




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=99						,		_SCREAMING_SNAKE_CASE=13						,		_SCREAMING_SNAKE_CASE=7						,		_SCREAMING_SNAKE_CASE=9						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE=5						,		_SCREAMING_SNAKE_CASE=4						,		_SCREAMING_SNAKE_CASE=37						,		_SCREAMING_SNAKE_CASE=8						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.002						,		_SCREAMING_SNAKE_CASE=1						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		):
								_UpperCAmelCase      =			parent
								_UpperCAmelCase      =			batch_size
								_UpperCAmelCase      =			encoder_seq_length
								_UpperCAmelCase      =			decoder_seq_length
								# For common tests
								_UpperCAmelCase      =			self.decoder_seq_length
								_UpperCAmelCase      =			is_training
								_UpperCAmelCase      =			use_attention_mask
								_UpperCAmelCase      =			use_labels
								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			hidden_size
								_UpperCAmelCase      =			num_hidden_layers
								_UpperCAmelCase      =			num_attention_heads
								_UpperCAmelCase      =			d_ff
								_UpperCAmelCase      =			relative_attention_num_buckets
								_UpperCAmelCase      =			dropout_rate
								_UpperCAmelCase      =			initializer_factor
								_UpperCAmelCase      =			eos_token_id
								_UpperCAmelCase      =			pad_token_id
								_UpperCAmelCase      =			decoder_start_token_id
								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			decoder_layers






		def 				UpperCAmelCase (    self			):
								return TaConfig.from_pretrained("""google/umt5-base"""			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		):
								if attention_mask is None:
														_UpperCAmelCase      =			input_ids.ne(config.pad_token_id			)
								if decoder_attention_mask is None:
														_UpperCAmelCase      =			decoder_input_ids.ne(config.pad_token_id			)
								if head_mask is None:
														_UpperCAmelCase      =			torch.ones(config.num_hidden_layers						,		config.num_attention_heads						,		device=_SCREAMING_SNAKE_CASE			)
								if decoder_head_mask is None:
														_UpperCAmelCase      =			torch.ones(config.num_decoder_layers						,		config.num_attention_heads						,		device=_SCREAMING_SNAKE_CASE			)
								if cross_attn_head_mask is None:
														_UpperCAmelCase      =			torch.ones(
														    config.num_decoder_layers						,		config.num_attention_heads						,		device=_SCREAMING_SNAKE_CASE			)
								return {
								    "input_ids": input_ids,
								    "decoder_input_ids": decoder_input_ids,
								    "attention_mask": attention_mask,
								    "decoder_attention_mask": decoder_attention_mask,
								    "head_mask": head_mask,
								    "decoder_head_mask": decoder_head_mask,
								    "cross_attn_head_mask": cross_attn_head_mask,
								}






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			ids_tensor([self.batch_size, self.encoder_seq_length]						,		self.vocab_size			)
								_UpperCAmelCase      =			ids_tensor([self.batch_size, self.decoder_seq_length]						,		self.vocab_size			)

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for NllbMoe the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								_UpperCAmelCase      =			input_ids.clamp(self.pad_token_id + 1			)
								_UpperCAmelCase      =			decoder_input_ids.clamp(self.pad_token_id + 1			)

								_UpperCAmelCase      =			self.get_config()
								_UpperCAmelCase      =			config.num_attention_heads
								_UpperCAmelCase      =			self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								return config, input_dict






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.prepare_config_and_inputs()
								return config, inputs_dict






		def 				UpperCAmelCase (    self			):
								return TaConfig(
								    vocab_size=166						,		d_model=self.hidden_size						,		d_ff=self.d_ff						,		d_kv=self.hidden_size // self.num_attention_heads						,		num_layers=self.num_hidden_layers						,		num_decoder_layers=self.decoder_layers						,		num_heads=self.num_attention_heads						,		relative_attention_num_buckets=self.relative_attention_num_buckets						,		dropout_rate=self.dropout_rate						,		initializer_factor=self.initializer_factor						,		eos_token_id=self.eos_token_id						,		bos_token_id=self.pad_token_id						,		pad_token_id=self.pad_token_id						,		decoder_start_token_id=self.decoder_start_token_id						,		)






