






"""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


SCREAMING_SNAKE_CASE						:						List[str]					   =       logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

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





class       snake_case_       (    pl.LightningModule       ):



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a=None  ,					__a="base"  ,					__a=None  ,					__a=None  ,					__a=None  ,					**__a  ,					):


					"""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						)
					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=__a  ,					**__a  ,					)
					else:
									A__							 =       config

					A__							 =       ('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:
									A__							 =       AutoTokenizer.from_pretrained(
									    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,					cache_dir=__a  ,					)
					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=__a  ,					)
					else:
									A__							 =       model


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					A__							 =       self.model_type.from_pretrained(*__a  ,					**__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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						)
					        ],  # 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:
									A__							 =       Adafactor(
									    __a  ,					lr=self.hparams.learning_rate  ,					scale_parameter=__a  ,					relative_step=__a						)

					else:
									A__							 =       AdamW(
									    __a  ,					lr=self.hparams.learning_rate  ,					eps=self.hparams.adam_epsilon						)
					A__							 =       optimizer

					A__							 =       self.get_lr_scheduler()

					return [optimizer], [scheduler]


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					return self.validation_step(__a  ,					__a						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.validation_end(__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a = False						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.train_loader


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""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						):


					"""simple docstring"""

					A__							 =       self.output_dir.joinpath('best_tfmr'						)
					A__							 =       self.step_count
					self.model.save_pretrained(__a						)
					self.tokenizer.save_pretrained(__a						)






	@staticmethod
	def       _UpperCAmelCase						(      __a  ,					__a						):


					"""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       snake_case_       (    pl.Callback       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""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       snake_case_       (    pl.Callback       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

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





class       snake_case_       (    pl.Callback       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					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(__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					rank_zero_info('***** Validation results *****'						)
					A__							 =       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						):


					"""simple docstring"""

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







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

				# init model
				A__							 =       Path(model.hparams.output_dir				)
				odir.mkdir(exist_ok=lowerCAmelCase__				)

				# 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(lowerCAmelCase__				)
				if logging_callback is None:
								A__							 =       LoggingCallback()

				A__							 =       {}

				if args.fpaa:
								A__							 =       16

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

				A__							 =       args.accumulate_grad_batches
				A__							 =       None
				A__							 =       'auto'

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

				if args.do_train:
								trainer.fit(lowerCAmelCase__				)

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










"""simple docstring"""






import os

import numpy
import onnx



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       a.name
				A__							 =       b.name

				A__							 =       ''
				A__							 =       ''

				A__							 =       a == b

				A__							 =       name_a
				A__							 =       name_b

				return res



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for i, input_name in enumerate(node_proto.input				):
								if input_name == name:
												node_proto.input.insert(lowerCAmelCase__       ,lowerCAmelCase__				)
												node_proto.input.pop(i + 1				)

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
								_graph_replace_input_with(node_proto.attribute[1].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(model.graph.initializer				)
				A__							 =       list(model_without_ext.graph.initializer				)
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								A__							 =       inits[i].name
								A__							 =       inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]				)

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph       ,lowerCAmelCase__       ,lowerCAmelCase__				)







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       os.path.dirname(lowerCAmelCase__				)
				A__							 =       os.path.basename(lowerCAmelCase__				)

				A__							 =       onnx.load(os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)

				A__							 =       list(model.graph.initializer				)

				A__							 =       set()
				A__							 =       {}
				A__							 =       []

				A__							 =       0

				for i in range(len(lowerCAmelCase__				)				):
								if i in dup_set:
												continue

								for j in range(i + 1       ,len(lowerCAmelCase__				)				):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]       ,inits[j]				):
																dup_set.add(lowerCAmelCase__				)
																dup_set.add(lowerCAmelCase__				)

																A__							 =       inits[j].data_type
																A__							 =       numpy.prod(inits[j].dims				)
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 11:
																				mem_size *= 8
																else:
																				print('unexpected data type: '       ,lowerCAmelCase__				)
																total_reduced_size += mem_size

																A__							 =       inits[i].name
																A__							 =       inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCAmelCase__				)
																else:
																				A__							 =       [name_j]
																ind_to_replace.append((j, i)				)

				print('total reduced size: '       ,total_reduced_size / 1024 / 1024 / 1024       ,'GB'				)

				A__							 =       sorted(lowerCAmelCase__				)
				_remove_dup_initializers_from_model(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				A__							 =       'optimized_' + model_file_name
				A__							 =       os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)
				onnx.save(lowerCAmelCase__       ,lowerCAmelCase__				)

				return new_model


