





"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_snake_case	:						Union[str, Any]					=					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_snake_case	:						Dict					=					{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_snake_case	:						Optional[Any]					=					{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_snake_case	:						List[str]					=					sorted(arg_to_scheduler.keys())
_snake_case	:						List[str]					=					'{' + ', '.join(arg_to_scheduler_choices) + '}'






class    _UpperCAmelCase  (      pl.LightningModule					):


					def __init__(   self     :Dict	,			__UpperCamelCase     :argparse.Namespace	,			__UpperCamelCase     :Dict=None	,			__UpperCamelCase     :List[str]="base"	,			__UpperCamelCase     :Tuple=None	,			__UpperCamelCase     :Dict=None	,			__UpperCamelCase     :Any=None	,			**__UpperCamelCase     :Tuple	,			):
												super().__init__()
												# TODO: move to self.save_hyperparameters()
												# self.save_hyperparameters()
												# can also expand arguments into trainer signature for easier reading

												self.save_hyperparameters(__UpperCamelCase						)
												A						=    0
												A						=    Path(self.hparams.output_dir						)
												A						=    self.hparams.cache_dir if self.hparams.cache_dir else None
												if config is None:
																			A						=    AutoConfig.from_pretrained(
																			    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path	,			**({"num_labels": num_labels} if num_labels is not None else {})	,			cache_dir=__UpperCamelCase	,			**__UpperCamelCase	,			)
												else:
																			A						=    config

												A						=    ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
												for p in extra_model_params:
																			if getattr(self.hparams	,			__UpperCamelCase	,			__UpperCamelCase						):
																										assert hasattr(self.config	,			__UpperCamelCase						), f"model config doesn't have a `{p}` attribute"
																										setattr(self.config	,			__UpperCamelCase	,			getattr(self.hparams	,			__UpperCamelCase						)						)

												if tokenizer is None:
																			A						=    AutoTokenizer.from_pretrained(
																			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,			cache_dir=__UpperCamelCase	,			)
												else:
																			A						=    tokenizer
												A						=    MODEL_MODES[mode]
												if model is None:
																			A						=    self.model_type.from_pretrained(
																			    self.hparams.model_name_or_path	,			from_tf=bool(".ckpt" in self.hparams.model_name_or_path						)	,			config=self.config	,			cache_dir=__UpperCamelCase	,			)
												else:
																			A						=    model


					def       lowerCamelCase			(   self     :Any	,			*__UpperCamelCase     :str	,			**__UpperCamelCase     :Tuple						):
												A						=    self.model_type.from_pretrained(*__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str						):
												A						=    arg_to_scheduler[self.hparams.lr_scheduler]
												A						=    get_schedule_func(
												    self.opt	,			num_warmup_steps=self.hparams.warmup_steps	,			num_training_steps=self.total_steps()						)
												A						=    {"scheduler": scheduler, "interval": "step", "frequency": 1}
												return scheduler


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    self.model
												A						=    ["bias", "LayerNorm.weight"]
												A						=    [
												    {
												        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
												        "weight_decay": self.hparams.weight_decay,
												    },
												    {
												        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
												        "weight_decay": 0.0,
												    },
												]
												if self.hparams.adafactor:
																			A						=    Adafactor(
																			    __UpperCamelCase	,			lr=self.hparams.learning_rate	,			scale_parameter=__UpperCamelCase	,			relative_step=__UpperCamelCase						)

												else:
																			A						=    AdamW(
																			    __UpperCamelCase	,			lr=self.hparams.learning_rate	,			eps=self.hparams.adam_epsilon						)
												A						=    optimizer

												A						=    self.get_lr_scheduler()

												return [optimizer], [scheduler]


					def       lowerCamelCase			(   self     :List[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :Tuple						):
												return self.validation_step(__UpperCamelCase	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[str]	,			__UpperCamelCase     :int						):
												return self.validation_end(__UpperCamelCase						)


					def       lowerCamelCase			(   self     :int						):
												A						=    max(1	,			self.hparams.gpus						)  # TODO: consider num_tpu_cores
												A						=    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
												return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