		def 				UpperCAmelCase (    self			):
								return TaConfig(
								    vocab_size=self.vocab_size						,		d_model=self.hidden_size						,		d_ff=self.d_ff						,		d_kv=self.hidden_size // self.num_attention_heads						,		num_layers=self.num_hidden_layers						,		num_decoder_layers=self.decoder_layers						,		num_heads=self.num_attention_heads						,		relative_attention_num_buckets=self.relative_attention_num_buckets						,		dropout_rate=self.dropout_rate						,		initializer_factor=self.initializer_factor						,		eos_token_id=self.eos_token_id						,		bos_token_id=self.pad_token_id						,		pad_token_id=self.pad_token_id						,		decoder_start_token_id=self.decoder_start_token_id						,		)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			UMTaModel(config=_SCREAMING_SNAKE_CASE			)
								model.to(_SCREAMING_SNAKE_CASE			)
								model.eval()
								_UpperCAmelCase      =			model(
								    input_ids=_SCREAMING_SNAKE_CASE						,		decoder_input_ids=_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE						,		decoder_attention_mask=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			model(input_ids=_SCREAMING_SNAKE_CASE						,		decoder_input_ids=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			result.last_hidden_state
								_UpperCAmelCase      =			result.past_key_values
								_UpperCAmelCase      =			result.encoder_last_hidden_state

								self.parent.assertEqual(encoder_output.size()						,		(self.batch_size, self.encoder_seq_length, self.hidden_size)			)
								self.parent.assertEqual(decoder_output.size()						,		(self.batch_size, self.decoder_seq_length, self.hidden_size)			)
								# There should be `num_layers` key value embeddings stored in decoder_past
								self.parent.assertEqual(len(_SCREAMING_SNAKE_CASE			)						,		config.num_layers			)
								# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
								self.parent.assertEqual(len(decoder_past[0]			)						,		4			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			UMTaModel(config=_SCREAMING_SNAKE_CASE			).get_decoder().to(_SCREAMING_SNAKE_CASE			).eval()
								# first forward pass
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE						,		use_cache=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE						,		use_cache=_SCREAMING_SNAKE_CASE			)

								self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE			) == len(_SCREAMING_SNAKE_CASE			)			)
								self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE			) == len(_SCREAMING_SNAKE_CASE			) + 1			)

								_UpperCAmelCase    ,					_UpperCAmelCase      =			outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								_UpperCAmelCase      =			ids_tensor((self.batch_size, 1)						,		config.vocab_size			)

								# append to next input_ids and
								_UpperCAmelCase      =			torch.cat([input_ids, next_tokens]						,		dim=-1			)

								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE						,		past_key_values=_SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]

								# select random slice
								_UpperCAmelCase      =			ids_tensor((1,)						,		output_from_past.shape[-1]			).item()
								_UpperCAmelCase      =			output_from_no_past[:, -1, random_slice_idx].detach()
								_UpperCAmelCase      =			output_from_past[:, 0, random_slice_idx].detach()

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			UMTaModel(config=_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			).half().eval()
								_UpperCAmelCase      =			model(**_SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]
								self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE			).any().item()			)




@require_torch
class       _A				(		__lowercase       ,     __lowercase       ,     __lowercase       ,     unittest.TestCase     ):
		__a					    =       (
		    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
		)
		__a					    =       (UMTaForConditionalGeneration,) if is_torch_available() else ()
		__a					    =       (
		    {
		        """conversational""": UMTaForConditionalGeneration,
		        """feature-extraction""": UMTaModel,
		        """summarization""": UMTaForConditionalGeneration,
		        """text2text-generation""": UMTaForConditionalGeneration,
		        """translation""": UMTaForConditionalGeneration,
		        """question-answering""": UMTaForQuestionAnswering,
		    }
		    if is_torch_available()
		    else {}
		)
		__a					    =       True
		__a					    =       False
		__a					    =       False
		__a					    =       True
		__a					    =       True
		# The small UMT5 model needs higher percentages for CPU/MP tests
		__a					    =       [0.8, 0.9]






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			UMTaModelTester(self			)






		@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""			)
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs()
								_UpperCAmelCase      =			UMTaModel(config_and_inputs[0]			).to(_SCREAMING_SNAKE_CASE			)
								with tempfile.TemporaryDirectory() as tmpdirname:
														torch.onnx.export(
														    _SCREAMING_SNAKE_CASE						,		(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,		F"{tmpdirname}/t5_test.onnx"						,		export_params=_SCREAMING_SNAKE_CASE						,		opset_version=9						,		input_names=["""input_ids""", """decoder_input_ids"""]						,		)






		@unittest.skipIf(torch_device == """cpu"""						,		"""Cant do half precision"""			)
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
								_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs()
								_UpperCAmelCase      =			config_and_inputs[0]
								_UpperCAmelCase      =			UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE			).eval()
								model.to(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			{
								    """head_mask""": torch.zeros(config.num_layers						,		config.num_heads						,		device=_SCREAMING_SNAKE_CASE			),
								    """decoder_head_mask""": torch.zeros(config.num_decoder_layers						,		config.num_heads						,		device=_SCREAMING_SNAKE_CASE			),
								    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers						,		config.num_heads						,		device=_SCREAMING_SNAKE_CASE			),
								}

								for attn_name, (name, mask) in zip(_SCREAMING_SNAKE_CASE						,		head_masking.items()			):
														_UpperCAmelCase      =			{name: mask}
														# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
														if name == "head_mask":
																				_UpperCAmelCase      =			torch.ones(
																				    config.num_decoder_layers						,		config.num_heads						,		device=_SCREAMING_SNAKE_CASE			)

