
import functools
import operator

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


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

lowerCamelCase_  :	List[str] 				=					{
    """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			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Tuple    =	"""sew"""

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    Dict=32  ,		snake_case_	:    Dict=768  ,		snake_case_	:    Tuple=12  ,		snake_case_	:    Dict=12  ,		snake_case_	:    Tuple=3072  ,		snake_case_	:    Tuple=2  ,		snake_case_	:    Optional[Any]="gelu"  ,		snake_case_	:    Tuple=0.1  ,		snake_case_	:    Dict=0.1  ,		snake_case_	:    Tuple=0.1  ,		snake_case_	:    Dict=0.0  ,		snake_case_	:    List[Any]=0.1  ,		snake_case_	:    Union[str, Any]=0.1  ,		snake_case_	:    Dict=0.02  ,		snake_case_	:    Union[str, Any]=1e-5  ,		snake_case_	:    Tuple="group"  ,		snake_case_	:    Optional[Any]="gelu"  ,		snake_case_	:    List[str]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,		snake_case_	:    List[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,		snake_case_	:    Union[str, Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,		snake_case_	:    List[Any]=False  ,		snake_case_	:    Union[str, Any]=128  ,		snake_case_	:    List[str]=16  ,		snake_case_	:    Dict=True  ,		snake_case_	:    int=0.05  ,		snake_case_	:    List[Any]=10  ,		snake_case_	:    List[str]=2  ,		snake_case_	:    Optional[Any]=0.0  ,		snake_case_	:    List[str]=10  ,		snake_case_	:    Dict=0  ,		snake_case_	:    int="mean"  ,		snake_case_	:    int=False  ,		snake_case_	:    List[Any]=False  ,		snake_case_	:    Tuple=256  ,		snake_case_	:    Tuple=0  ,		snake_case_	:    Optional[int]=1  ,		snake_case_	:    Dict=2  ,		**snake_case_	:    Dict  ,		):
							super().__init__(**snake_case_  ,		pad_token_id=snake_case_  ,		bos_token_id=snake_case_  ,		eos_token_id=snake_case_ )
							UpperCamelCase_:							int		=  hidden_size
							UpperCamelCase_:							Optional[int]		=  feat_extract_norm
							UpperCamelCase_:							Dict		=  feat_extract_activation
							UpperCamelCase_:							List[str]		=  list(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  list(snake_case_ )
							UpperCamelCase_:							str		=  list(snake_case_ )
							UpperCamelCase_:							Tuple		=  conv_bias
							UpperCamelCase_:							Optional[int]		=  num_conv_pos_embeddings
							UpperCamelCase_:							List[str]		=  num_conv_pos_embedding_groups
							UpperCamelCase_:							str		=  len(self.conv_dim )
							UpperCamelCase_:							Dict		=  num_hidden_layers
							UpperCamelCase_:							str		=  intermediate_size
							UpperCamelCase_:							str		=  squeeze_factor
							UpperCamelCase_:							Union[str, Any]		=  hidden_act
							UpperCamelCase_:							Any		=  num_attention_heads
							UpperCamelCase_:							Union[str, Any]		=  hidden_dropout
							UpperCamelCase_:							str		=  attention_dropout
							UpperCamelCase_:							List[str]		=  activation_dropout
							UpperCamelCase_:							Union[str, Any]		=  feat_proj_dropout
							UpperCamelCase_:							Union[str, Any]		=  final_dropout
							UpperCamelCase_:							Tuple		=  layerdrop
							UpperCamelCase_:							List[Any]		=  layer_norm_eps
							UpperCamelCase_:							str		=  initializer_range
							UpperCamelCase_:							Union[str, Any]		=  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
							UpperCamelCase_:							Optional[int]		=  apply_spec_augment
							UpperCamelCase_:							Optional[int]		=  mask_time_prob
							UpperCamelCase_:							Dict		=  mask_time_length
							UpperCamelCase_:							Union[str, Any]		=  mask_time_min_masks
							UpperCamelCase_:							Optional[Any]		=  mask_feature_prob
							UpperCamelCase_:							Optional[int]		=  mask_feature_length
							UpperCamelCase_:							Optional[int]		=  mask_feature_min_masks

							# ctc loss
							UpperCamelCase_:							Union[str, Any]		=  ctc_loss_reduction
							UpperCamelCase_:							Optional[Any]		=  ctc_zero_infinity

							# sequence classification
							UpperCamelCase_:							List[Any]		=  use_weighted_layer_sum
							UpperCamelCase_:							Dict		=  classifier_proj_size




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








from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	["""image_processor""", """tokenizer"""]
			__UpperCamelCase :  List[Any]    =	"""AutoImageProcessor"""
			__UpperCamelCase :  Tuple    =	"""AutoTokenizer"""

			def __init__(    self	:    Any  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Any ):
							super().__init__(snake_case_  ,		snake_case_ )
							UpperCamelCase_:							int		=  self.image_processor

			def __call__(    self	:    str  ,		snake_case_	:    Optional[int]=None  ,		snake_case_	:    int=None  ,		snake_case_	:    Dict=None  ,		**snake_case_	:    Optional[int] ):

							if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none.""" )

							if text is not None:
											UpperCamelCase_:							List[Any]		=  self.tokenizer(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if images is not None:
											UpperCamelCase_:							Any		=  self.image_processor(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if text is not None and images is not None:
											UpperCamelCase_:							Tuple		=  image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**snake_case_ )  ,		tensor_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		*snake_case_	:    int  ,		**snake_case_	:    Optional[Any] ):
							return self.tokenizer.batch_decode(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    int  ,		*snake_case_	:    Optional[Any]  ,		**snake_case_	:    str ):
							return self.tokenizer.decode(*snake_case_  ,		**snake_case_ )




			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return ["input_ids", "attention_mask", "pixel_values"]






