






import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
					    RwkvForCausalLM,
					    RwkvModel,
					)
					from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
					snake_case_   :	Dict							      =False





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__=14							, lowercase__=7							, lowercase__=True							, lowercase__=False							, lowercase__=True							, lowercase__=True							, lowercase__=True							, lowercase__=99							, lowercase__=32							, lowercase__=5							, lowercase__=37							, lowercase__="gelu"							, lowercase__=0.1							, lowercase__=0.1							, lowercase__=512							, lowercase__=16							, lowercase__=2							, lowercase__=3							, lowercase__=4							, lowercase__=None							, )				->				Dict:
				__A    =							parent
				__A    =							batch_size
				__A    =							seq_length
				__A    =							is_training
				__A    =							use_token_type_ids
				__A    =							use_input_mask
				__A    =							use_labels
				__A    =							use_mc_token_ids
				__A    =							vocab_size
				__A    =							hidden_size
				__A    =							num_hidden_layers
				__A    =							intermediate_size
				__A    =							hidden_act
				__A    =							hidden_dropout_prob
				__A    =							attention_probs_dropout_prob
				__A    =							max_position_embeddings
				__A    =							type_vocab_size
				__A    =							type_sequence_label_size
				__A    =							num_labels
				__A    =							num_choices
				__A    =							scope
				__A    =							vocab_size - 1
				__A    =							vocab_size - 1
				__A    =							vocab_size - 1






		def 					_lowerCamelCase					(      self						)				->				str:
				return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"						)






		def 					_lowerCamelCase					(      self							, lowercase__=False							, lowercase__=False							, lowercase__=False						)				->				List[Any]:
				__A    =							ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

				__A    =							None
				if self.use_input_mask:
						__A    =							random_attention_mask([self.batch_size, self.seq_length]						)

				__A    =							None
				if self.use_token_type_ids:
						__A    =							ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size						)

				__A    =							None
				if self.use_mc_token_ids:
						__A    =							ids_tensor([self.batch_size, self.num_choices]							, self.seq_length						)

				__A    =							None
				__A    =							None
				__A    =							None
				if self.use_labels:
						__A    =							ids_tensor([self.batch_size]							, self.type_sequence_label_size						)
						__A    =							ids_tensor([self.batch_size, self.seq_length]							, self.num_labels						)
						__A    =							ids_tensor([self.batch_size]							, self.num_choices						)

				__A    =							self.get_config(
				    gradient_checkpointing=lowercase__							, scale_attn_by_inverse_layer_idx=lowercase__							, reorder_and_upcast_attn=lowercase__							, )

				return (
				    config,
				    input_ids,
				    input_mask,
				    None,
				    token_type_ids,
				    mc_token_ids,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				)






		def 					_lowerCamelCase					(      self							, lowercase__=False							, lowercase__=False							, lowercase__=False						)				->				Optional[Any]:
				return RwkvConfig(
				    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, intermediate_size=self.intermediate_size							, activation_function=self.hidden_act							, resid_pdrop=self.hidden_dropout_prob							, attn_pdrop=self.attention_probs_dropout_prob							, n_positions=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, use_cache=lowercase__							, bos_token_id=self.bos_token_id							, eos_token_id=self.eos_token_id							, pad_token_id=self.pad_token_id							, gradient_checkpointing=lowercase__							, scale_attn_by_inverse_layer_idx=lowercase__							, reorder_and_upcast_attn=lowercase__							, )






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							self.get_config()
				__A    =							300
				return config






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				(
				    (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   
				)    =							self.prepare_config_and_inputs()

				__A    =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
				__A    =							ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2						)

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				    token_type_ids,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__						)				->				Union[str, Any]:
				__A    =							True
				__A    =							RwkvModel(config=lowercase__						)
				model.to(lowercase__						)
				model.eval()

				__A    =							model(lowercase__						)

				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)						)
				self.parent.assertEqual(len(result.hidden_states						)							, config.num_hidden_layers + 1						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__						)				->				List[str]:
				__A    =							RwkvForCausalLM(lowercase__						)
				model.to(lowercase__						)
				model.eval()

				__A    =							model(lowercase__							, labels=lowercase__						)
				self.parent.assertEqual(result.loss.shape							, ()						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__						)				->				List[str]:
				__A    =							RwkvModel(config=lowercase__						)
				model.to(lowercase__						)
				model.eval()

				__A    =							model(lowercase__						)
				__A    =							outputs.last_hidden_state

				__A    =							model(input_ids[:, :2]						)
				__A    =							outputs.last_hidden_state

				# Using the state computed on the first inputs, we will get the same output
				__A    =							model(input_ids[:, 2:]							, state=outputs.state						)
				__A    =							outputs.last_hidden_state

				self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]							, dim=1						)							, lowercase__							, atol=1e-5						)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__							, lowercase__=False						)				->				Tuple:
				__A    =							RwkvForCausalLM(lowercase__						)
				model.to(lowercase__						)
				if gradient_checkpointing:
						model.gradient_checkpointing_enable()

