'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class 				_lowercase     (				lowerCAmelCase__  ,							lowerCAmelCase__  ,							lowerCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					StableUnCLIPPipeline
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					TEXT_TO_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE     :       Dict      	=					TEXT_TO_IMAGE_BATCH_PARAMS
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					TEXT_TO_IMAGE_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE     :       int      	=					TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					False
 def a					(      self   :	Dict							)  ->    str:
  __lowerCAmelCase											=						32
  __lowerCAmelCase											=						embedder_hidden_size

  # prior components

  torch.manual_seed(0							)
  __lowerCAmelCase											=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

  torch.manual_seed(0							)
  __lowerCAmelCase											=						CLIPTextModelWithProjection(
      CLIPTextConfig(
          bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=UpperCamelCase__      ,       projection_dim=UpperCamelCase__      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       )							)

  torch.manual_seed(0							)
  __lowerCAmelCase											=						PriorTransformer(
      num_attention_heads=2      ,       attention_head_dim=12      ,       embedding_dim=UpperCamelCase__      ,       num_layers=1      ,       )

  torch.manual_seed(0							)
  __lowerCAmelCase											=						DDPMScheduler(
      variance_type="""fixed_small_log"""      ,       prediction_type="""sample"""      ,       num_train_timesteps=10_00      ,       clip_sample=UpperCamelCase__      ,       clip_sample_range=5.0      ,       beta_schedule="""squaredcos_cap_v2"""      ,       )

  # regular denoising components

  torch.manual_seed(0							)
  __lowerCAmelCase											=						StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase__							)
  __lowerCAmelCase											=						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""							)

  torch.manual_seed(0							)
  __lowerCAmelCase											=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

  torch.manual_seed(0							)
  __lowerCAmelCase											=						CLIPTextModel(
      CLIPTextConfig(
          bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=UpperCamelCase__      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       )							)

  torch.manual_seed(0							)
  __lowerCAmelCase											=						UNetaDConditionModel(
      sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       block_out_channels=(32, 64)      ,       attention_head_dim=(2, 4)      ,       class_embed_type="""projection"""      ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,       cross_attention_dim=UpperCamelCase__      ,       layers_per_block=1      ,       upcast_attention=UpperCamelCase__      ,       use_linear_projection=UpperCamelCase__      ,       )

  torch.manual_seed(0							)
  __lowerCAmelCase											=						DDIMScheduler(
      beta_schedule="""scaled_linear"""      ,       beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       prediction_type="""v_prediction"""      ,       set_alpha_to_one=UpperCamelCase__      ,       steps_offset=1      ,       )

  torch.manual_seed(0							)
  __lowerCAmelCase											=						AutoencoderKL()

  __lowerCAmelCase											=						{
      # prior components
      "prior_tokenizer": prior_tokenizer,
      "prior_text_encoder": prior_text_encoder,
      "prior": prior,
      "prior_scheduler": prior_scheduler,
      # image noising components
      "image_normalizer": image_normalizer,
      "image_noising_scheduler": image_noising_scheduler,
      # regular denoising components
      "tokenizer": tokenizer,
      "text_encoder": text_encoder,
      "unet": unet,
      "scheduler": scheduler,
      "vae": vae,
  }

  return components
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=0							)  ->    str:
  if str(UpperCamelCase__							).startswith("""mps"""							):
   __lowerCAmelCase											=						torch.manual_seed(UpperCamelCase__							)
  else:
   __lowerCAmelCase											=						torch.Generator(device=UpperCamelCase__							).manual_seed(UpperCamelCase__							)
  __lowerCAmelCase											=						{
      "prompt": "A painting of a squirrel eating a burger",
      "generator": generator,
      "num_inference_steps": 2,
      "prior_num_inference_steps": 2,
      "output_type": "numpy",
  }
  return inputs
 def a					(      self   :	Union[str, Any]							)  ->    int:
  __lowerCAmelCase											=						torch_device == "cpu"

  self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase__							)





 def a					(      self   :	str							)  ->    Optional[Any]:
  __lowerCAmelCase											=						torch_device in ["cpu", "mps"]

  self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase__							)







