

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


lowerCamelCase      				=     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     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_6					,						_A=1_3					,						_A=7					,						_A=1_4					,						_A=1_0					,						_A=1_9					,						_A=5					,						_A=4					,						_A=True					,						_A=1_6					,						_A=2					,						_A=4					,						_A=4					,						_A="gelu"					,						_A=0.1					,						_A=0.1					,						_A=[1, 2, 3, 4, 5]					,						_A=2_5					,						_A=5					,						):
												__lowerCAmelCase					=							d_model
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							prediction_length
												__lowerCAmelCase					=							context_length
												__lowerCAmelCase					=							cardinality
												__lowerCAmelCase					=							num_time_features
												__lowerCAmelCase					=							lags_sequence
												__lowerCAmelCase					=							embedding_dimension
												__lowerCAmelCase					=							is_training
												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							num_hidden_layers
												__lowerCAmelCase					=							num_attention_heads
												__lowerCAmelCase					=							intermediate_size
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							hidden_dropout_prob
												__lowerCAmelCase					=							attention_probs_dropout_prob

												__lowerCAmelCase					=							context_length
												__lowerCAmelCase					=							prediction_length + label_length
												__lowerCAmelCase					=							label_length

												__lowerCAmelCase					=							moving_average
												__lowerCAmelCase					=							autocorrelation_factor




							def        A__  ( self							):
												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        A__  ( self					,						_A							):
												__lowerCAmelCase					=							config.context_length + max(config.lags_sequence							)

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

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

												__lowerCAmelCase					=							{
												    '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        A__  ( self							):
												__lowerCAmelCase					=							self.get_config()
												__lowerCAmelCase					=							self.prepare_autoformer_inputs_dict(_A							)
												return config, inputs_dict




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.prepare_config_and_inputs()
												return config, inputs_dict




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							AutoformerModel(config=_A							).to(_A							).eval()
												__lowerCAmelCase					=							model(**_A							)

												__lowerCAmelCase					=							outputs.encoder_last_hidden_state
												__lowerCAmelCase					=							outputs.last_hidden_state

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

												__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							model.create_network_inputs(**_A							)
												__lowerCAmelCase,	__lowerCAmelCase					=							model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]							)

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

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

												__lowerCAmelCase					=							torch.cat(
												    (
												        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)					,						dim=1							),
												        feature[:, config.context_length - config.label_length :, ...],
												    )					,						dim=-1					,						)
												__lowerCAmelCase					=							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:
																	__lowerCAmelCase					=							model.get_decoder()
																	decoder.save_pretrained(_A							)
																	__lowerCAmelCase					=							AutoformerDecoder.from_pretrained(_A							).to(_A							)

												__lowerCAmelCase					=							decoder(
												    trend=_A					,						inputs_embeds=_A					,						encoder_hidden_states=_A					,						)[0]

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





@require_torch
class     snake_case_			(				_a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
							__UpperCAmelCase       =(AutoformerForPrediction,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												self.config_tester.run_common_tests()




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(_A							)
																						__lowerCAmelCase,	__lowerCAmelCase					=							model_class.from_pretrained(_A					,						output_loading_info=_A							)
																	self.assertEqual(info['missing_keys']					,						[]							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_encoder_decoder_model_standalone(*_A							)




							@unittest.skip(reason='Model has no tokens embeddings'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase					=							inspect.signature(getattr(_A					,						'forward'							)							)
												# The main input is the name of the argument after `self`
												__lowerCAmelCase					=							list(model_signature.parameters.keys()							)[1]
												self.assertEqual(AutoformerModel.main_input_name					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=							[
																	    '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(_A							)]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True

												__lowerCAmelCase					=							getattr(self.model_tester					,						'seq_length'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'decoder_seq_length'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'encoder_seq_length'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'd_model'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'num_attention_heads'					,						_A							)
												__lowerCAmelCase					=							d_model // num_attention_heads