				__A    =							model(lowercase__							, labels=lowercase__						)
				self.parent.assertEqual(result.loss.shape							, ()						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)						)
				result.loss.backward()






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							self.prepare_config_and_inputs()

				(
				    (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   
				)    =							config_and_inputs

				__A    =							{"input_ids": input_ids}

				return config, inputs_dict





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,       reason='See https://github.com/huggingface/transformers/pull/24204'		)
@require_torch
class        a__      (	lowerCAmelCase__	,       lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Optional[int] 					= (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
		UpperCAmelCase_							:       Any 					= (
		    {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
		)
		# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
		UpperCAmelCase_							:       Any 					= False
		UpperCAmelCase_							:       Dict 					= False
		UpperCAmelCase_							:       str 					= False
		UpperCAmelCase_							:       List[str] 					= False
		UpperCAmelCase_							:       Any 					= False  # Rwkv does not support head masking






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							RwkvModelTester(self						)
				__A    =							ConfigTester(
				    self							, config_class=lowercase__							, n_embd=37							, common_properties=["hidden_size", "num_hidden_layers"]						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__=None						)				->				List[str]:
				if isinstance(lowercase__							, torch.Tensor						):
						__A ,   __A    =							member.max().item(), member.min().item()
				elif isinstance(lowercase__							, lowercase__						) or isinstance(lowercase__							, lowercase__						):
						__A ,   __A    =							max(lowercase__						), min(lowercase__						)

				if not isinstance(lowercase__							, lowercase__						):
						raise TypeError("container should be a list or tuple"						)
				elif len(lowercase__						) != 2:
						raise ValueError("container should have 2 elements"						)

				__A ,   __A    =							container

				__A    =							(min_value >= expected_min) and (max_value <= expected_max)

				if not is_inside_interval:
						__A    =							"%s not found in %s" % (safe_repr(lowercase__						), safe_repr(lowercase__						))
						self.fail(self._formatMessage(lowercase__							, lowercase__						)						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				self.config_tester.run_common_tests()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_rwkv_model(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_causl_lm(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_state_equivalency(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				str:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__A    =							model_class(config=lowercase__						)
						for name, param in model.named_parameters():
								if "time_decay" in name:
										if param.requires_grad:
												self.assertTrue(param.data.max().item() == 3.0						)
												self.assertTrue(param.data.min().item() == -5.0						)
								elif "time_first" in name:
										if param.requires_grad:
												# check if it's a ones like
												self.assertTrue(torch.allclose(param.data							, torch.ones_like(param.data						)							, atol=1e-5							, rtol=1e-5						)						)
								elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]						):
										if param.requires_grad:
												self.assertInterval(
												    param.data							, [0.0, 1.0]							, msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							, )
								elif "time_mix_value" in name:
										if param.requires_grad:
												self.assertInterval(
												    param.data							, [0.0, 1.3]							, msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							, )






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()
				__A    =							True

				__A    =							getattr(self.model_tester							, "seq_length"							, lowercase__						)

				for model_class in self.all_model_classes:
						__A    =							True
						__A    =							False
						__A    =							True
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()

						__A    =							self._prepare_for_class(lowercase__							, lowercase__						)
						__A    =							inputs["input_ids"].shape[0]
						with torch.no_grad():
								__A    =							model(**lowercase__						)
						__A    =							outputs.attentions
						self.assertEqual(len(lowercase__						)							, self.model_tester.num_hidden_layers						)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						__A    =							True
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()

						__A    =							self._prepare_for_class(lowercase__							, lowercase__						)
						__A    =							inputs["input_ids"].shape[0]
						with torch.no_grad():
								__A    =							model(**lowercase__						)
						__A    =							outputs.attentions
						self.assertEqual(len(lowercase__						)							, self.model_tester.num_hidden_layers						)

						self.assertListEqual(
						    list(attentions[0].shape[-3:]						)							, [batch_size, seq_len, config.hidden_size]							, )
						__A    =							len(lowercase__						)

						# Check attention is always last and order is fine
						__A    =							True
						__A    =							True
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()

						__A    =							self._prepare_for_class(lowercase__							, lowercase__						)
						__A    =							inputs["input_ids"].shape[0]
						with torch.no_grad():
								__A    =							model(**lowercase__						)

						__A    =							1
						self.assertEqual(out_len + added_hidden_states							, len(lowercase__						)						)

						__A    =							outputs.attentions

						self.assertEqual(len(lowercase__						)							, self.model_tester.num_hidden_layers						)
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:]						)							, [batch_size, seq_len, config.hidden_size]							, )






		@slow
		def 					_lowerCamelCase					(      self						)				->				List[str]:
				for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__A    =							RwkvModel.from_pretrained(lowercase__						)
						self.assertIsNotNone(lowercase__						)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,       reason='See https://github.com/huggingface/transformers/pull/24204'		)
@slow
class        a__      (	unittest.TestCase		):






