import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate


class _a       (	lowerCAmelCase__ ):

	'''simple docstring'''



	lowerCamelCase_   :      List[Any]       =	CONFIG_NAME

	@register_to_config
	def __init__(       self							,			__UpperCAmelCase = True							,			__UpperCAmelCase = 8							,			__UpperCAmelCase = "lanczos"							,			__UpperCAmelCase = True							,			__UpperCAmelCase = False							,			):
			super().__init__()

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			if images.ndim == 3:
					__A							:   str	  =				images[None, ...]
			__A							:   int	  =				(images * 255).round().astype("uint8"   )
			if images.shape[-1] == 1:
					# special case for grayscale (single channel) images
					__A							:   Optional[Any]	  =				[Image.fromarray(image.squeeze()							,			mode="L"   ) for image in images]
			else:
					__A							:   List[str]	  =				[Image.fromarray(__UpperCAmelCase   ) for image in images]

			return pil_images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			if not isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
					__A							:   Optional[Any]	  =				[images]
			__A							:   Any	  =				[np.array(__UpperCAmelCase   ).astype(np.floataa   ) / 2_55.0 for image in images]
			__A							:   Dict	  =				np.stack(__UpperCAmelCase							,			axis=0   )

			return images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			if images.ndim == 3:
					__A							:   Dict	  =				images[..., None]

			__A							:   int	  =				torch.from_numpy(images.transpose(0							,			3							,			1							,			2   )   )
			return images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			__A							:   str	  =				images.cpu().permute(0							,			2							,			3							,			1   ).float().numpy()
			return images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			return 2.0 * images - 1.0

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			return (images / 2 + 0.5).clamp(0							,			1   )

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			__A							:   Tuple	  =				image.convert("RGB"   )
			return image

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = None							,			):
			if height is None:
					__A							:   Dict	  =				image.height
			if width is None:
					__A							:   Any	  =				image.width

			__A  ,       __A							:   Optional[int]	  =				(
			    x - x % self.config.vae_scale_factor for x in (width, height)
			)  # resize to integer multiple of vae_scale_factor
			__A							:   List[str]	  =				image.resize((width, height)							,			resample=PIL_INTERPOLATION[self.config.resample]   )
			return image

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = None							,			):
			__A							:   Dict	  =				(PIL.Image.Image, np.ndarray, torch.Tensor)
			if isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
					__A							:   Optional[Any]	  =				[image]
			elif not (isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ) and all(isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ) for i in image   )):
					raise ValueError(
					    F"Input is in incorrect format: {[type(__UpperCAmelCase   ) for i in image]}. Currently, we only support {', '.join(__UpperCAmelCase   )}"   )

			if isinstance(image[0]							,			PIL.Image.Image   ):
					if self.config.do_convert_rgb:
							__A							:   Tuple	  =				[self.convert_to_rgb(__UpperCAmelCase   ) for i in image]
					if self.config.do_resize:
							__A							:   Dict	  =				[self.resize(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ) for i in image]
					__A							:   Optional[Any]	  =				self.pil_to_numpy(__UpperCAmelCase   )  # to np
					__A							:   Optional[Any]	  =				self.numpy_to_pt(__UpperCAmelCase   )  # to pt

			elif isinstance(image[0]							,			np.ndarray   ):
					__A							:   Optional[int]	  =				np.concatenate(__UpperCAmelCase							,			axis=0   ) if image[0].ndim == 4 else np.stack(__UpperCAmelCase							,			axis=0   )
					__A							:   Dict	  =				self.numpy_to_pt(__UpperCAmelCase   )
					__A  ,       __A  ,       __A  ,       __A							:   List[Any]	  =				image.shape
					if self.config.do_resize and (
					    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
					):
							raise ValueError(
							    F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
							    F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"   )

			elif isinstance(image[0]							,			torch.Tensor   ):
					__A							:   List[Any]	  =				torch.cat(__UpperCAmelCase							,			axis=0   ) if image[0].ndim == 4 else torch.stack(__UpperCAmelCase							,			axis=0   )
					__A  ,       __A  ,       __A  ,       __A							:   str	  =				image.shape

					# don't need any preprocess if the image is latents
					if channel == 4:
							return image

					if self.config.do_resize and (
					    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
					):
							raise ValueError(
							    F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
							    F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"   )

