from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__snake_case	( SCREAMING_SNAKE_CASE__		):





      '''simple docstring'''






      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				List[Any] = None    ,  )				->Optional[Any]:


          """simple docstring"""


          super().__init__()
          self.register_modules(transformer=snake_case__    ,  vae=snake_case__    ,  scheduler=snake_case__)

          # create a imagenet -> id dictionary for easier use
          _lowerCamelCase     :		Union[str, Any] 						=						{}
          if idalabel is not None:
              for key, value in idalabel.items():
                  for label in value.split(""","""):
                      _lowerCamelCase     :		Optional[int] 						=						int(snake_case__)
              _lowerCamelCase     :		Optional[Any] 						=						dict(sorted(self.labels.items()))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          if not isinstance(snake_case__    ,  snake_case__):
              _lowerCamelCase     :		int 						=						list(snake_case__)

          for l in label:
              if l not in self.labels:
                  raise ValueError(
                      F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""")

          return [self.labels[l] for l in label]




      @torch.no_grad()
      def __call__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Tuple = 4.0    ,  _UpperCamelCase     :				int = None    ,  _UpperCamelCase     :				List[str] = 50    ,  _UpperCamelCase     :				Any = "pil"    ,  _UpperCamelCase     :				Dict = True    ,  )				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						len(snake_case__)
          _lowerCamelCase     :		Any 						=						self.transformer.config.sample_size
          _lowerCamelCase     :		List[Any] 						=						self.transformer.config.in_channels

          _lowerCamelCase     :		List[Any] 						=						randn_tensor(
              shape=(batch_size, latent_channels, latent_size, latent_size)    ,  generator=snake_case__    ,  device=self.device    ,  dtype=self.transformer.dtype    ,  )
          _lowerCamelCase     :		str 						=						torch.cat([latents] * 2) if guidance_scale > 1 else latents

          _lowerCamelCase     :		List[str] 						=						torch.tensor(snake_case__    ,  device=self.device).reshape(-1)
          _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([1000] * batch_size    ,  device=self.device)
          _lowerCamelCase     :		int 						=						torch.cat([class_labels, class_null]    ,  0) if guidance_scale > 1 else class_labels

          # set step values
          self.scheduler.set_timesteps(snake_case__)

          for t in self.progress_bar(self.scheduler.timesteps):
              if guidance_scale > 1:
                  _lowerCamelCase     :		Optional[Any] 						=						latent_model_input[: len(snake_case__) // 2]
                  _lowerCamelCase     :		Tuple 						=						torch.cat([half, half]    ,  dim=0)
              _lowerCamelCase     :		Optional[int] 						=						self.scheduler.scale_model_input(snake_case__    ,  snake_case__)

              _lowerCamelCase     :		Union[str, Any] 						=						t
              if not torch.is_tensor(snake_case__):
                  # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                  # This would be a good case for the `match` statement (Python 3.10+)
                  _lowerCamelCase     :		Any 						=						latent_model_input.device.type == "mps"
                  if isinstance(snake_case__    ,  snake_case__):
                      _lowerCamelCase     :		Tuple 						=						torch.floataa if is_mps else torch.floataa
                  else:
                      _lowerCamelCase     :		Tuple 						=						torch.intaa if is_mps else torch.intaa
                  _lowerCamelCase     :		List[Any] 						=						torch.tensor([timesteps]    ,  dtype=snake_case__    ,  device=latent_model_input.device)
              elif len(timesteps.shape) == 0:
                  _lowerCamelCase     :		Dict 						=						timesteps[None].to(latent_model_input.device)
              # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
              _lowerCamelCase     :		Optional[int] 						=						timesteps.expand(latent_model_input.shape[0])
              # predict noise model_output
              _lowerCamelCase     :		Optional[Any] 						=						self.transformer(
                  snake_case__    ,  timestep=snake_case__    ,  class_labels=snake_case__).sample

