
'''simple docstring'''





# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCAmelCase            = 1e-10
UpperCAmelCase            = "This is incredible! I love it, this is the best chicken I have ever had."
UpperCAmelCase            = 100

class     a (					nn.Module     ):




				def __init__(				self						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					List[str]="gpt2-medium",     SCREAMING_SNAKE_CASE_						:					Tuple=False,     SCREAMING_SNAKE_CASE_						:					List[Any]="cpu"   ):
										super().__init__()
										snake_case							:  Optional[Any]			=     GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[int]			=     GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     self.encoder.transformer.config.hidden_size
										snake_case							:  Union[str, Any]			=     ClassificationHead(class_size=SCREAMING_SNAKE_CASE_,     embed_size=self.embed_size   )
										snake_case							:  str			=     cached_mode
										snake_case							:  Dict			=     device




				def 						__snake_case				(				self						:					Dict   ):
										return self.classifier_head




				def 						__snake_case				(				self						:					Optional[Any]   ):
										for param in self.encoder.parameters():
																snake_case							:  Optional[Any]			=     False
										self.classifier_head.train()




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										snake_case							:  Dict			=     x.ne(0   ).unsqueeze(2   ).repeat(1,     1,     self.embed_size   ).float().to(self.device   ).detach()
										snake_case							:  str			=     self.encoder.transformer(SCREAMING_SNAKE_CASE_   )['''last_hidden_state''']
										snake_case							:  Any			=     hidden * mask
										snake_case							:  Union[str, Any]			=     torch.sum(SCREAMING_SNAKE_CASE_,     dim=1   ) / (torch.sum(SCREAMING_SNAKE_CASE_,     dim=1   ).detach() + EPSILON)
										return avg_hidden




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										if self.cached_mode:
																snake_case							:  Union[str, Any]			=     x.to(self.device   )
										else:
																snake_case							:  Tuple			=     self.avg_representation(x.to(self.device   )   )

										snake_case							:  Any			=     self.classifier_head(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     nn.functional.log_softmax(SCREAMING_SNAKE_CASE_,     dim=-1   )

										return probs




class     a (					data.Dataset     ):




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any   ):
										snake_case							:  Tuple			=     X
										snake_case							:  Any			=     y




				def __len__(				self						:					Union[str, Any]   ):
										return len(self.X   )




				def __getitem__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										snake_case							:  List[str]			=     {}
										snake_case							:  int			=     self.X[index]
										snake_case							:  Any			=     self.y[index]
										return data




def   A			(							A_				:       List[str]							):
						def pad_sequences(A_				:       List[str]							):
												snake_case							:  Any			=     [len(A_							) for seq in sequences]

												snake_case							:  Tuple			=     torch.zeros(len(A_							)     ,				max(A_							)							).long()  # padding value = 0

												for i, seq in enumerate(A_							):
																		snake_case							:  str			=     lengths[i]
																		snake_case							:  List[Any]			=     seq[:end]

												return padded_sequences, lengths

						snake_case							:  str			=     {}
						for key in data[0].keys():
												snake_case							:  Dict			=     [d[key] for d in data]

						snake_case,       snake_case							:  Any			=     pad_sequences(item_info['''X''']							)
						snake_case							:  Optional[Any]			=     torch.tensor(item_info['''y''']     ,				dtype=torch.long							)

						return x_batch, y_batch

def   A			(							A_				:       Optional[int]							):
						snake_case							:  Tuple			=     {}
						for key in data[0].keys():
												snake_case							:  List[str]			=     [d[key] for d in data]

						snake_case							:  int			=     torch.cat(item_info['''X''']     ,				0							)
						snake_case							:  int			=     torch.tensor(item_info['''y''']     ,				dtype=torch.long							)

