

"""simple docstring"""




import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

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


A_				:      Union[str, Any]           =			1E-4

if is_torch_available():
						import torch

						from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
						from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder





@require_torch
class  lowerCamelCase    :

			def __init__(       self					:    Any     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Tuple=1_6     ,					__UpperCAmelCase					:    Union[str, Any]=1_3     ,					__UpperCAmelCase					:    Dict=7     ,					__UpperCAmelCase					:    List[Any]=1_4     ,					__UpperCAmelCase					:    List[str]=1_0     ,					__UpperCAmelCase					:    str=1_9     ,					__UpperCAmelCase					:    List[str]=5     ,					__UpperCAmelCase					:    Any=4     ,					__UpperCAmelCase					:    Tuple=True     ,					__UpperCAmelCase					:    List[Any]=1_6     ,					__UpperCAmelCase					:    List[Any]=2     ,					__UpperCAmelCase					:    str=4     ,					__UpperCAmelCase					:    Any=4     ,					__UpperCAmelCase					:    Optional[Any]="gelu"     ,					__UpperCAmelCase					:    Tuple=0.1     ,					__UpperCAmelCase					:    str=0.1     ,					__UpperCAmelCase					:    str=[1, 2, 3, 4, 5]     ,					__UpperCAmelCase					:    List[str]=2_5     ,					__UpperCAmelCase					:    int=5     ,					) ->     str:
						SCREAMING_SNAKE_CASE__										=   d_model
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   prediction_length
						SCREAMING_SNAKE_CASE__										=   context_length
						SCREAMING_SNAKE_CASE__										=   cardinality
						SCREAMING_SNAKE_CASE__										=   num_time_features
						SCREAMING_SNAKE_CASE__										=   lags_sequence
						SCREAMING_SNAKE_CASE__										=   embedding_dimension
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob

						SCREAMING_SNAKE_CASE__										=   context_length
						SCREAMING_SNAKE_CASE__										=   prediction_length + label_length
						SCREAMING_SNAKE_CASE__										=   label_length

						SCREAMING_SNAKE_CASE__										=   moving_average
						SCREAMING_SNAKE_CASE__										=   autocorrelation_factor

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Tuple:
						return AutoformerConfig(
						    d_model=self.d_model     ,					encoder_layers=self.num_hidden_layers     ,					decoder_layers=self.num_hidden_layers     ,					encoder_attention_heads=self.num_attention_heads     ,					decoder_attention_heads=self.num_attention_heads     ,					encoder_ffn_dim=self.intermediate_size     ,					decoder_ffn_dim=self.intermediate_size     ,					dropout=self.hidden_dropout_prob     ,					attention_dropout=self.attention_probs_dropout_prob     ,					prediction_length=self.prediction_length     ,					context_length=self.context_length     ,					label_length=self.label_length     ,					lags_sequence=self.lags_sequence     ,					num_time_features=self.num_time_features     ,					num_static_categorical_features=1     ,					cardinality=[self.cardinality]     ,					embedding_dimension=[self.embedding_dimension]     ,					moving_average=self.moving_average     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    Any							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   config.context_length + max(config.lags_sequence							)

						SCREAMING_SNAKE_CASE__										=   ids_tensor([self.batch_size, 1]     ,					config.cardinality[0]							)
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, _past_length, config.num_time_features]							)
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, _past_length]							)
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, _past_length]							) > 0.5

						# decoder inputs
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]							)
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, config.prediction_length]							)

						SCREAMING_SNAKE_CASE__										=   {
						    """past_values""": past_values,
						    """static_categorical_features""": static_categorical_features,
						    """past_time_features""": past_time_features,
						    """past_observed_mask""": past_observed_mask,
						    """future_time_features""": future_time_features,
						    """future_values""": future_values,
						}
						return inputs_dict

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.get_config()
						SCREAMING_SNAKE_CASE__										=   self.prepare_autoformer_inputs_dict(__UpperCAmelCase							)
						return config, inputs_dict

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						return config, inputs_dict





			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Optional[int]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   AutoformerModel(config=__UpperCAmelCase							).to(__UpperCAmelCase							).eval()
						SCREAMING_SNAKE_CASE__										=   model(**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   outputs.encoder_last_hidden_state
						SCREAMING_SNAKE_CASE__										=   outputs.last_hidden_state

						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   model.get_encoder()
									encoder.save_pretrained(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   AutoformerEncoder.from_pretrained(__UpperCAmelCase							).to(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   model.create_network_inputs(**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]							)

