


"""simple docstring"""







from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__lowercase    					=      logging.get_logger(__name__)

# General docstring
__lowercase    					=      """ResNetConfig"""

# Base docstring
__lowercase    					=      """microsoft/resnet-50"""
__lowercase    					=      [1, 2048, 7, 7]

# Image classification docstring
__lowercase    					=      """microsoft/resnet-50"""
__lowercase    					=      """tiger cat"""

__lowercase    					=      [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = 3				, __UpperCAmelCase				:    int = 1				, __UpperCAmelCase				:    str = "relu"):
      super().__init__()
      a						:		List[Any]      							=			nn.Convad(
          __UpperCAmelCase				, __UpperCAmelCase				, kernel_size=__UpperCAmelCase				, stride=__UpperCAmelCase				, padding=kernel_size // 2				, bias=__UpperCAmelCase)
      a						:		str      							=			nn.BatchNormad(__UpperCAmelCase)
      a						:		Optional[int]      							=			ACTaFN[activation] if activation is not None else nn.Identity()






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    Tensor):
      a						:		Union[str, Any]      							=			self.convolution(__UpperCAmelCase)
      a						:		Optional[Any]      							=			self.normalization(__UpperCAmelCase)
      a						:		Optional[int]      							=			self.activation(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Optional[int]				, __UpperCAmelCase				:    ResNetConfig):
      super().__init__()
      a						:		str      							=			ResNetConvLayer(
          config.num_channels				, config.embedding_size				, kernel_size=7				, stride=2				, activation=config.hidden_act)
      a						:		List[str]      							=			nn.MaxPoolad(kernel_size=3				, stride=2				, padding=1)
      a						:		str      							=			config.num_channels






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    Tensor):
      a						:		Optional[int]      							=			pixel_values.shape[1]
      if num_channels != self.num_channels:
        raise ValueError(
            "Make sure that the channel dimension of the pixel values match with the one set in the configuration.")
      a						:		Optional[int]      							=			self.embedder(__UpperCAmelCase)
      a						:		Optional[int]      							=			self.pooler(__UpperCAmelCase)
      return embedding






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Any				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = 2):
      super().__init__()
      a						:		Union[str, Any]      							=			nn.Convad(__UpperCAmelCase				, __UpperCAmelCase				, kernel_size=1				, stride=__UpperCAmelCase				, bias=__UpperCAmelCase)
      a						:		int      							=			nn.BatchNormad(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    Tensor):
      a						:		Any      							=			self.convolution(__UpperCAmelCase)
      a						:		Any      							=			self.normalization(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = 1				, __UpperCAmelCase				:    str = "relu"):
      super().__init__()
      a						:		Optional[int]      							=			in_channels != out_channels or stride != 1
      a						:		List[str]      							=			(
          ResNetShortCut(__UpperCAmelCase				, __UpperCAmelCase				, stride=__UpperCAmelCase) if should_apply_shortcut else nn.Identity()
      )
      a						:		str      							=			nn.Sequential(
          ResNetConvLayer(__UpperCAmelCase				, __UpperCAmelCase				, stride=__UpperCAmelCase)				, ResNetConvLayer(__UpperCAmelCase				, __UpperCAmelCase				, activation=__UpperCAmelCase)				, )
      a						:		Optional[int]      							=			ACTaFN[activation]






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    Any):
      a						:		Optional[int]      							=			hidden_state
      a						:		Optional[int]      							=			self.layer(__UpperCAmelCase)
      a						:		Optional[Any]      							=			self.shortcut(__UpperCAmelCase)
      hidden_state += residual
      a						:		str      							=			self.activation(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Tuple				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = 1				, __UpperCAmelCase				:    str = "relu"				, __UpperCAmelCase				:    int = 4):
      super().__init__()
      a						:		Optional[Any]      							=			in_channels != out_channels or stride != 1
      a						:		List[str]      							=			out_channels // reduction
      a						:		Optional[Any]      							=			(
          ResNetShortCut(__UpperCAmelCase				, __UpperCAmelCase				, stride=__UpperCAmelCase) if should_apply_shortcut else nn.Identity()
      )
      a						:		int      							=			nn.Sequential(
          ResNetConvLayer(__UpperCAmelCase				, __UpperCAmelCase				, kernel_size=1)				, ResNetConvLayer(__UpperCAmelCase				, __UpperCAmelCase				, stride=__UpperCAmelCase)				, ResNetConvLayer(__UpperCAmelCase				, __UpperCAmelCase				, kernel_size=1				, activation=__UpperCAmelCase)				, )
      a						:		Union[str, Any]      							=			ACTaFN[activation]