						return x_batch, y_batch

def   A			(							A_				:       Tuple     ,				A_				:       List[Any]     ,				A_				:       List[str]     ,				A_				:       Dict=0     ,				A_				:       int=10     ,				A_				:       int="cpu"							):
						snake_case							:  Dict			=     0
						discriminator.train_custom()
						for batch_idx, (input_t, target_t) in enumerate(A_							):
												snake_case,       snake_case							:  Any			=     input_t.to(A_							), target_t.to(A_							)

												optimizer.zero_grad()

												snake_case							:  int			=     discriminator(A_							)
												snake_case							:  str			=     nn.functional.nll_loss(A_     ,				A_							)
												loss.backward(retain_graph=A_							)
												optimizer.step()

												samples_so_far += len(A_							)

												if batch_idx % log_interval == 0:
																		print(
																		    '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
																		        epoch + 1     ,				A_     ,				len(data_loader.dataset							)     ,				100 * samples_so_far / len(data_loader.dataset							)     ,				loss.item()     ,				)							)

def   A			(							A_				:       Any     ,				A_				:       Tuple     ,				A_				:       Optional[Any]="cpu"							):
						discriminator.eval()
						snake_case							:  str			=     0
						snake_case							:  str			=     0
						with torch.no_grad():
												for input_t, target_t in data_loader:
																		snake_case,       snake_case							:  Optional[int]			=     input_t.to(A_							), target_t.to(A_							)
																		snake_case							:  Any			=     discriminator(A_							)
																		# sum up batch loss
																		test_loss += nn.functional.nll_loss(A_     ,				A_     ,				reduction='''sum'''							).item()
																		# get the index of the max log-probability
																		snake_case							:  str			=     output_t.argmax(dim=1     ,				keepdim=A_							)
																		correct += pred_t.eq(target_t.view_as(A_							)							).sum().item()

						test_loss /= len(data_loader.dataset							)

						print(
						    '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
						        A_     ,				A_     ,				len(data_loader.dataset							)     ,				100.0 * correct / len(data_loader.dataset							)							)							)

def   A			(							A_				:       Optional[Any]     ,				A_				:       List[Any]     ,				A_				:       Tuple     ,				A_				:       Dict=False     ,				A_				:       int="cpu"							):
						snake_case							:  Dict			=     model.tokenizer.encode(A_							)
						snake_case							:  Dict			=     torch.tensor([input_t]     ,				dtype=torch.long     ,				device=A_							)
						if cached:
												snake_case							:  int			=     model.avg_representation(A_							)

						snake_case							:  Optional[Any]			=     model(A_							).data.cpu().numpy().flatten().tolist()
						print('''Input sentence:'''     ,				A_							)
						print(
						    '''Predictions:'''     ,				''', '''.join('''{}: {:.4f}'''.format(A_     ,				math.exp(A_							)							) for c, log_prob in zip(A_     ,				A_							)							)     ,				)

def   A			(							A_				:       Dict     ,				A_				:       str     ,				A_				:       Tuple     ,				A_				:       int=False     ,				A_				:       List[Any]="cpu"							):
						snake_case							:  str			=     torch.utils.data.DataLoader(dataset=A_     ,				batch_size=A_     ,				collate_fn=A_							)

						snake_case							:  Optional[Any]			=     []
						snake_case							:  Dict			=     []
						for batch_idx, (x, y) in enumerate(tqdm(A_     ,				ascii=A_							)							):
												with torch.no_grad():
																		snake_case							:  List[Any]			=     x.to(A_							)
																		snake_case							:  Tuple			=     discriminator.avg_representation(A_							).cpu().detach()
																		snake_case							:  Optional[Any]			=     torch.unbind(avg_rep.unsqueeze(1							)							)
																		xs += avg_rep_list
																		ys += y.cpu().numpy().tolist()

						snake_case							:  Any			=     torch.utils.data.DataLoader(
						    dataset=Dataset(A_     ,				A_							)     ,				batch_size=A_     ,				shuffle=A_     ,				collate_fn=A_							)

