



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__		:					List[Any]							=logging.get_logger(__name__)

lowerCAmelCase__		:					int							={
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

			'''simple docstring'''




			UpperCamelCase__      :			Optional[int]      =  '''roberta'''






			def __init__(   self				,							_A=50_265				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=512				,							_A=2				,							_A=0.0_2				,							_A=1e-12				,							_A=1				,							_A=0				,							_A=2				,							_A="absolute"				,							_A=True				,							_A=None				,							**_A				,							):







										'''simple docstring'''

										super().__init__(pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A				,							**_A		)

										__SCREAMING_SNAKE_CASE									= vocab_size
										__SCREAMING_SNAKE_CASE									= hidden_size
										__SCREAMING_SNAKE_CASE									= num_hidden_layers
										__SCREAMING_SNAKE_CASE									= num_attention_heads
										__SCREAMING_SNAKE_CASE									= hidden_act
										__SCREAMING_SNAKE_CASE									= intermediate_size
										__SCREAMING_SNAKE_CASE									= hidden_dropout_prob
										__SCREAMING_SNAKE_CASE									= attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE									= max_position_embeddings
										__SCREAMING_SNAKE_CASE									= type_vocab_size
										__SCREAMING_SNAKE_CASE									= initializer_range
										__SCREAMING_SNAKE_CASE									= layer_norm_eps
										__SCREAMING_SNAKE_CASE									= position_embedding_type
										__SCREAMING_SNAKE_CASE									= use_cache
										__SCREAMING_SNAKE_CASE									= classifier_dropout







class        UpperCAmelCase_  (       UpperCamelCase_	):

			'''simple docstring'''




			@property
			def 	_A     (   self		):







										'''simple docstring'''

										if self.task == "multiple-choice":
																	__SCREAMING_SNAKE_CASE									= {0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	__SCREAMING_SNAKE_CASE									= {0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]		)











import functools
import operator

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


lowerCAmelCase__		:					Dict							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''sew'''






						def __init__(   self				,							_A=32				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A=2				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=0.0				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_A=1e-5				,							_A="group"				,							_A="gelu"				,							_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,							_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,							_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,							_A=False				,							_A=128				,							_A=16				,							_A=True				,							_A=0.0_5				,							_A=10				,							_A=2				,							_A=0.0				,							_A=10				,							_A=0				,							_A="mean"				,							_A=False				,							_A=False				,							_A=256				,							_A=0				,							_A=1				,							_A=2				,							**_A				,							):







													'''simple docstring'''

													super().__init__(**_A				,							pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A		)
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= feat_extract_norm
													__SCREAMING_SNAKE_CASE									= feat_extract_activation
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= conv_bias
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embeddings
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embedding_groups
													__SCREAMING_SNAKE_CASE									= len(self.conv_dim		)
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= squeeze_factor
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= hidden_dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= activation_dropout
													__SCREAMING_SNAKE_CASE									= feat_proj_dropout
													__SCREAMING_SNAKE_CASE									= final_dropout
													__SCREAMING_SNAKE_CASE									= layerdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= vocab_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)`,'
																				    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
																				    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													__SCREAMING_SNAKE_CASE									= apply_spec_augment
													__SCREAMING_SNAKE_CASE									= mask_time_prob
													__SCREAMING_SNAKE_CASE									= mask_time_length
													__SCREAMING_SNAKE_CASE									= mask_time_min_masks
													__SCREAMING_SNAKE_CASE									= mask_feature_prob
													__SCREAMING_SNAKE_CASE									= mask_feature_length
													__SCREAMING_SNAKE_CASE									= mask_feature_min_masks

													# ctc loss
													__SCREAMING_SNAKE_CASE									= ctc_loss_reduction
													__SCREAMING_SNAKE_CASE									= ctc_zero_infinity

													# sequence classification
													__SCREAMING_SNAKE_CASE									= use_weighted_layer_sum
													__SCREAMING_SNAKE_CASE									= classifier_proj_size






						@property
						def 	_A     (   self		):







													'''simple docstring'''

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






