

from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
    from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor







class  __lowerCamelCase				(	__snake_case	):







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(lowerCamelCase							,  """hidden_sizes"""   )   )
        self.parent.assertTrue(hasattr(lowerCamelCase							,  """neck_hidden_sizes"""   )   )
        self.parent.assertTrue(hasattr(lowerCamelCase							,  """num_attention_heads"""   )   )







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase=13							,  lowerCamelCase=32							,  lowerCamelCase=2							,  lowerCamelCase=3							,  lowerCamelCase=640							,  lowerCamelCase=4							,  lowerCamelCase="silu"							,  lowerCamelCase=3							,  lowerCamelCase=32							,  lowerCamelCase=0.1							,  lowerCamelCase=0.1							,  lowerCamelCase=0.1							,  lowerCamelCase=0.02							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase=10							,  lowerCamelCase=None							,  )						->       List[str]:
        snake_case_       						=      parent
        snake_case_       						=      batch_size
        snake_case_       						=      image_size
        snake_case_       						=      patch_size
        snake_case_       						=      num_channels
        snake_case_       						=      last_hidden_size
        snake_case_       						=      num_attention_heads
        snake_case_       						=      hidden_act
        snake_case_       						=      conv_kernel_size
        snake_case_       						=      output_stride
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      classifier_dropout_prob
        snake_case_       						=      use_labels
        snake_case_       						=      is_training
        snake_case_       						=      num_labels
        snake_case_       						=      initializer_range
        snake_case_       						=      scope







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

        snake_case_       						=      self.get_config()

        return config, pixel_values, labels, pixel_labels







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        return MobileViTConfig(
            image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  num_attention_heads=self.num_attention_heads							,  hidden_act=self.hidden_act							,  conv_kernel_size=self.conv_kernel_size							,  output_stride=self.output_stride							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  classifier_dropout_prob=self.classifier_dropout_prob							,  initializer_range=self.initializer_range							,  )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      TFMobileViTModel(config=lowerCamelCase   )
        snake_case_       						=      model(lowerCamelCase							,  training=lowerCamelCase   )
        snake_case_       						=      snake_case_       						=      self.image_size // self.output_stride
        self.parent.assertEqual(
            result.last_hidden_state.shape							,  (self.batch_size, self.last_hidden_size, expected_height, expected_width)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[int]:
        snake_case_       						=      self.num_labels
        snake_case_       						=      TFMobileViTForImageClassification(lowerCamelCase   )
        snake_case_       						=      model(lowerCamelCase							,  labels=lowerCamelCase							,  training=lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      self.num_labels
        snake_case_       						=      TFMobileViTForSemanticSegmentation(lowerCamelCase   )
        snake_case_       						=      snake_case_       						=      self.image_size // self.output_stride

        snake_case_       						=      model(lowerCamelCase							,  training=lowerCamelCase   )
        self.parent.assertEqual(
            result.logits.shape							,  (self.batch_size, self.num_labels, expected_height, expected_width)   )

        snake_case_       						=      model(lowerCamelCase							,  labels=lowerCamelCase							,  training=lowerCamelCase   )
        self.parent.assertEqual(
            result.logits.shape							,  (self.batch_size, self.num_labels, expected_height, expected_width)   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.prepare_config_and_inputs()
        snake_case_						,				snake_case_						,				snake_case_						,				snake_case_       						=      config_and_inputs
        snake_case_       						=      {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_tf
class  __lowerCamelCase				(	__snake_case  ,							__snake_case  ,							unittest.TestCase	):
      lowerCamelCase_	:      Tuple          =					(
          (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
          if is_tf_available()
          else ()
      )
      lowerCamelCase_	:      List[str]          =					(
          {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
          if is_tf_available()
          else {}
      )

      lowerCamelCase_	:      Any          =					False
      lowerCamelCase_	:      Dict          =					False
      lowerCamelCase_	:      int          =					False
      lowerCamelCase_	:      Union[str, Any]          =					False
      lowerCamelCase_	:      Dict          =					False