						return data_loader

def   A			(							A_				:       Any     ,				A_				:       Any=None     ,				A_				:       List[str]="gpt2-medium"     ,				A_				:       Any=10     ,				A_				:       str=64     ,				A_				:       int=10     ,				A_				:       Any=False     ,				A_				:       Optional[int]=False     ,				A_				:       Optional[Any]=False     ,				):
						snake_case							:  int			=     '''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

						print('''Preprocessing {} dataset...'''.format(A_							)							)
						snake_case							:  Optional[Any]			=     time.time()

						if dataset == "SST":
												snake_case							:  List[str]			=     ['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
												snake_case							:  Dict			=     {c: i for i, c in enumerate(A_							)}

												snake_case							:  Optional[int]			=     Discriminator(
												    class_size=len(A_							)     ,				pretrained_model=A_     ,				cached_mode=A_     ,				device=A_							).to(A_							)

												snake_case							:  Optional[int]			=     torchtext_data.Field()
												snake_case							:  Union[str, Any]			=     torchtext_data.Field(sequential=A_							)
												snake_case,       snake_case,       snake_case							:  Dict			=     datasets.SST.splits(
												    A_     ,				A_     ,				fine_grained=A_     ,				train_subtrees=A_     ,				)

												snake_case							:  Optional[Any]			=     []
												snake_case							:  Any			=     []
												for i in trange(len(A_							)     ,				ascii=A_							):
																		snake_case							:  Tuple			=     TreebankWordDetokenizer().detokenize(vars(train_data[i]							)['''text''']							)
																		snake_case							:  Dict			=     discriminator.tokenizer.encode(A_							)
																		snake_case							:  Optional[int]			=     torch.tensor([50256] + seq     ,				device=A_     ,				dtype=torch.long							)
																		x.append(A_							)
																		y.append(classaidx[vars(train_data[i]							)['''label''']]							)
												snake_case							:  Tuple			=     Dataset(A_     ,				A_							)

												snake_case							:  Dict			=     []
												snake_case							:  Optional[Any]			=     []
												for i in trange(len(A_							)     ,				ascii=A_							):
																		snake_case							:  int			=     TreebankWordDetokenizer().detokenize(vars(test_data[i]							)['''text''']							)
																		snake_case							:  Union[str, Any]			=     discriminator.tokenizer.encode(A_							)
																		snake_case							:  Tuple			=     torch.tensor([50256] + seq     ,				device=A_     ,				dtype=torch.long							)
																		test_x.append(A_							)
																		test_y.append(classaidx[vars(test_data[i]							)['''label''']]							)
												snake_case							:  Optional[Any]			=     Dataset(A_     ,				A_							)

												snake_case							:  List[str]			=     {
												    '''class_size''': len(A_							),
												    '''embed_size''': discriminator.embed_size,
												    '''pretrained_model''': pretrained_model,
												    '''class_vocab''': classaidx,
												    '''default_class''': 2,
												}

						elif dataset == "clickbait":
												snake_case							:  List[str]			=     ['''non_clickbait''', '''clickbait''']
												snake_case							:  int			=     {c: i for i, c in enumerate(A_							)}

												snake_case							:  Union[str, Any]			=     Discriminator(
												    class_size=len(A_							)     ,				pretrained_model=A_     ,				cached_mode=A_     ,				device=A_							).to(A_							)

												with open('''datasets/clickbait/clickbait_train_prefix.txt'''							) as f:
																		snake_case							:  Union[str, Any]			=     []
																		for i, line in enumerate(A_							):
																								try:
																														data.append(eval(A_							)							)
																								except Exception:
																														print('''Error evaluating line {}: {}'''.format(A_     ,				A_							)							)
																														continue
												snake_case							:  str			=     []
												snake_case							:  Optional[Any]			=     []
												with open('''datasets/clickbait/clickbait_train_prefix.txt'''							) as f:
																		for i, line in enumerate(tqdm(A_     ,				ascii=A_							)							):
																								try:
																														snake_case							:  Union[str, Any]			=     eval(A_							)
																														snake_case							:  Union[str, Any]			=     discriminator.tokenizer.encode(d['''text''']							)

