

'''simple docstring'''



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

import requests


def       snake_case   (	snake_case      : Tuple					,		snake_case      : int=None			) ->  Union[str, Any]:


    """simple docstring"""


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

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

    try:
        job_links.update({job['name']: job['html_url'] for job in result['jobs']}			)
        lowerCAmelCase 						=							math.ceil((result['total_count'] - 100) / 100			)

        for i in range(snake_case			):
            lowerCAmelCase 						=							requests.get(url + F'&page={i + 2}'					,		headers=snake_case			).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       snake_case   (	snake_case      : List[str]					,		snake_case      : str=None			) ->  Dict:


    """simple docstring"""


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

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

    try:
        artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}			)
        lowerCAmelCase 						=							math.ceil((result['total_count'] - 100) / 100			)

        for i in range(snake_case			):
            lowerCAmelCase 						=							requests.get(url + F'&page={i + 2}'					,		headers=snake_case			).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       snake_case   (	snake_case      : List[Any]					,		snake_case      : Dict					,		snake_case      : Any					,		snake_case      : int			) ->  Optional[Any]:


    """simple docstring"""


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

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


def       snake_case   (	snake_case      : Dict					,		snake_case      : Dict=None			) ->  Dict:


    """simple docstring"""


    lowerCAmelCase 						=							[]
    lowerCAmelCase 						=							[]
    lowerCAmelCase 						=							None

    with zipfile.ZipFile(snake_case			) as z:
        for filename in z.namelist():
            if not os.path.isdir(snake_case			):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(snake_case			) as f:
                        for line in f:
                            lowerCAmelCase 						=							line.decode('UTF-8'			).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    lowerCAmelCase 						=							line[: line.index(': '			)]
                                    lowerCAmelCase 						=							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
                                lowerCAmelCase 						=							line[len('FAILED '			) :]
                                failed_tests.append(snake_case			)
                            elif filename == "job_name.txt":
                                lowerCAmelCase 						=							line

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

    lowerCAmelCase 						=							None
    if job_name and job_links:
        lowerCAmelCase 						=							job_links.get(snake_case					,		snake_case			)

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

    return result


def       snake_case   (	snake_case      : Optional[Any]					,		snake_case      : Optional[Any]=None			) ->  Optional[int]:


    """simple docstring"""


    lowerCAmelCase 						=							[]

    lowerCAmelCase 						=							[os.path.join(snake_case					,		snake_case			) for p in os.listdir(snake_case			) if p.endswith('.zip'			)]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(snake_case					,		job_links=snake_case			)			)

    return errors


def       snake_case   (	snake_case      : Union[str, Any]					,		snake_case      : str=None			) ->  Optional[Any]:


    """simple docstring"""


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

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


def       snake_case   (	snake_case      : Optional[int]			) ->  List[str]:


    """simple docstring"""


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

    return test


def       snake_case   (	snake_case      : Tuple					,		snake_case      : Union[str, Any]=None			) ->  int:


    """simple docstring"""


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

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

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


def       snake_case   (	snake_case      : List[str]			) ->  Optional[int]:


    """simple docstring"""


    lowerCAmelCase 						=							'| no. | error | status |'
    lowerCAmelCase 						=							'|-:|:-|:-|'
    lowerCAmelCase 						=							[header, sep]
    for error in reduced_by_error:
        lowerCAmelCase 						=							reduced_by_error[error]['count']
        lowerCAmelCase 						=							F'| {count} | {error[:100]} |  |'
        lines.append(snake_case			)

    return "\n".join(snake_case			)


def       snake_case   (	snake_case      : int			) ->  Tuple:


    """simple docstring"""


    lowerCAmelCase 						=							'| model | no. of errors | major error | count |'
    lowerCAmelCase 						=							'|-:|-:|-:|-:|'
    lowerCAmelCase 						=							[header, sep]
    for model in reduced_by_model:
        lowerCAmelCase 						=							reduced_by_model[model]['count']
        lowerCAmelCase						,   lowerCAmelCase 						=							list(reduced_by_model[model]['errors'].items()			)[0]
        lowerCAmelCase 						=							F'| {model} | {count} | {error[:60]} | {_count} |'
        lines.append(snake_case			)

    return "\n".join(snake_case			)


if __name__ == "__main__":
     _UpperCamelCase						:  List[Any]							       =				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.")
     _UpperCamelCase						:  Dict							       =				parser.parse_args()

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

     _UpperCamelCase						:  Optional[Any]							       =				get_job_links(args.workflow_run_id, token=args.token)
     _UpperCamelCase						:  Union[str, Any]							       =				{}
     # 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:
                    _UpperCamelCase						:  Optional[Any]							       =				k.find(" / ")
                    _UpperCamelCase						:  int							       =				k[index + len(" / ") :]
               _UpperCamelCase						:  Tuple							       =				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)

     _UpperCamelCase						:  Optional[int]							       =				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)

