



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


__UpperCAmelCase	       =	logging.get_logger(__name__)

# General docstring
__UpperCAmelCase	       =	'''ResNetConfig'''

# Base docstring
__UpperCAmelCase	       =	'''microsoft/resnet-50'''
__UpperCAmelCase	       =	[1, 2_048, 7, 7]

# Image classification docstring
__UpperCAmelCase	       =	'''microsoft/resnet-50'''
__UpperCAmelCase	       =	'''tiger cat'''

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



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






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tensor:
          UpperCamelCase   :  List[Any]						     =     self.convolution(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     self.normalization(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     self.activation(SCREAMING_SNAKE_CASE_	)
          return hidden_state






class     lowerCAmelCase_		(  nn.Module							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          super().__init__()
          UpperCamelCase   :  Optional[Any]						     =     ResNetConvLayer(
              config.num_channels,      config.embedding_size,      kernel_size=7,      stride=2,      activation=config.hidden_act	)
          UpperCamelCase   :  int						     =     nn.MaxPoolad(kernel_size=3,      stride=2,      padding=1	)
          UpperCamelCase   :  Dict						     =     config.num_channels






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tensor:
          UpperCamelCase   :  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.'	)
          UpperCamelCase   :  Optional[Any]						     =     self.embedder(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     self.pooler(SCREAMING_SNAKE_CASE_	)
          return embedding






class     lowerCAmelCase_		(  nn.Module							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 2	)					->		int:
          super().__init__()
          UpperCamelCase   :  Any						     =     nn.Convad(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      kernel_size=1,      stride=SCREAMING_SNAKE_CASE_,      bias=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     nn.BatchNormad(SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tensor:
          UpperCamelCase   :  Optional[int]						     =     self.convolution(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     self.normalization(SCREAMING_SNAKE_CASE_	)
          return hidden_state






class     lowerCAmelCase_		(  nn.Module							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = "relu"	)					->		Union[str, Any]:
          super().__init__()
          UpperCamelCase   :  str						     =     in_channels != out_channels or stride != 1
          UpperCamelCase   :  int						     =     (
              ResNetShortCut(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      stride=SCREAMING_SNAKE_CASE_	) if should_apply_shortcut else nn.Identity()
          )
          UpperCamelCase   :  Optional[int]						     =     nn.Sequential(
              ResNetConvLayer(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      stride=SCREAMING_SNAKE_CASE_	),      ResNetConvLayer(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      activation=SCREAMING_SNAKE_CASE_	),      )
          UpperCamelCase   :  Dict						     =     ACTaFN[activation]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     hidden_state
          UpperCamelCase   :  Optional[int]						     =     self.layer(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     self.shortcut(SCREAMING_SNAKE_CASE_	)
          hidden_state += residual
          UpperCamelCase   :  List[str]						     =     self.activation(SCREAMING_SNAKE_CASE_	)
          return hidden_state






class     lowerCAmelCase_		(  nn.Module							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = "relu",      SCREAMING_SNAKE_CASE_ = 4	)					->		List[Any]:
          super().__init__()
          UpperCamelCase   :  List[Any]						     =     in_channels != out_channels or stride != 1
          UpperCamelCase   :  Any						     =     out_channels // reduction
          UpperCamelCase   :  Optional[Any]						     =     (
              ResNetShortCut(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      stride=SCREAMING_SNAKE_CASE_	) if should_apply_shortcut else nn.Identity()
          )
          UpperCamelCase   :  Dict						     =     nn.Sequential(
              ResNetConvLayer(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      kernel_size=1	),      ResNetConvLayer(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      stride=SCREAMING_SNAKE_CASE_	),      ResNetConvLayer(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      kernel_size=1,      activation=SCREAMING_SNAKE_CASE_	),      )
          UpperCamelCase   :  Union[str, Any]						     =     ACTaFN[activation]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          UpperCamelCase   :  int						     =     hidden_state
          UpperCamelCase   :  str						     =     self.layer(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     self.shortcut(SCREAMING_SNAKE_CASE_	)
          hidden_state += residual
          UpperCamelCase   :  Dict						     =     self.activation(SCREAMING_SNAKE_CASE_	)
          return hidden_state






class     lowerCAmelCase_		(  nn.Module							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 2,      SCREAMING_SNAKE_CASE_ = 2,      )					->		int:
          super().__init__()

