


"""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__    (		_UpperCamelCase       ,   _UpperCamelCase       ,   _UpperCamelCase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =StableUnCLIPImgaImgPipeline
       a  :    Optional[Any]       =TEXT_GUIDED_IMAGE_VARIATION_PARAMS
       a  :    Optional[Any]       =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       a  :    Union[str, Any]       =frozenset(
           []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       a  :    Union[str, Any]       =frozenset([]    )

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							32
         lowerCAmelCase					:					Optional[Any]								=							embedder_hidden_size

         # image encoding components

         lowerCAmelCase					:					Optional[Any]								=							CLIPImageProcessor(crop_size=32		,					size=32						)

         torch.manual_seed(0						)
         lowerCAmelCase					:					Any								=							CLIPVisionModelWithProjection(
             CLIPVisionConfig(
                 hidden_size=_SCREAMING_SNAKE_CASE		,					projection_dim=_SCREAMING_SNAKE_CASE		,					num_hidden_layers=5		,					num_attention_heads=4		,					image_size=32		,					intermediate_size=37		,					patch_size=1		,					)						)

         # regular denoising components

         torch.manual_seed(0						)
         lowerCAmelCase					:					Any								=							StableUnCLIPImageNormalizer(embedding_dim=_SCREAMING_SNAKE_CASE						)
         lowerCAmelCase					:					Union[str, Any]								=							DDPMScheduler(beta_schedule="squaredcos_cap_v2"						)

         torch.manual_seed(0						)
         lowerCAmelCase					:					Optional[int]								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

         torch.manual_seed(0						)
         lowerCAmelCase					:					Tuple								=							CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0		,					eos_token_id=2		,					hidden_size=_SCREAMING_SNAKE_CASE		,					projection_dim=32		,					intermediate_size=37		,					layer_norm_eps=1e-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1_000		,					)						)

         torch.manual_seed(0						)
         lowerCAmelCase					:					List[Any]								=							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=_SCREAMING_SNAKE_CASE		,					layers_per_block=1		,					upcast_attention=_SCREAMING_SNAKE_CASE		,					use_linear_projection=_SCREAMING_SNAKE_CASE		,					)

         torch.manual_seed(0						)
         lowerCAmelCase					:					List[str]								=							DDIMScheduler(
             beta_schedule="scaled_linear"		,					beta_start=0.00085		,					beta_end=0.012		,					prediction_type="v_prediction"		,					set_alpha_to_one=_SCREAMING_SNAKE_CASE		,					steps_offset=1		,					)

         torch.manual_seed(0						)
         lowerCAmelCase					:					Optional[Any]								=							AutoencoderKL()

         lowerCAmelCase					:					Optional[int]								=							{
             # 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 	lowercase__							(						self		,					snake_case__		,					snake_case__=0		,					snake_case__=True						):






         """simple docstring"""





         if str(_SCREAMING_SNAKE_CASE						).startswith("mps"						):
           lowerCAmelCase					:					List[Any]								=							torch.manual_seed(_SCREAMING_SNAKE_CASE						)
         else:
           lowerCAmelCase					:					str								=							torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)

         lowerCAmelCase					:					Any								=							floats_tensor((1, 3, 32, 32)		,					rng=random.Random(_SCREAMING_SNAKE_CASE						)						).to(_SCREAMING_SNAKE_CASE						)

         if pil_image:
           lowerCAmelCase					:					Any								=							input_image * 0.5 + 0.5
           lowerCAmelCase					:					Any								=							input_image.clamp(0		,					1						)
           lowerCAmelCase					:					Dict								=							input_image.cpu().permute(0		,					2		,					3		,					1						).float().numpy()
           lowerCAmelCase					:					List[str]								=							DiffusionPipeline.numpy_to_pil(_SCREAMING_SNAKE_CASE						)[0]

