

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


A									=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A									=  {
    '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
A									=  {
    '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
}
A									=  sorted(arg_to_scheduler.keys())
A									=  '{' + ', '.join(arg_to_scheduler_choices) + '}'


class SCREAMING_SNAKE_CASE	(		pl.LightningModule				):






			"""simple docstring"""




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

				"""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(__UpperCamelCase				)
				snake_case_   		=			0
				snake_case_   		=			Path(self.hparams.output_dir				)
				snake_case_   		=			self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
					snake_case_   		=			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:
					snake_case_   		=			config

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


			def       __lowerCAmelCase			(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.model_type.from_pretrained(*__UpperCamelCase			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model
				snake_case_   		=			['bias', 'LayerNorm.weight']
				snake_case_   		=			[
				    {
				        '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:
					snake_case_   		=			Adafactor(
					    __UpperCamelCase			,					lr=self.hparams.learning_rate			,					scale_parameter=__UpperCamelCase			,					relative_step=__UpperCamelCase				)

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

				snake_case_   		=			self.get_lr_scheduler()

				return [optimizer], [scheduler]


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				return self.validation_step(__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				return self.validation_end(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			max(1			,					self.hparams.gpus				)  # TODO: consider num_tpu_cores
				snake_case_   		=			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			,					__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = False				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.train_loader


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				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			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.output_dir.joinpath('best_tfmr'				)
				snake_case_   		=			self.step_count
				self.model.save_pretrained(__UpperCamelCase				)
				self.tokenizer.save_pretrained(__UpperCamelCase				)







			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				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=str(Path(__UpperCamelCase				).parent / 'test_run' / 'cache'				)			,					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 SCREAMING_SNAKE_CASE	(		pl.Callback				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""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				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


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







class SCREAMING_SNAKE_CASE	(		pl.Callback				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			trainer.lr_schedulers[0]['scheduler']
				snake_case_   		=			{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
				pl_module.logger.log_metrics(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				rank_zero_info('***** Validation results *****'				)
				snake_case_   		=			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			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				rank_zero_info('***** Test results *****'				)
				snake_case_   		=			trainer.callback_metrics
				# Log and save results to file
				snake_case_   		=			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(lowercase__      ,			lowercase__						):





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






def a(lowercase__      ,			lowercase__      ,			lowercase__=None      ,			lowercase__=True      ,			lowercase__=[]      ,			lowercase__=None      ,			lowercase__=None      ,			**lowercase__      ,			):





	'''simple docstring'''
	pl.seed_everything(args.seed						)

	# init model
	snake_case_   		=			Path(model.hparams.output_dir						)
	odir.mkdir(exist_ok=lowercase__						)

	# add custom checkpoints
	if checkpoint_callback is None:
		snake_case_   		=			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(lowercase__						)
	if logging_callback is None:
		snake_case_   		=			LoggingCallback()

	snake_case_   		=			{}

	if args.fpaa:
		snake_case_   		=			16

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

	snake_case_   		=			args.accumulate_grad_batches
	snake_case_   		=			None
	snake_case_   		=			'auto'

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

	if args.do_train:
		trainer.fit(lowercase__						)

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



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	None
			__A    	=	BloomTokenizerFast
			__A    	=	BloomTokenizerFast
			__A    	=	True
			__A    	=	False
			__A    	=	"""tokenizer_file"""
			__A    	=	{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().setUp()
				snake_case_   		=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'				)
				tokenizer.save_pretrained(self.tmpdirname				)


			def       __lowerCAmelCase			(     self			,					**__UpperCamelCase				):

				"""simple docstring"""


				kwargs.update(self.special_tokens_map				)
				return BloomTokenizerFast.from_pretrained(self.tmpdirname			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()

				snake_case_   		=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
				snake_case_   		=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

				snake_case_   		=			tokenizer.batch_encode_plus(__UpperCamelCase				)['input_ids']
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)

				snake_case_   		=			tokenizer.batch_decode(__UpperCamelCase				)
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=6				):

				"""simple docstring"""


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
						snake_case_   		=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase			,					**__UpperCamelCase				)
						# tokenizer_r.pad_token = None # Hotfixing padding = None
						# Simple input
						snake_case_   		=			'This is a simple input'
						snake_case_   		=			['This is a simple input 1', 'This is a simple input 2']
						snake_case_   		=			('This is a simple input', 'This is a pair')
						snake_case_   		=			[
						    ('This is a simple input 1', 'This is a simple input 2'),
						    ('This is a simple pair 1', 'This is a simple pair 2'),
						]

						# Simple input tests
						try:
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)

							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
						except ValueError:
							self.fail('Bloom Tokenizer should be able to deal with padding'				)

						snake_case_   		=			None  # Hotfixing padding = None
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()
				snake_case_   		=			load_dataset('xnli'			,					'all_languages'			,					split='test'			,					streaming=__UpperCamelCase				)

				snake_case_   		=			next(iter(__UpperCamelCase				)				)['premise']  # pick up one data
				snake_case_   		=			list(sample_data.values()				)

				snake_case_   		=			list(map(tokenizer.encode			,					__UpperCamelCase				)				)
				snake_case_   		=			[tokenizer.decode(__UpperCamelCase			,					clean_up_tokenization_spaces=__UpperCamelCase				) for x in output_tokens]
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)			,					1				)
				self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)			,					1				)
