import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
					import importlib.resources as pkg_resources
except ImportError:
					# Try backported to PY<37 `importlib_resources`.
					import importlib_resources as pkg_resources

from . import resources


UpperCamelCase      			=     'https://github.com/huggingface/datasets/tree/main/src/datasets/utils'
UpperCamelCase      			=     F'''{BASE_REF_URL}/{__file__}'''
UpperCamelCase      			=     logging.getLogger(__name__)



def    lowerCamelCase_   (  _lowercase    )							->	Tuple[Any, str]:
		__A							:   int	  =				pkg_resources.read_text(_lowercase ,     _lowercase    )
		return yaml.safe_load(_lowercase    ), F"{BASE_REF_URL}/resources/{resource}"


UpperCamelCase			,   UpperCamelCase      			=     load_yaml_resource('readme_structure.yaml')

UpperCamelCase      			=     [
    '[Needs More Information]',
    '[More Information Needed]',
    '(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)',
]

# Dictionary representation of section/readme, error_list, warning_list
UpperCamelCase      			=     Tuple[dict, List[str], List[str]]


class _a       :

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = False   ):
			__A							:   Union[str, Any]	  =				name
			__A							:   Any	  =				level
			__A							:   Optional[int]	  =				lines
			__A							:   Dict	  =				""
			__A							:   List[Any]	  =				True
			__A							:   Tuple	  =				{}
			__A							:   Optional[int]	  =				[]
			__A							:   Dict	  =				[]
			if self.lines is not None:
					self.parse(suppress_parsing_errors=__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase = False   ):
			__A							:   Optional[int]	  =				""
			__A							:   int	  =				[]
			__A							:   Union[str, Any]	  =				False
			for line in self.lines:
					if line.strip(" \n"   ) == "":
							continue
					elif line.strip(" \n"   )[:3] == "```":
							__A							:   int	  =				not code_start
					elif line.split()[0] == self.level + "#" and not code_start:
							if current_sub_level != "":
									__A							:   Dict	  =				Section(__UpperCAmelCase							,			self.level + "#"							,			__UpperCAmelCase   )
									__A							:   Any	  =				[]
							else:
									if current_lines != []:
											self.text += "".join(__UpperCAmelCase   ).strip()
											if self.text != "" and self.text not in FILLER_TEXT:
													__A							:   str	  =				False
											__A							:   List[str]	  =				[]

							__A							:   Dict	  =				" ".join(line.split()[1:]   ).strip(" \n"   )
					else:
							current_lines.append(__UpperCAmelCase   )
			else:
					if current_sub_level != "":
							if current_sub_level in self.content:
									self.parsing_error_list.append(
									    F"Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections."   )
							__A							:   str	  =				Section(__UpperCAmelCase							,			self.level + "#"							,			__UpperCAmelCase   )
					else:
							if current_lines != []:
									self.text += "".join(__UpperCAmelCase   ).strip()
									if self.text != "" and self.text not in FILLER_TEXT:
											__A							:   Dict	  =				False

			if self.level == "" and not suppress_parsing_errors:
					if self.parsing_error_list != [] or self.parsing_warning_list != []:
							__A							:   Tuple	  =				"\n".join("-\t" + x for x in self.parsing_error_list + self.parsing_warning_list   )
							__A							:   str	  =				F"The following issues were found while parsing the README at `{self.name}`:\n" + errors
							raise ValueError(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   Dict	  =				[]
			__A							:   str	  =				[]
			if structure["allow_empty"] is False:
					# If content is expected
					if self.is_empty_text and self.content == {}:
							# If no content is found, mention it in the error_list
							error_list.append(F"Expected some content in section `{self.name}` but it is empty."   )

			if structure["allow_empty_text"] is False:
					# If some text is expected
					if self.is_empty_text:
							# If no text is found, mention it in the error_list
							error_list.append(
							    F"Expected some text in section `{self.name}` but it is empty (text in subsections are ignored)."   )
        # Subsections Validation
			if structure["subsections"] is not None:
					# If subsections are expected
					if self.content == {}:
							# If no subsections are present
							__A							:   List[Any]	  =				[subsection["name"] for subsection in structure["subsections"]]
							# Mention the expected values in the error_list
							error_list.append(
							    F"Section `{self.name}` expected the following subsections: {', '.join(['`'+x+'`' for x in values]   )}. Found 'None'."   )
					else:
							# If some subsections are present
							__A							:   Optional[int]	  =				[subsection["name"] for subsection in structure["subsections"]]
							__A							:   List[str]	  =				False
							for idx, name in enumerate(__UpperCAmelCase   ):
									if name not in self.content:
											# If the expected subsection is not present
											error_list.append(F"Section `{self.name}` is missing subsection: `{name}`."   )
											__A							:   Dict	  =				True
									else:
											# If the subsection is present, validate subsection, return the result
											# and concat the errors from subsection to section error_list

