
import argparse
from collections import defaultdict

import yaml


__lowerCamelCase							=       """docs/source/en/_toctree.yml"""


def 			UpperCamelCase					(    __lowerCamelCase     : Optional[Any]    ):
       snake_case       :   Any							=      defaultdict(__lowerCamelCase    )
       snake_case       :   List[Any]							=      []
       snake_case       :   List[str]							=      []
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({"local": doc["local"], "title": doc["title"]}    )
              else:
                     new_doc_list.append(__lowerCamelCase    )

       snake_case       :   List[str]							=      new_doc_list
       snake_case       :   Optional[int]							=      [key for key, value in counts.items() if value > 1]

       snake_case       :   Any							=      []
       for duplicate_key in duplicates:
              snake_case       :   List[str]							=      list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}    )
              if len(__lowerCamelCase    ) > 1:
                     raise ValueError(
                         f"""{duplicate_key} is present several times in the documentation table of content at """
                         "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
                         "others."    )
              # Only add this once
              new_doc.append({"local": duplicate_key, "title": titles[0]}    )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1]    )
       snake_case       :   Union[str, Any]							=      sorted(__lowerCamelCase				,					key=lambda __lowerCamelCase    : s["title"].lower()    )

       # "overview" gets special treatment and is always first
       if len(__lowerCamelCase    ) > 1:
              raise ValueError("{doc_list} has two 'overview' docs which is not allowed."    )

       overview_doc.extend(__lowerCamelCase    )

       # Sort
       return overview_doc


def 			UpperCamelCase					(    __lowerCamelCase     : Any=False    ):
       with open(__lowerCamelCase				,					encoding="utf-8"    ) as f:
              snake_case       :   Optional[Any]							=      yaml.safe_load(f.read()    )

       # Get to the API doc
       snake_case       :   List[str]							=      0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       snake_case       :   str							=      content[api_idx]["sections"]

       # Then to the model doc
       snake_case       :   Optional[int]							=      0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       snake_case       :   Dict							=      api_doc[scheduler_idx]["sections"]
       snake_case       :   Any							=      clean_doc_toc(__lowerCamelCase    )

       snake_case       :   str							=      False
       if new_scheduler_doc != scheduler_doc:
              snake_case       :   Dict							=      True
              if overwrite:
                     snake_case       :   int							=      new_scheduler_doc

       if diff:
              if overwrite:
                     snake_case       :   Dict							=      api_doc
                     with open(__lowerCamelCase				,					"w"				,					encoding="utf-8"    ) as f:
                            f.write(yaml.dump(__lowerCamelCase				,					allow_unicode=__lowerCamelCase    )    )
              else:
                     raise ValueError(
                         "The model doc part of the table of content is not properly sorted, run `make style` to fix this."    )


def 			UpperCamelCase					(    __lowerCamelCase     : int=False    ):
       with open(__lowerCamelCase				,					encoding="utf-8"    ) as f:
              snake_case       :   str							=      yaml.safe_load(f.read()    )

       # Get to the API doc
       snake_case       :   str							=      0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       snake_case       :   List[Any]							=      content[api_idx]["sections"]

       # Then to the model doc
       snake_case       :   Tuple							=      0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       snake_case       :   int							=      False
       snake_case       :   Optional[int]							=      api_doc[pipeline_idx]["sections"]
       snake_case       :   Any							=      []

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     snake_case       :   str							=      pipeline_doc["section"]
                     snake_case       :   Optional[int]							=      clean_doc_toc(__lowerCamelCase    )
                     if overwrite:
                            snake_case       :   Any							=      new_sub_pipeline_doc
              new_pipeline_docs.append(__lowerCamelCase    )

       # sort overall pipeline doc
       snake_case       :   Tuple							=      clean_doc_toc(__lowerCamelCase    )

       if new_pipeline_docs != pipeline_docs:
              snake_case       :   Optional[int]							=      True
              if overwrite:
                     snake_case       :   Optional[int]							=      new_pipeline_docs

       if diff:
              if overwrite:
                     snake_case       :   int							=      api_doc
                     with open(__lowerCamelCase				,					"w"				,					encoding="utf-8"    ) as f:
                            f.write(yaml.dump(__lowerCamelCase				,					allow_unicode=__lowerCamelCase    )    )
              else:
                     raise ValueError(
                         "The model doc part of the table of content is not properly sorted, run `make style` to fix this."    )


if __name__ == "__main__":
   __lowerCamelCase							=       argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   __lowerCamelCase							=       parser.parse_args()

   check_scheduler_doc(args.fix_and_overwrite)
   check_pipeline_doc(args.fix_and_overwrite)







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					(    __lowerCamelCase     : Dataset				,					__lowerCamelCase     : Dict[str, str]    ):
       snake_case       :   int							=      args.log_outputs
       snake_case       :   Dict							=      "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

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

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

       # print & log results
       snake_case       :   int							=      f"""WER: {wer_result}\nCER: {cer_result}"""
       print(__lowerCamelCase    )

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

       # log all results in text file. Possibly interesting for analysis
       if log_outputs is not None:
              snake_case       :   int							=      f"""log_{dataset_id}_predictions.txt"""
              snake_case       :   List[Any]							=      f"""log_{dataset_id}_targets.txt"""

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

                     result.map(__lowerCamelCase				,					with_indices=__lowerCamelCase    )


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

       snake_case       :   List[Any]							=      re.sub(__lowerCamelCase				,					""				,					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       :   Optional[Any]							=      ["\n\n", "\n", "   ", "  "]

       for t in token_sequences_to_ignore:
              snake_case       :   Dict							=      " ".join(text.split(__lowerCamelCase    )    )

       return text


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       # load dataset
       snake_case       :   str							=      load_dataset(args.dataset				,					args.config				,					split=args.split				,					use_auth_token=__lowerCamelCase    )

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

       # load processor
       snake_case       :   List[Any]							=      AutoFeatureExtractor.from_pretrained(args.model_id    )
       snake_case       :   Union[str, Any]							=      feature_extractor.sampling_rate

       # resample audio
       snake_case       :   Union[str, Any]							=      dataset.cast_column("audio"				,					Audio(sampling_rate=__lowerCamelCase    )    )

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

       # map function to decode audio
       def map_to_pred(__lowerCamelCase     : int    ):
              snake_case       :   Dict							=      asr(
                  batch["audio"]["array"]				,					chunk_length_s=args.chunk_length_s				,					stride_length_s=args.stride_length_s    )

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

       # run inference on all examples
       snake_case       :   Dict							=      dataset.map(__lowerCamelCase				,					remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
   __lowerCamelCase							=       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.""",
   )
   __lowerCamelCase							=       parser.parse_args()

   main(args)





