

'''simple docstring'''






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_	:




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   torch.manual_seed(0			)
   __lowercase							  = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''			)

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

   torch.manual_seed(0			)
   __lowercase							  = 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			)
   __lowercase							  = DDPMScheduler(
       num_train_timesteps=1_0_0_0					,beta_schedule='''squaredcos_cap_v2'''					,beta_start=0.0_0_0_1					,beta_end=0.0_2					,thresholding=lowercase__					,dynamic_thresholding_ratio=0.9_5					,sample_max_value=1.0					,prediction_type='''epsilon'''					,variance_type='''learned_range'''					,)

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

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

   torch.manual_seed(0			)
   __lowercase							  = 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=1.4_1_4					,time_embedding_act_fn='''gelu'''					,time_embedding_dim=3_2					,)
   unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

   torch.manual_seed(0			)
   __lowercase							  = DDPMScheduler(
       num_train_timesteps=1_0_0_0					,beta_schedule='''squaredcos_cap_v2'''					,beta_start=0.0_0_0_1					,beta_end=0.0_2					,thresholding=lowercase__					,dynamic_thresholding_ratio=0.9_5					,sample_max_value=1.0					,prediction_type='''epsilon'''					,variance_type='''learned_range'''					,)

   torch.manual_seed(0			)
   __lowercase							  = DDPMScheduler(
       num_train_timesteps=1_0_0_0					,beta_schedule='''squaredcos_cap_v2'''					,beta_start=0.0_0_0_1					,beta_end=0.0_2					,)

   torch.manual_seed(0			)
   __lowercase							  = 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   :     Optional[Any]			):
   __lowercase							  = self.get_dummy_components()
   __lowercase							  = self.pipeline_class(**lowercase__			)
   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = self.get_dummy_inputs(lowercase__			)

   __lowercase							  = inputs['prompt']
   __lowercase							  = inputs['generator']
   __lowercase							  = inputs['num_inference_steps']
   __lowercase							  = inputs['output_type']

   if "image" in inputs:
    __lowercase							  = inputs['image']
   else:
    __lowercase							  = None

   if "mask_image" in inputs:
    __lowercase							  = inputs['mask_image']
   else:
    __lowercase							  = None

   if "original_image" in inputs:
    __lowercase							  = inputs['original_image']
   else:
    __lowercase							  = None

   __lowercase							  = pipe.encode_prompt(lowercase__			)

   # inputs with prompt converted to embeddings
   __lowercase							  = {
       '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:
    __lowercase							  = image

   if mask_image is not None:
    __lowercase							  = mask_image

   if original_image is not None:
    __lowercase							  = original_image

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

   __lowercase							  = pipe(**lowercase__			)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
    pipe.save_pretrained(lowercase__			)
    __lowercase							  = self.pipeline_class.from_pretrained(lowercase__			)
    pipe_loaded.to(lowercase__			)
    pipe_loaded.set_progress_bar_config(disable=lowercase__			)

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

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

   __lowercase							  = self.get_dummy_inputs(lowercase__			)

   __lowercase							  = inputs['generator']
   __lowercase							  = inputs['num_inference_steps']
   __lowercase							  = inputs['output_type']

   # inputs with prompt converted to embeddings
   __lowercase							  = {
       '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:
    __lowercase							  = image

   if mask_image is not None:
    __lowercase							  = mask_image

   if original_image is not None:
    __lowercase							  = original_image

   __lowercase							  = pipe_loaded(**lowercase__			)[0]

   __lowercase							  = np.abs(to_np(lowercase__			) - to_np(lowercase__			)			).max()
   self.assertLess(lowercase__					,1e-4			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_dummy_components()
   __lowercase							  = self.pipeline_class(**lowercase__			)
   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = self.get_dummy_inputs(lowercase__			)
   __lowercase							  = pipe(**lowercase__			)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
    pipe.save_pretrained(lowercase__			)
    __lowercase							  = self.pipeline_class.from_pretrained(lowercase__			)
    pipe_loaded.to(lowercase__			)
    pipe_loaded.set_progress_bar_config(disable=lowercase__			)

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

   __lowercase							  = self.get_dummy_inputs(lowercase__			)
   __lowercase							  = pipe_loaded(**lowercase__			)[0]

   __lowercase							  = np.abs(to_np(lowercase__			) - to_np(lowercase__			)			).max()
   self.assertLess(lowercase__					,1e-4			)



import re
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:

			if len(re.findall('[ATCG]'       ,							SCREAMING_SNAKE_CASE_     )     ) != len(SCREAMING_SNAKE_CASE_     ):
						raise ValueError('Invalid Strand'     )

			return dna.translate(dna.maketrans('ATCG'       ,							'TAGC'     )     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()