


"""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     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						int:
						if key.endswith('.model.1.bias'  ) and len(key.split('.'  )  ) > 10:
												SCREAMING_SNAKE_CASE													=					key.replace('.model.1.bias' ,			'.conv1d_1.bias'  )
						elif key.endswith('.model.1.weight'  ) and len(key.split('.'  )  ) > 10:
												SCREAMING_SNAKE_CASE													=					key.replace('.model.1.weight' ,			'.conv1d_1.weight'  )
						elif key.endswith('.model.3.bias'  ) and len(key.split('.'  )  ) > 10:
												SCREAMING_SNAKE_CASE													=					key.replace('.model.3.bias' ,			'.conv1d_2.bias'  )
						elif key.endswith('.model.3.weight'  ) and len(key.split('.'  )  ) > 10:
												SCREAMING_SNAKE_CASE													=					key.replace('.model.3.weight' ,			'.conv1d_2.weight'  )

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

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

						if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
												SCREAMING_SNAKE_CASE													=					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:
												SCREAMING_SNAKE_CASE													=					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     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Optional[Any]:
						SCREAMING_SNAKE_CASE													=					{}
						import re

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

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

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

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

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

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

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

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

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

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

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

												# keep original key
												else:
																		SCREAMING_SNAKE_CASE													=					original_key

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

												SCREAMING_SNAKE_CASE													=					original_key
												SCREAMING_SNAKE_CASE													=					value

						return new_dict


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

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

						SCREAMING_SNAKE_CASE													=					JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					JukeboxModel(SCREAMING_SNAKE_CASE_  )

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

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

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

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

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

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

						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)







from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


a_   :List[Any]        =      logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase_     )
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], **_snake_case  :  str					)   ->Dict:
            super().__init__(**_snake_case					)

            if self.framework != "pt":
                   raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''					)
            # No specific FOR_XXX available yet






     def __call__(       self  :  Union[str, Any], _snake_case  :  Union[np.ndarray, bytes, str], **_snake_case  :  Tuple					)   ->Dict:
            return super().__call__(_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Tuple, **_snake_case  :  Any					)   ->Union[str, Any]:
            snake_case__							:	str   	=	{}
            if "candidate_labels" in kwargs:
                   snake_case__							:	str   	=	kwargs['candidate_labels']
            if "hypothesis_template" in kwargs:
                   snake_case__							:	str   	=	kwargs['hypothesis_template']

            return preprocess_params, {}, {}






     def    lowercase_   (       self  :  Dict, _snake_case  :  str, _snake_case  :  Optional[int]=None, _snake_case  :  List[str]="This is a sound of {}."					)   ->int:
            if isinstance(_snake_case, _snake_case					):
                   if audio.startswith('http://'					) or audio.startswith('https://'					):
                          # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                          # like http_huggingface_co.png
                          snake_case__							:	List[Any]   	=	requests.get(_snake_case					).content
                   else:
                          with open(_snake_case, 'rb'					) as f:
                                 snake_case__							:	Union[str, Any]   	=	f.read()

            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	List[Any]   	=	ffmpeg_read(_snake_case, self.feature_extractor.sampling_rate					)

            if not isinstance(_snake_case, np.ndarray					):
                   raise ValueError('We expect a numpy ndarray as input'					)
            if len(audio.shape					) != 1:
                   raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'					)

            snake_case__							:	Tuple   	=	self.feature_extractor(
                [audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='pt'					)
            snake_case__							:	int   	=	candidate_labels
            snake_case__							:	int   	=	[hypothesis_template.format(_snake_case					) for x in candidate_labels]
            snake_case__							:	Optional[int]   	=	self.tokenizer(_snake_case, return_tensors=self.framework, padding=_snake_case					)
            snake_case__							:	List[Any]   	=	[text_inputs]
            return inputs






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[Any]					)   ->int:
            snake_case__							:	Optional[int]   	=	model_inputs.pop('candidate_labels'					)
            snake_case__							:	str   	=	model_inputs.pop('text_inputs'					)
            if isinstance(text_inputs[0], _snake_case					):
                   snake_case__							:	Optional[Any]   	=	text_inputs[0]
            else:
                   # Batching case.
                   snake_case__							:	int   	=	text_inputs[0][0]

            snake_case__							:	Any   	=	self.model(**_snake_case, **_snake_case					)

            snake_case__							:	List[Any]   	=	{
                'candidate_labels': candidate_labels,
                'logits': outputs.logits_per_audio,
            }
            return model_outputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str					)   ->List[str]:
            snake_case__							:	int   	=	model_outputs.pop('candidate_labels'					)
            snake_case__							:	List[Any]   	=	model_outputs['logits'][0]

            if self.framework == "pt":
                   snake_case__							:	Tuple   	=	logits.softmax(dim=0					)
                   snake_case__							:	Union[str, Any]   	=	probs.tolist()
            else:
                   raise ValueError('`tf` framework not supported.'					)

            snake_case__							:	Union[str, Any]   	=	[
                {'score': score, 'label': candidate_label}
                for score, candidate_label in sorted(zip(_snake_case, _snake_case					), key=lambda _snake_case					: -x[0]					)
            ]
            return result