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

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

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

																	__lowerCAmelCase					=							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(_A					,						_A							)

																	# decoder attentions
																	__lowerCAmelCase					=							outputs.decoder_attentions
																	self.assertIsInstance(_A					,						(list, tuple)							)
																	self.assertEqual(len(_A							)					,						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
																	__lowerCAmelCase					=							outputs.cross_attentions
																	self.assertIsInstance(_A					,						(list, tuple)							)
																	self.assertEqual(len(_A							)					,						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
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							model_class(_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(**self._prepare_for_class(_A					,						_A							)							)

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

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

												self.assertEqual(len(_A							)					,						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        A__  ( self							):
												super().test_retain_grad_hidden_states_attentions()


def        __lowercase    (  UpperCAmelCase__="train-batch.pt"      ):
					__lowerCAmelCase					=							hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'		,					filename=UpperCAmelCase__		,					repo_type='dataset'      )
					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location=UpperCAmelCase__      )
					return batch





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


							"""simple docstring"""





							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'							).to(_A							)
												__lowerCAmelCase					=							prepare_batch()

												with torch.no_grad():
																	__lowerCAmelCase					=							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]

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

												__lowerCAmelCase					=							torch.tensor(
												    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]					,						device=_A							)
												self.assertTrue(torch.allclose(output[0, :3, :3]					,						_A					,						atol=_A							)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'							).to(_A							)
												__lowerCAmelCase					=							prepare_batch('val-batch.pt'							)
												with torch.no_grad():
																	__lowerCAmelCase					=							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
												__lowerCAmelCase					=							torch.Size((6_4, model.config.context_length, model.config.d_model)							)
												self.assertEqual(output.shape					,						_A							)

												__lowerCAmelCase					=							torch.tensor(
												    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]					,						device=_A							)
												self.assertTrue(torch.allclose(output[0, :3, :3]					,						_A					,						atol=_A							)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'							).to(_A							)
												__lowerCAmelCase					=							prepare_batch('val-batch.pt'							)
												with torch.no_grad():
																	__lowerCAmelCase					=							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']					,						)
												__lowerCAmelCase					=							torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)							)
												self.assertEqual(outputs.sequences.shape					,						_A							)

												__lowerCAmelCase					=							torch.tensor([3130.6763, 4056.5293, 7053.0786]					,						device=_A							)
												__lowerCAmelCase					=							outputs.sequences.mean(dim=1							)
												self.assertTrue(torch.allclose(mean_prediction[0, -3:]					,						_A					,						rtol=1e-1							)							)









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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__ = None		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							{}

					if train_file is not None:
										__lowerCAmelCase					=							[train_file]
					if eval_file is not None:
										__lowerCAmelCase					=							[eval_file]
					if test_file is not None:
										__lowerCAmelCase					=							[test_file]

					__lowerCAmelCase					=							datasets.load_dataset('csv'		,					data_files=UpperCAmelCase__      )
					__lowerCAmelCase					=							list(ds[list(files.keys()      )[0]].features.keys()      )
					__lowerCAmelCase					=							features_name.pop(UpperCAmelCase__      )
					__lowerCAmelCase					=							list(set(ds[list(files.keys()      )[0]][label_name]      )      )
					__lowerCAmelCase					=							{label: i for i, label in enumerate(UpperCAmelCase__      )}
					__lowerCAmelCase					=							tokenizer.model_input_names
					__lowerCAmelCase					=							{}

					if len(UpperCAmelCase__      ) == 1:
										for k in files.keys():
															__lowerCAmelCase					=							ds[k].map(
															    lambda UpperCAmelCase__      : tokenizer.batch_encode_plus(
															        example[features_name[0]]		,					truncation=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='max_length'      )		,					batched=UpperCAmelCase__		,					)
					elif len(UpperCAmelCase__      ) == 2:
										for k in files.keys():
															__lowerCAmelCase					=							ds[k].map(
															    lambda UpperCAmelCase__      : tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])		,					truncation=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='max_length'		,					)		,					batched=UpperCAmelCase__		,					)

					def gen_train():
										for ex in transformed_ds[datasets.Split.TRAIN]:
															__lowerCAmelCase					=							{k: v for k, v in ex.items() if k in input_names}
															__lowerCAmelCase					=							labelaid[ex[label_name]]
															yield (d, label)

					def gen_val():
										for ex in transformed_ds[datasets.Split.VALIDATION]:
															__lowerCAmelCase					=							{k: v for k, v in ex.items() if k in input_names}
															__lowerCAmelCase					=							labelaid[ex[label_name]]
															yield (d, label)

					def gen_test():
										for ex in transformed_ds[datasets.Split.TEST]:
															__lowerCAmelCase					=							{k: v for k, v in ex.items() if k in input_names}
															__lowerCAmelCase					=							labelaid[ex[label_name]]
															yield (d, label)