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    Dict):
      a						:		List[str]      							=			hidden_state
      a						:		List[Any]      							=			self.layer(__UpperCAmelCase)
      a						:		Optional[int]      							=			self.shortcut(__UpperCAmelCase)
      hidden_state += residual
      a						:		str      							=			self.activation(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    ResNetConfig				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int = 2				, __UpperCAmelCase				:    int = 2				, ):
      super().__init__()

      a						:		Tuple      							=			ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

      a						:		Dict      							=			nn.Sequential(
          # downsampling is done in the first layer with stride of 2
          layer(__UpperCAmelCase				, __UpperCAmelCase				, stride=__UpperCAmelCase				, activation=config.hidden_act)				, *[layer(__UpperCAmelCase				, __UpperCAmelCase				, activation=config.hidden_act) for _ in range(depth - 1)]				, )






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    Tensor):
      a						:		str      							=			input
      for layer in self.layers:
        a						:		Tuple      							=			layer(__UpperCAmelCase)
      return hidden_state






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    ResNetConfig):
      super().__init__()
      a						:		List[str]      							=			nn.ModuleList([])
      # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
      self.stages.append(
          ResNetStage(
              __UpperCAmelCase				, config.embedding_size				, config.hidden_sizes[0]				, stride=2 if config.downsample_in_first_stage else 1				, depth=config.depths[0]				, ))
      a						:		Any      							=			zip(config.hidden_sizes				, config.hidden_sizes[1:])
      for (in_channels, out_channels), depth in zip(__UpperCAmelCase				, config.depths[1:]):
        self.stages.append(ResNetStage(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, depth=__UpperCAmelCase))






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    Tensor				, __UpperCAmelCase				:    bool = False				, __UpperCAmelCase				:    bool = True):
      a						:		Tuple      							=			() if output_hidden_states else None

      for stage_module in self.stages:
        if output_hidden_states:
          a						:		Any      							=			hidden_states + (hidden_state,)

        a						:		int      							=			stage_module(__UpperCAmelCase)

      if output_hidden_states:
        a						:		List[Any]      							=			hidden_states + (hidden_state,)

      if not return_dict:
        return tuple(v for v in [hidden_state, hidden_states] if v is not None)

      return BaseModelOutputWithNoAttention(
          last_hidden_state=__UpperCAmelCase				, hidden_states=__UpperCAmelCase				, )






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       ResNetConfig
    UpperCAmelCase       :							List[Any]	=       """resnet"""
    UpperCAmelCase       :							Optional[Any]	=       """pixel_values"""
    UpperCAmelCase       :							str	=       True






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Tuple):
      if isinstance(__UpperCAmelCase				, nn.Convad):
        nn.init.kaiming_normal_(module.weight				, mode="fan_out"				, nonlinearity="relu")
      elif isinstance(__UpperCAmelCase				, (nn.BatchNormad, nn.GroupNorm)):
        nn.init.constant_(module.weight				, 1)
        nn.init.constant_(module.bias				, 0)






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Any=False):
      if isinstance(__UpperCAmelCase				, __UpperCAmelCase):
        a						:		Tuple      							=			value






__lowercase    					=      R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

__lowercase    					=      R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""    ,_a    ,)
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    Union[str, Any]):
      super().__init__(__UpperCAmelCase)
      a						:		Tuple      							=			config
      a						:		str      							=			ResNetEmbeddings(__UpperCAmelCase)
      a						:		List[str]      							=			ResNetEncoder(__UpperCAmelCase)
      a						:		Dict      							=			nn.AdaptiveAvgPoolad((1, 1))
      # Initialize weights and apply final processing
      self.post_init()






    @add_start_docstrings_to_model_forward(__UpperCAmelCase)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC				, output_type=__UpperCAmelCase				, config_class=_CONFIG_FOR_DOC				, modality="vision"				, expected_output=_EXPECTED_OUTPUT_SHAPE				, )
    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    Tensor				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[bool] = None):
      a						:		str      							=			(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      a						:		List[str]      							=			return_dict if return_dict is not None else self.config.use_return_dict

      a						:		Any      							=			self.embedder(__UpperCAmelCase)

      a						:		Optional[int]      							=			self.encoder(
          __UpperCAmelCase				, output_hidden_states=__UpperCAmelCase				, return_dict=__UpperCAmelCase)

      a						:		Tuple      							=			encoder_outputs[0]

      a						:		Dict      							=			self.pooler(__UpperCAmelCase)

      if not return_dict:
        return (last_hidden_state, pooled_output) + encoder_outputs[1:]

      return BaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=__UpperCAmelCase				, pooler_output=__UpperCAmelCase				, hidden_states=encoder_outputs.hidden_states				, )






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """    ,_a    ,)
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    List[str]):
      super().__init__(__UpperCAmelCase)
      a						:		List[str]      							=			config.num_labels
      a						:		Union[str, Any]      							=			ResNetModel(__UpperCAmelCase)
      # classification head
      a						:		Tuple      							=			nn.Sequential(
          nn.Flatten()				, nn.Linear(config.hidden_sizes[-1]				, config.num_labels) if config.num_labels > 0 else nn.Identity()				, )
      # initialize weights and apply final processing
      self.post_init()






