


import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	str		,     __a				:	str=13		,     __a				:	Optional[int]=7		,     __a				:	Optional[int]=True		,     __a				:	int=True		,     __a				:	Any=True		,     __a				:	List[Any]=True		,     __a				:	Any=True		,     __a				:	Union[str, Any]=False		,     __a				:	Dict=False		,     __a				:	List[Any]=False		,     __a				:	List[str]=2		,     __a				:	Union[str, Any]=99		,     __a				:	int=0		,     __a				:	List[Any]=32		,     __a				:	Dict=5		,     __a				:	Optional[int]=4		,     __a				:	List[Any]=0.1		,     __a				:	Optional[Any]=0.1		,     __a				:	List[Any]=512		,     __a				:	Tuple=12		,     __a				:	str=2		,     __a				:	List[Any]=0.02		,     __a				:	int=3		,     __a				:	int=4		,     __a				:	Optional[int]="last"		,     __a				:	str=None		,     __a				:	Optional[Any]=None		,     )			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     parent
												__lowercase      :							List[str]				=     batch_size
												__lowercase      :							Union[str, Any]				=     seq_length
												__lowercase      :							Any				=     is_training
												__lowercase      :							Any				=     use_input_lengths
												__lowercase      :							str				=     use_token_type_ids
												__lowercase      :							Union[str, Any]				=     use_labels
												__lowercase      :							Optional[int]				=     gelu_activation
												__lowercase      :							Optional[Any]				=     sinusoidal_embeddings
												__lowercase      :							List[str]				=     causal
												__lowercase      :							List[Any]				=     asm
												__lowercase      :							List[str]				=     n_langs
												__lowercase      :							Dict				=     vocab_size
												__lowercase      :							Optional[int]				=     n_special
												__lowercase      :							List[Any]				=     hidden_size
												__lowercase      :							Union[str, Any]				=     num_hidden_layers
												__lowercase      :							List[Any]				=     num_attention_heads
												__lowercase      :							Any				=     hidden_dropout_prob
												__lowercase      :							List[str]				=     attention_probs_dropout_prob
												__lowercase      :							Any				=     max_position_embeddings
												__lowercase      :							Dict				=     type_vocab_size
												__lowercase      :							Tuple				=     type_sequence_label_size
												__lowercase      :							int				=     initializer_range
												__lowercase      :							Union[str, Any]				=     num_labels
												__lowercase      :							List[str]				=     num_choices
												__lowercase      :							int				=     summary_type
												__lowercase      :							Any				=     use_proj
												__lowercase      :							int				=     scope



					def       lowerCAmelCase				(  self				:	Tuple							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size							)
												__lowercase      :							int				=     random_attention_mask([self.batch_size, self.seq_length]							)

												__lowercase      :							Optional[int]				=     None
												if self.use_input_lengths:
																			__lowercase      :							Union[str, Any]				=     (
																			    ids_tensor([self.batch_size]		,     vocab_size=2							) + self.seq_length - 2
																			)  # small variation of seq_length

												__lowercase      :							Tuple				=     None
												if self.use_token_type_ids:
																			__lowercase      :							Union[str, Any]				=     ids_tensor([self.batch_size, self.seq_length]		,     self.n_langs							)

												__lowercase      :							int				=     None
												__lowercase      :							List[str]				=     None
												__lowercase      :							str				=     None
												if self.use_labels:
																			__lowercase      :							List[str]				=     ids_tensor([self.batch_size]		,     self.type_sequence_label_size							)
																			__lowercase      :							Dict				=     ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels							)
																			__lowercase      :							List[Any]				=     ids_tensor([self.batch_size]		,     2							).float()
																			__lowercase      :							Dict				=     ids_tensor([self.batch_size]		,     self.num_choices							)

												__lowercase      :							List[str]				=     self.get_config()

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    input_lengths,
												    sequence_labels,
												    token_labels,
												    is_impossible_labels,
												    choice_labels,
												    input_mask,
												)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Dict:


												"""simple docstring"""

												return FlaubertConfig(
												    vocab_size=self.vocab_size		,     n_special=self.n_special		,     emb_dim=self.hidden_size		,     n_layers=self.num_hidden_layers		,     n_heads=self.num_attention_heads		,     dropout=self.hidden_dropout_prob		,     attention_dropout=self.attention_probs_dropout_prob		,     gelu_activation=self.gelu_activation		,     sinusoidal_embeddings=self.sinusoidal_embeddings		,     asm=self.asm		,     causal=self.causal		,     n_langs=self.n_langs		,     max_position_embeddings=self.max_position_embeddings		,     initializer_range=self.initializer_range		,     summary_type=self.summary_type		,     use_proj=self.use_proj		,     )



					def       lowerCAmelCase				(  self				:	Optional[Any]		,     __a				:	Any		,     __a				:	Optional[int]		,     __a				:	List[Any]		,     __a				:	List[str]		,     __a				:	Optional[Any]		,     __a				:	int		,     __a				:	Tuple		,     __a				:	Optional[int]		,     __a				:	Optional[Any]		,     )			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Any				=     FlaubertModel(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Any				=     model(__a		,     lengths=__a		,     langs=__a							)
												__lowercase      :							Dict				=     model(__a		,     langs=__a							)
												__lowercase      :							str				=     model(__a							)
												self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	str		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	Any		,     __a				:	Tuple		,     __a				:	str		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Tuple		,     )			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     FlaubertWithLMHeadModel(__a							)
												model.to(__a							)
												model.eval()

												__lowercase      :							Tuple				=     model(__a		,     token_type_ids=__a		,     labels=__a							)
												self.parent.assertEqual(result.loss.shape		,     ()							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)							)



					def       lowerCAmelCase				(  self				:	Optional[Any]		,     __a				:	int		,     __a				:	Optional[int]		,     __a				:	Optional[int]		,     __a				:	List[Any]		,     __a				:	int		,     __a				:	int		,     __a				:	Optional[Any]		,     __a				:	Optional[int]		,     __a				:	Dict		,     )			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     FlaubertForQuestionAnsweringSimple(__a							)
												model.to(__a							)
												model.eval()

												__lowercase      :							Tuple				=     model(__a							)

												__lowercase      :							int				=     model(__a		,     start_positions=__a		,     end_positions=__a							)
												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				:	Any		,     __a				:	Dict		,     __a				:	Dict		,     __a				:	Any		,     __a				:	Tuple		,     __a				:	List[str]		,     __a				:	Any		,     __a				:	List[Any]		,     __a				:	Optional[Any]		,     __a				:	Dict		,     )			->  str:


												"""simple docstring"""

												__lowercase      :							int				=     FlaubertForQuestionAnswering(__a							)
												model.to(__a							)
												model.eval()

												__lowercase      :							List[str]				=     model(__a							)

												__lowercase      :							str				=     model(
												    __a		,     start_positions=__a		,     end_positions=__a		,     cls_index=__a		,     is_impossible=__a		,     p_mask=__a		,     )

												__lowercase      :							List[str]				=     model(
												    __a		,     start_positions=__a		,     end_positions=__a		,     cls_index=__a		,     is_impossible=__a		,     )

												(__lowercase			)      :							Optional[int]				=     result_with_labels.to_tuple()

												__lowercase      :							List[Any]				=     model(__a		,     start_positions=__a		,     end_positions=__a							)

												(__lowercase			)      :							int				=     result_with_labels.to_tuple()

												self.parent.assertEqual(result_with_labels.loss.shape		,     ()							)
												self.parent.assertEqual(result.start_top_log_probs.shape		,     (self.batch_size, model.config.start_n_top)							)
												self.parent.assertEqual(result.start_top_index.shape		,     (self.batch_size, model.config.start_n_top)							)
												self.parent.assertEqual(
												    result.end_top_log_probs.shape		,     (self.batch_size, model.config.start_n_top * model.config.end_n_top)							)
												self.parent.assertEqual(
												    result.end_top_index.shape		,     (self.batch_size, model.config.start_n_top * model.config.end_n_top)							)
												self.parent.assertEqual(result.cls_logits.shape		,     (self.batch_size,)							)



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Dict		,     __a				:	int		,     __a				:	List[str]		,     __a				:	str		,     __a				:	List[Any]		,     __a				:	Union[str, Any]		,     __a				:	Any		,     __a				:	Optional[int]		,     __a				:	Dict		,     )			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Any				=     FlaubertForSequenceClassification(__a							)
												model.to(__a							)
												model.eval()