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_       						=      TFMobileViTModelTester(self   )
        snake_case_       						=      TFMobileViTConfigTester(self							,  config_class=lowerCamelCase							,  has_text_modality=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        self.config_tester.run_common_tests()







      @unittest.skip(reason="""MobileViT does not use inputs_embeds"""   )
      def 	lowerCAmelCase_    (     self   )						->       int:
        pass







      @unittest.skip(reason="""MobileViT does not support input and output embeddings"""   )
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        pass







      @unittest.skip(reason="""MobileViT does not output attentions"""   )
      def 	lowerCAmelCase_    (     self   )						->       int:
        pass







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

          snake_case_       						=      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        def check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ):
          snake_case_       						=      model_class(lowerCamelCase   )

          snake_case_       						=      model(**self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )   )

          snake_case_       						=      outputs.hidden_states

          snake_case_       						=      5
          self.assertEqual(len(lowerCamelCase   )							,  lowerCamelCase   )

          # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
          # with the width and height being successively divided by 2.
          snake_case_       						=      2
          for i in range(len(lowerCamelCase   )   ):
            self.assertListEqual(
                list(hidden_states[i].shape[-2:]   )							,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,  )
            divisor *= 2

          self.assertEqual(self.model_tester.output_stride							,  divisor // 2   )

        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          snake_case_       						=      True
          check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          snake_case_       						=      True

          check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase   )







      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        super().test_dataset_conversion()







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase=2e-1							,  lowerCamelCase=2e-1   )						->       Optional[Any]:
        self.assertTrue(np.allclose(lowerCamelCase							,  lowerCamelCase							,  atol=lowerCamelCase							,  rtol=lowerCamelCase   )   )







      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
          if model_class.__name__ != "TFMobileViTModel":
            snake_case_       						=      model_class(lowerCamelCase   )
            if getattr(lowerCamelCase							,  """hf_compute_loss"""							,  lowerCamelCase   ):
              super().test_keras_fit()







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
          # set an ignore index to correctly test the masked loss used in
          # `TFMobileViTForSemanticSegmentation`.
          if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
            snake_case_       						=      5

          snake_case_       						=      model_class(lowerCamelCase   )
          if getattr(lowerCamelCase							,  """hf_compute_loss"""							,  lowerCamelCase   ):
            # The number of elements in the loss should be the same as the number of elements in the label
            snake_case_       						=      self._prepare_for_class(inputs_dict.copy()							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
            snake_case_       						=      prepared_for_class[
                sorted(prepared_for_class.keys() - inputs_dict.keys()							,  reverse=lowerCamelCase   )[0]
            ]
            snake_case_       						=      added_label.shape.as_list()[:1]

            # Test that model correctly compute the loss with kwargs
            snake_case_       						=      self._prepare_for_class(inputs_dict.copy()							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
            snake_case_       						=      {"""input_ids""", """pixel_values""", """input_features"""}
            snake_case_       						=      possible_input_names.intersection(set(lowerCamelCase   )   ).pop()
            snake_case_       						=      prepared_for_class.pop(lowerCamelCase   )

            snake_case_       						=      model(lowerCamelCase							,  **lowerCamelCase   )[0]
            self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )

            # Test that model correctly compute the loss when we mask some positions
            snake_case_       						=      self._prepare_for_class(inputs_dict.copy()							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
            snake_case_       						=      {"""input_ids""", """pixel_values""", """input_features"""}
            snake_case_       						=      possible_input_names.intersection(set(lowerCamelCase   )   ).pop()
            snake_case_       						=      prepared_for_class.pop(lowerCamelCase   )
            if "labels" in prepared_for_class:
              snake_case_       						=      prepared_for_class["""labels"""].numpy()
              if len(labels.shape   ) > 1 and labels.shape[1] != 1:
                # labels[0] = -100
                snake_case_       						=      tf.convert_to_tensor(lowerCamelCase   )
                snake_case_       						=      model(lowerCamelCase							,  **lowerCamelCase   )[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )
                self.assertTrue(not np.any(np.isnan(loss.numpy()   )   )   )