					def       lowerCamelCase			(   self     :List[Any]	,			__UpperCamelCase     :Any						):
												if mode == "test":
																			A						=    len(self.test_dataloader().dataset						)
												else:
																			A						=    self.get_dataloader("train"	,			self.hparams.train_batch_size	,			shuffle=__UpperCamelCase						)
																			A						=    len(self.train_dataloader().dataset						)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :int	,			__UpperCamelCase     :bool = False						):
												raise NotImplementedError("You must implement this for your task"						)


					def       lowerCamelCase			(   self     :List[str]						):
												return self.train_loader


					def       lowerCamelCase			(   self     :Optional[int]						):
												return self.get_dataloader("dev"	,			self.hparams.eval_batch_size	,			shuffle=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[int]						):
												return self.get_dataloader("test"	,			self.hparams.eval_batch_size	,			shuffle=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :Union[str, Any]						):
												return os.path.join(
												    self.hparams.data_dir	,			"cached_{}_{}_{}".format(
												        __UpperCamelCase	,			list(filter(__UpperCamelCase	,			self.hparams.model_name_or_path.split("/"						)						)						).pop()	,			str(self.hparams.max_seq_length						)	,			)	,			)


					@pl.utilities.rank_zero_only
					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Dict[str, Any]						):
												A						=    self.output_dir.joinpath("best_tfmr"						)
												A						=    self.step_count
												self.model.save_pretrained(__UpperCamelCase						)
												self.tokenizer.save_pretrained(__UpperCamelCase						)


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :Any	,			__UpperCamelCase     :int						):
												parser.add_argument(
												    "--model_name_or_path"	,			default=__UpperCamelCase	,			type=__UpperCamelCase	,			required=__UpperCamelCase	,			help="Path to pretrained model or model identifier from huggingface.co/models"	,			)
												parser.add_argument(
												    "--config_name"	,			default=""	,			type=__UpperCamelCase	,			help="Pretrained config name or path if not the same as model_name"						)
												parser.add_argument(
												    "--tokenizer_name"	,			default=__UpperCamelCase	,			type=__UpperCamelCase	,			help="Pretrained tokenizer name or path if not the same as model_name"	,			)
												parser.add_argument(
												    "--cache_dir"	,			default=""	,			type=__UpperCamelCase	,			help="Where do you want to store the pre-trained models downloaded from huggingface.co"	,			)
												parser.add_argument(
												    "--encoder_layerdrop"	,			type=__UpperCamelCase	,			help="Encoder layer dropout probability (Optional). Goes into model.config"	,			)
												parser.add_argument(
												    "--decoder_layerdrop"	,			type=__UpperCamelCase	,			help="Decoder layer dropout probability (Optional). Goes into model.config"	,			)
												parser.add_argument(
												    "--dropout"	,			type=__UpperCamelCase	,			help="Dropout probability (Optional). Goes into model.config"	,			)
												parser.add_argument(
												    "--attention_dropout"	,			type=__UpperCamelCase	,			help="Attention dropout probability (Optional). Goes into model.config"	,			)
												parser.add_argument("--learning_rate"	,			default=5e-5	,			type=__UpperCamelCase	,			help="The initial learning rate for Adam."						)
												parser.add_argument(
												    "--lr_scheduler"	,			default="linear"	,			choices=__UpperCamelCase	,			metavar=__UpperCamelCase	,			type=__UpperCamelCase	,			help="Learning rate scheduler"	,			)
												parser.add_argument("--weight_decay"	,			default=0.0	,			type=__UpperCamelCase	,			help="Weight decay if we apply some."						)
												parser.add_argument("--adam_epsilon"	,			default=1e-8	,			type=__UpperCamelCase	,			help="Epsilon for Adam optimizer."						)
												parser.add_argument("--warmup_steps"	,			default=0	,			type=__UpperCamelCase	,			help="Linear warmup over warmup_steps."						)
												parser.add_argument("--num_workers"	,			default=4	,			type=__UpperCamelCase	,			help="kwarg passed to DataLoader"						)
												parser.add_argument("--num_train_epochs"	,			dest="max_epochs"	,			default=3	,			type=__UpperCamelCase						)
												parser.add_argument("--train_batch_size"	,			default=32	,			type=__UpperCamelCase						)
												parser.add_argument("--eval_batch_size"	,			default=32	,			type=__UpperCamelCase						)
												parser.add_argument("--adafactor"	,			action="store_true"						)







