


'''simple docstring'''






import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
		from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder


def  _snake_case       (					A			,   A			,   A			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   )						->   Tuple:
							if attention_mask is None:
														lowerCAmelCase__	       =     input_ids.ne(config.pad_token_id	)
							if decoder_attention_mask is None:
														lowerCAmelCase__	       =     decoder_input_ids.ne(config.pad_token_id	)
							if head_mask is None:
														lowerCAmelCase__	       =     torch.ones(config.encoder_layers			,   config.encoder_attention_heads			,   device=A	)
							if decoder_head_mask is None:
														lowerCAmelCase__	       =     torch.ones(config.decoder_layers			,   config.decoder_attention_heads			,   device=A	)
							if cross_attn_head_mask is None:
														lowerCAmelCase__	       =     torch.ones(config.decoder_layers			,   config.decoder_attention_heads			,   device=A	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=13 ,   lowerCamelCase_=7 ,   lowerCamelCase_=True ,   lowerCamelCase_=False ,   lowerCamelCase_=99 ,   lowerCamelCase_=16 ,   lowerCamelCase_=2 ,   lowerCamelCase_=4 ,   lowerCamelCase_=4 ,   lowerCamelCase_="relu" ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=20 ,   lowerCamelCase_=2 ,   lowerCamelCase_=1 ,   lowerCamelCase_=0 ,   )	->							Any:
														lowerCAmelCase__	       =     parent
														lowerCAmelCase__	       =     batch_size
														lowerCAmelCase__	       =     seq_length
														lowerCAmelCase__	       =     is_training
														lowerCAmelCase__	       =     use_labels
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     hidden_size
														lowerCAmelCase__	       =     num_hidden_layers
														lowerCAmelCase__	       =     num_attention_heads
														lowerCAmelCase__	       =     intermediate_size
														lowerCAmelCase__	       =     hidden_act
														lowerCAmelCase__	       =     hidden_dropout_prob
														lowerCAmelCase__	       =     attention_probs_dropout_prob
														lowerCAmelCase__	       =     encoder_layerdrop
														lowerCAmelCase__	       =     decoder_layerdrop
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     eos_token_id
														lowerCAmelCase__	       =     pad_token_id
														lowerCAmelCase__	       =     bos_token_id







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size       )
														lowerCAmelCase__	       =     self.eos_token_id  # Eos Token
														lowerCAmelCase__	       =     ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size       )

														# we need to clamp the input ids here to avoid having pad token in between
														# this is because for M2M100 the position_ids are prepared such that
														# all pad tokens have pos id = 2 and rest are between 2..seq_length
														# and the seq_length here is seq_length - num_pad_tokens
														# but when using past, there is no way of knowing if the past input ids had
														# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
														# position_ids being off by num_pad_tokens in past input
														lowerCAmelCase__	       =     input_ids.clamp(self.pad_token_id + 1       )
														lowerCAmelCase__	       =     decoder_input_ids.clamp(self.pad_token_id + 1       )

														lowerCAmelCase__	       =     self.get_config()
														lowerCAmelCase__	       =     prepare_mam_aaa_inputs_dict(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )
														return config, inputs_dict







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														return MaMaaaConfig(
														    vocab_size=self.vocab_size ,   d_model=self.hidden_size ,   encoder_layers=self.num_hidden_layers ,   decoder_layers=self.num_hidden_layers ,   encoder_attention_heads=self.num_attention_heads ,   decoder_attention_heads=self.num_attention_heads ,   encoder_ffn_dim=self.intermediate_size ,   decoder_ffn_dim=self.intermediate_size ,   dropout=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   encoder_layerdrop=self.encoder_layerdrop ,   decoder_layerdrop=self.decoder_layerdrop ,   max_position_embeddings=self.max_position_embeddings ,   eos_token_id=self.eos_token_id ,   bos_token_id=self.bos_token_id ,   pad_token_id=self.pad_token_id ,   )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.prepare_config_and_inputs()
														return config, inputs_dict







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Any:
														lowerCAmelCase__	       =     MaMaaaModel(config=lowerCamelCase_       ).get_decoder().to(lowerCamelCase_       ).eval()
														lowerCAmelCase__	       =     inputs_dict['''input_ids''']
														lowerCAmelCase__	       =     inputs_dict['''attention_mask''']
														lowerCAmelCase__	       =     inputs_dict['''head_mask''']

														# first forward pass
														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_ ,   head_mask=lowerCamelCase_ ,   use_cache=lowerCamelCase_       )

														lowerCAmelCase__       , lowerCAmelCase__	       =     outputs.to_tuple()

