"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a					:	List[str]							=		logging.get_logger(__name__)

a					:	int							=		{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class 		a_						( _UpperCAmelCase	):
    a      :				Optional[int]     =				'wavlm'







    def __init__(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Dict=32     ,					__UpperCamelCase			:       int=7_68     ,					__UpperCamelCase			:       Dict=12     ,					__UpperCamelCase			:       Optional[int]=12     ,					__UpperCamelCase			:       Optional[Any]=30_72     ,					__UpperCamelCase			:       Optional[int]="gelu"     ,					__UpperCamelCase			:       int=0.1     ,					__UpperCamelCase			:       List[Any]=0.1     ,					__UpperCamelCase			:       str=0.1     ,					__UpperCamelCase			:       List[str]=0.0     ,					__UpperCamelCase			:       Optional[Any]=0.1     ,					__UpperCamelCase			:       List[Any]=0.1     ,					__UpperCamelCase			:       int=0.0_2     ,					__UpperCamelCase			:       int=1e-5     ,					__UpperCamelCase			:       int="group"     ,					__UpperCamelCase			:       Any="gelu"     ,					__UpperCamelCase			:       List[str]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)     ,					__UpperCamelCase			:       Dict=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCamelCase			:       Any=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCamelCase			:       int=False     ,					__UpperCamelCase			:       Optional[Any]=1_28     ,					__UpperCamelCase			:       Union[str, Any]=16     ,					__UpperCamelCase			:       Optional[int]=3_20     ,					__UpperCamelCase			:       str=8_00     ,					__UpperCamelCase			:       Tuple=False     ,					__UpperCamelCase			:       Dict=True     ,					__UpperCamelCase			:       int=0.0_5     ,					__UpperCamelCase			:       Any=10     ,					__UpperCamelCase			:       Optional[int]=2     ,					__UpperCamelCase			:       int=0.0     ,					__UpperCamelCase			:       Optional[Any]=10     ,					__UpperCamelCase			:       List[Any]=3_20     ,					__UpperCamelCase			:       int=2     ,					__UpperCamelCase			:       Union[str, Any]=0.1     ,					__UpperCamelCase			:       str=1_00     ,					__UpperCamelCase			:       Union[str, Any]=2_56     ,					__UpperCamelCase			:       Tuple=2_56     ,					__UpperCamelCase			:       Tuple=0.1     ,					__UpperCamelCase			:       Dict="mean"     ,					__UpperCamelCase			:       Optional[int]=False     ,					__UpperCamelCase			:       Optional[int]=False     ,					__UpperCamelCase			:       Dict=2_56     ,					__UpperCamelCase			:       List[Any]=(5_12, 5_12, 5_12, 5_12, 15_00)     ,					__UpperCamelCase			:       Optional[int]=(5, 3, 3, 1, 1)     ,					__UpperCamelCase			:       Optional[Any]=(1, 2, 3, 1, 1)     ,					__UpperCamelCase			:       int=5_12     ,					__UpperCamelCase			:       Optional[Any]=80     ,					__UpperCamelCase			:       List[Any]=0     ,					__UpperCamelCase			:       List[Any]=1     ,					__UpperCamelCase			:       Optional[int]=2     ,					__UpperCamelCase			:       str=False     ,					__UpperCamelCase			:       Any=3     ,					__UpperCamelCase			:       str=2     ,					__UpperCamelCase			:       str=3     ,					__UpperCamelCase			:       List[Any]=None     ,					**__UpperCamelCase			:       Tuple     ,					)		->List[str]:






        '''simple docstring'''






        super().__init__(**__UpperCamelCase     ,					pad_token_id=__UpperCamelCase     ,					bos_token_id=__UpperCamelCase     ,					eos_token_id=__UpperCamelCase     )
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   feat_extract_norm
        _UpperCAmelCase          =   feat_extract_activation
        _UpperCAmelCase          =   list(__UpperCamelCase     )
        _UpperCAmelCase          =   list(__UpperCamelCase     )
        _UpperCAmelCase          =   list(__UpperCamelCase     )
        _UpperCAmelCase          =   conv_bias
        _UpperCAmelCase          =   num_buckets
        _UpperCAmelCase          =   max_bucket_distance
        _UpperCAmelCase          =   num_conv_pos_embeddings
        _UpperCAmelCase          =   num_conv_pos_embedding_groups
        _UpperCAmelCase          =   len(self.conv_dim     )
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   intermediate_size
        _UpperCAmelCase          =   hidden_act
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   hidden_dropout
        _UpperCAmelCase          =   attention_dropout
        _UpperCAmelCase          =   activation_dropout
        _UpperCAmelCase          =   feat_proj_dropout
        _UpperCAmelCase          =   final_dropout
        _UpperCAmelCase          =   layerdrop
        _UpperCAmelCase          =   layer_norm_eps
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   num_ctc_classes
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   do_stable_layer_norm
        _UpperCAmelCase          =   use_weighted_layer_sum
        _UpperCAmelCase          =   classifier_proj_size

