




from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


A      = [
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
A      = "UperNetConfig"


class     lowercase__  (					nn.Module		):






  def __init__(					self      :					int		,						_lowercase      :					int		,						_lowercase      :					int		,						_lowercase      :					Union[int, Tuple[int, int]]		,						_lowercase      :					Union[int, Tuple[int, int], str] = 0		,						_lowercase      :					bool = False		,						_lowercase      :					Union[int, Tuple[int, int]] = 1		,						):




         """simple docstring"""







         super().__init__()
         UpperCAmelCase__										=     nn.Convad(
             in_channels=_lowercase		,						out_channels=_lowercase		,						kernel_size=_lowercase		,						padding=_lowercase		,						bias=_lowercase		,						dilation=_lowercase		,						)
         UpperCAmelCase__										=     nn.BatchNormad(_lowercase )
         UpperCAmelCase__										=     nn.ReLU()






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					torch.Tensor ):




         """simple docstring"""







         UpperCAmelCase__										=     self.conv(_lowercase )
         UpperCAmelCase__										=     self.batch_norm(_lowercase )
         UpperCAmelCase__										=     self.activation(_lowercase )

         return output




class     lowercase__  (					nn.Module		):






  def __init__(					self      :					Union[str, Any]		,						_lowercase      :					int		,						_lowercase      :					int		,						_lowercase      :					int ):




         """simple docstring"""







         super().__init__()
         UpperCAmelCase__										=     [
             nn.AdaptiveAvgPoolad(_lowercase ),
             UperNetConvModule(_lowercase		,						_lowercase		,						kernel_size=1 ),
         ]
         for i, layer in enumerate(self.layers ):
                self.add_module(str(_lowercase )		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					torch.Tensor ):




         """simple docstring"""







         UpperCAmelCase__										=     input
         for layer in self.layers:
                UpperCAmelCase__										=     layer(_lowercase )
         return hidden_state




class     lowercase__  (					nn.Module		):






  def __init__(					self      :					List[Any]		,						_lowercase      :					Tuple[int, ...]		,						_lowercase      :					int		,						_lowercase      :					int		,						_lowercase      :					bool ):




         """simple docstring"""







         super().__init__()
         UpperCAmelCase__										=     pool_scales
         UpperCAmelCase__										=     align_corners
         UpperCAmelCase__										=     in_channels
         UpperCAmelCase__										=     channels
         UpperCAmelCase__										=     []
         for i, pool_scale in enumerate(_lowercase ):
                UpperCAmelCase__										=     UperNetPyramidPoolingBlock(pool_scale=_lowercase		,						in_channels=_lowercase		,						channels=_lowercase )
                self.blocks.append(_lowercase )
                self.add_module(str(_lowercase )		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					torch.Tensor ):




         """simple docstring"""







         UpperCAmelCase__										=     []
         for ppm in self.blocks:
                UpperCAmelCase__										=     ppm(_lowercase )
                UpperCAmelCase__										=     nn.functional.interpolate(
                    _lowercase		,						size=x.size()[2:]		,						mode="bilinear"		,						align_corners=self.align_corners )
                ppm_outs.append(_lowercase )
         return ppm_outs




class     lowercase__  (					nn.Module		):






  def __init__(					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					List[str] ):




         """simple docstring"""







         super().__init__()

         UpperCAmelCase__										=     config
         UpperCAmelCase__										=     config.pool_scales  # e.g. (1, 2, 3, 6)
         UpperCAmelCase__										=     in_channels
         UpperCAmelCase__										=     config.hidden_size
         UpperCAmelCase__										=     False
         UpperCAmelCase__										=     nn.Convad(self.channels		,						config.num_labels		,						kernel_size=1 )

         # PSP Module
         UpperCAmelCase__										=     UperNetPyramidPoolingModule(
             self.pool_scales		,						self.in_channels[-1]		,						self.channels		,						align_corners=self.align_corners		,						)
         UpperCAmelCase__										=     UperNetConvModule(
             self.in_channels[-1] + len(self.pool_scales ) * self.channels		,						self.channels		,						kernel_size=3		,						padding=1		,						)
         # FPN Module
         UpperCAmelCase__										=     nn.ModuleList()
         UpperCAmelCase__										=     nn.ModuleList()
         for in_channels in self.in_channels[:-1]:  # skip the top layer
                UpperCAmelCase__										=     UperNetConvModule(_lowercase		,						self.channels		,						kernel_size=1 )
                UpperCAmelCase__										=     UperNetConvModule(self.channels		,						self.channels		,						kernel_size=3		,						padding=1 )
                self.lateral_convs.append(_lowercase )
                self.fpn_convs.append(_lowercase )

