import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class    __lowerCAmelCase  (       lowerCAmelCase__,	lowerCAmelCase__,	lowerCAmelCase__,	unittest.TestCase						):



 '''simple docstring'''
 _lowerCamelCase						:	Tuple				    = StableDiffusionModelEditingPipeline
 _lowerCamelCase						:	Optional[Any]				    = TEXT_TO_IMAGE_PARAMS
 _lowerCamelCase						:	str				    = TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase						:	Union[str, Any]				    = TEXT_TO_IMAGE_IMAGE_PARAMS
 _lowerCamelCase						:	Any				    = TEXT_TO_IMAGE_IMAGE_PARAMS

 def 					__snake_case					(			self       :		Optional[int]  ) ->							str:
        torch.manual_seed(0  )
        _UpperCamelCase     :       Optional[Any]  						=  UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)       ,			layers_per_block=2       ,			sample_size=3_2       ,			in_channels=4       ,			out_channels=4       ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,			cross_attention_dim=3_2       ,			)
        _UpperCamelCase     :       Any  						=  DDIMScheduler()
        torch.manual_seed(0  )
        _UpperCamelCase     :       Any  						=  AutoencoderKL(
            block_out_channels=[3_2, 6_4]       ,			in_channels=3       ,			out_channels=3       ,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,			latent_channels=4       ,			)
        torch.manual_seed(0  )
        _UpperCamelCase     :       List[Any]  						=  CLIPTextConfig(
            bos_token_id=0       ,			eos_token_id=2       ,			hidden_size=3_2       ,			intermediate_size=3_7       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			pad_token_id=1       ,			vocab_size=1_0_0_0       ,			)
        _UpperCamelCase     :       str  						=  CLIPTextModel(lowerCAmelCase_  )
        _UpperCamelCase     :       Tuple  						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

        _UpperCamelCase     :       Union[str, Any]  						=  {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components

 def 					__snake_case					(			self       :		int       ,			lowerCamelCase       :		Tuple       ,			lowerCamelCase       :		int=0  ) ->							str:
        _UpperCamelCase     :       Union[str, Any]  						=  torch.manual_seed(lowerCAmelCase_  )
        _UpperCamelCase     :       str  						=  {
            'prompt': 'A field of roses',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs

 def 					__snake_case					(			self       :		int  ) ->							int:
        _UpperCamelCase     :       Any  						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase     :       Optional[Any]  						=  self.get_dummy_components()
        _UpperCamelCase     :       Optional[int]  						=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
        _UpperCamelCase     :       Tuple  						=  sd_pipe.to(lowerCAmelCase_  )
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

        _UpperCamelCase     :       List[Any]  						=  self.get_dummy_inputs(lowerCAmelCase_  )
        _UpperCamelCase     :       Dict  						=  sd_pipe(**lowerCAmelCase_  ).images
        _UpperCamelCase     :       int  						=  image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase     :       List[str]  						=  np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]  )

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

 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Tuple:
        _UpperCamelCase     :       Union[str, Any]  						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase     :       List[Any]  						=  self.get_dummy_components()
        _UpperCamelCase     :       Optional[Any]  						=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
        _UpperCamelCase     :       Any  						=  sd_pipe.to(lowerCAmelCase_  )
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

        _UpperCamelCase     :       Dict  						=  self.get_dummy_inputs(lowerCAmelCase_  )
        _UpperCamelCase     :       str  						=  'french fries'
        _UpperCamelCase     :       List[str]  						=  sd_pipe(**lowerCAmelCase_       ,			negative_prompt=lowerCAmelCase_  )
        _UpperCamelCase     :       List[str]  						=  output.images
        _UpperCamelCase     :       List[Any]  						=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase     :       Optional[Any]  						=  np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]  )

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

 def 					__snake_case					(			self       :		int  ) ->							int:
        _UpperCamelCase     :       Union[str, Any]  						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase     :       Dict  						=  self.get_dummy_components()
        _UpperCamelCase     :       List[str]  						=  EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule='scaled_linear'  )
        _UpperCamelCase     :       Any  						=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
        _UpperCamelCase     :       int  						=  sd_pipe.to(lowerCAmelCase_  )
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

        _UpperCamelCase     :       Dict  						=  self.get_dummy_inputs(lowerCAmelCase_  )
        _UpperCamelCase     :       Any  						=  sd_pipe(**lowerCAmelCase_  ).images
        _UpperCamelCase     :       int  						=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase     :       int  						=  np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]  )

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

 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Tuple:
        _UpperCamelCase     :       List[str]  						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase     :       int  						=  self.get_dummy_components()
        _UpperCamelCase     :       Tuple  						=  PNDMScheduler()
        _UpperCamelCase     :       Optional[Any]  						=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
        _UpperCamelCase     :       Optional[int]  						=  sd_pipe.to(lowerCAmelCase_  )
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

