






import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def 				lowerCamelCase__      (    ):
  __UpperCAmelCase							:	Any           =  """https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
  __UpperCAmelCase							:	Union[str, Any]           =  Image.open(requests.get(__lowerCamelCase  ,		stream=__lowerCamelCase		).raw		).convert("""RGB"""		)

  return image






def 				lowerCamelCase__      (    __lowerCamelCase			:     int		):
  __UpperCAmelCase							:	int           =  []
  # fmt: off

  # vision encoder
  rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")		)
  rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")		)
  rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")		)
  rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")		)
  rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")		)
  rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")		)

  for i in range(config.vision_config.num_hidden_layers		):
    rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")		)
    rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")		)

  # QFormer
  rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.layernorm.weight""")		)
  rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""")		)

  # fmt: on
  return rename_keys






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     List[Any]  ,		__lowerCamelCase			:     Optional[int]		):
  __UpperCAmelCase							:	Optional[int]           =  dct.pop(__lowerCamelCase		)
  __UpperCAmelCase							:	Any           =  val






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     Optional[Any]		):
  for i in range(config.vision_config.num_hidden_layers		):
    # read in original q and v biases
    __UpperCAmelCase							:	str           =  state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""		)
    __UpperCAmelCase							:	Optional[Any]           =  state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""		)

    # next, set bias in the state dict
    __UpperCAmelCase							:	Optional[Any]           =  torch.cat((q_bias, torch.zeros_like(__lowerCamelCase  ,		requires_grad=__lowerCamelCase		), v_bias)		)
    __UpperCAmelCase							:	str           =  qkv_bias






def 				lowerCamelCase__      (    __lowerCamelCase			:     int  ,		__lowerCamelCase			:     int		):
  __UpperCAmelCase							:	Tuple           =  364 if """coco""" in model_name else 224
  __UpperCAmelCase							:	Optional[Any]           =  BlipaVisionConfig(image_size=__lowerCamelCase		).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "opt-2.7b" in model_name:
    __UpperCAmelCase							:	int           =  OPTConfig.from_pretrained("""facebook/opt-2.7b"""  ,		eos_token_id=__lowerCamelCase		).to_dict()
  elif "opt-6.7b" in model_name:
    __UpperCAmelCase							:	int           =  OPTConfig.from_pretrained("""facebook/opt-6.7b"""  ,		eos_token_id=__lowerCamelCase		).to_dict()
  elif "t5-xl" in model_name:
    __UpperCAmelCase							:	Union[str, Any]           =  TaConfig.from_pretrained("""google/flan-t5-xl"""  ,		dense_act_fn="""gelu"""  ,		bos_token_id=1		).to_dict()
  elif "t5-xxl" in model_name:
    __UpperCAmelCase							:	List[str]           =  TaConfig.from_pretrained("""google/flan-t5-xxl"""  ,		dense_act_fn="""gelu"""  ,		bos_token_id=1		).to_dict()

  __UpperCAmelCase							:	int           =  BlipaConfig(vision_config=__lowerCamelCase  ,		text_config=__lowerCamelCase		)

  return config, image_size






@torch.no_grad()
def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     Union[str, Any]=None  ,		__lowerCamelCase			:     Any=False		):
  __UpperCAmelCase							:	Optional[int]           =  (
      AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""		)
      if """opt""" in model_name
      else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""		)
  )
  __UpperCAmelCase							:	Union[str, Any]           =  tokenizer("""\n"""  ,		add_special_tokens=__lowerCamelCase		).input_ids[0]
  __UpperCAmelCase      , __UpperCAmelCase							:	str           =  get_blipa_config(__lowerCamelCase  ,		eos_token_id=__lowerCamelCase		)

  __UpperCAmelCase							:	Dict           =  BlipaForConditionalGeneration(__lowerCamelCase		).eval()

  __UpperCAmelCase							:	Optional[int]           =  {
      """blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
      """blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
      """blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
      """blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
      """blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
      """blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
      """blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
  }

