




'''simple docstring'''






import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
		pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				"align_text_model"







							def __init__(		self ,   lowerCamelCase_=3_05_22 ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=12 ,   lowerCamelCase_=12 ,   lowerCamelCase_=30_72 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=2 ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=1e-12 ,   lowerCamelCase_=0 ,   lowerCamelCase_="absolute" ,   lowerCamelCase_=True ,   **lowerCamelCase_ ,   )	->							Optional[Any]:
														super().__init__(**lowerCamelCase_       )

														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     hidden_size
														lowerCAmelCase__	       =     num_hidden_layers
														lowerCAmelCase__	       =     num_attention_heads
														lowerCAmelCase__	       =     hidden_act
														lowerCAmelCase__	       =     intermediate_size
														lowerCAmelCase__	       =     hidden_dropout_prob
														lowerCAmelCase__	       =     attention_probs_dropout_prob
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     type_vocab_size
														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     layer_norm_eps
														lowerCAmelCase__	       =     position_embedding_type
														lowerCAmelCase__	       =     use_cache
														lowerCAmelCase__	       =     pad_token_id







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							"PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase_       )

														lowerCAmelCase__       , lowerCAmelCase__	       =     cls.get_config_dict(lowerCamelCase_ ,   **lowerCamelCase_       )

														# get the text config dict if we are loading from AlignConfig
														if config_dict.get('''model_type'''       ) == "align":
																					lowerCAmelCase__	       =     config_dict['''text_config''']

														if "model_type" in config_dict and hasattr(cls ,   '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																					    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

														return cls.from_dict(lowerCamelCase_ ,   **lowerCamelCase_       )




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				"align_vision_model"







							def __init__(		self ,   lowerCamelCase_ = 3 ,   lowerCamelCase_ = 6_00 ,   lowerCamelCase_ = 2.0 ,   lowerCamelCase_ = 3.1 ,   lowerCamelCase_ = 8 ,   lowerCamelCase_ = [3, 3, 5, 3, 5, 5, 3] ,   lowerCamelCase_ = [32, 16, 24, 40, 80, 1_12, 1_92] ,   lowerCamelCase_ = [16, 24, 40, 80, 1_12, 1_92, 3_20] ,   lowerCamelCase_ = [] ,   lowerCamelCase_ = [1, 2, 2, 2, 1, 2, 1] ,   lowerCamelCase_ = [1, 2, 2, 3, 3, 4, 1] ,   lowerCamelCase_ = [1, 6, 6, 6, 6, 6, 6] ,   lowerCamelCase_ = 0.25 ,   lowerCamelCase_ = "swish" ,   lowerCamelCase_ = 25_60 ,   lowerCamelCase_ = "mean" ,   lowerCamelCase_ = 0.02 ,   lowerCamelCase_ = 0.001 ,   lowerCamelCase_ = 0.99 ,   lowerCamelCase_ = 0.2 ,   **lowerCamelCase_ ,   )	->							List[Any]:
														super().__init__(**lowerCamelCase_       )

														lowerCAmelCase__	       =     num_channels
														lowerCAmelCase__	       =     image_size
														lowerCAmelCase__	       =     width_coefficient
														lowerCAmelCase__	       =     depth_coefficient
														lowerCAmelCase__	       =     depth_divisor
														lowerCAmelCase__	       =     kernel_sizes
														lowerCAmelCase__	       =     in_channels
														lowerCAmelCase__	       =     out_channels
														lowerCAmelCase__	       =     depthwise_padding
														lowerCAmelCase__	       =     strides
														lowerCAmelCase__	       =     num_block_repeats
														lowerCAmelCase__	       =     expand_ratios
														lowerCAmelCase__	       =     squeeze_expansion_ratio
														lowerCAmelCase__	       =     hidden_act
														lowerCAmelCase__	       =     hidden_dim
														lowerCAmelCase__	       =     pooling_type
														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     batch_norm_eps
														lowerCAmelCase__	       =     batch_norm_momentum
														lowerCAmelCase__	       =     drop_connect_rate
														lowerCAmelCase__	       =     sum(lowerCamelCase_       ) * 4







