

import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs



class       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A=None						,		_A=True						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						config_class
      SCREAMING_SNAKE_CASE_									=						has_text_modality
      SCREAMING_SNAKE_CASE_									=						kwargs
      SCREAMING_SNAKE_CASE_									=						common_properties

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)
      SCREAMING_SNAKE_CASE_									=						(
          ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
          common_properties.extend(['vocab_size'])

      # Test that config has the common properties as getters
      for prop in common_properties:
          self.parent.assertTrue(hasattr(_A						,		_A)						,		msg=f"""`{prop}` does not exist""")

      # Test that config has the common properties as setter
      for idx, name in enumerate(_A):
          try:
              setattr(_A						,		_A						,		_A)
              self.parent.assertEqual(
                  getattr(_A						,		_A)						,		_A						,		msg=f"""`{name} value {idx} expected, but was {getattr(_A						,		_A)}""")
          except NotImplementedError:
              # Some models might not be able to implement setters for common_properties
              # In that case, a NotImplementedError is raised
              pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(_A):
          try:
              SCREAMING_SNAKE_CASE_									=						self.config_class(**{name: idx})
              self.parent.assertEqual(
                  getattr(_A						,		_A)						,		_A						,		msg=f"""`{name} value {idx} expected, but was {getattr(_A						,		_A)}""")
          except NotImplementedError:
              # Some models might not be able to implement setters for common_properties
              # In that case, a NotImplementedError is raised
              pass

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)
      SCREAMING_SNAKE_CASE_									=						json.loads(config.to_json_string())
      for key, value in self.inputs_dict.items():
          self.parent.assertEqual(obj[key]						,		_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
          SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		'config.json')
          config_first.to_json_file(_A)
          SCREAMING_SNAKE_CASE_									=						self.config_class.from_json_file(_A)

      self.parent.assertEqual(config_second.to_dict()						,		config_first.to_dict())

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
          config_first.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						self.config_class.from_pretrained(_A)

      self.parent.assertEqual(config_second.to_dict()						,		config_first.to_dict())

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)

      SCREAMING_SNAKE_CASE_									=						'test'
      with tempfile.TemporaryDirectory() as tmpdirname:
          SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		_A)
          config_first.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						self.config_class.from_pretrained(_A						,		subfolder=_A)

      self.parent.assertEqual(config_second.to_dict()						,		config_first.to_dict())

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict						,		num_labels=5)
      self.parent.assertEqual(len(config.idalabel)						,		5)
      self.parent.assertEqual(len(config.labelaid)						,		5)

      SCREAMING_SNAKE_CASE_									=						3
      self.parent.assertEqual(len(config.idalabel)						,		3)
      self.parent.assertEqual(len(config.labelaid)						,		3)

  def 						lowerCAmelCase__     (					self):
      if self.config_class.is_composition:
          return
      SCREAMING_SNAKE_CASE_									=						self.config_class()
      self.parent.assertIsNotNone(_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						copy.deepcopy(_A)
      SCREAMING_SNAKE_CASE_									=						self.config_class(**_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for key, value in config_common_kwargs.items():
          if key == "torch_dtype":
              if not is_torch_available():
                  continue
              else:
                  import torch

                  if config.torch_dtype != torch.floataa:
                      wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa))
          elif getattr(_A						,		_A) != value:
              wrong_values.append((key, getattr(_A						,		_A), value))

      if len(_A) > 0:
          SCREAMING_SNAKE_CASE_									=						'\n'.join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values])
          raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""")


  def 						lowerCAmelCase__     (					self):
      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()





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  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A						,		_A						,		_A = None						,		):
      super().__init__()
      self.register_modules(transformer=_A						,		vae=_A						,		scheduler=_A)

      # create a imagenet -> id dictionary for easier use
      SCREAMING_SNAKE_CASE_									=						{}
      if idalabel is not None:
          for key, value in idalabel.items():
              for label in value.split(','):
                  SCREAMING_SNAKE_CASE_									=						int(_A)
          SCREAMING_SNAKE_CASE_									=						dict(sorted(self.labels.items()))

  def 						lowerCAmelCase__     (					self						,		_A):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						list(_A)

      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						,		_A						,		_A = 4.0						,		_A = None						,		_A = 50						,		_A = "pil"						,		_A = True						,		):
      SCREAMING_SNAKE_CASE_									=						len(_A)
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.sample_size
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.in_channels

      SCREAMING_SNAKE_CASE_									=						randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)						,		generator=_A						,		device=self.device						,		dtype=self.transformer.dtype						,		)
      SCREAMING_SNAKE_CASE_									=						torch.cat([latents] * 2) if guidance_scale > 1 else latents

      SCREAMING_SNAKE_CASE_									=						torch.tensor(_A						,		device=self.device).reshape(-1)
      SCREAMING_SNAKE_CASE_									=						torch.tensor([1000] * batch_size						,		device=self.device)
      SCREAMING_SNAKE_CASE_									=						torch.cat([class_labels, class_null]						,		0) if guidance_scale > 1 else class_labels

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

      for t in self.progress_bar(self.scheduler.timesteps):
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_									=						latent_model_input[: len(_A) // 2]
              SCREAMING_SNAKE_CASE_									=						torch.cat([half, half]						,		dim=0)
          SCREAMING_SNAKE_CASE_									=						self.scheduler.scale_model_input(_A						,		_A)

          SCREAMING_SNAKE_CASE_									=						t
          if not torch.is_tensor(_A):
              # 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+)
              SCREAMING_SNAKE_CASE_									=						latent_model_input.device.type == 'mps'
              if isinstance(_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						torch.floataa if is_mps else torch.floataa
              else:
                  SCREAMING_SNAKE_CASE_									=						torch.intaa if is_mps else torch.intaa
              SCREAMING_SNAKE_CASE_									=						torch.tensor([timesteps]						,		dtype=_A						,		device=latent_model_input.device)
          elif len(timesteps.shape) == 0:
              SCREAMING_SNAKE_CASE_									=						timesteps[None].to(latent_model_input.device)
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          SCREAMING_SNAKE_CASE_									=						timesteps.expand(latent_model_input.shape[0])
          # predict noise model_output
          SCREAMING_SNAKE_CASE_									=						self.transformer(
              _A						,		timestep=_A						,		class_labels=_A).sample

          # perform guidance
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		len(_A) // 2						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              SCREAMING_SNAKE_CASE_									=						torch.cat([half_eps, half_eps]						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						torch.cat([eps, rest]						,		dim=1)

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		_A						,		dim=1)
          else:
              SCREAMING_SNAKE_CASE_									=						noise_pred

          # compute previous image: x_t -> x_t-1
          SCREAMING_SNAKE_CASE_									=						self.scheduler.step(_A						,		_A						,		_A).prev_sample

      if guidance_scale > 1:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						latent_model_input.chunk(2						,		dim=0)
      else:
          SCREAMING_SNAKE_CASE_									=						latent_model_input

      SCREAMING_SNAKE_CASE_									=						1 / self.vae.config.scaling_factor * latents
      SCREAMING_SNAKE_CASE_									=						self.vae.decode(_A).sample

      SCREAMING_SNAKE_CASE_									=						(samples / 2 + 0.5).clamp(0						,		1)

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

      if output_type == "pil":
          SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(_A)

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A)


