





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 :					Tuple 							='''src/diffusers'''
lowerCAmelCase :					Dict 							='''.'''


# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase :					Any 							=importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCAmelCase :					Optional[Any] 							=spec.loader.load_module()
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : Any			):
   return line.startswith(__lowerCamelCase			) or len(__lowerCamelCase			) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"     ,__lowerCamelCase			) is not None
def 							UpperCAmelCase_  (						__lowerCamelCase : int			):
   lowercase_      :Any         =			object_name.split("."			)
   lowercase_      :Any         =			0

   # First let's find the module where our object lives.
   lowercase_      :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			):
         lowercase_      :Union[str, Any]         =			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:
      lowercase_      :Union[str, Any]         =			f.readlines()

   # Now let's find the class / func in the code!
   lowercase_      :int         =			""
   lowercase_      :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).
   lowercase_      :List[Any]         =			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

   lowercase_      :int         =			lines[start_index:line_index]
   return "".join(__lowerCamelCase			)


lowerCAmelCase :					Any 							=re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
lowerCAmelCase :					str 							=re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
lowerCAmelCase :					Tuple 							=re.compile(r'''<FILL\s+[^>]*>''')
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]			):
   lowercase_      :List[str]         =			code.split("\n"			)
   lowercase_      :Tuple         =			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 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]			):
   lowercase_      :Any         =			len(get_indent(__lowerCamelCase			)			) > 0
   if has_indent:
      lowercase_      :Union[str, Any]         =			F'class Bla:\n{code}'
   lowercase_      :int         =			black.Mode(target_versions={black.TargetVersion.PYaa}     ,line_length=1_19     ,preview=__lowerCamelCase			)
   lowercase_      :Optional[Any]         =			black.format_str(__lowerCamelCase     ,mode=__lowerCamelCase			)
   lowercase_      :List[str]         =			style_docstrings_in_code(__lowerCamelCase			)
   return result[len("class Bla:\n"			) :] if has_indent else result
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : str=False			):
   with open(__lowerCamelCase     ,"r"     ,encoding="utf-8"     ,newline="\n"			) as f:
      lowercase_      :Optional[int]         =			f.readlines()
   lowercase_      :List[str]         =			[]
   lowercase_      :List[Any]         =			0
   # Not a for loop cause `lines` is going to change (if `overwrite=True`).
   while line_index < len(__lowerCamelCase			):
      lowercase_      :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.
      lowercase_      :str         =			search.groups()
      lowercase_      :Optional[int]         =			find_code_in_diffusers(__lowerCamelCase			)
      lowercase_      :Optional[int]         =			get_indent(__lowerCamelCase			)

      lowercase_      :str         =			line_index + 1 if indent == theoretical_indent else line_index + 2
      lowercase_      :List[str]         =			theoretical_indent
      lowercase_      :List[str]         =			start_index

      # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
      lowercase_      :str         =			True
      while line_index < len(__lowerCamelCase			) and should_continue:
         line_index += 1
         if line_index >= len(__lowerCamelCase			):
            break
         lowercase_      :List[Any]         =			lines[line_index]
         lowercase_      :Dict         =			_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

      lowercase_      :Any         =			lines[start_index:line_index]
      lowercase_      :List[Any]         =			"".join(__lowerCamelCase			)

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

      # Before comparing, use the `replace_pattern` on the original code.
      if len(__lowerCamelCase			) > 0:
         lowercase_      :Tuple         =			replace_pattern.replace("with"     ,""			).split(","			)
         lowercase_      :Optional[int]         =			[_re_replace_pattern.search(__lowerCamelCase			) for p in patterns]
         for pattern in patterns:
            if pattern is None:
               continue
            lowercase_      :Tuple         =			pattern.groups()
            lowercase_      :List[str]         =			re.sub(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)
            if option.strip() == "all-casing":
               lowercase_      :str         =			re.sub(obja.lower()     ,obja.lower()     ,__lowerCamelCase			)
               lowercase_      :List[Any]         =			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
         lowercase_      :Union[str, Any]         =			blackify(lines[start_index - 1] + theoretical_code			)
         lowercase_      :Union[str, 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:
            lowercase_      :str         =			lines[:start_index] + [theoretical_code] + lines[line_index:]
            lowercase_      :Dict         =			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 							UpperCAmelCase_  (						__lowerCamelCase : bool = False			):
   lowercase_      :Any         =			glob.glob(os.path.join(__lowerCamelCase     ,"**/*.py"			)     ,recursive=__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			[]
   for filename in all_files:
      lowercase_      :Tuple         =			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:
      lowercase_      :Tuple         =			"\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__":
 lowerCAmelCase :					Union[str, Any] 							=argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 lowerCAmelCase :					int 							=parser.parse_args()

 check_copies(args.fix_and_overwrite)









