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__     :   Dict		=   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  :




					def __init__(   self    ,       __UpperCamelCase    ,       __UpperCamelCase=16    ,       __UpperCamelCase=13    ,       __UpperCamelCase=7    ,       __UpperCamelCase=14    ,       __UpperCamelCase=10    ,       __UpperCamelCase=19    ,       __UpperCamelCase=5    ,       __UpperCamelCase=4    ,       __UpperCamelCase=True    ,       __UpperCamelCase=16    ,       __UpperCamelCase=2    ,       __UpperCamelCase=4    ,       __UpperCamelCase=4    ,       __UpperCamelCase="gelu"    ,       __UpperCamelCase=0.1    ,       __UpperCamelCase=0.1    ,       __UpperCamelCase=[1, 2, 3, 4, 5]    ,       __UpperCamelCase=25    ,       __UpperCamelCase=5    ,       )     ->  int:



									'''simple docstring'''







									snake_case__					:	int												=      d_model
									snake_case__					:	Optional[Any]												=      parent
									snake_case__					:	Optional[int]												=      batch_size
									snake_case__					:	str												=      prediction_length
									snake_case__					:	Any												=      context_length
									snake_case__					:	Dict												=      cardinality
									snake_case__					:	List[str]												=      num_time_features
									snake_case__					:	Tuple												=      lags_sequence
									snake_case__					:	Any												=      embedding_dimension
									snake_case__					:	Optional[int]												=      is_training
									snake_case__					:	str												=      hidden_size
									snake_case__					:	int												=      num_hidden_layers
									snake_case__					:	List[Any]												=      num_attention_heads
									snake_case__					:	Optional[Any]												=      intermediate_size
									snake_case__					:	Any												=      hidden_act
									snake_case__					:	str												=      hidden_dropout_prob
									snake_case__					:	List[Any]												=      attention_probs_dropout_prob

									snake_case__					:	Any												=      context_length
									snake_case__					:	Union[str, Any]												=      prediction_length + label_length
									snake_case__					:	Optional[int]												=      label_length

									snake_case__					:	Optional[int]												=      moving_average
									snake_case__					:	Union[str, Any]												=      autocorrelation_factor




					def 			__a			(   self		)     ->  int:



									'''simple docstring'''







									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    ,       __UpperCamelCase		)     ->  Union[str, Any]:



									'''simple docstring'''







									snake_case__					:	str												=      config.context_length + max(config.lags_sequence		)

									snake_case__					:	Dict												=      ids_tensor([self.batch_size, 1]    ,       config.cardinality[0]		)
									snake_case__					:	Any												=      floats_tensor([self.batch_size, _past_length, config.num_time_features]		)
									snake_case__					:	Optional[int]												=      floats_tensor([self.batch_size, _past_length]		)
									snake_case__					:	str												=      floats_tensor([self.batch_size, _past_length]		) > 0.5

									# decoder inputs
									snake_case__					:	str												=      floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]		)
									snake_case__					:	List[str]												=      floats_tensor([self.batch_size, config.prediction_length]		)

									snake_case__					:	List[str]												=      {
									    '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		)     ->  int:



									'''simple docstring'''







									snake_case__					:	str												=      self.get_config()
									snake_case__					:	Optional[Any]												=      self.prepare_autoformer_inputs_dict(_UpperCAmelCase		)
									return config, inputs_dict




					def 			__a			(   self		)     ->  Dict:



									'''simple docstring'''







									snake_case__					:	Union[str, Any]												=      self.prepare_config_and_inputs()
									return config, inputs_dict




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase		)     ->  List[Any]:



									'''simple docstring'''







									snake_case__					:	Any												=      AutoformerModel(config=_UpperCAmelCase		).to(_UpperCAmelCase		).eval()
									snake_case__					:	List[Any]												=      model(**_UpperCAmelCase		)

									snake_case__					:	List[Any]												=      outputs.encoder_last_hidden_state
									snake_case__					:	int												=      outputs.last_hidden_state

									with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case__					:	Optional[int]												=      model.get_encoder()
													encoder.save_pretrained(_UpperCAmelCase		)
													snake_case__					:	Union[str, Any]												=      AutoformerEncoder.from_pretrained(_UpperCAmelCase		).to(_UpperCAmelCase		)

									snake_case__					:	str												=      model.create_network_inputs(**_UpperCAmelCase		)
									snake_case__					:	str												=      model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]		)

									snake_case__					:	Union[str, Any]												=      torch.cat(
									    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])    ,       dim=-1    ,       )
									snake_case__					:	Union[str, Any]												=      encoder(inputs_embeds=_UpperCAmelCase		)[0]
									self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

