

"""simple docstring"""






from typing import Optional, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, 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_mobilenet_va import MobileNetVaConfig


_lowerCAmelCase						=		logging.get_logger(__name__)


# General docstring
_lowerCAmelCase						=		"""MobileNetV1Config"""

# Base docstring
_lowerCAmelCase						=		"""google/mobilenet_v1_1.0_224"""
_lowerCAmelCase						=		[1, 1_0_2_4, 7, 7]

# Image classification docstring
_lowerCAmelCase						=		"""google/mobilenet_v1_1.0_224"""
_lowerCAmelCase						=		"""tabby, tabby cat"""


_lowerCAmelCase						=		[
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {}

    if isinstance(_lowerCamelCase ,   _lowerCamelCase   ):
        _lowerCAmelCase       :   Tuple  =    model.mobilenet_va
    else:
        _lowerCAmelCase       :   Dict  =    model

    _lowerCAmelCase       :   Optional[int]  =    'MobilenetV1/Conv2d_0/'
    _lowerCAmelCase       :   Optional[int]  =    backbone.conv_stem.convolution.weight
    _lowerCAmelCase       :   Any  =    backbone.conv_stem.normalization.bias
    _lowerCAmelCase       :   List[Any]  =    backbone.conv_stem.normalization.weight
    _lowerCAmelCase       :   int  =    backbone.conv_stem.normalization.running_mean
    _lowerCAmelCase       :   Union[str, Any]  =    backbone.conv_stem.normalization.running_var

    for i in range(13   ):
        _lowerCAmelCase       :   int  =    i + 1
        _lowerCAmelCase       :   Optional[Any]  =    i * 2

        _lowerCAmelCase       :   Optional[Any]  =    backbone.layer[pt_index]
        _lowerCAmelCase       :   int  =    f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
        _lowerCAmelCase       :   str  =    pointer.convolution.weight
        _lowerCAmelCase       :   Optional[Any]  =    pointer.normalization.bias
        _lowerCAmelCase       :   Union[str, Any]  =    pointer.normalization.weight
        _lowerCAmelCase       :   int  =    pointer.normalization.running_mean
        _lowerCAmelCase       :   int  =    pointer.normalization.running_var

        _lowerCAmelCase       :   Optional[int]  =    backbone.layer[pt_index + 1]
        _lowerCAmelCase       :   str  =    f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
        _lowerCAmelCase       :   List[Any]  =    pointer.convolution.weight
        _lowerCAmelCase       :   List[Any]  =    pointer.normalization.bias
        _lowerCAmelCase       :   str  =    pointer.normalization.weight
        _lowerCAmelCase       :   Optional[Any]  =    pointer.normalization.running_mean
        _lowerCAmelCase       :   Optional[int]  =    pointer.normalization.running_var

    if isinstance(_lowerCamelCase ,   _lowerCamelCase   ):
        _lowerCAmelCase       :   Optional[Any]  =    'MobilenetV1/Logits/Conv2d_1c_1x1/'
        _lowerCAmelCase       :   Tuple  =    model.classifier.weight
        _lowerCAmelCase       :   Dict  =    model.classifier.bias

    return tf_to_pt_map

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    try:
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
            'https://www.tensorflow.org/install/ for installation instructions.'   )
        raise

    # Load weights from TF model
    _lowerCAmelCase       :   int  =    tf.train.list_variables(_lowerCamelCase   )
    _lowerCAmelCase       :   Dict  =    {}
    for name, shape in init_vars:
        logger.info(f"""Loading TF weight {name} with shape {shape}"""   )
        _lowerCAmelCase       :   Any  =    tf.train.load_variable(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   List[str]  =    array

    # Build TF to PyTorch weights loading map
    _lowerCAmelCase       :   str  =    _build_tf_to_pytorch_map(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

    for name, pointer in tf_to_pt_map.items():
        logger.info(f"""Importing {name}"""   )
        if name not in tf_weights:
            logger.info(f"""{name} not in tf pre-trained weights, skipping"""   )
            continue

