






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
	    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
	    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    LayoutLMvaConfig,
	    LayoutLMvaForQuestionAnswering,
	    LayoutLMvaForSequenceClassification,
	    LayoutLMvaForTokenClassification,
	    LayoutLMvaModel,
	)
	from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import LayoutLMvaImageProcessor

class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	List[str]		,     __a				:	Optional[int]		,     __a				:	Any=2		,     __a				:	Any=3		,     __a				:	List[str]=4		,     __a				:	Optional[Any]=2		,     __a				:	Optional[Any]=7		,     __a				:	int=True		,     __a				:	Dict=True		,     __a				:	Dict=True		,     __a				:	List[Any]=True		,     __a				:	int=99		,     __a				:	Optional[int]=36		,     __a				:	Dict=3		,     __a				:	Union[str, Any]=4		,     __a				:	Dict=37		,     __a				:	List[str]="gelu"		,     __a				:	List[str]=0.1		,     __a				:	int=0.1		,     __a				:	Union[str, Any]=512		,     __a				:	Optional[Any]=16		,     __a				:	Any=2		,     __a				:	Optional[Any]=0.02		,     __a				:	List[Any]=6		,     __a				:	Optional[Any]=6		,     __a				:	List[Any]=3		,     __a				:	List[str]=4		,     __a				:	Dict=None		,     __a				:	Dict=1000		,     )			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     parent
												__lowercase      :							Tuple				=     batch_size
												__lowercase      :							Tuple				=     num_channels
												__lowercase      :							Union[str, Any]				=     image_size
												__lowercase      :							Optional[Any]				=     patch_size
												__lowercase      :							Optional[Any]				=     text_seq_length
												__lowercase      :							Tuple				=     is_training
												__lowercase      :							Any				=     use_input_mask
												__lowercase      :							str				=     use_token_type_ids
												__lowercase      :							Union[str, Any]				=     use_labels
												__lowercase      :							str				=     vocab_size
												__lowercase      :							Tuple				=     hidden_size
												__lowercase      :							Union[str, Any]				=     num_hidden_layers
												__lowercase      :							List[Any]				=     num_attention_heads
												__lowercase      :							int				=     intermediate_size
												__lowercase      :							Union[str, Any]				=     hidden_act
												__lowercase      :							List[Any]				=     hidden_dropout_prob
												__lowercase      :							Any				=     attention_probs_dropout_prob
												__lowercase      :							Optional[Any]				=     max_position_embeddings
												__lowercase      :							Any				=     type_vocab_size
												__lowercase      :							Any				=     type_sequence_label_size
												__lowercase      :							Tuple				=     initializer_range
												__lowercase      :							Tuple				=     coordinate_size
												__lowercase      :							List[str]				=     shape_size
												__lowercase      :							Tuple				=     num_labels
												__lowercase      :							int				=     num_choices
												__lowercase      :							Dict				=     scope
												__lowercase      :							Optional[Any]				=     range_bbox

												# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
												__lowercase      :							int				=     text_seq_length
												__lowercase      :							List[str]				=     (image_size // patch_size) ** 2 + 1
												__lowercase      :							Any				=     self.text_seq_length + self.image_seq_length



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


												"""simple docstring"""

												__lowercase      :							int				=     ids_tensor([self.batch_size, self.text_seq_length]		,     self.vocab_size							)

												__lowercase      :							str				=     ids_tensor([self.batch_size, self.text_seq_length, 4]		,     self.range_bbox							)
												# Ensure that bbox is legal
												for i in range(bbox.shape[0]							):
																			for j in range(bbox.shape[1]							):
																										if bbox[i, j, 3] < bbox[i, j, 1]:
																																	__lowercase      :							List[Any]				=     bbox[i, j, 3]
																																	__lowercase      :							str				=     bbox[i, j, 1]
																																	__lowercase      :							Union[str, Any]				=     t
																										if bbox[i, j, 2] < bbox[i, j, 0]:
																																	__lowercase      :							Union[str, Any]				=     bbox[i, j, 2]
																																	__lowercase      :							List[str]				=     bbox[i, j, 0]
																																	__lowercase      :							int				=     t

												__lowercase      :							Dict				=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

												__lowercase      :							List[str]				=     None
												if self.use_input_mask:
																			__lowercase      :							Optional[Any]				=     random_attention_mask([self.batch_size, self.text_seq_length]							)

												__lowercase      :							int				=     None
												if self.use_token_type_ids:
																			__lowercase      :							int				=     ids_tensor([self.batch_size, self.text_seq_length]		,     self.type_vocab_size							)

												__lowercase      :							Optional[int]				=     None
												__lowercase      :							Any				=     None
												if self.use_labels:
																			__lowercase      :							List[Any]				=     ids_tensor([self.batch_size]		,     self.type_sequence_label_size							)
																			__lowercase      :							Optional[Any]				=     ids_tensor([self.batch_size, self.text_seq_length]		,     self.num_labels							)

												__lowercase      :							Optional[Any]				=     LayoutLMvaConfig(
												    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		,     coordinate_size=self.coordinate_size		,     shape_size=self.shape_size		,     input_size=self.image_size		,     patch_size=self.patch_size		,     )

												return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels



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


												"""simple docstring"""