						SCREAMING_SNAKE_CASE__										=   torch.cat(
						    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])     ,					dim=-1     ,					)
						SCREAMING_SNAKE_CASE__										=   encoder(inputs_embeds=__UpperCAmelCase							)[0]
						self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3							)

						SCREAMING_SNAKE_CASE__										=   (
						    torch.mean(transformer_inputs[:, : config.context_length, ...]     ,					dim=1							)
						    .unsqueeze(1							)
						    .repeat(1     ,					config.prediction_length     ,					1							)
						)
						SCREAMING_SNAKE_CASE__										=   torch.zeros(
						    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]     ,					device=enc_input.device     ,					)

						SCREAMING_SNAKE_CASE__										=   torch.cat(
						    (
						        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)     ,					dim=1							),
						        feature[:, config.context_length - config.label_length :, ...],
						    )     ,					dim=-1     ,					)
						SCREAMING_SNAKE_CASE__										=   torch.cat(
						    (
						        torch.cat((trend_input[:, -config.label_length :, ...], mean)     ,					dim=1							),
						        feature[:, config.context_length - config.label_length :, ...],
						    )     ,					dim=-1     ,					)

						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   model.get_decoder()
									decoder.save_pretrained(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   AutoformerDecoder.from_pretrained(__UpperCAmelCase							).to(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   decoder(
						    trend=__UpperCAmelCase     ,					inputs_embeds=__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase     ,					)[0]

						self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3							)





@require_torch
class  lowerCamelCase    (A__      ,A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
			lowerCamelCase__ :		Optional[Any]       		=				(AutoformerForPrediction,) if is_torch_available() else ()
			lowerCamelCase__ :		List[str]       		=				{'feature-extraction': AutoformerModel} if is_torch_available() else {}
			lowerCamelCase__ :		List[Any]       		=				False
			lowerCamelCase__ :		str       		=				False
			lowerCamelCase__ :		List[Any]       		=				False
			lowerCamelCase__ :		List[Any]       		=				False
			lowerCamelCase__ :		Optional[int]       		=				False
			lowerCamelCase__ :		Optional[int]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     int:
						SCREAMING_SNAKE_CASE__										=   AutoformerModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase							)

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)

									with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   model_class.from_pretrained(__UpperCAmelCase     ,					output_loading_info=__UpperCAmelCase							)
									self.assertEqual(info["""missing_keys"""]     ,					[]							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*__UpperCAmelCase							)

			@unittest.skip(reason="""Model has no tokens embeddings"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     int:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   inspect.signature(getattr(__UpperCAmelCase     ,					"""forward"""							)							)
						# The main input is the name of the argument after `self`
						SCREAMING_SNAKE_CASE__										=   list(model_signature.parameters.keys()							)[1]
						self.assertEqual(AutoformerModel.main_input_name     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__										=   [
									    """past_values""",
									    """past_time_features""",
									    """past_observed_mask""",
									    """static_categorical_features""",
									    """static_real_features""",
									    """future_values""",
									    """future_time_features""",
									]

									if model.__class__.__name__ in ["AutoformerForPrediction"]:
												expected_arg_names.append("""future_observed_mask"""							)

									expected_arg_names.extend(
									    [
									        """decoder_attention_mask""",
									        """head_mask""",
									        """decoder_head_mask""",
									        """cross_attn_head_mask""",
									        """encoder_outputs""",
									        """past_key_values""",
									        """output_hidden_states""",
									        """output_attentions""",
									        """use_cache""",
									        """return_dict""",
									    ]							)

									self.assertListEqual(arg_names[: len(__UpperCAmelCase							)]     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__										=   True

						SCREAMING_SNAKE_CASE__										=   getattr(self.model_tester     ,					"""seq_length"""     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   getattr(self.model_tester     ,					"""decoder_seq_length"""     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   getattr(self.model_tester     ,					"""encoder_seq_length"""     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   getattr(self.model_tester     ,					"""d_model"""     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   getattr(self.model_tester     ,					"""num_attention_heads"""     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   d_model // num_attention_heads

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   True
									SCREAMING_SNAKE_CASE__										=   False
									SCREAMING_SNAKE_CASE__										=   True
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
									model.to(__UpperCAmelCase							)
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__										=   model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)							)
									SCREAMING_SNAKE_CASE__										=   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase							)     ,					self.model_tester.num_hidden_layers							)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE__										=   True
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
									model.to(__UpperCAmelCase							)
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__										=   model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)							)
									SCREAMING_SNAKE_CASE__										=   outputs.encoder_attentions
									self.assertEqual(len(__UpperCAmelCase							)     ,					self.model_tester.num_hidden_layers							)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]							)     ,					[self.model_tester.num_attention_heads, encoder_seq_length, dim]     ,					)
									SCREAMING_SNAKE_CASE__										=   len(__UpperCAmelCase							)

