



'''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


__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 	__magic_name__						:







							def __init__(							self					:       int	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       List[Any]=16	,_UpperCAmelCase					:       int=13	,_UpperCAmelCase					:       Union[str, Any]=7	,_UpperCAmelCase					:       List[Any]=14	,_UpperCAmelCase					:       List[str]=10	,_UpperCAmelCase					:       Any=19	,_UpperCAmelCase					:       List[str]=5	,_UpperCAmelCase					:       Tuple=4	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       List[str]=16	,_UpperCAmelCase					:       List[Any]=2	,_UpperCAmelCase					:       Dict=4	,_UpperCAmelCase					:       Union[str, Any]=4	,_UpperCAmelCase					:       List[Any]="gelu"	,_UpperCAmelCase					:       Optional[int]=0.1	,_UpperCAmelCase					:       Union[str, Any]=0.1	,_UpperCAmelCase					:       int=[1, 2, 3, 4, 5]	,_UpperCAmelCase					:       Union[str, Any]=25	,_UpperCAmelCase					:       Optional[int]=5	,):
									_a     :      int        =							d_model
									_a     :      Optional[Any]        =							parent
									_a     :      Optional[int]        =							batch_size
									_a     :      str        =							prediction_length
									_a     :      Any        =							context_length
									_a     :      Dict        =							cardinality
									_a     :      List[str]        =							num_time_features
									_a     :      Tuple        =							lags_sequence
									_a     :      Any        =							embedding_dimension
									_a     :      Optional[int]        =							is_training
									_a     :      str        =							hidden_size
									_a     :      int        =							num_hidden_layers
									_a     :      List[Any]        =							num_attention_heads
									_a     :      Optional[Any]        =							intermediate_size
									_a     :      Any        =							hidden_act
									_a     :      str        =							hidden_dropout_prob
									_a     :      List[Any]        =							attention_probs_dropout_prob

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

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







							def 							__lowercase			(							self					:       List[Any]		):
									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 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       List[Any]		):
									_a     :      str        =							config.context_length + max(config.lags_sequence		)

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

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

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







							def 							__lowercase			(							self					:       List[str]		):
									_a       ,					_a     :      Union[str, Any]        =							self.prepare_config_and_inputs()
									return config, inputs_dict







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int		):
									_a     :      Any        =							AutoformerModel(config=_UpperCAmelCase		).to(_UpperCAmelCase		).eval()
									_a     :      List[Any]        =							model(**_UpperCAmelCase		)

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

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

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

									_a     :      Union[str, Any]        =							torch.cat(
									    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])	,dim=-1	,)
									_a     :      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		)

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

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

									_a     :      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 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Union[str, Any]   				=					(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
							lowerCAmelCase       :    Any   				=					(AutoformerForPrediction,) if is_torch_available() else ()
							lowerCAmelCase       :    Any   				=					{'feature-extraction': AutoformerModel} if is_torch_available() else {}
							lowerCAmelCase       :    int   				=					False
							lowerCAmelCase       :    str   				=					False
							lowerCAmelCase       :    Optional[Any]   				=					False
							lowerCAmelCase       :    Optional[int]   				=					False
							lowerCAmelCase       :    Dict   				=					False
							lowerCAmelCase       :    Any   				=					False







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      int        =							AutoformerModelTester(self		)
									_a     :      Union[str, Any]        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]		):
									self.config_tester.run_common_tests()







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

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







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      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 							__lowercase			(							self					:       int		):
									pass







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







							def 							__lowercase			(							self					:       List[str]		):
									_a       ,					_a     :      Union[str, Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a     :      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 							__lowercase			(							self					:       str		):
									_a       ,					_a     :      Dict        =							self.model_tester.prepare_config_and_inputs_for_common()
									_a     :      str        =							True

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

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

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

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

									_a     :      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 							__lowercase			(							self					:       str		):
									super().test_retain_grad_hidden_states_attentions()



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

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







							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[Any]        =							AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'		).to(_UpperCAmelCase		)
									_a     :      Optional[Any]        =							prepare_batch()

									with torch.no_grad():
											_a     :      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]

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

									_a     :      Any        =							torch.tensor(
									    [[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]]	,device=_UpperCAmelCase		)
									self.assertTrue(torch.allclose(output[0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)







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

									_a     :      Union[str, Any]        =							torch.tensor(
									    [[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]]	,device=_UpperCAmelCase		)
									self.assertTrue(torch.allclose(output[0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)







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

									_a     :      List[str]        =							torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86]	,device=_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							outputs.sequences.mean(dim=1		)
									self.assertTrue(torch.allclose(mean_prediction[0, -3:]	,_UpperCAmelCase	,rtol=1E-1		)		)









'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




