
import functools
import operator

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


_UpperCAmelCase					: Any      =       logging.get_logger(__name__)

_UpperCAmelCase					: Any      =       {
    "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 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       int	     =				"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_=0.02				,      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_=0.05				,      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_				,      )  ->					Dict:
				"""simple docstring"""
				super().__init__(**A_				,      pad_token_id=A_				,      bos_token_id=A_				,      eos_token_id=A_       )
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     feat_extract_norm
				UpperCamelCase		=     feat_extract_activation
				UpperCamelCase		=     list(A_       )
				UpperCamelCase		=     list(A_       )
				UpperCamelCase		=     list(A_       )
				UpperCamelCase		=     conv_bias
				UpperCamelCase		=     num_conv_pos_embeddings
				UpperCamelCase		=     num_conv_pos_embedding_groups
				UpperCamelCase		=     len(self.conv_dim       )
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     squeeze_factor
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     hidden_dropout
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     activation_dropout
				UpperCamelCase		=     feat_proj_dropout
				UpperCamelCase		=     final_dropout
				UpperCamelCase		=     layerdrop
				UpperCamelCase		=     layer_norm_eps
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     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		=     apply_spec_augment
				UpperCamelCase		=     mask_time_prob
				UpperCamelCase		=     mask_time_length
				UpperCamelCase		=     mask_time_min_masks
				UpperCamelCase		=     mask_feature_prob
				UpperCamelCase		=     mask_feature_length
				UpperCamelCase		=     mask_feature_min_masks

				# ctc loss
				UpperCamelCase		=     ctc_loss_reduction
				UpperCamelCase		=     ctc_zero_infinity

				# sequence classification
				UpperCamelCase		=     use_weighted_layer_sum
				UpperCamelCase		=     classifier_proj_size



		@property
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				return functools.reduce(operator.mul				,      self.conv_stride				,      1       )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase					: Union[str, Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
