





'''simple docstring'''






from manim import *




class 					UpperCAmelCase	(     UpperCAmelCase__  ):




      def 							lowercase__       (     self     :					Dict    )							-> List[str]:
            _lowerCAmelCase					=				Rectangle(height=0.5     ,     width=0.5    )
            _lowerCAmelCase					=				Rectangle(height=0.46     ,     width=0.46    ).set_stroke(width=0    )

            _lowerCAmelCase					=				Rectangle(height=0.25     ,     width=0.25    )

            _lowerCAmelCase					=				[mem.copy() for i in range(6    )]
            _lowerCAmelCase					=				[mem.copy() for i in range(6    )]
            _lowerCAmelCase					=				VGroup(*__lowercase    ).arrange(__lowercase     ,     buff=0    )
            _lowerCAmelCase					=				VGroup(*__lowercase    ).arrange(__lowercase     ,     buff=0    )
            _lowerCAmelCase					=				VGroup(__lowercase     ,     __lowercase    ).arrange(__lowercase     ,     buff=0    )
            _lowerCAmelCase					=				Text("""CPU"""     ,     font_size=24    )
            _lowerCAmelCase					=				Group(__lowercase     ,     __lowercase    ).arrange(__lowercase     ,     buff=0.5     ,     aligned_edge=__lowercase    )
            cpu.move_to([-2.5, -0.5, 0]    )
            self.add(__lowercase    )

            _lowerCAmelCase					=				[mem.copy() for i in range(4    )]
            _lowerCAmelCase					=				VGroup(*__lowercase    ).arrange(__lowercase     ,     buff=0    )
            _lowerCAmelCase					=				Text("""GPU"""     ,     font_size=24    )
            _lowerCAmelCase					=				Group(__lowercase     ,     __lowercase    ).arrange(__lowercase     ,     buff=0.5     ,     aligned_edge=__lowercase    )
            gpu.move_to([-1, -1, 0]    )
            self.add(__lowercase    )

            _lowerCAmelCase					=				[mem.copy() for i in range(6    )]
            _lowerCAmelCase					=				VGroup(*__lowercase    ).arrange(__lowercase     ,     buff=0    )

            _lowerCAmelCase					=				Text("""Model"""     ,     font_size=24    )
            _lowerCAmelCase					=				Group(__lowercase     ,     __lowercase    ).arrange(__lowercase     ,     buff=0.5     ,     aligned_edge=__lowercase    )
            model.move_to([3, -1.0, 0]    )
            self.add(__lowercase    )

            _lowerCAmelCase					=				[]
            _lowerCAmelCase					=				[]

            for i, rect in enumerate(__lowercase    ):
                  _lowerCAmelCase					=				fill.copy().set_fill(__lowercase     ,     opacity=0.8    )
                  target.move_to(__lowercase    )
                  model_arr.append(__lowercase    )

                  _lowerCAmelCase					=				Rectangle(height=0.46     ,     width=0.46    ).set_stroke(width=0.0    ).set_fill(__lowercase     ,     opacity=0.8    )
                  cpu_target.move_to(cpu_left_col_base[i]    )
                  model_cpu_arr.append(__lowercase    )

            self.add(*__lowercase     ,     *__lowercase    )

            _lowerCAmelCase					=				[meta_mem.copy() for i in range(6    )]
            _lowerCAmelCase					=				[meta_mem.copy() for i in range(6    )]
            _lowerCAmelCase					=				VGroup(*__lowercase    ).arrange(__lowercase     ,     buff=0    )
            _lowerCAmelCase					=				VGroup(*__lowercase    ).arrange(__lowercase     ,     buff=0    )
            _lowerCAmelCase					=				VGroup(__lowercase     ,     __lowercase    ).arrange(__lowercase     ,     buff=0    )
            _lowerCAmelCase					=				Text("""Disk"""     ,     font_size=24    )
            _lowerCAmelCase					=				Group(__lowercase     ,     __lowercase    ).arrange(__lowercase     ,     buff=0.5     ,     aligned_edge=__lowercase    )
            disk.move_to([-4, -1.25, 0]    )
            self.add(__lowercase     ,     __lowercase    )

