





'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			StableUnCLIPImgaImgPipeline
     lowerCAmelCase						     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS
     lowerCAmelCase						     =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase						     =			frozenset(
         []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     lowerCAmelCase						     =			frozenset([]				)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			32
          __A		:					List[str]						=			embedder_hidden_size

          # image encoding components

          __A		:					List[Any]						=			CLIPImageProcessor(crop_size=32		,      size=32)

          torch.manual_seed(0)
          __A		:					Optional[int]						=			CLIPVisionModelWithProjection(
              CLIPVisionConfig(
                  hidden_size=_UpperCAmelCase		,      projection_dim=_UpperCAmelCase		,      num_hidden_layers=5		,      num_attention_heads=4		,      image_size=32		,      intermediate_size=37		,      patch_size=1		,      ))

          # regular denoising components

          torch.manual_seed(0)
          __A		:					Union[str, Any]						=			StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase)
          __A		:					Dict						=			DDPMScheduler(beta_schedule='squaredcos_cap_v2')

          torch.manual_seed(0)
          __A		:					List[Any]						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

          torch.manual_seed(0)
          __A		:					Union[str, Any]						=			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=1000		,      ))

          torch.manual_seed(0)
          __A		:					Dict						=			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)
          __A		:					Union[str, Any]						=			DDIMScheduler(
              beta_schedule='scaled_linear'		,      beta_start=0.00085		,      beta_end=0.012		,      prediction_type='v_prediction'		,      set_alpha_to_one=_UpperCAmelCase		,      steps_offset=1		,      )

          torch.manual_seed(0)
          __A		:					Any						=			AutoencoderKL()

          __A		:					List[str]						=			{
              # image encoding components
              'feature_extractor': feature_extractor,
              'image_encoder': image_encoder.eval(),
              # image noising components
              'image_normalizer': image_normalizer.eval(),
              'image_noising_scheduler': image_noising_scheduler,
              # regular denoising components
              'tokenizer': tokenizer,
              'text_encoder': text_encoder.eval(),
              'unet': unet.eval(),
              'scheduler': scheduler,
              'vae': vae.eval(),
          }

          return components


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=0		,      _UpperCAmelCase=True):

          '''simple docstring'''






          if str(_UpperCAmelCase).startswith('mps'):
               __A		:					Tuple						=			torch.manual_seed(_UpperCAmelCase)
          else:
               __A		:					Any						=			torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)

          __A		:					str						=			floats_tensor((1, 3, 32, 32)		,      rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)

          if pil_image:
               __A		:					List[Any]						=			input_image * 0.5 + 0.5
               __A		:					Dict						=			input_image.clamp(0		,      1)
               __A		:					Optional[int]						=			input_image.cpu().permute(0		,      2		,      3		,      1).float().numpy()
               __A		:					Tuple						=			DiffusionPipeline.numpy_to_pil(_UpperCAmelCase)[0]

          return {
              "prompt": "An anime racoon running a marathon",
              "image": input_image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "np",
          }


     @skip_mps
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			'cpu'  # ensure determinism for the device-dependent torch.Generator
          __A		:					Union[str, Any]						=			self.get_dummy_components()
          __A		:					Dict						=			StableUnCLIPImgaImgPipeline(**_UpperCAmelCase)
          __A		:					Dict						=			sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A		:					Dict						=			self.get_dummy_inputs(_UpperCAmelCase)
          inputs.update({'image_embeds': None})
          __A		:					Any						=			sd_pipe(**_UpperCAmelCase).images
          __A		:					Optional[Any]						=			image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __A		:					List[Any]						=			np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			torch_device in ['cpu', 'mps']

          self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			torch_device in ['cpu', 'mps']

          self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase)


     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()		,      reason='XFormers attention is only available with CUDA and `xformers` installed'		,      )
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCAmelCase)




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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png')

          __A		:					Optional[int]						=			load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy')

          __A		:					Any						=			StableUnCLIPImgaImgPipeline.from_pretrained(
              'fusing/stable-unclip-2-1-l-img2img'		,      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()

          __A		:					Optional[Any]						=			torch.Generator(device='cpu').manual_seed(0)
          __A		:					Dict						=			pipe(_UpperCAmelCase		,      'anime turle'		,      generator=_UpperCAmelCase		,      output_type='np')

          __A		:					Dict						=			output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(_UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png')

          __A		:					List[Any]						=			load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy')

          __A		:					Optional[Any]						=			StableUnCLIPImgaImgPipeline.from_pretrained(
              'fusing/stable-unclip-2-1-h-img2img'		,      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()

          __A		:					str						=			torch.Generator(device='cpu').manual_seed(0)
          __A		:					Any						=			pipe(_UpperCAmelCase		,      'anime turle'		,      generator=_UpperCAmelCase		,      output_type='np')

