




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      :  Tuple      						=						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      :		Union[str, Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Tuple=16		,	_UpperCAmelCase      :		List[str]=13		,	_UpperCAmelCase      :		List[Any]=7		,	_UpperCAmelCase      :		List[str]=14		,	_UpperCAmelCase      :		Optional[Any]=10		,	_UpperCAmelCase      :		Union[str, Any]=19		,	_UpperCAmelCase      :		List[Any]=5		,	_UpperCAmelCase      :		Optional[Any]=4		,	_UpperCAmelCase      :		int=True		,	_UpperCAmelCase      :		List[str]=16		,	_UpperCAmelCase      :		Tuple=2		,	_UpperCAmelCase      :		Optional[Any]=4		,	_UpperCAmelCase      :		int=4		,	_UpperCAmelCase      :		Tuple="gelu"		,	_UpperCAmelCase      :		Dict=0.1		,	_UpperCAmelCase      :		str=0.1		,	_UpperCAmelCase      :		Optional[int]=[1, 2, 3, 4, 5]		,	_UpperCAmelCase      :		Optional[int]=25		,	_UpperCAmelCase      :		str=5		,	)  ->    Dict:





        '''simple docstring'''




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

        _lowerCAmelCase    :    List[Any]										=      context_length
        _lowerCAmelCase    :    str										=      prediction_length + label_length
        _lowerCAmelCase    :    Tuple										=      label_length

        _lowerCAmelCase    :    Dict										=      moving_average
        _lowerCAmelCase    :    Dict										=      autocorrelation_factor



    def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Optional[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       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		int      )  ->    List[Any]:





        '''simple docstring'''




        _lowerCAmelCase    :    List[Any]										=      config.context_length + max(config.lags_sequence      )

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

        # decoder inputs
        _lowerCAmelCase    :    Optional[Any]										=      floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]      )
        _lowerCAmelCase    :    Dict										=      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       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    int:





        '''simple docstring'''




        _lowerCAmelCase    :    Dict										=      self.get_config()
        _lowerCAmelCase    :    Union[str, Any]										=      self.prepare_autoformer_inputs_dict(_UpperCAmelCase      )
        return config, inputs_dict



    def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    List[Any]:





        '''simple docstring'''




        _lowerCAmelCase    :    int										=      self.prepare_config_and_inputs()
        return config, inputs_dict



    def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[str]:





        '''simple docstring'''




        _lowerCAmelCase    :    List[str]										=      AutoformerModel(config=_UpperCAmelCase      ).to(_UpperCAmelCase      ).eval()
        _lowerCAmelCase    :    Union[str, Any]										=      model(**_UpperCAmelCase      )

        _lowerCAmelCase    :    Union[str, Any]										=      outputs.encoder_last_hidden_state
        _lowerCAmelCase    :    Optional[int]										=      outputs.last_hidden_state

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

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

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

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

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

        _lowerCAmelCase    :    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  (_a						,	_a						,	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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Dict:





        '''simple docstring'''




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



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





        '''simple docstring'''




        self.config_tester.run_common_tests()



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





        '''simple docstring'''




        _lowerCAmelCase    :    List[str]										=      self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            _lowerCAmelCase    :    int										=      model_class(_UpperCAmelCase      )

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



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





        '''simple docstring'''




        _lowerCAmelCase    :    Union[str, Any]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





        '''simple docstring'''




        pass



    def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[str]:





        '''simple docstring'''




        _lowerCAmelCase    :    Any										=      inspect.signature(getattr(_UpperCAmelCase		,	"""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		,	_UpperCAmelCase      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[int]:





        '''simple docstring'''




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

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

            _lowerCAmelCase    :    List[str]										=      [
                """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       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Optional[Any]:





        '''simple docstring'''




        _lowerCAmelCase    :    List[Any]										=      self.model_tester.prepare_config_and_inputs_for_common()
        _lowerCAmelCase    :    str										=      True