																														if len(A_							) < max_length_seq:
																																				snake_case							:  Dict			=     torch.tensor([50256] + seq     ,				device=A_     ,				dtype=torch.long							)
																														else:
																																				print('''Line {} is longer than maximum length {}'''.format(A_     ,				A_							)							)
																																				continue
																														x.append(A_							)
																														y.append(d['''label''']							)
																								except Exception:
																														print('''Error evaluating / tokenizing line {}, skipping it'''.format(A_							)							)
																														pass

												snake_case							:  str			=     Dataset(A_     ,				A_							)
												snake_case							:  List[Any]			=     int(0.9 * len(A_							)							)
												snake_case							:  Optional[int]			=     len(A_							) - train_size
												snake_case,       snake_case							:  Optional[Any]			=     torch.utils.data.random_split(A_     ,				[train_size, test_size]							)

												snake_case							:  Any			=     {
												    '''class_size''': len(A_							),
												    '''embed_size''': discriminator.embed_size,
												    '''pretrained_model''': pretrained_model,
												    '''class_vocab''': classaidx,
												    '''default_class''': 1,
												}

						elif dataset == "toxic":
												snake_case							:  str			=     ['''non_toxic''', '''toxic''']
												snake_case							:  Union[str, Any]			=     {c: i for i, c in enumerate(A_							)}

												snake_case							:  int			=     Discriminator(
												    class_size=len(A_							)     ,				pretrained_model=A_     ,				cached_mode=A_     ,				device=A_							).to(A_							)

												snake_case							:  Optional[Any]			=     []
												snake_case							:  Any			=     []
												with open('''datasets/toxic/toxic_train.txt'''							) as f:
																		for i, line in enumerate(tqdm(A_     ,				ascii=A_							)							):
																								try:
																														snake_case							:  Optional[int]			=     eval(A_							)
																														snake_case							:  str			=     discriminator.tokenizer.encode(d['''text''']							)

																														if len(A_							) < max_length_seq:
																																				snake_case							:  Optional[int]			=     torch.tensor([50256] + seq     ,				device=A_     ,				dtype=torch.long							)
																														else:
																																				print('''Line {} is longer than maximum length {}'''.format(A_     ,				A_							)							)
																																				continue
																														x.append(A_							)
																														y.append(int(np.sum(d['''label''']							) > 0							)							)
																								except Exception:
																														print('''Error evaluating / tokenizing line {}, skipping it'''.format(A_							)							)
																														pass

												snake_case							:  Dict			=     Dataset(A_     ,				A_							)
												snake_case							:  Optional[int]			=     int(0.9 * len(A_							)							)
												snake_case							:  Optional[Any]			=     len(A_							) - train_size
												snake_case,       snake_case							:  Union[str, Any]			=     torch.utils.data.random_split(A_     ,				[train_size, test_size]							)

												snake_case							:  List[str]			=     {
												    '''class_size''': len(A_							),
												    '''embed_size''': discriminator.embed_size,
												    '''pretrained_model''': pretrained_model,
												    '''class_vocab''': classaidx,
												    '''default_class''': 0,
												}

						else:  # if dataset == "generic":
												# This assumes the input dataset is a TSV with the following structure:
												# class \t text

												if dataset_fp is None:
																		raise ValueError('''When generic dataset is selected, dataset_fp needs to be specified aswell.'''							)

												snake_case							:  Optional[int]			=     set()
												with open(A_							) as f:
																		snake_case							:  List[Any]			=     csv.reader(A_     ,				delimiter='''\t'''							)
																		for row in tqdm(A_     ,				ascii=A_							):
																								if row:
																														classes.add(row[0]							)

												snake_case							:  int			=     sorted(A_							)
												snake_case							:  Any			=     {c: i for i, c in enumerate(A_							)}

												snake_case							:  str			=     Discriminator(
												    class_size=len(A_							)     ,				pretrained_model=A_     ,				cached_mode=A_     ,				device=A_							).to(A_							)

												snake_case							:  Union[str, Any]			=     []
												snake_case							:  Tuple			=     []
												with open(A_							) as f:
																		snake_case							:  Dict			=     csv.reader(A_     ,				delimiter='''\t'''							)
																		for i, row in enumerate(tqdm(A_     ,				ascii=A_							)							):
																								if row:
																														snake_case							:  Union[str, Any]			=     row[0]
																														snake_case							:  Union[str, Any]			=     row[1]

