

"""simple docstring"""






import collections.abc
from typing import Optional, Tuple, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


_lowerCAmelCase						=		logging.get_logger(__name__)

# General docstring
_lowerCAmelCase						=		"""PoolFormerConfig"""

# Base docstring
_lowerCAmelCase						=		"""sail/poolformer_s12"""
_lowerCAmelCase						=		[1, 5_1_2, 7, 7]

# Image classification docstring
_lowerCAmelCase						=		"""sail/poolformer_s12"""
_lowerCAmelCase						=		"""tabby, tabby cat"""

_lowerCAmelCase						=		[
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase = 0.0 ,   _lowerCamelCase = False   ):

    '''simple docstring'''



    if drop_prob == 0.0 or not training:
        return input
    _lowerCAmelCase       :   List[str]  =    1 - drop_prob
    _lowerCAmelCase       :   List[str]  =    (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    _lowerCAmelCase       :   str  =    keep_prob + torch.rand(_lowerCamelCase ,   dtype=input.dtype ,   device=input.device   )
    random_tensor.floor_()  # binarize
    _lowerCAmelCase       :   Any  =    input.div(_lowerCamelCase   ) * random_tensor
    return output







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A = None      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   int  =    drop_prob

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return drop_path(_A    ,self.drop_prob    ,self.training      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return "p={}".format(self.drop_prob      )







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A=None      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[Any]  =    patch_size if isinstance(_A    ,collections.abc.Iterable      ) else (patch_size, patch_size)
      _lowerCAmelCase       :   Union[str, Any]  =    stride if isinstance(_A    ,collections.abc.Iterable      ) else (stride, stride)
      _lowerCAmelCase       :   Optional[Any]  =    padding if isinstance(_A    ,collections.abc.Iterable      ) else (padding, padding)

      _lowerCAmelCase       :   List[Any]  =    nn.Convad(_A    ,_A    ,kernel_size=_A    ,stride=_A    ,padding=_A      )
      _lowerCAmelCase       :   Any  =    norm_layer(_A      ) if norm_layer else nn.Identity()







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.projection(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.norm(_A      )
      return embeddings







class 	__UpperCamelCase    (    nn.GroupNorm						):

  def __init__(		self    ,_A    ,**_A      ):
      '''simple docstring'''

      super().__init__(1    ,_A    ,**_A      )







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.AvgPoolad(_A    ,stride=1    ,padding=pool_size // 2    ,count_include_pad=_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.pool(_A      ) - hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   str  =    nn.Convad(_A    ,_A    ,1      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.Convad(_A    ,_A    ,1      )
      _lowerCAmelCase       :   Union[str, Any]  =    PoolFormerDropPath(_A      )
      if isinstance(config.hidden_act    ,_A      ):
          _lowerCAmelCase       :   Optional[int]  =    ACTaFN[config.hidden_act]
      else:
          _lowerCAmelCase       :   str  =    config.hidden_act







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.conva(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self.act_fn(_A      )
      _lowerCAmelCase       :   List[str]  =    self.drop(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.conva(_A      )
      _lowerCAmelCase       :   Any  =    self.drop(_A      )

      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   int  =    PoolFormerPooling(_A      )
      _lowerCAmelCase       :   int  =    PoolFormerOutput(_A    ,_A    ,_A    ,_A      )
      _lowerCAmelCase       :   List[Any]  =    PoolFormerGroupNorm(_A      )
      _lowerCAmelCase       :   Dict  =    PoolFormerGroupNorm(_A      )

      # Useful for training neural nets
      _lowerCAmelCase       :   Optional[Any]  =    PoolFormerDropPath(_A      ) if drop_path > 0.0 else nn.Identity()
      _lowerCAmelCase       :   Any  =    config.use_layer_scale
      if config.use_layer_scale:
          _lowerCAmelCase       :   List[str]  =    nn.Parameter(
              config.layer_scale_init_value * torch.ones((_A)      )    ,requires_grad=_A      )
          _lowerCAmelCase       :   Optional[Any]  =    nn.Parameter(
              config.layer_scale_init_value * torch.ones((_A)      )    ,requires_grad=_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if self.use_layer_scale:
          _lowerCAmelCase       :   Optional[int]  =    self.pooling(self.before_norm(_A      )      )
          _lowerCAmelCase       :   List[str]  =    self.layer_scale_a.unsqueeze(-1      ).unsqueeze(-1      ) * pooling_output
          # First residual connection
          _lowerCAmelCase       :   Union[str, Any]  =    hidden_states + self.drop_path(_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    ()

          _lowerCAmelCase       :   Optional[int]  =    self.output(self.after_norm(_A      )      )
          _lowerCAmelCase       :   Optional[Any]  =    self.layer_scale_a.unsqueeze(-1      ).unsqueeze(-1      ) * layer_output
          # Second residual connection
          _lowerCAmelCase       :   int  =    hidden_states + self.drop_path(_A      )

