


'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   RobertaTokenizer
    a													=   RobertaTokenizerFast
    a													=   True
    a													=   {"cls_token": "<s>"}


    def        A   (  self      :							List[Any]):
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           _A					:     str     = [
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           _A					:     Tuple     = dict(zip(SCREAMING_SNAKE_CASE				,							range(len(SCREAMING_SNAKE_CASE))))
           _A					:     Optional[int]     = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           _A					:     Dict     = {'unk_token': '<unk>'}

           _A					:     str     = os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file'])
           _A					:     Optional[int]     = os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['merges_file'])
           with open(self.vocab_file				,							'w'				,							encoding='utf-8') as fp:
                  fp.write(json.dumps(SCREAMING_SNAKE_CASE) + '\n')
           with open(self.merges_file				,							'w'				,							encoding='utf-8') as fp:
                  fp.write('\n'.join(SCREAMING_SNAKE_CASE))


    def        A   (  self      :							Tuple				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           kwargs.update(self.special_tokens_map)
           return self.tokenizer_class.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           kwargs.update(self.special_tokens_map)
           return RobertaTokenizerFast.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Optional[int]     = 'lower newer'
           _A					:     int     = 'lower newer'
           return input_text, output_text


    def        A   (  self      :							Dict):
           _A					:     str     = self.tokenizer_class(self.vocab_file				,							self.merges_file				,							**self.special_tokens_map)
           _A					:     Tuple     = 'lower newer'
           _A					:     str     = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
           _A					:     Tuple     = tokenizer.tokenize(SCREAMING_SNAKE_CASE)  # , add_prefix_space=True)
           self.assertListEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           _A					:     Any     = tokens + [tokenizer.unk_token]
           _A					:     int     = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE)				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = self.get_tokenizer()

           self.assertListEqual(tokenizer.encode('Hello world!'				,							add_special_tokens=SCREAMING_SNAKE_CASE)				,							[0, 31414, 232, 328, 2])
           self.assertListEqual(
               tokenizer.encode('Hello world! cécé herlolip 418'				,							add_special_tokens=SCREAMING_SNAKE_CASE)				,							[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]				,							)


    @slow
    def        A   (  self      :							Dict):
           _A					:     int     = self.tokenizer_class.from_pretrained('roberta-base')

           _A					:     List[str]     = tokenizer.encode('sequence builders'				,							add_special_tokens=SCREAMING_SNAKE_CASE)
           _A					:     int     = tokenizer.encode('multi-sequence build'				,							add_special_tokens=SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = tokenizer.encode(
               'sequence builders'				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE)
           _A					:     Dict     = tokenizer.encode(
               'sequence builders'				,							'multi-sequence build'				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode


    def        A   (  self      :							Tuple):
           _A					:     int     = self.get_tokenizer()

           _A					:     Tuple     = 'Encode this sequence.'
           _A					:     int     = tokenizer.byte_encoder[' '.encode('utf-8')[0]]

           # Testing encoder arguments
           _A					:     str     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE)
           _A					:     Dict     = tokenizer.convert_ids_to_tokens(encoded[0])[0]
           self.assertNotEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           _A					:     List[str]     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = tokenizer.convert_ids_to_tokens(encoded[0])[0]
           self.assertEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           tokenizer.add_special_tokens({'bos_token': '<s>'})
           _A					:     Any     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = tokenizer.convert_ids_to_tokens(encoded[1])[0]
           self.assertNotEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           # Testing spaces after special tokens
           _A					:     List[Any]     = '<mask>'
           tokenizer.add_special_tokens(
               {'mask_token': AddedToken(SCREAMING_SNAKE_CASE				,							lstrip=SCREAMING_SNAKE_CASE				,							rstrip=SCREAMING_SNAKE_CASE)})  # mask token has a left space
           _A					:     Optional[int]     = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE)

           _A					:     str     = 'Encode <mask> sequence'
           _A					:     Optional[Any]     = 'Encode <mask>sequence'

