
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 A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

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

          torch.manual_seed(0 )
          snake_case__						=				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 )
          snake_case__						=				DDPMScheduler(
              num_train_timesteps=10_00		,	beta_schedule="squaredcos_cap_v2"		,	beta_start=0.0_0_0_1		,	beta_end=0.0_2		,	thresholding=lowerCamelCase		,	dynamic_thresholding_ratio=0.9_5		,	sample_max_value=1.0		,	prediction_type="epsilon"		,	variance_type="learned_range"		,	)

          torch.manual_seed(0 )
          snake_case__						=				IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

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

          torch.manual_seed(0 )
          snake_case__						=				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=1.4_1_4		,	time_embedding_act_fn="gelu"		,	time_embedding_dim=32		,	)
          unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

          torch.manual_seed(0 )
          snake_case__						=				DDPMScheduler(
              num_train_timesteps=10_00		,	beta_schedule="squaredcos_cap_v2"		,	beta_start=0.0_0_0_1		,	beta_end=0.0_2		,	thresholding=lowerCamelCase		,	dynamic_thresholding_ratio=0.9_5		,	sample_max_value=1.0		,	prediction_type="epsilon"		,	variance_type="learned_range"		,	)

          torch.manual_seed(0 )
          snake_case__						=				DDPMScheduler(
              num_train_timesteps=10_00		,	beta_schedule="squaredcos_cap_v2"		,	beta_start=0.0_0_0_1		,	beta_end=0.0_2		,	)

          torch.manual_seed(0 )
          snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.get_dummy_components()
          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          pipe.to(lowerCamelCase )
          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )

          snake_case__						=				inputs["prompt"]
          snake_case__						=				inputs["generator"]
          snake_case__						=				inputs["num_inference_steps"]
          snake_case__						=				inputs["output_type"]

          if "image" in inputs:
              snake_case__						=				inputs["image"]
          else:
              snake_case__						=				None

          if "mask_image" in inputs:
              snake_case__						=				inputs["mask_image"]
          else:
              snake_case__						=				None

          if "original_image" in inputs:
              snake_case__						=				inputs["original_image"]
          else:
              snake_case__						=				None

          snake_case__						,					snake_case__						=				pipe.encode_prompt(lowerCamelCase )

          # inputs with prompt converted to embeddings
          snake_case__						=				{
              "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:
              snake_case__						=				image

          if mask_image is not None:
              snake_case__						=				mask_image

          if original_image is not None:
              snake_case__						=				original_image

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

          snake_case__						=				pipe(**lowerCamelCase )[0]

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

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

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

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )

          snake_case__						=				inputs["generator"]
          snake_case__						=				inputs["num_inference_steps"]
          snake_case__						=				inputs["output_type"]

          # inputs with prompt converted to embeddings
          snake_case__						=				{
              "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:
              snake_case__						=				image

          if mask_image is not None:
              snake_case__						=				mask_image

          if original_image is not None:
              snake_case__						=				original_image

          snake_case__						=				pipe_loaded(**lowerCamelCase )[0]

          snake_case__						=				np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
          self.assertLess(lowerCamelCase		,	1e-4 )



      def A_			(			self ):
          snake_case__						=				self.get_dummy_components()
          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          pipe.to(lowerCamelCase )
          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				pipe(**lowerCamelCase )[0]

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

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

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				pipe_loaded(**lowerCamelCase )[0]

          snake_case__						=				np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
          self.assertLess(lowerCamelCase		,	1e-4 )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						,					snake_case__						=				set(__lowerCAmelCase							), [start]

    while stack:
        snake_case__						=				stack.pop()
        explored.add(__lowerCAmelCase							)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]							):
            if adj not in explored:
                stack.append(__lowerCAmelCase							)
    return explored


__magic_name__            =  {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))



