

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()
__A			:					str						=			logging.get_logger(__name__)


__A			:					str						=			'https://openaipublic.azureedge.net/jukebox/models/'
__A			:					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 	__a     (      A__			:  Union[str, Any]       ):
    if key.endswith(".model.1.bias"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.1.bias"			,  ".conv1d_1.bias"       )
    elif key.endswith(".model.1.weight"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.1.weight"			,  ".conv1d_1.weight"       )
    elif key.endswith(".model.3.bias"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.3.bias"			,  ".conv1d_2.bias"       )
    elif key.endswith(".model.3.weight"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.3.weight"			,  ".conv1d_2.weight"       )

    if "conditioner_blocks.0." in key:
        SCREAMING_SNAKE_CASE										=   key.replace("conditioner_blocks.0"			,  "conditioner_blocks"       )

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

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        SCREAMING_SNAKE_CASE										=   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:
        SCREAMING_SNAKE_CASE										=   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 	__a     (      A__			:  List[str]			,  A__			:  Optional[Any]			,  A__			:  List[Any]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   {}
    import re

    SCREAMING_SNAKE_CASE										=   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
    SCREAMING_SNAKE_CASE										=   re.compile(
        R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
    SCREAMING_SNAKE_CASE										=   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

    SCREAMING_SNAKE_CASE										=   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
    SCREAMING_SNAKE_CASE										=   re.compile(
        R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
    SCREAMING_SNAKE_CASE										=   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

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

        elif re_encoder_block_resnet.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_encoder_block_resnet.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   int(groups[2]       ) * 2 + int(groups[3]       )
            SCREAMING_SNAKE_CASE										=   {"1": 1, "3": 2}[groups[-2]]
            SCREAMING_SNAKE_CASE										=   F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
            SCREAMING_SNAKE_CASE										=   F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   prefix + resnet_block
            SCREAMING_SNAKE_CASE										=   re_encoder_block_resnet.sub(A__			,  A__       )

        elif re_encoder_block_proj_out.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_encoder_block_proj_out.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   re_encoder_block_proj_out.sub(A__			,  A__       )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_decoder_block_conv_out.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   int(groups[2]       ) * 2 + int(groups[3]       ) - 2
            SCREAMING_SNAKE_CASE										=   F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   re_decoder_block_conv_out.sub(A__			,  A__       )

        elif re_decoder_block_resnet.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_decoder_block_resnet.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   int(groups[2]       ) * 2 + int(groups[3]       ) - 2
            SCREAMING_SNAKE_CASE										=   {"1": 1, "3": 2}[groups[-2]]
            SCREAMING_SNAKE_CASE										=   F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
            SCREAMING_SNAKE_CASE										=   F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   prefix + resnet_block
            SCREAMING_SNAKE_CASE										=   re_decoder_block_resnet.sub(A__			,  A__       )

        elif re_decoder_block_proj_in.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_decoder_block_proj_in.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   re_decoder_block_proj_in.sub(A__			,  A__       )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_prior_cond_conv_out.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   int(groups[1]       ) * 2 + int(groups[2]       ) - 2
            SCREAMING_SNAKE_CASE										=   F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   re_prior_cond_conv_out.sub(A__			,  A__       )

        elif re_prior_cond_resnet.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_prior_cond_resnet.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   int(groups[1]       ) * 2 + int(groups[2]       ) - 2
            SCREAMING_SNAKE_CASE										=   {"1": 1, "3": 2}[groups[-2]]
            SCREAMING_SNAKE_CASE										=   F"conditioner_blocks.upsampler.upsample_block.{block_index}."
            SCREAMING_SNAKE_CASE										=   F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   prefix + resnet_block
            SCREAMING_SNAKE_CASE										=   re_prior_cond_resnet.sub(A__			,  A__       )

        elif re_prior_cond_proj_in.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_prior_cond_proj_in.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   re_prior_cond_proj_in.sub(A__			,  A__       )