        _lowerCAmelCase       :   Dict  =    tf_weights[name]

        if "depthwise_weights" in name:
            logger.info('Transposing depthwise'   )
            _lowerCAmelCase       :   str  =    np.transpose(_lowerCamelCase ,   (2, 3, 0, 1)   )
        elif "weights" in name:
            logger.info('Transposing'   )
            if len(pointer.shape   ) == 2:  # copying into linear layer
                _lowerCAmelCase       :   Dict  =    array.squeeze().transpose()
            else:
                _lowerCAmelCase       :   List[str]  =    np.transpose(_lowerCamelCase ,   (3, 2, 0, 1)   )

        if pointer.shape != array.shape:
            raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched"""   )

        logger.info(f"""Initialize PyTorch weight {name} {array.shape}"""   )
        _lowerCAmelCase       :   Any  =    torch.from_numpy(_lowerCamelCase   )

        tf_weights.pop(_lowerCamelCase ,   _lowerCamelCase   )
        tf_weights.pop(name + '/RMSProp' ,   _lowerCamelCase   )
        tf_weights.pop(name + '/RMSProp_1' ,   _lowerCamelCase   )
        tf_weights.pop(name + '/ExponentialMovingAverage' ,   _lowerCamelCase   )

    logger.info(f"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys()   )}"""   )
    return model

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    features.shape[-2:]
    _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    conv_layer.stride
    _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    conv_layer.kernel_size

    if in_height % stride_height == 0:
        _lowerCAmelCase       :   Dict  =    max(kernel_height - stride_height ,   0   )
    else:
        _lowerCAmelCase       :   Dict  =    max(kernel_height - (in_height % stride_height) ,   0   )

    if in_width % stride_width == 0:
        _lowerCAmelCase       :   List[str]  =    max(kernel_width - stride_width ,   0   )
    else:
        _lowerCAmelCase       :   Optional[Any]  =    max(kernel_width - (in_width % stride_width) ,   0   )

    _lowerCAmelCase       :   Union[str, Any]  =    pad_along_width // 2
    _lowerCAmelCase       :   str  =    pad_along_width - pad_left
    _lowerCAmelCase       :   List[str]  =    pad_along_height // 2
    _lowerCAmelCase       :   Optional[int]  =    pad_along_height - pad_top

    _lowerCAmelCase       :   int  =    (pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(_lowerCamelCase ,   _lowerCamelCase ,   'constant' ,   0.0   )







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A = 1    ,_A = 1    ,_A = False    ,_A = True    ,_A = True    ,):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Any  =    config

      if in_channels % groups != 0:
          raise ValueError(F"""Input channels ({in_channels}) are not divisible by {groups} groups."""      )
      if out_channels % groups != 0:
          raise ValueError(F"""Output channels ({out_channels}) are not divisible by {groups} groups."""      )

      _lowerCAmelCase       :   Union[str, Any]  =    0 if config.tf_padding else int((kernel_size - 1) / 2      )

      _lowerCAmelCase       :   Optional[Any]  =    nn.Convad(
          in_channels=_A    ,out_channels=_A    ,kernel_size=_A    ,stride=_A    ,padding=_A    ,groups=_A    ,bias=_A    ,padding_mode='zeros'    ,)

      if use_normalization:
          _lowerCAmelCase       :   Union[str, Any]  =    nn.BatchNormad(
              num_features=_A    ,eps=config.layer_norm_eps    ,momentum=0.9_9_9_7    ,affine=_A    ,track_running_stats=_A    ,)
      else:
          _lowerCAmelCase       :   Dict  =    None

      if use_activation:
          if isinstance(_A    ,_A      ):
              _lowerCAmelCase       :   Optional[int]  =    ACTaFN[use_activation]
          elif isinstance(config.hidden_act    ,_A      ):
              _lowerCAmelCase       :   List[Any]  =    ACTaFN[config.hidden_act]
          else:
              _lowerCAmelCase       :   Dict  =    config.hidden_act
      else:
          _lowerCAmelCase       :   Any  =    None