												__lowercase      :							List[Any]				=     model(__a							)
												__lowercase      :							int				=     model(__a		,     labels=__a							)

												self.parent.assertEqual(result.loss.shape		,     ()							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)							)



					def       lowerCAmelCase				(  self				:	Dict		,     __a				:	Optional[Any]		,     __a				:	Optional[Any]		,     __a				:	Dict		,     __a				:	Tuple		,     __a				:	List[Any]		,     __a				:	Any		,     __a				:	str		,     __a				:	int		,     __a				:	Any		,     )			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.num_labels
												__lowercase      :							str				=     FlaubertForTokenClassification(__a							)
												model.to(__a							)
												model.eval()

												__lowercase      :							Any				=     model(__a		,     attention_mask=__a		,     labels=__a							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[str]		,     __a				:	Dict		,     __a				:	Tuple		,     __a				:	Optional[Any]		,     __a				:	Dict		,     __a				:	Any		,     __a				:	List[str]		,     __a				:	Optional[Any]		,     __a				:	int		,     )			->  Any:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.num_choices
												__lowercase      :							Optional[int]				=     FlaubertForMultipleChoice(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							int				=     input_ids.unsqueeze(1							).expand(-1		,     self.num_choices		,     -1							).contiguous()
												__lowercase      :							Optional[int]				=     token_type_ids.unsqueeze(1							).expand(-1		,     self.num_choices		,     -1							).contiguous()
												__lowercase      :							List[Any]				=     input_mask.unsqueeze(1							).expand(-1		,     self.num_choices		,     -1							).contiguous()
												__lowercase      :							List[str]				=     model(
												    __a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.prepare_config_and_inputs()
												(
												    __lowercase			
												)      :							Optional[int]				=     config_and_inputs
												__lowercase      :							Dict				=     {
												    """input_ids""": input_ids,
												    """token_type_ids""": token_type_ids,
												    """lengths""": input_lengths,
												    """attention_mask""": input_mask,
												}
												return config, inputs_dict







@require_torch
class 		lowerCAmelCase     ( __a      ,       __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Optional[int]									=      (
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					_A						:			List[str]									=      (
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	Union[str, Any]		,     __a				:	str		,     __a				:	Optional[int]		,     __a				:	Optional[int]		,     __a				:	Union[str, Any]							)			->  Tuple:


												"""simple docstring"""

												if (
												    pipeline_test_casse_name == "QAPipelineTests"
												    and tokenizer_name is not None
												    and not tokenizer_name.endswith("""Fast"""							)
												):
																			# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																			# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																			# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																			return True

												return False



					def       lowerCAmelCase				(  self				:	Optional[Any]		,     __a				:	str		,     __a				:	List[Any]		,     __a				:	List[str]=False							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     super()._prepare_for_class(__a		,     __a		,     return_labels=__a							)

												if return_labels:
																			if model_class.__name__ == "FlaubertForQuestionAnswering":
																										__lowercase      :							Any				=     torch.zeros(
																										    self.model_tester.batch_size		,     dtype=torch.long		,     device=__a							)
																										__lowercase      :							str				=     torch.zeros(
																										    self.model_tester.batch_size		,     dtype=torch.long		,     device=__a							)

												return inputs_dict



					def       lowerCAmelCase				(  self				:	Any							)			->  str:


												"""simple docstring"""

												__lowercase      :							Tuple				=     FlaubertModelTester(self							)
												__lowercase      :							str				=     ConfigTester(self		,     config_class=__a		,     emb_dim=37							)



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Dict:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  str:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_model(*__a							)



					def       lowerCAmelCase				(  self				:	int							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_lm_head(*__a							)



					def       lowerCAmelCase				(  self				:	str							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_simple_qa(*__a							)



					def       lowerCAmelCase				(  self				:	Any							)			->  str:


												"""simple docstring"""

												__lowercase      :							int				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_qa(*__a							)



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


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_sequence_classif(*__a							)



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


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_token_classif(*__a							)



					def       lowerCAmelCase				(  self				:	int							)			->  str:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_multiple_choice(*__a							)



					@slow
					def       lowerCAmelCase				(  self				:	Dict							)			->  Tuple:


												"""simple docstring"""

												for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__lowercase      :							Any				=     FlaubertModel.from_pretrained(__a							)
																			self.assertIsNotNone(__a							)



					@slow
					@require_torch_gpu
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																			# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
																			if model_class == FlaubertForMultipleChoice:
																										return

