


import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


SCREAMING_SNAKE_CASE_:Optional[Any]					=						re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
SCREAMING_SNAKE_CASE_:Optional[int]					=						10
SCREAMING_SNAKE_CASE_:Dict					=						256




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    if len(a__		) < MIN_NUM_TOKENS:
        return None
    A			:	Optional[int]										=       MinHash(num_perm=a__		)
    for token in set(a__		):
        min_hash.update(token.encode()		)
    return min_hash




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  int:



    """simple docstring"""






    return {t for t in NON_ALPHA.split(a__		) if len(t.strip()		) > 0}






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		*,
        lowerCamelCase__ = 0.85,		):
        A			:	Dict										=       duplication_jaccard_threshold
        A			:	Union[str, Any]										=       NUM_PERM
        A			:	Optional[Any]										=       MinHashLSH(threshold=self._duplication_jaccard_threshold,		num_perm=self._num_perm		)

        A			:	int										=       defaultdict(_lowerCamelCase		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Dict										=       self._index.query(_lowerCamelCase		)
        if code_key in self._index.keys:
            print(f'''Duplicate key {code_key}'''		)
            return

        self._index.insert(_lowerCamelCase,		_lowerCamelCase		)
        if len(_lowerCamelCase		) > 0:
            for base_duplicate in close_duplicates:
                if base_duplicate in self._duplicate_clusters:
                    self._duplicate_clusters[base_duplicate].add(_lowerCamelCase		)
                    break
            else:
                self._duplicate_clusters[close_duplicates[0]].add(_lowerCamelCase		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       []
        for base, duplicates in self._duplicate_clusters.items():
            A			:	Optional[Any]										=       [base] + list(_lowerCamelCase		)
            # reformat the cluster to be a list of dict
            A			:	Optional[Any]										=       [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
            duplicate_clusters.append(_lowerCamelCase		)
        return duplicate_clusters






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	List[Any]										=       self.get_duplicate_clusters()
        with open(_lowerCamelCase,		"""w"""		) as f:
            json.dump(_lowerCamelCase,		_lowerCamelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Any										=       element
    A			:	Dict										=       get_min_hash([t for t in NON_ALPHA.split(data["""content"""]		) if len(t.strip()		) > 0]		)
    if min_hash is not None:
        return (index, data["repo_name"], data["path"]), min_hash




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash				,					ThreadedIterator(a__				,					max_queue_size=1_0000		)				,					chunksize=100				,					):
            if data is not None:
                yield data




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Tuple										=       DuplicationIndex(duplication_jaccard_threshold=a__		)

    for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a__		)		)				,					max_queue_size=100		)		):
        di.add(a__				,					a__		)

    # Returns a List[Cluster] where Cluster is List[str] with the filenames.
    return di.get_duplicate_clusters()




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    A			:	Any										=       get_tokens(a__		)
    A			:	Union[str, Any]										=       get_tokens(a__		)
    return len(tokensa & tokensa		) / len(tokensa | tokensa		)


SCREAMING_SNAKE_CASE_:List[str]					=						None




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[Any]:



    """simple docstring"""






    A			:	List[Any]										=       []
    for elementa in cluster:
        A			:	List[Any]										=       _shared_dataset[elementa['''base_index''']]['''content''']
        for elementa in extremes:
            A			:	str										=       _shared_dataset[elementa['''base_index''']]['''content''']
            if jaccard_similarity(a__				,					a__		) >= jaccard_threshold:
                elementa["copies"] += 1
                break
        else:
            A			:	Optional[Any]										=       1
            extremes.append(a__		)
    return extremes




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    global _shared_dataset
    A			:	Any										=       dataset
    A			:	List[Any]										=       []
    A			:	str										=       partial(_find_cluster_extremes_shared				,					jaccard_threshold=a__		)
    with mp.Pool() as pool:
        for extremes in tqdm(
            pool.imap_unordered(
                a__				,					a__				,					)				,					total=len(a__		)				,					):
            extremes_list.append(a__		)
    return extremes_list




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase = 0.85		)	->  Any:



    """simple docstring"""






    A			:	str										=       make_duplicate_clusters(a__				,					a__		)
    A			:	Optional[Any]										=       {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
    A			:	List[Any]										=       {}
    A			:	Tuple										=       find_extremes(a__				,					a__				,					a__		)
    for extremes in extremes_clusters:
        for element in extremes:
            A			:	Optional[int]										=       element
    A			:	int										=       duplicate_indices - set(extreme_dict.keys()		)
    A			:	Dict										=       dataset.filter(lambda _lowerCAmelCase				,					_lowerCAmelCase		: idx not in remove_indices				,					with_indices=a__		)

    # update duplicate_clusters
    for cluster in duplicate_clusters:
        for element in cluster:
            A			:	Optional[Any]										=       element['''base_index'''] in extreme_dict
            if element["is_extreme"]:
                A			:	Union[str, Any]										=       extreme_dict[element['''base_index''']]['''copies''']

    print(f'''Original dataset size: {len(a__		)}'''		)
    print(f'''Number of duplicate clusters: {len(a__		)}'''		)
    print(f'''Files in duplicate cluster: {len(a__		)}'''		)
    print(f'''Unique files in duplicate cluster: {len(a__		)}'''		)
    print(f'''Filtered dataset size: {len(a__		)}'''		)

    return ds_filter, duplicate_clusters







import argparse
import json
import os
from pathlib import Path

import requests
import torch

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


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


SCREAMING_SNAKE_CASE_:Union[str, Any]					=						"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """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     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






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

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

    if "prime_prior" in key:
        A			:	Tuple										=       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			:	List[str]										=       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			:	Optional[int]										=       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     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	List[str]										=       {}
    import re

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

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

    A			:	Optional[Any]										=       re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""		)
    A			:	Tuple										=       re.compile(
        R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	List[Any]										=       re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"""		)

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

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

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

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

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

        elif re_decoder_block_proj_in.fullmatch(_lowerCAmelCase		):
            A			:	Optional[Any]										=       re_decoder_block_proj_in.match(_lowerCAmelCase		)
            A			:	Any										=       regex_match.groups()
            A			:	Optional[Any]										=       f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
            A			:	Dict										=       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		):
            A			:	Optional[int]										=       re_prior_cond_conv_out.match(_lowerCAmelCase		)
            A			:	List[Any]										=       regex_match.groups()
            A			:	List[Any]										=       int(groups[1]		) * 2 + int(groups[2]		) - 2
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
            A			:	List[str]										=       re_prior_cond_conv_out.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

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

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

        # keep original key
        else:
            A			:	str										=       original_key

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

        A			:	Union[str, Any]										=       original_key
        A			:	List[str]										=       value

    return new_dict




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase=None				,					_lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"		)[-1]}'''		):
            A			:	Optional[Any]										=       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		)

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

    A			:	List[Any]										=       JukeboxConfig.from_pretrained(_lowerCAmelCase		)
    A			:	Dict										=       JukeboxModel(_lowerCAmelCase		)

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

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

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

    A			:	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__":
     SCREAMING_SNAKE_CASE_:List[Any]					=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""jukebox-5b-lyrics""",
         type=str,
         help="""Name of the model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""jukebox-5b-lyrics-converted""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     SCREAMING_SNAKE_CASE_:int					=						parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


