



import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


lowerCAmelCase:      Optional[Any]		 =		TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('', '|', '|'),
    datarow=DataRow('', '|', '|'),
    padding=1,
    with_header_hide=None,
)


lowerCAmelCase:      Optional[Any]		 =		[]
lowerCAmelCase:      Optional[Any]		 =		[]

lowerCAmelCase:      Any		 =		{'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}

lowerCAmelCase:      Optional[int]		 =		[
    {
        'type': 'header',
        'text': {
            'type': 'plain_text',
            'text': F"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
            'emoji': True,
        },
    }
]

lowerCAmelCase:      str		 =		0
for log in Path().glob('*.log'):
     lowerCAmelCase:      Optional[Any]		 =		0
     with open(log, 'r') as f:
          for line in f:
               lowerCAmelCase:      Optional[int]		 =		json.loads(line)
               if line.get('nodeid', '') != "":
                    lowerCAmelCase:      Optional[Any]		 =		line['nodeid']
                    if line.get('duration', None) is not None:
                         lowerCAmelCase:      str		 =		F"{line['duration']:.4f}"
                         if line.get('outcome', '') == "failed":
                              section_num_failed += 1
                              failed.append([test, duration, log.name.split('_')[0]])
                              total_num_failed += 1
     group_info.append([str(log), section_num_failed, failed])
     lowerCAmelCase:      List[Any]		 =		[]
     log.unlink()

lowerCAmelCase:      List[str]		 =		''
lowerCAmelCase:      Optional[Any]		 =		[]
if total_num_failed > 0:
     for name, num_failed, failed_tests in group_info:
          if num_failed > 0:
               if num_failed == 1:
                    message += F"*{name[1:]}: {num_failed} failed test*\n"
               else:
                    message += F"*{name[1:]}: {num_failed} failed tests*\n"
               lowerCAmelCase:      Optional[int]		 =		[]
               lowerCAmelCase:      Any		 =		{}
               for test in failed_tests:
                    lowerCAmelCase:      Tuple		 =		test[0].split('::')
                    lowerCAmelCase:      str		 =		data[0].split('/')[-1]
                    if data[0] not in filesafailed:
                         lowerCAmelCase:      Optional[int]		 =		[data[1:]]
                    else:
                         filesafailed[data[0]] += [data[1:]]
                    failed_table.append(data)

               lowerCAmelCase:      str		 =		[test[0] for test in failed_table]
               lowerCAmelCase:      Any		 =		list(set(files))
               # Count number of instances in failed_tests
               lowerCAmelCase:      Optional[Any]		 =		[]
               for file in individual_files:
                    table.append([file, len(filesafailed[file])])

               lowerCAmelCase:      Dict		 =		tabulate(
                   table,
                   headers=['Test Location', 'Num Failed'],
                   tablefmt=hf_table_format,
                   stralign='right',
               )
               message += F"\n```\n{failed_table}\n```"
               all_filesafailed.append(filesafailed)
     if len(message) > 3_0_0_0:
          lowerCAmelCase:      str		 =		'Too many failed tests, please see the full report in the Action results.'
          lowerCAmelCase:      List[str]		 =		len(err) + 1_0
          lowerCAmelCase:      Dict		 =		message[: 3_0_0_0 - offset] + F"\n...\n```\n{err}"
     print(F"### {message}")
else:
     lowerCAmelCase:      List[Any]		 =		'No failed tests! 🤗'
     print(F"## {message}")
     payload.append(no_error_payload)

if os.environ.get('TEST_TYPE', '') != "":
     from slack_sdk import WebClient

     lowerCAmelCase:      Any		 =		WebClient(token=os.environ['SLACK_API_TOKEN'])
     if message != "No failed tests! 🤗":
          lowerCAmelCase:      Optional[int]		 =		{
              'type': 'section',
              'text': {
                  'type': 'mrkdwn',
                  'text': message,
              },
          }
          payload.append(md_report)
          lowerCAmelCase:      Optional[Any]		 =		{
              'type': 'section',
              'text': {
                  'type': 'mrkdwn',
                  'text': '*For more details:*',
              },
              'accessory': {
                  'type': 'button',
                  'text': {
                      'type': 'plain_text',
                      'text': 'Check Action results',
                      'emoji': True,
                  },
                  'url': F"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
              },
          }
          payload.append(action_button)
          lowerCAmelCase:      Optional[Any]		 =		{
              'type': 'context',
              'elements': [
                  {
                      'type': 'plain_text',
                      'text': F"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
                  }
              ],
          }
          payload.append(date_report)
     lowerCAmelCase:      List[Any]		 =		client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
     lowerCAmelCase:      Tuple		 =		response.data['ts']
     for failed_file in all_filesafailed:
          for test_location, test_failures in failed_file.items():
               # Keep only the first instance of the test name
               lowerCAmelCase:      Union[str, Any]		 =		''
               for i, row in enumerate(test_failures):
                    if row[0] != test_class:
                         lowerCAmelCase:      Dict		 =		row[0]
                    else:
                         lowerCAmelCase:      Optional[int]		 =		''