																			__lowercase      :							Tuple				=     True
																			__lowercase      :							List[Any]				=     model_class(config=__a							)

																			__lowercase      :							List[Any]				=     self._prepare_for_class(__a		,     __a							)
																			__lowercase      :							Tuple				=     torch.jit.trace(
																			    __a		,     (inputs_dict["""input_ids"""].to("""cpu"""							), inputs_dict["""attention_mask"""].to("""cpu"""							))							)

																			with tempfile.TemporaryDirectory() as tmp:
																										torch.jit.save(__a		,     os.path.join(__a		,     """traced_model.pt"""							)							)
																										__lowercase      :							Dict				=     torch.jit.load(os.path.join(__a		,     """traced_model.pt"""							)		,     map_location=__a							)
																										loaded(inputs_dict["""input_ids"""].to(__a							)		,     inputs_dict["""attention_mask"""].to(__a							)							)







@require_torch
class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					@slow
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""							)
												__lowercase      :							Optional[Any]				=     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
												with torch.no_grad():
																			__lowercase      :							Dict				=     model(__a							)[0]
												__lowercase      :							Tuple				=     torch.Size((1, 11, 768)							)
												self.assertEqual(output.shape		,     __a							)
												__lowercase      :							Optional[int]				=     torch.tensor(
												    [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]							)

												self.assertTrue(torch.allclose(output[:, :3, :3]		,     __a		,     atol=1E-4							)							)







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 : List[Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	List[str]		,     __a				:	str = None		,     __a				:	uuid.UUID = None		,     __a				:	Any=None		,     __a				:	List[Any]=None							)			->  List[Any]:


												"""simple docstring"""

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

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



					def __eq__(  self				:	Dict		,     __a				:	Dict							)			->  Any:


												"""simple docstring"""

												if not isinstance(__a		,     __a							):
																			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				:	List[str]		,     __a				:	str		,     __a				:	bool = 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}\"."							)
																										__lowercase      :							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:
																			__lowercase      :							Dict				=     text



					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	str							)			->  List[Any]:


												"""simple docstring"""

												self.generated_responses.append(__a							)



					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[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				:	int							)			->  str:


												"""simple docstring"""

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







@add_end_docstrings(
    __a      ,       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     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Any		,     *__a				:	int		,     **__a				:	str							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__(*__a		,     **__a							)
												if self.tokenizer.pad_token_id is None:
																			__lowercase      :							List[Any]				=     self.tokenizer.eos_token



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	int=None		,     __a				:	Tuple=None		,     __a				:	Any=None		,     **__a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     {}
												__lowercase      :							Tuple				=     {}
												__lowercase      :							List[str]				=     {}

												if min_length_for_response is not None:
																			__lowercase      :							Dict				=     min_length_for_response
												if minimum_tokens is not None:
																			__lowercase      :							Union[str, Any]				=     minimum_tokens

												if "max_length" in generate_kwargs:
																			__lowercase      :							Union[str, 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:
																			__lowercase      :							Union[str, Any]				=     clean_up_tokenization_spaces

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



					def __call__(  self				:	Optional[int]		,     __a				:	Union[Conversation, List[Conversation]]		,     __a				:	Dict=0		,     **__a				:	List[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     super().__call__(__a		,     num_workers=__a		,     **__a							)
												if isinstance(__a		,     __a							) and len(__a							) == 1:
																			return outputs[0]
												return outputs



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Conversation		,     __a				:	Tuple=32							)			->  Dict[str, Any]:


												"""simple docstring"""

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

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



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	Dict		,     __a				:	Any=10		,     **__a				:	Dict							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     generate_kwargs.get("""max_length"""		,     self.model.config.max_length							)

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



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Tuple		,     __a				:	List[Any]=True							)			->  List[str]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Conversation							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.tokenizer.eos_token_id
												__lowercase      :							Optional[Any]				=     []
												for is_user, text in conversation.iter_texts():
																			if eos_token_id is not None:
																										input_ids.extend(self.tokenizer.encode(__a		,     add_special_tokens=__a							) + [eos_token_id]							)
																			else:
																										input_ids.extend(self.tokenizer.encode(__a		,     add_special_tokens=__a							)							)

												if len(__a							) > self.tokenizer.model_max_length:
																			__lowercase      :							List[Any]				=     input_ids[-self.tokenizer.model_max_length :]
												return input_ids