        # keep original key
        else:
            SCREAMING_SNAKE_CASE										=   original_key

        SCREAMING_SNAKE_CASE										=   replace_key(A__       )

        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:
            SCREAMING_SNAKE_CASE										=   model_state_dict[F"{key_prefix}.{key}"]
            print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"       )
            SCREAMING_SNAKE_CASE										=   original_key

        SCREAMING_SNAKE_CASE										=   original_key
        SCREAMING_SNAKE_CASE										=   value

    return new_dict





@torch.no_grad()
def 	__a     (      A__			:  Tuple=None			,  A__			:  List[Any]=None       ):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"       ):
            SCREAMING_SNAKE_CASE										=   requests.get(F"{PREFIX}{file}"			,  allow_redirects=A__       )
            os.makedirs(F"{pytorch_dump_folder_path}/"			,  exist_ok=A__       )
            open(F"{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"			,  "wb"       ).write(r.content       )

    SCREAMING_SNAKE_CASE										=   MODEL_MAPPING[model_name.split("/"       )[-1]]

    SCREAMING_SNAKE_CASE										=   JukeboxConfig.from_pretrained(A__       )
    SCREAMING_SNAKE_CASE										=   JukeboxModel(A__       )

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

        SCREAMING_SNAKE_CASE										=   {}
        for k in old_dic.keys():
            if k.endswith(".b"       ):
                SCREAMING_SNAKE_CASE										=   old_dic[k]
            elif k.endswith(".w"       ):
                SCREAMING_SNAKE_CASE										=   old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                SCREAMING_SNAKE_CASE										=   old_dic[k]
            else:
                SCREAMING_SNAKE_CASE										=   old_dic[k]

        SCREAMING_SNAKE_CASE										=   "vqvae" if i == 0 else F"priors.{3 - i}"
        SCREAMING_SNAKE_CASE										=   fix_jukebox_keys(A__			,  model.state_dict()			,  A__			,  A__       )
        weight_dict.append(A__       )

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

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

    print(F"Saving model {model_name} to {pytorch_dump_folder_path}"       )
    model.save_pretrained(A__       )

    return weight_dict


if __name__ == "__main__":
    __A			:					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.',
    )
    __A			:					List[Any]						=			parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase						:  Tuple									=			logging.get_logger(__name__)


_UpperCAmelCase						:  Optional[int]									=			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_UpperCAmelCase						:  Union[str, Any]									=			{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

_UpperCAmelCase						:  Union[str, Any]									=			{"""facebook/blenderbot_small-90M""": 512}





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       set()
							snake_case_     							=       word[0]
							for char in word[1:]:
														pairs.add((prev_char, char) )
														snake_case_     							=       char

							snake_case_     							=       set(UpperCamelCase__ )
							return pairs







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE   :					Any			     =     PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE   :					str			     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     ['''input_ids''', '''attention_mask''']




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case="__start__" ,  snake_case="__end__" ,  snake_case="__unk__" ,  snake_case="__null__" ,  **snake_case ,  ):
											super().__init__(unk_token=snake_case ,  bos_token=snake_case ,  eos_token=snake_case ,  pad_token=snake_case ,  **snake_case			)

											with open(snake_case ,  encoding='utf-8'			) as vocab_handle:
																		snake_case_     							=       json.load(snake_case			)
											snake_case_     							=       {v: k for k, v in self.encoder.items()}
											with open(snake_case ,  encoding='utf-8'			) as merges_handle:
																		snake_case_     							=       merges_handle.read().split('\n'			)[1:-1]
											snake_case_     							=       [tuple(merge.split()			) for merge in merges]
											snake_case_     							=       dict(zip(snake_case ,  range(len(snake_case			)			)			)			)
											snake_case_     							=       {}