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							"PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase_       )

														lowerCAmelCase__       , lowerCAmelCase__	       =     cls.get_config_dict(lowerCamelCase_ ,   **lowerCamelCase_       )

														# get the vision config dict if we are loading from AlignConfig
														if config_dict.get('''model_type'''       ) == "align":
																					lowerCAmelCase__	       =     config_dict['''vision_config''']

														if "model_type" in config_dict and hasattr(cls ,   '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																					    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

														return cls.from_dict(lowerCamelCase_ ,   **lowerCamelCase_       )




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				"align"
							lowercase__      :       str     						=				True







							def __init__(		self ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   lowerCamelCase_=6_40 ,   lowerCamelCase_=1.0 ,   lowerCamelCase_=0.02 ,   **lowerCamelCase_ ,   )	->							Optional[Any]:
														super().__init__(**lowerCamelCase_       )

														if text_config is None:
																					lowerCAmelCase__	       =     {}
																					logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''       )

														if vision_config is None:
																					lowerCAmelCase__	       =     {}
																					logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''       )

														lowerCAmelCase__	       =     AlignTextConfig(**lowerCamelCase_       )
														lowerCAmelCase__	       =     AlignVisionConfig(**lowerCamelCase_       )

														lowerCAmelCase__	       =     projection_dim
														lowerCAmelCase__	       =     temperature_init_value
														lowerCAmelCase__	       =     initializer_range







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							Optional[Any]:
														return cls(text_config=text_config.to_dict() ,   vision_config=vision_config.to_dict() ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     copy.deepcopy(self.__dict__       )
														lowerCAmelCase__	       =     self.text_config.to_dict()
														lowerCAmelCase__	       =     self.vision_config.to_dict()
														lowerCAmelCase__	       =     self.__class__.model_type
														return output



'''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()
__UpperCAmelCase       =							logging.get_logger(__name__)


__UpperCAmelCase       =							'''https://openaipublic.azureedge.net/jukebox/models/'''
__UpperCAmelCase       =							{
    '''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  _snake_case       (					A	)						->   Union[str, Any]:
							if key.endswith('''.model.1.bias'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.1.bias'''			,   '''.conv1d_1.bias'''	)
							elif key.endswith('''.model.1.weight'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.1.weight'''			,   '''.conv1d_1.weight'''	)
							elif key.endswith('''.model.3.bias'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.3.bias'''			,   '''.conv1d_2.bias'''	)
							elif key.endswith('''.model.3.weight'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.3.weight'''			,   '''.conv1d_2.weight'''	)

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

							if "prime_prior" in key:
														lowerCAmelCase__	       =     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__	       =     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__	       =     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  _snake_case       (					A			,   A			,   A			,   A	)						->   Optional[int]:
							lowerCAmelCase__	       =     {}
							import re

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

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

							lowerCAmelCase__	       =     re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''	)
							lowerCAmelCase__	       =     re.compile(
							    R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''	)
							lowerCAmelCase__	       =     re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''	)

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(A	):
																					lowerCAmelCase__	       =     re_encoder_block_conv_in.match(A	)
																					lowerCAmelCase__	       =     regex_match.groups()
																					lowerCAmelCase__	       =     int(groups[2]	) * 2 + int(groups[3]	)
																					lowerCAmelCase__	       =     F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
																					lowerCAmelCase__	       =     re_encoder_block_conv_in.sub(A			,   A	)

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

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

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

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

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

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

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

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

														# keep original key
														else:
																					lowerCAmelCase__	       =     original_key

														lowerCAmelCase__	       =     replace_key(A	)

														if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
																					print(F"""failed converting {original_key} to {key}, does not match"""	)

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

														lowerCAmelCase__	       =     original_key
														lowerCAmelCase__	       =     value

							return new_dict


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

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

							lowerCAmelCase__	       =     JukeboxConfig.from_pretrained(A	)
							lowerCAmelCase__	       =     JukeboxModel(A	)

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

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

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

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

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

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

							return weight_dict


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