

'''simple docstring'''







import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      np.array:
       A_						       =   F'''{sampling_rate}'''
       A_						       =   '''1'''
       A_						       =   '''f32le'''
       A_						       =   [
           '''ffmpeg''',
           '''-i''',
           '''pipe:0''',
           '''-ac''',
           ac,
           '''-ar''',
           ar,
           '''-f''',
           format_for_conversion,
           '''-hide_banner''',
           '''-loglevel''',
           '''quiet''',
           '''pipe:1''',
       ]

       try:
              with subprocess.Popen(__a,							stdin=subprocess.PIPE,							stdout=subprocess.PIPE					) as ffmpeg_process:
                     A_						       =   ffmpeg_process.communicate(__a					)
       except FileNotFoundError as error:
              raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""					) from error
       A_						       =   output_stream[0]
       A_						       =   np.frombuffer(__a,							np.floataa					)
       if audio.shape[0] == 0:
              raise ValueError("""Malformed soundfile"""					)
       return audio
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = "f32le",							)      ->      List[Any]:
       A_						       =   F'''{sampling_rate}'''
       A_						       =   '''1'''
       if format_for_conversion == "s16le":
              A_						       =   2
       elif format_for_conversion == "f32le":
              A_						       =   4
       else:
              raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

       A_						       =   platform.system()
       if system == "Linux":
              A_						       =   '''alsa'''
              A_						       =   '''default'''
       elif system == "Darwin":
              A_						       =   '''avfoundation'''
              A_						       =   ''':0'''
       elif system == "Windows":
              A_						       =   '''dshow'''
              A_						       =   '''default'''

       A_						       =   [
           '''ffmpeg''',
           '''-f''',
           format_,
           '''-i''',
           input_,
           '''-ac''',
           ac,
           '''-ar''',
           ar,
           '''-f''',
           format_for_conversion,
           '''-fflags''',
           '''nobuffer''',
           '''-hide_banner''',
           '''-loglevel''',
           '''quiet''',
           '''pipe:1''',
       ]
       A_						       =   int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
       A_						       =   _ffmpeg_stream(__a,							__a					)
       for item in iterator:
              yield item
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = None,							UpperCAmelCase__ = None,							UpperCAmelCase__ = "f32le",							)      ->      str:






       if stream_chunk_s is not None:
              A_						       =   stream_chunk_s
       else:
              A_						       =   chunk_length_s

       A_						       =   ffmpeg_microphone(__a,							__a,							format_for_conversion=__a					)
       if format_for_conversion == "s16le":
              A_						       =   np.intaa
              A_						       =   2
       elif format_for_conversion == "f32le":
              A_						       =   np.floataa
              A_						       =   4
       else:
              raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

       if stride_length_s is None:
              A_						       =   chunk_length_s / 6
       A_						       =   int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
       if isinstance(__a,							(int, float)					):
              A_						       =   [stride_length_s, stride_length_s]

       A_						       =   int(round(sampling_rate * stride_length_s[0]					)					) * size_of_sample
       A_						       =   int(round(sampling_rate * stride_length_s[1]					)					) * size_of_sample
       A_						       =   datetime.datetime.now()
       A_						       =   datetime.timedelta(seconds=__a					)
       for item in chunk_bytes_iter(__a,							__a,							stride=(stride_left, stride_right),							stream=__a					):
              # Put everything back in numpy scale
              A_						       =   np.frombuffer(item["""raw"""],							dtype=__a					)
              A_						       =   (
                  item['''stride'''][0] // size_of_sample,
                  item['''stride'''][1] // size_of_sample,
              )
              A_						       =   sampling_rate
              audio_time += delta
              if datetime.datetime.now() > audio_time + 10 * delta:
                     # We're late !! SKIP
                     continue
              yield item
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = False					)      ->      Tuple:
       A_						       =   B''''''
       A_						       =   stride
       if stride_left + stride_right >= chunk_len:
              raise ValueError(
                  F'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''					)
       A_						       =   0
       for raw in iterator:
              acc += raw
              if stream and len(__a					) < chunk_len:
                     A_						       =   (_stride_left, 0)
                     yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
              else:
                     while len(__a					) >= chunk_len:
                            # We are flushing the accumulator
                            A_						       =   (_stride_left, stride_right)
                            A_						       =   {'''raw''': acc[:chunk_len], '''stride''': stride}
                            if stream:
                                   A_						       =   False
                            yield item
                            A_						       =   stride_left
                            A_						       =   acc[chunk_len - stride_left - stride_right :]
    # Last chunk
       if len(__a					) > stride_left:
              A_						       =   {'''raw''': acc, '''stride''': (_stride_left, 0)}
              if stream:
                     A_						       =   False
              yield item







