import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()
class 							a_					(		SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   StableDiffusionXLPipeline
 UpperCAmelCase_      					=   TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase_      					=   TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase_      					=   TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase_      					=   TEXT_TO_IMAGE_IMAGE_PARAMS




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







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__	      =   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')     ,       attention_head_dim=(2, 4)     ,       use_linear_projection=lowercase__     ,       addition_embed_type='text_time'     ,       addition_time_embed_dim=8     ,       transformer_layers_per_block=(1, 2)     ,       projection_class_embeddings_input_dim=80     ,       cross_attention_dim=64     ,       )
        lowerCAmelCase__	      =   EulerDiscreteScheduler(
            beta_start=0.00_085     ,       beta_end=0.012     ,       steps_offset=1     ,       beta_schedule='scaled_linear'     ,       timestep_spacing='leading'     ,       )
        torch.manual_seed(0)
        lowerCAmelCase__	      =   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)
        lowerCAmelCase__	      =   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=1_000     ,       hidden_act='gelu'     ,       projection_dim=32     ,       )
        lowerCAmelCase__	      =   CLIPTextModel(lowercase__)
        lowerCAmelCase__	      =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     ,       local_files_only=lowercase__)

        lowerCAmelCase__	      =   CLIPTextModelWithProjection(lowercase__)
        lowerCAmelCase__	      =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     ,       local_files_only=lowercase__)

        lowerCAmelCase__	      =   {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'text_encoder_2': text_encoder_a,
            'tokenizer_2': tokenizer_a,
            # "safety_checker": None,
            # "feature_extractor": None,
        }
        return components




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    str     ,       lowercase__   :    Optional[int]=0):







        '''simple docstring'''



        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__	      =   torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__	      =   torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__	      =   {
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 5.0,
            'output_type': 'numpy',
        }
        return inputs




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__	      =   self.get_dummy_components()
        lowerCAmelCase__	      =   StableDiffusionXLPipeline(**lowercase__)
        lowerCAmelCase__	      =   sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__	      =   self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__	      =   sd_pipe(**lowercase__).images
        lowerCAmelCase__	      =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCAmelCase__	      =   np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747])

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




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.get_dummy_components()
        lowerCAmelCase__	      =   StableDiffusionXLPipeline(**lowercase__)
        lowerCAmelCase__	      =   sd_pipe.to(lowercase__)
        lowerCAmelCase__	      =   sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        # forward without prompt embeds
        lowerCAmelCase__	      =   self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__	      =   3 * ['this is a negative prompt']
        lowerCAmelCase__	      =   negative_prompt
        lowerCAmelCase__	      =   3 * [inputs['prompt']]

        lowerCAmelCase__	      =   sd_pipe(**lowercase__)
        lowerCAmelCase__	      =   output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        lowerCAmelCase__	      =   self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__	      =   3 * ['this is a negative prompt']
        lowerCAmelCase__	      =   3 * [inputs.pop('prompt')]

        (
            (
            lowerCAmelCase__
        )      ,   (
            lowerCAmelCase__
        )      ,   (
            lowerCAmelCase__
        )      ,   (
            lowerCAmelCase__
        )      ,   
        )	      =   sd_pipe.encode_prompt(lowercase__     ,       negative_prompt=lowercase__)

        lowerCAmelCase__	      =   sd_pipe(
            **lowercase__     ,       prompt_embeds=lowercase__     ,       negative_prompt_embeds=lowercase__     ,       pooled_prompt_embeds=lowercase__     ,       negative_pooled_prompt_embeds=lowercase__     ,       )
        lowerCAmelCase__	      =   output.images[0, -3:, -3:, -1]

        # make sure that it's equal
        assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        super().test_inference_batch_single_identical(expected_max_diff=3e-3)






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






 '''simple docstring'''






 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    int     ,       lowercase__   :    str="cpu"     ,       lowercase__   :    List[Any]=torch.floataa     ,       lowercase__   :    Optional[int]=0):







        '''simple docstring'''



        lowerCAmelCase__	      =   torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__	      =   np.random.RandomState(lowercase__).standard_normal((1, 4, 64, 64))
        lowerCAmelCase__	      =   torch.from_numpy(lowercase__).to(device=lowercase__     ,       dtype=lowercase__)
        lowerCAmelCase__	      =   {
            'prompt': 'a photograph of an astronaut riding a horse',
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base')
        pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__	      =   self.get_inputs(lowercase__)
        lowerCAmelCase__	      =   pipe(**lowercase__).images
        lowerCAmelCase__	      =   image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase__	      =   np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506])
        assert np.abs(image_slice - expected_slice).max() < 7e-3

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   ArgumentParser(
           description=(
               'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
           )      )

       # Optional arguments for the launch helper
       parser.add_argument('--num_cores'      ,    type=lowerCAmelCase__      ,    default=1      ,    help='Number of TPU cores to use (1 or 8).'      )

       # positional
       parser.add_argument(
           'training_script'      ,    type=lowerCAmelCase__      ,    help=(
               'The full path to the single TPU training '
               'program/script to be launched in parallel, '
               'followed by all the arguments for the '
               'training script'
           )      ,    )

       # rest from the training program
       parser.add_argument('training_script_args'      ,    nargs=lowerCAmelCase__      )

       return parser.parse_args()






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   parse_args()

       # Import training_script as a module.
       lowerCAmelCase__	      =   Path(args.training_script      )
       sys.path.append(str(script_fpath.parent.resolve()      )      )
       lowerCAmelCase__	      =   script_fpath.stem
       lowerCAmelCase__	      =   importlib.import_module(lowerCAmelCase__      )

       # Patch sys.argv
       lowerCAmelCase__	      =   [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

       xmp.spawn(mod._mp_fn      ,    args=()      ,    nprocs=args.num_cores      )


if __name__ == "__main__":
    main()
