
"""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()
_a														=		logging.get_logger(__name__)


_a														=		"""https://openaipublic.azureedge.net/jukebox/models/"""
_a														=		{
    """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 				lowerCamelCase__		(      __snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					if key.endswith('''.model.1.bias'''     ) and len(key.split('''.'''     )     ) > 10:
										_UpperCamelCase										=  key.replace('''.model.1.bias''',					'''.conv1d_1.bias'''     )
					elif key.endswith('''.model.1.weight'''     ) and len(key.split('''.'''     )     ) > 10:
										_UpperCamelCase										=  key.replace('''.model.1.weight''',					'''.conv1d_1.weight'''     )
					elif key.endswith('''.model.3.bias'''     ) and len(key.split('''.'''     )     ) > 10:
										_UpperCamelCase										=  key.replace('''.model.3.bias''',					'''.conv1d_2.bias'''     )
					elif key.endswith('''.model.3.weight'''     ) and len(key.split('''.'''     )     ) > 10:
										_UpperCamelCase										=  key.replace('''.model.3.weight''',					'''.conv1d_2.weight'''     )

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

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

					if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
										_UpperCamelCase										=  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:
										_UpperCamelCase										=  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 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		Tuple:


					"""simple docstring"""





					_UpperCamelCase										=  {}
					import re

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

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

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

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

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

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

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

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

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

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

										elif re_prior_cond_proj_in.fullmatch(__snake_case     ):
															_UpperCamelCase										=  re_prior_cond_proj_in.match(__snake_case     )
															_UpperCamelCase										=  regex_match.groups()
															_UpperCamelCase										=  F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
															_UpperCamelCase										=  re_prior_cond_proj_in.sub(__snake_case,					__snake_case     )

										# keep original key
										else:
															_UpperCamelCase										=  original_key

										_UpperCamelCase										=  replace_key(__snake_case     )

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

										_UpperCamelCase										=  original_key
										_UpperCamelCase										=  value

					return new_dict



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case=None,					__snake_case=None     )							->		Optional[Any]:


					"""simple docstring"""





					for file in MODEL_MAPPING[model_name]:
										if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/"     )[-1]}'''     ):
															_UpperCamelCase										=  requests.get(F'''{PREFIX}{file}''',					allow_redirects=__snake_case     )
															os.makedirs(F'''{pytorch_dump_folder_path}/''',					exist_ok=__snake_case     )
															open(F'''{pytorch_dump_folder_path}/{file.split("/"     )[-1]}''',					'''wb'''     ).write(r.content     )

					_UpperCamelCase										=  MODEL_MAPPING[model_name.split('''/'''     )[-1]]

					_UpperCamelCase										=  JukeboxConfig.from_pretrained(__snake_case     )
					_UpperCamelCase										=  JukeboxModel(__snake_case     )

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

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

										_UpperCamelCase										=  '''vqvae''' if i == 0 else F'''priors.{3 - i}'''
										_UpperCamelCase										=  fix_jukebox_keys(__snake_case,					model.state_dict(),					__snake_case,					__snake_case     )
										weight_dict.append(__snake_case     )

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

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

					print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''     )
					model.save_pretrained(__snake_case     )

					return weight_dict


if __name__ == "__main__":
				_a														=		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														=		parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_a														=		{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TrOCRForCausalLM""",
				    """TrOCRPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
				from .processing_trocr import TrOCRProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