        if (
            (len(self.conv_stride     ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
            or (len(self.conv_dim     ) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                f""" {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        _UpperCAmelCase          =   apply_spec_augment
        _UpperCAmelCase          =   mask_time_prob
        _UpperCAmelCase          =   mask_time_length
        _UpperCAmelCase          =   mask_time_min_masks
        _UpperCAmelCase          =   mask_feature_prob
        _UpperCAmelCase          =   mask_feature_length

        # parameters for pretraining with codevector quantized representations
        _UpperCAmelCase          =   num_codevectors_per_group
        _UpperCAmelCase          =   num_codevector_groups
        _UpperCAmelCase          =   contrastive_logits_temperature
        _UpperCAmelCase          =   num_negatives
        _UpperCAmelCase          =   codevector_dim
        _UpperCAmelCase          =   proj_codevector_dim
        _UpperCAmelCase          =   diversity_loss_weight

        # ctc loss
        _UpperCAmelCase          =   ctc_loss_reduction
        _UpperCAmelCase          =   ctc_zero_infinity

        # adapter
        _UpperCAmelCase          =   add_adapter
        _UpperCAmelCase          =   adapter_kernel_size
        _UpperCAmelCase          =   adapter_stride
        _UpperCAmelCase          =   num_adapter_layers
        _UpperCAmelCase          =   output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        _UpperCAmelCase          =   classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        _UpperCAmelCase          =   list(__UpperCamelCase     )
        _UpperCAmelCase          =   list(__UpperCamelCase     )
        _UpperCAmelCase          =   list(__UpperCamelCase     )
        _UpperCAmelCase          =   xvector_output_dim







    @property
    def      _snake_case					(			self			:       List[str]     )		->Tuple:






        '''simple docstring'''






        return functools.reduce(operator.mul     ,					self.conv_stride     ,					1     )

"""simple docstring"""



import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _UpperCAmelCase          =   requests.get(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

    try:
        job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}      )
        _UpperCAmelCase          =   math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

        for i in range(_A      ):
            _UpperCAmelCase          =   requests.get(url + F"""&page={i + 2}"""					,      headers=_A      ).json()
            job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}      )

        return job_links
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""      )

    return {}


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _UpperCAmelCase          =   requests.get(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

    try:
        artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}      )
        _UpperCAmelCase          =   math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

        for i in range(_A      ):
            _UpperCAmelCase          =   requests.get(url + F"""&page={i + 2}"""					,      headers=_A      ).json()
            artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}      )

        return artifacts
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""      )

    return {}


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   requests.get(_A					,      headers=_A					,      allow_redirects=_A      )
    _UpperCAmelCase          =   result.headers["""Location"""]
    _UpperCAmelCase          =   requests.get(_A					,      allow_redirects=_A      )
    _UpperCAmelCase          =   os.path.join(_A					,      F"""{artifact_name}.zip"""      )
    with open(_A					,      """wb"""      ) as fp:
        fp.write(response.content      )


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   None

    with zipfile.ZipFile(_A      ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_A      ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_A      ) as f:
                        for line in f:
                            _UpperCAmelCase          =   line.decode("""UTF-8"""      ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _UpperCAmelCase          =   line[: line.index(""": """      )]
                                    _UpperCAmelCase          =   line[line.index(""": """      ) + len(""": """      ) :]
                                    errors.append([error_line, error]      )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith("""FAILED """      ):
                                # `test` is the test method that failed
                                _UpperCAmelCase          =   line[len("""FAILED """      ) :]
                                failed_tests.append(_A      )
                            elif filename == "job_name.txt":
                                _UpperCAmelCase          =   line

    if len(_A      ) != len(_A      ):
        raise ValueError(
            F"""`errors` and `failed_tests` should have the same number of elements. Got {len(_A      )} for `errors` """
            F"""and {len(_A      )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            """ problem."""      )