          __A		:					List[str]						=			output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(_UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png')

          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          __A		:					Optional[Any]						=			StableUnCLIPImgaImgPipeline.from_pretrained(
              'fusing/stable-unclip-2-1-h-img2img'		,      torch_dtype=torch.floataa)
          __A		:					int						=			pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()

          __A		:					Optional[int]						=			pipe(
              _UpperCAmelCase		,      'anime turtle'		,      num_inference_steps=2		,      output_type='np'		,      )

          __A		:					Optional[int]						=			torch.cuda.max_memory_allocated()
          # make sure that less than 7 GB is allocated
          assert mem_bytes < 7 * 10**9






'''simple docstring'''




import argparse
import os
import re


lowercase__       : Optional[int]         =       '''src/diffusers'''

# Pattern that looks at the indentation in a line.
lowercase__       : Dict         =       re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__       : List[str]         =       re.compile(r'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase__       : Tuple         =       re.compile(r'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase__       : str         =       re.compile(r'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase__       : str         =       re.compile(r'''\[([^\]]+)\]''')






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Tuple:
     __A		:					List[Any]						=			_re_indent.search(__snake_case     )
     return "" if search is None else search.groups()[0]






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]					,				__snake_case    :					str=""					,				__snake_case    :					Any=None					,				__snake_case    :					List[Any]=None     )	->	Optional[int]:
     __A		:					Tuple						=			0
     __A		:					Optional[int]						=			code.split('\n'     )
     if start_prompt is not None:
          while not lines[index].startswith(__snake_case     ):
               index += 1
          __A		:					Optional[int]						=			['\n'.join(lines[:index]     )]
     else:
          __A		:					Any						=			[]

     # We split into blocks until we get to the `end_prompt` (or the end of the block).
     __A		:					Tuple						=			[lines[index]]
     index += 1
     while index < len(__snake_case     ) and (end_prompt is None or not lines[index].startswith(__snake_case     )):
          if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
               if len(__snake_case     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + ' '     ):
                    current_block.append(lines[index]     )
                    blocks.append('\n'.join(__snake_case     )     )
                    if index < len(__snake_case     ) - 1:
                         __A		:					Union[str, Any]						=			[lines[index + 1]]
                         index += 1
                    else:
                         __A		:					Union[str, Any]						=			[]
               else:
                    blocks.append('\n'.join(__snake_case     )     )
                    __A		:					Optional[Any]						=			[lines[index]]
          else:
               current_block.append(lines[index]     )
          index += 1

     # Adds current block if it's nonempty.
     if len(__snake_case     ) > 0:
          blocks.append('\n'.join(__snake_case     )     )

     # Add final block after end_prompt if provided.
     if end_prompt is not None and index < len(__snake_case     ):
          blocks.append('\n'.join(lines[index:]     )     )

     return blocks






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	int:

     def _inner(__snake_case    :					List[Any]     ):
          return key(__snake_case     ).lower().replace('_'					,				''     )

     return _inner






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any=None     )	->	List[Any]:

     # If no key is provided, we use a noop.
     def noop(__snake_case    :					List[Any]     ):
          return x

     if key is None:
          __A		:					Optional[Any]						=			noop
     # Constants are all uppercase, they go first.
     __A		:					str						=			[obj for obj in objects if key(__snake_case     ).isupper()]
     # Classes are not all uppercase but start with a capital, they go second.
     __A		:					List[str]						=			[obj for obj in objects if key(__snake_case     )[0].isupper() and not key(__snake_case     ).isupper()]
     # Functions begin with a lowercase, they go last.
     __A		:					str						=			[obj for obj in objects if not key(__snake_case     )[0].isupper()]

     __A		:					Tuple						=			ignore_underscore(__snake_case     )
     return sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]     )	->	Tuple:

     # This inner function sort imports between [ ].
     def _replace(__snake_case    :					Tuple     ):
          __A		:					List[str]						=			match.groups()[0]
          if "," not in imports:
               return f'[{imports}]'
          __A		:					int						=			[part.strip().replace('"'					,				''     ) for part in imports.split(','     )]
          # We will have a final empty element if the line finished with a comma.
          if len(keys[-1]     ) == 0:
               __A		:					Dict						=			keys[:-1]
          return "[" + ", ".join([f'"{k}"' for k in sort_objects(__snake_case     )]     ) + "]"

     __A		:					List[Any]						=			import_statement.split('\n'     )
     if len(__snake_case     ) > 3:
          # Here we have to sort internal imports that are on several lines (one per name):
          # key: [
          #     "object1",
          #     "object2",
          #     ...
          # ]