        _lowerCAmelCase    :    Dict										=      getattr(self.model_tester		,	"""seq_length"""		,	_UpperCAmelCase      )
        _lowerCAmelCase    :    int										=      getattr(self.model_tester		,	"""decoder_seq_length"""		,	_UpperCAmelCase      )
        _lowerCAmelCase    :    Optional[Any]										=      getattr(self.model_tester		,	"""encoder_seq_length"""		,	_UpperCAmelCase      )
        _lowerCAmelCase    :    Union[str, Any]										=      getattr(self.model_tester		,	"""d_model"""		,	_UpperCAmelCase      )
        _lowerCAmelCase    :    str										=      getattr(self.model_tester		,	"""num_attention_heads"""		,	_UpperCAmelCase      )
        _lowerCAmelCase    :    Optional[Any]										=      d_model // num_attention_heads

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

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

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

        _lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Tuple:





        '''simple docstring'''




        super().test_retain_grad_hidden_states_attentions()






def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]="train-batch.pt"       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Dict										=      hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""      ,    filename=UpperCamelCase_      ,    repo_type="""dataset"""       )
    _lowerCAmelCase    :    str										=      torch.load(UpperCamelCase_      ,    map_location=UpperCamelCase_       )
    return batch





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



    def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Optional[int]:





        '''simple docstring'''




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

        with torch.no_grad():
            _lowerCAmelCase    :    Tuple										=      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    :    Any										=      torch.Size(
            (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)      )
        self.assertEqual(output.shape		,	_UpperCAmelCase      )

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



    def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    int:





        '''simple docstring'''




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

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



    def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





        '''simple docstring'''




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

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







import cva
import numpy as np
class        __snake_case  :



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		float		,	_UpperCAmelCase      :		int      )  ->    int:





								'''simple docstring'''




								if k in (0.04, 0.06):
												_lowerCAmelCase    :    str										=      k
												_lowerCAmelCase    :    Optional[Any]										=      window_size
								else:
												raise ValueError("""invalid k value"""      )



				def __str__(      self      :		int      )  ->    str:





								'''simple docstring'''




								return str(self.k      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		str      )  ->    tuple[cva.Mat, list[list[int]]]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      cva.imread(_UpperCAmelCase		,	0      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      img.shape
								_lowerCAmelCase    :    list[list[int]]										=      []
								_lowerCAmelCase    :    int										=      img.copy()
								_lowerCAmelCase    :    str										=      cva.cvtColor(_UpperCAmelCase		,	cva.COLOR_GRAY2RGB      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      np.gradient(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      dx**2
								_lowerCAmelCase    :    Optional[int]										=      dy**2
								_lowerCAmelCase    :    Optional[Any]										=      dx * dy
								_lowerCAmelCase    :    Dict										=      0.04
								_lowerCAmelCase    :    Tuple										=      self.window_size // 2
								for y in range(_UpperCAmelCase		,	h - offset      ):
												for x in range(_UpperCAmelCase		,	w - offset      ):
																_lowerCAmelCase    :    Optional[int]										=      ixx[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    int										=      iyy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    Any										=      ixy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()

																_lowerCAmelCase    :    Dict										=      (wxx * wyy) - (wxy**2)
																_lowerCAmelCase    :    Union[str, Any]										=      wxx + wyy
																_lowerCAmelCase    :    int										=      det - k * (trace**2)
																# Can change the value
																if r > 0.5:
																				corner_list.append([x, y, r]      )
																				color_img.itemset((y, x, 0)		,	0      )
																				color_img.itemset((y, x, 1)		,	0      )
																				color_img.itemset((y, x, 2)		,	255      )
								return color_img, corner_list


if __name__ == "__main__":
	_lowerCamelCase      :  int      						=						HarrisCorner(0.0_4, 3)
	_lowerCamelCase						, _lowerCamelCase      :  Any      						=						edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)