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

       @skip_mps
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							"cpu"  # ensure determinism for the device-dependent torch.Generator
         lowerCAmelCase					:					int								=							self.get_dummy_components()
         lowerCAmelCase					:					Tuple								=							StableUnCLIPImgaImgPipeline(**_SCREAMING_SNAKE_CASE						)
         lowerCAmelCase					:					Dict								=							sd_pipe.to(_SCREAMING_SNAKE_CASE						)
         sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

         lowerCAmelCase					:					Any								=							self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
         inputs.update({"image_embeds": None}						)
         lowerCAmelCase					:					Optional[Any]								=							sd_pipe(**_SCREAMING_SNAKE_CASE						).images
         lowerCAmelCase					:					List[Any]								=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         lowerCAmelCase					:					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 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							torch_device in ["cpu", "mps"]

         self._test_attention_slicing_forward_pass(test_max_difference=_SCREAMING_SNAKE_CASE						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							torch_device in ["cpu", "mps"]

         self._test_inference_batch_single_identical(test_max_difference=_SCREAMING_SNAKE_CASE						)







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






         """simple docstring"""





         self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_SCREAMING_SNAKE_CASE						)


@slow
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"						)

         lowerCAmelCase					:					Any								=							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"						)

         lowerCAmelCase					:					Union[str, Any]								=							StableUnCLIPImgaImgPipeline.from_pretrained(
             "fusing/stable-unclip-2-1-l-img2img"		,					torch_dtype=torch.floataa						)
         pipe.to(_SCREAMING_SNAKE_CASE						)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
         # 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					:					List[str]								=							torch.Generator(device="cpu"						).manual_seed(0						)
         lowerCAmelCase					:					Dict								=							pipe(_SCREAMING_SNAKE_CASE		,					"anime turle"		,					generator=_SCREAMING_SNAKE_CASE		,					output_type="np"						)

         lowerCAmelCase					:					Tuple								=							output.images[0]

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

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"						)

         lowerCAmelCase					:					Tuple								=							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"						)

         lowerCAmelCase					:					List[Any]								=							StableUnCLIPImgaImgPipeline.from_pretrained(
             "fusing/stable-unclip-2-1-h-img2img"		,					torch_dtype=torch.floataa						)
         pipe.to(_SCREAMING_SNAKE_CASE						)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
         # 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					:					Optional[Any]								=							torch.Generator(device="cpu"						).manual_seed(0						)
         lowerCAmelCase					:					Tuple								=							pipe(_SCREAMING_SNAKE_CASE		,					"anime turle"		,					generator=_SCREAMING_SNAKE_CASE		,					output_type="np"						)

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

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

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							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()

         lowerCAmelCase					:					Tuple								=							StableUnCLIPImgaImgPipeline.from_pretrained(
             "fusing/stable-unclip-2-1-h-img2img"		,					torch_dtype=torch.floataa						)
         lowerCAmelCase					:					int								=							pipe.to(_SCREAMING_SNAKE_CASE						)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase					:					List[Any]								=							pipe(
             _SCREAMING_SNAKE_CASE		,					"anime turtle"		,					num_inference_steps=2		,					output_type="np"		,					)

         lowerCAmelCase					:					Dict								=							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 glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase__				    =       '''src/diffusers'''
lowerCAmelCase__				    =       '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase__				    =       importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCAmelCase__				    =       spec.loader.load_module()
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  return line.startswith(SCREAMING_SNAKE_CASE    ) or len(SCREAMING_SNAKE_CASE    ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"    ,				SCREAMING_SNAKE_CASE    ) is not None
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Dict								=							object_name.split("."    )
  lowerCAmelCase					:					Optional[int]								=							0

