



"""simple docstring"""




import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__A       =						False
__A       =						False



def 	__a					(   lowerCAmelCase_       : Namespace    )				->			int:






						'''simple docstring'''



						return TrainCommand(lowerCAmelCase_    )






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						ArgumentParser					)      ->			Optional[Any]:
							UpperCAmelCase_=       parser.add_parser("""train"""						,  help="""CLI tool to train a model on a task."""					)

							train_parser.add_argument(
							    """--train_data"""						,  type=__UpperCAmelCase						,  required=__UpperCAmelCase						,  help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""						,  )
							train_parser.add_argument(
							    """--column_label"""						,  type=__UpperCAmelCase						,  default=0						,  help="""Column of the dataset csv file with example labels."""					)
							train_parser.add_argument(
							    """--column_text"""						,  type=__UpperCAmelCase						,  default=1						,  help="""Column of the dataset csv file with example texts."""					)
							train_parser.add_argument(
							    """--column_id"""						,  type=__UpperCAmelCase						,  default=2						,  help="""Column of the dataset csv file with example ids."""					)
							train_parser.add_argument(
							    """--skip_first_row"""						,  action="""store_true"""						,  help="""Skip the first row of the csv file (headers)."""					)

							train_parser.add_argument("""--validation_data"""						,  type=__UpperCAmelCase						,  default=""""""						,  help="""path to validation dataset."""					)
							train_parser.add_argument(
							    """--validation_split"""						,  type=__UpperCAmelCase						,  default=0.1						,  help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""						,  )

							train_parser.add_argument("""--output"""						,  type=__UpperCAmelCase						,  default="""./"""						,  help="""path to saved the trained model."""					)

							train_parser.add_argument(
							    """--task"""						,  type=__UpperCAmelCase						,  default="""text_classification"""						,  help="""Task to train the model on."""					)
							train_parser.add_argument(
							    """--model"""						,  type=__UpperCAmelCase						,  default="""bert-base-uncased"""						,  help="""Model's name or path to stored model."""					)
							train_parser.add_argument("""--train_batch_size"""						,  type=__UpperCAmelCase						,  default=32						,  help="""Batch size for training."""					)
							train_parser.add_argument("""--valid_batch_size"""						,  type=__UpperCAmelCase						,  default=64						,  help="""Batch size for validation."""					)
							train_parser.add_argument("""--learning_rate"""						,  type=__UpperCAmelCase						,  default=3E-5						,  help="""Learning rate."""					)
							train_parser.add_argument("""--adam_epsilon"""						,  type=__UpperCAmelCase						,  default=1E-08						,  help="""Epsilon for Adam optimizer."""					)
							train_parser.set_defaults(func=__UpperCAmelCase					)




	def __init__(     self     :						str						,  __UpperCAmelCase     :						Namespace					)      ->			Optional[int]:
							UpperCAmelCase_=       logging.get_logger("""transformers-cli/training"""					)

							UpperCAmelCase_=       """tf""" if is_tf_available() else """torch"""

							os.makedirs(args.output						,  exist_ok=__UpperCAmelCase					)
							UpperCAmelCase_=       args.output

							UpperCAmelCase_=       args.column_label
							UpperCAmelCase_=       args.column_text
							UpperCAmelCase_=       args.column_id

							self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""					)
							if args.task == "text_classification":
													UpperCAmelCase_=       TextClassificationPipeline.from_pretrained(args.model					)
							elif args.task == "token_classification":
													raise NotImplementedError
							elif args.task == "question_answering":
													raise NotImplementedError

							self.logger.info(F"""Loading dataset from {args.train_data}"""					)
							UpperCAmelCase_=       Processor.create_from_csv(
							    args.train_data						,  column_label=args.column_label						,  column_text=args.column_text						,  column_id=args.column_id						,  skip_first_row=args.skip_first_row						,  )
							UpperCAmelCase_=       None
							if args.validation_data:
													self.logger.info(F"""Loading validation dataset from {args.validation_data}"""					)
													UpperCAmelCase_=       Processor.create_from_csv(
													    args.validation_data						,  column_label=args.column_label						,  column_text=args.column_text						,  column_id=args.column_id						,  skip_first_row=args.skip_first_row						,  )

							UpperCAmelCase_=       args.validation_split
							UpperCAmelCase_=       args.train_batch_size
							UpperCAmelCase_=       args.valid_batch_size
							UpperCAmelCase_=       args.learning_rate
							UpperCAmelCase_=       args.adam_epsilon




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[Any]:
							if self.framework == "tf":
													return self.run_tf()
							return self.run_torch()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							raise NotImplementedError




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							self.pipeline.fit(
							    self.train_dataset						,  validation_data=self.valid_dataset						,  validation_split=self.validation_split						,  learning_rate=self.learning_rate						,  adam_epsilon=self.adam_epsilon						,  train_batch_size=self.train_batch_size						,  valid_batch_size=self.valid_batch_size						,  )

							# Save trained pipeline
							self.pipeline.save_pretrained(self.output					)









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 	__a					(   lowerCAmelCase_       : List[Any]    )				->			Tuple:






						'''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 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any    )				->			Optional[Any]:






						'''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(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_encoder_block_conv_in.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_encoder_block_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_encoder_block_resnet.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												# rename vqvae.decoder keys
												elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_conv_out.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_decoder_block_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_resnet.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       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_    ):
																		UpperCAmelCase_=       re_prior_cond_conv_out.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_prior_cond_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_prior_cond_resnet.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												# keep original key
												else:
																		UpperCAmelCase_=       original_key

												UpperCAmelCase_=       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:
																		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 	__a					(   lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : str=None    )				->			List[str]:






						'''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=lowerCAmelCase_    )
																		os.makedirs(F"""{pytorch_dump_folder_path}/"""      ,exist_ok=lowerCAmelCase_    )
																		open(F"""{pytorch_dump_folder_path}/{file.split("/"    )[-1]}"""      ,"""wb"""    ).write(r.content    )

						UpperCAmelCase_=       MODEL_MAPPING[model_name.split("""/"""    )[-1]]

						UpperCAmelCase_=       JukeboxConfig.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       JukeboxModel(lowerCAmelCase_    )

						UpperCAmelCase_=       []
						UpperCAmelCase_=       {}
						for i, dict_name in enumerate(lowerCAmelCase_    ):
												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(lowerCAmelCase_      ,model.state_dict()      ,lowerCAmelCase_      ,lowerCAmelCase_    )
												weight_dict.append(lowerCAmelCase_    )

						UpperCAmelCase_=       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__":
	__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)




