






"""simple docstring"""






import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


UpperCAmelCase_   : Optional[int]   			= (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


UpperCAmelCase_   : Tuple   			= logging.get_logger(__name__)  # pylint: disable=invalid-name



def 					_A			() ->       Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		"https://pypi.org/pypi/diffusers/json"
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		json.loads(request.urlopen(__lowerCamelCase							).read()							)["releases"].keys()
    return sorted(__lowerCamelCase    ,				key=lambda __a							: version.Version(__lowerCamelCase							)							)



def 					_A			() ->       Optional[Any]:
    """simple docstring"""






    if HF_MODULES_CACHE in sys.path:
        return

    sys.path.append(__lowerCamelCase							)
    os.makedirs(__lowerCamelCase    ,				exist_ok=__lowerCamelCase							)
    SCREAMING_SNAKE_CASE_      :  int    						=		Path(__lowerCamelCase							) / "__init__.py"
    if not init_path.exists():
        init_path.touch()



def 					_A			(__a							) ->       List[Any]:
    """simple docstring"""






    init_hf_modules()
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Path(__lowerCamelCase							) / name
    # If the parent module does not exist yet, recursively create it.
    if not dynamic_module_path.parent.exists():
        create_dynamic_module(dynamic_module_path.parent							)
    os.makedirs(__lowerCamelCase    ,				exist_ok=__lowerCamelCase							)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		dynamic_module_path / "__init__.py"
    if not init_path.exists():
        init_path.touch()



def 					_A			(__a							) ->       List[Any]:
    """simple docstring"""






    with open(__lowerCamelCase    ,				'''r'''    ,				encoding='''utf-8'''							) as f:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		f.read()

    # Imports of the form `import .xxx`
    SCREAMING_SNAKE_CASE_      :  Any    						=		re.findall('''^\s*import\s+\.(\S+)\s*$'''    ,				__lowerCamelCase    ,				flags=re.MULTILINE							)
    # Imports of the form `from .xxx import yyy`
    relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import'''    ,				__lowerCamelCase    ,				flags=re.MULTILINE							)
    # Unique-ify
    return list(set(__lowerCamelCase							)							)



def 					_A			(__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		False
    SCREAMING_SNAKE_CASE_      :  str    						=		[module_file]
    SCREAMING_SNAKE_CASE_      :  str    						=		[]

    # Let's recurse through all relative imports
    while not no_change:
        SCREAMING_SNAKE_CASE_      :  int    						=		[]
        for f in files_to_check:
            new_imports.extend(get_relative_imports(__lowerCamelCase							)							)

        SCREAMING_SNAKE_CASE_      :  Any    						=		Path(__lowerCamelCase							).parent
        SCREAMING_SNAKE_CASE_      :  str    						=		[str(module_path / m							) for m in new_imports]
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		[f for f in new_import_files if f not in all_relative_imports]
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[f'{f}.py' for f in new_import_files]

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		len(__lowerCamelCase							) == 0
        all_relative_imports.extend(__lowerCamelCase							)

    return all_relative_imports



def 					_A			(__a							) ->       int:
    """simple docstring"""






    with open(__lowerCamelCase    ,				'''r'''    ,				encoding='''utf-8'''							) as f:
        SCREAMING_SNAKE_CASE_      :  int    						=		f.read()

    # Imports of the form `import xxx`
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		re.findall('''^\s*import\s+(\S+)\s*$'''    ,				__lowerCamelCase    ,				flags=re.MULTILINE							)
    # Imports of the form `from xxx import yyy`
    imports += re.findall('''^\s*from\s+(\S+)\s+import'''    ,				__lowerCamelCase    ,				flags=re.MULTILINE							)
    # Only keep the top-level module
    SCREAMING_SNAKE_CASE_      :  Dict    						=		[imp.split('''.'''							)[0] for imp in imports if not imp.startswith('''.'''							)]

    # Unique-ify and test we got them all
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		list(set(__lowerCamelCase							)							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[]
    for imp in imports:
        try:
            importlib.import_module(__lowerCamelCase							)
        except ImportError:
            missing_packages.append(__lowerCamelCase							)

    if len(__lowerCamelCase							) > 0:
        raise ImportError(
            '''This modeling file requires the following packages that were not found in your environment: '''
            f'{", ".join(__lowerCamelCase							)}. Run `pip install {" ".join(__lowerCamelCase							)}`'							)

    return get_relative_imports(__lowerCamelCase							)



def 					_A			(__a    ,				__a							) ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		module_path.replace(os.path.sep    ,				'''.'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		importlib.import_module(__lowerCamelCase							)

    if class_name is None:
        return find_pipeline_class(__lowerCamelCase							)
    return getattr(__lowerCamelCase    ,				__lowerCamelCase							)



def 					_A			(__a							) ->       int:
    """simple docstring"""






    from ..pipelines import DiffusionPipeline

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		dict(inspect.getmembers(__lowerCamelCase    ,				inspect.isclass							)							)

    SCREAMING_SNAKE_CASE_      :  str    						=		None
    for cls_name, cls in cls_members.items():
        if (
            cls_name != DiffusionPipeline.__name__
            and issubclass(cls    ,				__lowerCamelCase							)
            and cls.__module__.split('''.'''							)[0] != "diffusers"
        ):
            if pipeline_class is not None:
                raise ValueError(
                    f'Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'
                    f' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'
                    f' {loaded_module}.'							)
            SCREAMING_SNAKE_CASE_      :  Dict    						=		cls

    return pipeline_class



def 					_A			(__a    ,				__a    ,				__a = None    ,				__a = False    ,				__a = False    ,				__a = None    ,				__a = None    ,				__a = None    ,				__a = False    ,				) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		str(__lowerCamelCase							)