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

      if self.config.tf_padding:
          _lowerCAmelCase       :   Any  =    apply_tf_padding(_A    ,self.convolution      )
      _lowerCAmelCase       :   int  =    self.convolution(_A      )
      if self.normalization is not None:
          _lowerCAmelCase       :   Tuple  =    self.normalization(_A      )
      if self.activation is not None:
          _lowerCAmelCase       :   Optional[int]  =    self.activation(_A      )
      return features







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		MobileNetVaConfig
  _UpperCAmelCase          =		load_tf_weights_in_mobilenet_va
  _UpperCAmelCase          =		"mobilenet_v1"
  _UpperCAmelCase          =		"pixel_values"
  _UpperCAmelCase          =		False

  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.BatchNormad      ):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0      )





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

    Parameters:
        config ([`MobileNetV1Config`]): 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
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""







@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."							,							a__							,							)
class 	__UpperCamelCase    (    a__						):

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

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

      _lowerCAmelCase       :   Any  =    32
      _lowerCAmelCase       :   List[Any]  =    max(int(depth * config.depth_multiplier      )    ,config.min_depth      )

      _lowerCAmelCase       :   Any  =    MobileNetVaConvLayer(
          _A    ,in_channels=config.num_channels    ,out_channels=_A    ,kernel_size=3    ,stride=2    ,)

      _lowerCAmelCase       :   List[str]  =    [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

      _lowerCAmelCase       :   Tuple  =    nn.ModuleList()
      for i in range(13      ):
          _lowerCAmelCase       :   Any  =    out_channels

          if strides[i] == 2 or i == 0:
              depth *= 2
              _lowerCAmelCase       :   str  =    max(int(depth * config.depth_multiplier      )    ,config.min_depth      )

          self.layer.append(
              MobileNetVaConvLayer(
                  _A    ,in_channels=_A    ,out_channels=_A    ,kernel_size=3    ,stride=strides[i]    ,groups=_A    ,)      )

          self.layer.append(
              MobileNetVaConvLayer(
                  _A    ,in_channels=_A    ,out_channels=_A    ,kernel_size=1    ,)      )

      _lowerCAmelCase       :   Optional[Any]  =    nn.AdaptiveAvgPoolad((1, 1)      ) if add_pooling_layer else None

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

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

      raise NotImplementedError







  @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       :   Tuple  =    (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      _lowerCAmelCase       :   Union[str, Any]  =    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       :   Any  =    self.conv_stem(_A      )

      _lowerCAmelCase       :   Optional[int]  =    () if output_hidden_states else None

      for i, layer_module in enumerate(self.layer      ):
          _lowerCAmelCase       :   Tuple  =    layer_module(_A      )

          if output_hidden_states:
              _lowerCAmelCase       :   int  =    all_hidden_states + (hidden_states,)

      _lowerCAmelCase       :   Dict  =    hidden_states

      if self.pooler is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    torch.flatten(self.pooler(_A      )    ,start_dim=1      )
      else:
          _lowerCAmelCase       :   Tuple  =    None

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

      return BaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=_A    ,pooler_output=_A    ,hidden_states=_A    ,)







@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "							,							a__							,							)
class 	__UpperCamelCase    (    a__						):

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

      super().__init__(_A      )

      _lowerCAmelCase       :   Dict  =    config.num_labels
      _lowerCAmelCase       :   List[str]  =    MobileNetVaModel(_A      )

      _lowerCAmelCase       :   List[str]  =    self.mobilenet_va.layer[-1].convolution.out_channels

      # Classifier head
      _lowerCAmelCase       :   Dict  =    nn.Dropout(config.classifier_dropout_prob    ,inplace=_A      )
      _lowerCAmelCase       :   List[str]  =    nn.Linear(_A    ,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       :   Any  =    self.mobilenet_va(_A    ,output_hidden_states=_A    ,return_dict=_A      )

      _lowerCAmelCase       :   int  =    outputs.pooler_output if return_dict else outputs[1]

      _lowerCAmelCase       :   Optional[int]  =    self.classifier(self.dropout(_A      )      )

      _lowerCAmelCase       :   Dict  =    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       :   List[Any]  =    'single_label_classification'
              else:
                  _lowerCAmelCase       :   Tuple  =    'multi_label_classification'

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

      if not return_dict:
          _lowerCAmelCase       :   Union[str, 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)