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


 '''simple docstring'''






 def a					(      self   :	int							)  ->    Dict:
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a					(      self   :	Optional[int]							)  ->    str:
  __lowerCAmelCase											=						load_numpy(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""							)

  __lowerCAmelCase											=						StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      ,       torch_dtype=torch.floataa							)
  pipe.to(UpperCamelCase__							)
  pipe.set_progress_bar_config(disable=UpperCamelCase__							)
  # stable unclip will oom when integration tests are run on a V100,
  # so turn on memory savings
  pipe.enable_attention_slicing()
  pipe.enable_sequential_cpu_offload()

  __lowerCAmelCase											=						torch.Generator(device="""cpu"""							).manual_seed(0							)
  __lowerCAmelCase											=						pipe("""anime turle"""      ,       generator=UpperCamelCase__      ,       output_type="""np"""							)

  __lowerCAmelCase											=						output.images[0]

  assert image.shape == (7_68, 7_68, 3)

  assert_mean_pixel_difference(UpperCamelCase__      ,       UpperCamelCase__							)





 def a					(      self   :	Optional[int]							)  ->    List[str]:
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()

  __lowerCAmelCase											=						StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      ,       torch_dtype=torch.floataa							)
  __lowerCAmelCase											=						pipe.to(UpperCamelCase__							)
  pipe.set_progress_bar_config(disable=UpperCamelCase__							)
  pipe.enable_attention_slicing()
  pipe.enable_sequential_cpu_offload()

  __lowerCAmelCase											=						pipe(
      """anime turtle"""      ,       prior_num_inference_steps=2      ,       num_inference_steps=2      ,       output_type="""np"""      ,       )

  __lowerCAmelCase											=						torch.cuda.max_memory_allocated()
  # make sure that less than 7 GB is allocated
  assert mem_bytes < 7 * 10**9












import argparse
import os
import re


SCREAMING_SNAKE_CASE__       :      List[Any]  =   'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__       :      Tuple  =   re.compile(r'\s*\(\s*"(\S[^"]+)"')

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False    )	-> int:
       with open(_SCREAMING_SNAKE_CASE ,"r" ,encoding="utf-8"    ) as f:
              lowerCamelCase   :      List[Any]			 =					f.read()

       lowerCamelCase   :      str			 =					content.split("\n"    )
       lowerCamelCase   :      int			 =					[]
       lowerCamelCase   :      List[Any]			 =					0
       while line_idx < len(_SCREAMING_SNAKE_CASE    ):
              if _re_intro_mapping.search(lines[line_idx]    ) is not None:
                     lowerCamelCase   :      Optional[int]			 =					len(re.search(r"^(\s*)\S" ,lines[line_idx]    ).groups()[0]    ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("    ):
                            new_lines.append(lines[line_idx]    )
                            line_idx += 1

                     lowerCamelCase   :      Optional[int]			 =					[]
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   lowerCamelCase   :      List[str]			 =					line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"    ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]    )    )
                            else:
                                   blocks.append(lines[line_idx]    )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     lowerCamelCase   :      Union[str, Any]			 =					sorted(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE    : _re_identifier.search(_SCREAMING_SNAKE_CASE    ).groups()[0]    )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]    )
                     line_idx += 1

       if overwrite:
              with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
                     f.write("\n".join(_SCREAMING_SNAKE_CASE    )    )
       elif "\n".join(_SCREAMING_SNAKE_CASE    ) != content:
              return True

def      A      (      _SCREAMING_SNAKE_CASE = False    )	-> List[str]:
       lowerCamelCase   :      str			 =					[os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) for f in os.listdir(_SCREAMING_SNAKE_CASE    ) if f.endswith(".py"    )]
       lowerCamelCase   :      Union[str, Any]			 =					[sort_auto_mapping(_SCREAMING_SNAKE_CASE ,overwrite=_SCREAMING_SNAKE_CASE    ) for fname in fnames]

       if not overwrite and any(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      str			 =					[f for f, d in zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) if d]
              raise ValueError(
                  f'''The following files have auto mappings that need sorting: {", ".join(_SCREAMING_SNAKE_CASE    )}. Run `make style` to fix'''
                  " this."    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[str]  =   argparse.ArgumentParser()
       parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()

       sort_all_auto_mappings(not args.check_only)






