import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A_:   int   =      16
A_:   List[str]   =      32


def        __lowerCAmelCase      (	_A       ):



							"""simple docstring"""


							return int(x / 2**20       )


class _lowercase       :





							"""simple docstring"""




							def __enter__(     self	):







														'''simple docstring'''






														gc.collect()
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
														_lowercase					=						torch.cuda.memory_allocated()
														return self



							def __exit__(     self  , *UpperCAmelCase	):







														'''simple docstring'''






														gc.collect()
														torch.cuda.empty_cache()
														_lowercase					=						torch.cuda.memory_allocated()
														_lowercase					=						torch.cuda.max_memory_allocated()
														_lowercase					=						bamb(self.end - self.begin	)
														_lowercase					=						bamb(self.peak - self.begin	)
														# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def        __lowerCAmelCase      (	_A			,_A = 16			,_A = "bert-base-cased"			,_A = 320			,_A = 160			,):



							"""simple docstring"""


							_lowercase					=						AutoTokenizer.from_pretrained(A_       )
							_lowercase					=						load_dataset(
							    """glue"""			,"""mrpc"""			,split={"""train""": f'''train[:{n_train}]''', """validation""": f'''validation[:{n_val}]'''}       )

							def tokenize_function(_A       ):
														# max_length=None => use the model max length (it's actually the default)
														_lowercase					=						tokenizer(examples["""sentence1"""]			,examples["""sentence2"""]			,truncation=A_			,max_length=A_       )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							_lowercase					=						datasets.map(
							    A_			,batched=A_			,remove_columns=["""idx""", """sentence1""", """sentence2"""]			,load_from_cache_file=A_       )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							_lowercase					=						tokenized_datasets.rename_column("""label"""			,"""labels"""       )

							def collate_fn(_A       ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(A_			,padding="""max_length"""			,max_length=128			,return_tensors="""pt"""       )
														return tokenizer.pad(A_			,padding="""longest"""			,return_tensors="""pt"""       )

							# Instantiate dataloaders.
							_lowercase					=						DataLoader(
							    tokenized_datasets["""train"""]			,shuffle=A_			,collate_fn=A_			,batch_size=A_       )
							_lowercase					=						DataLoader(
							    tokenized_datasets["""validation"""]			,shuffle=A_			,collate_fn=A_			,batch_size=A_       )

							return train_dataloader, eval_dataloader


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_lowercase					=						config['''lr''']
							_lowercase					=						int(config["""num_epochs"""]       )
							_lowercase					=						int(config["""seed"""]       )
							_lowercase					=						int(config["""batch_size"""]       )
							_lowercase					=						args.model_name_or_path

							set_seed(A_       )
							_lowercase					=						get_dataloaders(A_			,A_			,A_			,args.n_train			,args.n_val       )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_lowercase					=						AutoModelForSequenceClassification.from_pretrained(A_			,return_dict=A_       )

							# Instantiate optimizer
							_lowercase					=						(
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							_lowercase					=						optimizer_cls(params=model.parameters()			,lr=A_       )

							if accelerator.state.deepspeed_plugin is not None:
														_lowercase					=						accelerator.state.deepspeed_plugin.deepspeed_config[
														    '''gradient_accumulation_steps'''
														]
							else:
														_lowercase					=						1
							_lowercase					=						(len(A_       ) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														_lowercase					=						get_linear_schedule_with_warmup(
														    optimizer=A_			,num_warmup_steps=0			,num_training_steps=A_			,)
							else:
														_lowercase					=						DummyScheduler(A_			,total_num_steps=A_			,warmup_num_steps=0       )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							_lowercase					=						accelerator.prepare(
							    A_			,A_			,A_			,A_			,A_       )

							# We need to keep track of how many total steps we have iterated over
							_lowercase					=						0
							# We also need to keep track of the stating epoch so files are named properly
							_lowercase					=						0

							# Now we train the model
							_lowercase					=						{}
							for epoch in range(A_			,A_       ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(A_       ):
																												_lowercase					=						model(**A_       )
																												_lowercase					=						outputs.loss
																												_lowercase					=						loss / gradient_accumulation_steps
																												accelerator.backward(A_       )
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

