




import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
   from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor




class 			a__       :







     def __init__(	self  ,		A  ,		A=3  ,		A=32  ,		A=3  ,		A=10  ,		A=[10, 20, 30, 40]  ,		A=[1, 1, 2, 1]  ,		A=True  ,		A=True  ,		A="relu"  ,		A=3  ,		A=None  ,		A=["stage2", "stage3", "stage4"]  ,		A=[2, 3, 4]  ,		)  ->    Optional[int]:



          '''simple docstring'''



          a      =      parent
          a      =      batch_size
          a      =      image_size
          a      =      num_channels
          a      =      embeddings_size
          a      =      hidden_sizes
          a      =      depths
          a      =      is_training
          a      =      use_labels
          a      =      hidden_act
          a      =      num_labels
          a      =      scope
          a      =      len(A )
          a      =      out_features
          a      =      out_indices







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

          a      =      None
          if self.use_labels:
               a      =      ids_tensor([self.batch_size]  ,		self.num_labels )

          a      =      self.get_config()

          return config, pixel_values, labels







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          return ResNetConfig(
              num_channels=self.num_channels  ,		embeddings_size=self.embeddings_size  ,		hidden_sizes=self.hidden_sizes  ,		depths=self.depths  ,		hidden_act=self.hidden_act  ,		num_labels=self.num_labels  ,		out_features=self.out_features  ,		out_indices=self.out_indices  ,		)







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      ResNetModel(config=A )
          model.to(A )
          model.eval()
          a      =      model(A )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape  ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,		)







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          a      =      self.num_labels
          a      =      ResNetForImageClassification(A )
          model.to(A )
          model.eval()
          a      =      model(A  ,		labels=A )
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels) )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      ResNetBackbone(config=A )
          model.to(A )
          model.eval()
          a      =      model(A )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps )  ,		len(config.out_features ) )
          self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,		[self.batch_size, self.hidden_sizes[1], 4, 4] )

          # verify channels
          self.parent.assertEqual(len(model.channels )  ,		len(config.out_features ) )
          self.parent.assertListEqual(model.channels  ,		config.hidden_sizes[1:] )

          # verify backbone works with out_features=None
          a      =      None
          a      =      ResNetBackbone(config=A )
          model.to(A )
          model.eval()
          a      =      model(A )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps )  ,		1 )
          self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,		[self.batch_size, self.hidden_sizes[-1], 1, 1] )

          # verify channels
          self.parent.assertEqual(len(model.channels )  ,		1 )
          self.parent.assertListEqual(model.channels  ,		[config.hidden_sizes[-1]] )







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      self.prepare_config_and_inputs()
          a  ,      a  ,      a      =      config_and_inputs
          a      =      {"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 			a__       (					UpperCamelCase__   ,					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Tuple							=	(
         (
             ResNetModel,
             ResNetForImageClassification,
             ResNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     a		:			List[str]							=	(
         {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
         if is_torch_available()
         else {}
     )

     a		:			List[Any]							=	True
     a		:			List[str]							=	False
     a		:			Tuple							=	False
     a		:			List[Any]							=	False
     a		:			Union[str, Any]							=	False







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      ResNetModelTester(self )
          a      =      ConfigTester(self  ,		config_class=A  ,		has_text_modality=A )







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          return







     @unittest.skip(reason="ResNet does not use inputs_embeds" )
     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          pass







     @unittest.skip(reason="ResNet does not support input and output embeddings" )
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          pass







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a  ,      a      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a      =      model_class(A )
               a      =      inspect.signature(model.forward )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               a      =      [*signature.parameters.keys()]

               a      =      ["pixel_values"]
               self.assertListEqual(arg_names[:1]  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*A )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a  ,      a      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a      =      model_class(config=A )
               for name, module in model.named_modules():
                    if isinstance(A  ,		(nn.BatchNormad, nn.GroupNorm) ):
                         self.assertTrue(
                             torch.all(module.weight == 1 )  ,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''  ,		)
                         self.assertTrue(
                             torch.all(module.bias == 0 )  ,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''  ,		)







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          def check_hidden_states_output(A  ,		A  ,		A ):
               a      =      model_class(A )
               model.to(A )
               model.eval()

               with torch.no_grad():
                    a      =      model(**self._prepare_for_class(A  ,		A ) )

               a      =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               a      =      self.model_tester.num_stages
               self.assertEqual(len(A )  ,		expected_num_stages + 1 )

               # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:] )  ,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,		)

          a  ,      a      =      self.model_tester.prepare_config_and_inputs_for_common()
          a      =      ["basic", "bottleneck"]
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    a      =      layer_type
                    a      =      True
                    check_hidden_states_output(A  ,		A  ,		A )

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    a      =      True

                    check_hidden_states_output(A  ,		A  ,		A )







     @unittest.skip(reason="ResNet does not use feedforward chunking" )
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          pass







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a      =      ResNetModel.from_pretrained(A )
               self.assertIsNotNone(A )







def 					SCREAMING_SNAKE_CASE							(   )			->     Union[str, Any]:
     a      =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     return image




