import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_MAPPING,
         BeitForImageClassification,
         BeitForMaskedImageModeling,
         BeitForSemanticSegmentation,
         BeitModel,
     )
     from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     import PIL
     from PIL import Image

     from transformers import BeitImageProcessor



class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=100   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=30   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=37   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=10   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=[0, 1, 2, 3]   ,				)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      100
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_labels
       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__										=      type_sequence_label_size
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      scope
       UpperCamelCase__										=      out_indices
       UpperCamelCase__										=      num_labels

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__										=      (image_size // patch_size) ** 2
       UpperCamelCase__										=      num_patches + 1






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

       UpperCamelCase__										=      None
       UpperCamelCase__										=      None
       if self.use_labels:
            UpperCamelCase__										=      ids_tensor([self.batch_size]   ,				self.type_sequence_label_size	)
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels	)

       UpperCamelCase__										=      self.get_config()

       return config, pixel_values, labels, pixel_labels






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return BeitConfig(
           vocab_size=self.vocab_size   ,				image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				is_decoder=SCREAMING_SNAKE_CASE_   ,				initializer_range=self.initializer_range   ,				out_indices=self.out_indices   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      BeitModel(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      BeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length - 1, self.vocab_size)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.type_sequence_label_size
       UpperCamelCase__										=      BeitForImageClassification(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)	)

       # test greyscale images
       UpperCamelCase__										=      1
       UpperCamelCase__										=      BeitForImageClassification(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()

       UpperCamelCase__										=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.num_labels
       UpperCamelCase__										=      BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(
           result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(
           result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      config_and_inputs
       UpperCamelCase__										=      {'''pixel_values''': pixel_values}
       return config, inputs_dict



@require_torch
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	str						=(
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=(
      {
          "feature-extraction": BeitModel,
          "image-classification": BeitForImageClassification,
          "image-segmentation": BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE_	:	Union[str, Any]						=False
  SCREAMING_SNAKE_CASE_	:	Tuple						=False
  SCREAMING_SNAKE_CASE_	:	Tuple						=False






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      BeitModelTester(self	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_   ,				has_text_modality=SCREAMING_SNAKE_CASE_   ,				hidden_size=37	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  @unittest.skip(reason='''BEiT does not use inputs_embeds'''	)
  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       pass






  @require_torch_multi_gpu
  @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''	)
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)	)
            UpperCamelCase__										=      model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_   ,				nn.Linear	)	)






  def 		_a					(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(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      inspect.signature(model.forward	)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCamelCase__										=      [*signature.parameters.keys()]

            UpperCamelCase__										=      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       if not self.model_tester.is_training:
            return

       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__										=      True

       for model_class in self.all_model_classes:
            # we don't test BeitForMaskedImageModeling
            if model_class in [*get_values(SCREAMING_SNAKE_CASE_	), BeitForMaskedImageModeling]:
                 continue

            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            model.to(SCREAMING_SNAKE_CASE_	)
            model.train()
            UpperCamelCase__										=      self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				return_labels=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	).loss
            loss.backward()






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





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
            return

       UpperCamelCase__										=      False
       UpperCamelCase__										=      True

       for model_class in self.all_model_classes:
            # we don't test BeitForMaskedImageModeling
            if (
                model_class in [*get_values(SCREAMING_SNAKE_CASE_	), BeitForMaskedImageModeling]
                or not model_class.supports_gradient_checkpointing
            ):
                 continue

            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            model.gradient_checkpointing_enable()
            model.to(SCREAMING_SNAKE_CASE_	)
            model.train()
            UpperCamelCase__										=      self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				return_labels=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	).loss
            loss.backward()






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__										=      _config_zero_init(SCREAMING_SNAKE_CASE_	)
       for model_class in self.all_model_classes:
            UpperCamelCase__										=      model_class(config=SCREAMING_SNAKE_CASE_	)
            for name, param in model.named_parameters():
                 # we skip lambda parameters as these require special initial values
                 # determined by config.layer_scale_init_value
                 if "lambda" in name:
                      continue
                 if param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()   ,				[0.0, 1.0]   ,				msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,				)






  @slow
  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__										=      BeitModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image



@require_torch
@require_vision
class     _A   (    unittest.TestCase							):






  @cached_property
  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''	) if is_vision_available() else None






  @slow
  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''	).to(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).pixel_values.to(SCREAMING_SNAKE_CASE_	)

       # prepare bool_masked_pos
       UpperCamelCase__										=      torch.ones((1, 196)   ,				dtype=torch.bool	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(pixel_values=SCREAMING_SNAKE_CASE_   ,				bool_masked_pos=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      outputs.logits

