import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class 		_lowercase						:

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						int:

  """simple docstring"""





  torch.manual_seed(0			)
  UpperCamelCase_					:						int =					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'			)

  torch.manual_seed(0			)
  UpperCamelCase_					:						Tuple =					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'			)

  torch.manual_seed(0			)
  UpperCamelCase_					:						int =					UNetaDConditionModel(
      sample_size=3_2   ,      layers_per_block=1   ,      block_out_channels=[3_2, 6_4]   ,      down_block_types=[
          'ResnetDownsampleBlock2D',
          'SimpleCrossAttnDownBlock2D',
      ]   ,      mid_block_type='UNetMidBlock2DSimpleCrossAttn'   ,      up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']   ,      in_channels=3   ,      out_channels=6   ,      cross_attention_dim=3_2   ,      encoder_hid_dim=3_2   ,      attention_head_dim=8   ,      addition_embed_type='text'   ,      addition_embed_type_num_heads=2   ,      cross_attention_norm='group_norm'   ,      resnet_time_scale_shift='scale_shift'   ,      act_fn='gelu'   ,      )
  unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

  torch.manual_seed(0			)
  UpperCamelCase_					:						Any =					DDPMScheduler(
      num_train_timesteps=1_0_0_0   ,      beta_schedule='squaredcos_cap_v2'   ,      beta_start=0.0001   ,      beta_end=0.02   ,      thresholding=snake_case   ,      dynamic_thresholding_ratio=0.95   ,      sample_max_value=1.0   ,      prediction_type='epsilon'   ,      variance_type='learned_range'   ,      )

  torch.manual_seed(0			)
  UpperCamelCase_					:						Union[str, Any] =					IFWatermarker()

  return {
      "text_encoder": text_encoder,
      "tokenizer": tokenizer,
      "unet": unet,
      "scheduler": scheduler,
      "watermarker": watermarker,
      "safety_checker": None,
      "feature_extractor": None,
  }

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

  """simple docstring"""





  torch.manual_seed(0			)
  UpperCamelCase_					:						Dict =					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'			)

  torch.manual_seed(0			)
  UpperCamelCase_					:						List[str] =					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'			)

  torch.manual_seed(0			)
  UpperCamelCase_					:						int =					UNetaDConditionModel(
      sample_size=3_2   ,      layers_per_block=[1, 2]   ,      block_out_channels=[3_2, 6_4]   ,      down_block_types=[
          'ResnetDownsampleBlock2D',
          'SimpleCrossAttnDownBlock2D',
      ]   ,      mid_block_type='UNetMidBlock2DSimpleCrossAttn'   ,      up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']   ,      in_channels=6   ,      out_channels=6   ,      cross_attention_dim=3_2   ,      encoder_hid_dim=3_2   ,      attention_head_dim=8   ,      addition_embed_type='text'   ,      addition_embed_type_num_heads=2   ,      cross_attention_norm='group_norm'   ,      resnet_time_scale_shift='scale_shift'   ,      act_fn='gelu'   ,      class_embed_type='timestep'   ,      mid_block_scale_factor=1.414   ,      time_embedding_act_fn='gelu'   ,      time_embedding_dim=3_2   ,      )
  unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

  torch.manual_seed(0			)
  UpperCamelCase_					:						Optional[Any] =					DDPMScheduler(
      num_train_timesteps=1_0_0_0   ,      beta_schedule='squaredcos_cap_v2'   ,      beta_start=0.0001   ,      beta_end=0.02   ,      thresholding=snake_case   ,      dynamic_thresholding_ratio=0.95   ,      sample_max_value=1.0   ,      prediction_type='epsilon'   ,      variance_type='learned_range'   ,      )

  torch.manual_seed(0			)
  UpperCamelCase_					:						Dict =					DDPMScheduler(
      num_train_timesteps=1_0_0_0   ,      beta_schedule='squaredcos_cap_v2'   ,      beta_start=0.0001   ,      beta_end=0.02   ,      )

  torch.manual_seed(0			)
  UpperCamelCase_					:						int =					IFWatermarker()

  return {
      "text_encoder": text_encoder,
      "tokenizer": tokenizer,
      "unet": unet,
      "scheduler": scheduler,
      "image_noising_scheduler": image_noising_scheduler,
      "watermarker": watermarker,
      "safety_checker": None,
      "feature_extractor": None,
  }

