


import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @require_torch
    def       _lowerCAmelCase (	self		):
        # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
        # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
        # while running an external program

        # python one-liner segments

        # this must be loaded before socket.socket is monkey-patched
        A			:	Tuple										=       """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

        A			:	List[Any]										=       """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

        A			:	Any										=       """
import socket
def offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")
socket.socket = offline_socket
        """

        # Force fetching the files so that we can use the cache
        A			:	Dict										=       """hf-internal-testing/tiny-random-bert"""
        BertConfig.from_pretrained(lowerCamelCase__		)
        BertModel.from_pretrained(lowerCamelCase__		)
        BertTokenizer.from_pretrained(lowerCamelCase__		)
        pipeline(task="""fill-mask""",		model=lowerCamelCase__		)

        # baseline - just load from_pretrained with normal network
        A			:	str										=       [sys.executable, """-c""", """\n""".join([load, run, mock]		)]

        # should succeed
        A			:	List[str]										=       self.get_env()
        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        A			:	str										=       """1"""
        A			:	Any										=       subprocess.run(lowerCamelCase__,		env=lowerCamelCase__,		check=lowerCamelCase__,		capture_output=lowerCamelCase__		)
        self.assertEqual(result.returncode,		0,		result.stderr		)
        self.assertIn("""success""",		result.stdout.decode()		)






    @require_torch
    def       _lowerCAmelCase (	self		):
        # python one-liner segments
        # this must be loaded before socket.socket is monkey-patched
        A			:	Any										=       """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

        A			:	List[Any]										=       """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

        A			:	Tuple										=       """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")
socket.socket = offline_socket
        """

        # Force fetching the files so that we can use the cache
        A			:	List[str]										=       """hf-internal-testing/tiny-random-bert"""
        BertConfig.from_pretrained(lowerCamelCase__		)
        BertModel.from_pretrained(lowerCamelCase__		)
        BertTokenizer.from_pretrained(lowerCamelCase__		)
        pipeline(task="""fill-mask""",		model=lowerCamelCase__		)

        # baseline - just load from_pretrained with normal network
        A			:	int										=       [sys.executable, """-c""", """\n""".join([load, run, mock]		)]

        # should succeed
        A			:	str										=       self.get_env()
        A			:	List[Any]										=       subprocess.run(lowerCamelCase__,		env=lowerCamelCase__,		check=lowerCamelCase__,		capture_output=lowerCamelCase__		)
        self.assertEqual(result.returncode,		0,		result.stderr		)
        self.assertIn("""success""",		result.stdout.decode()		)






    @require_torch
    def       _lowerCAmelCase (	self		):
        # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
        # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
        # while running an external program

        # python one-liner segments

        # this must be loaded before socket.socket is monkey-patched
        A			:	Optional[Any]										=       """
from transformers import BertConfig, BertModel, BertTokenizer
        """

        A			:	List[str]										=       """
mname = \"hf-internal-testing/tiny-random-bert-sharded\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print(\"success\")
        """

        A			:	List[Any]										=       """
import socket
def offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")
socket.socket = offline_socket
        """

        # baseline - just load from_pretrained with normal network
        A			:	Tuple										=       [sys.executable, """-c""", """\n""".join([load, run]		)]

        # should succeed
        A			:	Any										=       self.get_env()
        A			:	Optional[Any]										=       subprocess.run(lowerCamelCase__,		env=lowerCamelCase__,		check=lowerCamelCase__,		capture_output=lowerCamelCase__		)
        self.assertEqual(result.returncode,		0,		result.stderr		)
        self.assertIn("""success""",		result.stdout.decode()		)

        # next emulate no network
        A			:	List[str]										=       [sys.executable, """-c""", """\n""".join([load, mock, run]		)]

        # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
        # env["TRANSFORMERS_OFFLINE"] = "0"
        # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
        # self.assertEqual(result.returncode, 1, result.stderr)

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        A			:	Optional[Any]										=       """1"""
        A			:	Union[str, Any]										=       subprocess.run(lowerCamelCase__,		env=lowerCamelCase__,		check=lowerCamelCase__,		capture_output=lowerCamelCase__		)
        self.assertEqual(result.returncode,		0,		result.stderr		)
        self.assertIn("""success""",		result.stdout.decode()		)






    @require_torch
    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       """
from transformers import pipeline
        """
        A			:	Optional[int]										=       """
mname = \"hf-internal-testing/tiny-random-bert\"
pipe = pipeline(model=mname)
        """

        A			:	Union[str, Any]										=       """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")
socket.socket = offline_socket
        """
        A			:	str										=       self.get_env()
        A			:	List[Any]										=       """1"""
        A			:	Union[str, Any]										=       [sys.executable, """-c""", """\n""".join([load, mock, run]		)]
        A			:	int										=       subprocess.run(lowerCamelCase__,		env=lowerCamelCase__,		check=lowerCamelCase__,		capture_output=lowerCamelCase__		)
        self.assertEqual(result.returncode,		1,		result.stderr		)
        self.assertIn(
            """You cannot infer task automatically within `pipeline` when using offline mode""",		result.stderr.decode().replace("""\n""",		""""""		),		)






    @require_torch
    def       _lowerCAmelCase (	self		):
        A			:	str										=       """
from transformers import AutoModel
        """
        A			:	Dict										=       """
mname = \"hf-internal-testing/test_dynamic_model\"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print(\"success\")
        """

        # baseline - just load from_pretrained with normal network
        A			:	str										=       [sys.executable, """-c""", """\n""".join([load, run]		)]

        # should succeed
        A			:	Union[str, Any]										=       self.get_env()
        A			:	Union[str, Any]										=       subprocess.run(lowerCamelCase__,		env=lowerCamelCase__,		check=lowerCamelCase__,		capture_output=lowerCamelCase__		)
        self.assertEqual(result.returncode,		0,		result.stderr		)
        self.assertIn("""success""",		result.stdout.decode()		)

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        A			:	Optional[int]										=       """1"""
        A			:	int										=       subprocess.run(lowerCamelCase__,		env=lowerCamelCase__,		check=lowerCamelCase__,		capture_output=lowerCamelCase__		)
        self.assertEqual(result.returncode,		0,		result.stderr		)
        self.assertIn("""success""",		result.stdout.decode()		)







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)