@require_torch
@require_vision
class 			a__       (					unittest.TestCase      ):







     @cached_property
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return (
              AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
              if is_vision_available()
              else None
          )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A )

          a      =      self.default_image_processor
          a      =      prepare_img()
          a      =      image_processor(images=A  ,		return_tensors="pt" ).to(A )

          # forward pass
          with torch.no_grad():
               a      =      model(**A )

          # verify the logits
          a      =      torch.Size((1, 1000) )
          self.assertEqual(outputs.logits.shape  ,		A )

          a      =      torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7] ).to(A )

          self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,		A  ,		atol=1e-4 ) )







@require_torch
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Optional[int]							=	(ResNetBackbone,) if is_torch_available() else ()
     a		:			Dict							=	False
     a		:			List[Any]							=	ResNetConfig







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      ResNetModelTester(self )







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


lowercase__      :			List[Any]           =					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase__      :			List[str]           =					{
    "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
lowercase__      :			Union[str, Any]           =					{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase__      :			Any           =					sorted(arg_to_scheduler.keys())
lowercase__      :			Union[str, Any]           =					"{" + ", ".join(arg_to_scheduler_choices) + "}"




class 			a__       (					pl.LightningModule      ):







     def __init__(	self  ,		A  ,		A=None  ,		A="base"  ,		A=None  ,		A=None  ,		A=None  ,		**A  ,		)  ->    Any:



          '''simple docstring'''



          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(A )
          a      =      0
          a      =      Path(self.hparams.output_dir )
          a      =      self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
               a      =      AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path  ,		**({"num_labels": num_labels} if num_labels is not None else {})  ,		cache_dir=A  ,		**A  ,		)
          else:
               a      =      config

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

          if tokenizer is None:
               a      =      AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,		cache_dir=A  ,		)
          else:
               a      =      tokenizer
          a      =      MODEL_MODES[mode]
          if model is None:
               a      =      self.model_type.from_pretrained(
                   self.hparams.model_name_or_path  ,		from_tf=bool(".ckpt" in self.hparams.model_name_or_path )  ,		config=self.config  ,		cache_dir=A  ,		)
          else:
               a      =      model







     def 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    Dict:



          '''simple docstring'''



          a      =      self.model_type.from_pretrained(*A  ,		**A )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



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

          else:
               a      =      AdamW(
                   A  ,		lr=self.hparams.learning_rate  ,		eps=self.hparams.adam_epsilon )
          a      =      optimizer

          a      =      self.get_lr_scheduler()

          return [optimizer], [scheduler]







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



          return self.validation_step(A  ,		A )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          return self.validation_end(A )







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    List[Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A = False )  ->    int:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          return self.train_loader







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Optional[int]:



          '''simple docstring'''



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







     @pl.utilities.rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A )  ->    None:



          '''simple docstring'''



          a      =      self.output_dir.joinpath("best_tfmr" )
          a      =      self.step_count
          self.model.save_pretrained(A )
          self.tokenizer.save_pretrained(A )







     @staticmethod
     def 			lowerCAmelCase_				(	A  ,		A )  ->    Any:



          '''simple docstring'''



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







class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Tuple:



          '''simple docstring'''



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







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     None:
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir"		,					default=__UpperCamelCase		,					type=__UpperCamelCase		,					required=__UpperCamelCase		,					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=__UpperCamelCase		,					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=__UpperCamelCase)
     parser.add_argument("--max_grad_norm"		,					dest="gradient_clip_val"		,					default=1.0		,					type=__UpperCamelCase		,					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=__UpperCamelCase		,					default=1		,					help="Number of updates steps to accumulate before performing a backward/update pass."		,					)
     parser.add_argument("--seed"		,					type=__UpperCamelCase		,					default=42		,					help="random seed for initialization")
     parser.add_argument(
         "--data_dir"		,					default=__UpperCamelCase		,					type=__UpperCamelCase		,					required=__UpperCamelCase		,					help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,					)





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=None		,					__UpperCamelCase=True		,					__UpperCamelCase=[]		,					__UpperCamelCase=None		,					__UpperCamelCase=None		,					**__UpperCamelCase		,					)			->     Optional[Any]:
     pl.seed_everything(args.seed)

     # init model
     a      =      Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=__UpperCamelCase)

     # add custom checkpoints
     if checkpoint_callback is None:
          a      =      pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir		,					prefix="checkpoint"		,					monitor="val_loss"		,					mode="min"		,					save_top_k=1)
     if early_stopping_callback:
          extra_callbacks.append(__UpperCamelCase)
     if logging_callback is None:
          a      =      LoggingCallback()

     a      =      {}

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

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

     a      =      args.accumulate_grad_batches
     a      =      extra_train_kwargs.get("accelerator"		,					__UpperCamelCase)
     a      =      extra_train_kwargs.get("profiler"		,					__UpperCamelCase)

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

     if args.do_train:
          trainer.fit(__UpperCamelCase)

     return trainer