          # We may have to ignore one or two lines on each side.
          __A		:					Optional[int]						=			2 if lines[1].strip() == '[' else 1
          __A		:					Any						=			[(i, _re_strip_line.search(__snake_case     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
          __A		:					Optional[int]						=			sort_objects(__snake_case					,				key=lambda __snake_case     : x[1]     )
          __A		:					Any						=			[lines[x[0] + idx] for x in sorted_indices]
          return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
     elif len(__snake_case     ) == 3:
          # Here we have to sort internal imports that are on one separate line:
          # key: [
          #     "object1", "object2", ...
          # ]
          if _re_bracket_content.search(lines[1]     ) is not None:
               __A		:					Union[str, Any]						=			_re_bracket_content.sub(_replace					,				lines[1]     )
          else:
               __A		:					Dict						=			[part.strip().replace('"'					,				''     ) for part in lines[1].split(','     )]
               # We will have a final empty element if the line finished with a comma.
               if len(keys[-1]     ) == 0:
                    __A		:					Tuple						=			keys[:-1]
               __A		:					List[Any]						=			get_indent(lines[1]     ) + ', '.join([f'"{k}"' for k in sort_objects(__snake_case     )]     )
          return "\n".join(__snake_case     )
     else:
          # Finally we have to deal with imports fitting on one line
          __A		:					Optional[Any]						=			_re_bracket_content.sub(_replace					,				__snake_case     )
          return import_statement






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					List[Any]=True     )	->	Optional[Any]:
     with open(__snake_case					,				'r'     ) as f:
          __A		:					Dict						=			f.read()

     if "_import_structure" not in code:
          return

     # Blocks of indent level 0
     __A		:					str						=			split_code_in_indented_blocks(
         __snake_case					,				start_prompt='_import_structure = {'					,				end_prompt='if TYPE_CHECKING:'     )

     # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
     for block_idx in range(1					,				len(__snake_case     ) - 1     ):
          # Check if the block contains some `_import_structure`s thingy to sort.
          __A		:					Tuple						=			main_blocks[block_idx]
          __A		:					int						=			block.split('\n'     )

          # Get to the start of the imports.
          __A		:					Tuple						=			0
          while line_idx < len(__snake_case     ) and "_import_structure" not in block_lines[line_idx]:
               # Skip dummy import blocks
               if "import dummy" in block_lines[line_idx]:
                    __A		:					Optional[int]						=			len(__snake_case     )
               else:
                    line_idx += 1
          if line_idx >= len(__snake_case     ):
               continue

          # Ignore beginning and last line: they don't contain anything.
          __A		:					Dict						=			'\n'.join(block_lines[line_idx:-1]     )
          __A		:					int						=			get_indent(block_lines[1]     )
          # Slit the internal block into blocks of indent level 1.
          __A		:					Optional[int]						=			split_code_in_indented_blocks(__snake_case					,				indent_level=__snake_case     )
          # We have two categories of import key: list or _import_structure[key].append/extend
          __A		:					Any						=			_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
          # Grab the keys, but there is a trap: some lines are empty or just comments.
          __A		:					Dict						=			[(pattern.search(__snake_case     ).groups()[0] if pattern.search(__snake_case     ) is not None else None) for b in internal_blocks]
          # We only sort the lines with a key.
          __A		:					Optional[Any]						=			[(i, key) for i, key in enumerate(__snake_case     ) if key is not None]
          __A		:					Tuple						=			[x[0] for x in sorted(__snake_case					,				key=lambda __snake_case     : x[1]     )]

          # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
          __A		:					str						=			0
          __A		:					Any						=			[]
          for i in range(len(__snake_case     )     ):
               if keys[i] is None:
                    reordered_blocks.append(internal_blocks[i]     )
               else:
                    __A		:					str						=			sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
                    reordered_blocks.append(__snake_case     )
                    count += 1

        # And we put our main block back together with its first and last line.
          __A		:					int						=			'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]     )

     if code != "\n".join(__snake_case     ):
          if check_only:
               return True
          else:
               print(f'Overwriting {file}.'     )
               with open(__snake_case					,				'w'     ) as f:
                    f.write('\n'.join(__snake_case     )     )






def 			_lowerCAmelCase     (    __snake_case    :					int=True     )	->	Optional[Any]:
     __A		:					Tuple						=			[]
     for root, _, files in os.walk(__snake_case     ):
          if "__init__.py" in files:
               __A		:					List[Any]						=			sort_imports(os.path.join(__snake_case					,				'__init__.py'     )					,				check_only=__snake_case     )
               if result:
                    __A		:					Dict						=			[os.path.join(__snake_case					,				'__init__.py'     )]
     if len(__snake_case     ) > 0:
          raise ValueError(f'Would overwrite {len(__snake_case     )} files, run `make style`.'     )


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

  sort_imports_in_all_inits(check_only=args.check_only)