                # Test that model correctly compute the loss with a dict
            snake_case_       						=      self._prepare_for_class(inputs_dict.copy()							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
            snake_case_       						=      model(lowerCamelCase   )[0]
            self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )

            # Test that model correctly compute the loss with a tuple
            snake_case_       						=      self._prepare_for_class(inputs_dict.copy()							,  lowerCamelCase							,  return_labels=lowerCamelCase   )

            # Get keys that were added with the _prepare_for_class function
            snake_case_       						=      prepared_for_class.keys() - inputs_dict.keys()
            snake_case_       						=      inspect.signature(model.call   ).parameters
            snake_case_       						=      list(signature.keys()   )

            # Create a dictionary holding the location of the tensors in the tuple
            snake_case_       						=      {0: input_name}
            for label_key in label_keys:
              snake_case_       						=      signature_names.index(lowerCamelCase   )
              snake_case_       						=      label_key
            snake_case_       						=      sorted(tuple_index_mapping.items()   )
            # Initialize a list with their default values, update the values and convert to a tuple
            snake_case_       						=      []

            for name in signature_names:
              if name != "kwargs":
                list_input.append(signature[name].default   )

            for index, value in sorted_tuple_index_mapping:
              snake_case_       						=      prepared_for_class[value]

            snake_case_       						=      tuple(lowerCamelCase   )

            # Send to model
            snake_case_       						=      model(tuple_input[:-1]   )[0]

            self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case_       						=      TFMobileViTModel.from_pretrained(lowerCamelCase   )
          self.assertIsNotNone(lowerCamelCase   )



def    UpperCamelCase(					)	->				Dict:





  '''simple docstring'''





  snake_case_       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
  return image







@require_tf
class  __lowerCamelCase				(	unittest.TestCase	):







      @slow
      def 	lowerCAmelCase_    (     self   )						->       Any:
        snake_case_       						=      TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""   )

        snake_case_       						=      MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""   )
        snake_case_       						=      prepare_img()
        snake_case_       						=      image_processor(images=lowerCamelCase							,  return_tensors="""tf"""   )

        # forward pass
        snake_case_       						=      model(**lowerCamelCase							,  training=lowerCamelCase   )

        # verify the logits
        snake_case_       						=      tf.TensorShape((1, 1000)   )
        self.assertEqual(outputs.logits.shape							,  lowerCamelCase   )

        snake_case_       						=      tf.constant([-1.9364, -1.2327, -0.4653]   )

        tf.debugging.assert_near(outputs.logits[0, :3]							,  lowerCamelCase							,  atol=1e-4							,  rtol=1e-04   )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # `from_pt` will be removed
        snake_case_       						=      TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )

        snake_case_       						=      MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )

        snake_case_       						=      prepare_img()
        snake_case_       						=      image_processor(images=lowerCamelCase							,  return_tensors="""tf"""   )

        # forward pass
        snake_case_       						=      model(inputs.pixel_values							,  training=lowerCamelCase   )
        snake_case_       						=      outputs.logits

        # verify the logits
        snake_case_       						=      tf.TensorShape((1, 21, 32, 32)   )
        self.assertEqual(logits.shape							,  lowerCamelCase   )

        snake_case_       						=      tf.constant(
            [
                [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
            ]   )

        tf.debugging.assert_near(logits[0, :3, :3, :3]							,  lowerCamelCase							,  rtol=1e-4							,  atol=1e-4   )

'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=0.999 ,       lowerCAmelCase_="cosine" ,       ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase_       ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCAmelCase_       ):
               return math.exp(t * -12.0       )

     else:
          raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"       )

     _UpperCAmelCase					:  str         =    []
     for i in range(lowerCAmelCase_       ):
          _UpperCAmelCase					:  List[str]         =    i / num_diffusion_timesteps
          _UpperCAmelCase					:  str         =    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(lowerCAmelCase_       ) / alpha_bar_fn(lowerCAmelCase_       ) ,       lowerCAmelCase_       )       )
     return torch.tensor(lowerCAmelCase_ ,       dtype=torch.floataa       )







class 	__lowerCAmelCase    (  __a       ,					__a				):
     snake_case   :    Any							      =      [e.name for e in KarrasDiffusionSchedulers]
     snake_case   :    List[Any]							      =      2



