





"""simple docstring"""




from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
lowerCamelCase_	:		int         =   20

# Time between time steps in seconds
lowerCamelCase_	:		Optional[Any]         =   INTERVAL / 10_00

class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_ = 1.0   , snake_case_ = 1.0   , snake_case_ = "blue"   , ):

        """simple docstring"""

        A_    :			str									=    position_x
        A_    :			List[Any]									=    position_y
        A_    :			List[str]									=    velocity_x
        A_    :			str									=    velocity_y
        A_    :			Tuple									=    mass
        A_    :			int									=    size
        A_    :			Optional[int]									=    color

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.position_x, self.position_y

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.velocity_x, self.velocity_y

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        self.velocity_x += force_x * delta_time
        self.velocity_y += force_y * delta_time

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        self.position_x += self.velocity_x * delta_time
        self.position_y += self.velocity_y * delta_time


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_ = 1.0   , snake_case_ = 1.0   , snake_case_ = 0.0   , ):

        """simple docstring"""

        A_    :			List[Any]									=    bodies
        A_    :			Union[str, Any]									=    gravitation_constant
        A_    :			Dict									=    time_factor
        A_    :			Optional[int]									=    softening_factor

      def __len__(	self    ):

        """simple docstring"""

        return len(self.bodies    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        for bodya in self.bodies:
          A_    :			List[str]									=    0.0
          A_    :			Optional[Any]									=    0.0
          for bodya in self.bodies:
            if bodya != bodya:
              A_    :			Union[str, Any]									=    bodya.position_x - bodya.position_x
              A_    :			Dict									=    bodya.position_y - bodya.position_y

              # Calculation of the distance using Pythagoras's theorem
              # Extra factor due to the softening technique
              A_    :			int									=    (dif_x**2 + dif_y**2 + self.softening_factor) ** (
                  1 / 2
              )

              # Newton's law of universal gravitation.
              force_x += (
                  self.gravitation_constant * bodya.mass * dif_x / distance**3
              )
              force_y += (
                  self.gravitation_constant * bodya.mass * dif_y / distance**3
              )

            # Update the body's velocity once all the force components have been added
          bodya.update_velocity(snake_case_   , snake_case_   , delta_time * self.time_factor    )

        # Update the positions only after all the velocities have been updated
        for body in self.bodies:
          body.update_position(delta_time * self.time_factor    )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  body_system.update_system(_UpperCAmelCase   )

  # Update the positions of the patches
  for patch, body in zip(_UpperCAmelCase					,   body_system.bodies   ):
    A_    :			Optional[Any]									=    (body.position_x, body.position_y)





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = -1					,   _UpperCAmelCase = 1					,   _UpperCAmelCase = -1					,   _UpperCAmelCase = 1					,   ):






  """simple docstring"""





  A_    :			Dict									=    plt.figure()
  fig.canvas.set_window_title(_UpperCAmelCase   )
  A_    :			Tuple									=    plt.axes(
      xlim=(x_start, x_end)					,   ylim=(y_start, y_end)   )  # Set section to be plotted
  plt.gca().set_aspect('equal'   )  # Fix aspect ratio

  # Each body is drawn as a patch by the plt-function
  A_    :			Tuple									=    [
      plt.Circle((body.position_x, body.position_y)					,   body.size					,   fc=body.color   )
      for body in body_system.bodies
  ]

  for patch in patches:
    ax.add_patch(_UpperCAmelCase   )

  # Function called at each step of the animation
  def update(_UpperCAmelCase   ) -> list[plt.Circle]:
    update_step(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   )
    return patches

  A_    :			Dict									=    animation.FuncAnimation(  # noqa: F841
      _UpperCAmelCase					,   _UpperCAmelCase					,   interval=_UpperCAmelCase					,   blit=_UpperCAmelCase   )

  plt.show()





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			List[Any]									=    0.9_700_436
  A_    :			Optional[int]									=    -0.24_308_753
  A_    :			Dict									=    0.466_203_685
  A_    :			Optional[int]									=    0.43_236_573

