




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase  :      int						=      logging.get_logger(__name__)

_lowerCAmelCase  :      int						=      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowerCAmelCase  :      Tuple						=      {
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_lowerCAmelCase  :      Any						=      {
    "ctrl": 2_5_6,
}

_lowerCAmelCase  :      Optional[Any]						=      {
    "Pregnancy": 1_6_8_6_2_9,
    "Christianity": 7_6_7_5,
    "Explain": 1_0_6_4_2_3,
    "Fitness": 6_3_4_4_0,
    "Saving": 6_3_1_6_3,
    "Ask": 2_7_1_7_1,
    "Ass": 9_5_9_8_5,
    "Joke": 1_6_3_5_0_9,
    "Questions": 4_5_6_2_2,
    "Thoughts": 4_9_6_0_5,
    "Retail": 5_2_3_4_2,
    "Feminism": 1_6_4_3_3_8,
    "Writing": 1_1_9_9_2,
    "Atheism": 1_9_2_2_6_3,
    "Netflix": 4_8_6_1_6,
    "Computing": 3_9_6_3_9,
    "Opinion": 4_3_2_1_3,
    "Alone": 4_4_9_6_7,
    "Funny": 5_8_9_1_7,
    "Gaming": 4_0_3_5_8,
    "Human": 4_0_8_8,
    "India": 1_3_3_1,
    "Joker": 7_7_1_3_8,
    "Diet": 3_6_2_0_6,
    "Legal": 1_1_8_5_9,
    "Norman": 4_9_3_9,
    "Tip": 7_2_6_8_9,
    "Weight": 5_2_3_4_3,
    "Movies": 4_6_2_7_3,
    "Running": 2_3_4_2_5,
    "Science": 2_0_9_0,
    "Horror": 3_7_7_9_3,
    "Confession": 6_0_5_7_2,
    "Finance": 1_2_2_5_0,
    "Politics": 1_6_3_6_0,
    "Scary": 1_9_1_9_8_5,
    "Support": 1_2_6_5_4,
    "Technologies": 3_2_5_1_6,
    "Teenage": 6_6_1_6_0,
    "Event": 3_2_7_6_9,
    "Learned": 6_7_4_6_0,
    "Notion": 1_8_2_7_7_0,
    "Wikipedia": 3_7_5_8_3,
    "Books": 6_6_6_5,
    "Extract": 7_6_0_5_0,
    "Confessions": 1_0_2_7_0_1,
    "Conspiracy": 7_5_9_3_2,
    "Links": 6_3_6_7_4,
    "Narcissus": 1_5_0_4_2_5,
    "Relationship": 5_4_7_6_6,
    "Relationships": 1_3_4_7_9_6,
    "Reviews": 4_1_6_7_1,
    "News": 4_2_5_6,
    "Translation": 2_6_8_2_0,
    "multilingual": 1_2_8_4_0_6,
}



def   lowerCAmelCase						(						_lowerCAmelCase							:      int		):




 """simple docstring"""







 UpperCAmelCase__							       =  set()
 UpperCAmelCase__							       =  word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  UpperCAmelCase__							       =  char

 UpperCAmelCase__							       =  set(_lowerCAmelCase		)
 return pairs


class 	_UpperCamelCase					( UpperCAmelCase__						):
      UpperCAmelCase_						=						VOCAB_FILES_NAMES
      UpperCAmelCase_						=						PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_						=						CONTROL_CODES
      def __init__(	self      :List[str]       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :int       ,					lowerCamelCase      :Dict="<unk>"       ,					**lowerCamelCase      :List[str]					)   ->	Tuple:
       super().__init__(unk_token=_SCREAMING_SNAKE_CASE       ,					**_SCREAMING_SNAKE_CASE					)

       with open(_SCREAMING_SNAKE_CASE       ,					encoding="utf-8"					) as vocab_handle:
        UpperCAmelCase__							       =  json.load(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  {v: k for k, v in self.encoder.items()}
       with open(_SCREAMING_SNAKE_CASE       ,					encoding="utf-8"					) as merges_handle:
        UpperCAmelCase__							       =  merges_handle.read().split("\n"					)[1:-1]
       UpperCAmelCase__							       =  [tuple(merge.split()					) for merge in merges]
       UpperCAmelCase__							       =  dict(zip(_SCREAMING_SNAKE_CASE       ,					range(len(_SCREAMING_SNAKE_CASE					)					)					)					)
       UpperCAmelCase__							       =  {}
      @property
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	Union[str, Any]:
       return len(self.encoder					)
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	Optional[Any]:
       return dict(self.encoder       ,					**self.added_tokens_encoder					)
      def 		UpperCAmelCase_  (	self      :int       ,					lowerCamelCase      :int					)   ->	Union[str, Any]:




       if token in self.cache:
        return self.cache[token]
       UpperCAmelCase__							       =  tuple(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  tuple(list(word[:-1]					) + [word[-1] + "</w>"]					)
       UpperCAmelCase__							       =  get_pairs(_SCREAMING_SNAKE_CASE					)

       if not pairs:
        return token

       while True:
        UpperCAmelCase__							       =  min(_SCREAMING_SNAKE_CASE       ,					key=lambda lowerCamelCase					: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE       ,					float("inf"					)					)					)
        if bigram not in self.bpe_ranks:
         break
        UpperCAmelCase__							       =  bigram
        UpperCAmelCase__							       =  []
        UpperCAmelCase__							       =  0
        while i < len(_SCREAMING_SNAKE_CASE					):
         try:
          UpperCAmelCase__							       =  word.index(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)
         except ValueError:
          new_word.extend(word[i:]					)
          break
         else:
          new_word.extend(word[i:j]					)
          UpperCAmelCase__							       =  j

         if word[i] == first and i < len(_SCREAMING_SNAKE_CASE					) - 1 and word[i + 1] == second:
          new_word.append(first + second					)
          i += 2
         else:
          new_word.append(word[i]					)
          i += 1
        UpperCAmelCase__							       =  tuple(_SCREAMING_SNAKE_CASE					)
        UpperCAmelCase__							       =  new_word
        if len(_SCREAMING_SNAKE_CASE					) == 1:
         break
        else:
         UpperCAmelCase__							       =  get_pairs(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  """@@ """.join(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  word[:-4]
       UpperCAmelCase__							       =  word
       return word
      def 		UpperCAmelCase_  (	self      :Dict       ,					lowerCamelCase      :Any					)   ->	int:
       UpperCAmelCase__							       =  []

       UpperCAmelCase__							       =  re.findall(r"\S+\n?"       ,					_SCREAMING_SNAKE_CASE					)

       for token in words:
        split_tokens.extend(list(self.bpe(_SCREAMING_SNAKE_CASE					).split(" "					)					)					)
       return split_tokens
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :Any					)   ->	Optional[int]:
       return self.encoder.get(_SCREAMING_SNAKE_CASE       ,					self.encoder.get(self.unk_token					)					)
      def 		UpperCAmelCase_  (	self      :Union[str, Any]       ,					lowerCamelCase      :Union[str, Any]					)   ->	Optional[int]:
       return self.decoder.get(_SCREAMING_SNAKE_CASE       ,					self.unk_token					)
      def 		UpperCAmelCase_  (	self      :Optional[Any]       ,					lowerCamelCase      :str					)   ->	Union[str, Any]:
       UpperCAmelCase__							       =  """ """.join(_SCREAMING_SNAKE_CASE					).replace("@@ "       ,					""					).strip()
       return out_string







      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :int       ,					lowerCamelCase      :List[Any] = None					)   ->	Tuple[str]:




       if not os.path.isdir(_SCREAMING_SNAKE_CASE					):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
        return
       UpperCAmelCase__							       =  os.path.join(
           _SCREAMING_SNAKE_CASE       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
       UpperCAmelCase__							       =  os.path.join(
           _SCREAMING_SNAKE_CASE       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

       with open(_SCREAMING_SNAKE_CASE       ,					"w"       ,					encoding="utf-8"					) as f:
        f.write(json.dumps(self.encoder       ,					indent=2       ,					sort_keys=_SCREAMING_SNAKE_CASE       ,					ensure_ascii=_SCREAMING_SNAKE_CASE					) + "\n"					)

       UpperCAmelCase__							       =  0
       with open(_SCREAMING_SNAKE_CASE       ,					"w"       ,					encoding="utf-8"					) as writer:
        writer.write("#version: 0.2\n"					)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,					key=lambda lowerCamelCase					: kv[1]					):
         if index != token_index:
          logger.warning(
              f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
              " Please check that the tokenizer is not corrupted!"					)
          UpperCAmelCase__							       =  token_index
         writer.write(" ".join(_SCREAMING_SNAKE_CASE					) + "\n"					)
         index += 1

       return vocab_file, merge_file

      # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
      #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
      #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
      #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
      #     return ''.join(tokens_generated_so_far)










"""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       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

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

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

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

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

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

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

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

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

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

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

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

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

 # load eval pipeline
 if args.device is None:
  UpperCAmelCase	:					Optional[int]	=	0 if torch.cuda.is_available() else -1
 UpperCAmelCase	:					Tuple	=	pipeline("""automatic-speech-recognition"""			,		model=args.model_id			,		device=args.device						)

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

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
      A:   List[Any]								=		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:   Union[str, Any]								=		parser.parse_args()

      main(args)
