import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=set()
     # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
     for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
          SCREAMING_SNAKE_CASE										=getattr(transformers.models.auto,   lowerCAmelCase_,   lowerCAmelCase_      )
          if module is None:
               continue
          # all mappings in a single auto modeling file
          SCREAMING_SNAKE_CASE										=[
              x
              for x in dir(lowerCAmelCase_      )
              if x.endswith('_MAPPING_NAMES'      )
              and (x.startswith('MODEL_'      ) or x.startswith('TF_MODEL_'      ) or x.startswith('FLAX_MODEL_'      ))
          ]
          for name in mapping_names:
               SCREAMING_SNAKE_CASE										=getattr(lowerCAmelCase_,   lowerCAmelCase_      )
               if mapping is not None:
                    for v in mapping.values():
                         if isinstance(lowerCAmelCase_,   (list, tuple)      ):
                              model_names.update(lowerCAmelCase_      )
                         elif isinstance(lowerCAmelCase_,   lowerCAmelCase_      ):
                              model_names.add(lowerCAmelCase_      )

     return sorted(lowerCAmelCase_      )







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=set(get_all_model_names()      )

     with open('tests/utils/tiny_model_summary.json'      ) as fp:
          SCREAMING_SNAKE_CASE										=json.load(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=set()
     for model_base_name in tiny_model_info:
          tiny_models_names.update(tiny_model_info[model_base_name]['model_classes']      )

     # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
     SCREAMING_SNAKE_CASE										=model_names.difference(lowerCAmelCase_      )
     for model_name in copy.copy(lowerCAmelCase_      ):
          if not model_name.startswith('TF'      ) and F'TF{model_name}' in not_on_hub:
               tiny_models_names.remove(lowerCAmelCase_      )
          elif model_name.startswith('TF'      ) and model_name[2:] in not_on_hub:
               tiny_models_names.remove(lowerCAmelCase_      )

     return sorted(lowerCAmelCase_      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=COMPOSITE_MODELS.values()

     # All tiny model base names on Hub
     SCREAMING_SNAKE_CASE										=get_all_model_names()
     SCREAMING_SNAKE_CASE										=hf_api.list_models(
         filter=ModelFilter(
             author='hf-internal-testing',   )      )
     SCREAMING_SNAKE_CASE										=set()
     for x in models:
          SCREAMING_SNAKE_CASE										=x.modelId
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=model.split('/'      )
          if not model.startswith('tiny-random-'      ):
               continue
          SCREAMING_SNAKE_CASE										=model.replace('tiny-random-',   ''      )
          if not model[0].isupper():
               continue
          if model not in model_names and model not in special_models:
               continue
          _models.add(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=sorted(_models      )
     # All tiny model names on Hub
     SCREAMING_SNAKE_CASE										={}
     for model in models:
          SCREAMING_SNAKE_CASE										=F'hf-internal-testing/tiny-random-{model}'
          SCREAMING_SNAKE_CASE										=model.split('-'      )[0]
          try:
               SCREAMING_SNAKE_CASE										=hf_api.repo_info(lowerCAmelCase_      )
               SCREAMING_SNAKE_CASE										={
                   'tokenizer_classes': set(),
                   'processor_classes': set(),
                   'model_classes': set(),
                   'sha': repo_info.sha,
               }
          except Exception:
               continue
          try:
               time.sleep(1      )
               SCREAMING_SNAKE_CASE										=AutoTokenizer.from_pretrained(lowerCAmelCase_      )
               content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__      )
          except Exception:
               pass
          try:
               time.sleep(1      )
               SCREAMING_SNAKE_CASE										=AutoTokenizer.from_pretrained(lowerCAmelCase_,   use_fast=lowerCAmelCase_      )
               content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__      )
          except Exception:
               pass
          try:
               time.sleep(1      )
               SCREAMING_SNAKE_CASE										=AutoImageProcessor.from_pretrained(lowerCAmelCase_      )
               content["processor_classes"].add(img_p.__class__.__name__      )
          except Exception:
               pass
          try:
               time.sleep(1      )
               SCREAMING_SNAKE_CASE										=AutoFeatureExtractor.from_pretrained(lowerCAmelCase_      )
               if not isinstance(lowerCAmelCase_,   lowerCAmelCase_      ):
                    content["processor_classes"].add(feat_p.__class__.__name__      )
          except Exception:
               pass
          try:
               time.sleep(1      )
               SCREAMING_SNAKE_CASE										=getattr(lowerCAmelCase_,   lowerCAmelCase_      )
               SCREAMING_SNAKE_CASE										=model_class.from_pretrained(lowerCAmelCase_      )
               content["model_classes"].add(m.__class__.__name__      )
          except Exception:
               pass
          try:
               time.sleep(1      )
               SCREAMING_SNAKE_CASE										=getattr(lowerCAmelCase_,   F'TF{model}'      )
               SCREAMING_SNAKE_CASE										=model_class.from_pretrained(lowerCAmelCase_      )
               content["model_classes"].add(m.__class__.__name__      )
          except Exception:
               pass

          SCREAMING_SNAKE_CASE										=sorted(content['tokenizer_classes']      )
          SCREAMING_SNAKE_CASE										=sorted(content['processor_classes']      )
          SCREAMING_SNAKE_CASE										=sorted(content['model_classes']      )

          SCREAMING_SNAKE_CASE										=content
          with open(os.path.join(lowerCAmelCase_,   'hub_tiny_model_summary.json'      ),   'w'      ) as fp:
               json.dump(lowerCAmelCase_,   lowerCAmelCase_,   ensure_ascii=lowerCAmelCase_,   indent=4      )


if __name__ == "__main__":
 _lowerCamelCase								=argparse.ArgumentParser()
 parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
 _lowerCamelCase								=parser.parse_args()

 # This has to be `spawn` to avoid hanging forever!
 multiprocessing.set_start_method("spawn")

 _lowerCamelCase								="tiny_models"
 _lowerCamelCase								=True
 _lowerCamelCase								=None
 _lowerCamelCase								=get_tiny_model_names_from_repo()
 _lowerCamelCase								=True
 _lowerCamelCase								=True
 _lowerCamelCase								="hf-internal-testing"

 create_tiny_models(
     output_path,
     all,
     model_types,
     models_to_skip,
     no_check,
     upload,
     organization,
     token=os.environ.get("TOKEN", None),
     num_workers=args.num_workers,
 )




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               'image': image,
               'mask_image': mask,
               'generator': generator,
               'num_inference_steps': 5,
               'eta': 0.0,
               'jump_length': 2,
               'jump_n_sample': 2,
               'output_type': 'numpy',
           }
           return inputs
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

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

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







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



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