																														try:
																																				snake_case							:  int			=     discriminator.tokenizer.encode(A_							)
																																				if len(A_							) < max_length_seq:
																																										snake_case							:  Tuple			=     torch.tensor([50256] + seq     ,				device=A_     ,				dtype=torch.long							)

																																				else:
																																										print('''Line {} is longer than maximum length {}'''.format(A_     ,				A_							)							)
																																										continue

																																				x.append(A_							)
																																				y.append(classaidx[label]							)

																														except Exception:
																																				print('''Error tokenizing line {}, skipping it'''.format(A_							)							)
																																				pass

												snake_case							:  Tuple			=     Dataset(A_     ,				A_							)
												snake_case							:  Any			=     int(0.9 * len(A_							)							)
												snake_case							:  List[str]			=     len(A_							) - train_size
												snake_case,       snake_case							:  int			=     torch.utils.data.random_split(A_     ,				[train_size, test_size]							)

												snake_case							:  Any			=     {
												    '''class_size''': len(A_							),
												    '''embed_size''': discriminator.embed_size,
												    '''pretrained_model''': pretrained_model,
												    '''class_vocab''': classaidx,
												    '''default_class''': 0,
												}

						snake_case							:  Union[str, Any]			=     time.time()
						print('''Preprocessed {} data points'''.format(len(A_							) + len(A_							)							)							)
						print('''Data preprocessing took: {:.3f}s'''.format(end - start							)							)

						if cached:
												print('''Building representation cache...'''							)

												snake_case							:  Optional[int]			=     time.time()

												snake_case							:  Optional[int]			=     get_cached_data_loader(A_     ,				A_     ,				A_     ,				shuffle=A_     ,				device=A_							)

												snake_case							:  Union[str, Any]			=     get_cached_data_loader(A_     ,				A_     ,				A_     ,				device=A_							)

												snake_case							:  Dict			=     time.time()
												print('''Building representation cache took: {:.3f}s'''.format(end - start							)							)

						else:
												snake_case							:  int			=     torch.utils.data.DataLoader(
												    dataset=A_     ,				batch_size=A_     ,				shuffle=A_     ,				collate_fn=A_							)
												snake_case							:  Tuple			=     torch.utils.data.DataLoader(dataset=A_     ,				batch_size=A_     ,				collate_fn=A_							)

						if save_model:
												with open('''{}_classifier_head_meta.json'''.format(A_							)     ,				'''w'''							) as meta_file:
																		json.dump(A_     ,				A_							)

						snake_case							:  Dict			=     optim.Adam(discriminator.parameters()     ,				lr=0.0001							)

						for epoch in range(A_							):
												snake_case							:  Dict			=     time.time()
												print('''\nEpoch'''     ,				epoch + 1							)

												train_epoch(
												    discriminator=A_     ,				data_loader=A_     ,				optimizer=A_     ,				epoch=A_     ,				log_interval=A_     ,				device=A_     ,				)
												evaluate_performance(data_loader=A_     ,				discriminator=A_     ,				device=A_							)

												snake_case							:  Optional[int]			=     time.time()
												print('''Epoch took: {:.3f}s'''.format(end - start							)							)

												print('''\nExample prediction'''							)
												predict(A_     ,				A_     ,				A_     ,				cached=A_     ,				device=A_							)