																												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
														accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin       )       )       )
														accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used       )       )
														accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked       )       )
														accelerator.print(
														    """Total Peak Memory consumed during the train (max): {}""".format(
														        tracemalloc.peaked + bamb(tracemalloc.begin       )       )       )
														_lowercase					=						tracemalloc.peaked + bamb(tracemalloc.begin       )
														if args.peak_memory_upper_bound is not None:
																					assert (
																					    train_total_peak_memory[f'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
																					), "Peak memory usage exceeded the upper bound"

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir			,"""peak_memory_utilization.json"""       )			,"""w"""       ) as f:
																					json.dump(A_			,A_       )


def        __lowerCAmelCase      (	):



							"""simple docstring"""


							_lowercase					=						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""       )
							parser.add_argument(
							    """--model_name_or_path"""			,type=A_			,default="""bert-base-cased"""			,help="""Path to pretrained model or model identifier from huggingface.co/models."""			,required=A_			,)
							parser.add_argument(
							    """--output_dir"""			,type=A_			,default="""."""			,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,)
							parser.add_argument(
							    """--peak_memory_upper_bound"""			,type=A_			,default=A_			,help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""			,)
							parser.add_argument(
							    """--n_train"""			,type=A_			,default=320			,help="""Number of training examples to use."""			,)
							parser.add_argument(
							    """--n_val"""			,type=A_			,default=160			,help="""Number of validation examples to use."""			,)
							parser.add_argument(
							    """--num_epochs"""			,type=A_			,default=1			,help="""Number of train epochs."""			,)
							_lowercase					=						parser.parse_args()
							_lowercase					=						{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
							training_function(A_			,A_       )


if __name__ == "__main__":
				main()





"""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


__UpperCamelCase    : Optional[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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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

						lowerCAmelCase__      :		Tuple    							=      context_length
						lowerCAmelCase__      :		Optional[Any]    							=      prediction_length + label_length
						lowerCAmelCase__      :		Union[str, Any]    							=      label_length

						lowerCAmelCase__      :		Optional[int]    							=      moving_average
						lowerCAmelCase__      :		Dict    							=      autocorrelation_factor







					def 			__lowerCAmelCase					(			self     :							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 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							str						):
						lowerCAmelCase__      :		str    							=      config.context_length + max(config.lags_sequence						)

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

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

						lowerCAmelCase__      :		Optional[int]    							=      {
						    '''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 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Dict    							=      self.get_config()
						lowerCAmelCase__      :		Dict    							=      self.prepare_autoformer_inputs_dict(lowercase_						)
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      self.prepare_config_and_inputs()
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Optional[int]  ,lowercase_     :							Union[str, Any]						):
						lowerCAmelCase__      :		Any    							=      AutoformerModel(config=lowercase_						).to(lowercase_						).eval()
						lowerCAmelCase__      :		Tuple    							=      model(**lowercase_						)

						lowerCAmelCase__      :		Any    							=      outputs.encoder_last_hidden_state
						lowerCAmelCase__      :		str    							=      outputs.last_hidden_state

						with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase__      :		List[Any]    							=      model.get_encoder()
							encoder.save_pretrained(lowercase_						)
							lowerCAmelCase__      :		Union[str, Any]    							=      AutoformerEncoder.from_pretrained(lowercase_						).to(lowercase_						)

						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      model.create_network_inputs(**lowercase_						)
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]						)

						lowerCAmelCase__      :		int    							=      torch.cat(
						    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])  ,dim=-1  ,)
						lowerCAmelCase__      :		Dict    							=      encoder(inputs_embeds=lowercase_						)[0]
						self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3						)

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

						lowerCAmelCase__      :		List[Any]    							=      torch.cat(
						    (
						        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)  ,dim=1						),
						        feature[:, config.context_length - config.label_length :, ...],
						    )  ,dim=-1  ,)
						lowerCAmelCase__      :		Any    							=      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__      :		Optional[int]    							=      model.get_decoder()
							decoder.save_pretrained(lowercase_						)
							lowerCAmelCase__      :		Tuple    							=      AutoformerDecoder.from_pretrained(lowercase_						).to(lowercase_						)

						lowerCAmelCase__      :		Union[str, Any]    							=      decoder(
						    trend=lowercase_  ,inputs_embeds=lowercase_  ,encoder_hidden_states=lowercase_  ,)[0]

