




import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
	import torch
	from torch import nn

	from transformers import DonutSwinModel
	from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST

class 		lowercase_       :




					def __init__(   self:		Optional[Any],     _lowercase:		str,     _lowercase:		List[str]=13,     _lowercase:		Optional[int]=32,     _lowercase:		Optional[int]=2,     _lowercase:		Optional[int]=3,     _lowercase:		Any=16,     _lowercase:		List[Any]=[1, 2, 1],     _lowercase:		Tuple=[2, 2, 4],     _lowercase:		List[str]=2,     _lowercase:		Dict=2.0,     _lowercase:		Union[str, Any]=True,     _lowercase:		Optional[Any]=0.0,     _lowercase:		Union[str, Any]=0.0,     _lowercase:		Optional[Any]=0.1,     _lowercase:		Union[str, Any]="gelu",     _lowercase:		Optional[int]=False,     _lowercase:		str=True,     _lowercase:		Union[str, Any]=0.02,     _lowercase:		int=1e-5,     _lowercase:		Dict=True,     _lowercase:		str=None,     _lowercase:		int=True,     _lowercase:		List[Any]=10,     _lowercase:		Optional[int]=8,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  patch_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  embed_dim
									__lowerCAmelCase					=  depths
									__lowerCAmelCase					=  num_heads
									__lowerCAmelCase					=  window_size
									__lowerCAmelCase					=  mlp_ratio
									__lowerCAmelCase					=  qkv_bias
									__lowerCAmelCase					=  hidden_dropout_prob
									__lowerCAmelCase					=  attention_probs_dropout_prob
									__lowerCAmelCase					=  drop_path_rate
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  use_absolute_embeddings
									__lowerCAmelCase					=  patch_norm
									__lowerCAmelCase					=  layer_norm_eps
									__lowerCAmelCase					=  initializer_range
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  scope
									__lowerCAmelCase					=  use_labels
									__lowerCAmelCase					=  type_sequence_label_size
									__lowerCAmelCase					=  encoder_stride




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


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

									__lowerCAmelCase					=  None
									if self.use_labels:
													__lowerCAmelCase					=  ids_tensor([self.batch_size],     self.type_sequence_label_size)

									__lowerCAmelCase					=  self.get_config()

									return config, pixel_values, labels




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									return DonutSwinConfig(
									    image_size=self.image_size,     patch_size=self.patch_size,     num_channels=self.num_channels,     embed_dim=self.embed_dim,     depths=self.depths,     num_heads=self.num_heads,     window_size=self.window_size,     mlp_ratio=self.mlp_ratio,     qkv_bias=self.qkv_bias,     hidden_dropout_prob=self.hidden_dropout_prob,     attention_probs_dropout_prob=self.attention_probs_dropout_prob,     drop_path_rate=self.drop_path_rate,     hidden_act=self.hidden_act,     use_absolute_embeddings=self.use_absolute_embeddings,     path_norm=self.patch_norm,     layer_norm_eps=self.layer_norm_eps,     initializer_range=self.initializer_range,     encoder_stride=self.encoder_stride,     )




					def     _lowercase						(   self:		str,     _lowercase:		int,     _lowercase:		List[Any],     _lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  DonutSwinModel(config=_lowercase)
									model.to(_lowercase)
									model.eval()
									__lowerCAmelCase					=  model(_lowercase)

									__lowerCAmelCase					=  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
									__lowerCAmelCase					=  int(config.embed_dim * 2 ** (len(config.depths) - 1))

									self.parent.assertEqual(result.last_hidden_state.shape,     (self.batch_size, expected_seq_len, expected_dim))







					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


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





@require_torch
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (DonutSwinModel,) if is_torch_available() else ()
					__UpperCamelCase     	= {"feature-extraction": DonutSwinModel} if is_torch_available() else {}
					__UpperCamelCase     	= True

					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  DonutSwinModelTester(self)
									__lowerCAmelCase					=  ConfigTester(self,     config_class=_lowercase,     embed_dim=37)




					def     _lowercase						(   self:		Optional[Any]):


									'''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     _lowercase						(   self:		Any):


									'''simple docstring'''


									return




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)
													self.assertIsInstance(model.get_input_embeddings(),     (nn.Module))
													__lowerCAmelCase					=  model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_lowercase,     nn.Linear))




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase					=  ["""pixel_values"""]
													self.assertListEqual(arg_names[:1],     _lowercase)




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCAmelCase					=  True

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													__lowerCAmelCase					=  False
													__lowerCAmelCase					=  True
													__lowerCAmelCase					=  model_class(_lowercase)
													model.to(_lowercase)
													model.eval()
													with torch.no_grad():
																	__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))
													__lowerCAmelCase					=  outputs.attentions
													__lowerCAmelCase					=  len(self.model_tester.depths)
													self.assertEqual(len(_lowercase),     _lowercase)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													__lowerCAmelCase					=  True
													__lowerCAmelCase					=  config.window_size**2
													__lowerCAmelCase					=  model_class(_lowercase)
													model.to(_lowercase)
													model.eval()
													with torch.no_grad():
																	__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))
													__lowerCAmelCase					=  outputs.attentions
													self.assertEqual(len(_lowercase),     _lowercase)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]),     [self.model_tester.num_heads[0], window_size_squared, window_size_squared],     )
													__lowerCAmelCase					=  len(_lowercase)

													# Check attention is always last and order is fine
													__lowerCAmelCase					=  True
													__lowerCAmelCase					=  True
													__lowerCAmelCase					=  model_class(_lowercase)
													model.to(_lowercase)
													model.eval()
													with torch.no_grad():
																	__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))

													if hasattr(self.model_tester,     """num_hidden_states_types"""):
																	__lowerCAmelCase					=  self.model_tester.num_hidden_states_types
													else:
																	# also another +1 for reshaped_hidden_states
																	__lowerCAmelCase					=  2
													self.assertEqual(out_len + added_hidden_states,     len(_lowercase))

													__lowerCAmelCase					=  outputs.attentions

													self.assertEqual(len(_lowercase),     _lowercase)

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]),     [self.model_tester.num_heads[0], window_size_squared, window_size_squared],     )




					def     _lowercase						(   self:		int,     _lowercase:		List[str],     _lowercase:		Any,     _lowercase:		List[Any],     _lowercase:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  model_class(_lowercase)
									model.to(_lowercase)
									model.eval()

									with torch.no_grad():
													__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))

									__lowerCAmelCase					=  outputs.hidden_states

									__lowerCAmelCase					=  getattr(
									    self.model_tester,     """expected_num_hidden_layers""",     len(self.model_tester.depths) + 1)
									self.assertEqual(len(_lowercase),     _lowercase)

									# DonutSwin has a different seq_length
									__lowerCAmelCase					=  (
									    config.patch_size
									    if isinstance(config.patch_size,     collections.abc.Iterable)
									    else (config.patch_size, config.patch_size)
									)

									__lowerCAmelCase					=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]),     [num_patches, self.model_tester.embed_dim],     )

									__lowerCAmelCase					=  outputs.reshaped_hidden_states
									self.assertEqual(len(_lowercase),     _lowercase)

									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  reshaped_hidden_states[0].shape
									__lowerCAmelCase					=  (
									    reshaped_hidden_states[0].view(_lowercase,     _lowercase,     height * width).permute(0,     2,     1)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]),     [num_patches, self.model_tester.embed_dim],     )




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									__lowerCAmelCase					=  (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size,     collections.abc.Iterable)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													self.check_hidden_states_output(_lowercase,     _lowercase,     _lowercase,     _lowercase)

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

													self.check_hidden_states_output(_lowercase,     _lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCAmelCase					=  3

									__lowerCAmelCase					=  (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size,     collections.abc.Iterable)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									__lowerCAmelCase					=  (
									    config.patch_size
									    if isinstance(config.patch_size,     collections.abc.Iterable)
									    else (config.patch_size, config.patch_size)
									)

									__lowerCAmelCase					=  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									__lowerCAmelCase					=  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													self.check_hidden_states_output(_lowercase,     _lowercase,     _lowercase,     (padded_height, padded_width))

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__lowerCAmelCase					=  True
													self.check_hidden_states_output(_lowercase,     _lowercase,     _lowercase,     (padded_height, padded_width))




					@slow
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase					=  DonutSwinModel.from_pretrained(_lowercase)
													self.assertIsNotNone(_lowercase)







					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									__lowerCAmelCase					=  _config_zero_init(_lowercase)
									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(config=_lowercase)
													for name, param in model.named_parameters():
																	if "embeddings" not in name and param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1e9).round() / 1e9).item(),     [0.0, 1.0],     msg=f'''Parameter {name} of model {model_class} seems not properly initialized''',     )












