



'''simple docstring'''





import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class 						UpperCAmelCase_					( a     ,      unittest.TestCase):
							lowerCamelCase__         =	BertTokenizer
							lowerCamelCase__         =	BertTokenizerFast
							lowerCamelCase__         =	True
							lowerCamelCase__         =	True
							lowerCamelCase__         =	filter_non_english







							def      snake_case__   (     self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase  :							Tuple				     =					[
														    "[UNK]",
														    "[CLS]",
														    "[SEP]",
														    "[PAD]",
														    "[MASK]",
														    "want",
														    "##want",
														    "##ed",
														    "wa",
														    "un",
														    "runn",
														    "##ing",
														    ",",
														    "low",
														    "lowest",
														]
														_lowerCAmelCase  :							Dict				     =					os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES["vocab_file"])
														with open(self.vocab_file,    "w",    encoding="utf-8") as vocab_writer:
																					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					"UNwant\u00E9d,running"
														_lowerCAmelCase  :							Dict				     =					"unwanted, running"
														return input_text, output_text







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					self.tokenizer_class(self.vocab_file)

														_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer.tokenize("UNwant\u00E9d,running")
														self.assertListEqual(__a,    ["un", "##want", "##ed", ",", "runn", "##ing"])
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),    [9, 6, 7, 12, 10, 11])







							def      snake_case__   (     self):

														'''simple docstring'''

														if not self.test_rust_tokenizer:
																					return

														_lowerCAmelCase  :							List[str]				     =					self.get_tokenizer()
														_lowerCAmelCase  :							Tuple				     =					self.get_rust_tokenizer()

														_lowerCAmelCase  :							Optional[int]				     =					"UNwant\u00E9d,running"

														_lowerCAmelCase  :							List[str]				     =					tokenizer.tokenize(__a)
														_lowerCAmelCase  :							Dict				     =					rust_tokenizer.tokenize(__a)
														self.assertListEqual(__a,    __a)

														_lowerCAmelCase  :							List[str]				     =					tokenizer.encode(__a,    add_special_tokens=__a)
														_lowerCAmelCase  :							str				     =					rust_tokenizer.encode(__a,    add_special_tokens=__a)
														self.assertListEqual(__a,    __a)

														_lowerCAmelCase  :							Optional[Any]				     =					self.get_rust_tokenizer()
														_lowerCAmelCase  :							Optional[int]				     =					tokenizer.encode(__a)
														_lowerCAmelCase  :							Any				     =					rust_tokenizer.encode(__a)
														self.assertListEqual(__a,    __a)

														# With lower casing
														_lowerCAmelCase  :							Optional[Any]				     =					self.get_tokenizer(do_lower_case=__a)
														_lowerCAmelCase  :							Dict				     =					self.get_rust_tokenizer(do_lower_case=__a)

														_lowerCAmelCase  :							Union[str, Any]				     =					"UNwant\u00E9d,running"

														_lowerCAmelCase  :							Optional[Any]				     =					tokenizer.tokenize(__a)
														_lowerCAmelCase  :							List[str]				     =					rust_tokenizer.tokenize(__a)
														self.assertListEqual(__a,    __a)

														_lowerCAmelCase  :							List[str]				     =					tokenizer.encode(__a,    add_special_tokens=__a)
														_lowerCAmelCase  :							List[str]				     =					rust_tokenizer.encode(__a,    add_special_tokens=__a)
														self.assertListEqual(__a,    __a)

