
"""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								=						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        _UpperCAmelCase:



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






							'''simple docstring'''
							_UpperCamelCase									=    d_model
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    prediction_length
							_UpperCamelCase									=    context_length
							_UpperCamelCase									=    cardinality
							_UpperCamelCase									=    num_time_features
							_UpperCamelCase									=    lags_sequence
							_UpperCamelCase									=    embedding_dimension
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob

							_UpperCamelCase									=    context_length
							_UpperCamelCase									=    prediction_length + label_length
							_UpperCamelCase									=    label_length

							_UpperCamelCase									=    moving_average
							_UpperCamelCase									=    autocorrelation_factor



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''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 		UpperCAmelCase				(   self							,						__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    config.context_length + max(config.lags_sequence)

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

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

							_UpperCamelCase									=    {
							    '''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 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_config()
							_UpperCamelCase									=    self.prepare_autoformer_inputs_dict(__a)
							return config, inputs_dict



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






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.prepare_config_and_inputs()
							return config, inputs_dict




		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerModel(config=__a).to(__a).eval()
							_UpperCamelCase									=    model(**__a)

							_UpperCamelCase									=    outputs.encoder_last_hidden_state
							_UpperCamelCase									=    outputs.last_hidden_state

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

							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    model.create_network_inputs(**__a)
							_UpperCamelCase					,    _UpperCamelCase									=    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...])

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

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

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

							_UpperCamelCase									=    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        _UpperCAmelCase(       SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	unittest.TestCase      ):
		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 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)

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



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    inspect.signature(getattr(__a							,						'''forward'''))
							# The main input is the name of the argument after `self`
							_UpperCamelCase									=    list(model_signature.parameters.keys())[1]
							self.assertEqual(AutoformerModel.main_input_name							,						__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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

												_UpperCamelCase									=    [
												    '''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 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase									=    True

							_UpperCamelCase									=    getattr(self.model_tester							,						'''seq_length'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''decoder_seq_length'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''encoder_seq_length'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''d_model'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''num_attention_heads'''							,						__a)
							_UpperCamelCase									=    d_model // num_attention_heads

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    True
												_UpperCamelCase									=    False
												_UpperCamelCase									=    True
												_UpperCamelCase									=    model_class(__a)
												model.to(__a)
												model.eval()
												with torch.no_grad():
																	_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))
												_UpperCamelCase									=    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"]
												_UpperCamelCase									=    True
												_UpperCamelCase									=    model_class(__a)
												model.to(__a)
												model.eval()
												with torch.no_grad():
																	_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))
												_UpperCamelCase									=    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]							,						)
												_UpperCamelCase									=    len(__a)

												_UpperCamelCase									=    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
												_UpperCamelCase									=    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
												_UpperCamelCase									=    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
							_UpperCamelCase									=    True
							_UpperCamelCase									=    True
							_UpperCamelCase									=    model_class(__a)
							model.to(__a)
							model.eval()
							with torch.no_grad():
												_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))

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

							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							super().test_retain_grad_hidden_states_attentions()





def  lowerCamelCase__						(     __snake_case="train-batch.pt"      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''',	filename=__snake_case,	repo_type='''dataset'''      )
					_UpperCamelCase									=    torch.load(__snake_case,	map_location=__snake_case      )
					return batch






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



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''').to(__a)
							_UpperCamelCase									=    prepare_batch()

							with torch.no_grad():
												_UpperCamelCase									=    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]

							_UpperCamelCase									=    torch.Size(
							    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size))
							self.assertEqual(output.shape							,						__a)

							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''').to(__a)
							_UpperCamelCase									=    prepare_batch('''val-batch.pt''')
							with torch.no_grad():
												_UpperCamelCase									=    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
							_UpperCamelCase									=    torch.Size((64, model.config.context_length, model.config.d_model))
							self.assertEqual(output.shape							,						__a)

							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''').to(__a)
							_UpperCamelCase									=    prepare_batch('''val-batch.pt''')
							with torch.no_grad():
												_UpperCamelCase									=    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''']							,						)
							_UpperCamelCase									=    torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length))
							self.assertEqual(outputs.sequences.shape							,						__a)

							_UpperCamelCase									=    torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86]							,						device=__a)
							_UpperCamelCase									=    outputs.sequences.mean(dim=1)
							self.assertTrue(torch.allclose(mean_prediction[0, -3:]							,						__a							,						rtol=1e-1))












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						gather(SCREAMING_SNAKE_CASE )
			assert gathered_tensor.tolist() == list(range(1       ,    state.num_processes**2 + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[state.process_index]
			SCREAMING_SNAKE_CASE_								=						gather_object(SCREAMING_SNAKE_CASE )
			assert len(SCREAMING_SNAKE_CASE ) == state.num_processes, F'{gathered_obj}, {len(SCREAMING_SNAKE_CASE )} != {state.num_processes}'
			assert gathered_obj == list(range(state.num_processes ) ), F'{gathered_obj} != {list(range(state.num_processes ) )}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						broadcast(SCREAMING_SNAKE_CASE )
			assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
			assert broadcasted_tensor.tolist() == list(range(1       ,    state.num_processes + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if state.is_main_process:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes + 1 ).to(state.device )
			else:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes ).to(state.device )
			SCREAMING_SNAKE_CASE_								=						pad_across_processes(SCREAMING_SNAKE_CASE )
			assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
			if not state.is_main_process:
						assert padded_tensor.tolist() == list(range(0       ,    state.num_processes ) ) + [0]

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'sum' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([4.0, 6] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'mean' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([2.0, 3] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			main()

def lowercase    (  )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						PartialState()
			state.print(F'State: {state}' )
			state.print('testing gather' )
			test_gather(SCREAMING_SNAKE_CASE )
			state.print('testing gather_object' )
			test_gather_object(SCREAMING_SNAKE_CASE )
			state.print('testing broadcast' )
			test_broadcast(SCREAMING_SNAKE_CASE )
			state.print('testing pad_across_processes' )
			test_pad_across_processes(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_sum' )
			test_reduce_sum(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_mean' )
			test_reduce_mean(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				main()
