
"""simple docstring"""







import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import BertGenerationDecoder, BertGenerationEncoder




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=13       ,		_SCREAMING_SNAKE_CASE=7       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=99       ,		_SCREAMING_SNAKE_CASE=32       ,		_SCREAMING_SNAKE_CASE=5       ,		_SCREAMING_SNAKE_CASE=4       ,		_SCREAMING_SNAKE_CASE=37       ,		_SCREAMING_SNAKE_CASE="gelu"       ,		_SCREAMING_SNAKE_CASE=0.1       ,		_SCREAMING_SNAKE_CASE=0.1       ,		_SCREAMING_SNAKE_CASE=50       ,		_SCREAMING_SNAKE_CASE=0.02       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=None       ,		):
			__lowerCAmelCase			:						Union[str, Any]									=      parent
			__lowerCAmelCase			:						Optional[int]									=      batch_size
			__lowerCAmelCase			:						Optional[Any]									=      seq_length
			__lowerCAmelCase			:						Union[str, Any]									=      is_training
			__lowerCAmelCase			:						Dict									=      use_input_mask
			__lowerCAmelCase			:						Tuple									=      vocab_size
			__lowerCAmelCase			:						Tuple									=      hidden_size
			__lowerCAmelCase			:						int									=      num_hidden_layers
			__lowerCAmelCase			:						Tuple									=      num_attention_heads
			__lowerCAmelCase			:						Union[str, Any]									=      intermediate_size
			__lowerCAmelCase			:						Any									=      hidden_act
			__lowerCAmelCase			:						List[str]									=      hidden_dropout_prob
			__lowerCAmelCase			:						Union[str, Any]									=      attention_probs_dropout_prob
			__lowerCAmelCase			:						List[str]									=      max_position_embeddings
			__lowerCAmelCase			:						List[Any]									=      initializer_range
			__lowerCAmelCase			:						int									=      use_labels
			__lowerCAmelCase			:						List[str]									=      scope

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size )

			__lowerCAmelCase			:						Optional[Any]									=      None
			if self.use_input_mask:
				__lowerCAmelCase			:						Any									=      random_attention_mask([self.batch_size, self.seq_length] )

			if self.use_labels:
				__lowerCAmelCase			:						Dict									=      ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size )

			__lowerCAmelCase			:						Any									=      self.get_config()

			return config, input_ids, input_mask, token_labels

		def 							__lowerCamelCase							(      self ):
			return BertGenerationConfig(
			    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       ,		is_decoder=_SCREAMING_SNAKE_CASE       ,		initializer_range=self.initializer_range       ,		)

		def 							__lowerCamelCase							(      self ):
			(
			    (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   
			)			:						str									=      self.prepare_config_and_inputs()

			__lowerCAmelCase			:						Union[str, Any]									=      True
			__lowerCAmelCase			:						Union[str, Any]									=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
			__lowerCAmelCase			:						Optional[int]									=      ids_tensor([self.batch_size, self.seq_length]       ,		vocab_size=2 )

			return (
			    config,
			    input_ids,
			    input_mask,
			    token_labels,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						Union[str, Any]									=      BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE )
			model.to(_SCREAMING_SNAKE_CASE )
			model.eval()
			__lowerCAmelCase			:						str									=      model(_SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      model(_SCREAMING_SNAKE_CASE )
			self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size) )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						List[Any]									=      True
			__lowerCAmelCase			:						Union[str, Any]									=      BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE )
			model.to(_SCREAMING_SNAKE_CASE )
			model.eval()
			__lowerCAmelCase			:						Optional[int]									=      model(
			    _SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE       ,		encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,		encoder_attention_mask=_SCREAMING_SNAKE_CASE       ,		)
			__lowerCAmelCase			:						Dict									=      model(
			    _SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE       ,		encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,		)
			self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size) )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						Optional[int]									=      True
			__lowerCAmelCase			:						Dict									=      True
			__lowerCAmelCase			:						str									=      BertGenerationDecoder(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).eval()

