






import pickle

import numpy as np
from matplotlib import pyplot as plt
class     a    :



      """simple docstring"""


      def __init__(      self					:			Union[str, Any]		,      __lowercase					:			Any		,      __lowercase					:			List[str]		,      __lowercase					:			Any		,      __lowercase					:			int		,      __lowercase					:			Tuple		,      __lowercase					:			Tuple=0.2		,      __lowercase					:			Dict=0.2					)							->      Optional[Any]:
        __UpperCAmelCase							:	str           =  bp_numa
        __UpperCAmelCase							:	Optional[int]           =  bp_numa
        __UpperCAmelCase							:	Any           =  bp_numa
        __UpperCAmelCase							:	str           =  conva_get[:2]
        __UpperCAmelCase							:	Tuple           =  conva_get[2]
        __UpperCAmelCase							:	Optional[int]           =  size_pa
        __UpperCAmelCase							:	str           =  rate_w
        __UpperCAmelCase							:	List[Any]           =  rate_t
        __UpperCAmelCase							:	Optional[int]           =  [
            np.mat(-1 * np.random.rand(self.conva[0]		,      self.conva[0]					) + 0.5					)
            for i in range(self.conva[1]					)
        ]
        __UpperCAmelCase							:	Union[str, Any]           =  np.mat(-1 * np.random.rand(self.num_bpa		,      self.num_bpa					) + 0.5					)
        __UpperCAmelCase							:	List[Any]           =  np.mat(-1 * np.random.rand(self.num_bpa		,      self.num_bpa					) + 0.5					)
        __UpperCAmelCase							:	Union[str, Any]           =  -2 * np.random.rand(self.conva[1]					) + 1
        __UpperCAmelCase							:	List[str]           =  -2 * np.random.rand(self.num_bpa					) + 1
        __UpperCAmelCase							:	List[Any]           =  -2 * np.random.rand(self.num_bpa					) + 1
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			Union[str, Any]					)							->      Optional[Any]:
        # save model dict with pickle
        __UpperCAmelCase							:	str           =  {
            """num_bp1""": self.num_bpa,
            """num_bp2""": self.num_bpa,
            """num_bp3""": self.num_bpa,
            """conv1""": self.conva,
            """step_conv1""": self.step_conva,
            """size_pooling1""": self.size_poolinga,
            """rate_weight""": self.rate_weight,
            """rate_thre""": self.rate_thre,
            """w_conv1""": self.w_conva,
            """wkj""": self.wkj,
            """vji""": self.vji,
            """thre_conv1""": self.thre_conva,
            """thre_bp2""": self.thre_bpa,
            """thre_bp3""": self.thre_bpa,
        }
        with open(__lowercase		,      """wb"""					) as f:
          pickle.dump(__lowercase		,      __lowercase					)

        print(f"""Model saved： {save_path}"""					)
      @classmethod
      def 	UpperCAmelCase      (      cls					:			List[str]		,      __lowercase					:			Optional[Any]					)							->      List[str]:
        # read saved model
        with open(__lowercase		,      """rb"""					) as f:
          __UpperCAmelCase							:	Optional[int]           =  pickle.load(__lowercase					)  # noqa: S301