														_lowerCAmelCase  :							List[Any]				     =					self.get_rust_tokenizer()
														_lowerCAmelCase  :							List[Any]				     =					tokenizer.encode(__a)
														_lowerCAmelCase  :							Dict				     =					rust_tokenizer.encode(__a)
														self.assertListEqual(__a,    __a)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					BasicTokenizer()

														self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"),    ["ah", "\u535A", "\u63A8", "zz"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					BasicTokenizer(do_lower_case=__a)

														self.assertListEqual(
														    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "),    ["hello", "!", "how", "are", "you", "?"])
														self.assertListEqual(tokenizer.tokenize("H\u00E9llo"),    ["hello"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					BasicTokenizer(do_lower_case=__a,    strip_accents=__a)

														self.assertListEqual(
														    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),    ["hällo", "!", "how", "are", "you", "?"])
														self.assertListEqual(tokenizer.tokenize("H\u00E9llo"),    ["h\u00E9llo"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					BasicTokenizer(do_lower_case=__a,    strip_accents=__a)

														self.assertListEqual(
														    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),    ["hallo", "!", "how", "are", "you", "?"])
														self.assertListEqual(tokenizer.tokenize("H\u00E9llo"),    ["hello"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					BasicTokenizer(do_lower_case=__a)

														self.assertListEqual(
														    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),    ["hallo", "!", "how", "are", "you", "?"])
														self.assertListEqual(tokenizer.tokenize("H\u00E9llo"),    ["hello"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					BasicTokenizer(do_lower_case=__a)

														self.assertListEqual(
														    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "),    ["HeLLo", "!", "how", "Are", "yoU", "?"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					BasicTokenizer(do_lower_case=__a,    strip_accents=__a)

														self.assertListEqual(
														    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),    ["HäLLo", "!", "how", "Are", "yoU", "?"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					BasicTokenizer(do_lower_case=__a,    strip_accents=__a)

														self.assertListEqual(
														    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),    ["HaLLo", "!", "how", "Are", "yoU", "?"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					BasicTokenizer(do_lower_case=__a,    never_split=["[UNK]"])

														self.assertListEqual(
														    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"),    ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					BasicTokenizer()
														_lowerCAmelCase  :							Optional[int]				     =					"a\n'll !!to?'d of, can't."
														_lowerCAmelCase  :							int				     =					["a", "'", "ll", "!", "!", "to", "?", "'", "d", "of", ",", "can", "'", "t", "."]
														self.assertListEqual(tokenizer.tokenize(__a),    __a)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

														_lowerCAmelCase  :							int				     =					{}
														for i, token in enumerate(__a):
																					_lowerCAmelCase  :							Dict				     =					i
														_lowerCAmelCase  :							Tuple				     =					WordpieceTokenizer(vocab=__a,    unk_token="[UNK]")

														self.assertListEqual(tokenizer.tokenize(""),    [])

														self.assertListEqual(tokenizer.tokenize("unwanted running"),    ["un", "##want", "##ed", "runn", "##ing"])

														self.assertListEqual(tokenizer.tokenize("unwantedX running"),    ["[UNK]", "runn", "##ing"])







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertTrue(_is_whitespace(" "))
														self.assertTrue(_is_whitespace("\t"))
														self.assertTrue(_is_whitespace("\r"))
														self.assertTrue(_is_whitespace("\n"))
														self.assertTrue(_is_whitespace("\u00A0"))

														self.assertFalse(_is_whitespace("A"))
														self.assertFalse(_is_whitespace("-"))







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertTrue(_is_control("\u0005"))

														self.assertFalse(_is_control("A"))
														self.assertFalse(_is_control(" "))
														self.assertFalse(_is_control("\t"))
														self.assertFalse(_is_control("\r"))







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertTrue(_is_punctuation("-"))
														self.assertTrue(_is_punctuation("$"))
														self.assertTrue(_is_punctuation("`"))
														self.assertTrue(_is_punctuation("."))

														self.assertFalse(_is_punctuation("A"))
														self.assertFalse(_is_punctuation(" "))







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					self.get_tokenizer()
														_lowerCAmelCase  :							List[str]				     =					self.get_rust_tokenizer()

														# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
														self.assertListEqual([tokenizer.tokenize(__a) for t in ["Test", "\xad", "test"]],    [["[UNK]"], [], ["[UNK]"]])

														self.assertListEqual(
														    [rust_tokenizer.tokenize(__a) for t in ["Test", "\xad", "test"]],    [["[UNK]"], [], ["[UNK]"]])