  __UpperCAmelCase      , __UpperCAmelCase							:	Union[str, Any]           =  model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""		)
  __UpperCAmelCase							:	str           =  """cuda""" if torch.cuda.is_available() else """cpu"""
  __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	List[str]           =  load_model_and_preprocess(
      name=__lowerCamelCase  ,		model_type=__lowerCamelCase  ,		is_eval=__lowerCamelCase  ,		device=__lowerCamelCase		)
  original_model.eval()
  print("""Done!"""		)

  # update state dict keys
  __UpperCAmelCase							:	Dict           =  original_model.state_dict()
  __UpperCAmelCase							:	Any           =  create_rename_keys(__lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    __UpperCAmelCase							:	int           =  state_dict.pop(__lowerCamelCase		)
    if key.startswith("""Qformer.bert"""		):
      __UpperCAmelCase							:	Dict           =  key.replace("""Qformer.bert"""  ,		"""qformer"""		)
    if "attention.self" in key:
      __UpperCAmelCase							:	Optional[int]           =  key.replace("""self"""  ,		"""attention"""		)
    if "opt_proj" in key:
      __UpperCAmelCase							:	Optional[int]           =  key.replace("""opt_proj"""  ,		"""language_projection"""		)
    if "t5_proj" in key:
      __UpperCAmelCase							:	List[str]           =  key.replace("""t5_proj"""  ,		"""language_projection"""		)
    if key.startswith("""opt"""		):
      __UpperCAmelCase							:	Optional[int]           =  key.replace("""opt"""  ,		"""language"""		)
    if key.startswith("""t5"""		):
      __UpperCAmelCase							:	List[str]           =  key.replace("""t5"""  ,		"""language"""		)
    __UpperCAmelCase							:	int           =  val

  # read in qv biases
  read_in_q_v_bias(__lowerCamelCase  ,		__lowerCamelCase		)

  __UpperCAmelCase      , __UpperCAmelCase							:	int           =  hf_model.load_state_dict(__lowerCamelCase  ,		strict=__lowerCamelCase		)
  assert len(__lowerCamelCase		) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  __UpperCAmelCase							:	str           =  load_demo_image()
  __UpperCAmelCase							:	Tuple           =  vis_processors["""eval"""](__lowerCamelCase		).unsqueeze(0		).to(__lowerCamelCase		)
  __UpperCAmelCase							:	Any           =  tokenizer(["""\n"""]  ,		return_tensors="""pt"""		).input_ids.to(__lowerCamelCase		)

  # create processor
  __UpperCAmelCase							:	Dict           =  BlipImageProcessor(
      size={"""height""": image_size, """width""": image_size}  ,		image_mean=__lowerCamelCase  ,		image_std=__lowerCamelCase		)
  __UpperCAmelCase							:	Optional[int]           =  BlipaProcessor(image_processor=__lowerCamelCase  ,		tokenizer=__lowerCamelCase		)
  __UpperCAmelCase							:	Any           =  processor(images=__lowerCamelCase  ,		return_tensors="""pt"""		).pixel_values.to(__lowerCamelCase		)

  # make sure processor creates exact same pixel values
  assert torch.allclose(__lowerCamelCase  ,		__lowerCamelCase		)

  original_model.to(__lowerCamelCase		)
  hf_model.to(__lowerCamelCase		)
  with torch.no_grad():
    if "opt" in model_name:
      __UpperCAmelCase							:	Optional[Any]           =  original_model({"""image""": original_pixel_values, """text_input""": [""""""]}		).logits
      __UpperCAmelCase							:	List[Any]           =  hf_model(__lowerCamelCase  ,		__lowerCamelCase		).logits
    else:
      __UpperCAmelCase							:	Optional[Any]           =  original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}		).logits
      __UpperCAmelCase							:	Union[str, Any]           =  input_ids.masked_fill(input_ids == tokenizer.pad_token_id  ,		-100		)
      __UpperCAmelCase							:	Union[str, Any]           =  hf_model(__lowerCamelCase  ,		__lowerCamelCase  ,		labels=__lowerCamelCase		).logits

  assert original_logits.shape == logits.shape
  print("""First values of original logits:"""  ,		original_logits[0, :3, :3]		)
  print("""First values of HF logits:"""  ,		logits[0, :3, :3]		)