        __UpperCAmelCase							:	Union[str, Any]           =  model_dic.get("""conv1"""					)
        conv_get.append(model_dic.get("""step_conv1"""					)					)
        __UpperCAmelCase							:	Tuple           =  model_dic.get("""size_pooling1"""					)
        __UpperCAmelCase							:	Any           =  model_dic.get("""num_bp1"""					)
        __UpperCAmelCase							:	List[Any]           =  model_dic.get("""num_bp2"""					)
        __UpperCAmelCase							:	Any           =  model_dic.get("""num_bp3"""					)
        __UpperCAmelCase							:	List[Any]           =  model_dic.get("""rate_weight"""					)
        __UpperCAmelCase							:	str           =  model_dic.get("""rate_thre"""					)
        # create model instance
        __UpperCAmelCase							:	str           =  CNN(__lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase					)
        # modify model parameter
        __UpperCAmelCase							:	int           =  model_dic.get("""w_conv1"""					)
        __UpperCAmelCase							:	Any           =  model_dic.get("""wkj"""					)
        __UpperCAmelCase							:	Any           =  model_dic.get("""vji"""					)
        __UpperCAmelCase							:	Union[str, Any]           =  model_dic.get("""thre_conv1"""					)
        __UpperCAmelCase							:	List[str]           =  model_dic.get("""thre_bp2"""					)
        __UpperCAmelCase							:	List[Any]           =  model_dic.get("""thre_bp3"""					)
        return conv_ins
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Any					)							->      Optional[Any]:
        return 1 / (1 + np.exp(-1 * x					))
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			Any					)							->      List[str]:
        return round(__lowercase		,      3					)
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			str		,      __lowercase					:			Dict		,      __lowercase					:			Tuple		,      __lowercase					:			List[Any]		,      __lowercase					:			str					)							->      Optional[Any]:
        # convolution process
        __UpperCAmelCase							:	Tuple           =  convs[0]
        __UpperCAmelCase							:	List[Any]           =  convs[1]
        __UpperCAmelCase							:	Dict           =  np.shape(__lowercase					)[0]
        # get the data slice of original image data, data_focus
        __UpperCAmelCase							:	str           =  []
        for i_focus in range(0		,      size_data - size_conv + 1		,      __lowercase					):
          for j_focus in range(0		,      size_data - size_conv + 1		,      __lowercase					):
            __UpperCAmelCase							:	Any           =  data[
                i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
            ]
            data_focus.append(__lowercase					)
        # calculate the feature map of every single kernel, and saved as list of matrix
        __UpperCAmelCase							:	Optional[int]           =  []
        __UpperCAmelCase							:	Any           =  int((size_data - size_conv) / conv_step + 1					)
        for i_map in range(__lowercase					):
          __UpperCAmelCase							:	int           =  []
          for i_focus in range(len(__lowercase					)					):
            __UpperCAmelCase							:	Optional[int]           =  (
                np.sum(np.multiply(data_focus[i_focus]		,      w_convs[i_map]					)					)
                - thre_convs[i_map]
            )
            featuremap.append(self.sig(__lowercase					)					)
          __UpperCAmelCase							:	Union[str, Any]           =  np.asmatrix(__lowercase					).reshape(
              __lowercase		,      __lowercase					)
          data_featuremap.append(__lowercase					)