         UpperCAmelCase__										=     UperNetConvModule(
             len(self.in_channels ) * self.channels		,						self.channels		,						kernel_size=3		,						padding=1		,						)






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         self.apply(self._init_weights )






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         if isinstance(_lowercase		,						nn.Convad ):
                module.weight.data.normal_(mean=0.0		,						std=self.config.initializer_range )
                if module.bias is not None:
                       module.bias.data.zero_()






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     inputs[-1]
         UpperCAmelCase__										=     [x]
         psp_outs.extend(self.psp_modules(_lowercase ) )
         UpperCAmelCase__										=     torch.cat(_lowercase		,						dim=1 )
         UpperCAmelCase__										=     self.bottleneck(_lowercase )

         return output






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					torch.Tensor ):




         """simple docstring"""







         UpperCAmelCase__										=     [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]

         laterals.append(self.psp_forward(_lowercase ) )

         # build top-down path
         UpperCAmelCase__										=     len(_lowercase )
         for i in range(used_backbone_levels - 1		,						0		,						-1 ):
                UpperCAmelCase__										=     laterals[i - 1].shape[2:]
                UpperCAmelCase__										=     laterals[i - 1] + nn.functional.interpolate(
                    laterals[i]		,						size=_lowercase		,						mode="bilinear"		,						align_corners=self.align_corners )

         # build outputs
         UpperCAmelCase__										=     [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
         # append psp feature
         fpn_outs.append(laterals[-1] )

         for i in range(used_backbone_levels - 1		,						0		,						-1 ):
                UpperCAmelCase__										=     nn.functional.interpolate(
                    fpn_outs[i]		,						size=fpn_outs[0].shape[2:]		,						mode="bilinear"		,						align_corners=self.align_corners )
         UpperCAmelCase__										=     torch.cat(_lowercase		,						dim=1 )
         UpperCAmelCase__										=     self.fpn_bottleneck(_lowercase )
         UpperCAmelCase__										=     self.classifier(_lowercase )

         return output




class     lowercase__  (					nn.Module		):






  def __init__(					self      :					Tuple		,						_lowercase      :					List[Any]		,						_lowercase      :					int = 2		,						_lowercase      :					int = 3		,						_lowercase      :					Union[int, Tuple[int, int]] = 1 ):




         """simple docstring"""







         super().__init__()

         UpperCAmelCase__										=     config
         UpperCAmelCase__										=     config.auxiliary_in_channels
         UpperCAmelCase__										=     config.auxiliary_channels
         UpperCAmelCase__										=     config.auxiliary_num_convs
         UpperCAmelCase__										=     config.auxiliary_concat_input
         UpperCAmelCase__										=     in_index

         UpperCAmelCase__										=     (kernel_size // 2) * dilation
         UpperCAmelCase__										=     []
         convs.append(
             UperNetConvModule(
                 self.in_channels		,						self.channels		,						kernel_size=_lowercase		,						padding=_lowercase		,						dilation=_lowercase ) )
         for i in range(self.num_convs - 1 ):
                convs.append(
                    UperNetConvModule(
                        self.channels		,						self.channels		,						kernel_size=_lowercase		,						padding=_lowercase		,						dilation=_lowercase ) )
         if self.num_convs == 0:
                UpperCAmelCase__										=     nn.Identity()
         else:
                UpperCAmelCase__										=     nn.Sequential(*_lowercase )
         if self.concat_input:
                UpperCAmelCase__										=     UperNetConvModule(
                    self.in_channels + self.channels		,						self.channels		,						kernel_size=_lowercase		,						padding=kernel_size // 2 )

         UpperCAmelCase__										=     nn.Convad(self.channels		,						config.num_labels		,						kernel_size=1 )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         self.apply(self._init_weights )






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					int ):




         """simple docstring"""







         if isinstance(_lowercase		,						nn.Convad ):
                module.weight.data.normal_(mean=0.0		,						std=self.config.initializer_range )
                if module.bias is not None:
                       module.bias.data.zero_()






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					torch.Tensor ):




         """simple docstring"""







         UpperCAmelCase__										=     encoder_hidden_states[self.in_index]
         UpperCAmelCase__										=     self.convs(_lowercase )
         if self.concat_input:
                UpperCAmelCase__										=     self.conv_cat(torch.cat([hidden_states, output]		,						dim=1 ) )
         UpperCAmelCase__										=     self.classifier(_lowercase )
         return output




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  UperNetConfig
  A__=  'pixel_values'
  A__=  True






  def 			_UpperCAmelCase  (					self      :					int		,						_lowercase      :					int ):




         """simple docstring"""







         if isinstance(_lowercase		,						_lowercase ):
                module.backbone.init_weights()
                module.decode_head.init_weights()
                module.auxiliary_head.init_weights()






