


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 			__lowerCAmelCase       ()->    Optional[Any]:




      """simple docstring"""

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

      return sorted(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       ()->    Tuple:




      """simple docstring"""

      snake_case_			= set(get_all_model_names()   )

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

      return sorted(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Union[str, Any]:




      """simple docstring"""

      snake_case_			= COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      snake_case_			= get_all_model_names()
      snake_case_			= hf_api.list_models(
          filter=ModelFilter(
              author='''hf-internal-testing'''   ,   )   )
      snake_case_			= set()
      for x in models:
            snake_case_			= x.modelId
            snake_case_			,       snake_case_			= model.split('''/'''   )
            if not model.startswith('''tiny-random-'''   ):
                  continue
            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(SCREAMING_SNAKE_CASE   )

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

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

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



import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )