]							=     align_corners
       _UpperCAmelCase		:							Any							=     in_channels
       _UpperCAmelCase		:							Tuple							=     channels
       _UpperCAmelCase		:							Any							=     []
       for i, pool_scale in enumerate(A_    ):
         _UpperCAmelCase		:							Dict							=     UperNetPyramidPoolingBlock(pool_scale=A_							,					in_channels=A_							,					channels=A_    )
         self.blocks.append(A_    )
         self.add_module(str(A_    )							,					A_    )




     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     []
       for ppm in self.blocks:
         _UpperCAmelCase		:							Dict							=     ppm(A_    )
         _UpperCAmelCase		:							Dict							=     nn.functional.interpolate(
             A_							,					size=x.size()[2:]							,					mode="""bilinear"""							,					align_corners=self.align_corners    )
         ppm_outs.append(A_    )
       return ppm_outs


class 		_SCREAMING_SNAKE_CASE     (						nn.Module     ):


     def __init__(  self							,					A_							,					A_    ):
       super().__init__()

       _UpperCAmelCase		:							str							=     config
       _UpperCAmelCase		:							List[str]							=     config.pool_scales  # e.g. (1, 2, 3, 6)
       _UpperCAmelCase		:							List[Any]							=     in_channels
       _UpperCAmelCase		:							Any							=     config.hidden_size
       _UpperCAmelCase		:							Any							=     False
       _UpperCAmelCase		:							int							=     nn.Convad(self.channels							,					config.num_labels							,					kernel_size=1    )

       # PSP Module
       _UpperCAmelCase		:							Any							=     UperNetPyramidPoolingModule(
           self.pool_scales							,					self.in_channels[-1]							,					self.channels							,					align_corners=self.align_corners							,					)
       _UpperCAmelCase		:							str							=     UperNetConvModule(
           self.in_channels[-1] + len(self.pool_scales    ) * self.channels							,					self.channels							,					kernel_size=3							,					padding=1							,					)
       # FPN Module
       _UpperCAmelCase		:							str							=     nn.ModuleList()
       _UpperCAmelCase		:							Tuple							=     nn.ModuleList()
       for in_channels in self.in_channels[:-1]:  # skip the top layer
         _UpperCAmelCase		:							int							=     UperNetConvModule(A_							,					self.channels							,					kernel_size=1    )
         _UpperCAmelCase		:							int							=     UperNetConvModule(self.channels							,					self.channels							,					kernel_size=3							,					padding=1    )
         self.lateral_convs.append(A_    )
         self.fpn_convs.append(A_    )

       _UpperCAmelCase		:							Optional[Any]							=     UperNetConvModule(
           len(self.in_channels    ) * self.channels							,					self.channels							,					kernel_size=3							,					padding=1							,					)


     def        __snake_case(  self    ):
       self.apply(self._init_weights    )


     def        __snake_case(  self							,					A_    ):
       if isinstance(A_							,					nn.Convad    ):
         module.weight.data.normal_(mean=0.0							,					std=self.config.initializer_range    )
         if module.bias is not None:
           module.bias.data.zero_()


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							List[Any]							=     inputs[-1]
       _UpperCAmelCase		:							int							=     [x]
       psp_outs.extend(self.psp_modules(A_    )    )
       _UpperCAmelCase		:							List[str]							=     torch.cat(A_							,					dim=1    )
       _UpperCAmelCase		:							str							=     self.bottleneck(A_    )

       return output




     def        __snake_case(  self							,					A_    ):
       # build laterals
       _UpperCAmelCase		:							Optional[int]							=     [lateral_conv(encoder_hidden_states[i]    ) for i, lateral_conv in enumerate(self.lateral_convs    )]

       laterals.append(self.psp_forward(A_    )    )

       # build top-down path
       _UpperCAmelCase		:							str							=     len(A_    )
       for i in range(used_backbone_levels - 1							,					0							,					-1    ):
         _UpperCAmelCase		:							Union[str, Any]							=     laterals[i - 1].shape[2:]
         _UpperCAmelCase		:							List[Any]							=     laterals[i - 1] + nn.functional.interpolate(
             laterals[i]							,					size=A_							,					mode="""bilinear"""							,					align_corners=self.align_corners    )