			# first forward pass
			__lowerCAmelCase			:						Union[str, Any]									=      model(
			    _SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE       ,		encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,		encoder_attention_mask=_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE       ,		)
			__lowerCAmelCase			:						Tuple									=      outputs.past_key_values

			# create hypothetical multiple next token and extent to next_input_ids
			__lowerCAmelCase			:						Any									=      ids_tensor((self.batch_size, 3)       ,		config.vocab_size )
			__lowerCAmelCase			:						List[str]									=      ids_tensor((self.batch_size, 3)       ,		vocab_size=2 )

			# append to next input_ids and
			__lowerCAmelCase			:						Union[str, Any]									=      torch.cat([input_ids, next_tokens]       ,		dim=-1 )
			__lowerCAmelCase			:						Optional[int]									=      torch.cat([input_mask, next_mask]       ,		dim=-1 )

			__lowerCAmelCase			:						List[str]									=      model(
			    _SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE       ,		encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,		encoder_attention_mask=_SCREAMING_SNAKE_CASE       ,		output_hidden_states=_SCREAMING_SNAKE_CASE       ,		)['hidden_states'][0]
			__lowerCAmelCase			:						Union[str, Any]									=      model(
			    _SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE       ,		encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,		encoder_attention_mask=_SCREAMING_SNAKE_CASE       ,		past_key_values=_SCREAMING_SNAKE_CASE       ,		output_hidden_states=_SCREAMING_SNAKE_CASE       ,		)['hidden_states'][0]

			# select random slice
			__lowerCAmelCase			:						Optional[int]									=      ids_tensor((1,)       ,		output_from_past.shape[-1] ).item()
			__lowerCAmelCase			:						Tuple									=      output_from_no_past[:, -3:, random_slice_idx].detach()
			__lowerCAmelCase			:						Union[str, Any]									=      output_from_past[:, :, random_slice_idx].detach()

			self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

			# test that outputs are equal for slice
			self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		atol=1E-3 ) )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		*_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						Tuple									=      BertGenerationDecoder(_SCREAMING_SNAKE_CASE )
			model.to(_SCREAMING_SNAKE_CASE )
			model.eval()
			__lowerCAmelCase			:						Dict									=      model(_SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE       ,		labels=_SCREAMING_SNAKE_CASE )
			self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size) )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      self.prepare_config_and_inputs()
			__lowerCAmelCase			:						List[str]									=      {'input_ids': input_ids, 'attention_mask': input_mask}
			return config, inputs_dict




@require_torch
class  A__       (	_lowerCamelCase       ,     _lowerCamelCase       ,     _lowerCamelCase       ,     unittest.TestCase):
		A_						:       str				   =	(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
		A_						:       Any				   =	(BertGenerationDecoder,) if is_torch_available() else ()
		A_						:       Any				   =	(
		    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
		    if is_torch_available()
		    else {}
		)

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      BertGenerationEncoderTester(self )
			__lowerCAmelCase			:						List[Any]									=      ConfigTester(self       ,		config_class=_SCREAMING_SNAKE_CASE       ,		hidden_size=37 )

		def 							__lowerCamelCase							(      self ):
			self.config_tester.run_common_tests()

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      self.model_tester.prepare_config_and_inputs()
			__lowerCAmelCase			:						str									=      'bert'
			self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_model_as_decoder(*_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			# This regression test was failing with PyTorch < 1.3
			(
			    (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   (
			    __lowerCAmelCase
			)			,   
			)			:						Dict									=      self.model_tester.prepare_config_and_inputs_for_decoder()

			__lowerCAmelCase			:						Dict									=      None

			self.model_tester.create_and_check_model_as_decoder(
			    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		)

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Any									=      self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_for_causal_lm(*_SCREAMING_SNAKE_CASE )







		@slow
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
			self.assertIsNotNone(_SCREAMING_SNAKE_CASE )




@require_torch
class  A__       (	unittest.TestCase):

		@slow
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
			__lowerCAmelCase			:						Dict									=      torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
			with torch.no_grad():
				__lowerCAmelCase			:						int									=      model(_SCREAMING_SNAKE_CASE )[0]
			__lowerCAmelCase			:						Union[str, Any]									=      torch.Size([1, 8, 10_24] )
			self.assertEqual(output.shape       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      torch.tensor(
			    [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
			self.assertTrue(torch.allclose(output[:, :3, :3]       ,		_SCREAMING_SNAKE_CASE       ,		atol=1E-4 ) )