    @add_start_docstrings_to_model_forward(__UpperCAmelCase)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT				, output_type=__UpperCAmelCase				, config_class=_CONFIG_FOR_DOC				, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				, )
    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Optional[torch.FloatTensor] = None				, __UpperCAmelCase				:    Optional[torch.LongTensor] = None				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[bool] = None				, ):
      a						:		Optional[Any]      							=			return_dict if return_dict is not None else self.config.use_return_dict

      a						:		int      							=			self.resnet(__UpperCAmelCase				, output_hidden_states=__UpperCAmelCase				, return_dict=__UpperCAmelCase)

      a						:		Dict      							=			outputs.pooler_output if return_dict else outputs[1]

      a						:		List[str]      							=			self.classifier(__UpperCAmelCase)

      a						:		Tuple      							=			None

      if labels is not None:
        if self.config.problem_type is None:
          if self.num_labels == 1:
            a						:		Dict      							=			"regression"
          elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
            a						:		int      							=			"single_label_classification"
          else:
            a						:		Tuple      							=			"multi_label_classification"
        if self.config.problem_type == "regression":
          a						:		Dict      							=			MSELoss()
          if self.num_labels == 1:
            a						:		Any      							=			loss_fct(logits.squeeze()				, labels.squeeze())
          else:
            a						:		Any      							=			loss_fct(__UpperCAmelCase				, __UpperCAmelCase)
        elif self.config.problem_type == "single_label_classification":
          a						:		Any      							=			CrossEntropyLoss()
          a						:		Optional[int]      							=			loss_fct(logits.view(-1				, self.num_labels)				, labels.view(-1))
        elif self.config.problem_type == "multi_label_classification":
          a						:		Optional[Any]      							=			BCEWithLogitsLoss()
          a						:		Optional[int]      							=			loss_fct(__UpperCAmelCase				, __UpperCAmelCase)

      if not return_dict:
        a						:		int      							=			(logits,) + outputs[2:]
        return (loss,) + output if loss is not None else output

      return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase				, logits=__UpperCAmelCase				, hidden_states=outputs.hidden_states)






@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """    ,_a    ,)
class 						_A  (  _a    ,_a  ):







    """simple docstring"""




    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Dict):
      super().__init__(__UpperCAmelCase)
      super()._init_backbone(__UpperCAmelCase)

      a						:		List[str]      							=			[config.embedding_size] + config.hidden_sizes
      a						:		Tuple      							=			ResNetEmbeddings(__UpperCAmelCase)
      a						:		List[str]      							=			ResNetEncoder(__UpperCAmelCase)

      # initialize weights and apply final processing
      self.post_init()






    @add_start_docstrings_to_model_forward(__UpperCAmelCase)
    @replace_return_docstrings(output_type=__UpperCAmelCase				, config_class=_CONFIG_FOR_DOC)
    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    Tensor				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[bool] = None):
      a						:		Tuple      							=			return_dict if return_dict is not None else self.config.use_return_dict
      a						:		List[str]      							=			(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )

      a						:		str      							=			self.embedder(__UpperCAmelCase)

      a						:		Union[str, Any]      							=			self.encoder(__UpperCAmelCase				, output_hidden_states=__UpperCAmelCase				, return_dict=__UpperCAmelCase)

      a						:		Optional[int]      							=			outputs.hidden_states

      a						:		Union[str, Any]      							=			()
      for idx, stage in enumerate(self.stage_names):
        if stage in self.out_features:
          feature_maps += (hidden_states[idx],)

      if not return_dict:
        a						:		str      							=			(feature_maps,)
        if output_hidden_states:
          output += (outputs.hidden_states,)
        return output

      return BackboneOutput(
          feature_maps=__UpperCAmelCase				, hidden_states=outputs.hidden_states if output_hidden_states else None				, attentions=__UpperCAmelCase				, )














from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowerCamelCase	:							Tuple				=_symbol_database.Default()


lowerCamelCase	:							List[str]				=_descriptor_pool.Default().AddSerializedFile(
    b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

lowerCamelCase	:							str				=globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
 lowerCamelCase	:							Optional[int]				=None
 lowerCamelCase	:							Tuple				=b'''H\003'''
 # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
 # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
 # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
 # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
 # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
 lowerCamelCase	:							List[str]				=45
 lowerCamelCase	:							List[Any]				=1581
 lowerCamelCase	:							Optional[int]				=1517
 lowerCamelCase	:							Tuple				=1570
 lowerCamelCase	:							Dict				=1584
 lowerCamelCase	:							Optional[Any]				=1793
 lowerCamelCase	:							Dict				=1795
 lowerCamelCase	:							Any				=1916
 lowerCamelCase	:							Dict				=1864
 lowerCamelCase	:							Dict				=1905
 lowerCamelCase	:							Dict				=1919
 lowerCamelCase	:							Union[str, Any]				=2429
 lowerCamelCase	:							List[Any]				=2208
 lowerCamelCase	:							List[Any]				=2418
 lowerCamelCase	:							List[str]				=2323
 lowerCamelCase	:							Dict				=2407
# @@protoc_insertion_point(module_scope)