          _lowerCAmelCase       :   int  =    (output,) + outputs
          return outputs

      else:
          _lowerCAmelCase       :   List[Any]  =    self.drop_path(self.pooling(self.before_norm(_A      )      )      )
          # First residual connection
          _lowerCAmelCase       :   int  =    pooling_output + hidden_states
          _lowerCAmelCase       :   List[str]  =    ()

          # Second residual connection inside the PoolFormerOutput block
          _lowerCAmelCase       :   Tuple  =    self.drop_path(self.output(self.after_norm(_A      )      )      )
          _lowerCAmelCase       :   str  =    hidden_states + layer_output

          _lowerCAmelCase       :   Union[str, Any]  =    (output,) + outputs
          return outputs







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[int]  =    config
      # stochastic depth decay rule
      _lowerCAmelCase       :   str  =    [x.item() for x in torch.linspace(0    ,config.drop_path_rate    ,sum(config.depths      )      )]

      # patch embeddings
      _lowerCAmelCase       :   Optional[Any]  =    []
      for i in range(config.num_encoder_blocks      ):
          embeddings.append(
              PoolFormerEmbeddings(
                  patch_size=config.patch_sizes[i]    ,stride=config.strides[i]    ,padding=config.padding[i]    ,num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]    ,hidden_size=config.hidden_sizes[i]    ,)      )
      _lowerCAmelCase       :   Dict  =    nn.ModuleList(_A      )

      # Transformer blocks
      _lowerCAmelCase       :   List[Any]  =    []
      _lowerCAmelCase       :   Tuple  =    0
      for i in range(config.num_encoder_blocks      ):
          # each block consists of layers
          _lowerCAmelCase       :   int  =    []
          if i != 0:
              cur += config.depths[i - 1]
          for j in range(config.depths[i]      ):
              layers.append(
                  PoolFormerLayer(
                      _A    ,num_channels=config.hidden_sizes[i]    ,pool_size=config.pool_size    ,hidden_size=config.hidden_sizes[i]    ,intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio      )    ,drop_path=dpr[cur + j]    ,)      )
          blocks.append(nn.ModuleList(_A      )      )

      _lowerCAmelCase       :   Tuple  =    nn.ModuleList(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=False    ,_A=True      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    () if output_hidden_states else None

      _lowerCAmelCase       :   str  =    pixel_values
      for idx, layers in enumerate(zip(self.patch_embeddings    ,self.block      )      ):
          _lowerCAmelCase       :   Optional[int]  =    layers
          # Get patch embeddings from hidden_states
          _lowerCAmelCase       :   Dict  =    embedding_layer(_A      )
          # Send the embeddings through the blocks
          for _, blk in enumerate(_A      ):
              _lowerCAmelCase       :   Optional[int]  =    blk(_A      )
              _lowerCAmelCase       :   int  =    layer_outputs[0]

          if output_hidden_states:
              _lowerCAmelCase       :   List[str]  =    all_hidden_states + (hidden_states,)

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

      return BaseModelOutputWithNoAttention(last_hidden_state=_A    ,hidden_states=_A      )







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		PoolFormerConfig
  _UpperCAmelCase          =		"poolformer"
  _UpperCAmelCase          =		"pixel_values"
  _UpperCAmelCase          =		True

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if isinstance(_A    ,(nn.Linear, nn.Convad)      ):
          module.weight.data.normal_(mean=0.0    ,std=self.config.initializer_range      )
          if module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(_A    ,nn.LayerNorm      ):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0      )







  def __lowerCamelCase		(		self    ,_A    ,_A=False      ):
      '''simple docstring'''

      if isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   Any  =    value





_lowerCAmelCase						=		r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

_lowerCAmelCase						=		r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""







@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."							,							a__							,							)
class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      super().__init__(_A      )
      _lowerCAmelCase       :   List[Any]  =    config

      _lowerCAmelCase       :   int  =    PoolFormerEncoder(_A      )

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.embeddings.patch_embeddings







  @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    ,_A = None    ,_A = None    ,_A = None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      _lowerCAmelCase       :   List[str]  =    return_dict if return_dict is not None else self.config.use_return_dict

      if pixel_values is None:
          raise ValueError('You have to specify pixel_values'      )

      _lowerCAmelCase       :   List[Any]  =    self.encoder(
          _A    ,output_hidden_states=_A    ,return_dict=_A    ,)
      _lowerCAmelCase       :   Optional[int]  =    encoder_outputs[0]

      if not return_dict:
          return (sequence_output, None) + encoder_outputs[1:]

      return BaseModelOutputWithNoAttention(
          last_hidden_state=_A    ,hidden_states=encoder_outputs.hidden_states    ,)







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Dict  =    nn.Linear(config.hidden_size    ,config.hidden_size      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.dense(_A      )
      return output







@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "							,							a__							,							)
class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      super().__init__(_A      )
      _lowerCAmelCase       :   Optional[int]  =    config.num_labels
      _lowerCAmelCase       :   Optional[int]  =    PoolFormerModel(_A      )

