'''simple docstring'''

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 			UpperCAmelCase  (						a__    ,						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    StableDiffusionDiffEditPipeline
       SCREAMING_SNAKE_CASE							=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
       SCREAMING_SNAKE_CASE							=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
       SCREAMING_SNAKE_CASE							=    frozenset(
           []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       SCREAMING_SNAKE_CASE							=    frozenset([]					)

       def _lowerCAmelCase(						self )  ->  Tuple:
        torch.manual_seed(0 )
        lowercase__				:    Optional[int]  =					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	,   attention_head_dim=(2, 4)	,   use_linear_projection=__lowerCAmelCase	,   )
        lowercase__				:    Optional[int]  =					DDIMScheduler(
            beta_start=0.0_0_0_8_5	,   beta_end=0.0_1_2	,   beta_schedule='''scaled_linear'''	,   clip_sample=__lowerCAmelCase	,   set_alpha_to_one=__lowerCAmelCase	,   )
        lowercase__				:    List[str]  =					DDIMInverseScheduler(
            beta_start=0.0_0_0_8_5	,   beta_end=0.0_1_2	,   beta_schedule='''scaled_linear'''	,   clip_sample=__lowerCAmelCase	,   set_alpha_to_zero=__lowerCAmelCase	,   )
        torch.manual_seed(0 )
        lowercase__				:    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	,   sample_size=128	,   )
        torch.manual_seed(0 )
        lowercase__				:    List[str]  =					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=1000	,   hidden_act='''gelu'''	,   projection_dim=512	,   )
        lowercase__				:    Optional[int]  =					CLIPTextModel(__lowerCAmelCase )
        lowercase__				:    str  =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        lowercase__				:    int  =					{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''inverse_scheduler''': inverse_scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }

        return components

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  int:
        lowercase__				:    Optional[Any]  =					floats_tensor((1, 16, 16)	,   rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
        lowercase__				:    str  =					floats_tensor((1, 2, 4, 16, 16)	,   rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    int  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    List[Any]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					{
            '''prompt''': '''a dog and a newt''',
            '''mask_image''': mask,
            '''image_latents''': latents,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  Optional[Any]:
        lowercase__				:    Tuple  =					floats_tensor((1, 3, 32, 32)	,   rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
        lowercase__				:    str  =					image.cpu().permute(0	,   2	,   3	,   1 )[0]
        lowercase__				:    str  =					Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert('''RGB''' )
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    List[str]  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    Optional[int]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    Any  =					{
            '''image''': image,
            '''source_prompt''': '''a cat and a frog''',
            '''target_prompt''': '''a dog and a newt''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''num_maps_per_mask''': 2,
            '''mask_encode_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  Optional[int]:
        lowercase__				:    Any  =					floats_tensor((1, 3, 32, 32)	,   rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
        lowercase__				:    int  =					image.cpu().permute(0	,   2	,   3	,   1 )[0]
        lowercase__				:    Any  =					Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert('''RGB''' )
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    int  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    Union[str, Any]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					{
            '''image''': image,
            '''prompt''': '''a cat and a frog''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''decode_latents''': True,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(						self )  ->  str:
        if not hasattr(self.pipeline_class	,   '''_optional_components''' ):
         return

        lowercase__				:    Union[str, Any]  =					self.get_dummy_components()
        lowercase__				:    Union[str, Any]  =					self.pipeline_class(**__lowerCAmelCase )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        # set all optional components to None and update pipeline config accordingly
        for optional_component in pipe._optional_components:
         setattr(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )

        lowercase__				:    Optional[Any]  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					pipe(**__lowerCAmelCase )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(__lowerCAmelCase )
         lowercase__				:    Tuple  =					self.pipeline_class.from_pretrained(__lowerCAmelCase )
         pipe_loaded.to(__lowerCAmelCase )
         pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase )

        for optional_component in pipe._optional_components:
         self.assertTrue(
             getattr(__lowerCAmelCase	,   __lowerCAmelCase ) is None	,   F"""`{optional_component}` did not stay set to None after loading."""	,   )

        lowercase__				:    Dict  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    List[Any]  =					pipe_loaded(**__lowerCAmelCase )[0]

        lowercase__				:    int  =					np.abs(output - output_loaded ).max()
        self.assertLess(__lowerCAmelCase	,   1E-4 )

       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    Tuple  =					'''cpu'''

        lowercase__				:    List[str]  =					self.get_dummy_components()
        lowercase__				:    str  =					self.pipeline_class(**__lowerCAmelCase )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Dict  =					self.get_dummy_mask_inputs(__lowerCAmelCase )
        lowercase__				:    str  =					pipe.generate_mask(**__lowerCAmelCase )
        lowercase__				:    Dict  =					mask[0, -3:, -3:]

        self.assertEqual(mask.shape	,   (1, 16, 16) )
        lowercase__				:    Union[str, Any]  =					np.array([0] * 9 )
        lowercase__				:    List[str]  =					np.abs(mask_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(__lowerCAmelCase	,   1E-3 )
        self.assertEqual(mask[0, -3, -4]	,   0 )

