


import argparse
import json
import os
from pathlib import Path

import requests
import torch

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


logging.set_verbosity_info()
UpperCamelCase					:       Union[str, Any]			    =     logging.get_logger(__name__)


UpperCamelCase					:       Tuple			    =     '''https://openaipublic.azureedge.net/jukebox/models/'''
UpperCamelCase					:       Dict			    =     {
    '''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 UpperCamelCase_				(				__a      )	->   List[Any]:







      if key.endswith(".model.1.bias"      ) and len(key.split("."      )      ) > 10:
            a__	:  Union[str, Any]     =   key.replace(".model.1.bias"			,				".conv1d_1.bias"      )
      elif key.endswith(".model.1.weight"      ) and len(key.split("."      )      ) > 10:
            a__	:  Any     =   key.replace(".model.1.weight"			,				".conv1d_1.weight"      )
      elif key.endswith(".model.3.bias"      ) and len(key.split("."      )      ) > 10:
            a__	:  Tuple     =   key.replace(".model.3.bias"			,				".conv1d_2.bias"      )
      elif key.endswith(".model.3.weight"      ) and len(key.split("."      )      ) > 10:
            a__	:  List[Any]     =   key.replace(".model.3.weight"			,				".conv1d_2.weight"      )

      if "conditioner_blocks.0." in key:
            a__	:  Union[str, Any]     =   key.replace("conditioner_blocks.0"			,				"conditioner_blocks"      )

      if "prime_prior" in key:
            a__	:  List[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:
            a__	:  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:
            a__	:  List[str]     =   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 UpperCamelCase_				(				__a			,				__a			,				__a			,				__a      )	->   Optional[Any]:
      a__	:  Tuple     =   {}
      import re

      a__	:  Union[str, Any]     =   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"      )
      a__	:  List[str]     =   re.compile(
          R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"      )
      a__	:  Optional[int]     =   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"      )

      a__	:  Any     =   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"      )
      a__	:  List[Any]     =   re.compile(
          R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"      )
      a__	:  Any     =   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"      )

      a__	:  Tuple     =   re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"      )
      a__	:  int     =   re.compile(
          R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"      )
      a__	:  Dict     =   re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"      )

      for original_key, value in state_dict.items():
            # rename vqvae.encoder keys
            if re_encoder_block_conv_in.fullmatch(_UpperCamelCase      ):
                  a__	:  List[Any]     =   re_encoder_block_conv_in.match(_UpperCamelCase      )
                  a__	:  str     =   regex_match.groups()
                  a__	:  Optional[Any]     =   int(groups[2]      ) * 2 + int(groups[3]      )
                  a__	:  List[Any]     =   f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
                  a__	:  List[str]     =   re_encoder_block_conv_in.sub(_UpperCamelCase			,				_UpperCamelCase      )

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

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

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

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

            elif re_decoder_block_proj_in.fullmatch(_UpperCamelCase      ):
                  a__	:  Tuple     =   re_decoder_block_proj_in.match(_UpperCamelCase      )
                  a__	:  str     =   regex_match.groups()
                  a__	:  str     =   f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
                  a__	:  Dict     =   re_decoder_block_proj_in.sub(_UpperCamelCase			,				_UpperCamelCase      )

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

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

            elif re_prior_cond_proj_in.fullmatch(_UpperCamelCase      ):
                  a__	:  Any     =   re_prior_cond_proj_in.match(_UpperCamelCase      )
                  a__	:  List[Any]     =   regex_match.groups()
                  a__	:  Any     =   f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
                  a__	:  List[Any]     =   re_prior_cond_proj_in.sub(_UpperCamelCase			,				_UpperCamelCase      )

            # keep original key
            else:
                  a__	:  Union[str, Any]     =   original_key

            a__	:  Tuple     =   replace_key(_UpperCamelCase      )

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

            # handle missmatched shape
            elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
                  a__	:  str     =   model_state_dict[f'''{key_prefix}.{key}''']
                  print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''      )
                  a__	:  Optional[Any]     =   original_key

            a__	:  List[str]     =   original_key
            a__	:  List[Any]     =   value

      return new_dict



@torch.no_grad()
def UpperCamelCase_				(				__a=None			,				__a=None      )	->   Any:







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

      a__	:  Optional[int]     =   MODEL_MAPPING[model_name.split("/"      )[-1]]

      a__	:  Any     =   JukeboxConfig.from_pretrained(_UpperCamelCase      )
      a__	:  Optional[int]     =   JukeboxModel(_UpperCamelCase      )

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

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

            a__	:  int     =   "vqvae" if i == 0 else f'''priors.{3 - i}'''
            a__	:  Union[str, Any]     =   fix_jukebox_keys(_UpperCamelCase			,				model.state_dict()			,				_UpperCamelCase			,				_UpperCamelCase      )
            weight_dict.append(_UpperCamelCase      )

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

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

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

      return weight_dict


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





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a			:			List[str]												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 3_8_4}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = do_resize
								__lowercase			       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowercase			       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowercase			       = resample
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_normalize
								__lowercase			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								if "shortest_edge" not in size:
											raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
								__lowercase			       = size['''shortest_edge''']

								if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__lowercase			       = int(shortest_edge / crop_pct    )
											__lowercase			       = get_resize_output_image_size(snake_case_			,							size=snake_case_			,							default_to_square=snake_case_    )
											__lowercase			       = resize(image=snake_case_			,							size=snake_case_			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=snake_case_			,							size=(shortest_edge, shortest_edge)			,							data_format=snake_case_			,							**snake_case_    )
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    snake_case_			,							size=(shortest_edge, shortest_edge)			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

								return rescale(snake_case_			,							scale=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								return normalize(snake_case_			,							mean=snake_case_			,							std=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      PIL.Image.Image:







								'''simple docstring'''

								__lowercase			       = do_resize if do_resize is not None else self.do_resize
								__lowercase			       = crop_pct if crop_pct is not None else self.crop_pct
								__lowercase			       = resample if resample is not None else self.resample
								__lowercase			       = do_rescale if do_rescale is not None else self.do_rescale
								__lowercase			       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase			       = do_normalize if do_normalize is not None else self.do_normalize
								__lowercase			       = image_mean if image_mean is not None else self.image_mean
								__lowercase			       = image_std if image_std is not None else self.image_std

								__lowercase			       = size if size is not None else self.size
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = make_list_of_images(snake_case_    )

								if not valid_images(snake_case_    ):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

								if do_resize and size is None or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''    )

								if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.'''    )

								if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

								if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

								# All transformations expect numpy arrays.
								__lowercase			       = [to_numpy_array(snake_case_    ) for image in images]

								if do_resize:
											__lowercase			       = [self.resize(image=snake_case_			,							size=snake_case_			,							crop_pct=snake_case_			,							resample=snake_case_    ) for image in images]

								if do_rescale:
											__lowercase			       = [self.rescale(image=snake_case_			,							scale=snake_case_    ) for image in images]

								if do_normalize:
											__lowercase			       = [self.normalize(image=snake_case_			,							mean=snake_case_			,							std=snake_case_    ) for image in images]

								__lowercase			       = [to_channel_dimension_format(snake_case_			,							snake_case_    ) for image in images]

								__lowercase			       = {'''pixel_values''': images}
								return BatchFeature(data=snake_case_			,							tensor_type=snake_case_    )