          UpperCamelCase   :  int						     =     ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

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






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tensor:
          UpperCamelCase   :  Optional[int]						     =     input
          for layer in self.layers:
             UpperCamelCase   :  Dict						     =     layer(SCREAMING_SNAKE_CASE_	)
          return hidden_state






class     lowerCAmelCase_		(  nn.Module							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          super().__init__()
          UpperCamelCase   :  Optional[Any]						     =     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(
                  SCREAMING_SNAKE_CASE_,      config.embedding_size,      config.hidden_sizes[0],      stride=2 if config.downsample_in_first_stage else 1,      depth=config.depths[0],      )	)
          UpperCamelCase   :  Any						     =     zip(config.hidden_sizes,      config.hidden_sizes[1:]	)
          for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE_,      config.depths[1:]	):
             self.stages.append(ResNetStage(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      depth=SCREAMING_SNAKE_CASE_	)	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = True	)					->		BaseModelOutputWithNoAttention:
          UpperCamelCase   :  int						     =     () if output_hidden_states else None

          for stage_module in self.stages:
             if output_hidden_states:
                UpperCamelCase   :  Union[str, Any]						     =     hidden_states + (hidden_state,)

             UpperCamelCase   :  int						     =     stage_module(SCREAMING_SNAKE_CASE_	)

          if output_hidden_states:
             UpperCamelCase   :  Optional[int]						     =     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=SCREAMING_SNAKE_CASE_,      hidden_states=SCREAMING_SNAKE_CASE_,      )






class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  ResNetConfig
       UpperCAmelCase__    : List[str]       					=  "resnet"
       UpperCAmelCase__    : int       					=  "pixel_values"
       UpperCAmelCase__    : Optional[Any]       					=  True
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict:
          if isinstance(SCREAMING_SNAKE_CASE_,      nn.Convad	):
             nn.init.kaiming_normal_(module.weight,      mode='fan_out',      nonlinearity='relu'	)
          elif isinstance(SCREAMING_SNAKE_CASE_,      (nn.BatchNormad, nn.GroupNorm)	):
             nn.init.constant_(module.weight,      1	)
             nn.init.constant_(module.bias,      0	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False	)					->		Union[str, Any]:
          if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Union[str, Any]						     =     value






__UpperCAmelCase	       =	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.
'''

__UpperCAmelCase	       =	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     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_	)					->		int:
          super().__init__(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     config
          UpperCamelCase   :  Dict						     =     ResNetEmbeddings(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     ResNetEncoder(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     nn.AdaptiveAvgPoolad((1, 1)	)
          # Initialize weights and apply final processing
          self.post_init()






       @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_	)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC,      output_type=SCREAMING_SNAKE_CASE_,      config_class=_CONFIG_FOR_DOC,      modality='vision',      expected_output=_EXPECTED_OUTPUT_SHAPE,      )
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None	)					->		BaseModelOutputWithPoolingAndNoAttention:
          UpperCamelCase   :  Optional[Any]						     =     (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          UpperCamelCase   :  Optional[int]						     =     return_dict if return_dict is not None else self.config.use_return_dict

          UpperCamelCase   :  List[Any]						     =     self.embedder(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     self.encoder(
              SCREAMING_SNAKE_CASE_,      output_hidden_states=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[Any]						     =     encoder_outputs[0]

          UpperCamelCase   :  Any						     =     self.pooler(SCREAMING_SNAKE_CASE_	)