              # perform guidance
              if guidance_scale > 1:
                  _lowerCamelCase     :		Optional[int] 						=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                  _lowerCamelCase     :		Dict 						=						torch.split(snake_case__    ,  len(snake_case__) // 2    ,  dim=0)

                  _lowerCamelCase     :		Optional[Any] 						=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                  _lowerCamelCase     :		Tuple 						=						torch.cat([half_eps, half_eps]    ,  dim=0)

                  _lowerCamelCase     :		Optional[int] 						=						torch.cat([eps, rest]    ,  dim=1)

              # learned sigma
              if self.transformer.config.out_channels // 2 == latent_channels:
                  _lowerCamelCase     :		Dict 						=						torch.split(snake_case__    ,  snake_case__    ,  dim=1)
              else:
                  _lowerCamelCase     :		str 						=						noise_pred

              # compute previous image: x_t -> x_t-1
              _lowerCamelCase     :		Any 						=						self.scheduler.step(snake_case__    ,  snake_case__    ,  snake_case__).prev_sample

          if guidance_scale > 1:
              _lowerCamelCase     :		Any 						=						latent_model_input.chunk(2    ,  dim=0)
          else:
              _lowerCamelCase     :		List[str] 						=						latent_model_input

          _lowerCamelCase     :		Any 						=						1 / self.vae.config.scaling_factor * latents
          _lowerCamelCase     :		Dict 						=						self.vae.decode(snake_case__).sample

          _lowerCamelCase     :		Dict 						=						(samples / 2 + 0.5).clamp(0    ,  1)

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          _lowerCamelCase     :		Tuple 						=						samples.cpu().permute(0    ,  2    ,  3    ,  1).float().numpy()

          if output_type == "pil":
              _lowerCamelCase     :		Tuple 						=						self.numpy_to_pil(snake_case__)

          if not return_dict:
              return (samples,)

          return ImagePipelineOutput(images=snake_case__)





import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)




class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						["""a""", """b""", """c"""]

          # Defaults to last layer if both are None
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""c"""])
          self.assertEqual(_UpperCamelCase    ,  [2])

          # Out indices set to match out features
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(["""a""", """c"""]    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features set to match out indices
          _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [0, 2]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features selected from negative indices
          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [-3, -1]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [-3, -1])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->int:


          """simple docstring"""


          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  _UpperCamelCase)

          # Out features must be a list
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(("""a""", """b""")    ,  (0, 1)    ,  ["""a""", """b"""])

          # Out features must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  ["""a"""])

          # Out indices must be a list or tuple
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  0    ,  ["""a""", """b"""])

          # Out indices must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  (0, 1)    ,  ["""a"""])

          # Out features and out indices must be the same length
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0,)    ,  ["""a""", """b""", """c"""])

          # Out features should match out indices
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 2)    ,  ["""a""", """b""", """c"""])

          # Out features and out indices should be in order
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""b""", """a"""]    ,  (0, 1)    ,  ["""a""", """b"""])

          # Check passes with valid inputs
          verify_out_features_out_indices(["""a""", """b""", """d"""]    ,  (0, 1, -1)    ,  ["""a""", """b""", """c""", """d"""])




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						BackboneMixin()

          _lowerCamelCase     :		Union[str, Any] 						=						["""a""", """b""", """c"""]
          _lowerCamelCase     :		Tuple 						=						["""a""", """c"""]
          _lowerCamelCase     :		List[Any] 						=						[0, 2]

          # Check that the output features and indices are set correctly
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [0, 2])

          # Check out features and indices are updated correctly
          _lowerCamelCase     :		str 						=						["""a""", """b"""]
          self.assertEqual(backbone.out_features    ,  ["""a""", """b"""])
          self.assertEqual(backbone.out_indices    ,  [0, 1])

          _lowerCamelCase     :		Optional[int] 						=						[-3, -1]
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [-3, -1])