            _lowerCAmelCase					=				Square(side_length=2.2    )
            key.move_to([-5, 2, 0]    )

            _lowerCAmelCase					=				MarkupText(
                f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"     ,     font_size=18     ,     )

            key_text.move_to([-5, 2.4, 0]    )

            self.add(__lowercase     ,     __lowercase    )

            _lowerCAmelCase					=				MarkupText(
                f"<span fgcolor='{BLUE}'>●</span> Checkpoint"     ,     font_size=18     ,     )

            blue_text.next_to(__lowercase     ,     DOWN * 2.4     ,     aligned_edge=key_text.get_left()    )
            self.add(__lowercase    )

            _lowerCAmelCase					=				MarkupText(
                f"Now watch as an input is passed through the model\nand how the memory is utilized and handled."     ,     font_size=24     ,     )
            step_a.move_to([2, 2, 0]    )

            self.play(Write(__lowercase    )    )

            _lowerCAmelCase					=				Square(0.3    )
            input.set_fill(__lowercase     ,     opacity=1.0    )
            input.set_stroke(width=0.0    )
            input.next_to(model_base[0]     ,     __lowercase     ,     buff=0.5    )

            self.play(Write(__lowercase    )    )

            input.generate_target()
            input.target.next_to(model_arr[0]     ,     direction=__lowercase     ,     buff=0.02    )
            self.play(MoveToTarget(__lowercase    )    )

            self.play(FadeOut(__lowercase    )    )

            _lowerCAmelCase					=				Arrow(start=__lowercase     ,     end=__lowercase     ,     color=__lowercase     ,     buff=0.5    )
            a.next_to(model_arr[0].get_left()     ,     __lowercase     ,     buff=0.2    )

            model_cpu_arr[0].generate_target()
            model_cpu_arr[0].target.move_to(gpu_rect[0]    )

            _lowerCAmelCase					=				MarkupText(
                f"As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."     ,     font_size=24     ,     )
            step_a.move_to([2, 2, 0]    )

            self.play(Write(__lowercase     ,     run_time=3    )    )

            _lowerCAmelCase					=				{"""run_time""": 1, """fade_in""": True, """fade_out""": True, """buff""": 0.02}

            self.play(
                Write(__lowercase    )     ,     Circumscribe(model_arr[0]     ,     color=__lowercase     ,     **__lowercase    )     ,     Circumscribe(model_cpu_arr[0]     ,     color=__lowercase     ,     **__lowercase    )     ,     Circumscribe(gpu_rect[0]     ,     color=__lowercase     ,     **__lowercase    )     ,     )
            self.play(MoveToTarget(model_cpu_arr[0]    )    )

            _lowerCAmelCase					=				a.copy()
            for i in range(6    ):
                  a_c.next_to(model_arr[i].get_right() + 0.02     ,     __lowercase     ,     buff=0.2    )

                  input.generate_target()
                  input.target.move_to(model_arr[i].get_right() + 0.02    )

                  _lowerCAmelCase					=				AnimationGroup(
                      FadeOut(__lowercase     ,     run_time=0.5    )     ,     MoveToTarget(__lowercase     ,     run_time=0.5    )     ,     FadeIn(__lowercase     ,     run_time=0.5    )     ,     lag_ratio=0.2    )

                  self.play(__lowercase    )

                  model_cpu_arr[i].generate_target()
                  model_cpu_arr[i].target.move_to(cpu_left_col_base[i]    )

                  if i < 5:
                        model_cpu_arr[i + 1].generate_target()
                        model_cpu_arr[i + 1].target.move_to(gpu_rect[0]    )
                        if i >= 1:
                              _lowerCAmelCase					=				0.7