        # expanding the data slice to One dimenssion
        __UpperCAmelCase							:	Optional[Any]           =  []
        for each_focus in data_focus:
          focusa_list.extend(self.Expand_Mat(__lowercase					)					)
        __UpperCAmelCase							:	int           =  np.asarray(__lowercase					)
        return focus_list, data_featuremap
      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Optional[int]		,      __lowercase					:			Tuple="average_pool"					)							->      Any:
        # pooling process
        __UpperCAmelCase							:	Dict           =  len(featuremaps[0]					)
        __UpperCAmelCase							:	Any           =  int(size_map / size_pooling					)
        __UpperCAmelCase							:	List[Any]           =  []
        for i_map in range(len(__lowercase					)					):
          __UpperCAmelCase							:	Optional[Any]           =  featuremaps[i_map]
          __UpperCAmelCase							:	List[str]           =  []
          for i_focus in range(0		,      __lowercase		,      __lowercase					):
            for j_focus in range(0		,      __lowercase		,      __lowercase					):
              __UpperCAmelCase							:	Union[str, Any]           =  feature_map[
                  i_focus : i_focus + size_pooling,
                  j_focus : j_focus + size_pooling,
              ]
              if pooling_type == "average_pool":
                # average pooling
                map_pooled.append(np.average(__lowercase					)					)
              elif pooling_type == "max_pooling":
                # max pooling
                map_pooled.append(np.max(__lowercase					)					)
          __UpperCAmelCase							:	Optional[int]           =  np.asmatrix(__lowercase					).reshape(__lowercase		,      __lowercase					)
          featuremap_pooled.append(__lowercase					)
        return featuremap_pooled
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			str					)							->      Optional[Any]:
        # expanding three dimension data to one dimension list
        __UpperCAmelCase							:	Optional[Any]           =  []
        for i in range(len(__lowercase					)					):
          __UpperCAmelCase							:	Union[str, Any]           =  np.shape(data[i]					)
          __UpperCAmelCase							:	Any           =  data[i].reshape(1		,      shapes[0] * shapes[1]					)
          __UpperCAmelCase							:	Union[str, Any]           =  data_listed.getA().tolist()[0]
          data_expanded.extend(__lowercase					)
        __UpperCAmelCase							:	List[str]           =  np.asarray(__lowercase					)
        return data_expanded
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			List[Any]					)							->      Dict:
        # expanding matrix to one dimension list
        __UpperCAmelCase							:	Dict           =  np.asarray(__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  np.shape(__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  data_mat.reshape(1		,      shapes[0] * shapes[1]					)
        return data_expanded
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			List[str]		,      __lowercase					:			str		,      __lowercase					:			Any		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	Union[str, Any]           =  []
        __UpperCAmelCase							:	str           =  0
        for i_map in range(__lowercase					):
          __UpperCAmelCase							:	Optional[Any]           =  np.ones((size_map, size_map)					)
          for i in range(0		,      __lowercase		,      __lowercase					):
            for j in range(0		,      __lowercase		,      __lowercase					):
              __UpperCAmelCase							:	List[Any]           =  pd_pool[
                  i_pool
              ]
              __UpperCAmelCase							:	Optional[Any]           =  i_pool + 1
          __UpperCAmelCase							:	Tuple           =  np.multiply(
              __lowercase		,      np.multiply(out_map[i_map]		,      (1 - out_map[i_map])					)					)
          pd_all.append(__lowercase					)
        return pd_all
      def 	UpperCAmelCase      (      self					:			Union[str, Any]		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			Tuple		,      __lowercase					:			List[Any]		,      __lowercase					:			List[str]		,      __lowercase					:			Optional[int]		,      __lowercase					:			int=bool					)							->      Union[str, Any]:
        # model traning
        print("""----------------------Start Training-------------------------"""					)
        print((""" - - Shape: Train_Data  """, np.shape(__lowercase					))					)
        print((""" - - Shape: Teach_Data  """, np.shape(__lowercase					))					)
        __UpperCAmelCase							:	Optional[Any]           =  0
        __UpperCAmelCase							:	int           =  []
        __UpperCAmelCase							:	Tuple           =  10000
        while rp < n_repeat and mse >= error_accuracy:
          __UpperCAmelCase							:	Any           =  0
          print(f"""-------------Learning Time {rp}--------------"""					)
          for p in range(len(__lowercase					)					):
            # print('------------Learning Image: %d--------------'%p)
            __UpperCAmelCase							:	int           =  np.asmatrix(datas_train[p]					)
            __UpperCAmelCase							:	str           =  np.asarray(datas_teach[p]					)
            __UpperCAmelCase      , __UpperCAmelCase							:	str           =  self.convolute(
                __lowercase		,      self.conva		,      self.w_conva		,      self.thre_conva		,      conv_step=self.step_conva		,      )
            __UpperCAmelCase							:	Any           =  self.pooling(__lowercase		,      self.size_poolinga					)
            __UpperCAmelCase							:	Optional[Any]           =  np.shape(__lowercase					)
            __UpperCAmelCase							:	Tuple           =  self._expand(__lowercase					)
            __UpperCAmelCase							:	Tuple           =  data_bp_input

            __UpperCAmelCase							:	Optional[Any]           =  np.dot(__lowercase		,      self.vji.T					) - self.thre_bpa
            __UpperCAmelCase							:	Optional[Any]           =  self.sig(__lowercase					)
            __UpperCAmelCase							:	List[Any]           =  np.dot(__lowercase		,      self.wkj.T					) - self.thre_bpa
            __UpperCAmelCase							:	Dict           =  self.sig(__lowercase					)

            # --------------Model Leaning ------------------------
            # calculate error and gradient---------------
            __UpperCAmelCase							:	List[Any]           =  np.multiply(
                (data_teach - bp_outa)		,      np.multiply(__lowercase		,      (1 - bp_outa)					)					)
            __UpperCAmelCase							:	Optional[int]           =  np.multiply(
                np.dot(__lowercase		,      self.wkj					)		,      np.multiply(__lowercase		,      (1 - bp_outa)					)					)
            __UpperCAmelCase							:	str           =  np.dot(__lowercase		,      self.vji					)

            __UpperCAmelCase							:	List[Any]           =  pd_i_all / (self.size_poolinga * self.size_poolinga)
            __UpperCAmelCase							:	Union[str, Any]           =  pd_conva_pooled.T.getA().tolist()
            __UpperCAmelCase							:	List[str]           =  self._calculate_gradient_from_pool(
                __lowercase		,      __lowercase		,      shape_featuremapa[0]		,      shape_featuremapa[1]		,      self.size_poolinga		,      )
            # weight and threshold learning process---------
            # convolution layer
            for k_conv in range(self.conva[1]					):
              __UpperCAmelCase							:	List[str]           =  self._expand_mat(pd_conva_all[k_conv]					)
              __UpperCAmelCase							:	List[Any]           =  self.rate_weight * np.dot(__lowercase		,      __lowercase					)

