





"""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
lowercase				:	Tuple      						=  """src/diffusers"""
lowercase				:	Dict      						=  """."""


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



def A_     (		A__		,				A__    )  ->		Optional[Any]:
       return line.startswith(_A    ) or len(_A    ) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$'		,				_A    ) is not None



def A_     (		A__    )  ->		int:
       a__						:     Optional[Any]			=    object_name.split('.'    )
       a__						:     Optional[Any]			=    0

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

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

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

       a__						:     Dict			=    lines[start_index:line_index]
       return "".join(_A    )


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



def A_     (		A__    )  ->		Optional[Any]:
       a__						:     Optional[int]			=    code.split('\n'    )
       a__						:     Optional[Any]			=    0
       while idx < len(_A    ) and len(lines[idx]    ) == 0:
              idx += 1
       if idx < len(_A    ):
              return re.search(R'^(\s*)\S'		,				lines[idx]    ).groups()[0]
       return ""



def A_     (		A__    )  ->		List[Any]:
       a__						:     Optional[Any]			=    len(get_indent(_A    )    ) > 0
       if has_indent:
              a__						:     int			=    F'class Bla:\n{code}'
       a__						:     Union[str, Any]			=    black.Mode(target_versions={black.TargetVersion.PYaa}		,				line_length=119		,				preview=_A    )
       a__						:     List[str]			=    black.format_str(_A		,				mode=_A    )
       a__						:     List[str]			=    style_docstrings_in_code(_A    )
       return result[len('class Bla:\n'    ) :] if has_indent else result



def A_     (		A__		,				A__=False    )  ->		int:







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

              a__						:     str			=    line_index + 1 if indent == theoretical_indent else line_index + 2
              a__						:     Union[str, Any]			=    theoretical_indent
              a__						:     Dict			=    start_index

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

              a__						:     Union[str, Any]			=    lines[start_index:line_index]
              a__						:     Optional[int]			=    ''.join(_A    )

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

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

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

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



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

    check_copies(args.fix_and_overwrite)









import pickle

import numpy as np
from matplotlib import pyplot as plt





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=0.2 ,  lowercase=0.2)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    bp_numa
          a__						:     Union[str, Any]			=    bp_numa
          a__						:     Optional[int]			=    bp_numa
          a__						:     Optional[int]			=    conva_get[:2]
          a__						:     Optional[Any]			=    conva_get[2]
          a__						:     Optional[int]			=    size_pa
          a__						:     Union[str, Any]			=    rate_w
          a__						:     Dict			=    rate_t
          a__						:     int			=    [
              np.mat(-1 * np.random.rand(self.conva[0] ,  self.conva[0]) + 0.5)
              for i in range(self.conva[1])
          ]
          a__						:     Optional[int]			=    np.mat(-1 * np.random.rand(self.num_bpa ,  self.num_bpa) + 0.5)
          a__						:     Optional[int]			=    np.mat(-1 * np.random.rand(self.num_bpa ,  self.num_bpa) + 0.5)
          a__						:     Any			=    -2 * np.random.rand(self.conva[1]) + 1
          a__						:     Optional[Any]			=    -2 * np.random.rand(self.num_bpa) + 1
          a__						:     Optional[int]			=    -2 * np.random.rand(self.num_bpa) + 1

   def 							__lowercase		( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    {
              '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 							__lowercase		( cls ,  lowercase)      ->						Any:







          '''simple docstring'''

          with open(lowercase ,  'rb') as f:
                 a__						:     Any			=    pickle.load(lowercase)  # noqa: S301

          a__						:     Dict			=    model_dic.get('conv1')
          conv_get.append(model_dic.get('step_conv1'))
          a__						:     Tuple			=    model_dic.get('size_pooling1')
          a__						:     Optional[int]			=    model_dic.get('num_bp1')
          a__						:     Tuple			=    model_dic.get('num_bp2')
          a__						:     Optional[Any]			=    model_dic.get('num_bp3')
          a__						:     Optional[Any]			=    model_dic.get('rate_weight')
          a__						:     int			=    model_dic.get('rate_thre')
          # create model instance
          a__						:     Union[str, Any]			=    CNN(lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)
          # modify model parameter
          a__						:     str			=    model_dic.get('w_conv1')
          a__						:     Optional[int]			=    model_dic.get('wkj')
          a__						:     Tuple			=    model_dic.get('vji')
          a__						:     str			=    model_dic.get('thre_conv1')
          a__						:     List[str]			=    model_dic.get('thre_bp2')
          a__						:     Tuple			=    model_dic.get('thre_bp3')
          return conv_ins

   def 							__lowercase		( self ,  lowercase)      ->						Any:







          '''simple docstring'''

          return 1 / (1 + np.exp(-1 * x))

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          return round(lowercase ,  3)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    convs[0]
          a__						:     Tuple			=    convs[1]
          a__						:     Any			=    np.shape(lowercase)[0]
          # get the data slice of original image data, data_focus
          a__						:     Tuple			=    []
          for i_focus in range(0 ,  size_data - size_conv + 1 ,  lowercase):
                 for j_focus in range(0 ,  size_data - size_conv + 1 ,  lowercase):
                        a__						:     str			=    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
          a__						:     str			=    []
          a__						:     Union[str, Any]			=    int((size_data - size_conv) / conv_step + 1)
          for i_map in range(lowercase):
                 a__						:     Tuple			=    []
                 for i_focus in range(len(lowercase)):
                        a__						:     Optional[Any]			=    (
                            np.sum(np.multiply(data_focus[i_focus] ,  w_convs[i_map]))
                            - thre_convs[i_map]
                        )
                        featuremap.append(self.sig(lowercase))
                 a__						:     Dict			=    np.asmatrix(lowercase).reshape(
                     lowercase ,  lowercase)
                 data_featuremap.append(lowercase)

