

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 				SCREAMING_SNAKE_CASE__ :





    def 				snake_case_    (				self):
         torch.manual_seed(0)
         lowercase__   :       Union[str, Any]				=	TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

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

         torch.manual_seed(0)
         lowercase__   :       Tuple				=	UNetaDConditionModel(
             sample_size=32						,       layers_per_block=1						,       block_out_channels=[32, 64]						,       down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]						,       mid_block_type='UNetMidBlock2DSimpleCrossAttn'						,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']						,       in_channels=3						,       out_channels=6						,       cross_attention_dim=32						,       encoder_hid_dim=32						,       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__   :       Optional[Any]				=	DDPMScheduler(
             num_train_timesteps=1000						,       beta_schedule='squaredcos_cap_v2'						,       beta_start=0.0_001						,       beta_end=0.02						,       thresholding=a						,       dynamic_thresholding_ratio=0.95						,       sample_max_value=1.0						,       prediction_type='epsilon'						,       variance_type='learned_range'						,       )

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

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





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

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

         torch.manual_seed(0)
         lowercase__   :       str				=	UNetaDConditionModel(
             sample_size=32						,       layers_per_block=[1, 2]						,       block_out_channels=[32, 64]						,       down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]						,       mid_block_type='UNetMidBlock2DSimpleCrossAttn'						,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']						,       in_channels=6						,       out_channels=6						,       cross_attention_dim=32						,       encoder_hid_dim=32						,       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=32						,       )
         unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

         torch.manual_seed(0)
         lowercase__   :       str				=	DDPMScheduler(
             num_train_timesteps=1000						,       beta_schedule='squaredcos_cap_v2'						,       beta_start=0.0_001						,       beta_end=0.02						,       thresholding=a						,       dynamic_thresholding_ratio=0.95						,       sample_max_value=1.0						,       prediction_type='epsilon'						,       variance_type='learned_range'						,       )

         torch.manual_seed(0)
         lowercase__   :       str				=	DDPMScheduler(
             num_train_timesteps=1000						,       beta_schedule='squaredcos_cap_v2'						,       beta_start=0.0_001						,       beta_end=0.02						,       )

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

         lowercase__   :       Union[str, Any]				=	self.get_dummy_inputs(a)

         lowercase__   :       Any				=	inputs['prompt']
         lowercase__   :       str				=	inputs['generator']
         lowercase__   :       str				=	inputs['num_inference_steps']
         lowercase__   :       List[str]				=	inputs['output_type']

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

         if "mask_image" in inputs:
              lowercase__   :       Union[str, Any]				=	inputs['mask_image']
         else:
              lowercase__   :       Optional[int]				=	None

         if "original_image" in inputs:
              lowercase__   :       Tuple				=	inputs['original_image']
         else:
              lowercase__   :       Optional[int]				=	None

         lowercase__				,			lowercase__   :       Union[str, Any]				=	pipe.encode_prompt(a)

         # inputs with prompt converted to embeddings
         lowercase__   :       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:
              lowercase__   :       List[Any]				=	image

         if mask_image is not None:
              lowercase__   :       str				=	mask_image

         if original_image is not None:
              lowercase__   :       Any				=	original_image

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

         lowercase__   :       List[Any]				=	pipe(**a)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(a)
              lowercase__   :       Optional[int]				=	self.pipeline_class.from_pretrained(a)
              pipe_loaded.to(a)
              pipe_loaded.set_progress_bar_config(disable=a)

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

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

         lowercase__   :       List[Any]				=	self.get_dummy_inputs(a)

         lowercase__   :       Union[str, Any]				=	inputs['generator']
         lowercase__   :       List[Any]				=	inputs['num_inference_steps']
         lowercase__   :       Any				=	inputs['output_type']

         # inputs with prompt converted to embeddings
         lowercase__   :       str				=	{
             '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__   :       List[Any]				=	image

         if mask_image is not None:
              lowercase__   :       str				=	mask_image

         if original_image is not None:
              lowercase__   :       Any				=	original_image

         lowercase__   :       int				=	pipe_loaded(**a)[0]

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





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	self.get_dummy_components()
         lowercase__   :       List[Any]				=	self.pipeline_class(**a)
         pipe.to(a)
         pipe.set_progress_bar_config(disable=a)

         lowercase__   :       List[Any]				=	self.get_dummy_inputs(a)
         lowercase__   :       str				=	pipe(**a)[0]

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

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

         lowercase__   :       List[str]				=	self.get_dummy_inputs(a)
         lowercase__   :       str				=	pipe_loaded(**a)[0]

         lowercase__   :       Union[str, Any]				=	np.abs(to_np(a) - to_np(a)).max()
         self.assertLess(a						,       1e-4)




import numpy as np

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	int(np.ceil((x_end - xa) / h      )      )
     lowercase__   :       Dict				=	np.zeros((n + 1,)      )
     lowercase__   :       Dict				=	ya
     lowercase__   :       Union[str, Any]				=	xa

     for k in range(SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Optional[int]				=	f(SCREAMING_SNAKE_CASE_     ,					y[k]      )
          lowercase__   :       Dict				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       Any				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       List[Any]				=	f(x + h     ,					y[k] + h * ka      )
          lowercase__   :       Tuple				=	y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
          x += h

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()

