



import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 				A				(					_lowerCamelCase				, _lowerCamelCase=7  ):





							'''simple docstring'''




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

							# The id of a workflow (not of a workflow run)
							_lowerCAmelCase     :							Any    =    "636036"

							_lowerCAmelCase     :							List[Any]    =    F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
							# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
							url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

							_lowerCAmelCase     :							Optional[int]    =    requests.get(_lowerCamelCase				, headers=_lowerCamelCase  ).json()

							return result["workflow_runs"]






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    get_daily_ci_runs(_lowerCamelCase  )
							_lowerCAmelCase     :							List[Any]    =    None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					_lowerCAmelCase     :							str    =    workflow_run["id"]
																					break

							return workflow_run_id






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Any    =    get_last_daily_ci_runs(_lowerCamelCase  )
							if workflow_run_id is not None:
														_lowerCAmelCase     :							int    =    get_artifacts_links(worflow_run_id=_lowerCamelCase				, token=_lowerCamelCase  )
														for artifact_name in artifact_names:
																					if artifact_name in artifacts_links:
																												_lowerCAmelCase     :							Optional[int]    =    artifacts_links[artifact_name]
																												download_artifact(
																												    artifact_name=_lowerCamelCase				, artifact_url=_lowerCamelCase				, output_dir=_lowerCamelCase				, token=_lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							get_last_daily_ci_artifacts(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

							_lowerCAmelCase     :							str    =    {}
							for artifact_name in artifact_names:
														_lowerCAmelCase     :							Optional[int]    =    os.path.join(_lowerCamelCase				, F"{artifact_name}.zip"  )
														if os.path.isfile(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Dict    =    {}
																					with zipfile.ZipFile(_lowerCamelCase  ) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_lowerCamelCase  ):
																																										# read the file
																																										with z.open(_lowerCamelCase  ) as f:
																																																	_lowerCAmelCase     :							str    =    f.read().decode("UTF-8"  )

							return results





import functools
import operator

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


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'wav2vec2'







							def __init__(   self,			__a=32,			__a=768,			__a=12,			__a=12,			__a=3072,			__a="gelu",			__a=0.1,			__a=0.1,			__a=0.1,			__a=0.0,			__a=0.0,			__a=0.1,			__a=0.1,			__a=0.02,			__a=1E-5,			__a="group",			__a="gelu",			__a=(512, 512, 512, 512, 512, 512, 512),			__a=(5, 2, 2, 2, 2, 2, 2),			__a=(10, 3, 3, 3, 3, 2, 2),			__a=False,			__a=128,			__a=16,			__a=False,			__a=True,			__a=0.05,			__a=10,			__a=2,			__a=0.0,			__a=10,			__a=0,			__a=320,			__a=2,			__a=0.1,			__a=100,			__a=256,			__a=256,			__a=0.1,			__a="sum",			__a=False,			__a=False,			__a=256,			__a=(512, 512, 512, 512, 1500),			__a=(5, 3, 3, 1, 1),			__a=(1, 2, 3, 1, 1),			__a=512,			__a=0,			__a=1,			__a=2,			__a=False,			__a=3,			__a=2,			__a=3,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a,			pad_token_id=__a,			bos_token_id=__a,			eos_token_id=__a)
														_lowerCAmelCase     :							str    =    hidden_size
														_lowerCAmelCase     :							Optional[int]    =    feat_extract_norm
														_lowerCAmelCase     :							Union[str, Any]    =    feat_extract_activation
														_lowerCAmelCase     :							Optional[Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    conv_bias
														_lowerCAmelCase     :							str    =    num_conv_pos_embeddings
														_lowerCAmelCase     :							List[Any]    =    num_conv_pos_embedding_groups
														_lowerCAmelCase     :							str    =    len(self.conv_dim)
														_lowerCAmelCase     :							List[str]    =    num_hidden_layers
														_lowerCAmelCase     :							str    =    intermediate_size
														_lowerCAmelCase     :							Any    =    hidden_act
														_lowerCAmelCase     :							int    =    num_attention_heads
														_lowerCAmelCase     :							Optional[Any]    =    hidden_dropout
														_lowerCAmelCase     :							List[str]    =    attention_dropout
														_lowerCAmelCase     :							Tuple    =    activation_dropout
														_lowerCAmelCase     :							int    =    feat_proj_dropout
														_lowerCAmelCase     :							List[str]    =    final_dropout
														_lowerCAmelCase     :							int    =    layerdrop
														_lowerCAmelCase     :							int    =    layer_norm_eps
														_lowerCAmelCase     :							Union[str, Any]    =    initializer_range
														_lowerCAmelCase     :							str    =    vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    do_stable_layer_norm
														_lowerCAmelCase     :							Any    =    use_weighted_layer_sum

														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
														_lowerCAmelCase     :							str    =    apply_spec_augment
														_lowerCAmelCase     :							Optional[Any]    =    mask_time_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_time_length
														_lowerCAmelCase     :							List[str]    =    mask_time_min_masks
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_length
														_lowerCAmelCase     :							List[str]    =    mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														_lowerCAmelCase     :							Union[str, Any]    =    num_codevectors_per_group
														_lowerCAmelCase     :							str    =    num_codevector_groups
														_lowerCAmelCase     :							Optional[int]    =    contrastive_logits_temperature
														_lowerCAmelCase     :							Optional[int]    =    feat_quantizer_dropout
														_lowerCAmelCase     :							Optional[int]    =    num_negatives
														_lowerCAmelCase     :							Union[str, Any]    =    codevector_dim
														_lowerCAmelCase     :							Any    =    proj_codevector_dim
														_lowerCAmelCase     :							Optional[int]    =    diversity_loss_weight

														# ctc loss
														_lowerCAmelCase     :							Tuple    =    ctc_loss_reduction
														_lowerCAmelCase     :							Tuple    =    ctc_zero_infinity

														# adapter
														_lowerCAmelCase     :							List[Any]    =    add_adapter
														_lowerCAmelCase     :							List[str]    =    adapter_kernel_size
														_lowerCAmelCase     :							str    =    adapter_stride
														_lowerCAmelCase     :							List[str]    =    num_adapter_layers
														_lowerCAmelCase     :							str    =    output_hidden_size or hidden_size
														_lowerCAmelCase     :							Tuple    =    adapter_attn_dim

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

														# XVector-specific parameters. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							Tuple    =    xvector_output_dim







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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