  A_    :			Union[str, Any]									=    [
      Body(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   size=0.2					,   color='red'   ),
      Body(-position_x					,   -position_y					,   _UpperCAmelCase					,   _UpperCAmelCase					,   size=0.2					,   color='green'   ),
      Body(0					,   0					,   -2 * velocity_x					,   -2 * velocity_y					,   size=0.2					,   color='blue'   ),
  ]
  return BodySystem(_UpperCAmelCase					,   time_factor=3   )





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			int									=    7.34_76E22
  A_    :			Union[str, Any]									=    5.9_72E24
  A_    :			Dict									=    1022
  A_    :			Optional[int]									=    384399000
  A_    :			List[str]									=    6.6_74E-11

  # Calculation of the respective velocities so that total impulse is zero,
  # i.e. the two bodies together don't move
  A_    :			Tuple									=    earth_mass * velocity_dif / (earth_mass + moon_mass)
  A_    :			Dict									=    moon_velocity - velocity_dif

  A_    :			Optional[int]									=    Body(-earth_moon_distance					,   0					,   0					,   _UpperCAmelCase					,   _UpperCAmelCase					,   10000000					,   'grey'   )
  A_    :			Optional[Any]									=    Body(0					,   0					,   0					,   _UpperCAmelCase					,   _UpperCAmelCase					,   50000000					,   'blue'   )
  return BodySystem([earth, moon]					,   _UpperCAmelCase					,   time_factor=1000000   )





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Optional[Any]									=    []
  for _ in range(10   ):
    A_    :			Union[str, Any]									=    random.uniform(-0.5					,   0.5   )
    A_    :			List[str]									=    random.uniform(-0.5					,   0.5   )

    # Bodies are created pairwise with opposite velocities so that the
    # total impulse remains zero
    bodies.append(
        Body(
            random.uniform(-0.5					,   0.5   )					,   random.uniform(-0.5					,   0.5   )					,   _UpperCAmelCase					,   _UpperCAmelCase					,   size=0.05					,   )   )
    bodies.append(
        Body(
            random.uniform(-0.5					,   0.5   )					,   random.uniform(-0.5					,   0.5   )					,   -velocity_x					,   -velocity_y					,   size=0.05					,   )   )
  return BodySystem(_UpperCAmelCase					,   0.01					,   10					,   0.1   )


if __name__ == "__main__":
   plot('Figure-8 solution to the 3-body-problem', example_a(), -2, 2, -2, 2)
   plot(
       'Moon\'s orbit around the earth',
       example_a(),
       -4_30_00_00_00,
       4_30_00_00_00,
       -4_30_00_00_00,
       4_30_00_00_00,
   )
   plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)






'''simple docstring'''


import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
				from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
				_lowerCamelCase					:						Optional[Any]					     =							True
				from torch.cuda.amp import autocast


_lowerCamelCase					:						List[Any]					     =							logging.getLogger(__name__)



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Whether to log verbose messages or not.'''}		,       )






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



	"""simple docstring"""







	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      handlers=[logging.StreamHandler(sys.stdout     )]							,      )
	A					=   logging.WARNING
	if model_args.verbose_logging:
		A					=   logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank     ):
		A					=   logging.INFO
	logger.setLevel(UpperCAmelCase     )



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}    )
		__lowerCAmelCase								=     field(
		    default='''train'''		,       metadata={
		        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
		    }		,       )
		__lowerCAmelCase								=     field(
		    default='''validation'''		,       metadata={
		        '''help''': (
		            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
		        )
		    }		,       )
		__lowerCAmelCase								=     field(
		    default='''text'''		,       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}		,       )
		__lowerCAmelCase								=     field(
		    default='''file'''		,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}		,       )
		__lowerCAmelCase								=     field(
		    default='''librispeech'''		,       metadata={
		        '''help''': (
		            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
		            ''' \'buckwalter\'.'''
		        )
		    }		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}		,       )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     "|"
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     None







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   str   ):
			if name == "librispeech":
				return cls()
			if name == "timit":
				return cls(
				    do_lower_case=_lowerCAmelCase   ,   translation_table=str.maketrans({"""-""": """ """}   )   ,   )
			if name == "buckwalter":
				A					=   {
				    """-""": """ """,  # sometimes used to represent pauses
				    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
				}
				return cls(
				    vocab_file=pathlib.Path(_lowerCAmelCase   ).parent.joinpath("""vocab/buckwalter.json"""   )   ,   word_delimiter_token="""/"""   ,   translation_table=str.maketrans(_lowerCAmelCase   )   ,   words_to_remove={"""sil"""}   ,   untransliterator=arabic.buckwalter.untransliterate   ,   )
			raise ValueError(F"""Unsupported orthography: '{name}'."""   )







