import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						StableDiffusionXLImgaImgPipeline
  lowerCamelCase__     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
  lowerCamelCase__     =						PipelineTesterMixin.required_optional_params - {'''latents'''}
  lowerCamelCase__     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCamelCase__     =						IMAGE_TO_IMAGE_IMAGE_PARAMS
  lowerCamelCase__     =						IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    __A     (							self :       Tuple   )  ->	int:
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  UNetaDConditionModel(
            block_out_channels=(32, 64)  ,     layers_per_block=2  ,     sample_size=32  ,     in_channels=4  ,     out_channels=4  ,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,     attention_head_dim=(2, 4)  ,     use_linear_projection=__magic_name__  ,     addition_embed_type="text_time"  ,     addition_time_embed_dim=8  ,     transformer_layers_per_block=(1, 2)  ,     projection_class_embeddings_input_dim=80  ,     cross_attention_dim=64  ,     )
        SCREAMING_SNAKE_CASE_          =  EulerDiscreteScheduler(
            beta_start=0.0_0085  ,     beta_end=0.012  ,     steps_offset=1  ,     beta_schedule="scaled_linear"  ,     timestep_spacing="leading"  ,     )
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  AutoencoderKL(
            block_out_channels=[32, 64]  ,     in_channels=3  ,     out_channels=3  ,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]  ,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]  ,     latent_channels=4  ,     sample_size=128  ,     )
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  CLIPTextConfig(
            bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=32  ,     intermediate_size=37  ,     layer_norm_eps=1e-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_000  ,     hidden_act="gelu"  ,     projection_dim=32  ,     )
        SCREAMING_SNAKE_CASE_          =  CLIPTextModel(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  ,     local_files_only=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  CLIPTextModelWithProjection(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  ,     local_files_only=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_a,
            "tokenizer_2": tokenizer_a,
            # "safety_checker": None,
            # "feature_extractor": None,
        }
        return components







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Union[str, Any]=0   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  floats_tensor((1, 3, 32, 32)  ,     rng=random.Random(__magic_name__   )   ).to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  image / 2 + 0.5
        if str(__magic_name__   ).startswith("mps"   ):
              SCREAMING_SNAKE_CASE_          =  torch.manual_seed(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "output_type": "numpy",
            "strength": 0.75,
        }
        return inputs







  def    __A     (							self :       Dict   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  StableDiffusionXLImgaImgPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  sd_pipe.to(__magic_name__   )
        sd_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  sd_pipe(**__magic_name__   ).images
        SCREAMING_SNAKE_CASE_          =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)

        SCREAMING_SNAKE_CASE_          =  np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







  def    __A     (							self :       List[Any]   )  ->	Optional[Any]:
        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )







  def    __A     (							self :       int   )  ->	List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )







  def    __A     (							self :       str   )  ->	str:
        pass







  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  StableDiffusionXLImgaImgPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  sd_pipe.to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  sd_pipe.to(__magic_name__   )
        sd_pipe.set_progress_bar_config(disable=__magic_name__   )

        # forward without prompt embeds
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  3 * ["this is a negative prompt"]
        SCREAMING_SNAKE_CASE_          =  negative_prompt
        SCREAMING_SNAKE_CASE_          =  3 * [inputs["prompt"]]

        SCREAMING_SNAKE_CASE_          =  sd_pipe(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  3 * ["this is a negative prompt"]
        SCREAMING_SNAKE_CASE_          =  3 * [inputs.pop("prompt"   )]

        (
            (
            SCREAMING_SNAKE_CASE_ 
        )     , (
            SCREAMING_SNAKE_CASE_ 
        )     , (
            SCREAMING_SNAKE_CASE_ 
        )     , (
            SCREAMING_SNAKE_CASE_ 
        )     ,  
        )          =  sd_pipe.encode_prompt(__magic_name__  ,     negative_prompt=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  sd_pipe(
            **__magic_name__  ,     prompt_embeds=__magic_name__  ,     negative_prompt_embeds=__magic_name__  ,     pooled_prompt_embeds=__magic_name__  ,     negative_pooled_prompt_embeds=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  output.images[0, -3:, -3:, -1]

        # make sure that it's equal
        assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4


@slow
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Optional[int]   )  ->	List[str]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Dict="cpu"  ,     __magic_name__ :       Optional[int]=torch.floataa  ,     __magic_name__ :       List[Any]=0   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  np.random.RandomState(__magic_name__   ).standard_normal((1, 4, 64, 64)   )
        SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__magic_name__   ).to(device=__magic_name__  ,     dtype=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {
            "prompt": "a photograph of an astronaut riding a horse",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs







  def    __A     (							self :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  pipe(**__magic_name__   ).images
        SCREAMING_SNAKE_CASE_          =  image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]   )
        assert np.abs(image_slice - expected_slice   ).max() < 7e-3



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A     :  Dict					     =       TypeVar("T")


class   lowerCamelCase					(Generic[T]					):
  """simple docstring"""







  def __init__(							self :       Optional[int]  ,     __magic_name__ :       bool = True   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  {}  # dictionary of lists
        SCREAMING_SNAKE_CASE_          =  directed







  def    __A     (							self :       Dict  ,     __magic_name__ :       T  ,     __magic_name__ :       T   )  ->	GraphAdjacencyList[T]:

        if not self.directed:  # For undirected graphs
              # if both source vertex and destination vertex are both present in the
              # adjacency list, add destination vertex to source vertex list of adjacent
              # vertices and add source vertex to destination vertex list of adjacent
              # vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    self.adj_list[destination_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination vertex
              # to source vertex list of adjacent vertices, then create a new vertex with
              # destination vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
              # if only destination vertex is present in adjacency list, add source vertex
              # to destination vertex list of adjacent vertices, then create a new vertex
              # with source vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and assign a list
              # containing the destination vertex as it's first adjacent vertex also
              # create a new vertex with destination vertex as key and assign a list
              # containing the source vertex as it's first adjacent vertex.
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
        else:  # For directed graphs
              # if both source vertex and destination vertex are present in adjacency
              # list, add destination vertex to source vertex list of adjacent vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination
              # vertex to source vertex list of adjacent vertices and create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  []
              # if only destination vertex is present in adjacency list, create a new
              # vertex with source vertex as key and assign a list containing destination
              # vertex as first adjacent vertex
              elif destination_vertex in self.adj_list:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and a list containing
              # destination vertex as it's first adjacent vertex. Then create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  []

        return self







  def __repr__(							self :       int   )  ->	str:
        return pformat(self.adj_list   )