@require_torch
class  A__       (	unittest.TestCase):

		@slow
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
			__lowerCAmelCase			:						str									=      torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
			with torch.no_grad():
				__lowerCAmelCase			:						Tuple									=      model(_SCREAMING_SNAKE_CASE )[0]
			__lowerCAmelCase			:						Optional[int]									=      torch.Size([1, 8, 5_03_58] )
			self.assertEqual(output.shape       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[int]									=      torch.tensor(
			    [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
			self.assertTrue(torch.allclose(output[:, :3, :3]       ,		_SCREAMING_SNAKE_CASE       ,		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


_lowerCAmelCase      						=	logging.get_logger(__name__)






class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None      )    ->		str:
      if not conversation_id:
          lowerCAmelCase__ :  List[str]							      =					uuid.uuida()
      if past_user_inputs is None:
          lowerCAmelCase__ :  List[Any]							      =					[]
      if generated_responses is None:
          lowerCAmelCase__ :  str							      =					[]

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




  def __eq__(    self  ,__UpperCAmelCase      )    ->		Dict:
      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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = False      )    ->		Optional[Any]:
      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}\"."""      )
              lowerCAmelCase__ :  Optional[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:
          lowerCAmelCase__ :  Optional[Any]							      =					text




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      if self.new_user_input:
          self.past_user_inputs.append(self.new_user_input      )
      lowerCAmelCase__ :  Union[str, Any]							      =					None




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      self.generated_responses.append(__UpperCAmelCase      )




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







@add_end_docstrings(
    SCREAMING_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_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,*__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		Tuple:
      super().__init__(*__UpperCAmelCase  ,**__UpperCAmelCase      )
      if self.tokenizer.pad_token_id is None:
          lowerCAmelCase__ :  Tuple							      =					self.tokenizer.eos_token




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[Any]							      =					{}
      lowerCAmelCase__ :  Optional[int]							      =					{}
      lowerCAmelCase__ :  List[str]							      =					{}

      if min_length_for_response is not None:
          lowerCAmelCase__ :  Any							      =					min_length_for_response
      if minimum_tokens is not None:
          lowerCAmelCase__ :  Optional[int]							      =					minimum_tokens

      if "max_length" in generate_kwargs:
          lowerCAmelCase__ :  Optional[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:
          lowerCAmelCase__ :  int							      =					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]:
      lowerCAmelCase__ :  Optional[int]							      =					super().__call__(__UpperCAmelCase  ,num_workers=__UpperCAmelCase  ,**__UpperCAmelCase      )
      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) and len(__UpperCAmelCase      ) == 1:
          return outputs[0]
      return outputs




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=32      )    ->		Dict[str, Any]:
      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"""      ):
          lowerCAmelCase__ :  str							      =					self.tokenizer._build_conversation_input_ids(__UpperCAmelCase      )
      else:
          # If the tokenizer cannot handle conversations, we default to only the old version
          lowerCAmelCase__ :  List[Any]							      =					self._legacy_parse_and_tokenize(__UpperCAmelCase      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=10  ,**__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Optional[Any]							      =					generate_kwargs.get("""max_length"""  ,self.model.config.max_length      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=True      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					model_outputs["""output_ids"""]
      lowerCAmelCase__ :  Tuple							      =					self.tokenizer.decode(
          output_ids[0]  ,skip_special_tokens=__UpperCAmelCase  ,clean_up_tokenization_spaces=__UpperCAmelCase  ,)
      lowerCAmelCase__ :  Union[str, Any]							      =					model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__UpperCAmelCase      )
      return conversation






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Dict							      =					self.tokenizer.eos_token_id
      lowerCAmelCase__ :  int							      =					[]
      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:
          lowerCAmelCase__ :  Optional[Any]							      =					input_ids[-self.tokenizer.model_max_length :]
      return input_ids