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







@require_torch
class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



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







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		int    							=      AutoformerModelTester(self						)
						lowerCAmelCase__      :		Optional[int]    							=      ConfigTester(self  ,config_class=lowercase_  ,has_text_modality=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		Optional[Any]    							=      model_class(lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Tuple    							=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*lowercase_						)







					@unittest.skip(reason='''Model has no tokens embeddings'''						)
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						pass







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







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase__      :		Optional[int]    							=      [
							    '''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(lowercase_						)]  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__      :		List[str]    							=      True

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

						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		List[str]    							=      True
							lowerCAmelCase__      :		Optional[int]    							=      False
							lowerCAmelCase__      :		Optional[Any]    							=      True
							lowerCAmelCase__      :		Any    							=      model_class(lowercase_						)
							model.to(lowercase_						)
							model.eval()
							with torch.no_grad():
								lowerCAmelCase__      :		str    							=      model(**self._prepare_for_class(lowercase_  ,lowercase_						)						)
							lowerCAmelCase__      :		Optional[Any]    							=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(lowercase_						)  ,self.model_tester.num_hidden_layers						)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							lowerCAmelCase__      :		List[str]    							=      True
							lowerCAmelCase__      :		Union[str, Any]    							=      model_class(lowercase_						)
							model.to(lowercase_						)
							model.eval()
							with torch.no_grad():
								lowerCAmelCase__      :		Dict    							=      model(**self._prepare_for_class(lowercase_  ,lowercase_						)						)
							lowerCAmelCase__      :		int    							=      outputs.encoder_attentions
							self.assertEqual(len(lowercase_						)  ,self.model_tester.num_hidden_layers						)

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

							lowerCAmelCase__      :		List[str]    							=      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(lowercase_  ,lowercase_						)

							# decoder attentions
							lowerCAmelCase__      :		str    							=      outputs.decoder_attentions
							self.assertIsInstance(lowercase_  ,(list, tuple)						)
							self.assertEqual(len(lowercase_						)  ,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__      :		Any    							=      outputs.cross_attentions
							self.assertIsInstance(lowercase_  ,(list, tuple)						)
							self.assertEqual(len(lowercase_						)  ,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__      :		str    							=      True
						lowerCAmelCase__      :		Tuple    							=      True
						lowerCAmelCase__      :		Optional[Any]    							=      model_class(lowercase_						)
						model.to(lowercase_						)
						model.eval()
						with torch.no_grad():
							lowerCAmelCase__      :		Optional[Any]    							=      model(**self._prepare_for_class(lowercase_  ,lowercase_						)						)

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

						lowerCAmelCase__      :		Optional[Any]    							=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

						self.assertEqual(len(lowercase_						)  ,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 			__lowerCAmelCase					(			self     :							str						):
						super().test_retain_grad_hidden_states_attentions()

def     __SCREAMING_SNAKE_CASE    ( A_="train-batch.pt"					):
	lowerCAmelCase__      :		Any    							=      hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''		,	filename=A_		,	repo_type='''dataset'''					)
	lowerCAmelCase__      :		Union[str, Any]    							=      torch.load(A_		,	map_location=A_					)
	return batch







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






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		List[Any]    							=      AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		int    							=      prepare_batch()

						with torch.no_grad():
							lowerCAmelCase__      :		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]

						lowerCAmelCase__      :		Dict    							=      torch.Size(
						    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)						)
						self.assertEqual(output.shape  ,lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      prepare_batch('''val-batch.pt'''						)
						with torch.no_grad():
							lowerCAmelCase__      :		str    							=      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__      :		Any    							=      torch.Size((6_4, model.config.context_length, model.config.d_model)						)
						self.assertEqual(output.shape  ,lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      prepare_batch('''val-batch.pt'''						)
						with torch.no_grad():
							lowerCAmelCase__      :		int    							=      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__      :		List[Any]    							=      torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)						)
						self.assertEqual(outputs.sequences.shape  ,lowercase_						)

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




