




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


UpperCamelCase__						=		logging.get_logger(__name__)


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					["""input_values""", """attention_mask"""]

					def __init__(							self					,			__lowercase=1					,			__lowercase=16_000					,			__lowercase=0.0					,			__lowercase=False					,			__lowercase=True					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__(feature_size=__lowercase					,			sampling_rate=__lowercase					,			padding_value=__lowercase					,			**__lowercase   )
						lowerCamelCase_				:						Union[str, Any]				  =				return_attention_mask
						lowerCamelCase_				:						List[Any]				  =				do_normalize

					@staticmethod
					def       lowercase_				(							__lowercase					,			__lowercase					,			__lowercase = 0.0   ):







						'''simple docstring'''




						if attention_mask is not None:
							lowerCamelCase_				:						Union[str, Any]				  =				np.array(__lowercase					,			np.intaa   )
							lowerCamelCase_				:						str				  =				[]

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

								normed_input_values.append(__lowercase   )
						else:
							lowerCamelCase_				:						Any				  =				[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

						return normed_input_values





					def __call__(							self					,			__lowercase					,			__lowercase = False					,			__lowercase = None					,			__lowercase = False					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			**__lowercase					,			):







						'''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} was trained using a sampling rate of"
								    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` 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.'   )

						lowerCamelCase_				:						int				  =				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}"   )
						lowerCamelCase_				:						List[str]				  =				is_batched_numpy or (
						    isinstance(__lowercase					,			(list, tuple)   ) and (isinstance(raw_speech[0]					,			(np.ndarray, tuple, list)   ))
						)

						# always return batch
						if not is_batched:
							lowerCamelCase_				:						List[Any]				  =				[raw_speech]

						# convert into correct format for padding
						lowerCamelCase_				:						str				  =				BatchFeature({'input_values': raw_speech}   )

						lowerCamelCase_				:						str				  =				self.pad(
						    __lowercase					,			padding=__lowercase					,			max_length=__lowercase					,			truncation=__lowercase					,			pad_to_multiple_of=__lowercase					,			return_attention_mask=__lowercase					,			)

						# convert input values to correct format
						lowerCamelCase_				:						Tuple				  =				padded_inputs['input_values']
						if not isinstance(input_values[0]					,			np.ndarray   ):
							lowerCamelCase_				:						List[str]				  =				[np.asarray(__lowercase					,			dtype=np.floataa   ) for array in input_values]
						elif (
						    not isinstance(__lowercase					,			np.ndarray   )
						    and isinstance(input_values[0]					,			np.ndarray   )
						    and input_values[0].dtype is np.dtype(np.floataa   )
						):
							lowerCamelCase_				:						int				  =				[array.astype(np.floataa   ) for array in input_values]
						elif isinstance(__lowercase					,			np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
							lowerCamelCase_				:						List[str]				  =				input_values.astype(np.floataa   )

						# convert attention_mask to correct format
						lowerCamelCase_				:						Any				  =				padded_inputs.get('attention_mask'   )
						if attention_mask is not None:
							lowerCamelCase_				:						Optional[int]				  =				[np.asarray(__lowercase					,			dtype=np.intaa   ) for array in attention_mask]

						# zero-mean and unit-variance normalization
						if self.do_normalize:
							lowerCamelCase_				:						Tuple				  =				(
							    attention_mask
							    if self._get_padding_strategies(__lowercase					,			max_length=__lowercase   ) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							lowerCamelCase_				:						Any				  =				self.zero_mean_unit_var_norm(
							    padded_inputs['input_values']					,			attention_mask=__lowercase					,			padding_value=self.padding_value   )

						if return_tensors is not None:
							lowerCamelCase_				:						Any				  =				padded_inputs.convert_to_tensors(__lowercase   )

						return padded_inputs


"""simple docstring"""







import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


SCREAMING_SNAKE_CASE_  					= {}
for task, _ in pipeline_test_mapping.items():
  SCREAMING_SNAKE_CASE_  					= {'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
SCREAMING_SNAKE_CASE_  					= {
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							str:

   if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "pt"
   elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "tf"
   elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "flax"
   else:
      return None
def       __UpperCAmelCase		(		a_      :		Optional[Any]     ,	a_      :		int					)  ->							List[Any]:
   # Use the cached results
   if PIPELINE_TEST_MAPPING[task].get(a_     ,	a_					) is not None:
      return PIPELINE_TEST_MAPPING[task][framework]

   _UpperCAmelCase 						=       pipeline_test_mapping[task]["test"]
   _UpperCAmelCase 						=       None

   if framework == "pt":
      _UpperCAmelCase 						=       getattr(a_     ,	"model_mapping"     ,	a_					)
   elif framework == "tf":
      _UpperCAmelCase 						=       getattr(a_     ,	"tf_model_mapping"     ,	a_					)

   if mapping is not None:
      _UpperCAmelCase 						=       dict(mapping.items()					)