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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.get_dummy_components()
  UpperCamelCase_					:						Union[str, Any] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Any =					self.get_dummy_inputs(snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					inputs['prompt']
  UpperCamelCase_					:						List[Any] =					inputs['generator']
  UpperCamelCase_					:						Optional[int] =					inputs['num_inference_steps']
  UpperCamelCase_					:						Tuple =					inputs['output_type']

  if "image" in inputs:
   UpperCamelCase_					:						Any =					inputs['image']
  else:
   UpperCamelCase_					:						Optional[Any] =					None

  if "mask_image" in inputs:
   UpperCamelCase_					:						Tuple =					inputs['mask_image']
  else:
   UpperCamelCase_					:						Union[str, Any] =					None

  if "original_image" in inputs:
   UpperCamelCase_					:						Union[str, Any] =					inputs['original_image']
  else:
   UpperCamelCase_					:						List[str] =					None

  UpperCamelCase_, UpperCamelCase_					:						int =					pipe.encode_prompt(snake_case			)

  # inputs with prompt converted to embeddings
  UpperCamelCase_					:						Optional[int] =					{
      'prompt_embeds': prompt_embeds,
      'negative_prompt_embeds': negative_prompt_embeds,
      'generator': generator,
      'num_inference_steps': num_inference_steps,
      'output_type': output_type,
  }

  if image is not None:
   UpperCamelCase_					:						List[str] =					image

  if mask_image is not None:
   UpperCamelCase_					:						str =					mask_image

  if original_image is not None:
   UpperCamelCase_					:						Optional[Any] =					original_image

  # set all optional components to None
  for optional_component in pipe._optional_components:
   setattr(snake_case   ,      snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[str] =					pipe(**snake_case			)[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(snake_case			)
   UpperCamelCase_					:						List[str] =					self.pipeline_class.from_pretrained(snake_case			)
   pipe_loaded.to(snake_case			)
   pipe_loaded.set_progress_bar_config(disable=snake_case			)

  pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

  for optional_component in pipe._optional_components:
   self.assertTrue(
       getattr(snake_case   ,      snake_case			) is None   ,      f"`{optional_component}` did not stay set to None after loading."   ,      )

  UpperCamelCase_					:						Dict =					self.get_dummy_inputs(snake_case			)

  UpperCamelCase_					:						Optional[int] =					inputs['generator']
  UpperCamelCase_					:						Any =					inputs['num_inference_steps']
  UpperCamelCase_					:						Dict =					inputs['output_type']

  # inputs with prompt converted to embeddings
  UpperCamelCase_					:						Optional[Any] =					{
      'prompt_embeds': prompt_embeds,
      'negative_prompt_embeds': negative_prompt_embeds,
      'generator': generator,
      'num_inference_steps': num_inference_steps,
      'output_type': output_type,
  }

  if image is not None:
   UpperCamelCase_					:						List[Any] =					image

  if mask_image is not None:
   UpperCamelCase_					:						int =					mask_image

  if original_image is not None:
   UpperCamelCase_					:						List[str] =					original_image

  UpperCamelCase_					:						List[Any] =					pipe_loaded(**snake_case			)[0]

  UpperCamelCase_					:						int =					np.abs(to_np(snake_case			) - to_np(snake_case			)			).max()
  self.assertLess(snake_case   ,      1e-4			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.get_dummy_components()
  UpperCamelCase_					:						Optional[int] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Tuple =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						Dict =					pipe(**snake_case			)[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(snake_case			)
   UpperCamelCase_					:						Dict =					self.pipeline_class.from_pretrained(snake_case			)
   pipe_loaded.to(snake_case			)
   pipe_loaded.set_progress_bar_config(disable=snake_case			)

  pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

  UpperCamelCase_					:						List[Any] =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						List[Any] =					pipe_loaded(**snake_case			)[0]

  UpperCamelCase_					:						List[Any] =					np.abs(to_np(snake_case			) - to_np(snake_case			)			).max()
  self.assertLess(snake_case   ,      1e-4			)




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