     _UpperCamelCase						:  Tuple							       =				get_all_errors(args.output_dir, job_links=job_links)

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

     # print the top 30 most common test errors
     _UpperCamelCase						:  Tuple							       =				counter.most_common(30)
     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)

     _UpperCamelCase						:  List[Any]							       =				reduce_by_error(errors)
     _UpperCamelCase						:  Optional[int]							       =				reduce_by_model(errors)

     _UpperCamelCase						:  Optional[Any]							       =				make_github_table(reduced_by_error)
     _UpperCamelCase						:  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)








'''simple docstring'''



import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def       snake_case   (	snake_case      : Any			) ->  Any:


    """simple docstring"""


    return 1.0 / (1.0 + np.exp(-_outputs			))


def       snake_case   (	snake_case      : Dict			) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							np.max(_outputs					,		axis=-1					,		keepdims=snake_case			)
    lowerCAmelCase 						=							np.exp(_outputs - maxes			)
    return shifted_exp / shifted_exp.sum(axis=-1					,		keepdims=snake_case			)



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[str]				=     '''sigmoid'''
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''softmax'''
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     '''none'''



@add_end_docstrings(
    a_     , R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''     , )
class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     False
  SCREAMING_SNAKE_CASE			:       Dict				=     ClassificationFunction.NONE

  def __init__(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(**_SCREAMING_SNAKE_CASE	)

      self.check_model_type(
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
          if self.framework == 'tf'
          else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=""      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							tokenizer_kwargs

      lowerCAmelCase 						=							{}
      if hasattr(self.model.config      , 'return_all_scores'	) and return_all_scores is None:
          lowerCAmelCase 						=							self.model.config.return_all_scores

      if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) or top_k is None:
          lowerCAmelCase 						=							top_k
          lowerCAmelCase 						=							False
      elif return_all_scores is not None:
          warnings.warn(
              '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
              ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'      , _SCREAMING_SNAKE_CASE      , )
          if return_all_scores:
              lowerCAmelCase 						=							None
          else:
              lowerCAmelCase 						=							1

      if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          lowerCAmelCase 						=							ClassificationFunction[function_to_apply.upper()]

      if function_to_apply is not None:
          lowerCAmelCase 						=							function_to_apply
      return preprocess_params, {}, postprocess_params

  def __call__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							super().__call__(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      # TODO try and retrieve it in a nicer way from _sanitize_parameters.
      lowerCAmelCase 						=							'top_k' not in kwargs
      if isinstance(args[0]      , _SCREAMING_SNAKE_CASE	) and _legacy:
          # This pipeline is odd, and return a list when single item is run
          return [result]
      else:
          return result

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.framework
      if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          return self.tokenizer(**_SCREAMING_SNAKE_CASE      , return_tensors=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      elif isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) and len(_SCREAMING_SNAKE_CASE	) == 1 and isinstance(inputs[0]      , _SCREAMING_SNAKE_CASE	) and len(inputs[0]	) == 2:
          # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
          return self.tokenizer(
              text=inputs[0][0]      , text_pair=inputs[0][1]      , return_tensors=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      elif isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          # This is likely an invalid usage of the pipeline attempting to pass text pairs.
          raise ValueError(
              'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
              ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'	)
      return self.tokenizer(_SCREAMING_SNAKE_CASE      , return_tensors=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.model(**_SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=1      , _SCREAMING_SNAKE_CASE=True	):





      '''simple docstring'''

      if function_to_apply is None:
          if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
              lowerCAmelCase 						=							ClassificationFunction.SIGMOID
          elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
              lowerCAmelCase 						=							ClassificationFunction.SOFTMAX
          elif hasattr(self.model.config      , 'function_to_apply'	) and function_to_apply is None:
              lowerCAmelCase 						=							self.model.config.function_to_apply
          else:
              lowerCAmelCase 						=							ClassificationFunction.NONE

      lowerCAmelCase 						=							model_outputs['logits'][0]
      lowerCAmelCase 						=							outputs.numpy()

      if function_to_apply == ClassificationFunction.SIGMOID:
          lowerCAmelCase 						=							sigmoid(_SCREAMING_SNAKE_CASE	)
      elif function_to_apply == ClassificationFunction.SOFTMAX:
          lowerCAmelCase 						=							softmax(_SCREAMING_SNAKE_CASE	)
      elif function_to_apply == ClassificationFunction.NONE:
          lowerCAmelCase 						=							outputs
      else:
          raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}'	)

      if top_k == 1 and _legacy:
          return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

      lowerCAmelCase 						=							[
          {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(_SCREAMING_SNAKE_CASE	)
      ]
      if not _legacy:
          dict_scores.sort(key=lambda _SCREAMING_SNAKE_CASE	: x["score"]      , reverse=_SCREAMING_SNAKE_CASE	)
          if top_k is not None:
              lowerCAmelCase 						=							dict_scores[:top_k]
      return dict_scores