				@property
				def        a				(   self			):
											return len(self.encoder			)




				def        a				(   self			):
											return dict(self.encoder ,  **self.added_tokens_encoder			)




				def        a				(   self ,  snake_case			):
											if token in self.cache:
																		return self.cache[token]
											snake_case_     							=       re.sub('([.,!?()])' ,  R' \1' ,  snake_case			)
											snake_case_     							=       re.sub('(\')' ,  R' \1 ' ,  snake_case			)
											snake_case_     							=       re.sub(R'\s{2,}' ,  ' ' ,  snake_case			)
											if "\n" in token:
																		snake_case_     							=       token.replace('\n' ,  ' __newln__'			)

											snake_case_     							=       token.split(' '			)
											snake_case_     							=       []
											for token in tokens:
																		if not len(snake_case			):
																									continue

																		snake_case_     							=       token.lower()
																		snake_case_     							=       tuple(snake_case			)
																		snake_case_     							=       tuple(list(word[:-1]			) + [word[-1] + '</w>']			)
																		snake_case_     							=       get_pairs(snake_case			)

																		if not pairs:
																									words.append(snake_case			)
																									continue

																		while True:
																									snake_case_     							=       min(snake_case ,  key=lambda snake_case			: self.bpe_ranks.get(snake_case ,  float('inf'			)			)			)
																									if bigram not in self.bpe_ranks:
																																break
																									snake_case_							,       snake_case_     							=       bigram
																									snake_case_     							=       []
																									snake_case_     							=       0

																									while i < len(snake_case			):
																																try:
																																							snake_case_     							=       word.index(snake_case ,  snake_case			)
																																							new_word.extend(word[i:j]			)
																																							snake_case_     							=       j
																																except ValueError:
																																							new_word.extend(word[i:]			)
																																							break

																																if word[i] == first and i < len(snake_case			) - 1 and word[i + 1] == second:
																																							new_word.append(first + second			)
																																							i += 2
																																else:
																																							new_word.append(word[i]			)
																																							i += 1
																									snake_case_     							=       tuple(snake_case			)
																									snake_case_     							=       new_word
																									if len(snake_case			) == 1:
																																break
																									else:
																																snake_case_     							=       get_pairs(snake_case			)
																		snake_case_     							=       '@@ '.join(snake_case			)
																		snake_case_     							=       word[:-4]

																		snake_case_     							=       word
																		words.append(snake_case			)
											return " ".join(snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       []

											snake_case_     							=       re.findall(R'\S+\n?' ,  snake_case			)

											for token in words:
																		split_tokens.extend(list(self.bpe(snake_case			).split(' '			)			)			)
											return split_tokens




				def        a				(   self ,  snake_case			):
											snake_case_     							=       token.lower()
											return self.encoder.get(snake_case ,  self.encoder.get(self.unk_token			)			)




				def        a				(   self ,  snake_case			):
											return self.decoder.get(snake_case ,  self.unk_token			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       ' '.join(snake_case			).replace('@@ ' ,  ''			).strip()
											return out_string




				def        a				(   self ,  snake_case ,  snake_case = None			):
											if not os.path.isdir(snake_case			):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
																		return
											snake_case_     							=       os.path.join(
											    snake_case ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
											snake_case_     							=       os.path.join(
											    snake_case ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

											with open(snake_case ,  'w' ,  encoding='utf-8'			) as f:
																		f.write(json.dumps(self.encoder ,  indent=2 ,  sort_keys=snake_case ,  ensure_ascii=snake_case			) + '\n'			)

											snake_case_     							=       0
											with open(snake_case ,  'w' ,  encoding='utf-8'			) as writer:
																		writer.write('#version: 0.2\n'			)
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,  key=lambda snake_case			: kv[1]			):
																									if index != token_index:
																																logger.warning(
																																    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																																    ' Please check that the tokenizer is not corrupted!'			)
																																snake_case_     							=       token_index
																									writer.write(' '.join(snake_case			) + '\n'			)
																									index += 1

											return vocab_file, merge_file