                        self.play(
                            Circumscribe(model_arr[i]     ,     **__lowercase    )     ,     Circumscribe(cpu_left_col_base[i]     ,     **__lowercase    )     ,     Circumscribe(cpu_left_col_base[i + 1]     ,     color=__lowercase     ,     **__lowercase    )     ,     Circumscribe(gpu_rect[0]     ,     color=__lowercase     ,     **__lowercase    )     ,     Circumscribe(model_arr[i + 1]     ,     color=__lowercase     ,     **__lowercase    )     ,     )
                        if i < 1:
                              self.play(
                                  MoveToTarget(model_cpu_arr[i]    )     ,     MoveToTarget(model_cpu_arr[i + 1]    )     ,     )
                        else:
                              self.play(
                                  MoveToTarget(model_cpu_arr[i]     ,     run_time=0.7    )     ,     MoveToTarget(model_cpu_arr[i + 1]     ,     run_time=0.7    )     ,     )
                  else:
                        model_cpu_arr[i].generate_target()
                        model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]    )
                        input.generate_target()
                        input.target.next_to(model_arr[-1].get_right()     ,     RIGHT + 0.02     ,     buff=0.2    )

                        self.play(
                            Circumscribe(model_arr[-1]     ,     color=__lowercase     ,     **__lowercase    )     ,     Circumscribe(cpu_left_col_base[-1]     ,     color=__lowercase     ,     **__lowercase    )     ,     Circumscribe(gpu_rect[0]     ,     color=__lowercase     ,     **__lowercase    )     ,     )

                        self.play(MoveToTarget(model_cpu_arr[i]    )    )

                  _lowerCAmelCase					=				a_c
                  _lowerCAmelCase					=				a_c.copy()

            input.generate_target()
            input.target.next_to(model_base[-1]     ,     RIGHT + 0.02     ,     buff=0.5    )
            self.play(
                FadeOut(__lowercase    )     ,     FadeOut(__lowercase     ,     run_time=0.5    )     ,     )

            _lowerCAmelCase					=				MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed."     ,     font_size=24    )
            step_a.move_to([2, 2, 0]    )

            self.play(Write(__lowercase     ,     run_time=3    )     ,     MoveToTarget(__lowercase    )    )

            self.wait()












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

import numpy as np

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


lowercase__							:	Tuple											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''input_values''', '''attention_mask''']






						def __init__(		self				:	List[Any]			,      __lowercase				:	int = 1			,      __lowercase				:	int = 1_60_00			,      __lowercase				:	float = 0.0			,      __lowercase				:	bool = False			,      __lowercase				:	int = 80			,      __lowercase				:	int = 16			,      __lowercase				:	int = 64			,      __lowercase				:	str = "hann_window"			,      __lowercase				:	float = 1.0			,      __lowercase				:	float = 80			,      __lowercase				:	float = 76_00			,      __lowercase				:	float = 1E-10			,      __lowercase				:	int = 2			,      __lowercase				:	bool = True			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							super().__init__(feature_size=__lowercase			,      sampling_rate=__lowercase			,      padding_value=__lowercase			,      **__lowercase		)
							snake_case_						       =	do_normalize
							snake_case_						       =	return_attention_mask

							snake_case_						       =	num_mel_bins
							snake_case_						       =	hop_length
							snake_case_						       =	win_length
							snake_case_						       =	win_function
							snake_case_						       =	frame_signal_scale
							snake_case_						       =	fmin
							snake_case_						       =	fmax
							snake_case_						       =	mel_floor
							snake_case_						       =	reduction_factor

							snake_case_						       =	win_length * sampling_rate // 10_00
							snake_case_						       =	hop_length * sampling_rate // 10_00
							snake_case_						       =	optimal_fft_length(self.sample_size		)
							snake_case_						       =	(self.n_fft // 2) + 1

							snake_case_						       =	window_function(window_length=self.sample_size			,      name=self.win_function			,      periodic=__lowercase		)

							snake_case_						       =	mel_filter_bank(
							    num_frequency_bins=self.n_freqs			,      num_mel_filters=self.num_mel_bins			,      min_frequency=self.fmin			,      max_frequency=self.fmax			,      sampling_rate=self.sampling_rate			,      norm="slaney"			,      mel_scale="slaney"			,      )

							if frame_signal_scale != 1.0:
								warnings.warn(
								    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )
							if reduction_factor != 2.0:
								warnings.warn(
								    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )






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






							"""simple docstring"""






							if attention_mask is not None:
								snake_case_						       =	np.array(__lowercase			,      np.intaa		)
								snake_case_						       =	[]

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

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

							return normed_input_values






						def 			snake_case__		(		self				:	str			,      __lowercase				:	np.ndarray			,      ):






							"""simple docstring"""






							snake_case_						       =	spectrogram(
							    __lowercase			,      window=self.window			,      frame_length=self.sample_size			,      hop_length=self.sample_stride			,      fft_length=self.n_fft			,      mel_filters=self.mel_filters			,      mel_floor=self.mel_floor			,      log_mel="log10"			,      )
							return log_mel_spec.T






						def __call__(		self				:	Optional[int]			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      __lowercase				:	Optional[int] = None			,      **__lowercase				:	List[str]			,      ):






							"""simple docstring"""






							if audio is None and audio_target is None:
								raise ValueError("You must provide either `audio` or `audio_target` values."		)

							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 audio is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )
							else:
								snake_case_						       =	None

							if audio_target is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )

								if inputs is None:
									return inputs_target
								else:
									snake_case_						       =	inputs_target["input_values"]
									snake_case_						       =	inputs_target.get("attention_mask"		)
									if decoder_attention_mask is not None:
										snake_case_						       =	decoder_attention_mask

							return inputs






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,      __lowercase				:	bool = False			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	isinstance(__lowercase			,      np.ndarray		) and len(speech.shape		) > 1
							if is_batched_numpy and len(speech.shape		) > 2:
								raise ValueError(f"Only mono-channel audio is supported for input to {self}"		)
							snake_case_						       =	is_batched_numpy or (
							    isinstance(__lowercase			,      (list, tuple)		) and (isinstance(speech[0]			,      (np.ndarray, tuple, list)		))
							)

							if is_batched:
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.floataa		) for speech in speech]
							elif not is_batched and not isinstance(__lowercase			,      np.ndarray		):
								snake_case_						       =	np.asarray(__lowercase			,      dtype=np.floataa		)
							elif isinstance(__lowercase			,      np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
								snake_case_						       =	speech.astype(np.floataa		)

							# always return batch
							if not is_batched:
								snake_case_						       =	[speech]

							# needed to make pad() work on spectrogram inputs
							snake_case_						       =	self.feature_size

							# convert into correct format for padding
							if is_target:
								snake_case_						       =	[self._extract_mel_features(__lowercase		) for waveform in speech]
								snake_case_						       =	BatchFeature({"input_values": features}		)
								snake_case_						       =	self.num_mel_bins
							else:
								snake_case_						       =	BatchFeature({"input_values": speech}		)

							snake_case_						       =	self.pad(
							    __lowercase			,      padding=__lowercase			,      max_length=__lowercase			,      truncation=__lowercase			,      pad_to_multiple_of=__lowercase			,      return_attention_mask=__lowercase			,      **__lowercase			,      )

							snake_case_						       =	feature_size_hack

							# convert input values to correct format
							snake_case_						       =	padded_inputs["input_values"]
							if not isinstance(input_values[0]			,      np.ndarray		):
								snake_case_						       =	[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		)
							):
								snake_case_						       =	[array.astype(np.floataa		) for array in input_values]
							elif isinstance(__lowercase			,      np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
								snake_case_						       =	input_values.astype(np.floataa		)

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

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

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

							return padded_inputs






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							snake_case_						       =	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
							for name in names:
								if name in output:
									del output[name]

							return output
