

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class      __lowercase			(    unittest.TestCase					):

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  @property
  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					1
   UpperCAmelCase__					:  Optional[Any]					   =					3
   UpperCAmelCase__					:  str					   =					(32, 32)

   UpperCAmelCase__					:  List[Any]					   =					floats_tensor((batch_size, num_channels) + sizes					,rng=random.Random(0    )    ).to(A    )
   return image

  @property
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Any					   =					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""")					,cross_attention_dim=32					,)
   return model

  @property
  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Union[str, Any]					   =					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					,)
   return model

  @property
  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Optional[Any]					   =					RobertaSeriesConfig(
       hidden_size=32					,project_dim=32					,intermediate_size=37					,layer_norm_eps=1e-05					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=5_006					,)
   return RobertaSeriesModelWithTransformation(A    )

  @property
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   def extract(*A		:			Tuple					,**A		:			List[Any]    ):







    class      __lowercase			:

      def __init__(  self		:			Any    ):






       '''simple docstring'''
       UpperCAmelCase__					:  Union[str, Any]					   =					torch.ones([0]    )




      def    __lowercase       (  self		:			Any					,A		:			Dict    ):






       '''simple docstring'''
       self.pixel_values.to(A    )
       return self







    return Out()

   return extract

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase__					:  Optional[Any]					   =					self.dummy_cond_unet
   UpperCAmelCase__					:  int					   =					PNDMScheduler(skip_prk_steps=A    )
   UpperCAmelCase__					:  Optional[int]					   =					self.dummy_vae
   UpperCAmelCase__					:  Tuple					   =					self.dummy_text_encoder
   UpperCAmelCase__					:  Optional[int]					   =					XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
   UpperCAmelCase__					:  str					   =					77

   UpperCAmelCase__					:  List[str]					   =					self.dummy_image.to(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					init_image / 2 + 0.5

   # make sure here that pndm scheduler skips prk
   UpperCAmelCase__					:  List[str]					   =					AltDiffusionImgaImgPipeline(
       unet=A					,scheduler=A					,vae=A					,text_encoder=A					,tokenizer=A					,safety_checker=A					,feature_extractor=self.dummy_extractor					,)
   UpperCAmelCase__					:  Optional[int]					   =					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,do_normalize=A    )
   UpperCAmelCase__					:  Tuple					   =					alt_pipe.to(A    )
   alt_pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  Optional[Any]					   =					"""A painting of a squirrel eating a burger"""
   UpperCAmelCase__					:  str					   =					torch.Generator(device=A    ).manual_seed(0    )
   UpperCAmelCase__					:  Dict					   =					alt_pipe(
       [prompt]					,generator=A					,guidance_scale=6.0					,num_inference_steps=2					,output_type="""np"""					,image=A					,)

   UpperCAmelCase__					:  Union[str, Any]					   =					output.images

   UpperCAmelCase__					:  List[Any]					   =					torch.Generator(device=A    ).manual_seed(0    )
   UpperCAmelCase__					:  List[str]					   =					alt_pipe(
       [prompt]					,generator=A					,guidance_scale=6.0					,num_inference_steps=2					,output_type="""np"""					,image=A					,return_dict=A					,)[0]

   UpperCAmelCase__					:  str					   =					image[0, -3:, -3:, -1]
   UpperCAmelCase__					:  Union[str, Any]					   =					image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   UpperCAmelCase__					:  Optional[int]					   =					np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 5e-3

  @unittest.skipIf(torch_device != """cuda"""					,"""This test requires a GPU"""    )
  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.dummy_cond_unet
   UpperCAmelCase__					:  Optional[Any]					   =					PNDMScheduler(skip_prk_steps=A    )
   UpperCAmelCase__					:  List[Any]					   =					self.dummy_vae
   UpperCAmelCase__					:  List[str]					   =					self.dummy_text_encoder
   UpperCAmelCase__					:  int					   =					XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
   UpperCAmelCase__					:  Tuple					   =					77

   UpperCAmelCase__					:  Tuple					   =					self.dummy_image.to(A    )

   # put models in fp16
   UpperCAmelCase__					:  str					   =					unet.half()
   UpperCAmelCase__					:  List[str]					   =					vae.half()
   UpperCAmelCase__					:  List[str]					   =					bert.half()

   # make sure here that pndm scheduler skips prk
   UpperCAmelCase__					:  Optional[int]					   =					AltDiffusionImgaImgPipeline(
       unet=A					,scheduler=A					,vae=A					,text_encoder=A					,tokenizer=A					,safety_checker=A					,feature_extractor=self.dummy_extractor					,)
   UpperCAmelCase__					:  int					   =					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,do_normalize=A    )
   UpperCAmelCase__					:  str					   =					alt_pipe.to(A    )
   alt_pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  List[str]					   =					"""A painting of a squirrel eating a burger"""
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.manual_seed(0    )
   UpperCAmelCase__					:  List[Any]					   =					alt_pipe(
       [prompt]					,generator=A					,num_inference_steps=2					,output_type="""np"""					,image=A					,).images

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




  @unittest.skipIf(torch_device != """cuda"""					,"""This test requires a GPU"""    )
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/img2img/sketch-mountains-input.jpg"""    )
   # resize to resolution that is divisible by 8 but not 16 or 32
   UpperCAmelCase__					:  List[str]					   =					init_image.resize((760, 504)    )

   UpperCAmelCase__					:  str					   =					"""BAAI/AltDiffusion"""
   UpperCAmelCase__					:  List[str]					   =					AltDiffusionImgaImgPipeline.from_pretrained(
       A					,safety_checker=A					,)
   pipe.to(A    )
   pipe.set_progress_bar_config(disable=A    )
   pipe.enable_attention_slicing()