														# create hypothetical multiple next token and extent to next_input_ids
														lowerCAmelCase__	       =     ids_tensor((self.batch_size, 3) ,   config.vocab_size       )
														lowerCAmelCase__	       =     ids_tensor((self.batch_size, 3) ,   2       )

														# append to next input_ids and
														lowerCAmelCase__	       =     torch.cat([input_ids, next_tokens] ,   dim=-1       )
														lowerCAmelCase__	       =     torch.cat([attention_mask, next_attn_mask] ,   dim=-1       )

														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_       )['''last_hidden_state''']
														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_ ,   past_key_values=lowerCamelCase_       )[
														    '''last_hidden_state'''
														]

														# select random slice
														lowerCAmelCase__	       =     ids_tensor((1,) ,   output_from_past.shape[-1]       ).item()
														lowerCAmelCase__	       =     output_from_no_past[:, -3:, random_slice_idx].detach()
														lowerCAmelCase__	       =     output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(lowerCamelCase_ ,   lowerCamelCase_ ,   atol=1e-2       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							List[str]:
														lowerCAmelCase__	       =     MaMaaaModel(config=lowerCamelCase_       ).to(lowerCamelCase_       ).eval()
														lowerCAmelCase__	       =     model(**lowerCamelCase_       )

														lowerCAmelCase__	       =     outputs.encoder_last_hidden_state
														lowerCAmelCase__	       =     outputs.last_hidden_state

														with tempfile.TemporaryDirectory() as tmpdirname:
																					lowerCAmelCase__	       =     model.get_encoder()
																					encoder.save_pretrained(lowerCamelCase_       )
																					lowerCAmelCase__	       =     MaMaaaEncoder.from_pretrained(lowerCamelCase_       ).to(lowerCamelCase_       )

														lowerCAmelCase__	       =     encoder(inputs_dict['''input_ids'''] ,   attention_mask=inputs_dict['''attention_mask''']       )[
														    0
														]

														self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					lowerCAmelCase__	       =     model.get_decoder()
																					decoder.save_pretrained(lowerCamelCase_       )
																					lowerCAmelCase__	       =     MaMaaaDecoder.from_pretrained(lowerCamelCase_       ).to(lowerCamelCase_       )

														lowerCAmelCase__	       =     decoder(
														    input_ids=inputs_dict['''decoder_input_ids'''] ,   attention_mask=inputs_dict['''decoder_attention_mask'''] ,   encoder_hidden_states=lowerCamelCase_ ,   encoder_attention_mask=inputs_dict['''attention_mask'''] ,   )[0]

														self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3       )




@require_torch
class 		a__    (							a__    ,    a__    ,    a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       str     						=				(
							    (
							        MaMaaaModel,
							        MaMaaaForConditionalGeneration,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__      :       int     						=				(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
							lowercase__      :       str     						=				(
							    {
							        "conversational": MaMaaaForConditionalGeneration,
							        "feature-extraction": MaMaaaModel,
							        "summarization": MaMaaaForConditionalGeneration,
							        "text2text-generation": MaMaaaForConditionalGeneration,
							        "translation": MaMaaaForConditionalGeneration,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase__      :       Optional[int]     						=				True
							lowercase__      :       int     						=				True
							lowercase__      :       int     						=				False
							lowercase__      :       List[str]     						=				False







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Optional[Any]:
														if pipeline_test_casse_name == "TranslationPipelineTests":
																					# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
																					# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
																					return True

														return False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     MaMaaaModelTester(self       )
														lowerCAmelCase__	       =     ConfigTester(self ,   config_class=lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()
														for model_class in self.all_model_classes:
																					lowerCAmelCase__	       =     model_class(lowerCamelCase_       )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(lowerCamelCase_       )
																												lowerCAmelCase__       , lowerCAmelCase__	       =     model_class.from_pretrained(lowerCamelCase_ ,   output_loading_info=lowerCamelCase_       )
																					self.assertEqual(info['''missing_keys'''] ,   []       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_encoder_decoder_model_standalone(*lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																					lowerCAmelCase__	       =     model_class(lowerCamelCase_       )
																					model.to(lowerCamelCase_       )
																					model.eval()

																					lowerCAmelCase__	       =     copy.deepcopy(self._prepare_for_class(lowerCamelCase_ ,   lowerCamelCase_       )       )