class    _UpperCAmelCase  (      pl.Callback					):


					def       lowerCamelCase			(   self     :List[Any]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Dict						):
												A						=    trainer.lr_schedulers[0]["scheduler"]
												A						=    {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
												pl_module.logger.log_metrics(__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[str]	,			__UpperCamelCase     :pl.Trainer	,			__UpperCamelCase     :pl.LightningModule						):
												rank_zero_info("***** Validation results *****"						)
												A						=    trainer.callback_metrics
												# Log results
												for key in sorted(__UpperCamelCase						):
																			if key not in ["log", "progress_bar"]:
																										rank_zero_info("{} = {}\n".format(__UpperCamelCase	,			str(metrics[key]						)						)						)


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :pl.Trainer	,			__UpperCamelCase     :pl.LightningModule						):
												rank_zero_info("***** Test results *****"						)
												A						=    trainer.callback_metrics
												# Log and save results to file
												A						=    os.path.join(pl_module.hparams.output_dir	,			"test_results.txt"						)
												with open(__UpperCamelCase	,			"w"						) as writer:
																			for key in sorted(__UpperCamelCase						):
																										if key not in ["log", "progress_bar"]:
																																	rank_zero_info("{} = {}\n".format(__UpperCamelCase	,			str(metrics[key]						)						)						)
																																	writer.write("{} = {}\n".format(__UpperCamelCase	,			str(metrics[key]						)						)						)







def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    "--output_dir"   ,    default=UpperCamelCase   ,    type=UpperCamelCase   ,    required=UpperCamelCase   ,    help="The output directory where the model predictions and checkpoints will be written."   ,    )
							parser.add_argument(
							    "--fp16"   ,    action="store_true"   ,    help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   ,    )

							parser.add_argument(
							    "--fp16_opt_level"   ,    type=UpperCamelCase   ,    default="O2"   ,    help=(
							        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
							        "See details at https://nvidia.github.io/apex/amp.html"
							    )   ,    )
							parser.add_argument("--n_tpu_cores"   ,    dest="tpu_cores"   ,    type=UpperCamelCase			)
							parser.add_argument("--max_grad_norm"   ,    dest="gradient_clip_val"   ,    default=1.0   ,    type=UpperCamelCase   ,    help="Max gradient norm"			)
							parser.add_argument("--do_train"   ,    action="store_true"   ,    help="Whether to run training."			)
							parser.add_argument("--do_predict"   ,    action="store_true"   ,    help="Whether to run predictions on the test set."			)
							parser.add_argument(
							    "--gradient_accumulation_steps"   ,    dest="accumulate_grad_batches"   ,    type=UpperCamelCase   ,    default=1   ,    help="Number of updates steps to accumulate before performing a backward/update pass."   ,    )
							parser.add_argument("--seed"   ,    type=UpperCamelCase   ,    default=42   ,    help="random seed for initialization"			)
							parser.add_argument(
							    "--data_dir"   ,    default=UpperCamelCase   ,    type=UpperCamelCase   ,    required=UpperCamelCase   ,    help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."   ,    )






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase=None   ,    UpperCamelCase=True   ,    UpperCamelCase=[]   ,    UpperCamelCase=None   ,    UpperCamelCase=None   ,    **UpperCamelCase   ,    ):
							pl.seed_everything(args.seed			)

							# init model
							A						=    Path(model.hparams.output_dir			)
							odir.mkdir(exist_ok=UpperCamelCase			)

							# add custom checkpoints
							if checkpoint_callback is None:
														A						=    pl.callbacks.ModelCheckpoint(
														    filepath=args.output_dir   ,    prefix="checkpoint"   ,    monitor="val_loss"   ,    mode="min"   ,    save_top_k=1			)
							if early_stopping_callback:
														extra_callbacks.append(UpperCamelCase			)
							if logging_callback is None:
														A						=    LoggingCallback()

							A						=    {}

							# TODO: remove with PyTorch 1.6 since pl uses native amp
							if args.fpaa:
														A						=    16
														A						=    args.fpaa_opt_level

							if args.gpus > 1:
														A						=    "ddp"

							A						=    args.accumulate_grad_batches
							A						=    extra_train_kwargs.get("accelerator"   ,    UpperCamelCase			)
							A						=    extra_train_kwargs.get("profiler"   ,    UpperCamelCase			)

