

"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_UpperCamelCase			      =		False







@nightly
@require_torch_gpu
class   __lowercase				(unittest.TestCase			):
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





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



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(A_ )
       pipe.set_progress_bar_config(disable=A_ )

       __lowerCAmelCase :						int = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )

       __lowerCAmelCase :						Optional[int] = torch.manual_seed(0 )
       __lowerCAmelCase :						Optional[Any] = pipe(
           prompt='''first prompt'''				,							image=A_				,							text_to_image_strength=0.75				,							generator=A_				,							guidance_scale=7.5				,							num_inference_steps=2				,							output_type='''numpy'''				,							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A_ )
             __lowerCAmelCase :						Any = VersatileDiffusionDualGuidedPipeline.from_pretrained(A_ )

       pipe.to(A_ )
       pipe.set_progress_bar_config(disable=A_ )

       __lowerCAmelCase :						Any = generator.manual_seed(0 )
       __lowerCAmelCase :						Tuple = pipe(
           prompt='''first prompt'''				,							image=A_				,							text_to_image_strength=0.75				,							generator=A_				,							guidance_scale=7.5				,							num_inference_steps=2				,							output_type='''numpy'''				,							).images

       assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"





 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       pipe.remove_unused_weights()
       pipe.to(A_ )
       pipe.set_progress_bar_config(disable=A_ )

       __lowerCAmelCase :						str = "cyberpunk 2077"
       __lowerCAmelCase :						Any = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
       __lowerCAmelCase :						Optional[int] = torch.manual_seed(0 )
       __lowerCAmelCase :						str = pipe(
           prompt=A_				,							image=A_				,							text_to_image_strength=0.75				,							generator=A_				,							guidance_scale=7.5				,							num_inference_steps=50				,							output_type='''numpy'''				,							).images

       __lowerCAmelCase :						int = image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       __lowerCAmelCase :						Optional[Any] = np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779] )

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





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 		_lowercase			(					):
      __lowerCAmelCase :						Union[str, Any] = 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"]:
            __lowerCAmelCase :						Any = getattr(transformers.models.auto       ,     lowercase__       ,     lowercase__						)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            __lowerCAmelCase :						Optional[int] = [
                x
                for x in dir(lowercase__						)
                if x.endswith('''_MAPPING_NAMES'''						)
                and (x.startswith('''MODEL_'''						) or x.startswith('''TF_MODEL_'''						) or x.startswith('''FLAX_MODEL_'''						))
            ]
            for name in mapping_names:
                  __lowerCAmelCase :						int = getattr(lowercase__       ,     lowercase__						)
                  if mapping is not None:
                        for v in mapping.values():
                              if isinstance(lowercase__       ,     (list, tuple)						):
                                    model_names.update(lowercase__						)
                              elif isinstance(lowercase__       ,     lowercase__						):
                                    model_names.add(lowercase__						)

      return sorted(lowercase__						)







def 		_lowercase			(					):
      # All model names defined in auto mappings
      __lowerCAmelCase :						int = set(get_all_model_names()						)

      with open('''tests/utils/tiny_model_summary.json'''						) as fp:
            __lowerCAmelCase :						Tuple = json.load(lowercase__						)
      __lowerCAmelCase :						Tuple = 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.
      __lowerCAmelCase :						List[Any] = model_names.difference(lowercase__						)
      for model_name in copy.copy(lowercase__						):
            if not model_name.startswith('''TF'''						) and f"""TF{model_name}""" in not_on_hub:
                  tiny_models_names.remove(lowercase__						)
            elif model_name.startswith('''TF'''						) and model_name[2:] in not_on_hub:
                  tiny_models_names.remove(lowercase__						)

      return sorted(lowercase__						)







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      __lowerCAmelCase :						Optional[Any] = get_all_model_names()
      __lowerCAmelCase :						str = hf_api.list_models(
          filter=ModelFilter(
              author='''hf-internal-testing'''       ,     )						)
      __lowerCAmelCase :						List[str] = set()
      for x in models:
            __lowerCAmelCase :						str = x.modelId
            __lowerCAmelCase,							__lowerCAmelCase :						List[Any] = model.split('''/'''						)
            if not model.startswith('''tiny-random-'''						):
                  continue
            __lowerCAmelCase :						Dict = 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(lowercase__						)

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

            __lowerCAmelCase :						Tuple = sorted(content['''tokenizer_classes''']						)
            __lowerCAmelCase :						List[Any] = sorted(content['''processor_classes''']						)
            __lowerCAmelCase :						int = sorted(content['''model_classes''']						)

            __lowerCAmelCase :						Union[str, Any] = content
            with open(os.path.join(lowercase__       ,     '''hub_tiny_model_summary.json'''						)       ,     '''w'''						) as fp:
                  json.dump(lowercase__       ,     lowercase__       ,     ensure_ascii=lowercase__       ,     indent=4						)


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

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

 _UpperCamelCase			      =		"tiny_models"
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		None
 _UpperCamelCase			      =		get_tiny_model_names_from_repo()
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		"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,
 )