											# Skip sublevel validation if current level is `###`
											if self.level == "###":
													continue
											else:
													__A  ,       __A  ,       __A							:   Tuple	  =				self.content[name].validate(
													    structure["subsections"][idx]   )
											error_list += subsec_error_list
											warning_list += subsec_warning_list

							if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
									for name in self.content:
											if name not in structure_names:
													# If an extra subsection is present
													warning_list.append(
													    F"`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown."   )
			if error_list:
					# If there are errors, do not return the dictionary as it is invalid
					return {}, error_list, warning_list
			else:
					return self.to_dict(), error_list, warning_list







	def 					__UpperCAmelCase(       self   ):
			return {
			    "name": self.name,
			    "text": self.text,
			    "is_empty_text": self.is_empty_text,
			    "subsections": [value.to_dict() for value in self.content.values()],
			}


class _a       (	lowerCAmelCase__ ):  # Level 0

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = False   ):
			super().__init__(name=__UpperCAmelCase							,			level=""   )  # Not using lines here as we need to use a child class parse
			__A							:   Any	  =				structure
			__A							:   Union[str, Any]	  =				-2
			__A							:   Any	  =				0
			__A							:   Tuple	  =				lines
			if self.lines is not None:
					self.parse(suppress_parsing_errors=__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			if self.structure is None:
					__A  ,       __A  ,       __A							:   List[Any]	  =				self._validate(__UpperCAmelCase   )
			else:
					__A  ,       __A  ,       __A							:   Optional[Any]	  =				self._validate(self.structure   )
			if error_list != [] or warning_list != []:
					__A							:   List[Any]	  =				"\n".join(["-\t" + x for x in error_list + warning_list]   )
					__A							:   Union[str, Any]	  =				F"The following issues were found for the README at `{self.name}`:\n" + errors
					raise ValueError(__UpperCAmelCase   )

	@classmethod
	def 					__UpperCAmelCase(       cls							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = False   ):
			with open(__UpperCAmelCase							,			encoding="utf-8"   ) as f:
					__A							:   Any	  =				f.readlines()
			return cls(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			suppress_parsing_errors=__UpperCAmelCase   )

	@classmethod
	def 					__UpperCAmelCase(       cls							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = "root"							,			__UpperCAmelCase = False   ):
			__A							:   Any	  =				string.split("\n"   )
			return cls(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			suppress_parsing_errors=__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase = False   ):
			# Skip Tags
			__A							:   List[str]	  =				0

			for line in self.lines:
					self.yaml_tags_line_count += 1
					if line.strip(" \n"   ) == "---":
							self.tag_count += 1
							if self.tag_count == 2:
									break
					line_count += 1
			if self.tag_count == 2:
					__A							:   Optional[Any]	  =				self.lines[line_count + 1 :]  # Get the last + 1 th item.
			else:
					__A							:   Tuple	  =				self.lines[self.tag_count :]
			super().parse(suppress_parsing_errors=__UpperCAmelCase   )

	def __str__(       self   ):
			return str(self.to_dict()   )







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   Optional[Any]	  =				[]
			__A							:   Dict	  =				[]
			if self.yaml_tags_line_count == 0:
					warning_list.append("Empty YAML markers are present in the README."   )
			elif self.tag_count == 0:
					warning_list.append("No YAML markers are present in the README."   )
			elif self.tag_count == 1:
					warning_list.append("Only the start of YAML tags present in the README."   )
			# Check how many first level sections are present.
			__A							:   str	  =				len(self.content.keys()   )
			if num_first_level_keys > 1:
					# If more than one, add to the error list, continue
					error_list.append(
					    F"The README has several first-level headings: {', '.join(['`'+x+'`' for x in list(self.content.keys()   )]   )}. Only one heading is expected. Skipping further validation for this README."   )
			elif num_first_level_keys < 1:
					# If less than one, append error.
					error_list.append(
					    "The README has no first-level headings. One heading is expected. Skipping further validation for this README."   )

			else:
					# If one exactly
					__A							:   int	  =				list(self.content.keys()   )[0]  # Get the key
					if start_key.startswith("Dataset Card for"   ):  # Check correct start
							# If the starting is correct, validate all the sections
							__A  ,       __A  ,       __A							:   List[Any]	  =				self.content[start_key].validate(
							    readme_structure["subsections"][0]   )
							error_list += sec_error_list
							warning_list += sec_warning_list
					else:
							# If not found, append error
							error_list.append(
							    "No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."   )
			if error_list:
					# If there are errors, do not return the dictionary as it is invalid
					return {}, error_list, warning_list
			else:
					return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
					from argparse import ArgumentParser

					UpperCamelCase      			=     ArgumentParser(usage='Validate the content (excluding YAML tags) of a README.md file.')
					ap.add_argument('readme_filepath')
					UpperCamelCase      			=     ap.parse_args()
					UpperCamelCase      			=     Path(args.readme_filepath)
					UpperCamelCase      			=     ReadMe.from_readme(readme_filepath)






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


UpperCamelCase      			=     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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


class _a       (	pl.LightningModule ):