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.tokenizer_class.from_pretrained("bert-base-uncased")

														_lowerCAmelCase  :							str				     =					tokenizer.encode("sequence builders",    add_special_tokens=__a)
														_lowerCAmelCase  :							List[str]				     =					tokenizer.encode("multi-sequence build",    add_special_tokens=__a)

														_lowerCAmelCase  :							List[str]				     =					tokenizer.build_inputs_with_special_tokens(__a)
														_lowerCAmelCase  :							Any				     =					tokenizer.build_inputs_with_special_tokens(__a,    __a)

														assert encoded_sentence == [101] + text + [102]
														assert encoded_pair == [101] + text + [102] + text_a + [102]







							def      snake_case__   (     self):

														'''simple docstring'''

														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
																												_lowerCAmelCase  :							Any				     =					self.rust_tokenizer_class.from_pretrained(__a,    **__a)

																												_lowerCAmelCase  :							str				     =					f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
																												_lowerCAmelCase  :							Any				     =					tokenizer_r.encode_plus(
																												    __a,    return_attention_mask=__a,    return_token_type_ids=__a,    return_offsets_mapping=__a,    add_special_tokens=__a,    )

																												_lowerCAmelCase  :							Optional[int]				     =					tokenizer_r.do_lower_case if hasattr(__a,    "do_lower_case") else False
																												_lowerCAmelCase  :							str				     =					(
																												    [
																												        ((0, 0), tokenizer_r.cls_token),
																												        ((0, 1), "A"),
																												        ((1, 2), ","),
																												        ((3, 5), "na"),
																												        ((5, 6), "##ï"),
																												        ((6, 8), "##ve"),
																												        ((9, 15), tokenizer_r.mask_token),
																												        ((16, 21), "Allen"),
																												        ((21, 23), "##NL"),
																												        ((23, 24), "##P"),
																												        ((25, 33), "sentence"),
																												        ((33, 34), "."),
																												        ((0, 0), tokenizer_r.sep_token),
																												    ]
																												    if not do_lower_case
																												    else [
																												        ((0, 0), tokenizer_r.cls_token),
																												        ((0, 1), "a"),
																												        ((1, 2), ","),
																												        ((3, 8), "naive"),
																												        ((9, 15), tokenizer_r.mask_token),
																												        ((16, 21), "allen"),
																												        ((21, 23), "##nl"),
																												        ((23, 24), "##p"),
																												        ((25, 33), "sentence"),
																												        ((33, 34), "."),
																												        ((0, 0), tokenizer_r.sep_token),
																												    ]
																												)

																												self.assertEqual(
																												    [e[1] for e in expected_results],    tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]))
																												self.assertEqual([e[0] for e in expected_results],    tokens["offset_mapping"])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					["的", "人", "有"]
														_lowerCAmelCase  :							Optional[int]				     =					"".join(__a)
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
																												_lowerCAmelCase  :							Tuple				     =					True
																												_lowerCAmelCase  :							List[str]				     =					self.tokenizer_class.from_pretrained(__a,    **__a)
																												_lowerCAmelCase  :							List[str]				     =					self.rust_tokenizer_class.from_pretrained(__a,    **__a)

																												_lowerCAmelCase  :							List[Any]				     =					tokenizer_p.encode(__a,    add_special_tokens=__a)
																												_lowerCAmelCase  :							List[Any]				     =					tokenizer_r.encode(__a,    add_special_tokens=__a)

																												_lowerCAmelCase  :							Optional[Any]				     =					tokenizer_r.convert_ids_to_tokens(__a)
																												_lowerCAmelCase  :							int				     =					tokenizer_p.convert_ids_to_tokens(__a)

																												# it is expected that each Chinese character is not preceded by "##"
																												self.assertListEqual(__a,    __a)
																												self.assertListEqual(__a,    __a)