   # cache the results
   _UpperCAmelCase 						=       mapping
   return mapping
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework is None:
      return None
   _UpperCAmelCase 						=       get_mapping_for_task(a_     ,	a_					)
   if mapping is None:
      return None

   _UpperCAmelCase 						=       list({model_class.config_class for model_class in test_class.all_model_classes}					)
   if len(a_					) != 1:
      raise ValueError("There should be exactly one configuration class from `test_class.all_model_classes`."					)

   # This could be a list/tuple of model classes, but it's rare.
   _UpperCAmelCase 						=       mapping.get(config_classes[0]     ,	a_					)
   if isinstance(a_     ,	(tuple, list)					):
      _UpperCAmelCase 						=       sorted(a_     ,	key=lambda a_					: x.__name__					)

   return model_class
def       __UpperCAmelCase		(		a_      :		Tuple					)  ->							Tuple:
   _UpperCAmelCase 						=       [(task, get_model_for_pipeline_test(a_     ,	a_					)) for task in pipeline_test_mapping]
   _UpperCAmelCase 						=       sorted([(task, model) for task, model in mapping if model is not None]     ,	key=lambda a_					: x[0]					)

   return dict(a_					)
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework == "pt":
      _UpperCAmelCase 						=       "torch"
   _UpperCAmelCase 						=       "{}"

   _UpperCAmelCase 						=       get_pipeline_model_mapping(a_					)
   if len(a_					) == 0:
      return ""

   _UpperCAmelCase 						=       []
   for task, model_classes in mapping.items():
      if isinstance(a_     ,	(tuple, list)					):
         # A list/tuple of model classes
         _UpperCAmelCase 						=       "(" + ", ".join([x.__name__ for x in model_classes]					) + ")"
      else:
         # A single model class
         _UpperCAmelCase 						=       model_classes.__name__
      texts.append(F'''"{task}": {value}'''					)
   _UpperCAmelCase 						=       "{" + ", ".join(a_					) + "}"
   _UpperCAmelCase 						=       F'''pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'''

   return text
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
   if not issubclass(a_     ,	unittest.TestCase					):
      return False
   return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							Any:
   _UpperCAmelCase 						=       [x for x in get_test_classes(a_					) if is_valid_test_class(a_					)]

   _UpperCAmelCase 						=       None
   for test_class in test_classes:
      # If a test class has defined `pipeline_model_mapping`, let's take it
      if getattr(a_     ,	"pipeline_model_mapping"     ,	a_					) is not None:
         _UpperCAmelCase 						=       test_class
         break
    # Take the test class with the shortest name (just a heuristic)
   if target_test_class is None and len(a_					) > 0:
      _UpperCAmelCase 						=       sorted(a_     ,	key=lambda a_					: (len(x.__name__					), x.__name__)					)[0]

   return target_test_class
def       __UpperCAmelCase		(		a_      :		Union[str, Any]     ,	a_      :		str     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       start_idx
   for idx, line in enumerate(lines[start_idx:]					):
      _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
      if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
         _UpperCAmelCase 						=       start_idx + idx
      elif idx > 0 and indent <= indent_level:
         # Outside the definition block of `pipeline_model_mapping`
         break

   return end_idx
def       __UpperCAmelCase		(		a_      :		Tuple     ,	a_      :		Union[str, Any]=False					)  ->							str:
   if getattr(a_     ,	"pipeline_model_mapping"     ,	a_					) is not None:
      if not overwrite:
         return "", -1

   _UpperCAmelCase 						=       get_pipeline_model_mapping_string(a_					)
   if len(a_					) == 0:
      return "", -1
   _UpperCAmelCase 						=       line_to_add + "\n"

   # The code defined the class `test_class`
   _UpperCAmelCase						,				_UpperCAmelCase 						=       inspect.getsourcelines(a_					)
   # `inspect` gives the code for an object, including decorator(s) if any.
   # We (only) need the exact line of the class definition.
   for idx, line in enumerate(a_					):
      if line.lstrip().startswith("class "					):
         _UpperCAmelCase 						=       class_lines[idx:]
         class_start_line_no += idx
         break
   _UpperCAmelCase 						=       class_start_line_no + len(a_					) - 1

   # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
   # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
   # block that appears in a `test_class`.
   _UpperCAmelCase 						=       None
   # The indent level of the line at `class_lines[start_idx]` (if defined)
   _UpperCAmelCase 						=       0
   # To record if `pipeline_model_mapping` is found in `test_class`.
   _UpperCAmelCase 						=       None
   for idx, line in enumerate(a_					):
      if line.strip().startswith("all_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("all_generative_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("pipeline_model_mapping = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
         _UpperCAmelCase 						=       line
         break

   if start_idx is None:
      return "", -1
   # Find the ending index (inclusive) of the above found block.
   _UpperCAmelCase 						=       find_block_ending(a_     ,	a_     ,	a_					)

   # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
   # `is_timm_available()` instead of `is_torch_available()`.
   # Keep leading and trailing whitespaces
   _UpperCAmelCase 						=       re.compile(r"\s(is_\S+?_available\(\))\s"					)
   for line in class_lines[start_idx : end_idx + 1]:
      _UpperCAmelCase 						=       r.search(a_					)
      if backend_condition is not None:
         # replace the leading and trailing whitespaces to the space character " ".
         _UpperCAmelCase 						=       " " + backend_condition[0][1:-1] + " "
         _UpperCAmelCase 						=       r.sub(a_     ,	a_					)
         break

   if def_line is None:
      # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
      # `all_model_classes` or `all_generative_model_classes`.
      _UpperCAmelCase 						=       end_idx
   else:
      # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
      _UpperCAmelCase 						=       start_idx - 1
      # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
      for idx in range(a_     ,	end_idx + 1					):
         # These lines are going to be removed before writing to the test file.
         _UpperCAmelCase 						=       None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
   _UpperCAmelCase 						=       [x.__name__ for x in test_class.__bases__]
   if "PipelineTesterMixin" not in parent_classes:
      # Put `PipelineTesterMixin` just before `unittest.TestCase`
      _UpperCAmelCase 						=       [x for x in parent_classes if x != "TestCase"] + ["PipelineTesterMixin"]
      if "TestCase" in parent_classes:
         # Here we **assume** the original string is always with `unittest.TestCase`.
         _parent_classes.append("unittest.TestCase"					)
      _UpperCAmelCase 						=       ", ".join(_parent_classes					)
      for idx, line in enumerate(a_					):
         # Find the ending of the declaration of `test_class`
         if line.strip().endswith("):"					):
            # mark the lines of the declaration of `test_class` to be removed
            for _idx in range(idx + 1					):
               _UpperCAmelCase 						=       None  # noqa
            break
        # Add the new, one-line, class declaration for `test_class`
      _UpperCAmelCase 						=       F'''class {test_class.__name__}({parent_classes}):\n'''

   # Add indentation
   _UpperCAmelCase 						=       " " * indent_level + line_to_add
   # Insert `pipeline_model_mapping` to `class_lines`.
   # (The line at `target_idx` should be kept by definition!)
   _UpperCAmelCase 						=       class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
   # Remove the lines that are marked to be removed
   _UpperCAmelCase 						=       [x for x in class_lines if x is not None]

   # Move from test class to module (in order to write to the test file)
   _UpperCAmelCase 						=       inspect.getsourcelines(inspect.getmodule(a_					)					)[0]
   # Be careful with the 1-off between line numbers and array indices
   _UpperCAmelCase 						=       module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
   _UpperCAmelCase 						=       "".join(a_					)

   _UpperCAmelCase 						=       inspect.getsourcefile(a_					)
   with open(a_     ,	"w"     ,	encoding="UTF-8"     ,	newline="\n"					) as fp:
      fp.write(a_					)

   return line_to_add
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		Optional[Any]=False					)  ->							Dict:
   _UpperCAmelCase 						=       find_test_class(a_					)
   if test_class:
      add_pipeline_model_mapping(a_     ,	overwrite=a_					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_  					= argparse.ArgumentParser()
  parser.add_argument(
      '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
  )
  parser.add_argument(
      '--all',
      action='store_true',
      help='If to check and modify all test files.',
  )
  parser.add_argument(
      '--overwrite',
      action='store_true',
      help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
  )
  SCREAMING_SNAKE_CASE_  					= parser.parse_args()

  if not args.all and not args.test_file:
    raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
  elif args.all and args.test_file:
    raise ValueError('Only one of `--test_file` and `--all` could be specified.')

  SCREAMING_SNAKE_CASE_  					= []
  if args.test_file:
    SCREAMING_SNAKE_CASE_  					= [args.test_file]
  else:
    SCREAMING_SNAKE_CASE_  					= os.path.join('tests', 'models', '**', 'test_modeling_*.py')
    for test_file in glob.glob(pattern):
      # `Flax` is not concerned at this moment
      if not test_file.startswith('test_modeling_flax_'):
        test_files.append(test_file)

  for test_file in test_files:
    if test_file in TEST_FILE_TO_IGNORE:
      print(f'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
      continue
    add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)

