





'''simple docstring'''

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase				:      Union[str, Any]						      =get_tests_dir("fixtures/test_sentencepiece.model")






@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase      (			UpperCamelCase__     ,			unittest.TestCase							):
 __lowercase  						=							XGLMTokenizer
 __lowercase  						=							XGLMTokenizerFast
 __lowercase  						=							True
 __lowercase  						=							True
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     super().setUp()

     # We have a SentencePiece fixture for testing
     A__							=     XGLMTokenizer(lowercase_		,			keep_accents=lowercase_				)
     tokenizer.save_pretrained(self.tmpdirname				)
 def 	UpperCAmelCase_				(							self :Any				)->				int:
     A__							=     "<pad>"
     A__							=     1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_				)		,			lowercase_				)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_				)		,			lowercase_				)
 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     A__							=     list(self.get_tokenizer().get_vocab().keys()				)

     self.assertEqual(vocab_keys[0]		,			"<s>"				)
     self.assertEqual(vocab_keys[1]		,			"<pad>"				)
     self.assertEqual(len(lowercase_				)		,			10_08				)
 def 	UpperCAmelCase_				(							self :str				)->				Union[str, Any]:
     self.assertEqual(self.get_tokenizer().vocab_size		,			10_08				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Union[str, Any]:
     A__							=     XGLMTokenizer(lowercase_		,			keep_accents=lowercase_				)

     A__							=     tokenizer.tokenize("This is a test"				)
     self.assertListEqual(lowercase_		,			["▁This", "▁is", "▁a", "▁t", "est"]				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowercase_				)		,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,			)

     A__							=     tokenizer.tokenize("I was born in 92000, and this is falsé."				)
     self.assertListEqual(
         lowercase_		,			[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "9",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "é",
             ".",
         ]		,			)
     A__							=     tokenizer.convert_tokens_to_ids(lowercase_				)
     self.assertListEqual(
         lowercase_		,			[
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
         ]		,			)

     A__							=     tokenizer.convert_ids_to_tokens(lowercase_				)
     self.assertListEqual(
         lowercase_		,			[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "<unk>",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "<unk>",
             ".",
         ]		,			)
 @cached_property
 def 	UpperCAmelCase_				(							self :Dict				)->				Dict:
     return XGLMTokenizer.from_pretrained("facebook/xglm-564M"				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Dict:
     with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(lowercase_		,			f.name				)
         A__							=     XGLMTokenizer(f.name		,			keep_accents=lowercase_				)
         A__							=     pickle.dumps(lowercase_				)
     pickle.loads(lowercase_				)
 def 	UpperCAmelCase_				(							self :List[str]				)->				str:
     if not self.test_rust_tokenizer:
         return

     A__							=     self.get_tokenizer()
     A__							=     self.get_rust_tokenizer()

     A__							=     "I was born in 92000, and this is falsé."

     A__							=     tokenizer.tokenize(lowercase_				)
     A__							=     rust_tokenizer.tokenize(lowercase_				)
     self.assertListEqual(lowercase_		,			lowercase_				)

     A__							=     tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_				)
     A__							=     rust_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_				)
     self.assertListEqual(lowercase_		,			lowercase_				)

     A__							=     self.get_rust_tokenizer()
     A__							=     tokenizer.encode(lowercase_				)
     A__							=     rust_tokenizer.encode(lowercase_				)
     self.assertListEqual(lowercase_		,			lowercase_				)
 @slow
 def 	UpperCAmelCase_				(							self :Dict				)->				Tuple:
     A__							=     "Hello World!"
     A__							=     [2, 3_12_27, 44_47, 35]

     self.assertListEqual(lowercase_		,			self.big_tokenizer.encode(lowercase_				)				)
 @slow
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Dict:
     A__							=     (
         "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
         " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
     )
     # fmt: off
     A__							=     [2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
     # fmt: on

     self.assertListEqual(lowercase_		,			self.big_tokenizer.encode(lowercase_				)				)



 @slow
 def 	UpperCAmelCase_				(							self :Tuple				)->				str:
     # fmt: off
     A__							=     {
         "input_ids": [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
         "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
     }  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_		,			model_name="facebook/xglm-564M"		,			padding=lowercase_		,			)










'''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()
__lowerCAmelCase				:      Any						      =logging.get_logger(__name__)


__lowerCAmelCase				:      int						      ="https://openaipublic.azureedge.net/jukebox/models/"
__lowerCAmelCase				:      Any						      ={
    "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   :   str			):
    if key.endswith(".model.1.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.bias"		,			".conv1d_1.bias"			)
    elif key.endswith(".model.1.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.weight"		,			".conv1d_1.weight"			)
    elif key.endswith(".model.3.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.bias"		,			".conv1d_2.bias"			)
    elif key.endswith(".model.3.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.weight"		,			".conv1d_2.weight"			)

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

    if "prime_prior" in key:
        A__							=     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__							=     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__							=     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   :   Optional[Any]		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   str			):
    A__							=     {}
    import re

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

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

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

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

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

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

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

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_proj_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
            A__							=     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__							=     re_prior_cond_conv_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[1]			) * 2 + int(groups[2]			) - 2
            A__							=     F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
            A__							=     re_prior_cond_conv_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

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

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

        # keep original key
        else:
            A__							=     original_key

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

        A__							=     original_key
        A__							=     value

    return new_dict





@torch.no_grad()
def 		UpperCamelCase							(				_lowerCamelCase   :   str=None		,			_lowerCamelCase   :   Dict=None			):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"			):
            A__							=     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__							=     MODEL_MAPPING[model_name.split("/"			)[-1]]

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

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

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

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

    A__							=     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__":
  __lowerCAmelCase				:      Dict						      =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.",
  )
  __lowerCAmelCase				:      int						      =parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