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

												# text + image
												__lowercase      :							Dict				=     model(__a		,     pixel_values=__a							)
												__lowercase      :							int				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__a							)
												__lowercase      :							List[Any]				=     model(__a		,     bbox=__a		,     pixel_values=__a		,     token_type_ids=__a							)
												__lowercase      :							Union[str, Any]				=     model(__a		,     bbox=__a		,     pixel_values=__a							)

												self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)							)

												# text only
												__lowercase      :							Optional[Any]				=     model(__a							)
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,     (self.batch_size, self.text_seq_length, self.hidden_size)							)

												# image only
												__lowercase      :							Tuple				=     model(pixel_values=__a							)
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,     (self.batch_size, self.image_seq_length, self.hidden_size)							)



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


												"""simple docstring"""

												__lowercase      :							Any				=     self.num_labels
												__lowercase      :							str				=     LayoutLMvaForSequenceClassification(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							str				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)							)



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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.num_labels
												__lowercase      :							Optional[int]				=     LayoutLMvaForTokenClassification(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							int				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.text_seq_length, self.num_labels)							)



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


												"""simple docstring"""

												__lowercase      :							int				=     LayoutLMvaForQuestionAnswering(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Optional[Any]				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__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							)			->  int:


												"""simple docstring"""

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







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

					'''simple docstring'''

					_A						:			Optional[int]									=      False
					_A						:			List[Any]									=      False
					_A						:			Tuple									=      False

					_A						:			Union[str, Any]									=      (
					    (
					        LayoutLMvaModel,
					        LayoutLMvaForSequenceClassification,
					        LayoutLMvaForTokenClassification,
					        LayoutLMvaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					_A						:			Union[str, Any]									=      (
					    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
					    if is_torch_available()
					    else {}
					)



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


												"""simple docstring"""

												return True



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


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     LayoutLMvaModelTester(self							)
												__lowercase      :							int				=     ConfigTester(self		,     config_class=__a		,     hidden_size=37							)



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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     copy.deepcopy(__a							)
												if model_class in get_values(__a							):
																			__lowercase      :							str				=     {
																			    k: v.unsqueeze(1							).expand(-1		,     self.model_tester.num_choices		,     -1							).contiguous()
																			    if isinstance(__a		,     torch.Tensor							) and v.ndim > 1
																			    else v
																			    for k, v in inputs_dict.items()
																			}
												if return_labels:
																			if model_class in get_values(__a							):
																										__lowercase      :							Any				=     torch.ones(self.model_tester.batch_size		,     dtype=torch.long		,     device=__a							)
																			elif model_class in get_values(__a							):
																										__lowercase      :							Tuple				=     torch.zeros(
																										    self.model_tester.batch_size		,     dtype=torch.long		,     device=__a							)
																										__lowercase      :							Optional[Any]				=     torch.zeros(
																										    self.model_tester.batch_size		,     dtype=torch.long		,     device=__a							)
																			elif model_class in [
																			    *get_values(__a							),
																			]:
																										__lowercase      :							Union[str, Any]				=     torch.zeros(
																										    self.model_tester.batch_size		,     dtype=torch.long		,     device=__a							)
																			elif model_class in [
																			    *get_values(__a							),
																			]:
																										__lowercase      :							int				=     torch.zeros(
																										    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,     dtype=torch.long		,     device=__a		,     )

												return inputs_dict



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


												"""simple docstring"""

												self.config_tester.run_common_tests()



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__a							)



					def       lowerCAmelCase				(  self				:	int							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																			__lowercase      :							Optional[Any]				=     type
																			self.model_tester.create_and_check_model(*__a							)



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


												"""simple docstring"""

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



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


												"""simple docstring"""

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



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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__a							)



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


												"""simple docstring"""

												for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__lowercase      :							Optional[Any]				=     LayoutLMvaModel.from_pretrained(__a							)
																			self.assertIsNotNone(__a							)







def       snake_case_		(     ):
							__lowercase      :							Any				=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_torch
class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

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


												"""simple docstring"""

												return LayoutLMvaImageProcessor(apply_ocr=__a							) if is_vision_available() else None



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


												"""simple docstring"""

												__lowercase      :							Dict				=     LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""							).to(__a							)

												__lowercase      :							Tuple				=     self.default_image_processor
												__lowercase      :							Tuple				=     prepare_img()
												__lowercase      :							Optional[Any]				=     image_processor(images=__a		,     return_tensors="""pt"""							).pixel_values.to(__a							)

												__lowercase      :							Optional[Any]				=     torch.tensor([[1, 2]]							)
												__lowercase      :							int				=     torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]							).unsqueeze(0							)

												# forward pass
												__lowercase      :							Tuple				=     model(
												    input_ids=input_ids.to(__a							)		,     bbox=bbox.to(__a							)		,     pixel_values=pixel_values.to(__a							)		,     )

												# verify the logits
												__lowercase      :							Union[str, Any]				=     torch.Size((1, 199, 768)							)
												self.assertEqual(outputs.last_hidden_state.shape		,     __a							)

												__lowercase      :							Optional[Any]				=     torch.tensor(
												    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							).to(__a							)

												self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :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