




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)











import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class 			_UpperCamelCase      (				lowerCAmelCase		):
      # to overwrite at feature extractactor specific tests
      UpperCAmelCase_        =		None
      UpperCAmelCase_        =		None
      @property
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       return self.feat_extract_tester.prepare_feat_extract_dict()
      def 	UpperCAmelCase_						(			self   :Any      )     ->			str:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       self.assertTrue(hasattr(lowerCamelCase ,      "feature_size"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "sampling_rate"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "padding_value"      )      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       self.assertTrue(all(len(lowerCamelCase      ) == len(lowerCamelCase      ) for x, y in zip(lowerCamelCase ,      processed_features[input_name]      )      )      )

       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="np"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="pt"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="tf"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Union[str, Any]      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						self.feat_extract_tester.seq_length_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.max_seq_length + pad_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.min_seq_length
       UpperCAmelCase__							 =						self.feat_extract_tester.batch_size
       UpperCAmelCase__							 =						self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest"      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[-1]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length"      )[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )
       self.assertTrue(len(input_a[0]      ) == pad_min_length      )
       self.assertTrue(len(input_a[1]      ) == pad_min_length + pad_diff      )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]      ))      )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size      )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(all(len(lowerCamelCase      ) % 10 == 0 for x in input_a      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__							 =						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(lowerCamelCase      ) == expected_mult_pad_length for x in input_a      )      )
       self.assertEqual(input_a.shape[:2] ,      (batch_size, expected_mult_pad_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size      )

       # Check padding value is correct
       UpperCAmelCase__							 =						(np.ones(self.feat_extract_tester.feature_size      ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]      )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[1]      )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[2]      )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      ) < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)      )
           < 1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Any      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Optional[int] ,      lowerCamelCase   :str      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       # truncate to smallest
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to smallest with np
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np" ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to middle
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(len(input_a[-1]      ) == len(speech_inputs[-1]      )      )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      truncation=lowerCamelCase      )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						12
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       UpperCAmelCase__							 =						len(speech_inputs[0]      )
       if expected_length % pad_to_multiple_of != 0:
        UpperCAmelCase__							 =						((len(speech_inputs[0]      ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]      ) == expected_length      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			int:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="pt"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_pt.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="tf"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_tf.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			str:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(list(processed.attention_mask.shape      ) ,      list(processed[input_name].shape[:2]      )      )
       self.assertListEqual(processed.attention_mask.sum(-1      ).tolist() ,      lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )
       UpperCAmelCase__							 =						min(lowerCamelCase      )

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape      ) ,      [processed_pad[input_name].shape[0], max_length]      )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1      ).tolist() ,      [max_length for x in speech_inputs]      )