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({k: tf.intaa for k in input_names}, tf.intaa)		,					({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))		,					)
					    if datasets.Split.TRAIN in transformed_ds
					    else None
					)

					if train_ds is not None:
										__lowerCAmelCase					=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({k: tf.intaa for k in input_names}, tf.intaa)		,					({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))		,					)
					    if datasets.Split.VALIDATION in transformed_ds
					    else None
					)

					if val_ds is not None:
										__lowerCAmelCase					=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({k: tf.intaa for k in input_names}, tf.intaa)		,					({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))		,					)
					    if datasets.Split.TEST in transformed_ds
					    else None
					)

					if test_ds is not None:
										__lowerCAmelCase					=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

					return train_ds, val_ds, test_ds, labelaid


lowerCamelCase      				=     logging.getLogger(__name__)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(metadata={"""help""": """Which column contains the label"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the training file"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the development file"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the test file"""}							)
							__UpperCAmelCase       =field(
							    default=128			, metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """Set this flag to use fast tokenization."""}							)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			, )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
					__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir      )
					    and os.listdir(training_args.output_dir      )
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    ' --overwrite_output_dir to overcome.'      )

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,					datefmt='%m/%d/%Y %H:%M:%S'		,					level=logging.INFO		,					)
					logger.info(
					    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1      )}, """
					    F"""16-bits training: {training_args.fpaa}"""      )
					logger.info(F"""Training/evaluation parameters {training_args}"""      )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__lowerCAmelCase					=							AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,					cache_dir=model_args.cache_dir		,					)

					__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							get_tfds(
					    train_file=data_args.train_file		,					eval_file=data_args.dev_file		,					test_file=data_args.test_file		,					tokenizer=UpperCAmelCase__		,					label_column_id=data_args.label_column_id		,					max_seq_length=data_args.max_seq_length		,					)

					__lowerCAmelCase					=							AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,					num_labels=len(UpperCAmelCase__      )		,					labelaid=UpperCAmelCase__		,					idalabel={id: label for label, id in labelaid.items()}		,					finetuning_task='text-classification'		,					cache_dir=model_args.cache_dir		,					)

					with training_args.strategy.scope():
										__lowerCAmelCase					=							TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path		,					from_pt=bool('.bin' in model_args.model_name_or_path      )		,					config=UpperCAmelCase__		,					cache_dir=model_args.cache_dir		,					)

					def compute_metrics(UpperCAmelCase__      ) -> Dict:
										__lowerCAmelCase					=							np.argmax(p.predictions		,					axis=1      )

										return {"acc": (preds == p.label_ids).mean()}

					# Initialize our Trainer
					__lowerCAmelCase					=							TFTrainer(
					    model=UpperCAmelCase__		,					args=UpperCAmelCase__		,					train_dataset=UpperCAmelCase__		,					eval_dataset=UpperCAmelCase__		,					compute_metrics=UpperCAmelCase__		,					)

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir      )

					# Evaluation
					__lowerCAmelCase					=							{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'      )
										__lowerCAmelCase					=							trainer.evaluate()
										__lowerCAmelCase					=							os.path.join(training_args.output_dir		,					'eval_results.txt'      )

										with open(UpperCAmelCase__		,					'w'      ) as writer:
															logger.info('***** Eval results *****'      )

															for key, value in result.items():
																				logger.info(F"""  {key} = {value}"""      )
																				writer.write(F"""{key} = {value}\n"""      )

															results.update(UpperCAmelCase__      )

					return results


if __name__ == "__main__":
		main()






