






'''simple docstring'''




import functools
import operator

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


__lowerCamelCase   :					Any   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Any   							=					{
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''unispeech'''

							def __init__(		self			:Union[str, Any]       ,							lowerCAmelCase__			:Any=32       ,							lowerCAmelCase__			:Tuple=768       ,							lowerCAmelCase__			:Optional[int]=12       ,							lowerCAmelCase__			:Optional[int]=12       ,							lowerCAmelCase__			:List[str]=3_072       ,							lowerCAmelCase__			:Dict="gelu"       ,							lowerCAmelCase__			:Optional[int]=0.1       ,							lowerCAmelCase__			:Dict=0.1       ,							lowerCAmelCase__			:int=0.1       ,							lowerCAmelCase__			:str=0.0       ,							lowerCAmelCase__			:List[str]=0.0       ,							lowerCAmelCase__			:Any=0.1       ,							lowerCAmelCase__			:Optional[int]=0.1       ,							lowerCAmelCase__			:str=0.0_2       ,							lowerCAmelCase__			:Tuple=1E-5       ,							lowerCAmelCase__			:Optional[Any]="group"       ,							lowerCAmelCase__			:Optional[int]="gelu"       ,							lowerCAmelCase__			:Optional[Any]=(512, 512, 512, 512, 512, 512, 512)       ,							lowerCAmelCase__			:Optional[int]=(5, 2, 2, 2, 2, 2, 2)       ,							lowerCAmelCase__			:Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)       ,							lowerCAmelCase__			:Dict=False       ,							lowerCAmelCase__			:Tuple=128       ,							lowerCAmelCase__			:Optional[Any]=16       ,							lowerCAmelCase__			:List[str]=False       ,							lowerCAmelCase__			:List[str]=True       ,							lowerCAmelCase__			:List[str]=0.0_5       ,							lowerCAmelCase__			:Any=10       ,							lowerCAmelCase__			:Dict=2       ,							lowerCAmelCase__			:List[Any]=0.0       ,							lowerCAmelCase__			:Any=10       ,							lowerCAmelCase__			:str=0       ,							lowerCAmelCase__			:List[str]=320       ,							lowerCAmelCase__			:Optional[Any]=2       ,							lowerCAmelCase__			:Dict=0.1       ,							lowerCAmelCase__			:List[Any]=100       ,							lowerCAmelCase__			:Tuple=256       ,							lowerCAmelCase__			:Optional[int]=256       ,							lowerCAmelCase__			:List[str]=0.1       ,							lowerCAmelCase__			:Optional[Any]="mean"       ,							lowerCAmelCase__			:Optional[int]=False       ,							lowerCAmelCase__			:Optional[int]=False       ,							lowerCAmelCase__			:List[str]=256       ,							lowerCAmelCase__			:Dict=80       ,							lowerCAmelCase__			:Tuple=0       ,							lowerCAmelCase__			:List[Any]=1       ,							lowerCAmelCase__			:List[Any]=2       ,							lowerCAmelCase__			:int=0.5       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  Optional[Any]:


										'''simple docstring'''




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

										# parameters for pretraining with codevector quantized representations
										snake_case_				:					Optional[Any]   =       num_codevectors_per_group
										snake_case_				:					Dict   =       num_codevector_groups
										snake_case_				:					List[Any]   =       contrastive_logits_temperature
										snake_case_				:					Dict   =       feat_quantizer_dropout
										snake_case_				:					Dict   =       num_negatives
										snake_case_				:					Optional[Any]   =       codevector_dim
										snake_case_				:					Any   =       proj_codevector_dim
										snake_case_				:					str   =       diversity_loss_weight

										# ctc loss
										snake_case_				:					Optional[Any]   =       ctc_loss_reduction
										snake_case_				:					List[Any]   =       ctc_zero_infinity

										# pretraining loss
										snake_case_				:					Optional[int]   =       replace_prob






							@property
							def 							_A	(		self			:Tuple				)				->  str:


										'''simple docstring'''




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












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)




