


"""simple docstring"""



import math

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


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class 					__magic_name__		(			__UpperCAmelCase     ):
							__A			:      Optional[Any]        =   "data2vec-audio"






							def __init__(   self  :    Tuple  ,     snake_case__  :    List[Any]=3_2  ,     snake_case__  :    List[Any]=7_6_8  ,     snake_case__  :    str=1_2  ,     snake_case__  :    Optional[Any]=1_2  ,     snake_case__  :    List[Any]=3_0_7_2  ,     snake_case__  :    Optional[int]="gelu"  ,     snake_case__  :    List[Any]=0.1  ,     snake_case__  :    List[str]=0.1  ,     snake_case__  :    List[str]=0.1  ,     snake_case__  :    Optional[Any]=0.0  ,     snake_case__  :    Tuple=0.1  ,     snake_case__  :    str=0.1  ,     snake_case__  :    Union[str, Any]=0.02  ,     snake_case__  :    Dict=1e-5  ,     snake_case__  :    List[str]="gelu"  ,     snake_case__  :    List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,     snake_case__  :    Dict=(5, 2, 2, 2, 2, 2, 2)  ,     snake_case__  :    List[Any]=(1_0, 3, 3, 3, 3, 2, 2)  ,     snake_case__  :    Tuple=False  ,     snake_case__  :    Optional[Any]=1_6  ,     snake_case__  :    Optional[Any]=1_9  ,     snake_case__  :    int=5  ,     snake_case__  :    Any=0.05  ,     snake_case__  :    List[str]=1_0  ,     snake_case__  :    int=2  ,     snake_case__  :    Union[str, Any]=0.0  ,     snake_case__  :    Optional[Any]=1_0  ,     snake_case__  :    Dict=0  ,     snake_case__  :    int="sum"  ,     snake_case__  :    str=False  ,     snake_case__  :    List[Any]=False  ,     snake_case__  :    Union[str, Any]=2_5_6  ,     snake_case__  :    List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)  ,     snake_case__  :    Optional[int]=(5, 3, 3, 1, 1)  ,     snake_case__  :    int=(1, 2, 3, 1, 1)  ,     snake_case__  :    str=5_1_2  ,     snake_case__  :    List[Any]=0  ,     snake_case__  :    List[str]=1  ,     snake_case__  :    Union[str, Any]=2  ,     snake_case__  :    Tuple=False  ,     snake_case__  :    Union[str, Any]=3  ,     snake_case__  :    List[Any]=2  ,     snake_case__  :    Any=3  ,     snake_case__  :    Dict=None  ,     **snake_case__  :    Tuple  ,     ):

														'''simple docstring'''






														super().__init__(**lowerCAmelCase_  ,     pad_token_id=lowerCAmelCase_  ,     bos_token_id=lowerCAmelCase_  ,     eos_token_id=lowerCAmelCase_				)
														lowercase		:Optional[Any]       							=				hidden_size
														lowercase		:Union[str, Any]       							=				feat_extract_activation
														lowercase		:Dict       							=				list(lowerCAmelCase_				)
														lowercase		:int       							=				list(lowerCAmelCase_				)
														lowercase		:str       							=				list(lowerCAmelCase_				)
														lowercase		:Optional[Any]       							=				conv_bias
														lowercase		:Optional[Any]       							=				num_conv_pos_embeddings
														lowercase		:Union[str, Any]       							=				num_conv_pos_embedding_groups
														lowercase		:int       							=				conv_pos_kernel_size
														lowercase		:Dict       							=				len(self.conv_dim				)
														lowercase		:Tuple       							=				num_hidden_layers
														lowercase		:Any       							=				intermediate_size
														lowercase		:str       							=				hidden_act
														lowercase		:List[str]       							=				num_attention_heads
														lowercase		:Optional[int]       							=				hidden_dropout
														lowercase		:Tuple       							=				attention_dropout
														lowercase		:Any       							=				activation_dropout
														lowercase		:Dict       							=				feat_proj_dropout
														lowercase		:List[Any]       							=				final_dropout
														lowercase		:str       							=				layerdrop
														lowercase		:Union[str, Any]       							=				layer_norm_eps
														lowercase		:Any       							=				initializer_range
														lowercase		:Optional[int]       							=				vocab_size
														lowercase		:Union[str, 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
														lowercase		:Dict       							=				mask_time_prob
														lowercase		:str       							=				mask_time_length
														lowercase		:Tuple       							=				mask_time_min_masks
														lowercase		:Tuple       							=				mask_feature_prob
														lowercase		:List[Any]       							=				mask_feature_length
														lowercase		:List[Any]       							=				mask_feature_min_masks

														# ctc loss
														lowercase		:int       							=				ctc_loss_reduction
														lowercase		:Union[str, Any]       							=				ctc_zero_infinity

														# adapter
														lowercase		:Union[str, Any]       							=				add_adapter
														lowercase		:Union[str, Any]       							=				adapter_kernel_size
														lowercase		:Union[str, Any]       							=				adapter_stride
														lowercase		:str       							=				num_adapter_layers
														lowercase		:List[Any]       							=				output_hidden_size or hidden_size

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														lowercase		:Union[str, Any]       							=				classifier_proj_size

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







							@property
							def  __snake_case						(   self  :    Dict				):

														'''simple docstring'''






														return math.prod(self.conv_stride				)





"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCAmelCase      		=			'''
Human: <<task>>

Assistant: '''


UpperCAmelCase      		=			'''huggingface-tools/default-prompts'''
UpperCAmelCase      		=			{'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def       lowerCamelCase					(a_						:int				,				a_						:str				,				a_						:Dict="run")     ->							Optional[Any]:
       if prompt_or_repo_id is None:
              lowercase		:Tuple       							=				DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search('''\\s'''				,				a_) is not None:
              return prompt_or_repo_id

       lowercase		:List[str]       							=				cached_file(
           a_				,				PROMPT_FILES[mode]				,				repo_type='''dataset'''				,				user_agent={'''agent''': agent_name})
       with open(a_				,				'''r'''				,				encoding='''utf-8''') as f:
              return f.read()

