import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


__a  :     Any       =  logging.get_logger(__name__)

__a  :     Union[str, Any]       =  {
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

__a  :     List[Any]       =  {
    """artists_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
    },
    """genres_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
    },
    """lyrics_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
    },
}

__a  :     Tuple       =  {
    """jukebox""": 5_1_2,
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						VOCAB_FILES_NAMES
			__a  : Dict			 =						PRETRAINED_VOCAB_FILES_MAP
			__a  : int			 =						PRETRAINED_LYRIC_TOKENS_SIZES
			__a  : Any			 =						['''input_ids''', '''attention_mask''']





			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=["v3", "v2", "v2"]      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__="<|endoftext|>"      ,   **lowerCAmelCase__      ,   )     ->					str:

							'''simple docstring'''


							__lowercase 			=							AddedToken(lowerCAmelCase__      ,   lstrip=lowerCAmelCase__      ,   rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						) else unk_token
							super().__init__(
							    unk_token=lowerCAmelCase__      ,   n_genres=lowerCAmelCase__      ,   version=lowerCAmelCase__      ,   max_n_lyric_tokens=lowerCAmelCase__      ,   **lowerCAmelCase__      ,   )
							__lowercase 			=							version
							__lowercase 			=							max_n_lyric_tokens
							__lowercase 			=							n_genres

							with open(lowerCAmelCase__      ,   encoding='''utf-8'''						) as vocab_handle:
											__lowercase 			=							json.load(lowerCAmelCase__						)

							with open(lowerCAmelCase__      ,   encoding='''utf-8'''						) as vocab_handle:
											__lowercase 			=							json.load(lowerCAmelCase__						)

							with open(lowerCAmelCase__      ,   encoding='''utf-8'''						) as vocab_handle:
											__lowercase 			=							json.load(lowerCAmelCase__						)

							__lowercase 			=							R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
							# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
							if len(self.lyrics_encoder						) == 79:
											__lowercase 			=							oov.replace(R'''\-\''''      ,   R'''\-+\''''						)

							__lowercase 			=							regex.compile(lowerCAmelCase__						)
							__lowercase 			=							{v: k for k, v in self.artists_encoder.items()}
							__lowercase 			=							{v: k for k, v in self.genres_encoder.items()}
							__lowercase 			=							{v: k for k, v in self.lyrics_encoder.items()}





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return len(self.artists_encoder						) + len(self.genres_encoder						) + len(self.lyrics_encoder						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							return dict(self.artists_encoder      ,   self.genres_encoder      ,   self.lyrics_encoder						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							[self.artists_encoder.get(lowerCAmelCase__      ,   0						) for artist in list_artists]
							for genres in range(len(lowerCAmelCase__						)						):
											__lowercase 			=							[self.genres_encoder.get(lowerCAmelCase__      ,   0						) for genre in list_genres[genres]]
											__lowercase 			=							list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]						))

							__lowercase 			=							[[self.lyrics_encoder.get(lowerCAmelCase__      ,   0						) for character in list_lyrics[0]], [], []]
							return artists_id, list_genres, lyric_ids





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Dict:

							'''simple docstring'''


							return list(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   **lowerCAmelCase__						)     ->					List[str]:

							'''simple docstring'''


							__lowercase			,     __lowercase			,     __lowercase 			=							self.prepare_for_tokenization(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase 			=							self._tokenize(lowerCAmelCase__						)
							return artist, genre, lyrics





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = False						)     ->					Tuple[str, str, str, Dict[str, Any]]:

							'''simple docstring'''


							for idx in range(len(self.version						)						):
											if self.version[idx] == "v3":
															__lowercase 			=							artists[idx].lower()
															__lowercase 			=							[genres[idx].lower()]
											else:
															__lowercase 			=							self._normalize(artists[idx]						) + '''.v2'''
															__lowercase 			=							[
															    self._normalize(lowerCAmelCase__						) + '''.v2''' for genre in genres[idx].split('''_'''						)
															]  # split is for the full dictionary with combined genres

							if self.version[0] == "v2":
											__lowercase 			=							regex.compile(R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''						)
											__lowercase 			=							'''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
											__lowercase 			=							{vocab[index]: index + 1 for index in range(len(lowerCAmelCase__						)						)}
											__lowercase 			=							0
											__lowercase 			=							len(lowerCAmelCase__						) + 1
											__lowercase 			=							self.vocab
											__lowercase 			=							{v: k for k, v in self.vocab.items()}
											__lowercase 			=							''''''
							else:
											__lowercase 			=							regex.compile(R'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'''						)

							__lowercase 			=							self._run_strip_accents(lowerCAmelCase__						)
							__lowercase 			=							lyrics.replace('''\\'''      ,   '''\n'''						)
							__lowercase 			=							self.out_of_vocab.sub(''''''      ,   lowerCAmelCase__						), [], []
							return artists, genres, lyrics





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							unicodedata.normalize('''NFD'''      ,   lowerCAmelCase__						)
							__lowercase 			=							[]
							for char in text:
											__lowercase 			=							unicodedata.category(lowerCAmelCase__						)
											if cat == "Mn":
															continue
											output.append(lowerCAmelCase__						)
							return "".join(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							(
							    [chr(lowerCAmelCase__						) for i in range(ord('''a'''						)      ,   ord('''z'''						) + 1						)]
							    + [chr(lowerCAmelCase__						) for i in range(ord('''A'''						)      ,   ord('''Z'''						) + 1						)]
							    + [chr(lowerCAmelCase__						) for i in range(ord('''0'''						)      ,   ord('''9'''						) + 1						)]
							    + ['''.''']
							)
							__lowercase 			=							frozenset(lowerCAmelCase__						)
							__lowercase 			=							re.compile(R'''_+'''						)
							__lowercase 			=							''''''.join([c if c in accepted else '''_''' for c in text.lower()]						)
							__lowercase 			=							pattern.sub('''_'''      ,   lowerCAmelCase__						).strip('''_'''						)
							return text





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					str:

							'''simple docstring'''


							return " ".join(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = False						)     ->					Optional[Any]:

							'''simple docstring'''


							if not isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						):
											__lowercase 			=							TensorType(lowerCAmelCase__						)

							# Get a function reference for the correct framework
							if tensor_type == TensorType.TENSORFLOW:
											if not is_tf_available():
															raise ImportError(
															    '''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'''						)
											import tensorflow as tf

											__lowercase 			=							tf.constant
											__lowercase 			=							tf.is_tensor
							elif tensor_type == TensorType.PYTORCH:
											if not is_torch_available():
															raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.'''						)
											import torch

											__lowercase 			=							torch.tensor
											__lowercase 			=							torch.is_tensor
							elif tensor_type == TensorType.JAX:
											if not is_flax_available():
															raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.'''						)
											import jax.numpy as jnp  # noqa: F811

											__lowercase 			=							jnp.array
											__lowercase 			=							_is_jax
							else:
											__lowercase 			=							np.asarray
											__lowercase 			=							_is_numpy

							# Do the tensor conversion in batch

							try:
											if prepend_batch_axis:
															__lowercase 			=							[inputs]

											if not is_tensor(lowerCAmelCase__						):
															__lowercase 			=							as_tensor(lowerCAmelCase__						)
							except:  # noqa E722
											raise ValueError(
											    '''Unable to create tensor, you should probably activate truncation and/or padding '''
											    '''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'''						)

							return inputs





			def __call__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=""      ,   lowerCAmelCase__="pt"						)     ->					BatchEncoding:

							'''simple docstring'''


							__lowercase 			=							[0, 0, 0]
							__lowercase 			=							[artist] * len(self.version						)
							__lowercase 			=							[genres] * len(self.version						)

							__lowercase			,     __lowercase			,     __lowercase 			=							self.tokenize(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase			,     __lowercase			,     __lowercase 			=							self._convert_token_to_id(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

							__lowercase 			=							[-INFINITY] * len(full_tokens[-1]						)
							__lowercase 			=							[
							    self.convert_to_tensors(
							        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]      ,   tensor_type=lowerCAmelCase__						)
							    for i in range(len(self.version						)						)
							]
							return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks}						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					Tuple[str]:

							'''simple docstring'''


							if not os.path.isdir(lowerCAmelCase__						):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
											return

							__lowercase 			=							os.path.join(
							    lowerCAmelCase__      ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file''']						)
							with open(lowerCAmelCase__      ,   '''w'''      ,   encoding='''utf-8'''						) as f:
											f.write(json.dumps(self.artists_encoder      ,   ensure_ascii=lowerCAmelCase__						)						)

							__lowercase 			=							os.path.join(
							    lowerCAmelCase__      ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file''']						)
							with open(lowerCAmelCase__      ,   '''w'''      ,   encoding='''utf-8'''						) as f:
											f.write(json.dumps(self.genres_encoder      ,   ensure_ascii=lowerCAmelCase__						)						)

							__lowercase 			=							os.path.join(
							    lowerCAmelCase__      ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file''']						)
							with open(lowerCAmelCase__      ,   '''w'''      ,   encoding='''utf-8'''						) as f:
											f.write(json.dumps(self.lyrics_encoder      ,   ensure_ascii=lowerCAmelCase__						)						)

							return (artists_file, genres_file, lyrics_file)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.artists_decoder.get(lowerCAmelCase__						)
							__lowercase 			=							[self.genres_decoder.get(lowerCAmelCase__						) for genre in genres_index]
							__lowercase 			=							[self.lyrics_decoder.get(lowerCAmelCase__						) for character in lyric_index]
							return artist, genres, lyrics
import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__a  :     str       =  1_6
__a  :     str       =  3_2





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				return int(x / 2**20							)




class    _UpperCamelCase      :


			"""simple docstring"""






			def __enter__(   self						)     ->					str:

							'''simple docstring'''