  # First let's find the module where our object lives.
  lowerCAmelCase					:					Any								=							parts[i]
  while i < len(SCREAMING_SNAKE_CASE    ) and not os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE    ,				f"""{module}.py"""    )    ):
    i += 1
    if i < len(SCREAMING_SNAKE_CASE    ):
      lowerCAmelCase					:					Optional[Any]								=							os.path.join(SCREAMING_SNAKE_CASE    ,				parts[i]    )
  if i >= len(SCREAMING_SNAKE_CASE    ):
    raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""    )

  with open(os.path.join(SCREAMING_SNAKE_CASE    ,				f"""{module}.py"""    )    ,				"r"    ,				encoding="utf-8"    ,				newline="\n"    ) as f:
    lowerCAmelCase					:					List[Any]								=							f.readlines()

  # Now let's find the class / func in the code!
  lowerCAmelCase					:					List[str]								=							""
  lowerCAmelCase					:					int								=							0
  for name in parts[i + 1 :]:
    while (
        line_index < len(SCREAMING_SNAKE_CASE    ) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)"""    ,				lines[line_index]    ) is None
    ):
      line_index += 1
    indent += "    "
    line_index += 1

  if line_index >= len(SCREAMING_SNAKE_CASE    ):
    raise ValueError(f""" {object_name} does not match any function or class in {module}."""    )

  # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
  lowerCAmelCase					:					List[str]								=							line_index
  while line_index < len(SCREAMING_SNAKE_CASE    ) and _should_continue(lines[line_index]    ,				SCREAMING_SNAKE_CASE    ):
    line_index += 1
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]    ) <= 1:
    line_index -= 1

  lowerCAmelCase					:					List[Any]								=							lines[start_index:line_index]
  return "".join(SCREAMING_SNAKE_CASE    )


lowerCAmelCase__				    =       re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
lowerCAmelCase__				    =       re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
lowerCAmelCase__				    =       re.compile(r'''<FILL\s+[^>]*>''')
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  lowerCAmelCase					:					int								=							code.split("\n"    )
  lowerCAmelCase					:					List[str]								=							0
  while idx < len(SCREAMING_SNAKE_CASE    ) and len(lines[idx]    ) == 0:
    idx += 1
  if idx < len(SCREAMING_SNAKE_CASE    ):
    return re.search(r"^(\s*)\S"    ,				lines[idx]    ).groups()[0]
  return ""
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							len(get_indent(SCREAMING_SNAKE_CASE    )    ) > 0
  if has_indent:
    lowerCAmelCase					:					Tuple								=							f"""class Bla:\n{code}"""
  lowerCAmelCase					:					Optional[Any]								=							black.Mode(target_versions={black.TargetVersion.PYaa}    ,				line_length=1_1_9    ,				preview=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Dict								=							black.format_str(SCREAMING_SNAKE_CASE    ,				mode=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase						,  lowerCAmelCase					:					List[Any]								=							style_docstrings_in_code(SCREAMING_SNAKE_CASE    )
  return result[len("class Bla:\n"    ) :] if has_indent else result
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   int=False    ):


  '''simple docstring'''







  with open(SCREAMING_SNAKE_CASE    ,				"r"    ,				encoding="utf-8"    ,				newline="\n"    ) as f:
    lowerCAmelCase					:					int								=							f.readlines()
  lowerCAmelCase					:					List[str]								=							[]
  lowerCAmelCase					:					str								=							0
  # Not a for loop cause `lines` is going to change (if `overwrite=True`).
  while line_index < len(SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					List[Any]								=							_re_copy_warning.search(lines[line_index]    )
    if search is None:
      line_index += 1
      continue

    # There is some copied code here, let's retrieve the original.
    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					List[Any]								=							search.groups()
    lowerCAmelCase					:					List[str]								=							find_code_in_diffusers(SCREAMING_SNAKE_CASE    )
    lowerCAmelCase					:					Optional[Any]								=							get_indent(SCREAMING_SNAKE_CASE    )

    lowerCAmelCase					:					Dict								=							line_index + 1 if indent == theoretical_indent else line_index + 2
    lowerCAmelCase					:					Optional[int]								=							theoretical_indent
    lowerCAmelCase					:					List[str]								=							start_index

    # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
    lowerCAmelCase					:					str								=							True
    while line_index < len(SCREAMING_SNAKE_CASE    ) and should_continue:
      line_index += 1
      if line_index >= len(SCREAMING_SNAKE_CASE    ):
        break
      lowerCAmelCase					:					Tuple								=							lines[line_index]
      lowerCAmelCase					:					str								=							_should_continue(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ) and re.search(f"""^{indent}# End copy"""    ,				SCREAMING_SNAKE_CASE    ) is None
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]    ) <= 1:
      line_index -= 1

    lowerCAmelCase					:					Tuple								=							lines[start_index:line_index]
    lowerCAmelCase					:					List[str]								=							"".join(SCREAMING_SNAKE_CASE    )

    # Remove any nested `Copied from` comments to avoid circular copies
    lowerCAmelCase					:					List[str]								=							[line for line in theoretical_code.split("\n"    ) if _re_copy_warning.search(SCREAMING_SNAKE_CASE    ) is None]
    lowerCAmelCase					:					Union[str, Any]								=							"\n".join(SCREAMING_SNAKE_CASE    )

    # Before comparing, use the `replace_pattern` on the original code.
    if len(SCREAMING_SNAKE_CASE    ) > 0:
      lowerCAmelCase					:					str								=							replace_pattern.replace("with"    ,				""    ).split(","    )
      lowerCAmelCase					:					List[str]								=							[_re_replace_pattern.search(SCREAMING_SNAKE_CASE    ) for p in patterns]
      for pattern in patterns:
        if pattern is None:
          continue
        lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					List[str]								=							pattern.groups()
        lowerCAmelCase					:					List[Any]								=							re.sub(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
        if option.strip() == "all-casing":
          lowerCAmelCase					:					Optional[Any]								=							re.sub(obja.lower()    ,				obja.lower()    ,				SCREAMING_SNAKE_CASE    )
          lowerCAmelCase					:					Dict								=							re.sub(obja.upper()    ,				obja.upper()    ,				SCREAMING_SNAKE_CASE    )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
      lowerCAmelCase					:					Union[str, Any]								=							blackify(lines[start_index - 1] + theoretical_code    )
      lowerCAmelCase					:					List[str]								=							theoretical_code[len(lines[start_index - 1]    ) :]

    # Test for a diff and act accordingly.
    if observed_code != theoretical_code:
      diffs.append([object_name, start_index]    )
      if overwrite:
        lowerCAmelCase					:					Tuple								=							lines[:start_index] + [theoretical_code] + lines[line_index:]
        lowerCAmelCase					:					int								=							start_index + 1

  if overwrite and len(SCREAMING_SNAKE_CASE    ) > 0:
    # Warn the user a file has been modified.
    print(f"""Detected changes, rewriting {filename}."""    )
    with open(SCREAMING_SNAKE_CASE    ,				"w"    ,				encoding="utf-8"    ,				newline="\n"    ) as f:
      f.writelines(SCREAMING_SNAKE_CASE    )
  return diffs
def  a__      (    SCREAMING_SNAKE_CASE   :   bool = False    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							glob.glob(os.path.join(SCREAMING_SNAKE_CASE    ,				"**/*.py"    )    ,				recursive=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					str								=							[]
  for filename in all_files:
    lowerCAmelCase					:					List[Any]								=							is_copy_consistent(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
    diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
  if not overwrite and len(SCREAMING_SNAKE_CASE    ) > 0:
    lowerCAmelCase					:					List[Any]								=							"\n".join(SCREAMING_SNAKE_CASE    )
    raise Exception(
        "Found the following copy inconsistencies:\n"
        + diff
        + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       argparse.ArgumentParser()
     parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
     lowerCAmelCase__				    =       parser.parse_args()

     check_copies(args.fix_and_overwrite)





