


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

from tabulate import DataRow, TableFormat, tabulate


snake_case							:       List[str]										=	TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


snake_case							:       Optional[int]										=	[]
snake_case							:       List[str]										=	[]

snake_case							:       List[Any]										=	{'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}

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

snake_case							:       Optional[Any]										=	0
for log in Path().glob('''*.log'''):
     snake_case							:       List[Any]										=	0
     with open(log, '''r''') as f:
          for line in f:
               snake_case							:       int										=	json.loads(line)
               if line.get('''nodeid''', '''''') != "":
                    snake_case							:       str										=	line['''nodeid''']
                    if line.get('''duration''', None) is not None:
                         snake_case							:       int										=	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])
     snake_case							:       Any										=	[]
     log.unlink()

snake_case							:       Tuple										=	''''''
snake_case							:       Optional[int]										=	[]
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"
               snake_case							:       Optional[Any]										=	[]
               snake_case							:       Optional[Any]										=	{}
               for test in failed_tests:
                    snake_case							:       Dict										=	test[0].split('''::''')
                    snake_case							:       Tuple										=	data[0].split('''/''')[-1]
                    if data[0] not in filesafailed:
                         snake_case							:       Dict										=	[data[1:]]
                    else:
                         filesafailed[data[0]] += [data[1:]]
                    failed_table.append(data)

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

               snake_case							:       int										=	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) > 30_00:
          snake_case							:       List[Any]										=	'''Too many failed tests, please see the full report in the Action results.'''
          snake_case							:       List[Any]										=	len(err) + 10
          snake_case							:       str										=	message[: 30_00 - offset] + F"""\n...\n```\n{err}"""
     print(F"""### {message}""")
else:
     snake_case							:       int										=	'''No failed tests! 🤗'''
     print(F"""## {message}""")
     payload.append(no_error_payload)

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

     snake_case							:       List[Any]										=	WebClient(token=os.environ['''SLACK_API_TOKEN'''])
     if message != "No failed tests! 🤗":
          snake_case							:       List[str]										=	{
              '''type''': '''section''',
              '''text''': {
                  '''type''': '''mrkdwn''',
                  '''text''': message,
              },
          }
          payload.append(md_report)
          snake_case							:       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)
          snake_case							:       List[Any]										=	{
              '''type''': '''context''',
              '''elements''': [
                  {
                      '''type''': '''plain_text''',
                      '''text''': F"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
                  }
              ],
          }
          payload.append(date_report)
     snake_case							:       List[str]										=	client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
     snake_case							:       List[Any]										=	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
               snake_case							:       List[Any]										=	''''''
               for i, row in enumerate(test_failures):
                    if row[0] != test_class:
                         snake_case							:       Dict										=	row[0]
                    else:
                         snake_case							:       int										=	''''''

               snake_case							:       str										=	{
                   '''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],
               )









import functools
import operator

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


snake_case							:       Optional[int]										=	logging.get_logger(__name__)

snake_case							:       Optional[Any]										=	{
    '''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 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'wavlm'




 def __init__(						self   ,    _lowerCamelCase=32   ,    _lowerCamelCase=768   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-5   ,    _lowerCamelCase="group"   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,    _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,    _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,    _lowerCamelCase=False   ,    _lowerCamelCase=128   ,    _lowerCamelCase=16   ,    _lowerCamelCase=320   ,    _lowerCamelCase=800   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=0.05   ,    _lowerCamelCase=10   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=10   ,    _lowerCamelCase=320   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=100   ,    _lowerCamelCase=256   ,    _lowerCamelCase=256   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase="mean"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False   ,    _lowerCamelCase=256   ,    _lowerCamelCase=(512, 512, 512, 512, 1500)   ,    _lowerCamelCase=(5, 3, 3, 1, 1)   ,    _lowerCamelCase=(1, 2, 3, 1, 1)   ,    _lowerCamelCase=512   ,    _lowerCamelCase=80   ,    _lowerCamelCase=0   ,    _lowerCamelCase=1   ,    _lowerCamelCase=2   ,    _lowerCamelCase=False   ,    _lowerCamelCase=3   ,    _lowerCamelCase=2   ,    _lowerCamelCase=3   ,    _lowerCamelCase=None   ,    **_lowerCamelCase   ,    ):
       super().__init__(**_lowerCamelCase   ,    pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase       )
       a		:Union[str, Any]									=			hidden_size
       a		:List[Any]									=			feat_extract_norm
       a		:Tuple									=			feat_extract_activation
       a		:Tuple									=			list(_lowerCamelCase       )
       a		:Optional[Any]									=			list(_lowerCamelCase       )
       a		:Optional[Any]									=			list(_lowerCamelCase       )
       a		:int									=			conv_bias
       a		:Tuple									=			num_buckets
       a		:Tuple									=			max_bucket_distance
       a		:int									=			num_conv_pos_embeddings
       a		:List[str]									=			num_conv_pos_embedding_groups
       a		:Tuple									=			len(self.conv_dim       )
       a		:Any									=			num_hidden_layers
       a		:Tuple									=			intermediate_size
       a		:Any									=			hidden_act
       a		:List[str]									=			num_attention_heads
       a		:int									=			hidden_dropout
       a		:Optional[Any]									=			attention_dropout
       a		:Dict									=			activation_dropout
       a		:Any									=			feat_proj_dropout
       a		:Dict									=			final_dropout
       a		:int									=			layerdrop
       a		:Tuple									=			layer_norm_eps
       a		:Union[str, Any]									=			initializer_range
       a		:Union[str, Any]									=			num_ctc_classes
       a		:Optional[Any]									=			vocab_size
       a		:str									=			do_stable_layer_norm
       a		:Dict									=			use_weighted_layer_sum
       a		:Any									=			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
       a		:List[Any]									=			apply_spec_augment
       a		:Optional[int]									=			mask_time_prob
       a		:Optional[Any]									=			mask_time_length
       a		:Tuple									=			mask_time_min_masks
       a		:Dict									=			mask_feature_prob
       a		:List[Any]									=			mask_feature_length

       # parameters for pretraining with codevector quantized representations
       a		:Tuple									=			num_codevectors_per_group
       a		:List[str]									=			num_codevector_groups
       a		:List[Any]									=			contrastive_logits_temperature
       a		:Union[str, Any]									=			num_negatives
       a		:Union[str, Any]									=			codevector_dim
       a		:Dict									=			proj_codevector_dim
       a		:Union[str, Any]									=			diversity_loss_weight

       # ctc loss
       a		:Dict									=			ctc_loss_reduction
       a		:Optional[int]									=			ctc_zero_infinity

       # adapter
       a		:Any									=			add_adapter
       a		:str									=			adapter_kernel_size
       a		:Tuple									=			adapter_stride
       a		:Dict									=			num_adapter_layers
       a		:Any									=			output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       a		:Tuple									=			classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       a		:List[Any]									=			list(_lowerCamelCase       )
       a		:Optional[int]									=			list(_lowerCamelCase       )
       a		:List[str]									=			list(_lowerCamelCase       )
       a		:Optional[int]									=			xvector_output_dim




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return functools.reduce(operator.mul   ,    self.conv_stride   ,    1       )