									SCREAMING_SNAKE_CASE__										=   7

									if "last_hidden_state" in outputs:
												correct_outlen += 1

									if "trend" in outputs:
												correct_outlen += 1

									if "past_key_values" in outputs:
												correct_outlen += 1  # past_key_values have been returned

									if "loss" in outputs:
												correct_outlen += 1

									if "params" in outputs:
												correct_outlen += 1

									self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

									# decoder attentions
									SCREAMING_SNAKE_CASE__										=   outputs.decoder_attentions
									self.assertIsInstance(__UpperCAmelCase     ,					(list, tuple)							)
									self.assertEqual(len(__UpperCAmelCase							)     ,					self.model_tester.num_hidden_layers							)
									self.assertListEqual(
									    list(decoder_attentions[0].shape[-3:]							)     ,					[self.model_tester.num_attention_heads, decoder_seq_length, dim]     ,					)

									# cross attentions
									SCREAMING_SNAKE_CASE__										=   outputs.cross_attentions
									self.assertIsInstance(__UpperCAmelCase     ,					(list, tuple)							)
									self.assertEqual(len(__UpperCAmelCase							)     ,					self.model_tester.num_hidden_layers							)
									self.assertListEqual(
									    list(cross_attentions[0].shape[-3:]							)     ,					[self.model_tester.num_attention_heads, decoder_seq_length, dim]     ,					)

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

						self.assertEqual(out_len + 2     ,					len(__UpperCAmelCase							)							)

						SCREAMING_SNAKE_CASE__										=   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

						self.assertEqual(len(__UpperCAmelCase							)     ,					self.model_tester.num_hidden_layers							)
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:]							)     ,					[self.model_tester.num_attention_heads, encoder_seq_length, dim]     ,					)





			@is_flaky()
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						super().test_retain_grad_hidden_states_attentions()

def 						A							(  snake_case__="train-batch.pt"     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""		,					filename=snake_case__		,					repo_type="""dataset"""     )
			SCREAMING_SNAKE_CASE__										=   torch.load(snake_case__		,					map_location=snake_case__     )
			return batch





@require_torch
@slow
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""							).to(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   prepare_batch()

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(
									    past_values=batch["""past_values"""]     ,					past_time_features=batch["""past_time_features"""]     ,					past_observed_mask=batch["""past_observed_mask"""]     ,					static_categorical_features=batch["""static_categorical_features"""]     ,					future_values=batch["""future_values"""]     ,					future_time_features=batch["""future_time_features"""]     ,					)[0]

						SCREAMING_SNAKE_CASE__										=   torch.Size(
						    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)							)
						self.assertEqual(output.shape     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   torch.tensor(
						    [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]     ,					device=__UpperCAmelCase							)
						self.assertTrue(torch.allclose(output[0, :3, :3]     ,					__UpperCAmelCase     ,					atol=__UpperCAmelCase							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""							).to(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   prepare_batch("""val-batch.pt"""							)
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(
									    past_values=batch["""past_values"""]     ,					past_time_features=batch["""past_time_features"""]     ,					past_observed_mask=batch["""past_observed_mask"""]     ,					static_categorical_features=batch["""static_categorical_features"""]     ,					).encoder_last_hidden_state
						SCREAMING_SNAKE_CASE__										=   torch.Size((6_4, model.config.context_length, model.config.d_model)							)
						self.assertEqual(output.shape     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   torch.tensor(
						    [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]     ,					device=__UpperCAmelCase							)
						self.assertTrue(torch.allclose(output[0, :3, :3]     ,					__UpperCAmelCase     ,					atol=__UpperCAmelCase							)							)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""							).to(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   prepare_batch("""val-batch.pt"""							)
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model.generate(
									    static_categorical_features=batch["""static_categorical_features"""]     ,					past_time_features=batch["""past_time_features"""]     ,					past_values=batch["""past_values"""]     ,					future_time_features=batch["""future_time_features"""]     ,					past_observed_mask=batch["""past_observed_mask"""]     ,					)
						SCREAMING_SNAKE_CASE__										=   torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)							)
						self.assertEqual(outputs.sequences.shape     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]     ,					device=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   outputs.sequences.mean(dim=1							)
						self.assertTrue(torch.allclose(mean_prediction[0, -3:]     ,					__UpperCAmelCase     ,					rtol=1e-1							)							)





"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