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

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






@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "	,   a__	,   )
class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          super().__init__(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     config.num_labels
          UpperCamelCase   :  Optional[int]						     =     ResNetModel(SCREAMING_SNAKE_CASE_	)
          # classification head
          UpperCamelCase   :  List[str]						     =     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(SCREAMING_SNAKE_CASE_	)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT,      output_type=SCREAMING_SNAKE_CASE_,      config_class=_CONFIG_FOR_DOC,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,      )
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      )					->		ImageClassifierOutputWithNoAttention:
          UpperCamelCase   :  List[str]						     =     return_dict if return_dict is not None else self.config.use_return_dict

          UpperCamelCase   :  Any						     =     self.resnet(SCREAMING_SNAKE_CASE_,      output_hidden_states=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     outputs.pooler_output if return_dict else outputs[1]

          UpperCamelCase   :  Optional[Any]						     =     self.classifier(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Tuple						     =     None

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

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

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






@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "	,   a__	,   )
class     lowerCAmelCase_		(  a__	,   a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict:
          super().__init__(SCREAMING_SNAKE_CASE_	)
          super()._init_backbone(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     [config.embedding_size] + config.hidden_sizes
          UpperCamelCase   :  int						     =     ResNetEmbeddings(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     ResNetEncoder(SCREAMING_SNAKE_CASE_	)

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






       @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_	)
       @replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE_,      config_class=_CONFIG_FOR_DOC	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None	)					->		BackboneOutput:
          UpperCamelCase   :  Dict						     =     return_dict if return_dict is not None else self.config.use_return_dict
          UpperCamelCase   :  Optional[int]						     =     (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )

          UpperCamelCase   :  Tuple						     =     self.embedder(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     self.encoder(SCREAMING_SNAKE_CASE_,      output_hidden_states=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     outputs.hidden_states

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

          if not return_dict:
             UpperCamelCase   :  Optional[Any]						     =     (feature_maps,)
             if output_hidden_states:
                output += (outputs.hidden_states,)
             return output

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








import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	       =	logging.get_logger(__name__)
__UpperCAmelCase	       =	{'''vocab_file''': '''spiece.model'''}

__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

__UpperCAmelCase	       =	{
    '''AI-Sweden/gpt-sw3-126m''': 2_048,
    '''AI-Sweden/gpt-sw3-350m''': 2_048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_048,
    '''AI-Sweden/gpt-sw3-20b''': 2_048,
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Union[str, Any]       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : Any       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : int       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : Optional[int]       					=  ["input_ids", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		None:
          UpperCamelCase   :  Optional[Any]						     =     {} if sp_model_kwargs is None else sp_model_kwargs

          UpperCamelCase   :  Dict						     =     kwargs.get('name_or_path'	)
          if name_or_path is None:
             logger.warning(
                 'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                 ' you are testing the model, this can safely be ignored'	)
             UpperCamelCase   :  Tuple						     =     'None'

          # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
          UpperCamelCase   :  str						     =     '<|endoftext|>' if eos_token is None else eos_token
          UpperCamelCase   :  Tuple						     =     '<unk>' if unk_token is None else unk_token
          if "gpt-sw3-7b" in name_or_path:
             UpperCamelCase   :  str						     =     unk_token if pad_token is None else pad_token
             UpperCamelCase   :  List[str]						     =     eos_token if bos_token is None else bos_token
          else:
             UpperCamelCase   :  List[Any]						     =     '<pad>' if pad_token is None else pad_token
             UpperCamelCase   :  Dict						     =     '<s>' if bos_token is None else bos_token

          super().__init__(
              do_lower_case=SCREAMING_SNAKE_CASE_,      remove_space=SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_,      bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      sp_model_kwargs=self.sp_model_kwargs,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  List[str]						     =     do_lower_case
          UpperCamelCase   :  List[str]						     =     remove_space
          UpperCamelCase   :  List[Any]						     =     keep_accents
          UpperCamelCase   :  List[str]						     =     vocab_file