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   str   ):
			# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
			if len(self.translation_table   ) > 0:
				A					=   text.translate(self.translation_table   )
			if len(self.words_to_remove   ) == 0:
				A					=   """ """.join(text.split()   )  # clean up whitespaces
			else:
				A					=   """ """.join(w for w in text.split() if w not in self.words_to_remove   )  # and clean up whilespaces
			return text







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   ModelArguments   ):
			A					=   WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   )
			if self.vocab_file:
				A					=   WavaVecaCTCTokenizer(
				    self.vocab_file   ,   cache_dir=model_args.cache_dir   ,   do_lower_case=self.do_lower_case   ,   word_delimiter_token=self.word_delimiter_token   ,   )
			else:
				A					=   WavaVecaCTCTokenizer.from_pretrained(
				    model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   ,   do_lower_case=self.do_lower_case   ,   word_delimiter_token=self.word_delimiter_token   ,   )
			return WavaVecaProcessor(_lowerCAmelCase   ,   _lowerCAmelCase   )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None







		def __call__(self    :   List[str]   ,   _lowerCAmelCase    :   List[Dict[str, Union[List[int], torch.Tensor]]]   ):
			# split inputs and labels since they have to be of different lenghts and need
			# different padding methods
			A					=   [{"""input_values""": feature["""input_values"""]} for feature in features]
			A					=   [{"""input_ids""": feature["""labels"""]} for feature in features]

			A					=   self.processor.pad(
			    _lowerCAmelCase   ,   padding=self.padding   ,   max_length=self.max_length   ,   pad_to_multiple_of=self.pad_to_multiple_of   ,   return_tensors="""pt"""   ,   )
			A					=   self.processor.pad(
			    labels=_lowerCAmelCase   ,   padding=self.padding   ,   max_length=self.max_length_labels   ,   pad_to_multiple_of=self.pad_to_multiple_of_labels   ,   return_tensors="""pt"""   ,   )

			# replace padding with -100 to ignore loss correctly
			A					=   labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1   )   ,   -100   )

			A					=   labels

			return batch






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   nn.Module   ,   _lowerCAmelCase    :   Dict[str, Union[torch.Tensor, Any]]   ):
			model.train()
			A					=   self._prepare_inputs(_lowerCAmelCase   )

			if self.use_amp:
				with autocast():
					A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )
			else:
				A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )

			if self.args.n_gpu > 1:
				if model.module.config.ctc_loss_reduction == "mean":
					A					=   loss.mean()
				elif model.module.config.ctc_loss_reduction == "sum":
					A					=   loss.sum() / (inputs["""labels"""] >= 0).sum()
				else:
					raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""   )

			if self.args.gradient_accumulation_steps > 1:
				A					=   loss / self.args.gradient_accumulation_steps

			if self.use_amp:
				self.scaler.scale(_lowerCAmelCase   ).backward()
			elif self.use_apex:
				with amp.scale_loss(_lowerCAmelCase   ,   self.optimizer   ) as scaled_loss:
					scaled_loss.backward()
			elif self.deepspeed:
				self.deepspeed.backward(_lowerCAmelCase   )
			else:
				loss.backward()

			return loss.detach()






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



	"""simple docstring"""







	A					=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

	A	,     A	,     A					=   parser.parse_args_into_dataclasses()
	configure_logger(UpperCAmelCase							,      UpperCAmelCase     )

	A					=   Orthography.from_name(data_args.orthography.lower()     )
	A					=   orthography.create_processor(UpperCAmelCase     )
	A					=   WavaVecaForCTC.from_pretrained(
	    model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      gradient_checkpointing=training_args.gradient_checkpointing							,      vocab_size=len(processor.tokenizer     )							,      )

	A					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.train_split_name     )
	A					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.validation_split_name     )