        # expected range [0,1], normalize to [-1,1]
			__A							:   Optional[int]	  =				self.config.do_normalize
			if image.min() < 0:
					warnings.warn(
					    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
					    F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"							,			__UpperCAmelCase							,			)
					__A							:   str	  =				False

			if do_normalize:
					__A							:   Optional[int]	  =				self.normalize(__UpperCAmelCase   )

			return image







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase = "pil"							,			__UpperCAmelCase = None							,			):
			if not isinstance(__UpperCAmelCase							,			torch.Tensor   ):
					raise ValueError(
					    F"Input for postprocessing is in incorrect format: {type(__UpperCAmelCase   )}. We only support pytorch tensor"   )
			if output_type not in ["latent", "pt", "np", "pil"]:
					__A							:   Any	  =				(
					    F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
					    "`pil`, `np`, `pt`, `latent`"
					)
					deprecate("Unsupported output_type"							,			"1.0.0"							,			__UpperCAmelCase							,			standard_warn=__UpperCAmelCase   )
					__A							:   Union[str, Any]	  =				"np"

			if output_type == "latent":
					return image

			if do_denormalize is None:
					__A							:   Union[str, Any]	  =				[self.config.do_normalize] * image.shape[0]

			__A							:   Dict	  =				torch.stack(
			    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

			if output_type == "pt":
					return image

			__A							:   List[Any]	  =				self.pt_to_numpy(__UpperCAmelCase   )

			if output_type == "np":
					return image

			if output_type == "pil":
					return self.numpy_to_pil(__UpperCAmelCase   )


class _a       (	lowerCAmelCase__ ):

	'''simple docstring'''



	lowerCamelCase_   :      List[Any]       =	CONFIG_NAME

	@register_to_config
	def __init__(       self							,			__UpperCAmelCase = True							,			__UpperCAmelCase = 8							,			__UpperCAmelCase = "lanczos"							,			__UpperCAmelCase = True							,			):
			super().__init__()

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			if images.ndim == 3:
					__A							:   Any	  =				images[None, ...]
			__A							:   Optional[int]	  =				(images * 255).round().astype("uint8"   )
			if images.shape[-1] == 1:
					# special case for grayscale (single channel) images
					__A							:   Optional[Any]	  =				[Image.fromarray(image.squeeze()							,			mode="L"   ) for image in images]
			else:
					__A							:   Optional[Any]	  =				[Image.fromarray(image[:, :, :3]   ) for image in images]

			return pil_images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			return image[:, :, 1] * 2**8 + image[:, :, 2]

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			if images.ndim == 3:
					__A							:   str	  =				images[None, ...]
			__A							:   Dict	  =				images[:, :, :, 3:]
			if images.shape[-1] == 6:
					__A							:   Union[str, Any]	  =				(images_depth * 255).round().astype("uint8"   )
					__A							:   Optional[int]	  =				[
					    Image.fromarray(self.rgblike_to_depthmap(__UpperCAmelCase   )							,			mode="I;16"   ) for image_depth in images_depth
					]
			elif images.shape[-1] == 4:
					__A							:   Optional[int]	  =				(images_depth * 6_55_35.0).astype(np.uintaa   )
					__A							:   Optional[int]	  =				[Image.fromarray(__UpperCAmelCase							,			mode="I;16"   ) for image_depth in images_depth]
			else:
					raise Exception("Not supported"   )

			return pil_images







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase = "pil"							,			__UpperCAmelCase = None							,			):
			if not isinstance(__UpperCAmelCase							,			torch.Tensor   ):
					raise ValueError(
					    F"Input for postprocessing is in incorrect format: {type(__UpperCAmelCase   )}. We only support pytorch tensor"   )
			if output_type not in ["latent", "pt", "np", "pil"]:
					__A							:   str	  =				(
					    F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
					    "`pil`, `np`, `pt`, `latent`"
					)
					deprecate("Unsupported output_type"							,			"1.0.0"							,			__UpperCAmelCase							,			standard_warn=__UpperCAmelCase   )
					__A							:   int	  =				"np"

			if do_denormalize is None:
					__A							:   List[str]	  =				[self.config.do_normalize] * image.shape[0]

			__A							:   Union[str, Any]	  =				torch.stack(
			    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

			__A							:   Union[str, Any]	  =				self.pt_to_numpy(__UpperCAmelCase   )

			if output_type == "np":
					if image.shape[-1] == 6:
							__A							:   str	  =				np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]							,			axis=0   )
					else:
							__A							:   Optional[int]	  =				image[:, :, :, 3:]
					return image[:, :, :, :3], image_depth

			if output_type == "pil":
					return self.numpy_to_pil(__UpperCAmelCase   ), self.numpy_to_depth(__UpperCAmelCase   )
			else:
					raise Exception(F"This type {output_type} is not supported"   )






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





