




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						:   int		=   "src/diffusers"
_lowerCAmelCase						:   Optional[int]		=   "."


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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						object_name.split("."	)
 UpperCAmelCase__							 =						0

 # First let's find the module where our object lives.
 UpperCAmelCase__							 =						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__							 =						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__							 =						f.readlines()

 # Now let's find the class / func in the code!
 UpperCAmelCase__							 =						""
 UpperCAmelCase__							 =						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__							 =						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__							 =						lines[start_index:line_index]
 return "".join(_lowerCAmelCase	)


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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						code.split("\n"	)
 UpperCAmelCase__							 =						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							:   Dict	):




 """simple docstring"""







 UpperCAmelCase__							 =						len(get_indent(_lowerCAmelCase	)	) > 0
 if has_indent:
  UpperCAmelCase__							 =						F'''class Bla:\n{code}'''
 UpperCAmelCase__							 =						black.Mode(target_versions={black.TargetVersion.PYaa}						,							line_length=119						,							preview=_lowerCAmelCase	)
 UpperCAmelCase__							 =						black.format_str(_lowerCAmelCase						,							mode=_lowerCAmelCase	)
 UpperCAmelCase__ , UpperCAmelCase__							 =						style_docstrings_in_code(_lowerCAmelCase	)
 return result[len("class Bla:\n"	) :] if has_indent else result



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   str=False	):




 """simple docstring"""







 with open(_lowerCAmelCase						,							"r"						,							encoding="utf-8"						,							newline="\n"	) as f:
  UpperCAmelCase__							 =						f.readlines()
 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(_lowerCAmelCase	):
  UpperCAmelCase__							 =						_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__							 =						search.groups()
  UpperCAmelCase__							 =						find_code_in_diffusers(_lowerCAmelCase	)
  UpperCAmelCase__							 =						get_indent(_lowerCAmelCase	)

  UpperCAmelCase__							 =						line_index + 1 if indent == theoretical_indent else line_index + 2
  UpperCAmelCase__							 =						theoretical_indent
  UpperCAmelCase__							 =						start_index

  # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
  UpperCAmelCase__							 =						True
  while line_index < len(_lowerCAmelCase	) and should_continue:
   line_index += 1
   if line_index >= len(_lowerCAmelCase	):
    break
   UpperCAmelCase__							 =						lines[line_index]
   UpperCAmelCase__							 =						_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__							 =						lines[start_index:line_index]
  UpperCAmelCase__							 =						"".join(_lowerCAmelCase	)

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

  # Before comparing, use the `replace_pattern` on the original code.
  if len(_lowerCAmelCase	) > 0:
   UpperCAmelCase__							 =						replace_pattern.replace("with"						,							""	).split(","	)
   UpperCAmelCase__							 =						[_re_replace_pattern.search(_lowerCAmelCase	) for p in patterns]
   for pattern in patterns:
    if pattern is None:
     continue
    UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						pattern.groups()
    UpperCAmelCase__							 =						re.sub(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)
    if option.strip() == "all-casing":
     UpperCAmelCase__							 =						re.sub(obja.lower()						,							obja.lower()						,							_lowerCAmelCase	)
     UpperCAmelCase__							 =						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__							 =						blackify(lines[start_index - 1] + theoretical_code	)
   UpperCAmelCase__							 =						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__							 =						lines[:start_index] + [theoretical_code] + lines[line_index:]
    UpperCAmelCase__							 =						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	):




 """simple docstring"""







 UpperCAmelCase__							 =						glob.glob(os.path.join(_lowerCAmelCase						,							"**/*.py"	)						,							recursive=_lowerCAmelCase	)
 UpperCAmelCase__							 =						[]
 for filename in all_files:
  UpperCAmelCase__							 =						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__							 =						"\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						:   List[str]		=   argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  _lowerCAmelCase						:   Optional[int]		=   parser.parse_args()

  check_copies(args.fix_and_overwrite)











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCAmelCase						:   Any		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""input_values""", """padding_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :int = 1 ,      lowerCamelCase   :int = 2_4000 ,      lowerCamelCase   :float = 0.0 ,      lowerCamelCase   :float = None ,      lowerCamelCase   :float = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			str:
       super().__init__(feature_size=lowerCamelCase ,      sampling_rate=lowerCamelCase ,      padding_value=lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						chunk_length_s
       UpperCAmelCase__							 =						overlap
      @property
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       if self.chunk_length_s is None:
        return None
       else:
        return int(self.chunk_length_s * self.sampling_rate      )
      @property
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       if self.chunk_length_s is None or self.overlap is None:
        return None
       else:
        return max(1 ,      int((1.0 - self.overlap) * self.chunk_length      )      )







      def __call__(			self   :List[Any] ,      lowerCamelCase   :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCamelCase   :Optional[Union[bool, str, PaddingStrategy]] = None ,      lowerCamelCase   :Optional[bool] = False ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :Optional[int] = None ,      )     ->			BatchFeature:
       if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
         raise ValueError(
             f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
             f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
             f''' {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."      )

       if padding and truncation:
        raise ValueError("Both padding and truncation were set. Make sure you only set one."      )
       elif padding is None:
        # by default let's pad the inputs
        UpperCAmelCase__							 =						True

       UpperCAmelCase__							 =						bool(
           isinstance(lowerCamelCase ,      (list, tuple)      ) and (isinstance(raw_audio[0] ,      (np.ndarray, tuple, list)      ))      )

       if is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ).T for audio in raw_audio]
       elif not is_batched and not isinstance(lowerCamelCase ,      np.ndarray      ):
        UpperCAmelCase__							 =						np.asarray(lowerCamelCase ,      dtype=np.floataa      )
       elif isinstance(lowerCamelCase ,      np.ndarray      ) and raw_audio.dtype is np.dtype(np.floataa      ):
        UpperCAmelCase__							 =						raw_audio.astype(np.floataa      )

       # always return batch
       if not is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase      ).T]

       # verify inputs are valid
       for idx, example in enumerate(lowerCamelCase      ):
        if example.ndim > 2:
         raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''      )
        if self.feature_size == 1 and example.ndim != 1:
         raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''      )
        if self.feature_size == 2 and example.shape[-1] != 2:
         raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						BatchFeature({"input_values": raw_audio}      )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
        if truncation:
         UpperCAmelCase__							 =						min(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.floor(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
         UpperCAmelCase__							 =						max(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.ceil(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
         UpperCAmelCase__							 =						"max_length"
        else:
         UpperCAmelCase__							 =						input_values

        # normal padding on batch
       if padded_inputs is None:
        UpperCAmelCase__							 =						self.pad(
            lowerCamelCase ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      padding=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      )
        if padding:
         UpperCAmelCase__							 =						padded_inputs.pop("attention_mask"      )

       UpperCAmelCase__							 =						[]
       for example in padded_inputs.pop("input_values"      ):
        if self.feature_size == 1:
         UpperCAmelCase__							 =						example[..., None]
        input_values.append(example.T      )

       UpperCAmelCase__							 =						input_values
       if return_tensors is not None:
        UpperCAmelCase__							 =						padded_inputs.convert_to_tensors(lowerCamelCase      )

       return padded_inputs