def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[str]:
       A_						       =   2**24  # 16Mo
       try:
              with subprocess.Popen(__a,							stdout=subprocess.PIPE,							bufsize=__a					) as ffmpeg_process:
                     while True:
                            A_						       =   ffmpeg_process.stdout.read(__a					)
                            if raw == b"":
                                   break
                            yield raw
       except FileNotFoundError as error:
              raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""					) from error




'''simple docstring'''
import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					test_file.split(os.path.sep  )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
										    f'''{test_file} instead.'''  )
					UpperCamelCase__  :List[Any]				   =					components[-1]
					if not test_fn.endswith('''py'''  ):
										raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''  )
					if not test_fn.startswith('''test_modeling_'''  ):
										raise ValueError(
										    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''  )

					UpperCamelCase__  :Optional[Any]				   =					components[:-1] + [test_fn.replace('''.py'''					,   ''''''  )]
					UpperCamelCase__  :str				   =					'''.'''.join(__a  )

					return test_module_path


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					get_module_path(__a  )
					UpperCamelCase__  :List[str]				   =					importlib.import_module(__a  )

					return test_module


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					[]
					UpperCamelCase__  :Optional[Any]				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										if attr.endswith('''ModelTester'''  ):
															tester_classes.append(getattr(__a					,   __a  )  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					[]
					UpperCamelCase__  :Dict				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										UpperCamelCase__  :Dict				   =					getattr(__a					,   __a  )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										UpperCamelCase__  :Optional[int]				   =					getattr(__a					,   '''all_model_classes'''					,   []  )
										if len(__a  ) > 0:
															test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					get_test_classes(__a  )
					UpperCamelCase__  :Any				   =					set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes  )

					# sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Optional[Any]				   =					test_class()
					if hasattr(__a					,   '''setUp'''  ):
										test.setUp()

					UpperCamelCase__  :Union[str, Any]				   =					None
					if hasattr(__a					,   '''model_tester'''  ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															UpperCamelCase__  :List[str]				   =					test.model_tester.__class__

					return model_tester


def      a (     __a					,   __a  )    ->      Any:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					get_test_classes(__a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a					,   __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					get_test_classes_for_model(__a					,   __a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										UpperCamelCase__  :int				   =					get_model_tester_from_test_class(__a  )
										if tester_class is not None:
															tester_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_test_classes(__a  )
					UpperCamelCase__  :Dict				   =					{test_class: get_model_tester_from_test_class(__a  ) for test_class in test_classes}
					return test_tester_mapping


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_model_classes(__a  )
					UpperCamelCase__  :Tuple				   =					{
					    model_class: get_test_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_test_mapping


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					get_model_classes(__a  )
					UpperCamelCase__  :Union[str, Any]				   =					{
					    model_class: get_tester_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_to_tester_mapping







def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					if isinstance(__a					,   __a  ):
										return o
					elif isinstance(__a					,   __a  ):
										return o.__name__
					elif isinstance(__a					,   (list, tuple)  ):
										return [to_json(__a  ) for x in o]
					elif isinstance(__a					,   __a  ):
										return {to_json(__a  ): to_json(__a  ) for k, v in o.items()}
					else:
										return o