          # expanding the data slice to One dimenssion
          a__						:     int			=    []
          for each_focus in data_focus:
                 focusa_list.extend(self.Expand_Mat(lowercase))
          a__						:     Optional[int]			=    np.asarray(lowercase)
          return focus_list, data_featuremap

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase="average_pool")      ->						str:







          '''simple docstring'''

          a__						:     Any			=    len(featuremaps[0])
          a__						:     int			=    int(size_map / size_pooling)
          a__						:     Optional[Any]			=    []
          for i_map in range(len(lowercase)):
                 a__						:     Any			=    featuremaps[i_map]
                 a__						:     Optional[int]			=    []
                 for i_focus in range(0 ,  lowercase ,  lowercase):
                        for j_focus in range(0 ,  lowercase ,  lowercase):
                               a__						:     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))
                 a__						:     List[str]			=    np.asmatrix(lowercase).reshape(lowercase ,  lowercase)
                 featuremap_pooled.append(lowercase)
          return featuremap_pooled

   def 							__lowercase		( self ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Any			=    []
          for i in range(len(lowercase)):
                 a__						:     Tuple			=    np.shape(data[i])
                 a__						:     List[str]			=    data[i].reshape(1 ,  shapes[0] * shapes[1])
                 a__						:     Optional[Any]			=    data_listed.getA().tolist()[0]
                 data_expanded.extend(lowercase)
          a__						:     Union[str, Any]			=    np.asarray(lowercase)
          return data_expanded

   def 							__lowercase		( self ,  lowercase)      ->						Dict:







          '''simple docstring'''

          a__						:     Dict			=    np.asarray(lowercase)
          a__						:     Optional[int]			=    np.shape(lowercase)
          a__						:     Any			=    data_mat.reshape(1 ,  shapes[0] * shapes[1])
          return data_expanded

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     int			=    []
          a__						:     Optional[int]			=    0
          for i_map in range(lowercase):
                 a__						:     Optional[Any]			=    np.ones((size_map, size_map))
                 for i in range(0 ,  lowercase ,  lowercase):
                        for j in range(0 ,  lowercase ,  lowercase):
                               a__						:     Union[str, Any]			=    pd_pool[
                                   i_pool
                               ]
                               a__						:     Tuple			=    i_pool + 1
                 a__						:     Optional[int]			=    np.multiply(
                     lowercase ,  np.multiply(out_map[i_map] ,  (1 - out_map[i_map])))
                 pd_all.append(lowercase)
          return pd_all

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=bool)      ->						str:







          '''simple docstring'''

          print('----------------------Start Training-------------------------')
          print((' - - Shape: Train_Data  ', np.shape(lowercase)))
          print((' - - Shape: Teach_Data  ', np.shape(lowercase)))
          a__						:     Dict			=    0
          a__						:     List[Any]			=    []
          a__						:     Optional[int]			=    1_0000
          while rp < n_repeat and mse >= error_accuracy:
                 a__						:     Dict			=    0
                 print(F'-------------Learning Time {rp}--------------')
                 for p in range(len(lowercase)):
                        # print('------------Learning Image: %d--------------'%p)
                        a__						:     Dict			=    np.asmatrix(datas_train[p])
                        a__						:     Any			=    np.asarray(datas_teach[p])
                        a__			,							a__						:     Optional[int]			=    self.convolute(
                            lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
                        a__						:     Dict			=    self.pooling(lowercase ,  self.size_poolinga)
                        a__						:     Optional[Any]			=    np.shape(lowercase)
                        a__						:     Union[str, Any]			=    self._expand(lowercase)
                        a__						:     List[Any]			=    data_bp_input

                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)
                        a__						:     Any			=    np.dot(lowercase ,  self.wkj.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)

                        # --------------Model Leaning ------------------------
                        # calculate error and gradient---------------
                        a__						:     Any			=    np.multiply(
                            (data_teach - bp_outa) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Optional[Any]			=    np.multiply(
                            np.dot(lowercase ,  self.wkj) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji)

                        a__						:     Union[str, Any]			=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                        a__						:     List[str]			=    pd_conva_pooled.T.getA().tolist()
                        a__						:     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]):
                               a__						:     Optional[int]			=    self._expand_mat(pd_conva_all[k_conv])
                               a__						:     int			=    self.rate_weight * np.dot(lowercase ,  lowercase)

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

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

          def draw_error():
                 a__						:     int			=    [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 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     str			=    []
          print('-------------------Start Testing-------------------------')
          print((' - - Shape: Test_Data  ', np.shape(lowercase)))
          for p in range(len(lowercase)):
                 a__						:     int			=    np.asmatrix(datas_test[p])
                 a__			,							a__						:     Optional[int]			=    self.convolute(
                     lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
                 a__						:     str			=    self.pooling(lowercase ,  self.size_poolinga)
                 a__						:     Optional[int]			=    self._expand(lowercase)

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

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     str			=    np.asmatrix(lowercase)
          a__			,							a__						:     str			=    self.convolute(
              lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
          a__						:     List[str]			=    self.pooling(lowercase ,  self.size_poolinga)

          return data_conveda, data_pooleda


if __name__ == "__main__":
    pass