    _UpperCAmelCase          =   None
    if job_name and job_links:
        _UpperCAmelCase          =   job_links.get(_A					,      _A      )

    # A list with elements of the form (line of error, error, failed test)
    _UpperCAmelCase          =   [x + [y] + [job_link] for x, y in zip(_A					,      _A      )]

    return result


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   []

    _UpperCAmelCase          =   [os.path.join(_A					,      _A      ) for p in os.listdir(_A      ) if p.endswith(""".zip"""      )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(_A					,      job_links=_A      )      )

    return errors


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   Counter()
    counter.update([x[1] for x in logs]      )
    _UpperCAmelCase          =   counter.most_common()
    _UpperCAmelCase          =   {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _UpperCAmelCase          =   {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

    _UpperCAmelCase          =   dict(sorted(r.items()					,      key=lambda _A      : item[1]["count"]					,      reverse=_A      )      )
    return r


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   test.split("""::"""      )[0]
    if test.startswith("""tests/models/"""      ):
        _UpperCAmelCase          =   test.split("""/"""      )[2]
    else:
        _UpperCAmelCase          =   None

    return test


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   [(x[0], x[1], get_model(x[2]      )) for x in logs]
    _UpperCAmelCase          =   [x for x in logs if x[2] is not None]
    _UpperCAmelCase          =   {x[2] for x in logs}

    _UpperCAmelCase          =   {}
    for test in tests:
        _UpperCAmelCase          =   Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]      )
        _UpperCAmelCase          =   counter.most_common()
        _UpperCAmelCase          =   {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _UpperCAmelCase          =   sum(error_counts.values()      )
        if n_errors > 0:
            _UpperCAmelCase          =   {"""count""": n_errors, """errors""": error_counts}

    _UpperCAmelCase          =   dict(sorted(r.items()					,      key=lambda _A      : item[1]["count"]					,      reverse=_A      )      )
    return r


def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   """| no. | error | status |"""
    _UpperCAmelCase          =   """|-:|:-|:-|"""
    _UpperCAmelCase          =   [header, sep]
    for error in reduced_by_error:
        _UpperCAmelCase          =   reduced_by_error[error]["""count"""]
        _UpperCAmelCase          =   F"""| {count} | {error[:1_0_0]} |  |"""
        lines.append(_A      )

    return "\n".join(_A      )


def  _UpperCamelCase	(						_A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   """| model | no. of errors | major error | count |"""
    _UpperCAmelCase          =   """|-:|-:|-:|-:|"""
    _UpperCAmelCase          =   [header, sep]
    for model in reduced_by_model:
        _UpperCAmelCase          =   reduced_by_model[model]["""count"""]
        _UpperCAmelCase    ,_UpperCAmelCase          =   list(reduced_by_model[model]["""errors"""].items()      )[0]
        _UpperCAmelCase          =   F"""| {model} | {count} | {error[:6_0]} | {_count} |"""
        lines.append(_A      )

    return "\n".join(_A      )


if __name__ == "__main__":
 a					:	Tuple							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
 parser.add_argument(
     '''--output_dir''',
     type=str,
     required=True,
     help='''Where to store the downloaded artifacts and other result files.''',
 )
 parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
 a					:	Dict							=		parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 a					:	Tuple							=		get_job_links(args.workflow_run_id, token=args.token)
 a					:	Tuple							=		{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    a					:	List[Any]							=		k.find(''' / ''')
    a					:	Tuple							=		k[index + len(''' / ''') :]
   a					:	int							=		v
 with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 a					:	Tuple							=		get_artifacts_links(args.workflow_run_id, token=args.token)
 with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

 for idx, (name, url) in enumerate(artifacts.items()):
  download_artifact(name, url, args.output_dir, args.token)
  # Be gentle to GitHub
  time.sleep(1)

 a					:	Optional[int]							=		get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 a					:	Union[str, Any]							=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 a					:	Optional[int]							=		counter.most_common(3_0)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 a					:	int							=		reduce_by_error(errors)
 a					:	str							=		reduce_by_model(errors)

 a					:	int							=		make_github_table(reduced_by_error)
 a					:	Optional[int]							=		make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)