
import functools
import operator

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


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

lowerCamelCase_						: int         =  {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Union[str, Any]					      = """sew-d"""

			def __init__(       self :       Optional[Any]						,	snake_case_ :       Tuple=32						,	snake_case_ :       Optional[int]=768						,	snake_case_ :       List[str]=12						,	snake_case_ :       List[Any]=12						,	snake_case_ :       Union[str, Any]=3072						,	snake_case_ :       int=2						,	snake_case_ :       Tuple=512						,	snake_case_ :       int=256						,	snake_case_ :       Optional[Any]=True						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       str=("p2c", "c2p")						,	snake_case_ :       Optional[Any]="layer_norm"						,	snake_case_ :       Dict="gelu_python"						,	snake_case_ :       str=0.1						,	snake_case_ :       str=0.1						,	snake_case_ :       Union[str, Any]=0.1						,	snake_case_ :       int=0.0						,	snake_case_ :       List[Any]=0.1						,	snake_case_ :       int=0.02						,	snake_case_ :       int=1e-7						,	snake_case_ :       Tuple=1e-5						,	snake_case_ :       Optional[int]="group"						,	snake_case_ :       Any="gelu"						,	snake_case_ :       str=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,	snake_case_ :       Optional[int]=(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_ :       Optional[Any]=False						,	snake_case_ :       List[str]=128						,	snake_case_ :       Optional[int]=16						,	snake_case_ :       List[str]=True						,	snake_case_ :       Optional[int]=0.05						,	snake_case_ :       Optional[Any]=10						,	snake_case_ :       str=2						,	snake_case_ :       Tuple=0.0						,	snake_case_ :       Optional[Any]=10						,	snake_case_ :       Dict=0						,	snake_case_ :       Union[str, Any]="mean"						,	snake_case_ :       Tuple=False						,	snake_case_ :       List[Any]=False						,	snake_case_ :       Union[str, Any]=256						,	snake_case_ :       Optional[int]=0						,	snake_case_ :       Optional[Any]=1						,	snake_case_ :       Any=2						,	**snake_case_ :       Tuple						,	):
							super().__init__(**snake_case_						,	pad_token_id=snake_case_						,	bos_token_id=snake_case_						,	eos_token_id=snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						hidden_size
							UpperCamelCase_:							Optional[int]	=						feat_extract_norm
							UpperCamelCase_:							int	=						feat_extract_activation
							UpperCamelCase_:							Union[str, Any]	=						list(snake_case_      )
							UpperCamelCase_:							int	=						list(snake_case_      )
							UpperCamelCase_:							Optional[int]	=						list(snake_case_      )
							UpperCamelCase_:							Dict	=						conv_bias
							UpperCamelCase_:							Optional[int]	=						num_conv_pos_embeddings
							UpperCamelCase_:							Any	=						num_conv_pos_embedding_groups
							UpperCamelCase_:							Union[str, Any]	=						len(self.conv_dim      )
							UpperCamelCase_:							str	=						num_hidden_layers
							UpperCamelCase_:							Optional[Any]	=						intermediate_size
							UpperCamelCase_:							int	=						squeeze_factor
							UpperCamelCase_:							int	=						max_position_embeddings
							UpperCamelCase_:							Union[str, Any]	=						position_buckets
							UpperCamelCase_:							Any	=						share_att_key
							UpperCamelCase_:							Optional[int]	=						relative_attention
							UpperCamelCase_:							Optional[int]	=						norm_rel_ebd
							UpperCamelCase_:							List[Any]	=						list(snake_case_      )
							UpperCamelCase_:							Union[str, Any]	=						hidden_act
							UpperCamelCase_:							str	=						num_attention_heads
							UpperCamelCase_:							str	=						hidden_dropout
							UpperCamelCase_:							List[Any]	=						attention_dropout
							UpperCamelCase_:							Any	=						activation_dropout
							UpperCamelCase_:							Union[str, Any]	=						feat_proj_dropout
							UpperCamelCase_:							str	=						final_dropout
							UpperCamelCase_:							Dict	=						layer_norm_eps
							UpperCamelCase_:							List[str]	=						feature_layer_norm_eps
							UpperCamelCase_:							str	=						initializer_range
							UpperCamelCase_:							Optional[int]	=						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_:							int	=						apply_spec_augment
							UpperCamelCase_:							str	=						mask_time_prob
							UpperCamelCase_:							Optional[Any]	=						mask_time_length
							UpperCamelCase_:							Tuple	=						mask_time_min_masks
							UpperCamelCase_:							Optional[int]	=						mask_feature_prob
							UpperCamelCase_:							Dict	=						mask_feature_length
							UpperCamelCase_:							List[Any]	=						mask_feature_min_masks

							# ctc loss
							UpperCamelCase_:							int	=						ctc_loss_reduction
							UpperCamelCase_:							List[str]	=						ctc_zero_infinity

							# sequence classification
							UpperCamelCase_:							Optional[int]	=						use_weighted_layer_sum
							UpperCamelCase_:							Dict	=						classifier_proj_size




			@property
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							return functools.reduce(operator.mul						,	self.conv_stride						,	1      )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: str         =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  ["""RoFormerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Any         =  [
							    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RoFormerForCausalLM""",
							    """RoFormerForMaskedLM""",
							    """RoFormerForMultipleChoice""",
							    """RoFormerForQuestionAnswering""",
							    """RoFormerForSequenceClassification""",
							    """RoFormerForTokenClassification""",
							    """RoFormerLayer""",
							    """RoFormerModel""",
							    """RoFormerPreTrainedModel""",
							    """load_tf_weights_in_roformer""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRoFormerForCausalLM""",
							    """TFRoFormerForMaskedLM""",
							    """TFRoFormerForMultipleChoice""",
							    """TFRoFormerForQuestionAnswering""",
							    """TFRoFormerForSequenceClassification""",
							    """TFRoFormerForTokenClassification""",
							    """TFRoFormerLayer""",
							    """TFRoFormerModel""",
							    """TFRoFormerPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxRoFormerForMaskedLM""",
							    """FlaxRoFormerForMultipleChoice""",
							    """FlaxRoFormerForQuestionAnswering""",
							    """FlaxRoFormerForSequenceClassification""",
							    """FlaxRoFormerForTokenClassification""",
							    """FlaxRoFormerModel""",
							    """FlaxRoFormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_						: Tuple         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