							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							__lowercase 			=							torch.cuda.memory_allocated()
							return self





			def __exit__(   self      ,   *lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							gc.collect()
							torch.cuda.empty_cache()
							__lowercase 			=							torch.cuda.memory_allocated()
							__lowercase 			=							torch.cuda.max_memory_allocated()
							__lowercase 			=							bamb(self.end - self.begin						)
							__lowercase 			=							bamb(self.peak - self.begin						)
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def 				UpperCAmelCase						(    lowercase							,			lowercase = 16							,			lowercase = "bert-base-cased"							,			lowercase = 320							,			lowercase = 160							,			):







				"""simple docstring"""



				__lowercase 			=							AutoTokenizer.from_pretrained(lowercase							)
				__lowercase 			=							load_dataset(
				    '''glue'''							,			'''mrpc'''							,			split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}							)

				def tokenize_function(lowercase							):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase 			=							tokenizer(examples['''sentence1''']							,			examples['''sentence2''']							,			truncation=lowercase							,			max_length=lowercase							)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				__lowercase 			=							datasets.map(
				    lowercase							,			batched=lowercase							,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,			load_from_cache_file=lowercase							)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase 			=							tokenized_datasets.rename_column('''label'''							,			'''labels'''							)

				def collate_fn(lowercase							):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowercase							,			padding='''max_length'''							,			max_length=128							,			return_tensors='''pt'''							)
								return tokenizer.pad(lowercase							,			padding='''longest'''							,			return_tensors='''pt'''							)

				# Instantiate dataloaders.
				__lowercase 			=							DataLoader(
				    tokenized_datasets['''train''']							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=lowercase							)
				__lowercase 			=							DataLoader(
				    tokenized_datasets['''validation''']							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=lowercase							)

				return train_dataloader, eval_dataloader





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase 			=							config['''lr''']
				__lowercase 			=							int(config['''num_epochs''']							)
				__lowercase 			=							int(config['''seed''']							)
				__lowercase 			=							int(config['''batch_size''']							)
				__lowercase 			=							args.model_name_or_path

				set_seed(lowercase							)
				__lowercase			,     __lowercase 			=							get_dataloaders(lowercase							,			lowercase							,			lowercase							,			args.n_train							,			args.n_val							)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase 			=							AutoModelForSequenceClassification.from_pretrained(lowercase							,			return_dict=lowercase							)

				# Instantiate optimizer
				__lowercase 			=							(
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				__lowercase 			=							optimizer_cls(params=model.parameters()							,			lr=lowercase							)

				if accelerator.state.deepspeed_plugin is not None:
								__lowercase 			=							accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								__lowercase 			=							1
				__lowercase 			=							(len(lowercase							) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								__lowercase 			=							get_linear_schedule_with_warmup(
								    optimizer=lowercase							,			num_warmup_steps=0							,			num_training_steps=lowercase							,			)
				else:
								__lowercase 			=							DummyScheduler(lowercase							,			total_num_steps=lowercase							,			warmup_num_steps=0							)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase 			=							accelerator.prepare(
				    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							)

				# We need to keep track of how many total steps we have iterated over
				__lowercase 			=							0
				# We also need to keep track of the stating epoch so files are named properly
				__lowercase 			=							0

				# Now we train the model
				__lowercase 			=							{}
				for epoch in range(lowercase							,			lowercase							):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowercase							):
																__lowercase 			=							model(**lowercase							)
																__lowercase 			=							outputs.loss
																__lowercase 			=							loss / gradient_accumulation_steps
																accelerator.backward(lowercase							)
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin							)							)							)
								accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used							)							)
								accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked							)							)
								accelerator.print(
								    '''Total Peak Memory consumed during the train (max): {}'''.format(
								        tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
								__lowercase 			=							tracemalloc.peaked + bamb(tracemalloc.begin							)
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir							,			'''peak_memory_utilization.json'''							)							,			'''w'''							) as f:
												json.dump(lowercase							,			lowercase							)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''							)
				parser.add_argument(
				    '''--model_name_or_path'''							,			type=lowercase							,			default='''bert-base-cased'''							,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,			required=lowercase							,			)
				parser.add_argument(
				    '''--output_dir'''							,			type=lowercase							,			default='''.'''							,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''							,			)
				parser.add_argument(
				    '''--peak_memory_upper_bound'''							,			type=lowercase							,			default=lowercase							,			help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''							,			)
				parser.add_argument(
				    '''--n_train'''							,			type=lowercase							,			default=320							,			help='''Number of training examples to use.'''							,			)
				parser.add_argument(
				    '''--n_val'''							,			type=lowercase							,			default=160							,			help='''Number of validation examples to use.'''							,			)
				parser.add_argument(
				    '''--num_epochs'''							,			type=lowercase							,			default=1							,			help='''Number of train epochs.'''							,			)
				__lowercase 			=							parser.parse_args()
				__lowercase 			=							{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase							,			lowercase							)


if __name__ == "__main__":
		main()