        _UpperCamelCase     :       List[str]  						=  self.get_dummy_inputs(lowerCAmelCase_  )
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowerCAmelCase_  ):
               _UpperCamelCase     :       Tuple  						=  sd_pipe(**lowerCAmelCase_  ).images

 def 					__snake_case					(			self       :		Tuple  ) ->							int:
        super().test_inference_batch_single_identical(expected_max_diff=5e-3  )

 def 					__snake_case					(			self       :		List[str]  ) ->							Tuple:
        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3  )




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



 '''simple docstring'''

 def 					__snake_case					(			self       :		Optional[int]  ) ->							Optional[Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def 					__snake_case					(			self       :		int       ,			lowerCamelCase       :		Optional[int]=0  ) ->							Dict:
        _UpperCamelCase     :       Optional[Any]  						=  torch.manual_seed(lowerCAmelCase_  )
        _UpperCamelCase     :       Optional[Any]  						=  {
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs

 def 					__snake_case					(			self       :		Optional[Any]  ) ->							str:
        _UpperCamelCase     :       Union[str, Any]  						=  'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase     :       Dict  						=  StableDiffusionModelEditingPipeline.from_pretrained(lowerCAmelCase_       ,			safety_checker=lowerCAmelCase_  )
        pipe.to(lowerCAmelCase_  )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
        pipe.enable_attention_slicing()

        _UpperCamelCase     :       Union[str, Any]  						=  self.get_inputs()
        _UpperCamelCase     :       Any  						=  pipe(**lowerCAmelCase_  ).images
        _UpperCamelCase     :       List[str]  						=  image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        _UpperCamelCase     :       Optional[int]  						=  np.array(
            [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]  )

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

        # make sure image changes after editing
        pipe.edit_model('A pack of roses'       ,			'A pack of blue roses'  )

        _UpperCamelCase     :       List[str]  						=  pipe(**lowerCAmelCase_  ).images
        _UpperCamelCase     :       Union[str, Any]  						=  image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        assert np.abs(expected_slice - image_slice  ).max() > 1e-1

 def 					__snake_case					(			self       :		Union[str, Any]  ) ->							Optional[int]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCamelCase     :       List[Any]  						=  'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase     :       str  						=  DDIMScheduler.from_pretrained(lowerCAmelCase_       ,			subfolder='scheduler'  )
        _UpperCamelCase     :       Optional[Any]  						=  StableDiffusionModelEditingPipeline.from_pretrained(
            lowerCAmelCase_       ,			scheduler=lowerCAmelCase_       ,			safety_checker=lowerCAmelCase_  )
        _UpperCamelCase     :       List[str]  						=  pipe.to(lowerCAmelCase_  )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
        pipe.enable_attention_slicing(1  )
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase     :       int  						=  self.get_inputs()
        _UpperCamelCase     :       Any  						=  pipe(**lowerCAmelCase_  )

        _UpperCamelCase     :       str  						=  torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 1_0**9




from __future__ import annotations


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int				):
       assert (
           c % greatest_common_divisor(lowercase_		,						lowercase_				) == 0
       )  # greatest_common_divisor(a,b) function implemented below
       ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       int  						=  extended_gcd(lowercase_		,						lowercase_				)  # extended_gcd(a,b) function implemented below
       _UpperCamelCase     :       List[Any]  						=  c / d
       return (r * x, r * y)


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int = 2				):
       ((_UpperCamelCase)						,      (_UpperCamelCase))     :       Tuple  						=  diophantine(lowercase_		,						lowercase_		,						lowercase_				)  # Initial value
       _UpperCamelCase     :       List[Any]  						=  greatest_common_divisor(lowercase_		,						lowercase_				)
       _UpperCamelCase     :       Optional[int]  						=  a // d
       _UpperCamelCase     :       Optional[Any]  						=  b // d

       for i in range(lowercase_				):
              _UpperCamelCase     :       Union[str, Any]  						=  xa + i * q
              _UpperCamelCase     :       List[Any]  						=  ya - i * p
              print(lowercase_		,						lowercase_				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
       if a < b:
              _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  b, a

       while a % b != 0:
              _UpperCamelCase						,      _UpperCamelCase     :       Union[str, Any]  						=  b, a % b

       return b


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
       assert a >= 0 and b >= 0

       if b == 0:
              _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  a, 1, 0
       else:
              ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       Any  						=  extended_gcd(lowercase_		,						a % b				)
              _UpperCamelCase     :       Any  						=  q
              _UpperCamelCase     :       str  						=  p - q * (a // b)

       assert a % d == 0 and b % d == 0
       assert d == a * x + b * y

       return (d, x, y)


if __name__ == "__main__":
 from doctest import testmod

 testmod(name='''diophantine''', verbose=True)
 testmod(name='''diophantine_all_soln''', verbose=True)
 testmod(name='''extended_gcd''', verbose=True)
 testmod(name='''greatest_common_divisor''', verbose=True)



