






import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class       UpperCAmelCase_						( lowercase,     lowercase,     unittest.TestCase			):



      """simple docstring"""
      UpperCamelCase_    : int									=IFImgaImgPipeline
      UpperCamelCase_    : List[Any]									=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
      UpperCamelCase_    : int									=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      UpperCamelCase_    : Tuple									=PipelineTesterMixin.required_optional_params - {'latents'}
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          return self._get_dummy_components()
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=0			)    ->				Union[str, Any]:
          if str(SCREAMING_SNAKE_CASE_			).startswith('''mps'''			):
              UpperCamelCase	:Optional[int]     							=       torch.manual_seed(SCREAMING_SNAKE_CASE_			)
          else:
              UpperCamelCase	:Optional[int]     							=       torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Optional[Any]     							=       floats_tensor((1, 3, 32, 32)   ,    rng=random.Random(SCREAMING_SNAKE_CASE_			)			).to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Union[str, Any]     							=       {
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''output_type''': '''numpy''',
          }

          return inputs
      def    UpperCAmelCase						(    self			)    ->				str:
          self._test_save_load_optional_components()
      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()   ,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,    )
      def    UpperCAmelCase						(    self			)    ->				str:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)
      @unittest.skipIf(torch_device != '''cuda'''   ,    reason='''float16 requires CUDA'''			)
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1			)
      @unittest.skipIf(torch_device != '''cuda'''   ,    reason='''float16 requires CUDA'''			)
      def    UpperCAmelCase						(    self			)    ->				int:
          super().test_floataa_inference(expected_max_diff=1e-1			)
      def    UpperCAmelCase						(    self			)    ->				Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          self._test_save_load_local()




      def    UpperCAmelCase						(    self			)    ->				Dict:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2   ,    )













import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