     @register_to_config
     def __init__(self						,	lowerCAmelCase__ = 1_0_0_0						,	lowerCAmelCase__ = 0.0_0_0_8_5						,	lowerCAmelCase__ = 0.0_1_2						,	lowerCAmelCase__ = "linear"						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = "epsilon"						,	lowerCAmelCase__ = "linspace"						,	lowerCAmelCase__ = 0						,	):
          if trained_betas is not None:
               _UpperCAmelCase					:  Optional[int]         =    torch.tensor(lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "linear":
               _UpperCAmelCase					:  Optional[int]         =    torch.linspace(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase					:  Tuple         =    (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowerCAmelCase__						,	dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase					:  Union[str, Any]         =    betas_for_alpha_bar(lowerCAmelCase__	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

          _UpperCAmelCase					:  Union[str, Any]         =    1.0 - self.betas
          _UpperCAmelCase					:  List[Any]         =    torch.cumprod(self.alphas						,	dim=0	)

          #  set all values
          self.set_timesteps(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__=None	):
          if schedule_timesteps is None:
               _UpperCAmelCase					:  Optional[Any]         =    self.timesteps

          _UpperCAmelCase					:  Optional[Any]         =    (schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter	) == 0:
               _UpperCAmelCase					:  Dict         =    1 if len(lowerCAmelCase__	) > 1 else 0
          else:
               _UpperCAmelCase					:  str         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
               _UpperCAmelCase					:  Any         =    self._index_counter[timestep_int]

          return indices[pos].item()



     @property
     def     snake_case_ (self	):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    self.index_for_timestep(lowerCAmelCase__	)

          if self.state_in_first_order:
               _UpperCAmelCase					:  Any         =    self.sigmas[step_index]
          else:
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]

          _UpperCAmelCase					:  Dict         =    sample / ((sigma**2 + 1) ** 0.5)
          return sample



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	):
          _UpperCAmelCase					:  Dict         =    num_inference_steps

          _UpperCAmelCase					:  int         =    num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
               _UpperCAmelCase					:  Dict         =    np.linspace(0						,	num_train_timesteps - 1						,	lowerCAmelCase__						,	dtype=lowerCAmelCase__	)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase					:  List[str]         =    num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               _UpperCAmelCase					:  Optional[Any]         =    (np.arange(0						,	lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase					:  Any         =    num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               _UpperCAmelCase					:  List[Any]         =    (np.arange(lowerCAmelCase__						,	0						,	-step_ratio	)).round().copy().astype(lowerCAmelCase__	)
               timesteps -= 1
          else:
               raise ValueError(
                   F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

          _UpperCAmelCase					:  List[str]         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Dict         =    torch.from_numpy(np.log(lowerCAmelCase__	)	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    np.interp(lowerCAmelCase__						,	np.arange(0						,	len(lowerCAmelCase__	)	)						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Dict         =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
          _UpperCAmelCase					:  Any         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)

          # compute up and down sigmas
          _UpperCAmelCase					:  str         =    sigmas.roll(-1	)
          _UpperCAmelCase					:  List[Any]         =    0.0
          _UpperCAmelCase					:  Optional[int]         =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          _UpperCAmelCase					:  Any         =    (sigmas_next**2 - sigmas_up**2) ** 0.5
          _UpperCAmelCase					:  Tuple         =    0.0

          # compute interpolated sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    sigmas.log().lerp(sigmas_down.log()						,	0.5	).exp()
          _UpperCAmelCase					:  int         =    0.0

          # set sigmas
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
          _UpperCAmelCase					:  str         =    torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
          _UpperCAmelCase					:  Optional[int]         =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

          if str(lowerCAmelCase__	).startswith("""mps"""	):
               # mps does not support float64
               _UpperCAmelCase					:  List[str]         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  int         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  int         =    self.sigma_to_t(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=timesteps.dtype	)
          _UpperCAmelCase					:  Optional[int]         =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1	).flatten()