  # assert values
  if model_name == "blip2-flan-t5-xl":
    __UpperCAmelCase							:	Optional[int]           =  torch.tensor(
        [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]  ,		device=__lowerCamelCase		)
    assert torch.allclose(logits[0, :3, :3]  ,		__lowerCamelCase  ,		atol=1E-4		)
  elif model_name == "blip2-flan-t5-xl-coco":
    __UpperCAmelCase							:	Optional[Any]           =  torch.tensor(
        [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]  ,		device=__lowerCamelCase		)
  else:
    # cast to same type
    __UpperCAmelCase							:	Optional[Any]           =  logits.dtype
    assert torch.allclose(original_logits.to(__lowerCamelCase		)  ,		__lowerCamelCase  ,		atol=1E-2		)
  print("""Looks ok!"""		)

  print("""Generating a caption..."""		)
  __UpperCAmelCase							:	Dict           =  """"""
  __UpperCAmelCase							:	Dict           =  tokenizer(__lowerCamelCase  ,		return_tensors="""pt"""		).input_ids.to(__lowerCamelCase		)

  __UpperCAmelCase							:	Tuple           =  original_model.generate({"""image""": original_pixel_values}		)
  __UpperCAmelCase							:	Union[str, Any]           =  hf_model.generate(
      __lowerCamelCase  ,		__lowerCamelCase  ,		do_sample=__lowerCamelCase  ,		num_beams=5  ,		max_length=30  ,		min_length=1  ,		top_p=0.9  ,		repetition_penalty=1.0  ,		length_penalty=1.0  ,		temperature=1  ,		)
  print("""Original generation:"""  ,		__lowerCamelCase		)
  __UpperCAmelCase							:	List[Any]           =  input_ids.shape[1]
  __UpperCAmelCase							:	List[Any]           =  processor.batch_decode(outputs[:, prompt_length:]  ,		skip_special_tokens=__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  [text.strip() for text in output_text]
  print("""HF generation:"""  ,		__lowerCamelCase		)

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(__lowerCamelCase		)
    hf_model.save_pretrained(__lowerCamelCase		)

  if push_to_hub:
    processor.push_to_hub(f"""nielsr/{model_name}"""		)
    hf_model.push_to_hub(f"""nielsr/{model_name}"""		)


if __name__ == "__main__":
       a		:       Dict			     = argparse.ArgumentParser()
       a		:       str			     = [
           "blip2-opt-2.7b",
           "blip2-opt-6.7b",
           "blip2-opt-2.7b-coco",
           "blip2-opt-6.7b-coco",
           "blip2-flan-t5-xl",
           "blip2-flan-t5-xl-coco",
           "blip2-flan-t5-xxl",
       ]
       parser.add_argument(
           "--model_name",
           default="blip2-opt-2.7b",
           choices=choices,
           type=str,
           help="Path to hf config.json of model to convert",
       )
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument(
           "--push_to_hub",
           action="store_true",
           help="Whether to push the model and processor to the hub after converting",
       )

       a		:       Dict			     = parser.parse_args()

       convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










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
a		:       List[str]			     = "src/diffusers"
a		:       str			     = "."


