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 				_SCREAMING_SNAKE_CASE( snake_case__							,   snake_case__							,   unittest.TestCase						):
   UpperCamelCase_					:    Optional[Any]			      = IFImgaImgPipeline
   UpperCamelCase_					:    Dict			      = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   UpperCamelCase_					:    List[Any]			      = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCamelCase_					:    Optional[Any]			      = PipelineTesterMixin.required_optional_params - {'latents'}
   def _lowercase(     self     )						->Any:
        return self._get_dummy_components()
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=0     )						->Tuple:
        if str(lowerCAmelCase_     ).startswith('''mps'''     ):
             a	:     List[str]						      =    torch.manual_seed(lowerCAmelCase_     )
        else:
             a	:     Optional[Any]						      =    torch.Generator(device=lowerCAmelCase_     ).manual_seed(lowerCAmelCase_     )

        a	:     List[Any]						      =    floats_tensor((1, 3, 32, 32)				,  rng=random.Random(lowerCAmelCase_     )     ).to(lowerCAmelCase_     )

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

        return inputs
   def _lowercase(     self     )						->Optional[Any]:
        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 _lowercase(     self     )						->Any:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )
   @unittest.skipIf(torch_device != '''cuda'''				,  reason='''float16 requires CUDA'''     )
   def _lowercase(     self     )						->Tuple:
        # 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 _lowercase(     self     )						->Union[str, Any]:
        super().test_floataa_inference(expected_max_diff=1e-1     )
   def _lowercase(     self     )						->Optional[int]:
        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )
   def _lowercase(     self     )						->List[str]:
        self._test_save_load_local()


   def _lowercase(     self     )						->int:
        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2				,  )
import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (inputs > threshold).type(inputs.type()     )
        if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             a	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Optional[Any]						      =    0
        a	:     List[Any]						      =    1
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Tuple						      =    0
        a	:     List[str]						      =    1
        return mask