												if save_model:
																		# torch.save(discriminator.state_dict(),
																		#           "{}_discriminator_{}.pt".format(
																		#               args.dataset, epoch + 1
																		#               ))
																		torch.save(
																		    discriminator.get_classifier().state_dict()     ,				'''{}_classifier_head_epoch_{}.pt'''.format(A_     ,				epoch + 1							)     ,				)


if __name__ == "__main__":
						UpperCAmelCase            = argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
						parser.add_argument(
						    "--dataset",
						    type=str,
						    default="SST",
						    choices=("SST", "clickbait", "toxic", "generic"),
						    help=(
						        "dataset to train the discriminator on."
						        "In case of generic, the dataset is expected"
						        "to be a TSBV file with structure: class \\t text"
						    ),
						)
						parser.add_argument(
						    "--dataset_fp",
						    type=str,
						    default="",
						    help="File path of the dataset to use. Needed only in case of generic datadset",
						)
						parser.add_argument(
						    "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
						)
						parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
						parser.add_argument(
						    "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
						)
						parser.add_argument(
						    "--log_interval",
						    type=int,
						    default=10,
						    metavar="N",
						    help="how many batches to wait before logging training status",
						)
						parser.add_argument("--save_model", action="store_true", help="whether to save the model")
						parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
						parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
						UpperCAmelCase            = parser.parse_args()

						train_discriminator(**(vars(args)))



'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen_decoder'''
				_snake_case		     =			['''past_key_values''']




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict=20_48,     SCREAMING_SNAKE_CASE_						:					List[str]=20_48,     SCREAMING_SNAKE_CASE_						:					int=24,     SCREAMING_SNAKE_CASE_						:					Dict=40_96,     SCREAMING_SNAKE_CASE_						:					str=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					List[Any]=10_24,     SCREAMING_SNAKE_CASE_						:					int=0.1,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					Any=False,     SCREAMING_SNAKE_CASE_						:					List[str]=4,     SCREAMING_SNAKE_CASE_						:					str=20_48,     SCREAMING_SNAKE_CASE_						:					int=20_48,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     **SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  Optional[int]			=     vocab_size
										snake_case							:  Optional[int]			=     max_position_embeddings
										snake_case							:  Dict			=     hidden_size
										snake_case							:  Any			=     ffn_dim
										snake_case							:  Optional[Any]			=     num_hidden_layers
										snake_case							:  Tuple			=     num_attention_heads
										snake_case							:  int			=     dropout
										snake_case							:  Union[str, Any]			=     attention_dropout
										snake_case							:  Optional[int]			=     activation_dropout
										snake_case							:  Union[str, Any]			=     activation_function
										snake_case							:  Tuple			=     initializer_factor
										snake_case							:  List[Any]			=     layerdrop
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Dict			=     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case							:  Tuple			=     num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_,     tie_word_embeddings=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )




class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen'''
				_snake_case		     =			True




				def __init__(				self						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''   )

										snake_case							:  Any			=     kwargs.pop('''text_encoder'''   )
										snake_case							:  List[Any]			=     text_encoder_config.pop('''model_type'''   )

										snake_case							:  Union[str, Any]			=     kwargs.pop('''audio_encoder'''   )
										snake_case							:  Tuple			=     audio_encoder_config.pop('''model_type'''   )

										snake_case							:  List[str]			=     kwargs.pop('''decoder'''   )

										snake_case							:  List[str]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     True




				@classmethod
				def 						__snake_case				(				cls						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					MusicgenDecoderConfig,     **SCREAMING_SNAKE_CASE_						:					Optional[Any],     ):
										return cls(
										    text_encoder=text_encoder_config.to_dict(),     audio_encoder=audio_encoder_config.to_dict(),     decoder=decoder_config.to_dict(),     **SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Dict			=     copy.deepcopy(self.__dict__   )
										snake_case							:  Tuple			=     self.text_encoder.to_dict()
										snake_case							:  str			=     self.audio_encoder.to_dict()
										snake_case							:  Dict			=     self.decoder.to_dict()
										snake_case							:  Optional[int]			=     self.__class__.model_type
										return output