          _UpperCAmelCase					:  Any         =    torch.cat([timesteps[:1], interleaved_timesteps]	)

          _UpperCAmelCase					:  Dict         =    None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          _UpperCAmelCase					:  Dict         =    defaultdict(lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          # get log sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sigma.log()

          # get distribution
          _UpperCAmelCase					:  str         =    log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          _UpperCAmelCase					:  str         =    dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
          _UpperCAmelCase					:  List[Any]         =    low_idx + 1

          _UpperCAmelCase					:  Optional[int]         =    self.log_sigmas[low_idx]
          _UpperCAmelCase					:  Dict         =    self.log_sigmas[high_idx]

          # interpolate sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    (low - log_sigma) / (low - high)
          _UpperCAmelCase					:  Union[str, Any]         =    w.clamp(0						,	1	)

          # transform interpolation to time range
          _UpperCAmelCase					:  Union[str, Any]         =    (1 - w) * low_idx + w * high_idx
          _UpperCAmelCase					:  str         =    t.view(sigma.shape	)
          return t



     @property
     def     snake_case_ (self	):
          return self.sample is None



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = True						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.index_for_timestep(lowerCAmelCase__	)

          # advance index counter by 1
          _UpperCAmelCase					:  Optional[Any]         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               _UpperCAmelCase					:  Tuple         =    self.sigmas[step_index]
               _UpperCAmelCase					:  List[str]         =    self.sigmas_interpol[step_index]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_up[step_index]
               _UpperCAmelCase					:  Optional[int]         =    self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               _UpperCAmelCase					:  List[Any]         =    self.sigmas[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]
               _UpperCAmelCase					:  List[Any]         =    self.sigmas_up[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_down[step_index - 1]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          _UpperCAmelCase					:  int         =    0
          _UpperCAmelCase					:  str         =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          _UpperCAmelCase					:  Optional[int]         =    model_output.device
          _UpperCAmelCase					:  Tuple         =    randn_tensor(model_output.shape						,	dtype=model_output.dtype						,	device=lowerCAmelCase__						,	generator=lowerCAmelCase__	)

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _UpperCAmelCase					:  int         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  List[Any]         =    sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               _UpperCAmelCase					:  Tuple         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  Dict         =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
          elif self.config.prediction_type == "sample":
               raise NotImplementedError("""prediction_type not implemented yet: sample"""	)
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

          if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               _UpperCAmelCase					:  Any         =    (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               _UpperCAmelCase					:  Any         =    sigma_interpol - sigma_hat

               # store for 2nd order step
               _UpperCAmelCase					:  List[str]         =    sample
               _UpperCAmelCase					:  Optional[Any]         =    dt
               _UpperCAmelCase					:  Optional[int]         =    sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _UpperCAmelCase					:  Optional[int]         =    (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               _UpperCAmelCase					:  List[Any]         =    sigma_down - sigma_hat

               _UpperCAmelCase					:  int         =    self.sample
               _UpperCAmelCase					:  Union[str, Any]         =    None

               _UpperCAmelCase					:  str         =    sample + derivative * dt
               _UpperCAmelCase					:  List[str]         =    prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCAmelCase					:  List[Any]         =    self.sigmas.to(device=original_samples.device						,	dtype=original_samples.dtype	)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__	):
               # mps does not support float64
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device						,	dtype=torch.floataa	)
               _UpperCAmelCase					:  Dict         =    timesteps.to(original_samples.device						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  Dict         =    self.timesteps.to(original_samples.device	)
               _UpperCAmelCase					:  Tuple         =    timesteps.to(original_samples.device	)

          _UpperCAmelCase					:  Any         =    [self.index_for_timestep(lowerCAmelCase__						,	lowerCAmelCase__	) for t in timesteps]

          _UpperCAmelCase					:  List[Any]         =    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase					:  Dict         =    sigma.unsqueeze(-1	)

          _UpperCAmelCase					:  str         =    original_samples + noise * sigma
          return noisy_samples



     def __len__(self	):
          return self.config.num_train_timesteps


