


"""simple docstring"""



import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
  )
  from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class _lowerCAmelCase							:







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__=13		,      UpperCamelCase__=7		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=99		,      UpperCamelCase__=16		,      UpperCamelCase__=36		,      UpperCamelCase__=6		,      UpperCamelCase__=6		,      UpperCamelCase__=6		,      UpperCamelCase__=37		,      UpperCamelCase__="gelu"		,      UpperCamelCase__=0.1		,      UpperCamelCase__=0.1		,      UpperCamelCase__=512		,      UpperCamelCase__=16		,      UpperCamelCase__=2		,      UpperCamelCase__=0.02		,      UpperCamelCase__=3		,      UpperCamelCase__=4		,      UpperCamelCase__=None		,      )      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : List[str]    =       parent
       snake_case       : Dict    =       batch_size
       snake_case       : Optional[Any]    =       seq_length
       snake_case       : int    =       is_training
       snake_case       : Union[str, Any]    =       use_input_mask
       snake_case       : List[Any]    =       use_token_type_ids
       snake_case       : List[str]    =       use_labels
       snake_case       : Optional[int]    =       vocab_size
       snake_case       : Any    =       embedding_size
       snake_case       : Union[str, Any]    =       hidden_size
       snake_case       : Tuple    =       num_hidden_layers
       snake_case       : Tuple    =       num_hidden_groups
       snake_case       : str    =       num_attention_heads
       snake_case       : Optional[int]    =       intermediate_size
       snake_case       : Union[str, Any]    =       hidden_act
       snake_case       : Tuple    =       hidden_dropout_prob
       snake_case       : Optional[Any]    =       attention_probs_dropout_prob
       snake_case       : Optional[int]    =       max_position_embeddings
       snake_case       : str    =       type_vocab_size
       snake_case       : int    =       type_sequence_label_size
       snake_case       : int    =       initializer_range
       snake_case       : Any    =       num_labels
       snake_case       : List[Any]    =       num_choices
       snake_case       : Union[str, Any]    =       scope







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : List[str]    =       ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size		)

       snake_case       : Optional[int]    =       None
       if self.use_input_mask:
           snake_case       : Union[str, Any]    =       random_attention_mask([self.batch_size, self.seq_length]		)

       snake_case       : List[Any]    =       None
       if self.use_token_type_ids:
           snake_case       : Union[str, Any]    =       ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size		)

       snake_case       : Tuple    =       None
       snake_case       : Optional[Any]    =       None
       snake_case       : Optional[Any]    =       None
       if self.use_labels:
           snake_case       : List[Any]    =       ids_tensor([self.batch_size]		,      self.type_sequence_label_size		)
           snake_case       : int    =       ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels		)
           snake_case       : Optional[int]    =       ids_tensor([self.batch_size]		,      self.num_choices		)

       snake_case       : Any    =       self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       return AlbertConfig(
           vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      type_vocab_size=self.type_vocab_size		,      initializer_range=self.initializer_range		,      num_hidden_groups=self.num_hidden_groups		,      )