               lowerCAmelCase:      Optional[Any]		 =		{
                   'type': 'section',
                   'text': {
                       'type': 'mrkdwn',
                       'text': F"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
                   },
               }

               client.chat_postMessage(
                   channel='#accelerate-ci-daily',
                   thread_ts=ts,
                   blocks=[payload],
               )




'''simple docstring'''






from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowerCAmelCase:      Optional[Any]		 =		logging.get_logger(__name__)

@add_end_docstrings(lowerCamelCase__			)
class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    **__snake_case				:						List[str]			):
      super().__init__(**__snake_case			)

      if self.framework != "pt":
        raise ValueError(F"""The {self.__class__} is only available in PyTorch."""			)
      # No specific FOR_XXX available yet







    def __call__(   self				:						Any		,    __snake_case				:						Union[np.ndarray, bytes, str]		,    **__snake_case				:						Optional[Any]			):
      return super().__call__(__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Dict		,    **__snake_case				:						Optional[int]			):
      a		:      str       =  {}
      if "candidate_labels" in kwargs:
        a		:      Optional[int]       =  kwargs['candidate_labels']
      if "hypothesis_template" in kwargs:
        a		:      Any       =  kwargs['hypothesis_template']

      return preprocess_params, {}, {}







    def     lowercase_ (   self				:						int		,    __snake_case				:						Optional[Any]		,    __snake_case				:						List[Any]=None		,    __snake_case				:						List[str]="This is a sound of {}."			):
      if isinstance(__snake_case		,    __snake_case			):
        if audio.startswith('http://'			) or audio.startswith('https://'			):
          # We need to actually check for a real protocol, otherwise it's impossible to use a local file
          # like http_huggingface_co.png
          a		:      List[Any]       =  requests.get(__snake_case			).content
        else:
          with open(__snake_case		,    'rb'			) as f:
            a		:      List[str]       =  f.read()

      if isinstance(__snake_case		,    __snake_case			):
        a		:      Optional[Any]       =  ffmpeg_read(__snake_case		,    self.feature_extractor.sampling_rate			)

      if not isinstance(__snake_case		,    np.ndarray			):
        raise ValueError('We expect a numpy ndarray as input'			)
      if len(audio.shape			) != 1:
        raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'			)

      a		:      str       =  self.feature_extractor(
          [audio]		,    sampling_rate=self.feature_extractor.sampling_rate		,    return_tensors='pt'			)
      a		:      Tuple       =  candidate_labels
      a		:      Union[str, Any]       =  [hypothesis_template.format(__snake_case			) for x in candidate_labels]
      a		:      int       =  self.tokenizer(__snake_case		,    return_tensors=self.framework		,    padding=__snake_case			)
      a		:      Any       =  [text_inputs]
      return inputs







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str			):
      a		:      Any       =  model_inputs.pop('candidate_labels'			)
      a		:      List[Any]       =  model_inputs.pop('text_inputs'			)
      if isinstance(text_inputs[0]		,    __snake_case			):
        a		:      str       =  text_inputs[0]
      else:
        # Batching case.
        a		:      Dict       =  text_inputs[0][0]

      a		:      List[Any]       =  self.model(**__snake_case		,    **__snake_case			)

      a		:      Any       =  {
          'candidate_labels': candidate_labels,
          'logits': outputs.logits_per_audio,
      }
      return model_outputs







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str			):
      a		:      Optional[int]       =  model_outputs.pop('candidate_labels'			)
      a		:      List[str]       =  model_outputs['logits'][0]

      if self.framework == "pt":
        a		:      str       =  logits.softmax(dim=0			)
        a		:      List[Any]       =  probs.tolist()
      else:
        raise ValueError('`tf` framework not supported.'			)

      a		:      Dict       =  [
          {'score': score, 'label': candidate_label}
          for score, candidate_label in sorted(zip(__snake_case		,    __snake_case			)		,    key=lambda __snake_case			: -x[0]			)
      ]
      return result