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

									snake_case__					:	Any												=      torch.cat(
									    (
									        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)    ,       dim=1		),
									        feature[:, config.context_length - config.label_length :, ...],
									    )    ,       dim=-1    ,       )
									snake_case__					:	List[str]												=      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:
													snake_case__					:	Union[str, Any]												=      model.get_decoder()
													decoder.save_pretrained(_UpperCAmelCase		)
													snake_case__					:	int												=      AutoformerDecoder.from_pretrained(_UpperCAmelCase		).to(_UpperCAmelCase		)

									snake_case__					:	Tuple												=      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  __snake_case  (			_UpperCamelCase    ,_UpperCamelCase    ,unittest.TestCase	):
					__lowerCamelCase       					=   (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
					__lowerCamelCase       					=   (AutoformerForPrediction,) if is_torch_available() else ()
					__lowerCamelCase       					=   {'feature-extraction': AutoformerModel} if is_torch_available() else {}
					__lowerCamelCase       					=   False
					__lowerCamelCase       					=   False
					__lowerCamelCase       					=   False
					__lowerCamelCase       					=   False
					__lowerCamelCase       					=   False
					__lowerCamelCase       					=   False




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



									'''simple docstring'''







									snake_case__					:	int												=      AutoformerModelTester(self		)
									snake_case__					:	Union[str, Any]												=      ConfigTester(self    ,       config_class=_UpperCAmelCase    ,       has_text_modality=_UpperCAmelCase		)




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



									'''simple docstring'''







									self.config_tester.run_common_tests()




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



									'''simple docstring'''







									snake_case__					:	Union[str, Any]												=      self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													snake_case__					:	Dict												=      model_class(_UpperCAmelCase		)

													with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(_UpperCAmelCase		)
																	snake_case__					:	List[str]												=      model_class.from_pretrained(_UpperCAmelCase    ,       output_loading_info=_UpperCAmelCase		)
													self.assertEqual(info['missing_keys']    ,       []		)




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



									'''simple docstring'''







									snake_case__					:	Dict												=      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 			__a			(   self		)     ->  Tuple:



									'''simple docstring'''







									pass




					def 			__a			(   self		)     ->  int:



									'''simple docstring'''







									snake_case__					:	Any												=      inspect.signature(getattr(_UpperCAmelCase    ,       'forward'		)		)
									# The main input is the name of the argument after `self`
									snake_case__					:	List[str]												=      list(model_signature.parameters.keys()		)[1]
									self.assertEqual(AutoformerModel.main_input_name    ,       _UpperCAmelCase		)




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



									'''simple docstring'''







									snake_case__					:	Union[str, Any]												=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													snake_case__					:	str												=      model_class(_UpperCAmelCase		)
													snake_case__					:	Tuple												=      inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													snake_case__					:	Optional[Any]												=      [*signature.parameters.keys()]

													snake_case__					:	Dict												=      [
													    '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 			__a			(   self		)     ->  str:



									'''simple docstring'''







									snake_case__					:	Dict												=      self.model_tester.prepare_config_and_inputs_for_common()
									snake_case__					:	str												=      True

									snake_case__					:	Optional[Any]												=      getattr(self.model_tester    ,       'seq_length'    ,       _UpperCAmelCase		)
									snake_case__					:	List[Any]												=      getattr(self.model_tester    ,       'decoder_seq_length'    ,       _UpperCAmelCase		)
									snake_case__					:	Optional[int]												=      getattr(self.model_tester    ,       'encoder_seq_length'    ,       _UpperCAmelCase		)
									snake_case__					:	Union[str, Any]												=      getattr(self.model_tester    ,       'd_model'    ,       _UpperCAmelCase		)
									snake_case__					:	Optional[int]												=      getattr(self.model_tester    ,       'num_attention_heads'    ,       _UpperCAmelCase		)
									snake_case__					:	Tuple												=      d_model // num_attention_heads