       # build outputs
       _UpperCAmelCase		:							str							=     [self.fpn_convs[i](laterals[i]    ) for i in range(used_backbone_levels - 1    )]
       # append psp feature
       fpn_outs.append(laterals[-1]    )

       for i in range(used_backbone_levels - 1							,					0							,					-1    ):
         _UpperCAmelCase		:							Union[str, Any]							=     nn.functional.interpolate(
             fpn_outs[i]							,					size=fpn_outs[0].shape[2:]							,					mode="""bilinear"""							,					align_corners=self.align_corners    )
       _UpperCAmelCase		:							Union[str, Any]							=     torch.cat(A_							,					dim=1    )
       _UpperCAmelCase		:							int							=     self.fpn_bottleneck(A_    )
       _UpperCAmelCase		:							Union[str, Any]							=     self.classifier(A_    )

       return output


class 		_SCREAMING_SNAKE_CASE     (						nn.Module     ):


     def __init__(  self							,					A_							,					A_ = 2							,					A_ = 3							,					A_ = 1    ):
       super().__init__()

       _UpperCAmelCase		:							Dict							=     config
       _UpperCAmelCase		:							List[str]							=     config.auxiliary_in_channels
       _UpperCAmelCase		:							str							=     config.auxiliary_channels
       _UpperCAmelCase		:							List[str]							=     config.auxiliary_num_convs
       _UpperCAmelCase		:							Any							=     config.auxiliary_concat_input
       _UpperCAmelCase		:							Optional[Any]							=     in_index

       _UpperCAmelCase		:							int							=     (kernel_size // 2) * dilation
       _UpperCAmelCase		:							Tuple							=     []
       convs.append(
           UperNetConvModule(
               self.in_channels							,					self.channels							,					kernel_size=A_							,					padding=A_							,					dilation=A_    )    )
       for i in range(self.num_convs - 1    ):
         convs.append(
             UperNetConvModule(
                 self.channels							,					self.channels							,					kernel_size=A_							,					padding=A_							,					dilation=A_    )    )
       if self.num_convs == 0:
         _UpperCAmelCase		:							List[str]							=     nn.Identity()
       else:
         _UpperCAmelCase		:							Union[str, Any]							=     nn.Sequential(*A_    )
       if self.concat_input:
         _UpperCAmelCase		:							Optional[int]							=     UperNetConvModule(
             self.in_channels + self.channels							,					self.channels							,					kernel_size=A_							,					padding=kernel_size // 2    )

       _UpperCAmelCase		:							List[str]							=     nn.Convad(self.channels							,					config.num_labels							,					kernel_size=1    )


     def        __snake_case(  self    ):
       self.apply(self._init_weights    )


     def        __snake_case(  self							,					A_    ):
       if isinstance(A_							,					nn.Convad    ):
         module.weight.data.normal_(mean=0.0							,					std=self.config.initializer_range    )
         if module.bias is not None:
           module.bias.data.zero_()




     def        __snake_case(  self							,					A_    ):
       # just take the relevant feature maps
       _UpperCAmelCase		:							Optional[int]							=     encoder_hidden_states[self.in_index]
       _UpperCAmelCase		:							int							=     self.convs(A_    )
       if self.concat_input:
         _UpperCAmelCase		:							int							=     self.conv_cat(torch.cat([hidden_states, output]							,					dim=1    )    )
       _UpperCAmelCase		:							Optional[Any]							=     self.classifier(A_    )
       return output


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    UperNetConfig
     __SCREAMING_SNAKE_CASE     					=    '''pixel_values'''
     __SCREAMING_SNAKE_CASE     					=    True


     def        __snake_case(  self							,					A_    ):
       if isinstance(A_							,					A_    ):
         module.backbone.init_weights()
         module.decode_head.init_weights()
         module.auxiliary_head.init_weights()


     def        __snake_case(  self    ):
       self.backbone.init_weights()
       self.decode_head.init_weights()
       self.auxiliary_head.init_weights()




     def        __snake_case(  self							,					A_							,					A_=False    ):
       if isinstance(A_							,					A_    ):
         _UpperCAmelCase		:							Union[str, Any]							=     value


SCREAMING_SNAKE_CASE__					:     Dict       =     R'\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): 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'

SCREAMING_SNAKE_CASE__					:     List[Any]       =     R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for 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(
    '''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'''							,	A							,	)
class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def __init__(  self							,					A_    ):
       super().__init__(A_    )

       _UpperCAmelCase		:							int							=     AutoBackbone.from_config(config.backbone_config    )

       # Semantic segmentation head(s)
       _UpperCAmelCase		:							Optional[Any]							=     UperNetHead(A_							,					in_channels=self.backbone.channels    )
       _UpperCAmelCase		:							Optional[Any]							=     UperNetFCNHead(A_    ) if config.use_auxiliary_head else None