       # verify the logits
       UpperCamelCase__										=      torch.Size((1, 196, 8192)	)
       self.assertEqual(logits.shape   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.tensor(
           [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]	).to(SCREAMING_SNAKE_CASE_	)

       self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-2	)	)






  @slow
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''	).to(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      outputs.logits

       # verify the logits
       UpperCamelCase__										=      torch.Size((1, 1000)	)
       self.assertEqual(logits.shape   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.tensor([-1.2385, -1.0987, -1.0108]	).to(SCREAMING_SNAKE_CASE_	)

       self.assertTrue(torch.allclose(logits[0, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)

       UpperCamelCase__										=      281
       self.assertEqual(logits.argmax(-1	).item()   ,				SCREAMING_SNAKE_CASE_	)






  @slow
  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''	).to(
           SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      outputs.logits

       # verify the logits
       UpperCamelCase__										=      torch.Size((1, 2_1841)	)
       self.assertEqual(logits.shape   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.tensor([1.6881, -0.2787, 0.5901]	).to(SCREAMING_SNAKE_CASE_	)

       self.assertTrue(torch.allclose(logits[0, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)

       UpperCamelCase__										=      2396
       self.assertEqual(logits.argmax(-1	).item()   ,				SCREAMING_SNAKE_CASE_	)






  @slow
  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''	)
       UpperCamelCase__										=      model.to(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE_   ,				size=640   ,				do_center_crop=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      load_dataset('''hf-internal-testing/fixtures_ade20k'''   ,				split='''test'''	)
       UpperCamelCase__										=      Image.open(ds[0]['''file''']	)
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      outputs.logits

       # verify the logits
       UpperCamelCase__										=      torch.Size((1, 150, 160, 160)	)
       self.assertEqual(logits.shape   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      version.parse(PIL.__version__	) < version.parse('''9.0.0'''	)

       if is_pillow_less_than_a:
            UpperCamelCase__										=      torch.tensor(
                [
                    [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                    [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                    [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                ]   ,				device=SCREAMING_SNAKE_CASE_   ,				)
       else:
            UpperCamelCase__										=      torch.tensor(
                [
                    [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
                    [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
                    [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
                ]   ,				device=SCREAMING_SNAKE_CASE_   ,				)

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)






  @slow
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''	)
       UpperCamelCase__										=      model.to(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE_   ,				size=640   ,				do_center_crop=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      load_dataset('''hf-internal-testing/fixtures_ade20k'''   ,				split='''test'''	)
       UpperCamelCase__										=      Image.open(ds[0]['''file''']	)
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      outputs.logits.detach().cpu()

       UpperCamelCase__										=      image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_   ,				target_sizes=[(500, 300)]	)
       UpperCamelCase__										=      torch.Size((500, 300)	)
       self.assertEqual(segmentation[0].shape   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      torch.Size((160, 160)	)
       self.assertEqual(segmentation[0].shape   ,				SCREAMING_SNAKE_CASE_	)




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
     __magic_name__								=pd.read_csv('''sample_data.csv''', header=None)
     __magic_name__								=df.shape[:1][0]
     # If you're using some other dataset input the target column
     __magic_name__								=df.iloc[:, 1:2]
     __magic_name__								=actual_data.values.reshape(len_data, 1)
     __magic_name__								=MinMaxScaler().fit_transform(actual_data)
     __magic_name__								=10
     __magic_name__								=5
     __magic_name__								=20
     __magic_name__								=len_data - periods * look_back
     __magic_name__								=actual_data[:division]
     __magic_name__								=actual_data[division - look_back :]
     __magic_name__		,  __magic_name__								=[], []
     __magic_name__		,  __magic_name__								=[], []

     for i in range(0, len(train_data) - forward_days - look_back + 1):
          train_x.append(train_data[i : i + look_back])
          train_y.append(train_data[i + look_back : i + look_back + forward_days])
     for i in range(0, len(test_data) - forward_days - look_back + 1):
          test_x.append(test_data[i : i + look_back])
          test_y.append(test_data[i + look_back : i + look_back + forward_days])
     __magic_name__								=np.array(train_x)
     __magic_name__								=np.array(test_x)
     __magic_name__								=np.array([list(i.ravel()) for i in train_y])
     __magic_name__								=np.array([list(i.ravel()) for i in test_y])

     __magic_name__								=Sequential()
     model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(128, 1)))
     model.add(Dense(forward_days))
     model.compile(loss='''mean_squared_error''', optimizer='''adam''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



