
"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

    from transformers.models.swin.modeling_tf_swin import (
        TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSwinForImageClassification,
        TFSwinForMaskedImageModeling,
        TFSwinModel,
    )


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class 						lowercase:



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=1_3    ,							__SCREAMING_SNAKE_CASE=3_2    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=3    ,							__SCREAMING_SNAKE_CASE=1_6    ,							__SCREAMING_SNAKE_CASE=[1, 2, 1]    ,							__SCREAMING_SNAKE_CASE=[2, 2, 4]    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=2.0    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE="gelu"    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=0.02    ,							__SCREAMING_SNAKE_CASE=1e-5    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=1_0    ,							__SCREAMING_SNAKE_CASE=8    ,							)		->    None:







         """simple docstring"""



         a__    = parent
         a__    = batch_size
         a__    = image_size
         a__    = patch_size
         a__    = num_channels
         a__    = embed_dim
         a__    = depths
         a__    = num_heads
         a__    = window_size
         a__    = mlp_ratio
         a__    = qkv_bias
         a__    = hidden_dropout_prob
         a__    = attention_probs_dropout_prob
         a__    = drop_path_rate
         a__    = hidden_act
         a__    = use_absolute_embeddings
         a__    = patch_norm
         a__    = layer_norm_eps
         a__    = initializer_range
         a__    = is_training
         a__    = scope
         a__    = use_labels
         a__    = type_sequence_label_size
         a__    = encoder_stride



     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



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

         a__    = None
         if self.use_labels:
             a__    = ids_tensor([self.batch_size]    ,							self.type_sequence_label_size      )

         a__    = self.get_config()

         return config, pixel_values, labels



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







         """simple docstring"""



         return SwinConfig(
             image_size=self.image_size    ,							patch_size=self.patch_size    ,							num_channels=self.num_channels    ,							embed_dim=self.embed_dim    ,							depths=self.depths    ,							num_heads=self.num_heads    ,							window_size=self.window_size    ,							mlp_ratio=self.mlp_ratio    ,							qkv_bias=self.qkv_bias    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							drop_path_rate=self.drop_path_rate    ,							hidden_act=self.hidden_act    ,							use_absolute_embeddings=self.use_absolute_embeddings    ,							path_norm=self.patch_norm    ,							layer_norm_eps=self.layer_norm_eps    ,							initializer_range=self.initializer_range    ,							encoder_stride=self.encoder_stride    ,							)



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Dict:







         """simple docstring"""



         a__    = TFSwinModel(config=__SCREAMING_SNAKE_CASE      )
         a__    = model(__SCREAMING_SNAKE_CASE      )

         a__    = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
         a__    = int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, expected_seq_len, expected_dim)      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         a__    = TFSwinForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE      )
         a__    = model(__SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(
             result.logits.shape    ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

         # test greyscale images
         a__    = 1
         a__    = TFSwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE      )

         a__    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         a__    = model(__SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, 1, self.image_size, self.image_size)      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         a__    = self.type_sequence_label_size
         a__    = TFSwinForImageClassification(__SCREAMING_SNAKE_CASE      )
         a__    = 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
         a__    = 1
         a__    = TFSwinForImageClassification(__SCREAMING_SNAKE_CASE      )
         a__    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         a__    = model(__SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size)      )




     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



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







@require_tf
class 						lowercase(_lowercase						,	_lowercase						,	unittest.TestCase					):
     __snake_case:  Any  				=    (
         (
             TFSwinModel,
             TFSwinForImageClassification,
             TFSwinForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     __snake_case:  List[str]  				=    (
         {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case:  int  				=    False
     __snake_case:  Union[str, Any]  				=    False
     __snake_case:  Optional[int]  				=    False
     __snake_case:  str  				=    False



     def  lowercase__     (       self      )		->    Dict:







         """simple docstring"""



         a__    = TFSwinModelTester(self      )
         a__    = ConfigTester(self    ,							config_class=__SCREAMING_SNAKE_CASE    ,							embed_dim=3_7      )



     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()



     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



         return



     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



         a__    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self      )		->    Dict:







         """simple docstring"""



         a__    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE      )



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







         """simple docstring"""



         a__    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE      )



     @unittest.skip(reason='Swin does not use inputs_embeds'      )
     def  lowercase__     (       self      )		->    Dict:







         """simple docstring"""



         pass



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







         """simple docstring"""