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




     @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("""batch_size, sequence_length"""    )    )
     @replace_return_docstrings(output_type=A_							,					config_class=_CONFIG_FOR_DOC    )
     def        __snake_case(  self							,					A_ = None							,					A_ = None							,					A_ = None							,					A_ = None							,					A_ = None							,					):
       _UpperCAmelCase		:							List[Any]							=     return_dict if return_dict is not None else self.config.use_return_dict
       _UpperCAmelCase		:							Union[str, Any]							=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _UpperCAmelCase		:							Optional[int]							=     output_attentions if output_attentions is not None else self.config.output_attentions

       _UpperCAmelCase		:							str							=     self.backbone.forward_with_filtered_kwargs(
           A_							,					output_hidden_states=A_							,					output_attentions=A_    )
       _UpperCAmelCase		:							List[Any]							=     outputs.feature_maps

       _UpperCAmelCase		:							Dict							=     self.decode_head(A_    )
       _UpperCAmelCase		:							int							=     nn.functional.interpolate(A_							,					size=pixel_values.shape[2:]							,					mode="""bilinear"""							,					align_corners=A_    )

       _UpperCAmelCase		:							List[Any]							=     None
       if self.auxiliary_head is not None:
         _UpperCAmelCase		:							int							=     self.auxiliary_head(A_    )
         _UpperCAmelCase		:							str							=     nn.functional.interpolate(
             A_							,					size=pixel_values.shape[2:]							,					mode="""bilinear"""							,					align_corners=A_    )

       _UpperCAmelCase		:							Optional[Any]							=     None
       if labels is not None:
         if self.config.num_labels == 1:
           raise ValueError("""The number of labels should be greater than one"""    )
         else:
           # compute weighted loss
           _UpperCAmelCase		:							List[Any]							=     CrossEntropyLoss(ignore_index=self.config.loss_ignore_index    )
           _UpperCAmelCase		:							Tuple							=     loss_fct(A_							,					A_    )
           _UpperCAmelCase		:							int							=     loss_fct(A_							,					A_    )
           _UpperCAmelCase		:							List[str]							=     main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

       if not return_dict:
         if output_hidden_states:
           _UpperCAmelCase		:							Optional[Any]							=     (logits,) + outputs[1:]
         else:
           _UpperCAmelCase		:							List[Any]							=     (logits,) + outputs[2:]
         return ((loss,) + output) if loss is not None else output

       return SemanticSegmenterOutput(
           loss=A_							,					logits=A_							,					hidden_states=outputs.hidden_states							,					attentions=outputs.attentions							,					)









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()
SCREAMING_SNAKE_CASE__					:     Any       =     logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__					:     Any       =     'https://openaipublic.azureedge.net/jukebox/models/'
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     {
    '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__    (		snake_case__					:							Union[str, Any]     ):
  if key.endswith(""".model.1.bias"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Dict							=     key.replace(""".model.1.bias"""						,   """.conv1d_1.bias"""     )
  elif key.endswith(""".model.1.weight"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							List[str]							=     key.replace(""".model.1.weight"""						,   """.conv1d_1.weight"""     )
  elif key.endswith(""".model.3.bias"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Union[str, Any]							=     key.replace(""".model.3.bias"""						,   """.conv1d_2.bias"""     )
  elif key.endswith(""".model.3.weight"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Optional[Any]							=     key.replace(""".model.3.weight"""						,   """.conv1d_2.weight"""     )

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

  if "prime_prior" in key:
    _UpperCAmelCase		:							Union[str, Any]							=     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		:							Union[str, Any]							=     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		:							List[Any]							=     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__    (		snake_case__					:							List[str]						,   snake_case__					:							Optional[Any]						,   snake_case__					:							Optional[Any]						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							int							=     {}
  import re

  _UpperCAmelCase		:							Tuple							=     re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""     )
  _UpperCAmelCase		:							Union[str, Any]							=     re.compile(
      r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
  _UpperCAmelCase		:							Any							=     re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""     )

  _UpperCAmelCase		:							List[str]							=     re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""     )
  _UpperCAmelCase		:							Optional[Any]							=     re.compile(
      r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
  _UpperCAmelCase		:							Optional[Any]							=     re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""     )

  _UpperCAmelCase		:							Union[str, Any]							=     re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""     )
  _UpperCAmelCase		:							Optional[int]							=     re.compile(
      r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
  _UpperCAmelCase		:							Optional[Any]							=     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(snake_case__     ):
      _UpperCAmelCase		:							str							=     re_encoder_block_conv_in.match(snake_case__     )
      _UpperCAmelCase		:							Tuple							=     regex_match.groups()
      _UpperCAmelCase		:							List[Any]							=     int(groups[2]     ) * 2 + int(groups[3]     )
      _UpperCAmelCase		:							int							=     f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
      _UpperCAmelCase		:							Union[str, Any]							=     re_encoder_block_conv_in.sub(snake_case__						,   snake_case__     )