							A						=    pl.Trainer.from_argparse_args(
							    UpperCamelCase   ,    weights_summary=UpperCamelCase   ,    callbacks=[logging_callback] + extra_callbacks   ,    logger=UpperCamelCase   ,    checkpoint_callback=UpperCamelCase   ,    **UpperCamelCase   ,    )

							if args.do_train:
														trainer.fit(UpperCamelCase			)

							return trainer









"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_snake_case	:						List[str]					=					logging.get_logger(__name__)






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				['''input_values''', '''attention_mask''']


					def __init__(   self     :str	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :Tuple=1_60_00	,			__UpperCamelCase     :Optional[int]=0.0	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :Optional[Any]=True	,			**__UpperCamelCase     :Optional[Any]	,			):
												super().__init__(feature_size=__UpperCamelCase	,			sampling_rate=__UpperCamelCase	,			padding_value=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    return_attention_mask
												A						=    do_normalize


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :float = 0.0						):
												if attention_mask is not None:
																			A						=    np.array(__UpperCamelCase	,			np.intaa						)
																			A						=    []

																			for vector, length in zip(__UpperCamelCase	,			attention_mask.sum(-1						)						):
																										A						=    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
																										if length < normed_slice.shape[0]:
																																	A						=    padding_value

																										normed_input_values.append(__UpperCamelCase						)
												else:
																			A						=    [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

												return normed_input_values


					def __call__(   self     :Tuple	,			__UpperCamelCase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,			__UpperCamelCase     :Union[bool, str, PaddingStrategy] = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :Optional[Union[str, TensorType]] = None	,			__UpperCamelCase     :Optional[int] = None	,			**__UpperCamelCase     :List[str]	,			):

												if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																										raise ValueError(
																										    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																										    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
																										    f" {self.sampling_rate} and not {sampling_rate}."						)
												else:
																			logger.warning(
																			    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."						)

												A						=    isinstance(__UpperCamelCase	,			np.ndarray						) and len(raw_speech.shape						) > 1
												if is_batched_numpy and len(raw_speech.shape						) > 2:
																			raise ValueError(f"Only mono-channel audio is supported for input to {self}"						)
												A						=    is_batched_numpy or (
												    isinstance(__UpperCamelCase	,			(list, tuple)						) and (isinstance(raw_speech[0]	,			(np.ndarray, tuple, list)						))
												)

												# always return batch
												if not is_batched:
																			A						=    [raw_speech]

												# convert into correct format for padding
												A						=    BatchFeature({"input_values": raw_speech}						)

												A						=    self.pad(
												    __UpperCamelCase	,			padding=__UpperCamelCase	,			max_length=__UpperCamelCase	,			truncation=__UpperCamelCase	,			pad_to_multiple_of=__UpperCamelCase	,			return_attention_mask=__UpperCamelCase	,			)

												# convert input values to correct format
												A						=    padded_inputs["input_values"]
												if not isinstance(input_values[0]	,			np.ndarray						):
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.floataa						) for array in input_values]
												elif (
												    not isinstance(__UpperCamelCase	,			np.ndarray						)
												    and isinstance(input_values[0]	,			np.ndarray						)
												    and input_values[0].dtype is np.dtype(np.floataa						)
												):
																			A						=    [array.astype(np.floataa						) for array in input_values]
												elif isinstance(__UpperCamelCase	,			np.ndarray						) and input_values.dtype is np.dtype(np.floataa						):
																			A						=    input_values.astype(np.floataa						)

												# convert attention_mask to correct format
												A						=    padded_inputs.get("attention_mask"						)
												if attention_mask is not None:
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.intaa						) for array in attention_mask]

												# zero-mean and unit-variance normalization
												if self.do_normalize:
																			A						=    (
																			    attention_mask
																			    if self._get_padding_strategies(__UpperCamelCase	,			max_length=__UpperCamelCase						) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			A						=    self.zero_mean_unit_var_norm(
																			    padded_inputs["input_values"]	,			attention_mask=__UpperCamelCase	,			padding_value=self.padding_value						)

												if return_tensors is not None:
																			A						=    padded_inputs.convert_to_tensors(__UpperCamelCase						)

												return padded_inputs