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=None							,			__UpperCAmelCase="base"							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			**__UpperCAmelCase							,			):
			super().__init__()
			# TODO: move to self.save_hyperparameters()
			# self.save_hyperparameters()
			# can also expand arguments into trainer signature for easier reading

			self.save_hyperparameters(__UpperCAmelCase   )
			__A							:   str	  =				0
			__A							:   Dict	  =				Path(self.hparams.output_dir   )
			__A							:   Any	  =				self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
					__A							:   Union[str, Any]	  =				AutoConfig.from_pretrained(
					    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,			**({"num_labels": num_labels} if num_labels is not None else {})							,			cache_dir=__UpperCAmelCase							,			**__UpperCAmelCase							,			)
			else:
					__A							:   PretrainedConfig	  =				config

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

			if tokenizer is None:
					__A							:   List[Any]	  =				AutoTokenizer.from_pretrained(
					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,			cache_dir=__UpperCAmelCase							,			)
			else:
					__A							:   PreTrainedTokenizer	  =				tokenizer
			__A							:   Union[str, Any]	  =				MODEL_MODES[mode]
			if model is None:
					__A							:   Tuple	  =				self.model_type.from_pretrained(
					    self.hparams.model_name_or_path							,			from_tf=bool(".ckpt" in self.hparams.model_name_or_path   )							,			config=self.config							,			cache_dir=__UpperCAmelCase							,			)
			else:
					__A							:   Tuple	  =				model

	def 					__UpperCAmelCase(       self							,			*__UpperCAmelCase							,			**__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				self.model_type.from_pretrained(*__UpperCAmelCase							,			**__UpperCAmelCase   )

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

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

			else:
					__A							:   Tuple	  =				AdamW(
					    __UpperCAmelCase							,			lr=self.hparams.learning_rate							,			eps=self.hparams.adam_epsilon   )
			__A							:   Optional[Any]	  =				optimizer

			__A							:   str	  =				self.get_lr_scheduler()

			return [optimizer], [scheduler]

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			return self.validation_step(__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			return self.validation_end(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				max(1							,			self.hparams.gpus   )  # TODO: consider num_tpu_cores
			__A							:   Optional[int]	  =				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							,			__UpperCAmelCase   ):
			if mode == "test":
					__A							:   List[str]	  =				len(self.test_dataloader().dataset   )
			else:
					__A							:   List[str]	  =				self.get_dataloader("train"							,			self.hparams.train_batch_size							,			shuffle=__UpperCAmelCase   )
					__A							:   str	  =				len(self.train_dataloader().dataset   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase = False   ):
			raise NotImplementedError("You must implement this for your task"   )

	def 					__UpperCAmelCase(       self   ):
			return self.train_loader

	def 					__UpperCAmelCase(       self   ):
			return self.get_dataloader("dev"							,			self.hparams.eval_batch_size							,			shuffle=__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			return self.get_dataloader("test"							,			self.hparams.eval_batch_size							,			shuffle=__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			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 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				self.output_dir.joinpath("best_tfmr"   )
			__A							:   Union[str, Any]	  =				self.step_count
			self.model.save_pretrained(__UpperCAmelCase   )
			self.tokenizer.save_pretrained(__UpperCAmelCase   )







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


class _a       (	pl.Callback ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				trainer.lr_schedulers[0]["scheduler"]
			__A							:   Optional[Any]	  =				{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
			pl_module.logger.log_metrics(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			rank_zero_info("***** Validation results *****"   )
			__A							:   Any	  =				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 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			rank_zero_info("***** Test results *****"   )
			__A							:   Dict	  =				trainer.callback_metrics
			# Log and save results to file
			__A							:   Tuple	  =				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    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	None:
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    "--output_dir" ,     default=_lowercase ,     type=_lowercase ,     required=_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=_lowercase ,     type=_lowercase ,     required=_lowercase ,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,     )





def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase=None ,     _lowercase=True ,     _lowercase=[] ,     _lowercase=None ,     _lowercase=None ,     **_lowercase ,     )							->	Union[str, Any]:
		pl.seed_everything(args.seed    )

		# init model
		__A							:   int	  =				Path(model.hparams.output_dir    )
		odir.mkdir(exist_ok=_lowercase    )

		# add custom checkpoints
		if checkpoint_callback is None:
				__A							:   Optional[int]	  =				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:
				__A							:   int	  =				LoggingCallback()

		__A							:   List[str]	  =				{}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				__A							:   Optional[int]	  =				16
				__A							:   Any	  =				args.fpaa_opt_level

		if args.gpus > 1:
				__A							:   int	  =				"ddp"

		__A							:   int	  =				args.accumulate_grad_batches
		__A							:   Dict	  =				extra_train_kwargs.get("accelerator" ,     _lowercase    )
		__A							:   Optional[int]	  =				extra_train_kwargs.get("profiler" ,     _lowercase    )

		__A							:   List[str]	  =				pl.Trainer.from_argparse_args(
		    _lowercase ,     weights_summary=_lowercase ,     callbacks=[logging_callback] + extra_callbacks ,     logger=_lowercase ,     checkpoint_callback=_lowercase ,     **_lowercase ,     )

		if args.do_train:
				trainer.fit(_lowercase    )

		return trainer