																					if not self.is_encoder_decoder:
																												lowerCAmelCase__	       =     inputs['''input_ids''']
																												del inputs["input_ids"]
																					else:
																												lowerCAmelCase__	       =     inputs['''input_ids''']
																												lowerCAmelCase__	       =     inputs.get('''decoder_input_ids''' ,   lowerCamelCase_       )
																												del inputs["input_ids"]
																												inputs.pop('''decoder_input_ids''' ,   lowerCamelCase_       )

																					lowerCAmelCase__	       =     model.get_input_embeddings()
																					if not self.is_encoder_decoder:
																												lowerCAmelCase__	       =     wte(lowerCamelCase_       )
																					else:
																												lowerCAmelCase__	       =     wte(lowerCamelCase_       )
																												lowerCAmelCase__	       =     wte(lowerCamelCase_       )

																					with torch.no_grad():
																												model(**lowerCamelCase_       )[0]







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()
														lowerCAmelCase__	       =     input_dict['''input_ids''']
														lowerCAmelCase__	       =     input_ids.ne(1       ).to(lowerCamelCase_       )
														lowerCAmelCase__	       =     MaMaaaForConditionalGeneration(lowerCamelCase_       ).eval().to(lowerCamelCase_       )
														if torch_device == "cuda":
																					model.half()
														model.generate(lowerCamelCase_ ,   attention_mask=lowerCamelCase_       )
														model.generate(num_beams=4 ,   do_sample=lowerCamelCase_ ,   early_stopping=lowerCamelCase_ ,   num_return_sequences=3       )


def  _snake_case       (					A	)						->   str:
							return torch.tensor(A			,   dtype=torch.long			,   device=A	)


__UpperCAmelCase       =							1e-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							@cached_property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''       ).to(lowerCamelCase_       )
														lowerCAmelCase__	       =     _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]       )
														lowerCAmelCase__	       =     _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]       )
														lowerCAmelCase__	       =     prepare_mam_aaa_inputs_dict(model.config ,   lowerCamelCase_ ,   lowerCamelCase_       )
														with torch.no_grad():
																					lowerCAmelCase__	       =     model(**lowerCamelCase_       )[0]
														lowerCAmelCase__	       =     torch.Size((1, 11, 10_24)       )
														self.assertEqual(output.shape ,   lowerCamelCase_       )
														# change to expected output here
														lowerCAmelCase__	       =     torch.tensor(
														    [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]] ,   device=lowerCamelCase_       )
														self.assertTrue(torch.allclose(output[:, :3, :3] ,   lowerCamelCase_ ,   atol=lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''       ).to(lowerCamelCase_       )

														# change to intended input
														lowerCAmelCase__	       =     _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]       )
														lowerCAmelCase__	       =     _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]       )
														lowerCAmelCase__	       =     prepare_mam_aaa_inputs_dict(model.config ,   lowerCamelCase_ ,   lowerCamelCase_       )
														with torch.no_grad():
																					lowerCAmelCase__	       =     model(**lowerCamelCase_       )[0]
														lowerCAmelCase__	       =     torch.Size((1, 11, model.config.vocab_size)       )
														self.assertEqual(output.shape ,   lowerCamelCase_       )
														# change to expected output here
														lowerCAmelCase__	       =     torch.tensor(
														    [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]] ,   device=lowerCamelCase_       )
														self.assertTrue(torch.allclose(output[:, :3, :3] ,   lowerCamelCase_ ,   atol=lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''       ).to(lowerCamelCase_       )
														lowerCAmelCase__	       =     MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''' ,   src_lang='''fr''' ,   tgt_lang='''en'''       )

														lowerCAmelCase__	       =     [
														    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
														    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
														    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
														    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
														    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
														]

														# The below article tests that we don't add any hypotheses outside of the top n_beams
														lowerCAmelCase__	       =     tokenizer(lowerCamelCase_ ,   padding=lowerCamelCase_ ,   return_tensors='''pt'''       )

														lowerCAmelCase__	       =     model.generate(
														    input_ids=dct['''input_ids'''].to(lowerCamelCase_       ) ,   attention_mask=dct['''attention_mask'''].to(lowerCamelCase_       ) ,   num_beams=5 ,   forced_bos_token_id=tokenizer.get_lang_id('''en'''       ) ,   )

														lowerCAmelCase__	       =     [
														    '''The NSA case highlights the total absence of intelligence debate''',
														    '''I think there are two levels of response from the French government.''',
														    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
														    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
														    ''' communications in France.''',
														]

														lowerCAmelCase__	       =     tokenizer.batch_decode(
														    hypotheses_batch.tolist() ,   clean_up_tokenization_spaces=lowerCamelCase_ ,   skip_special_tokens=lowerCamelCase_       )
														assert generated == expected_en



'''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)