




"""simple docstring"""


import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  args.log_outputs
  _snake_case							:		Union[str, Any]								=  """_""".join(args.dataset.split("""/"""    ) + [args.config, args.split]    )

  # load metric
  _snake_case							:		str								=  load_metric("""wer"""    )
  _snake_case							:		List[str]								=  load_metric("""cer"""    )

  # compute metrics
  _snake_case							:		List[str]								=  wer.compute(references=result["""target"""]		,   predictions=result["""prediction"""]    )
  _snake_case							:		Dict								=  cer.compute(references=result["""target"""]		,   predictions=result["""prediction"""]    )

  # print & log results
  _snake_case							:		Optional[int]								=  F"WER: {wer_result}\nCER: {cer_result}"
  print(__SCREAMING_SNAKE_CASE    )

  with open(F"{dataset_id}_eval_results.txt"		,   """w"""    ) as f:
    f.write(__SCREAMING_SNAKE_CASE    )

  # log all results in text file. Possibly interesting for analysis
  if log_outputs is not None:
    _snake_case							:		Tuple								=  F"log_{dataset_id}_predictions.txt"
    _snake_case							:		Optional[Any]								=  F"log_{dataset_id}_targets.txt"

    with open(__SCREAMING_SNAKE_CASE		,   """w"""    ) as p, open(__SCREAMING_SNAKE_CASE		,   """w"""    ) as t:
      # mapping function to write output
      def write_to_file(snake_case__							:					str		,   snake_case__							:					int    ):
        p.write(F"{i}" + """\n"""    )
        p.write(batch["""prediction"""] + """\n"""    )
        t.write(F"{i}" + """\n"""    )
        t.write(batch["""target"""] + """\n"""    )

      result.map(__SCREAMING_SNAKE_CASE		,   with_indices=__SCREAMING_SNAKE_CASE    )


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  _snake_case							:		Dict								=  """[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

  _snake_case							:		Optional[Any]								=  re.sub(__SCREAMING_SNAKE_CASE		,   """"""		,   text.lower()    )

  # In addition, we can normalize the target text, e.g. removing new lines characters etc...
  # note that order is important here!
  _snake_case							:		Dict								=  ["""\n\n""", """\n""", """   """, """  """]

  for t in token_sequences_to_ignore:
    _snake_case							:		List[Any]								=  """ """.join(text.split(__SCREAMING_SNAKE_CASE    )    )

  return text


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		str								=  load_dataset(args.dataset		,   args.config		,   split=args.split		,   use_auth_token=__SCREAMING_SNAKE_CASE    )

  # for testing: only process the first two examples as a test
  # dataset = dataset.select(range(10))

  # load processor
  _snake_case							:		Tuple								=  AutoFeatureExtractor.from_pretrained(args.model_id    )
  _snake_case							:		str								=  feature_extractor.sampling_rate

  # resample audio
  _snake_case							:		Any								=  dataset.cast_column("""audio"""		,   Audio(sampling_rate=__SCREAMING_SNAKE_CASE    )    )

  # load eval pipeline
  if args.device is None:
    _snake_case							:		Dict								=  0 if torch.cuda.is_available() else -1
  _snake_case							:		Dict								=  pipeline("""automatic-speech-recognition"""		,   model=args.model_id		,   device=args.device    )

  # map function to decode audio
  def map_to_pred(snake_case__							:					str    ):
    _snake_case							:		Any								=  asr(
        batch["""audio"""]["""array"""]		,   chunk_length_s=args.chunk_length_s		,   stride_length_s=args.stride_length_s    )

    _snake_case							:		int								=  prediction["""text"""]
    _snake_case							:		int								=  normalize_text(batch["""sentence"""]    )
    return batch

  # run inference on all examples
  _snake_case							:		str								=  dataset.map(__SCREAMING_SNAKE_CASE		,   remove_columns=dataset.column_names    )

  # compute and log_results
  # do not change function below
  log_results(__SCREAMING_SNAKE_CASE		,   __SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()

     parser.add_argument(
         '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
     )
     parser.add_argument(
         '''--dataset''',
         type=str,
         required=True,
         help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
     )
     parser.add_argument(
         '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
     )
     parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
     parser.add_argument(
         '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
     )
     parser.add_argument(
         '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
     )
     parser.add_argument(
         '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
     )
     parser.add_argument(
         '''--device''',
         type=int,
         default=None,
         help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
     )
     A_						=				parser.parse_args()

     main(args)




from manim import *







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Rectangle(height=0.5		,    width=0.5       )
		lowercase       =    Rectangle(height=0.25		,    width=0.25       )
		lowercase       =    Rectangle(height=0.46		,    width=0.46       ).set_stroke(width=0       )

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

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

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

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

		lowercase       =    []
		lowercase       =    []
		lowercase       =    []

		for i, rect in enumerate(snake_case       ):
			rect.set_stroke(snake_case       )

			lowercase       =    Rectangle(height=0.46 / 4		,    width=0.46 / 3       ).set_stroke(width=0.0       ).set_fill(snake_case		,    opacity=0.7       )

			if i == 0:
				cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT       )		,    buff=0.02		,    direction=snake_case       )
				cpu_target.set_x(cpu_target.get_x() + 0.1       )
			elif i == 3:
				cpu_target.next_to(model_cpu_arr[0]		,    direction=snake_case		,    buff=0.0       )
			else:
				cpu_target.next_to(model_cpu_arr[i - 1]		,    direction=snake_case		,    buff=0.0       )
			self.add(snake_case       )
			model_cpu_arr.append(snake_case       )

		self.add(*snake_case		,    *snake_case		,    *snake_case       )

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

		lowercase       =    Text('Loaded Checkpoint'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		checkpoint.move_to([3, 0.5, 0]       )

		self.add(snake_case       )

		lowercase       =    []
		lowercase       =    []

		for i, rect in enumerate(snake_case       ):
			lowercase       =    fill.copy().set_fill(snake_case		,    opacity=0.7       )
			target.move_to(snake_case       )
			ckpt_arr.append(snake_case       )

			lowercase       =    target.copy()
			if i < 5:
				cpu_target.move_to(cpu_left_col_base[i + 1]       )
			else:
				cpu_target.move_to(cpu_right_col_base[i - 5]       )
			ckpt_cpu_arr.append(snake_case       )
		self.add(*snake_case		,    *snake_case       )

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

		lowercase       =    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(snake_case		,    snake_case       )

		lowercase       =    MarkupText(
		    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,    font_size=18		,    )

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

		lowercase       =    MarkupText(
		    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'''		,    font_size=24		,    )
		step_a.move_to([2, 2, 0]       )

		lowercase       =    [meta_mem.copy() for i in range(6       )]
		lowercase       =    [meta_mem.copy() for i in range(6       )]
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    Text('Disk'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		disk.move_to([-4.0, -1.25, 0]       )
		self.play(Write(snake_case		,    run_time=3       )		,    Write(snake_case		,    run_time=1       )		,    Create(snake_case		,    run_time=1       )       )

		lowercase       =    []
		for i, rect in enumerate(snake_case       ):
			lowercase       =    rect.copy()
			target.generate_target()
			target.target.move_to(disk_left_col_base[i]       ).scale(0.5       )
			animations.append(MoveToTarget(snake_case		,    run_time=1.5       )       )
		self.play(*snake_case       )

		self.play(FadeOut(snake_case       )       )

		lowercase       =    MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''		,    font_size=24       )
		step_a.move_to([2, 2, 0]       )

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

		self.play(
		    FadeOut(snake_case		,    snake_case		,    *snake_case		,    *snake_case       )		,    )

		self.wait()