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		os.path.join(__lowerCamelCase    ,				__lowerCamelCase							)

    if os.path.isfile(__lowerCamelCase							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		module_file_or_url
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		"local"
    elif pretrained_model_name_or_path.count('''/'''							) == 0:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		get_diffusers_versions()
        # cut ".dev0"
        SCREAMING_SNAKE_CASE_      :  str    						=		"v" + ".".join(__version__.split('''.'''							)[:3]							)

        # retrieve github version that matches
        if revision is None:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		latest_version if latest_version[1:] in available_versions else "main"
            logger.info(f'Defaulting to latest_version: {revision}.'							)
        elif revision in available_versions:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		f'v{revision}'
        elif revision == "main":
            SCREAMING_SNAKE_CASE_      :  str    						=		revision
        else:
            raise ValueError(
                f'`custom_revision`: {revision} does not exist. Please make sure to choose one of'
                f' {", ".join(available_versions + ["main"]							)}.'							)

        # community pipeline on GitHub
        SCREAMING_SNAKE_CASE_      :  Any    						=		COMMUNITY_PIPELINES_URL.format(revision=__lowerCamelCase    ,				pipeline=__lowerCamelCase							)
        try:
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		cached_download(
                __lowerCamelCase    ,				cache_dir=__lowerCamelCase    ,				force_download=__lowerCamelCase    ,				proxies=__lowerCamelCase    ,				resume_download=__lowerCamelCase    ,				local_files_only=__lowerCamelCase    ,				use_auth_token=__lowerCamelCase    ,				)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		"git"
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		pretrained_model_name_or_path + ".py"
        except EnvironmentError:
            logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'							)
            raise
    else:
        try:
            # Load from URL or cache if already cached
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		hf_hub_download(
                __lowerCamelCase    ,				__lowerCamelCase    ,				cache_dir=__lowerCamelCase    ,				force_download=__lowerCamelCase    ,				proxies=__lowerCamelCase    ,				resume_download=__lowerCamelCase    ,				local_files_only=__lowerCamelCase    ,				use_auth_token=__lowerCamelCase    ,				)
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		os.path.join('''local'''    ,				'''--'''.join(pretrained_model_name_or_path.split('''/'''							)							)							)
        except EnvironmentError:
            logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'							)
            raise

    # Check we have all the requirements in our environment
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_imports(__lowerCamelCase							)

    # Now we move the module inside our cached dynamic modules.
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
    create_dynamic_module(__lowerCamelCase							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Path(__lowerCamelCase							) / full_submodule
    if submodule == "local" or submodule == "git":
        # We always copy local files (we could hash the file to see if there was a change, and give them the name of
        # that hash, to only copy when there is a modification but it seems overkill for now).
        # The only reason we do the copy is to avoid putting too many folders in sys.path.
        shutil.copy(__lowerCamelCase    ,				submodule_path / module_file							)
        for module_needed in modules_needed:
            SCREAMING_SNAKE_CASE_      :  str    						=		f'{module_needed}.py'
            shutil.copy(os.path.join(__lowerCamelCase    ,				__lowerCamelCase							)    ,				submodule_path / module_needed							)
    else:
        # Get the commit hash
        # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
        if isinstance(__lowerCamelCase    ,				__lowerCamelCase							):
            SCREAMING_SNAKE_CASE_      :  Any    						=		use_auth_token
        elif use_auth_token is True:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		HfFolder.get_token()
        else:
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		None

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model_info(__lowerCamelCase    ,				revision=__lowerCamelCase    ,				token=__lowerCamelCase							).sha

        # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
        # benefit of versioning.
        SCREAMING_SNAKE_CASE_      :  Any    						=		submodule_path / commit_hash
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		full_submodule + os.path.sep + commit_hash
        create_dynamic_module(__lowerCamelCase							)

        if not (submodule_path / module_file).exists():
            shutil.copy(__lowerCamelCase    ,				submodule_path / module_file							)
        # Make sure we also have every file with relative
        for module_needed in modules_needed:
            if not (submodule_path / module_needed).exists():
                get_cached_module_file(
                    __lowerCamelCase    ,				f'{module_needed}.py'    ,				cache_dir=__lowerCamelCase    ,				force_download=__lowerCamelCase    ,				resume_download=__lowerCamelCase    ,				proxies=__lowerCamelCase    ,				use_auth_token=__lowerCamelCase    ,				revision=__lowerCamelCase    ,				local_files_only=__lowerCamelCase    ,				)
    return os.path.join(__lowerCamelCase    ,				__lowerCamelCase							)





def 					_A			(__a    ,				__a    ,				__a = None    ,				__a = None    ,				__a = False    ,				__a = False    ,				__a = None    ,				__a = None    ,				__a = None    ,				__a = False    ,				**__a    ,				) ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		get_cached_module_file(
        __lowerCamelCase    ,				__lowerCamelCase    ,				cache_dir=__lowerCamelCase    ,				force_download=__lowerCamelCase    ,				resume_download=__lowerCamelCase    ,				proxies=__lowerCamelCase    ,				use_auth_token=__lowerCamelCase    ,				revision=__lowerCamelCase    ,				local_files_only=__lowerCamelCase    ,				)
    return get_class_in_module(__lowerCamelCase    ,				final_module.replace('''.py'''    ,				''''''							)							)




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)