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							"RWKV/rwkv-4-169m-pile"
				__A    =							AutoTokenizer.from_pretrained(self.model_id						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							"Hello my name is Jasmine and I am a newbie to the"
				__A    =							RwkvForCausalLM.from_pretrained(self.model_id						).to(lowercase__						)

				__A    =							self.tokenizer("Hello my name is"							, return_tensors="pt"						).input_ids.to(lowercase__						)
				__A    =							model.generate(lowercase__							, max_new_tokens=10						)
				__A    =							self.tokenizer.decode(output[0].tolist()						)

				self.assertEqual(lowercase__							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							"Hello my name is Jasmine and I am a newbie to the"

				__A    =							self.tokenizer("Hello my name is"							, return_tensors="pt"						).input_ids.to(lowercase__						)
				__A    =							RwkvForCausalLM.from_pretrained(self.model_id							, torch_dtype=torch.bfloataa						).to(lowercase__						)

				__A    =							model.generate(lowercase__							, max_new_tokens=10						)
				__A    =							self.tokenizer.decode(output[0].tolist()						)

				self.assertEqual(lowercase__							, lowercase__						)






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    __lowerCAmelCase					(	pl.LightningModule			):




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



									"""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(_snake_case						)
									_lowerCAmelCase         =       0
									_lowerCAmelCase         =       Path(self.hparams.output_dir						)
									_lowerCAmelCase         =       self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
														_lowerCAmelCase         =       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=_snake_case		,							**_snake_case		,							)
									else:
														_lowerCAmelCase         =       config

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

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




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model_type.from_pretrained(*_snake_case		,							**_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model
									_lowerCAmelCase         =       ["""bias""", """LayerNorm.weight"""]
									_lowerCAmelCase         =       [
									    {
									        """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:
														_lowerCAmelCase         =       Adafactor(
														    _snake_case		,							lr=self.hparams.learning_rate		,							scale_parameter=_snake_case		,							relative_step=_snake_case						)

									else:
														_lowerCAmelCase         =       AdamW(
														    _snake_case		,							lr=self.hparams.learning_rate		,							eps=self.hparams.adam_epsilon						)
									_lowerCAmelCase         =       optimizer

									_lowerCAmelCase         =       self.get_lr_scheduler()

									return [optimizer], [scheduler]




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									return self.validation_step(_snake_case		,							_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return self.validation_end(_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





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




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									if mode == "test":
														_lowerCAmelCase         =       len(self.test_dataloader().dataset						)
									else:
														_lowerCAmelCase         =       self.get_dataloader("""train"""		,							self.hparams.train_batch_size		,							shuffle=_snake_case						)
														_lowerCAmelCase         =       len(self.train_dataloader().dataset						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case = False						):



									"""simple docstring"""





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




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.train_loader




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.get_dataloader("""dev"""		,							self.hparams.eval_batch_size		,							shuffle=_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.get_dataloader("""test"""		,							self.hparams.eval_batch_size		,							shuffle=_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





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




				@pl.utilities.rank_zero_only
				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.output_dir.joinpath("""best_tfmr"""						)
									_lowerCAmelCase         =       self.step_count
									self.model.save_pretrained(_snake_case						)
									self.tokenizer.save_pretrained(_snake_case						)




				@staticmethod
				def 				snake_case  (				_snake_case		,							_snake_case						):



									"""simple docstring"""





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





class    __lowerCAmelCase					(	pl.Callback			):




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





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


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					parser.add_argument(
					    """--output_dir"""      ,		default=snake_case      ,		type=snake_case      ,		required=snake_case      ,		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=snake_case      ,		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=snake_case     )
					parser.add_argument("""--max_grad_norm"""      ,		dest="""gradient_clip_val"""      ,		default=1.0      ,		type=snake_case      ,		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=snake_case      ,		default=1      ,		help="""Number of updates steps to accumulate before performing a backward/update pass."""      ,		)
					parser.add_argument("""--seed"""      ,		type=snake_case      ,		default=42      ,		help="""random seed for initialization"""     )
					parser.add_argument(
					    """--data_dir"""      ,		default=snake_case      ,		type=snake_case      ,		required=snake_case      ,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""      ,		)


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case=None      ,		snake_case=True      ,		snake_case=[]      ,		snake_case=None      ,		snake_case=None      ,		**snake_case      ,		):





					"""simple docstring"""

					pl.seed_everything(args.seed     )

					# init model
					_lowerCAmelCase         =       Path(model.hparams.output_dir     )
					odir.mkdir(exist_ok=snake_case     )

					# add custom checkpoints
					if checkpoint_callback is None:
										_lowerCAmelCase         =       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(snake_case     )
					if logging_callback is None:
										_lowerCAmelCase         =       LoggingCallback()

					_lowerCAmelCase         =       {}

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

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

					_lowerCAmelCase         =       args.accumulate_grad_batches
					_lowerCAmelCase         =       extra_train_kwargs.get("""accelerator"""      ,		snake_case     )
					_lowerCAmelCase         =       extra_train_kwargs.get("""profiler"""      ,		snake_case     )

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

					if args.do_train:
										trainer.fit(snake_case     )

					return trainer