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

    elif re_encoder_block_proj_out.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							List[str]							=     re_encoder_block_proj_out.match(snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     regex_match.groups()
      _UpperCAmelCase		:							List[Any]							=     f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
      _UpperCAmelCase		:							Tuple							=     re_encoder_block_proj_out.sub(snake_case__						,   snake_case__     )

    # rename vqvae.decoder keys
    elif re_decoder_block_conv_out.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							Any							=     re_decoder_block_conv_out.match(snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     regex_match.groups()
      _UpperCAmelCase		:							Union[str, Any]							=     int(groups[2]     ) * 2 + int(groups[3]     ) - 2
      _UpperCAmelCase		:							Any							=     f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
      _UpperCAmelCase		:							Optional[Any]							=     re_decoder_block_conv_out.sub(snake_case__						,   snake_case__     )

    elif re_decoder_block_resnet.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							str							=     re_decoder_block_resnet.match(snake_case__     )
      _UpperCAmelCase		:							Optional[int]							=     regex_match.groups()
      _UpperCAmelCase		:							Any							=     int(groups[2]     ) * 2 + int(groups[3]     ) - 2
      _UpperCAmelCase		:							Tuple							=     {"""1""": 1, """3""": 2}[groups[-2]]
      _UpperCAmelCase		:							List[str]							=     f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
      _UpperCAmelCase		:							Dict							=     f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      _UpperCAmelCase		:							List[Any]							=     prefix + resnet_block
      _UpperCAmelCase		:							Union[str, Any]							=     re_decoder_block_resnet.sub(snake_case__						,   snake_case__     )

    elif re_decoder_block_proj_in.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							str							=     re_decoder_block_proj_in.match(snake_case__     )
      _UpperCAmelCase		:							int							=     regex_match.groups()
      _UpperCAmelCase		:							Union[str, Any]							=     f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      _UpperCAmelCase		:							Optional[Any]							=     re_decoder_block_proj_in.sub(snake_case__						,   snake_case__     )

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

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

    elif re_prior_cond_proj_in.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							Union[str, Any]							=     re_prior_cond_proj_in.match(snake_case__     )
      _UpperCAmelCase		:							Optional[int]							=     regex_match.groups()
      _UpperCAmelCase		:							Optional[Any]							=     f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      _UpperCAmelCase		:							Tuple							=     re_prior_cond_proj_in.sub(snake_case__						,   snake_case__     )

    # keep original key
    else:
      _UpperCAmelCase		:							Tuple							=     original_key

    _UpperCAmelCase		:							List[Any]							=     replace_key(snake_case__     )

    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		:							List[str]							=     model_state_dict[f'''{key_prefix}.{key}''']
      print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''     )
      _UpperCAmelCase		:							Tuple							=     original_key

    _UpperCAmelCase		:							List[Any]							=     original_key
    _UpperCAmelCase		:							int							=     value

  return new_dict




@torch.no_grad()
def    a__    (		snake_case__					:							Any=None						,   snake_case__					:							Optional[Any]=None     ):
  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"     )[-1]}'''     ):
      _UpperCAmelCase		:							Dict							=     requests.get(f'''{PREFIX}{file}'''						,   allow_redirects=snake_case__     )
      os.makedirs(f'''{pytorch_dump_folder_path}/'''						,   exist_ok=snake_case__     )
      open(f'''{pytorch_dump_folder_path}/{file.split("/"     )[-1]}'''						,   """wb"""     ).write(r.content     )

  _UpperCAmelCase		:							str							=     MODEL_MAPPING[model_name.split("""/"""     )[-1]]

  _UpperCAmelCase		:							Optional[Any]							=     JukeboxConfig.from_pretrained(snake_case__     )
  _UpperCAmelCase		:							int							=     JukeboxModel(snake_case__     )

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

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

    _UpperCAmelCase		:							Tuple							=     """vqvae""" if i == 0 else f'''priors.{3 - i}'''
    _UpperCAmelCase		:							str							=     fix_jukebox_keys(snake_case__						,   model.state_dict()						,   snake_case__						,   snake_case__     )
    weight_dict.append(snake_case__     )

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

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

  print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''     )
  model.save_pretrained(snake_case__     )

  return weight_dict


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     List[str]       =     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.',
 )
 SCREAMING_SNAKE_CASE__					:     List[Any]       =     parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