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         self.backbone.init_weights()
         self.decode_head.init_weights()
         self.auxiliary_head.init_weights()






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					List[str]		,						_lowercase      :					Dict=False ):




         """simple docstring"""







         if isinstance(_lowercase		,						_lowercase ):
                UpperCAmelCase__										=     value




A      = r"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

A      = r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"


@add_start_docstrings(
    'UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'					,							__SCREAMING_SNAKE_CASE					,							)
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Any		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         super().__init__(_lowercase )

         UpperCAmelCase__										=     AutoBackbone.from_config(config.backbone_config )

         # Semantic segmentation head(s)
         UpperCAmelCase__										=     UperNetHead(_lowercase		,						in_channels=self.backbone.channels )
         UpperCAmelCase__										=     UperNetFCNHead(_lowercase ) if config.use_auxiliary_head else None

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






  @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length" ) )
  @replace_return_docstrings(output_type=_lowercase		,						config_class=_CONFIG_FOR_DOC )
  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Optional[torch.Tensor] = None		,						_lowercase      :					Optional[bool] = None		,						_lowercase      :					Optional[bool] = None		,						_lowercase      :					Optional[torch.Tensor] = None		,						_lowercase      :					Optional[bool] = None		,						):




         """simple docstring"""







         UpperCAmelCase__										=     return_dict if return_dict is not None else self.config.use_return_dict
         UpperCAmelCase__										=     (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         UpperCAmelCase__										=     output_attentions if output_attentions is not None else self.config.output_attentions

         UpperCAmelCase__										=     self.backbone.forward_with_filtered_kwargs(
             _lowercase		,						output_hidden_states=_lowercase		,						output_attentions=_lowercase )
         UpperCAmelCase__										=     outputs.feature_maps

         UpperCAmelCase__										=     self.decode_head(_lowercase )
         UpperCAmelCase__										=     nn.functional.interpolate(_lowercase		,						size=pixel_values.shape[2:]		,						mode="bilinear"		,						align_corners=_lowercase )

         UpperCAmelCase__										=     None
         if self.auxiliary_head is not None:
                UpperCAmelCase__										=     self.auxiliary_head(_lowercase )
                UpperCAmelCase__										=     nn.functional.interpolate(
                    _lowercase		,						size=pixel_values.shape[2:]		,						mode="bilinear"		,						align_corners=_lowercase )

         UpperCAmelCase__										=     None
         if labels is not None:
                if self.config.num_labels == 1:
                       raise ValueError("The number of labels should be greater than one" )
                else:
                       # compute weighted loss
                       UpperCAmelCase__										=     CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
                       UpperCAmelCase__										=     loss_fct(_lowercase		,						_lowercase )
                       UpperCAmelCase__										=     loss_fct(_lowercase		,						_lowercase )
                       UpperCAmelCase__										=     main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

         if not return_dict:
                if output_hidden_states:
                       UpperCAmelCase__										=     (logits,) + outputs[1:]
                else:
                       UpperCAmelCase__										=     (logits,) + outputs[2:]
                return ((loss,) + output) if loss is not None else output

         return SemanticSegmenterOutput(
             loss=_lowercase		,						logits=_lowercase		,						hidden_states=outputs.hidden_states		,						attentions=outputs.attentions		,						)









import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


A      = logging.get_logger(__name__)


class     lowercase__  :






  def __init__(					self      :					List[str]		,						_lowercase      :					str = None		,						_lowercase      :					uuid.UUID = None		,						_lowercase      :					Dict=None		,						_lowercase      :					Any=None ):




         """simple docstring"""







         if not conversation_id:
                UpperCAmelCase__										=     uuid.uuida()
         if past_user_inputs is None:
                UpperCAmelCase__										=     []
         if generated_responses is None:
                UpperCAmelCase__										=     []

         UpperCAmelCase__										=     conversation_id
         UpperCAmelCase__										=     past_user_inputs
         UpperCAmelCase__										=     generated_responses
         UpperCAmelCase__										=     text






  def __eq__(					self      :					Any		,						_lowercase      :					str ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                return False
         if self.uuid == other.uuid:
                return True
         return (
             self.new_user_input == other.new_user_input
             and self.past_user_inputs == other.past_user_inputs
             and self.generated_responses == other.generated_responses
         )






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					str		,						_lowercase      :					bool = False ):




         """simple docstring"""







         if self.new_user_input:
                if overwrite:
                       logger.warning(
                           F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
                           F"""with: \"{text}\".""" )
                       UpperCAmelCase__										=     text
                else:
                       logger.warning(
                           F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
                           F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
         else:
                UpperCAmelCase__										=     text






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         if self.new_user_input:
                self.past_user_inputs.append(self.new_user_input )
         UpperCAmelCase__										=     None






