
'''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


lowerCAmelCase					:    Optional[int]           = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase					:    List[str]           = {
    '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
lowerCAmelCase					:    int           = {
    '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
}
lowerCAmelCase					:    Any           = sorted(arg_to_scheduler.keys())
lowerCAmelCase					:    Optional[int]           = '{' + ', '.join(arg_to_scheduler_choices) + '}'


class 			SCREAMING_SNAKE_CASE__      (     pl.LightningModule):







				def __init__(       self						,				A_						,				A_=None						,				A_="base"						,				A_=None						,				A_=None						,				A_=None						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__()
											# TODO: move to self.save_hyperparameters()
											# self.save_hyperparameters()
											# can also expand arguments into trainer signature for easier reading

											self.save_hyperparameters(A_     )
											UpperCamelCase   			=     0
											UpperCamelCase   			=     Path(self.hparams.output_dir     )
											UpperCamelCase   			=     self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
																		UpperCamelCase   			=     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=A_						,				**A_						,				)
											else:
																		UpperCamelCase   			=     config

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

											if tokenizer is None:
																		UpperCamelCase   			=     AutoTokenizer.from_pretrained(
																		    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,				cache_dir=A_						,				)
											else:
																		UpperCamelCase   			=     tokenizer
											UpperCamelCase   			=     MODEL_MODES[mode]
											if model is None:
																		UpperCamelCase   			=     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=A_						,				)
											else:
																		UpperCamelCase   			=     model







				def 						UpperCAmelCase_    (       self						,				*A_						,				**A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_type.from_pretrained(*A_						,				**A_     )







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







											'''simple docstring'''






											UpperCamelCase   			=     arg_to_scheduler[self.hparams.lr_scheduler]
											UpperCamelCase   			=     get_schedule_func(
											    self.opt						,				num_warmup_steps=self.hparams.warmup_steps						,				num_training_steps=self.total_steps()     )
											UpperCamelCase   			=     {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
											return scheduler







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model
											UpperCamelCase   			=     ['bias', 'LayerNorm.weight']
											UpperCamelCase   			=     [
											    {
											        'params': [
											            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )
											        ],  # check this named paramters
											        '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:
																		UpperCamelCase   			=     Adafactor(
																		    A_						,				lr=self.hparams.learning_rate						,				scale_parameter=A_						,				relative_step=A_     )

											else:
																		UpperCamelCase   			=     AdamW(
																		    A_						,				lr=self.hparams.learning_rate						,				eps=self.hparams.adam_epsilon     )
											UpperCamelCase   			=     optimizer

											UpperCamelCase   			=     self.get_lr_scheduler()

											return [optimizer], [scheduler]







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Optional[Any]:







											'''simple docstring'''






											return self.validation_step(A_						,				A_     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






											return self.validation_end(A_     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											if stage == "test":
																		UpperCamelCase   			=     len(self.test_dataloader().dataset     )
											else:
																		UpperCamelCase   			=     self.get_dataloader('train'						,				self.hparams.train_batch_size						,				shuffle=A_     )
																		UpperCamelCase   			=     len(self.train_dataloader().dataset     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_ = False     )->		Dict:







											'''simple docstring'''






											raise NotImplementedError('You must implement this for your task'     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.train_loader







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											return self.get_dataloader('dev'						,				self.hparams.eval_batch_size						,				shuffle=A_     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											return self.get_dataloader('test'						,				self.hparams.eval_batch_size						,				shuffle=A_     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											return os.path.join(
											    self.hparams.data_dir						,				'cached_{}_{}_{}'.format(
											        A_						,				list(filter(A_						,				self.hparams.model_name_or_path.split('/'     )     )     ).pop()						,				str(self.hparams.max_seq_length     )						,				)						,				)







				@pl.utilities.rank_zero_only
				def 						UpperCAmelCase_    (       self						,				A_     )->		None:







											'''simple docstring'''






											UpperCamelCase   			=     self.output_dir.joinpath('best_tfmr'     )
											UpperCamelCase   			=     self.step_count
											self.model.save_pretrained(A_     )
											self.tokenizer.save_pretrained(A_     )







				@staticmethod
				def 						UpperCAmelCase_    (       A_						,				A_     )->		Tuple:







											'''simple docstring'''






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


class 			SCREAMING_SNAKE_CASE__      (     pl.Callback):







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											if (
											    trainer.is_global_zero and trainer.global_rank == 0
											):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																		pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class 			SCREAMING_SNAKE_CASE__      (     pl.Callback):







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											for name, param in pl_module.model.rag.named_parameters():
																		if param.grad is None:
																									print(A_     )


class 			SCREAMING_SNAKE_CASE__      (     pl.Callback):







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     trainer.lr_schedulers[0]['scheduler']
											UpperCamelCase   			=     {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
											pl_module.logger.log_metrics(A_     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		List[str]:







											'''simple docstring'''






											rank_zero_info('***** Validation results *****'     )
											UpperCamelCase   			=     trainer.callback_metrics
											# Log results
											for key in sorted(A_     ):
																		if key not in ["log", "progress_bar"]:
																									rank_zero_info('{} = {}\n'.format(A_						,				str(metrics[key]     )     )     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






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







def 		A_(     A						:		Optional[int]		,     A						:		Tuple):
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    '--output_dir'		,     default=str(Path(A).parent / 'test_run' / 'model_checkpoints')		,     type=A		,     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=A		,     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=A)
							parser.add_argument('--max_grad_norm'		,     dest='gradient_clip_val'		,     default=1.0		,     type=A		,     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=A		,     default=1		,     help='Number of updates steps to accumulate before performing a backward/update pass.'		,     )
							parser.add_argument('--seed'		,     type=A		,     default=42		,     help='random seed for initialization')
							parser.add_argument(
							    '--data_dir'		,     default=str(Path(A).parent / 'test_run' / 'dummy-train-data')		,     type=A		,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'		,     )







def 		A_(     A						:		BaseTransformer		,     A						:		argparse.Namespace		,     A						:		Optional[int]=None		,     A						:		Optional[int]=True		,     A						:		Dict=[]		,     A						:		str=None		,     A						:		List[str]=None		,     **A						:		Tuple		,     ):
							pl.seed_everything(args.seed)

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

							# add custom checkpoints
							if checkpoint_callback is None:
														UpperCamelCase   			=     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(A)
							if logging_callback is None:
														UpperCamelCase   			=     LoggingCallback()

							UpperCamelCase   			=     {}

							if args.fpaa:
														UpperCamelCase   			=     16

							if args.gpus > 1:
														UpperCamelCase   			=     'auto'
														UpperCamelCase   			=     'ddp'

							UpperCamelCase   			=     args.accumulate_grad_batches
							UpperCamelCase   			=     None
							UpperCamelCase   			=     'auto'

							UpperCamelCase   			=     pl.Trainer.from_argparse_args(
							    A		,     weights_summary=A		,     callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,     logger=A		,     val_check_interval=1		,     num_sanity_val_steps=2		,     **A		,     )

							if args.do_train:
														trainer.fit(A)

							else:
														print('RAG modeling tests with new set functions successfuly executed!')
							return trainer






'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