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       UpperCamelCase_		(	pl.LightningModule       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=None					,    lowerCAmelCase_="base"					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    **lowerCAmelCase_					,    )		->				Union[str, Any]:
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

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

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

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




					def 						lowerCAmelCase      (		self					,    *lowerCAmelCase_					,    **lowerCAmelCase_	)		->				Union[str, Any]:
										_snake_case						=					self.model_type.from_pretrained(*lowerCAmelCase_					,    **lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					arg_to_scheduler[self.hparams.lr_scheduler]
										_snake_case						=					get_schedule_func(
										    self.opt					,    num_warmup_steps=self.hparams.warmup_steps					,    num_training_steps=self.total_steps()	)
										_snake_case						=					{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
										return scheduler




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					self.model
										_snake_case						=					['bias', 'LayerNorm.weight']
										_snake_case						=					[
										    {
										        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
										        'weight_decay': self.hparams.weight_decay,
										    },
										    {
										        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
										        'weight_decay': 0.0,
										    },
										]
										if self.hparams.adafactor:
															_snake_case						=					Adafactor(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    scale_parameter=lowerCAmelCase_					,    relative_step=lowerCAmelCase_	)

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

										_snake_case						=					self.get_lr_scheduler()

										return [optimizer], [scheduler]




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				int:
										return self.validation_step(lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Union[str, Any]:
										return self.validation_end(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					max(1					,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
										_snake_case						=					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				str:
										if mode == "test":
															_snake_case						=					len(self.test_dataloader().dataset	)
										else:
															_snake_case						=					self.get_dataloader('train'					,    self.hparams.train_batch_size					,    shuffle=lowerCAmelCase_	)
															_snake_case						=					len(self.train_dataloader().dataset	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = False	)		->				int:
										raise NotImplementedError('You must implement this for your task'	)




					def 						lowerCAmelCase      (		self	)		->				Any:
										return self.train_loader




					def 						lowerCAmelCase      (		self	)		->				Dict:
										return self.get_dataloader('dev'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										return self.get_dataloader('test'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Union[str, Any]:
										return os.path.join(
										    self.hparams.data_dir					,    'cached_{}_{}_{}'.format(
										        lowerCAmelCase_					,    list(filter(lowerCAmelCase_					,    self.hparams.model_name_or_path.split('/'	)	)	).pop()					,    str(self.hparams.max_seq_length	)					,    )					,    )




					@pl.utilities.rank_zero_only
					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				None:
										_snake_case						=					self.output_dir.joinpath('best_tfmr'	)
										_snake_case						=					self.step_count
										self.model.save_pretrained(lowerCAmelCase_	)
										self.tokenizer.save_pretrained(lowerCAmelCase_	)






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





class       UpperCamelCase_		(	pl.Callback       ):




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					trainer.lr_schedulers[0]['scheduler']
										_snake_case						=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
										pl_module.logger.log_metrics(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Dict:
										rank_zero_info('***** Validation results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log results
										for key in sorted(lowerCAmelCase_	):
															if key not in ["log", "progress_bar"]:
																				rank_zero_info('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)






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





def     lowerCamelCase__  (  UpperCamelCase__      :							int							,     UpperCamelCase__      :							Optional[Any]    )      ->			None:



					'''simple docstring'''




					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     lowerCamelCase__  (  UpperCamelCase__      :							BaseTransformer							,     UpperCamelCase__      :							argparse.Namespace							,     UpperCamelCase__      :							Any=None							,     UpperCamelCase__      :							str=True							,     UpperCamelCase__      :							int=[]							,     UpperCamelCase__      :							Any=None							,     UpperCamelCase__      :							Optional[int]=None							,     **UpperCamelCase__      :							Optional[int]							,     )      ->			str:



					'''simple docstring'''




					pl.seed_everything(args.seed    )

					# init model
					_snake_case						=					Path(model.hparams.output_dir    )
					odir.mkdir(exist_ok=UpperCamelCase__    )

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

					_snake_case						=					{}

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

					if args.gpus > 1:
										_snake_case						=					'ddp'

					_snake_case						=					args.accumulate_grad_batches
					_snake_case						=					extra_train_kwargs.get('accelerator'							,     UpperCamelCase__    )
					_snake_case						=					extra_train_kwargs.get('profiler'							,     UpperCamelCase__    )

					_snake_case						=					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