	A					=   datasets.load_metric("""wer"""     )
	A					=   processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
	A					=   """""".join(t for t in processor.tokenizer.get_vocab().keys() if len(UpperCAmelCase     ) == 1     )
	A					=   re.compile(  # remove characters not in vocabulary
	    Rf"""[^\s{re.escape(UpperCAmelCase     )}]"""							,      flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,      )
	A					=   []

	def prepare_example(UpperCAmelCase     ):  # TODO(elgeish) make use of multiprocessing?
		A	,     A					=   librosa.load(example[data_args.speech_file_column]							,      sr=UpperCAmelCase     )
		if data_args.max_duration_in_seconds is not None:
			A					=   len(example["""speech"""]     ) / example["""sampling_rate"""]
		# Normalize and clean up text; order matters!
		A					=   orthography.preprocess_for_training(example[data_args.target_text_column]     )
		A					=   vocabulary_text_cleaner.sub(""""""							,      UpperCAmelCase     )
		if updated_text != example[data_args.target_text_column]:
			text_updates.append((example[data_args.target_text_column], updated_text)     )
			A					=   updated_text
		return example

	A					=   train_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )
	A					=   val_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )

	if data_args.max_duration_in_seconds is not None:

		def filter_by_max_duration(UpperCAmelCase     ):
			return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

		A					=   len(UpperCAmelCase     )
		A					=   len(UpperCAmelCase     )
		A					=   train_dataset.filter(UpperCAmelCase							,      remove_columns=["""duration_in_seconds"""]     )
		A					=   val_dataset.filter(UpperCAmelCase							,      remove_columns=["""duration_in_seconds"""]     )
		if len(UpperCAmelCase     ) > old_train_size:
			logger.warning(
			    f"""Filtered out {len(UpperCAmelCase     ) - old_train_size} train example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s)."""     )
		if len(UpperCAmelCase     ) > old_val_size:
			logger.warning(
			    f"""Filtered out {len(UpperCAmelCase     ) - old_val_size} validation example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s)."""     )
	logger.info(f"""Split sizes: {len(UpperCAmelCase     )} train and {len(UpperCAmelCase     )} validation."""     )

	logger.warning(f"""Updated {len(UpperCAmelCase     )} transcript(s) using '{data_args.orthography}' orthography rules."""     )
	if logger.isEnabledFor(logging.DEBUG     ):
		for original_text, updated_text in text_updates:
			logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\""""     )
	A					=   None

	def prepare_dataset(UpperCAmelCase     ):
		# check that all files have the correct sampling rate
		assert (
		    len(set(batch["""sampling_rate"""]     )     ) == 1
		), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

		A					=   processor(
		    audio=batch["""speech"""]							,      text=batch[data_args.target_text_column]							,      sampling_rate=batch["""sampling_rate"""][0]     )
		batch.update(UpperCAmelCase     )
		return batch

	A					=   train_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )
	A					=   val_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )

	A					=   DataCollatorCTCWithPadding(processor=UpperCAmelCase							,      padding=UpperCAmelCase     )

	def compute_metrics(UpperCAmelCase     ):
		A					=   pred.predictions
		A					=   np.argmax(UpperCAmelCase							,      axis=-1     )

		A					=   processor.tokenizer.pad_token_id

		A					=   processor.batch_decode(UpperCAmelCase     )
		# we do not want to group tokens when computing the metrics
		A					=   processor.batch_decode(pred.label_ids							,      group_tokens=UpperCAmelCase     )
		if logger.isEnabledFor(logging.DEBUG     ):
			for reference, predicted in zip(UpperCAmelCase							,      UpperCAmelCase     ):
				logger.debug(f"""reference: \"{reference}\""""     )
				logger.debug(f"""predicted: \"{predicted}\""""     )
				if orthography.untransliterator is not None:
					logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(UpperCAmelCase     )}\""""     )
					logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(UpperCAmelCase     )}\""""     )

		A					=   wer_metric.compute(predictions=UpperCAmelCase							,      references=UpperCAmelCase     )

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	A					=   CTCTrainer(
	    model=UpperCAmelCase							,      data_collator=UpperCAmelCase							,      args=UpperCAmelCase							,      compute_metrics=UpperCAmelCase							,      train_dataset=UpperCAmelCase							,      eval_dataset=UpperCAmelCase							,      tokenizer=processor.feature_extractor							,      )

	trainer.train()


if __name__ == "__main__":
				main()