														_UpperCAmelCase      =			model.generate(
														    config_and_inputs[1]["""input_ids"""]						,		num_beams=1						,		max_length=3						,		output_attentions=_SCREAMING_SNAKE_CASE						,		return_dict_in_generate=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)
														# We check the state of decoder_attentions and cross_attentions just from the last step
														_UpperCAmelCase      =			out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
														self.assertEqual(sum([w.sum().item() for w in attn_weights]			)						,		0.0			)






		@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""			)
		def 				UpperCAmelCase (    self			):
								pass




@require_torch
@require_sentencepiece
@require_tokenizers
class       _A				(		unittest.TestCase     ):






		@slow
		@unittest.skip(
		    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""			)
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""						,		return_dict=_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			AutoTokenizer.from_pretrained("""google/umt5-small"""						,		use_fast=_SCREAMING_SNAKE_CASE						,		legacy=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			[
								    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
								    """No se como puedo <extra_id_0>.""",
								    """This is the reason why we <extra_id_0> them.""",
								    """The <extra_id_0> walks in <extra_id_1>, seats""",
								    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
								]
								_UpperCAmelCase      =			tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""						,		padding=_SCREAMING_SNAKE_CASE			).input_ids
								# fmt: off
								_UpperCAmelCase      =			torch.tensor(
								    [
								        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
								    ]			)
								# fmt: on
								torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			model.generate(input_ids.to(_SCREAMING_SNAKE_CASE			)			)
								_UpperCAmelCase      =			[
								    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
								    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								]
								_UpperCAmelCase      =			tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)





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

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


if is_tf_available():
				import tensorflow as tf

if is_torch_available():
				import torch


a													=			logging.get_logger(__name__)




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None			):
								if not conversation_id:
														_UpperCAmelCase      =			uuid.uuida()
								if past_user_inputs is None:
														_UpperCAmelCase      =			[]
								if generated_responses is None:
														_UpperCAmelCase      =			[]

								_UpperCAmelCase      =			conversation_id
								_UpperCAmelCase      =			past_user_inputs
								_UpperCAmelCase      =			generated_responses
								_UpperCAmelCase      =			text






		def __eq__(    self						,		_SCREAMING_SNAKE_CASE			):
								if not isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = False			):
								if self.new_user_input:
														if overwrite:
																				logger.warning(
																				    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
																				    F"with: \"{text}\"."			)
																				_UpperCAmelCase      =			text
														else:
																				logger.warning(
																				    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
																				    F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"			)
								else:
														_UpperCAmelCase      =			text






		def 				UpperCAmelCase (    self			):
								if self.new_user_input:
														self.past_user_inputs.append(self.new_user_input			)
								_UpperCAmelCase      =			None






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								self.generated_responses.append(_SCREAMING_SNAKE_CASE			)






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




@add_end_docstrings(
    __lowercase       ,     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       _A				(		__lowercase     ):






		def __init__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								super().__init__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								if self.tokenizer.pad_token_id is None:
														_UpperCAmelCase      =			self.tokenizer.eos_token






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}

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

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

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






		def __call__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			super().__call__(_SCREAMING_SNAKE_CASE						,		num_workers=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								if isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			) and len(_SCREAMING_SNAKE_CASE			) == 1:
														return outputs[0]
								return outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=32			):
								if not isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""			)
								if conversation.new_user_input is None:
														raise ValueError(
														    F"Conversation with UUID {type(conversation.uuid			)} does not contain new user input to process. "
														    """Add user inputs with the conversation's `add_user_input` method"""			)
								if hasattr(self.tokenizer						,		"""_build_conversation_input_ids"""			):
														_UpperCAmelCase      =			self.tokenizer._build_conversation_input_ids(_SCREAMING_SNAKE_CASE			)
								else:
														# If the tokenizer cannot handle conversations, we default to only the old version
														_UpperCAmelCase      =			self._legacy_parse_and_tokenize(_SCREAMING_SNAKE_CASE			)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=10						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			generate_kwargs.get("""max_length"""						,		self.model.config.max_length			)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=True			):
								_UpperCAmelCase      =			model_outputs["""output_ids"""]
								_UpperCAmelCase      =			self.tokenizer.decode(
								    output_ids[0]						,		skip_special_tokens=_SCREAMING_SNAKE_CASE						,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			model_outputs["""conversation"""]
								conversation.mark_processed()
								conversation.append_response(_SCREAMING_SNAKE_CASE			)
								return conversation






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

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