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					str ):




         """simple docstring"""







         self.generated_responses.append(_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         for user_input, generated_response in zip(self.past_user_inputs		,						self.generated_responses ):
                yield True, user_input
                yield False, generated_response
         if self.new_user_input:
                yield True, self.new_user_input






  def __repr__(					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     F"""Conversation id: {self.uuid} \n"""
         for is_user, text in self.iter_texts():
                UpperCAmelCase__										=     "user" if is_user else "bot"
                output += F"""{name} >> {text} \n"""
         return output




@add_end_docstrings(
    __SCREAMING_SNAKE_CASE					,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '					,							)
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					str		,						*_lowercase      :					Union[str, Any]		,						**_lowercase      :					Any ):




         """simple docstring"""







         super().__init__(*_lowercase		,						**_lowercase )
         if self.tokenizer.pad_token_id is None:
                UpperCAmelCase__										=     self.tokenizer.eos_token






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					int=None		,						_lowercase      :					Any=None		,						_lowercase      :					List[str]=None		,						**_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     {}
         UpperCAmelCase__										=     {}
         UpperCAmelCase__										=     {}

         if min_length_for_response is not None:
                UpperCAmelCase__										=     min_length_for_response
         if minimum_tokens is not None:
                UpperCAmelCase__										=     minimum_tokens

         if "max_length" in generate_kwargs:
                UpperCAmelCase__										=     generate_kwargs["max_length"]
                # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
         if clean_up_tokenization_spaces is not None:
                UpperCAmelCase__										=     clean_up_tokenization_spaces

         if generate_kwargs:
                forward_params.update(_lowercase )
         return preprocess_params, forward_params, postprocess_params






  def __call__(					self      :					Dict		,						_lowercase      :					Union[Conversation, List[Conversation]]		,						_lowercase      :					List[str]=0		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     super().__call__(_lowercase		,						num_workers=_lowercase		,						**_lowercase )
         if isinstance(_lowercase		,						_lowercase ) and len(_lowercase ) == 1:
                return outputs[0]
         return outputs






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Conversation		,						_lowercase      :					List[Any]=32 ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                raise ValueError("ConversationalPipeline, expects Conversation as inputs" )
         if conversation.new_user_input is None:
                raise ValueError(
                    F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
                    "Add user inputs with the conversation's `add_user_input` method" )
         if hasattr(self.tokenizer		,						"_build_conversation_input_ids" ):
                UpperCAmelCase__										=     self.tokenizer._build_conversation_input_ids(_lowercase )
         else:
                # If the tokenizer cannot handle conversations, we default to only the old version
                UpperCAmelCase__										=     self._legacy_parse_and_tokenize(_lowercase )

         if self.framework == "pt":
                UpperCAmelCase__										=     torch.LongTensor([input_ids] )
         elif self.framework == "tf":
                UpperCAmelCase__										=     tf.constant([input_ids] )
         return {"input_ids": input_ids, "conversation": conversation}






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[Any]=10		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     generate_kwargs.get("max_length"		,						self.model.config.max_length )

         UpperCAmelCase__										=     model_inputs["input_ids"].shape[1]
         if max_length - minimum_tokens < n:
                logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
                UpperCAmelCase__										=     max_length - minimum_tokens
                UpperCAmelCase__										=     model_inputs["input_ids"][:, -trim:]
                if "attention_mask" in model_inputs:
                       UpperCAmelCase__										=     model_inputs["attention_mask"][:, -trim:]
         UpperCAmelCase__										=     model_inputs.pop("conversation" )
         UpperCAmelCase__										=     max_length
         UpperCAmelCase__										=     self.model.generate(**_lowercase		,						**_lowercase )
         if self.model.config.is_encoder_decoder:
                UpperCAmelCase__										=     1
         else:
                UpperCAmelCase__										=     n
         return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[Any]=True ):




         """simple docstring"""







         UpperCAmelCase__										=     model_outputs["output_ids"]
         UpperCAmelCase__										=     self.tokenizer.decode(
             output_ids[0]		,						skip_special_tokens=_lowercase		,						clean_up_tokenization_spaces=_lowercase		,						)
         UpperCAmelCase__										=     model_outputs["conversation"]
         conversation.mark_processed()
         conversation.append_response(_lowercase )
         return conversation






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Conversation ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer.eos_token_id
         UpperCAmelCase__										=     []
         for is_user, text in conversation.iter_texts():
                if eos_token_id is not None:
                       input_ids.extend(self.tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase ) + [eos_token_id] )
                else:
                       input_ids.extend(self.tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase ) )

         if len(_lowercase ) > self.tokenizer.model_max_length:
                UpperCAmelCase__										=     input_ids[-self.tokenizer.model_max_length :]
         return input_ids