'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase :					Optional[int] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["input_features"]





     def __init__(			self		: Any     ,				lowercase		: Tuple=80     ,				lowercase		: Optional[int]=16_000     ,				lowercase		: Optional[Any]=160     ,				lowercase		: Optional[int]=30     ,				lowercase		: List[Any]=400     ,				lowercase		: Dict=0.0     ,				lowercase		: Tuple=False     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        super().__init__(
            feature_size=lowercase     ,				sampling_rate=lowercase     ,				padding_value=lowercase     ,				return_attention_mask=lowercase     ,				**lowercase     ,				)
        lowercase_      :Optional[int]         =			n_fft
        lowercase_      :List[Any]         =			hop_length
        lowercase_      :Tuple         =			chunk_length
        lowercase_      :List[str]         =			chunk_length * sampling_rate
        lowercase_      :Optional[Any]         =			self.n_samples // hop_length
        lowercase_      :Any         =			sampling_rate
        lowercase_      :List[Any]         =			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2     ,				num_mel_filters=lowercase     ,				min_frequency=0.0     ,				max_frequency=80_00.0     ,				sampling_rate=lowercase     ,				norm="slaney"     ,				mel_scale="slaney"     ,				)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.array						):





        """simple docstring"""




        lowercase_      :Any         =			spectrogram(
            lowercase     ,				window_function(self.n_fft     ,				"hann"						)     ,				frame_length=self.n_fft     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=self.mel_filters     ,				log_mel="log10"     ,				)
        lowercase_      :Any         =			log_spec[:, :-1]
        lowercase_      :List[Any]         =			np.maximum(lowercase     ,				log_spec.max() - 8.0						)
        lowercase_      :Dict         =			(log_spec + 4.0) / 4.0
        return log_spec





     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 							lowercase__				(			lowercase		: List[np.ndarray]     ,				lowercase		: List[np.ndarray]     ,				lowercase		: float = 0.0						):





        """simple docstring"""




        if attention_mask is not None:
           lowercase_      :Optional[int]         =			np.array(lowercase     ,				np.intaa						)
           lowercase_      :Any         =			[]

           for vector, length in zip(lowercase     ,				attention_mask.sum(-1						)						):
              lowercase_      :Dict         =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
              if length < normed_slice.shape[0]:
                 lowercase_      :List[Any]         =			padding_value

              normed_input_values.append(lowercase						)
        else:
           lowercase_      :List[Any]         =			[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

        return normed_input_values





     def __call__(			self		: Tuple     ,				lowercase		: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,				lowercase		: bool = True     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[str] = "max_length"     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Union[str, Any]     ,				):





        """simple docstring"""




        if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
              raise ValueError(
                  F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                  F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                  F' was sampled with {self.sampling_rate} and not {sampling_rate}.'						)
        else:
           logger.warning(
               "It is strongly recommended to pass the `sampling_rate` argument to this function. "
               "Failing to do so can result in silent errors that might be hard to debug."						)

        lowercase_      :List[str]         =			isinstance(lowercase     ,				np.ndarray						) and len(raw_speech.shape						) > 1
        if is_batched_numpy and len(raw_speech.shape						) > 2:
           raise ValueError(F'Only mono-channel audio is supported for input to {self}'						)
        lowercase_      :Optional[Any]         =			is_batched_numpy or (
            isinstance(lowercase     ,				(list, tuple)						) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)						))
        )

        if is_batched:
           lowercase_      :Any         =			[np.asarray([speech]     ,				dtype=np.floataa						).T for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase     ,				np.ndarray						):
           lowercase_      :List[Any]         =			np.asarray(lowercase     ,				dtype=np.floataa						)
        elif isinstance(lowercase     ,				np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
           lowercase_      :Union[str, Any]         =			raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
           lowercase_      :Optional[int]         =			[np.asarray([raw_speech]						).T]

        lowercase_      :int         =			BatchFeature({"input_features": raw_speech}						)

        # convert into correct format for padding

        lowercase_      :Tuple         =			self.pad(
            lowercase     ,				padding=lowercase     ,				max_length=max_length if max_length else self.n_samples     ,				truncation=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=return_attention_mask or do_normalize     ,				)

        # zero-mean and unit-variance normalization
        if do_normalize:
           lowercase_      :Union[str, Any]         =			self.zero_mean_unit_var_norm(
               padded_inputs["input_features"]     ,				attention_mask=padded_inputs["attention_mask"]     ,				padding_value=self.padding_value     ,				)
           lowercase_      :List[Any]         =			np.stack(padded_inputs["input_features"]     ,				axis=0						)

        # make sure list is in array format
        lowercase_      :Union[str, Any]         =			padded_inputs.get("input_features"						).transpose(2     ,				0     ,				1						)

        lowercase_      :List[str]         =			[self._np_extract_fbank_features(lowercase						) for waveform in input_features[0]]

        if isinstance(input_features[0]     ,				lowercase						):
           lowercase_      :Tuple         =			[np.asarray(lowercase     ,				dtype=np.floataa						) for feature in input_features]
        else:
           lowercase_      :Union[str, Any]         =			input_features

        if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           lowercase_      :Dict         =			padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
           lowercase_      :Tuple         =			padded_inputs.convert_to_tensors(lowercase						)

        return padded_inputs







     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			copy.deepcopy(self.__dict__						)
        lowercase_      :List[str]         =			self.__class__.__name__
        if "mel_filters" in output:
           del output["mel_filters"]
        return output