									for model_class in self.all_model_classes:
													snake_case__					:	Optional[int]												=      True
													snake_case__					:	int												=      False
													snake_case__					:	str												=      True
													snake_case__					:	Any												=      model_class(_UpperCAmelCase		)
													model.to(_UpperCAmelCase		)
													model.eval()
													with torch.no_grad():
																	snake_case__					:	List[str]												=      model(**self._prepare_for_class(_UpperCAmelCase    ,       _UpperCAmelCase		)		)
													snake_case__					:	int												=      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"]
													snake_case__					:	Optional[int]												=      True
													snake_case__					:	Optional[Any]												=      model_class(_UpperCAmelCase		)
													model.to(_UpperCAmelCase		)
													model.eval()
													with torch.no_grad():
																	snake_case__					:	Tuple												=      model(**self._prepare_for_class(_UpperCAmelCase    ,       _UpperCAmelCase		)		)
													snake_case__					:	int												=      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]    ,       )
													snake_case__					:	Tuple												=      len(_UpperCAmelCase		)

													snake_case__					:	Optional[Any]												=      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
													snake_case__					:	str												=      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
													snake_case__					:	Any												=      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
									snake_case__					:	str												=      True
									snake_case__					:	int												=      True
									snake_case__					:	Dict												=      model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									with torch.no_grad():
													snake_case__					:	Optional[int]												=      model(**self._prepare_for_class(_UpperCAmelCase    ,       _UpperCAmelCase		)		)

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

									snake_case__					:	Tuple												=      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 			__a			(   self		)     ->  Any:



									'''simple docstring'''







									super().test_retain_grad_hidden_states_attentions()




def  UpperCamelCase__						(				A__="train-batch.pt"    )   ->					List[Any]:
				snake_case__					:	Union[str, Any]												=      hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'			, filename=lowerCAmelCase_			, repo_type='dataset'    )
				snake_case__					:	Union[str, Any]												=      torch.load(lowerCAmelCase_			, map_location=lowerCAmelCase_    )
				return batch





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




					def 			__a			(   self		)     ->  Any:



									'''simple docstring'''







									snake_case__					:	Optional[Any]												=      AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'		).to(_UpperCAmelCase		)
									snake_case__					:	Optional[Any]												=      prepare_batch()

									with torch.no_grad():
													snake_case__					:	Union[str, Any]												=      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]

									snake_case__					:	List[str]												=      torch.Size(
									    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)		)
									self.assertEqual(output.shape    ,       _UpperCAmelCase		)

									snake_case__					:	Any												=      torch.tensor(
									    [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]    ,       device=_UpperCAmelCase		)
									self.assertTrue(torch.allclose(output[0, :3, :3]    ,       _UpperCAmelCase    ,       atol=_UpperCAmelCase		)		)




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



									'''simple docstring'''







									snake_case__					:	Optional[Any]												=      AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'		).to(_UpperCAmelCase		)
									snake_case__					:	Optional[int]												=      prepare_batch('val-batch.pt'		)
									with torch.no_grad():
													snake_case__					:	Optional[Any]												=      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
									snake_case__					:	Any												=      torch.Size((64, model.config.context_length, model.config.d_model)		)
									self.assertEqual(output.shape    ,       _UpperCAmelCase		)

									snake_case__					:	Union[str, Any]												=      torch.tensor(
									    [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]    ,       device=_UpperCAmelCase		)
									self.assertTrue(torch.allclose(output[0, :3, :3]    ,       _UpperCAmelCase    ,       atol=_UpperCAmelCase		)		)




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



									'''simple docstring'''







									snake_case__					:	Optional[Any]												=      AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'		).to(_UpperCAmelCase		)
									snake_case__					:	Tuple												=      prepare_batch('val-batch.pt'		)
									with torch.no_grad():
													snake_case__					:	Union[str, Any]												=      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']    ,       )
									snake_case__					:	List[Any]												=      torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)		)
									self.assertEqual(outputs.sequences.shape    ,       _UpperCAmelCase		)

									snake_case__					:	List[str]												=      torch.tensor([3130.6763, 4056.5293, 7053.0786]    ,       device=_UpperCAmelCase		)
									snake_case__					:	Union[str, Any]												=      outputs.sequences.mean(dim=1		)
									self.assertTrue(torch.allclose(mean_prediction[0, -3:]    ,       _UpperCAmelCase    ,       rtol=1E-1		)		)







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




