


"""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()
__snake_case             =   logging.get_logger(__name__)


__snake_case             =   """https://openaipublic.azureedge.net/jukebox/models/"""
__snake_case             =   {
    """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						(						lowercase  :  Any			)     ->						str:


  """simple docstring"""





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

  if "conditioner_blocks.0." in key:
    snake_case   :				List[str]				 =   key.replace("conditioner_blocks.0"						,							"conditioner_blocks"			)

  if "prime_prior" in key:
    snake_case   :				Dict				 =   key.replace("prime_prior"						,							"encoder"			)

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    snake_case   :				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:
    snake_case   :				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						(						lowercase  :  int						,							lowercase  :  List[str]						,							lowercase  :  List[Any]						,							lowercase  :  List[Any]			)     ->						Optional[Any]:


  """simple docstring"""





  snake_case   :				Dict				 =   {}
  import re

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

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

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

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

    elif re_encoder_block_proj_out.fullmatch(lowercase			):
      snake_case   :				List[str]				 =   re_encoder_block_proj_out.match(lowercase			)
      snake_case   :				Dict				 =   regex_match.groups()
      snake_case   :				List[str]				 =   F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
      snake_case   :				Optional[int]				 =   re_encoder_block_proj_out.sub(lowercase						,							lowercase			)

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

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

    elif re_decoder_block_proj_in.fullmatch(lowercase			):
      snake_case   :				List[str]				 =   re_decoder_block_proj_in.match(lowercase			)
      snake_case   :				Tuple				 =   regex_match.groups()
      snake_case   :				Tuple				 =   F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
      snake_case   :				Optional[int]				 =   re_decoder_block_proj_in.sub(lowercase						,							lowercase			)

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

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

    elif re_prior_cond_proj_in.fullmatch(lowercase			):
      snake_case   :				str				 =   re_prior_cond_proj_in.match(lowercase			)
      snake_case   :				Tuple				 =   regex_match.groups()
      snake_case   :				List[Any]				 =   F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
      snake_case   :				int				 =   re_prior_cond_proj_in.sub(lowercase						,							lowercase			)

    # keep original key
    else:
      snake_case   :				int				 =   original_key

    snake_case   :				Any				 =   replace_key(lowercase			)

    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:
      snake_case   :				Union[str, Any]				 =   model_state_dict[F'{key_prefix}.{key}']
      print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'			)
      snake_case   :				str				 =   original_key

    snake_case   :				Optional[int]				 =   original_key
    snake_case   :				Any				 =   value

  return new_dict







@torch.no_grad()
def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]=None						,							lowercase  :  List[str]=None			)     ->						Any:


  """simple docstring"""





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

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

  snake_case   :				Optional[int]				 =   JukeboxConfig.from_pretrained(lowercase			)
  snake_case   :				Tuple				 =   JukeboxModel(lowercase			)

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

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

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

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

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

  print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
  model.save_pretrained(lowercase			)

  return weight_dict


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









"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase			:	str							  =  '''LayoutLMv2ImageProcessor'''
   __UpperCAmelCase			:	Dict							  =  ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."						,     UpperCamelCase__						,     )
           snake_case   :				Dict				 =   kwargs.pop("feature_extractor"						)

       snake_case   :				int				 =   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(UpperCamelCase__						,     UpperCamelCase__						)







   def __call__(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		BatchEncoding:

       '''simple docstring'''




       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes "
               "if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       snake_case   :				Any				 =   self.image_processor(images=UpperCamelCase__						,     return_tensors=UpperCamelCase__						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				Optional[int]				 =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
           snake_case   :				Optional[Any]				 =   features["words"]

       snake_case   :				Dict				 =   self.tokenizer(
           text=text if text is not None else features["words"]						,     text_pair=text_pair if text_pair is not None else None						,     boxes=boxes if boxes is not None else features["boxes"]						,     word_labels=UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						,     padding=UpperCamelCase__						,     truncation=UpperCamelCase__						,     max_length=UpperCamelCase__						,     stride=UpperCamelCase__						,     pad_to_multiple_of=UpperCamelCase__						,     return_token_type_ids=UpperCamelCase__						,     return_attention_mask=UpperCamelCase__						,     return_overflowing_tokens=UpperCamelCase__						,     return_special_tokens_mask=UpperCamelCase__						,     return_offsets_mapping=UpperCamelCase__						,     return_length=UpperCamelCase__						,     verbose=UpperCamelCase__						,     return_tensors=UpperCamelCase__						,     **UpperCamelCase__						,     )

       # add pixel values
       snake_case   :				Dict				 =   features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           snake_case   :				Any				 =   self.get_overflowing_images(UpperCamelCase__						,     encoded_inputs["overflow_to_sample_mapping"]						)
       snake_case   :				str				 =   images

       return encoded_inputs







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(UpperCamelCase__						) != len(UpperCamelCase__						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F' {len(UpperCamelCase__						)} and {len(UpperCamelCase__						)}'						)

       return images_with_overflow







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       return self.tokenizer.batch_decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       return self.tokenizer.decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   @property
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       return ["input_ids", "bbox", "attention_mask", "image"]







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,     UpperCamelCase__						,     )
       return self.image_processor_class







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,     UpperCamelCase__						,     )
       return self.image_processor