																												_lowerCAmelCase  :							Union[str, Any]				     =					False
																												_lowerCAmelCase  :							Optional[Any]				     =					self.rust_tokenizer_class.from_pretrained(__a,    **__a)
																												_lowerCAmelCase  :							List[Any]				     =					self.tokenizer_class.from_pretrained(__a,    **__a)

																												_lowerCAmelCase  :							Optional[int]				     =					tokenizer_r.encode(__a,    add_special_tokens=__a)
																												_lowerCAmelCase  :							Any				     =					tokenizer_p.encode(__a,    add_special_tokens=__a)

																												_lowerCAmelCase  :							str				     =					tokenizer_r.convert_ids_to_tokens(__a)
																												_lowerCAmelCase  :							Dict				     =					tokenizer_p.convert_ids_to_tokens(__a)

																												# it is expected that only the first Chinese character is not preceded by "##".
																												_lowerCAmelCase  :							Any				     =					[
																												    f"##{token}" if idx != 0 else token for idx, token in enumerate(__a)
																												]
																												self.assertListEqual(__a,    __a)
																												self.assertListEqual(__a,    __a)





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        A       (      _lowerCamelCase						):





							'''simple docstring'''




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

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

							if "prime_prior" in key:
														_lowerCAmelCase  :							int				     =					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  :							int				     =					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  :							Tuple				     =					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       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					{}
							import re

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

							_lowerCAmelCase  :							List[Any]				     =					re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
							_lowerCAmelCase  :							List[str]				     =					re.compile(
							    r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
							_lowerCAmelCase  :							int				     =					re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

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

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

														elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							Union[str, Any]				     =					re_encoder_block_proj_out.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[Any]				     =					F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
																					_lowerCAmelCase  :							str				     =					re_encoder_block_proj_out.sub(_lowerCamelCase       ,				_lowerCamelCase						)

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

														elif re_decoder_block_resnet.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							List[str]				     =					re_decoder_block_resnet.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[str]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[Any]				     =					int(groups[2]						) * 2 + int(groups[3]						) - 2
																					_lowerCAmelCase  :							Union[str, Any]				     =					{"1": 1, "3": 2}[groups[-2]]
																					_lowerCAmelCase  :							Optional[Any]				     =					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  :							Dict				     =					prefix + resnet_block
																					_lowerCAmelCase  :							Dict				     =					re_decoder_block_resnet.sub(_lowerCamelCase       ,				_lowerCamelCase						)

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

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

														elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							int				     =					re_prior_cond_proj_in.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							Optional[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[int]				     =					F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
																					_lowerCAmelCase  :							List[str]				     =					re_prior_cond_proj_in.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														# keep original key
														else:
																					_lowerCAmelCase  :							Optional[int]				     =					original_key

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

														_lowerCAmelCase  :							List[Any]				     =					original_key
														_lowerCAmelCase  :							Optional[int]				     =					value

							return new_dict






@torch.no_grad()
def        A       (      _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  :							List[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						)

							_lowerCAmelCase  :							Optional[Any]				     =					MODEL_MAPPING[model_name.split("/"						)[-1]]

							_lowerCAmelCase  :							Tuple				     =					JukeboxConfig.from_pretrained(_lowerCamelCase						)
							_lowerCAmelCase  :							Optional[int]				     =					JukeboxModel(_lowerCamelCase						)

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

														_lowerCAmelCase  :							Union[str, Any]				     =					{}
														for k in old_dic.keys():
																					if k.endswith(".b"						):
																												_lowerCAmelCase  :							Dict				     =					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  :							Union[str, Any]				     =					old_dic[k]

														_lowerCAmelCase  :							Union[str, Any]				     =					"vqvae" if i == 0 else F"priors.{3 - i}"
														_lowerCAmelCase  :							Union[str, Any]				     =					fix_jukebox_keys(_lowerCamelCase       ,				model.state_dict()       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														weight_dict.append(_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[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__":
						_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)