# This is to make sure the diffusers module imported is the one in the repo.
a		:       Tuple			     = importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a		:       List[str]			     = spec.loader.load_module()






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






def 				lowerCamelCase__      (    __lowerCamelCase			:     Any		):
  __UpperCAmelCase							:	Optional[int]           =  object_name.split("""."""		)
  __UpperCAmelCase							:	List[Any]           =  0

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

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

  # Now let's find the class / func in the code!
  __UpperCAmelCase							:	List[str]           =  """"""
  __UpperCAmelCase							:	int           =  0
  for name in parts[i + 1 :]:
    while (
        line_index < len(__lowerCamelCase		) 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(__lowerCamelCase		):
    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).
  __UpperCAmelCase							:	List[str]           =  line_index
  while line_index < len(__lowerCamelCase		) and _should_continue(lines[line_index]  ,		__lowerCamelCase		):
    line_index += 1
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]		) <= 1:
    line_index -= 1

  __UpperCAmelCase							:	Dict           =  lines[start_index:line_index]
  return "".join(__lowerCamelCase		)


a		:       Any			     = re.compile(r"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a		:       Optional[int]			     = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)")
a		:       Dict			     = re.compile(r"<FILL\s+[^>]*>")






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[Any]		):
  __UpperCAmelCase							:	Optional[Any]           =  code.split("""\n"""		)
  __UpperCAmelCase							:	str           =  0
  while idx < len(__lowerCamelCase		) and len(lines[idx]		) == 0:
    idx += 1
  if idx < len(__lowerCamelCase		):
    return re.search(R"""^(\s*)\S"""  ,		lines[idx]		).groups()[0]
  return ""






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[str]		):
  __UpperCAmelCase							:	Tuple           =  len(get_indent(__lowerCamelCase		)		) > 0
  if has_indent:
    __UpperCAmelCase							:	Optional[Any]           =  f"""class Bla:\n{code}"""
  __UpperCAmelCase							:	Dict           =  black.Mode(target_versions={black.TargetVersion.PYaa}  ,		line_length=119  ,		preview=__lowerCamelCase		)
  __UpperCAmelCase							:	Dict           =  black.format_str(__lowerCamelCase  ,		mode=__lowerCamelCase		)
  __UpperCAmelCase      , __UpperCAmelCase							:	Any           =  style_docstrings_in_code(__lowerCamelCase		)
  return result[len("""class Bla:\n"""		) :] if has_indent else result






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Optional[Any]=False		):
  with open(__lowerCamelCase  ,		"""r"""  ,		encoding="""utf-8"""  ,		newline="""\n"""		) as f:
    __UpperCAmelCase							:	Optional[Any]           =  f.readlines()
  __UpperCAmelCase							:	Optional[int]           =  []
  __UpperCAmelCase							:	str           =  0
  # Not a for loop cause `lines` is going to change (if `overwrite=True`).
  while line_index < len(__lowerCamelCase		):
    __UpperCAmelCase							:	Dict           =  _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.
    __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	Union[str, Any]           =  search.groups()
    __UpperCAmelCase							:	Any           =  find_code_in_diffusers(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[int]           =  get_indent(__lowerCamelCase		)

    __UpperCAmelCase							:	Tuple           =  line_index + 1 if indent == theoretical_indent else line_index + 2
    __UpperCAmelCase							:	Any           =  theoretical_indent
    __UpperCAmelCase							:	Any           =  start_index

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

    __UpperCAmelCase							:	Optional[int]           =  lines[start_index:line_index]
    __UpperCAmelCase							:	int           =  """""".join(__lowerCamelCase		)

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

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

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

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






def 				lowerCamelCase__      (    __lowerCamelCase			:     bool = False		):
  __UpperCAmelCase							:	Tuple           =  glob.glob(os.path.join(__lowerCamelCase  ,		"""**/*.py"""		)  ,		recursive=__lowerCamelCase		)
  __UpperCAmelCase							:	Optional[int]           =  []
  for filename in all_files:
    __UpperCAmelCase							:	str           =  is_copy_consistent(__lowerCamelCase  ,		__lowerCamelCase		)
    diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
  if not overwrite and len(__lowerCamelCase		) > 0:
    __UpperCAmelCase							:	Union[str, Any]           =  """\n""".join(__lowerCamelCase		)
    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__":
       a		:       Dict			     = argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       a		:       Optional[int]			     = parser.parse_args()

       check_copies(args.fix_and_overwrite)