         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             a__    = model_class(__SCREAMING_SNAKE_CASE      )
             self.assertIsInstance(model.get_input_embeddings()    ,							tf.keras.layers.Layer      )
             a__    = model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE    ,							tf.keras.layers.Dense      )      )



     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()

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

             a__    = ['pixel_values']
             self.assertListEqual(arg_names[:1]    ,							__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



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

         for model_class in self.all_model_classes:
             a__    = True
             a__    = False
             a__    = True
             a__    = model_class(__SCREAMING_SNAKE_CASE      )
             a__    = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )      )
             a__    = outputs.attentions
             a__    = len(self.model_tester.depths      )
             self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE      )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             a__    = True
             a__    = config.window_size**2
             a__    = model_class(__SCREAMING_SNAKE_CASE      )
             a__    = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )      )
             a__    = outputs.attentions
             self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE      )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]      )    ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,							)
             a__    = len(__SCREAMING_SNAKE_CASE      )

             # Check attention is always last and order is fine
             a__    = True
             a__    = True
             a__    = model_class(__SCREAMING_SNAKE_CASE      )
             a__    = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )      )

             if hasattr(self.model_tester    ,							'num_hidden_states_types'      ):
                 a__    = self.model_tester.num_hidden_states_types
             else:
                 # also another +1 for reshaped_hidden_states
                 a__    = 2
             self.assertEqual(out_len + added_hidden_states    ,							len(__SCREAMING_SNAKE_CASE      )      )

             a__    = outputs.attentions

             self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE      )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]      )    ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,							)



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = model_class(__SCREAMING_SNAKE_CASE      )
         a__    = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )      )
         a__    = outputs.hidden_states

         a__    = getattr(
             self.model_tester    ,							'expected_num_hidden_layers'    ,							len(self.model_tester.depths      ) + 1      )
         self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE      )

         # Swin has a different seq_length
         a__    = to_atuple(config.patch_size      )

         a__    = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]      )    ,							[num_patches, self.model_tester.embed_dim]    ,							)

         a__    = outputs.reshaped_hidden_states
         self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE      )

         a__  ,    a__  ,    a__  ,    a__    = reshaped_hidden_states[0].shape

         a__    = tf.reshape(reshaped_hidden_states[0]    ,							(batch_size, num_channels, height * width)      )
         a__    = tf.transpose(__SCREAMING_SNAKE_CASE    ,							(0, 2, 1)      )

         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]      )    ,							[num_patches, self.model_tester.embed_dim]    ,							)



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







         """simple docstring"""



         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()

         a__    = to_atuple(self.model_tester.image_size      )

         for model_class in self.all_model_classes:
             a__    = True
             self.check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )

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

             self.check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )



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







         """simple docstring"""



         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()
         a__    = 3

         a__    = to_atuple(self.model_tester.image_size      )
         a__    = to_atuple(config.patch_size      )

         a__    = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         a__    = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
             a__    = True
             self.check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							(padded_height, padded_width)      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             a__    = True
             self.check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							(padded_height, padded_width)      )




     @slow
     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a__    = TFSwinModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )







@require_vision
@require_tf
class 						lowercase(unittest.TestCase					):



     @cached_property
     def  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         return (
             AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
             if is_vision_available()
             else None
         )




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







         """simple docstring"""



         a__    = TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
         a__    = self.default_image_processor

         a__    = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
         a__    = image_processor(images=__SCREAMING_SNAKE_CASE    ,							return_tensors='tf'      )

         # forward pass
         a__    = model(__SCREAMING_SNAKE_CASE      )

         # verify the logits
         a__    = tf.TensorShape((1, 1_0_0_0)      )
         self.assertEqual(outputs.logits.shape    ,							__SCREAMING_SNAKE_CASE      )
         a__    = tf.constant([-0.09_48, -0.64_54, -0.09_21]      )
         self.assertTrue(np.allclose(outputs.logits[0, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=1e-4      )      )





"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


a		:					List[Any]    	=					logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 						lowercase(_lowercase					):
     __snake_case:  torch.FloatTensor







class 						lowercase(_lowercase						,	_lowercase					):
     __snake_case:  List[Any]  				=    1



     @register_to_config
     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 4_0    ,							__SCREAMING_SNAKE_CASE = 0.0_02    ,							__SCREAMING_SNAKE_CASE = 80.0    ,							__SCREAMING_SNAKE_CASE = 0.5    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							__SCREAMING_SNAKE_CASE = 7.0    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Dict:







         """simple docstring"""



         a__    = sigma_max

         a__    = np.linspace(0    ,							1    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         # setable values
         a__    = None
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = False
         a__    = False



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None      )		->    Union[str, Any]:







         """simple docstring"""



         if schedule_timesteps is None:
             a__    = self.timesteps

         a__    = (schedule_timesteps == timestep).nonzero()
         return indices.item()



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    torch.FloatTensor:







         """simple docstring"""



         if isinstance(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )
         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigmas[step_idx]

         a__    = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         a__    = True
         return sample



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         if not isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray      ):
             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa      )

         a__    = 1_0_0_0 * 0.25 * np.log(sigmas + 1e-44      )

         return timesteps



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							)		->    Optional[int]:







         """simple docstring"""



         if num_inference_steps is None and timesteps is None:
             raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'      )

         if num_inference_steps is not None and timesteps is not None:
             raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'      )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
             for i in range(1    ,							len(__SCREAMING_SNAKE_CASE      )      ):
                 if timesteps[i] >= timesteps[i - 1]:
                     raise ValueError('`timesteps` must be in descending order.'      )

             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     f'`timesteps` must start before `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps}.'      )

             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.intaa      )
             a__    = True
         else:
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                     f' maximal {self.config.num_train_timesteps} timesteps.'      )

             a__    = num_inference_steps

             a__    = self.config.num_train_timesteps // self.num_inference_steps
             a__    = (np.arange(0    ,							__SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
             a__    = False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         a__    = self.config.num_train_timesteps
         a__    = timesteps[::-1].copy()
         a__    = ramp / (num_train_timesteps - 1)
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         a__    = np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )

         if str(__SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
             # mps does not support float64
             a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE    ,							dtype=torch.floataa      )
         else:
             a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.config.rho
         a__    = sigma_min ** (1 / rho)
         a__    = sigma_max ** (1 / rho)
         a__    = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



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







         """simple docstring"""



         a__    = self.config.sigma_data

         a__    = sigma_data**2 / (sigma**2 + sigma_data**2)
         a__    = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_data

         a__    = sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         a__    = (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:







         """simple docstring"""



         if (
             isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.IntTensor      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.LongTensor      )
         ):
             raise ValueError(
                 (
                     'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                     f' `{self.__class__}.step()` is not supported. Make sure to pass'
                     ' one of the `scheduler.timesteps` as a timestep.'
                 )    ,							)

         if not self.is_scale_input_called:
             logger.warning(
                 'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                 'See `StableDiffusionPipeline` for a usage example.'      )

         if isinstance(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )

         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )

         # sigma_next corresponds to next_t in original implementation
         a__    = self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
             a__    = self.sigmas[step_index + 1]
         else:
             # Set sigma_next to sigma_min
             a__    = self.sigmas[-1]

         # Get scalings for boundary conditions
         a__  ,    a__    = self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE      )

         # 1. Denoise model output using boundary conditions
         a__    = c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
             a__    = denoised.clamp(-1    ,							1      )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps      ) > 1:
             a__    = randn_tensor(
                 model_output.shape    ,							dtype=model_output.dtype    ,							device=model_output.device    ,							generator=__SCREAMING_SNAKE_CASE      )
         else:
             a__    = torch.zeros_like(__SCREAMING_SNAKE_CASE      )
         a__    = noise * self.config.s_noise

         a__    = sigma_next.clamp(min=__SCREAMING_SNAKE_CASE    ,							max=__SCREAMING_SNAKE_CASE      )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         a__    = denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    torch.FloatTensor:







         """simple docstring"""



         a__    = self.sigmas.to(device=original_samples.device    ,							dtype=original_samples.dtype      )
         if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE      ):
             # mps does not support float64
             a__    = self.timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
             a__    = timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
         else:
             a__    = self.timesteps.to(original_samples.device      )
             a__    = timesteps.to(original_samples.device      )

         a__    = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         a__    = sigmas[step_indices].flatten()
         while len(sigma.shape      ) < len(original_samples.shape      ):
             a__    = sigma.unsqueeze(-1      )

         a__    = original_samples + noise * sigma
         return noisy_samples




     def __len__(       self      )		->    str:







         """simple docstring"""



         return self.config.num_train_timesteps