      # Final norm
      _lowerCAmelCase       :   Tuple  =    PoolFormerGroupNorm(config.hidden_sizes[-1]      )
      # Classifier head
      _lowerCAmelCase       :   Tuple  =    (
          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    ,_A = None    ,_A = None    ,_A = None    ,_A = None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    return_dict if return_dict is not None else self.config.use_return_dict

      _lowerCAmelCase       :   Dict  =    self.poolformer(
          _A    ,output_hidden_states=_A    ,return_dict=_A    ,)

      _lowerCAmelCase       :   Tuple  =    outputs[0]

      _lowerCAmelCase       :   Any  =    self.classifier(self.norm(_A      ).mean([-2, -1]      )      )

      _lowerCAmelCase       :   List[Any]  =    None
      if labels is not None:
          if self.config.problem_type is None:
              if self.num_labels == 1:
                  _lowerCAmelCase       :   int  =    'regression'
              elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                  _lowerCAmelCase       :   str  =    'single_label_classification'
              else:
                  _lowerCAmelCase       :   Optional[int]  =    'multi_label_classification'

          if self.config.problem_type == "regression":
              _lowerCAmelCase       :   Tuple  =    MSELoss()
              if self.num_labels == 1:
                  _lowerCAmelCase       :   Union[str, Any]  =    loss_fct(logits.squeeze()    ,labels.squeeze()      )
              else:
                  _lowerCAmelCase       :   List[str]  =    loss_fct(_A    ,_A      )
          elif self.config.problem_type == "single_label_classification":
              _lowerCAmelCase       :   Any  =    CrossEntropyLoss()
              _lowerCAmelCase       :   List[Any]  =    loss_fct(logits.view(-1    ,self.num_labels      )    ,labels.view(-1      )      )
          elif self.config.problem_type == "multi_label_classification":
              _lowerCAmelCase       :   List[str]  =    BCEWithLogitsLoss()
              _lowerCAmelCase       :   Any  =    loss_fct(_A    ,_A      )

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

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








"""simple docstring"""






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()
_lowerCAmelCase						=		logging.get_logger(__name__)


_lowerCAmelCase						=		"""https://openaipublic.azureedge.net/jukebox/models/"""
_lowerCAmelCase						=		{
    """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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if key.endswith('.model.1.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.bias' ,   '.conv1d_1.bias'   )
    elif key.endswith('.model.1.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.weight' ,   '.conv1d_1.weight'   )
    elif key.endswith('.model.3.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   int  =    key.replace('.model.3.bias' ,   '.conv1d_2.bias'   )
    elif key.endswith('.model.3.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Tuple  =    key.replace('.model.3.weight' ,   '.conv1d_2.weight'   )

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

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

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        _lowerCAmelCase       :   Optional[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:
        _lowerCAmelCase       :   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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {}
    import re

    _lowerCAmelCase       :   Optional[Any]  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Optional[int]  =    re.compile(
        R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Union[str, Any]  =    re.compile(
        R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

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

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

        elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   str  =    re_encoder_block_proj_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
            _lowerCAmelCase       :   Any  =    re_encoder_block_proj_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Optional[int]  =    re_decoder_block_conv_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            _lowerCAmelCase       :   Dict  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_decoder_block_conv_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

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

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Any  =    re_decoder_block_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   str  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   str  =    re_decoder_block_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

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

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

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[str]  =    regex_match.groups()
            _lowerCAmelCase       :   List[Any]  =    f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # keep original key
        else:
            _lowerCAmelCase       :   Optional[Any]  =    original_key

        _lowerCAmelCase       :   List[Any]  =    replace_key(_lowerCamelCase   )

        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:
            _lowerCAmelCase       :   Dict  =    model_state_dict[f"""{key_prefix}.{key}"""]
            print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""   )
            _lowerCAmelCase       :   Optional[int]  =    original_key

        _lowerCAmelCase       :   Union[str, Any]  =    original_key
        _lowerCAmelCase       :   Optional[Any]  =    value

    return new_dict

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase=None ,   _lowerCamelCase=None   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   Union[str, Any]  =    MODEL_MAPPING[model_name.split('/'   )[-1]]

    _lowerCAmelCase       :   Optional[Any]  =    JukeboxConfig.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    JukeboxModel(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    _lowerCAmelCase       :   Any  =    {}
    for i, dict_name in enumerate(_lowerCamelCase   ):
        _lowerCAmelCase       :   Union[str, Any]  =    torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'   )[-1]}"""   )['model']

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

        _lowerCAmelCase       :   List[str]  =    'vqvae' if i == 0 else f"""priors.{3 - i}"""
        _lowerCAmelCase       :   Tuple  =    fix_jukebox_keys(_lowerCamelCase ,   model.state_dict() ,   _lowerCamelCase ,   _lowerCamelCase   )
        weight_dict.append(_lowerCamelCase   )

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

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

    print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(_lowerCamelCase   )

    return weight_dict


if __name__ == "__main__":
 _lowerCAmelCase						=		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.""",
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