       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    int  =					'''cpu'''

        lowercase__				:    List[Any]  =					self.get_dummy_components()
        lowercase__				:    Any  =					self.pipeline_class(**__lowerCAmelCase )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    int  =					self.get_dummy_inversion_inputs(__lowerCAmelCase )
        lowercase__				:    List[Any]  =					pipe.invert(**__lowerCAmelCase ).images
        lowercase__				:    int  =					image[0, -1, -3:, -3:]

        self.assertEqual(image.shape	,   (2, 32, 32, 3) )
        lowercase__				:    str  =					np.array(
            [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]	,   )
        lowercase__				:    str  =					np.abs(image_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(__lowerCAmelCase	,   1E-3 )

       def _lowerCAmelCase(						self )  ->  List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=5E-3 )


       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__				:    List[str]  =					'''cpu'''

        lowercase__				:    str  =					self.get_dummy_components()

        lowercase__				:    List[str]  =					{'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
        lowercase__				:    Any  =					DPMSolverMultistepScheduler(**__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					DPMSolverMultistepInverseScheduler(**__lowerCAmelCase )

        lowercase__				:    List[str]  =					self.pipeline_class(**__lowerCAmelCase )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Union[str, Any]  =					self.get_dummy_inversion_inputs(__lowerCAmelCase )
        lowercase__				:    str  =					pipe.invert(**__lowerCAmelCase ).images
        lowercase__				:    Dict  =					image[0, -1, -3:, -3:]

        self.assertEqual(image.shape	,   (2, 32, 32, 3) )
        lowercase__				:    List[str]  =					np.array(
            [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]	,   )
        lowercase__				:    List[Any]  =					np.abs(image_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(__lowerCAmelCase	,   1E-3 )



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





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  Dict:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       @classmethod
       def _lowerCAmelCase(						cls )  ->  int:
        lowercase__				:    Optional[Any]  =					load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )

        lowercase__				:    Union[str, Any]  =					raw_image.convert('''RGB''' ).resize((768, 768) )

        lowercase__				:    Any  =					raw_image

       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Optional[Any]  =					torch.manual_seed(0 )

        lowercase__				:    Tuple  =					StableDiffusionDiffEditPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2-1'''	,   safety_checker=__lowerCAmelCase	,   torch_dtype=torch.floataa )
        lowercase__				:    List[str]  =					DDIMScheduler.from_config(pipe.scheduler.config )
        lowercase__				:    List[Any]  =					DDIMInverseScheduler.from_config(pipe.scheduler.config )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    str  =					'''a bowl of fruit'''
        lowercase__				:    List[Any]  =					'''a bowl of pears'''

        lowercase__				:    Optional[Any]  =					pipe.generate_mask(
            image=self.raw_image	,   source_prompt=__lowerCAmelCase	,   target_prompt=__lowerCAmelCase	,   generator=__lowerCAmelCase	,   )

        lowercase__				:    Optional[Any]  =					pipe.invert(
            prompt=__lowerCAmelCase	,   image=self.raw_image	,   inpaint_strength=0.7	,   generator=__lowerCAmelCase ).latents

        lowercase__				:    Any  =					pipe(
            prompt=__lowerCAmelCase	,   mask_image=__lowerCAmelCase	,   image_latents=__lowerCAmelCase	,   generator=__lowerCAmelCase	,   negative_prompt=__lowerCAmelCase	,   inpaint_strength=0.7	,   output_type='''numpy'''	,   ).images[0]

        lowercase__				:    Optional[int]  =					(
            np.array(
                load_image(
                    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                    '''/diffedit/pears.png''' ).resize((768, 768) ) )
            / 255
        )
        assert np.abs((expected_image - image).max() ) < 5E-1


       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    Optional[int]  =					torch.manual_seed(0 )

        lowercase__				:    Dict  =					StableDiffusionDiffEditPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2-1'''	,   safety_checker=__lowerCAmelCase	,   torch_dtype=torch.floataa )
        lowercase__				:    Tuple  =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
        lowercase__				:    List[str]  =					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Union[str, Any]  =					'''a bowl of fruit'''
        lowercase__				:    int  =					'''a bowl of pears'''

        lowercase__				:    Tuple  =					pipe.generate_mask(
            image=self.raw_image	,   source_prompt=__lowerCAmelCase	,   target_prompt=__lowerCAmelCase	,   generator=__lowerCAmelCase	,   )

        lowercase__				:    Optional[int]  =					pipe.invert(
            prompt=__lowerCAmelCase	,   image=self.raw_image	,   inpaint_strength=0.7	,   generator=__lowerCAmelCase	,   num_inference_steps=25	,   ).latents

        lowercase__				:    Tuple  =					pipe(
            prompt=__lowerCAmelCase	,   mask_image=__lowerCAmelCase	,   image_latents=__lowerCAmelCase	,   generator=__lowerCAmelCase	,   negative_prompt=__lowerCAmelCase	,   inpaint_strength=0.7	,   num_inference_steps=25	,   output_type='''numpy'''	,   ).images[0]

        lowercase__				:    Union[str, Any]  =					(
            np.array(
                load_image(
                    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                    '''/diffedit/pears.png''' ).resize((768, 768) ) )
            / 255
        )
        assert np.abs((expected_image - image).max() ) < 5E-1


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