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : str    =       AlbertModel(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : Union[str, Any]    =       model(UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		)
       snake_case       : Any    =       model(UpperCamelCase__		,      token_type_ids=UpperCamelCase__		)
       snake_case       : Optional[Any]    =       model(UpperCamelCase__		)
       self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)		)
       self.parent.assertEqual(result.pooler_output.shape		,      (self.batch_size, self.hidden_size)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : List[str]    =       AlbertForPreTraining(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : Tuple    =       model(
           UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      labels=UpperCamelCase__		,      sentence_order_label=UpperCamelCase__		,      )
       self.parent.assertEqual(result.prediction_logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)		)
       self.parent.assertEqual(result.sop_logits.shape		,      (self.batch_size, config.num_labels)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Tuple    =       AlbertForMaskedLM(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : Tuple    =       model(UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      labels=UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Tuple    =       AlbertForQuestionAnswering(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : str    =       model(
           UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      start_positions=UpperCamelCase__		,      end_positions=UpperCamelCase__		,      )
       self.parent.assertEqual(result.start_logits.shape		,      (self.batch_size, self.seq_length)		)
       self.parent.assertEqual(result.end_logits.shape		,      (self.batch_size, self.seq_length)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : str    =       self.num_labels
       snake_case       : int    =       AlbertForSequenceClassification(UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : Dict    =       model(UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      labels=UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : int    =       self.num_labels
       snake_case       : List[Any]    =       AlbertForTokenClassification(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : Optional[Any]    =       model(UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      labels=UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.num_labels)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       self.num_choices
       snake_case       : Union[str, Any]    =       AlbertForMultipleChoice(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : List[str]    =       input_ids.unsqueeze(1		).expand(-1		,      self.num_choices		,      -1		).contiguous()
       snake_case       : Tuple    =       token_type_ids.unsqueeze(1		).expand(-1		,      self.num_choices		,      -1		).contiguous()
       snake_case       : str    =       input_mask.unsqueeze(1		).expand(-1		,      self.num_choices		,      -1		).contiguous()
       snake_case       : Dict    =       model(
           UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      labels=UpperCamelCase__		,      )
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices)		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : List[str]    =       self.prepare_config_and_inputs()
       (
           (
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,
       )       : Any    =       config_and_inputs
       snake_case       : Tuple    =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict







@require_torch
class _lowerCAmelCase							( snake_case_       ,				snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			Union[str, Any]				    =    (
       (
           AlbertModel,
           AlbertForPreTraining,
           AlbertForMaskedLM,
           AlbertForMultipleChoice,
           AlbertForSequenceClassification,
           AlbertForTokenClassification,
           AlbertForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase					:			Optional[Any]				    =    (
       {
           '''feature-extraction''': AlbertModel,
           '''fill-mask''': AlbertForMaskedLM,
           '''question-answering''': AlbertForQuestionAnswering,
           '''text-classification''': AlbertForSequenceClassification,
           '''token-classification''': AlbertForTokenClassification,
           '''zero-shot''': AlbertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase					:			List[str]				    =    True







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__=False		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       super()._prepare_for_class(UpperCamelCase__		,      UpperCamelCase__		,      return_labels=UpperCamelCase__		)

       if return_labels:
           if model_class in get_values(UpperCamelCase__		):
               snake_case       : Union[str, Any]    =       torch.zeros(
                   (self.model_tester.batch_size, self.model_tester.seq_length)		,      dtype=torch.long		,      device=UpperCamelCase__		)
               snake_case       : Optional[int]    =       torch.zeros(
                   self.model_tester.batch_size		,      dtype=torch.long		,      device=UpperCamelCase__		)
       return inputs_dict







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       AlbertModelTester(self		)
       snake_case       : Optional[Any]    =       ConfigTester(self		,      config_class=UpperCamelCase__		,      hidden_size=37		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : int    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : Any    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Any    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Dict    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : int    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : List[str]    =       self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
           snake_case       : Union[str, Any]    =       type
           self.model_tester.create_and_check_model(*UpperCamelCase__		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case       : List[str]    =       AlbertModel.from_pretrained(UpperCamelCase__		)
           self.assertIsNotNone(UpperCamelCase__		)







@require_torch
class _lowerCAmelCase							( unittest.TestCase      ):







   @slow
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : List[str]    =       AlbertModel.from_pretrained("albert-base-v2"		)
       snake_case       : List[str]    =       torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
       snake_case       : Tuple    =       torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
       with torch.no_grad():
           snake_case       : List[str]    =       model(UpperCamelCase__		,      attention_mask=UpperCamelCase__		)[0]
       snake_case       : List[Any]    =       torch.Size((1, 11, 768)		)
       self.assertEqual(output.shape		,      UpperCamelCase__		)
       snake_case       : Optional[Any]    =       torch.tensor(
           [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,      UpperCamelCase__		,      atol=1e-4		)		)









"""simple docstring"""



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


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							:







   def __init__(      self		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		)      ->						Optional[Any]:

       '''simple docstring'''




       if not conversation_id:
           snake_case       : Union[str, Any]    =       uuid.uuida()
       if past_user_inputs is None:
           snake_case       : Optional[Any]    =       []
       if generated_responses is None:
           snake_case       : Optional[Any]    =       []

       snake_case       : uuid.UUID    =       conversation_id
       snake_case       : List[str]    =       past_user_inputs
       snake_case       : List[str]    =       generated_responses
       snake_case       : Optional[str]    =       text







   def __eq__(      self		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       if not isinstance(UpperCamelCase__		,      UpperCamelCase__		):
           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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = False		)      ->						Dict:

       '''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}".'		)
               snake_case       : int    =       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:
           snake_case       : Any    =       text







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       if self.new_user_input:
           self.past_user_inputs.append(self.new_user_input		)
       snake_case       : Dict    =       None







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       self.generated_responses.append(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''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		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       F'Conversation id: {self.uuid} \n'
       for is_user, text in self.iter_texts():
           snake_case       : List[str]    =       "user" if is_user else "bot"
           output += F'{name} >> {text} \n'
       return output







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







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       super().__init__(*UpperCamelCase__		,      **UpperCamelCase__		)
       if self.tokenizer.pad_token_id is None:
           snake_case       : str    =       self.tokenizer.eos_token







   def 					lowerCamelCase						(      self		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      **UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Tuple    =       {}
       snake_case       : Optional[int]    =       {}
       snake_case       : Optional[Any]    =       {}

       if min_length_for_response is not None:
           snake_case       : int    =       min_length_for_response
       if minimum_tokens is not None:
           snake_case       : Dict    =       minimum_tokens

       if "max_length" in generate_kwargs:
           snake_case       : Any    =       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:
           snake_case       : Optional[Any]    =       clean_up_tokenization_spaces

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







   def __call__(      self		,      UpperCamelCase__		,      UpperCamelCase__=0		,      **UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=32		)      ->						Dict[str, Any]:

       '''simple docstring'''




       if not isinstance(UpperCamelCase__		,      UpperCamelCase__		):
           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"		):
           snake_case       : Optional[Any]    =       self.tokenizer._build_conversation_input_ids(UpperCamelCase__		)
       else:
           # If the tokenizer cannot handle conversations, we default to only the old version
           snake_case       : Optional[Any]    =       self._legacy_parse_and_tokenize(UpperCamelCase__		)

       if self.framework == "pt":
           snake_case       : Union[str, Any]    =       torch.LongTensor([input_ids]		)
       elif self.framework == "tf":
           snake_case       : Optional[int]    =       tf.constant([input_ids]		)
       return {"input_ids": input_ids, "conversation": conversation}







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=10		,      **UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Dict    =       generate_kwargs.get("max_length"		,      self.model.config.max_length		)

       snake_case       : Optional[Any]    =       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})'		)
           snake_case       : List[str]    =       max_length - minimum_tokens
           snake_case       : Dict    =       model_inputs["input_ids"][:, -trim:]
           if "attention_mask" in model_inputs:
               snake_case       : List[Any]    =       model_inputs["attention_mask"][:, -trim:]
       snake_case       : Union[str, Any]    =       model_inputs.pop("conversation"		)
       snake_case       : Union[str, Any]    =       max_length
       snake_case       : Any    =       self.model.generate(**UpperCamelCase__		,      **UpperCamelCase__		)
       if self.model.config.is_encoder_decoder:
           snake_case       : Optional[int]    =       1
       else:
           snake_case       : int    =       n
       return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=True		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       model_outputs["output_ids"]
       snake_case       : str    =       self.tokenizer.decode(
           output_ids[0]		,      skip_special_tokens=UpperCamelCase__		,      clean_up_tokenization_spaces=UpperCamelCase__		,      )
       snake_case       : List[Any]    =       model_outputs["conversation"]
       conversation.mark_processed()
       conversation.append_response(UpperCamelCase__		)
       return conversation







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




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

       if len(UpperCamelCase__		) > self.tokenizer.model_max_length:
           snake_case       : str    =       input_ids[-self.tokenizer.model_max_length :]
       return input_ids





