




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Any						=     LongformerTokenizer
				__a     :       Any						=     True
				__a     :       List[Any]						=     LongformerTokenizerFast
				__a     :       Any						=     True






				def 		snake_case	(			self ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							SCREAMING_SNAKE_CASE_   : Dict					  =      [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dict(zip(snake_case__      ,range(len(snake_case__ ) ) ) )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'unk_token': '<unk>'}

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file'] )
							SCREAMING_SNAKE_CASE_   : int					  =      os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file      ,'w'      ,encoding='utf-8' ) as fp:
										fp.write(json.dumps(snake_case__ ) + '\n' )
							with open(self.merges_file      ,'w'      ,encoding='utf-8' ) as fp:
										fp.write('\n'.join(snake_case__ ) )






				def 		snake_case	(			self      ,**snake_case__ ):
							kwargs.update(self.special_tokens_map )
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**snake_case__ )






				def 		snake_case	(			self      ,**snake_case__ ):
							kwargs.update(self.special_tokens_map )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'lower newer'
							SCREAMING_SNAKE_CASE_   : Any					  =      'lower newer'
							return input_text, output_text






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.tokenizer_class(self.vocab_file      ,self.merges_file      ,**self.special_tokens_map )
							SCREAMING_SNAKE_CASE_   : Any					  =      'lower newer'
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							SCREAMING_SNAKE_CASE_   : int					  =      tokenizer.tokenize(snake_case__ )  # , add_prefix_space=True)
							self.assertListEqual(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      tokens + [tokenizer.unk_token]
							SCREAMING_SNAKE_CASE_   : Tuple					  =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('Hello world!'      ,add_special_tokens=snake_case__ )      ,[0, 31414, 232, 328, 2] )
							self.assertListEqual(
							    tokenizer.encode('Hello world! cécé herlolip 418'      ,add_special_tokens=snake_case__ )      ,[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]      ,)






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      tokenizer.encode('sequence builders'      ,add_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer.encode('multi-sequence build'      ,add_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.encode(
							    'sequence builders'      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer.encode(
							    'sequence builders'      ,'multi-sequence build'      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.build_inputs_with_special_tokens(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer.build_inputs_with_special_tokens(snake_case__      ,snake_case__ )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Dict					  =      'Encode this sequence.'
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.byte_encoder[' '.encode('utf-8' )[0]]

							# Testing encoder arguments
							SCREAMING_SNAKE_CASE_   : int					  =      tokenizer.encode(snake_case__      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
							self.assertNotEqual(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer.encode(snake_case__      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
							self.assertEqual(snake_case__      ,snake_case__ )

							tokenizer.add_special_tokens({'bos_token': '<s>'} )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer.encode(snake_case__      ,add_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.convert_ids_to_tokens(encoded[1] )[0]
							self.assertNotEqual(snake_case__      ,snake_case__ )

							# Testing spaces after special tokens
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      '<mask>'
							tokenizer.add_special_tokens(
							    {'mask_token': AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ )} )  # mask token has a left space
							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.convert_tokens_to_ids(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'Encode <mask> sequence'
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'Encode <mask>sequence'

							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.encode(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      encoded.index(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
							self.assertEqual(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer.encode(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      encoded.index(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
							self.assertNotEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							pass






				def 		snake_case	(			self ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.rust_tokenizer_class.from_pretrained(snake_case__      ,**snake_case__ )
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.tokenizer_class.from_pretrained(snake_case__      ,**snake_case__ )
													SCREAMING_SNAKE_CASE_   : Dict					  =      'A, <mask> AllenNLP sentence.'
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer_r.encode_plus(snake_case__      ,add_special_tokens=snake_case__      ,return_token_type_ids=snake_case__ )
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer_p.encode_plus(snake_case__      ,add_special_tokens=snake_case__      ,return_token_type_ids=snake_case__ )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['token_type_ids'] )      ,sum(tokens_p['token_type_ids'] ) )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )      ,sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )      ,)

													SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['input_ids']      ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
													self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

													self.assertSequenceEqual(
													    snake_case__      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
													self.assertSequenceEqual(
													    snake_case__      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )






				def 		snake_case	(			self ):
							for trim_offsets, add_prefix_space in itertools.product([True, False]      ,repeat=2 ):
										SCREAMING_SNAKE_CASE_   : int					  =      self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )

										SCREAMING_SNAKE_CASE_   : List[str]					  =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

										self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,snake_case__ )

										self.assertEqual(post_processor_state['add_prefix_space']      ,snake_case__ )
										self.assertEqual(post_processor_state['trim_offsets']      ,snake_case__ )






				def 		snake_case	(			self ):
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
													SCREAMING_SNAKE_CASE_   : List[str]					  =      'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
													SCREAMING_SNAKE_CASE_   : Dict					  =      F'{text_of_1_token} {text_of_1_token}'

													SCREAMING_SNAKE_CASE_   : Tuple					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : str					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ) + 1, len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Any					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ) + 1, len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Dict					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : str					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ), len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : int					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ), len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : List[Any]					  =      F' {text}'

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													SCREAMING_SNAKE_CASE_   : Any					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : Tuple					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(1, 1 + len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(1 + len(snake_case__ ) + 1, 1 + len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, 1 + len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(1 + len(snake_case__ ), 1 + len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, 1 + len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(1 + len(snake_case__ ), 1 + len(snake_case__ ) + 1 + len(snake_case__ ))      ,)









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__ :   Tuple						=							logging.get_logger(__name__)


UpperCamelCase__ :   Optional[int]						=							'''https://openaipublic.azureedge.net/jukebox/models/'''
UpperCamelCase__ :   Optional[Any]						=							{
    '''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_ :     List[str]  )       ->  int:







			"""simple docstring"""





			if key.endswith('.model.1.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[str]					  =      key.replace('.model.1.bias'  ,							'.conv1d_1.bias'  )
			elif key.endswith('.model.1.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('.model.1.weight'  ,							'.conv1d_1.weight'  )
			elif key.endswith('.model.3.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      key.replace('.model.3.bias'  ,							'.conv1d_2.bias'  )
			elif key.endswith('.model.3.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('.model.3.weight'  ,							'.conv1d_2.weight'  )

			if "conditioner_blocks.0." in key:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('conditioner_blocks.0'  ,							'conditioner_blocks'  )

			if "prime_prior" in key:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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_   : Union[str, Any]					  =      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_   : Union[str, Any]					  =      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_ :     Dict  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     List[Any]  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      {}
			import re

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      re.compile(
			    R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : str					  =      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      re.compile(
			    R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

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

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

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

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

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

						elif re_decoder_block_proj_in.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re_decoder_block_proj_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : int					  =      F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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_  ):
									SCREAMING_SNAKE_CASE_   : List[str]					  =      re_prior_cond_conv_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : str					  =      int(groups[1]  ) * 2 + int(groups[2]  ) - 2
									SCREAMING_SNAKE_CASE_   : Any					  =      F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      re_prior_cond_conv_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						elif re_prior_cond_resnet.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      re_prior_cond_resnet.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[str]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : Tuple					  =      int(groups[1]  ) * 2 + int(groups[2]  ) - 2
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {'1': 1, '3': 2}[groups[-2]]
									SCREAMING_SNAKE_CASE_   : List[str]					  =      F'conditioner_blocks.upsampler.upsample_block.{block_index}.'
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Dict					  =      prefix + resnet_block
									SCREAMING_SNAKE_CASE_   : List[str]					  =      re_prior_cond_resnet.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

						# keep original key
						else:
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      original_key

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

						SCREAMING_SNAKE_CASE_   : int					  =      original_key
						SCREAMING_SNAKE_CASE_   : int					  =      value

			return new_dict





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     int=None  ,							lowerCamelCase_ :     int=None  )       ->  Dict:







			"""simple docstring"""





			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"  )[-1]}'  ):
									SCREAMING_SNAKE_CASE_   : int					  =      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  )

			SCREAMING_SNAKE_CASE_   : List[str]					  =      MODEL_MAPPING[model_name.split('/'  )[-1]]

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      JukeboxConfig.from_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      JukeboxModel(lowerCamelCase_  )

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

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

						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'vqvae' if i == 0 else F'priors.{3 - i}'
						SCREAMING_SNAKE_CASE_   : Any					  =      fix_jukebox_keys(lowerCamelCase_  ,							model.state_dict()  ,							lowerCamelCase_  ,							lowerCamelCase_  )
						weight_dict.append(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Tuple					  =      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__":
						UpperCamelCase__ :   Optional[int]						=							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__ :   str						=							parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