          UpperCamelCase   :  Union[str, Any]						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(SCREAMING_SNAKE_CASE_	)

          # Used for whitespace normalization in input texts
          # fmt : off
          UpperCamelCase   :  Dict						     =     {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
          # fmt : on

          # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
          UpperCamelCase   :  List[Any]						     =     re.compile(
              F"""[{"".join(map(SCREAMING_SNAKE_CASE_,      list(range(0,      9	)	) + list(range(11,      32	)	) + list(range(127,      160	)	) + [160, 173, 8203]	)	)}]"""	)
       def __getstate__(      self	)					->		Tuple:
          UpperCamelCase   :  List[Any]						     =     self.__dict__.copy()
          UpperCamelCase   :  Optional[int]						     =     None
          return state
       def __setstate__(      self,      SCREAMING_SNAKE_CASE_	)					->		Any:
          UpperCamelCase   :  Any						     =     d

          # for backward compatibility
          if not hasattr(self,      'sp_model_kwargs'	):
             UpperCamelCase   :  Optional[int]						     =     {}

          UpperCamelCase   :  int						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(self.vocab_file	)
       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def  snake_case_	(      self	)					->		int:
          return len(self.sp_model	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Dict						     =     self.non_printing_characters_re.sub('',      SCREAMING_SNAKE_CASE_	)

          # Normalize whitespaces
          UpperCamelCase   :  Any						     =     ''.join([char if char not in self.whitespaces else ' ' for char in text]	)

          # NFC Unicode normalization
          UpperCamelCase   :  Dict						     =     unicodedata.normalize('NFC',      SCREAMING_SNAKE_CASE_	)
          return text
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		List[str]:
          UpperCamelCase   :  Any						     =     self.preprocess_text(SCREAMING_SNAKE_CASE_	)
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,      out_type=SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		int:
          return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
       @staticmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		str:
          return out_string
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Optional[Any]						     =     []
          UpperCamelCase   :  List[Any]						     =     ''
          UpperCamelCase   :  str						     =     False
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                if not prev_is_special:
                   out_string += " "

                out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                UpperCamelCase   :  Dict						     =     True
                UpperCamelCase   :  Optional[Any]						     =     []
             else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
                UpperCamelCase   :  str						     =     False
          out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)

          return out_string
       def  snake_case_	(      self	)					->		Dict[str, int]:
          UpperCamelCase   :  Tuple						     =     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
          vocab.update(self.added_tokens_encoder	)
          return vocab
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
             return
          UpperCamelCase   :  List[str]						     =     os.path.join(
              SCREAMING_SNAKE_CASE_,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

          if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file,      SCREAMING_SNAKE_CASE_	)
          elif not os.path.isfile(self.vocab_file	):
             with open(SCREAMING_SNAKE_CASE_,      'wb'	) as fi:
                UpperCamelCase   :  Any						     =     self.sp_model.serialized_model_proto()
                fi.write(SCREAMING_SNAKE_CASE_	)

          return (out_vocab_file,)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False	)					->		Union[List[int], List[List[int]], "torch.Tensor"]:

          if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  List[str]						     =     self.preprocess_text(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Optional[Any]						     =     self.sp_model.encode(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  Union[str, Any]						     =     [self.preprocess_text(SCREAMING_SNAKE_CASE_	) for t in text]
             UpperCamelCase   :  Any						     =     self.sp_model.encode(SCREAMING_SNAKE_CASE_	)

          if return_tensors is True or return_tensors == "pt":
             UpperCamelCase   :  List[Any]						     =     torch.tensor(SCREAMING_SNAKE_CASE_	)

          return token_ids
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          return self.sp_model.decode(SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[int]:
          UpperCamelCase   :  List[Any]						     =     [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
          UpperCamelCase   :  Optional[Any]						     =     (
              F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(SCREAMING_SNAKE_CASE_	) + F"""{self.bos_token}Bot:"""
          )
          return self.encode(text=SCREAMING_SNAKE_CASE_	)