   UpperCAmelCase__					:  int					   =					"""A fantasy landscape, trending on artstation"""

   UpperCAmelCase__					:  Dict					   =					torch.manual_seed(0    )
   UpperCAmelCase__					:  int					   =					pipe(
       prompt=A					,image=A					,strength=0.7_5					,guidance_scale=7.5					,generator=A					,output_type="""np"""					,)
   UpperCAmelCase__					:  List[Any]					   =					output.images[0]

   UpperCAmelCase__					:  str					   =					image[255:258, 383:386, -1]

   assert image.shape == (504, 760, 3)
   UpperCAmelCase__					:  str					   =					np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]    )

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







@slow
@require_torch_gpu
class      __lowercase			(    unittest.TestCase					):

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/img2img/sketch-mountains-input.jpg"""    )
   UpperCAmelCase__					:  Union[str, Any]					   =					init_image.resize((768, 512)    )
   UpperCAmelCase__					:  int					   =					load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""    )

   UpperCAmelCase__					:  Dict					   =					"""BAAI/AltDiffusion"""
   UpperCAmelCase__					:  List[str]					   =					AltDiffusionImgaImgPipeline.from_pretrained(
       A					,safety_checker=A					,)
   pipe.to(A    )
   pipe.set_progress_bar_config(disable=A    )
   pipe.enable_attention_slicing()

   UpperCAmelCase__					:  List[str]					   =					"""A fantasy landscape, trending on artstation"""

   UpperCAmelCase__					:  str					   =					torch.manual_seed(0    )
   UpperCAmelCase__					:  Tuple					   =					pipe(
       prompt=A					,image=A					,strength=0.7_5					,guidance_scale=7.5					,generator=A					,output_type="""np"""					,)
   UpperCAmelCase__					:  Any					   =					output.images[0]

   assert image.shape == (512, 768, 3)
   # img2img is flaky across GPUs even in fp32, so using MAE here
   assert np.abs(expected_image - image    ).max() < 1e-2








"""simple docstring"""




from __future__ import annotations

from typing import Any







class      __lowercase			:

  def __init__(  self		:			Optional[int]					,A		:			int					,A		:			int					,A		:			float = 0    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					row, column
   UpperCAmelCase__					:  int					   =					[[default_value for c in range(A    )] for r in range(A    )]

  def __str__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					f"Matrix consist of {self.row} rows and {self.column} columns\n"

   # Make string identifier
   UpperCAmelCase__					:  Optional[Any]					   =					0
   for row_vector in self.array:
    for obj in row_vector:
     UpperCAmelCase__					:  Optional[int]					   =					max(A					,len(str(A    )    )    )
   UpperCAmelCase__					:  int					   =					f"%{max_element_length}s"

   # Make string and return
   def single_line(A		:			list[float]    ) -> str:
    nonlocal string_format_identifier
    UpperCAmelCase__					:  List[str]					   =					"""["""
    line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
    line += "]"
    return line

   s += "\n".join(single_line(A    ) for row_vector in self.array    )
   return s

  def __repr__(  self		:			Dict    ):






   '''simple docstring'''
   return str(self    )

  def    __lowercase       (  self		:			int					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   if not (isinstance(A					,(list, tuple)    ) and len(A    ) == 2):
    return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
    return False
   else:
    return True

  def __getitem__(  self		:			Optional[int]					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   return self.array[loc[0]][loc[1]]

  def __setitem__(  self		:			List[Any]					,A		:			tuple[int, int]					,A		:			float    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   UpperCAmelCase__					:  str					   =					value

  def __add__(  self		:			Any					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    )
   assert self.row == another.row and self.column == another.column

   # Add
   UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Union[str, Any]					   =					self[r, c] + another[r, c]
   return result

  def __neg__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  int					   =					-self[r, c]
   return result

  def __sub__(  self		:			str					,A		:			Matrix    ):






   '''simple docstring'''
   return self + (-another)

  def __mul__(  self		:			Union[str, Any]					,A		:			int | float | Matrix    ):






   '''simple docstring'''

   if isinstance(A					,(int, float)    ):  # Scalar multiplication
    UpperCAmelCase__					:  Dict					   =					Matrix(self.row					,self.column    )
    for r in range(self.row    ):
     for c in range(self.column    ):
      UpperCAmelCase__					:  Any					   =					self[r, c] * another
    return result
   elif isinstance(A					,A    ):  # Matrix multiplication
    assert self.column == another.row
    UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,another.column    )
    for r in range(self.row    ):
     for c in range(another.column    ):
      for i in range(self.column    ):
       result[r, c] += self[r, i] * another[i, c]
    return result
   else:
    UpperCAmelCase__					:  Union[str, Any]					   =					f"Unsupported type given for another ({type(A    )})"
    raise TypeError(A    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					Matrix(self.column					,self.row    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Optional[int]					   =					self[r, c]
   return result




  def    __lowercase       (  self		:			Any					,A		:			Matrix					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    ) and isinstance(A					,A    )
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   UpperCAmelCase__					:  Optional[Any]					   =					v.transpose()
   UpperCAmelCase__					:  Union[str, Any]					   =					(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
    return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    3							,    0      )
       for i in range(3      ):
        UpperCAmelCase__					:  List[str]					   =					1
       print(F"a^(-1) is {ainv}"      )
       # u, v
       UpperCAmelCase__					:  List[Any]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					1, 2, -3
       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					4, -2, 5
       print(F"u is {u}"      )
       print(F"v is {v}"      )
       print(F"uv^T is {u * v.transpose()}"      )
       # Sherman Morrison
       print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCamelCase							,    __UpperCamelCase      )}"      )
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       import doctest

       doctest.testmod()




      testa()