              __UpperCAmelCase							:	List[Any]           =  self.w_conva[k_conv] + delta_w.reshape(
                  (self.conva[0], self.conva[0])					)

              __UpperCAmelCase							:	Dict           =  (
                  self.thre_conva[k_conv]
                  - np.sum(pd_conva_all[k_conv]					) * self.rate_thre
              )
            # all connected layer
            __UpperCAmelCase							:	Union[str, Any]           =  self.wkj + pd_k_all.T * bp_outa * self.rate_weight
            __UpperCAmelCase							:	Optional[int]           =  self.vji + pd_j_all.T * bp_outa * self.rate_weight
            __UpperCAmelCase							:	str           =  self.thre_bpa - pd_k_all * self.rate_thre
            __UpperCAmelCase							:	Optional[Any]           =  self.thre_bpa - pd_j_all * self.rate_thre
            # calculate the sum error of all single image
            __UpperCAmelCase							:	int           =  np.sum(abs(data_teach - bp_outa					)					)
            error_count += errors
            # print('   ----Teach      ',data_teach)
            # print('   ----BP_output  ',bp_out3)
          __UpperCAmelCase							:	Optional[int]           =  rp + 1
          __UpperCAmelCase							:	List[str]           =  error_count / patterns
          all_mse.append(__lowercase					)

        def draw_error():
          __UpperCAmelCase							:	Optional[Any]           =  [error_accuracy for i in range(int(n_repeat * 1.2					)					)]
          plt.plot(__lowercase		,      """+-"""					)
          plt.plot(__lowercase		,      """r--"""					)
          plt.xlabel("""Learning Times"""					)
          plt.ylabel("""All_mse"""					)
          plt.grid(__lowercase		,      alpha=0.5					)
          plt.show()

        print("""------------------Training Complished---------------------"""					)
        print((""" - - Training epoch: """, rp, f"""     - - Mse: {mse:.6f}""")					)
        if draw_e:
          draw_error()
        return mse
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			Any					)							->      Union[str, Any]:
        # model predict
        __UpperCAmelCase							:	Optional[Any]           =  []
        print("""-------------------Start Testing-------------------------"""					)
        print((""" - - Shape: Test_Data  """, np.shape(__lowercase					))					)
        for p in range(len(__lowercase					)					):
          __UpperCAmelCase							:	Dict           =  np.asmatrix(datas_test[p]					)
          __UpperCAmelCase      , __UpperCAmelCase							:	Union[str, Any]           =  self.convolute(
              __lowercase		,      self.conva		,      self.w_conva		,      self.thre_conva		,      conv_step=self.step_conva		,      )
          __UpperCAmelCase							:	Tuple           =  self.pooling(__lowercase		,      self.size_poolinga					)
          __UpperCAmelCase							:	List[str]           =  self._expand(__lowercase					)

          __UpperCAmelCase							:	Union[str, Any]           =  data_bp_input
          __UpperCAmelCase							:	Dict           =  bp_outa * self.vji.T - self.thre_bpa
          __UpperCAmelCase							:	Any           =  self.sig(__lowercase					)
          __UpperCAmelCase							:	int           =  bp_outa * self.wkj.T - self.thre_bpa
          __UpperCAmelCase							:	str           =  self.sig(__lowercase					)
          produce_out.extend(bp_outa.getA().tolist()					)
        __UpperCAmelCase							:	int           =  [list(map(self.do_round		,      __lowercase					)					) for each in produce_out]
        return np.asarray(__lowercase					)




      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			Optional[int]					)							->      Dict:
        # return the data of image after convoluting process so we can check it out
        __UpperCAmelCase							:	Optional[Any]           =  np.asmatrix(__lowercase					)
        __UpperCAmelCase      , __UpperCAmelCase							:	Dict           =  self.convolute(
            __lowercase		,      self.conva		,      self.w_conva		,      self.thre_conva		,      conv_step=self.step_conva		,      )
        __UpperCAmelCase							:	str           =  self.pooling(__lowercase		,      self.size_poolinga					)

        return data_conveda, data_pooleda


if __name__ == "__main__":
       pass










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)