           _A					:     List[Any]     = tokenizer.encode(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = encoded.index(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
           self.assertEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           _A					:     str     = tokenizer.encode(SCREAMING_SNAKE_CASE)
           _A					:     Dict     = encoded.index(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
           self.assertNotEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           pass


    def        A   (  self      :							Optional[int]):
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                         _A					:     Optional[Any]     = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
                         _A					:     List[Any]     = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
                         _A					:     Union[str, Any]     = 'A, <mask> AllenNLP sentence.'
                         _A					:     List[str]     = tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							return_token_type_ids=SCREAMING_SNAKE_CASE)
                         _A					:     Tuple     = tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							return_token_type_ids=SCREAMING_SNAKE_CASE)

                         # token_type_ids should put 0 everywhere
                         self.assertEqual(sum(tokens_r['token_type_ids'])				,							sum(tokens_p['token_type_ids']))

                         # attention_mask should put 1 everywhere, so sum over length should be 1
                         self.assertEqual(
                             sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])				,							sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])				,							)

                         _A					:     Optional[Any]     = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
                         _A					:     Optional[Any]     = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

                         # Rust correctly handles the space before the mask while python doesnt
                         self.assertSequenceEqual(tokens_p['input_ids']				,							[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
                         self.assertSequenceEqual(tokens_r['input_ids']				,							[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

                         self.assertSequenceEqual(
                             SCREAMING_SNAKE_CASE				,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
                         self.assertSequenceEqual(
                             SCREAMING_SNAKE_CASE				,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])


    def        A   (  self      :							int):
           for trim_offsets, add_prefix_space in itertools.product([True, False]				,							repeat=2):
                  _A					:     Union[str, Any]     = self.rust_tokenizer_class.from_pretrained(
                      self.tmpdirname				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)

                  _A					:     Dict     = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
                  _A					:     Union[str, Any]     = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

                  self.assertEqual(pre_tokenizer_state['add_prefix_space']				,							SCREAMING_SNAKE_CASE)

                  self.assertEqual(post_processor_state['add_prefix_space']				,							SCREAMING_SNAKE_CASE)
                  self.assertEqual(post_processor_state['trim_offsets']				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple):
           # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
           # `trim_offsets`
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                         _A					:     Union[str, Any]     = 'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                         _A					:     Union[str, Any]     = F'{text_of_1_token} {text_of_1_token}'

                         _A					:     Tuple     = self.rust_tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)
                         _A					:     Any     = tokenizer_r(SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(encoding.offset_mapping[0]				,							(0, len(SCREAMING_SNAKE_CASE)))
                         self.assertEqual(
                             encoding.offset_mapping[1]				,							(len(SCREAMING_SNAKE_CASE) + 1, len(SCREAMING_SNAKE_CASE) + 1 + len(SCREAMING_SNAKE_CASE))				,							)

                         _A					:     Optional[int]     = self.rust_tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)
                         _A					:     Dict     = tokenizer_r(SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(encoding.offset_mapping[0]				,							(0, len(SCREAMING_SNAKE_CASE)))
                         self.assertEqual(
                             encoding.offset_mapping[1]				,							(len(SCREAMING_SNAKE_CASE) + 1, len(SCREAMING_SNAKE_CASE) + 1 + len(SCREAMING_SNAKE_CASE))				,							)

                         _A					:     Optional[int]     = self.rust_tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)
                         _A					:     Dict     = tokenizer_r(SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(encoding.offset_mapping[0]				,							(0, len(SCREAMING_SNAKE_CASE)))
                         self.assertEqual(
                             encoding.offset_mapping[1]				,							(len(SCREAMING_SNAKE_CASE), len(SCREAMING_SNAKE_CASE) + 1 + len(SCREAMING_SNAKE_CASE))				,							)

                         _A					:     Optional[int]     = self.rust_tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)
                         _A					:     List[Any]     = tokenizer_r(SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(encoding.offset_mapping[0]				,							(0, len(SCREAMING_SNAKE_CASE)))
                         self.assertEqual(
                             encoding.offset_mapping[1]				,							(len(SCREAMING_SNAKE_CASE), len(SCREAMING_SNAKE_CASE) + 1 + len(SCREAMING_SNAKE_CASE))				,							)

                         _A					:     str     = F' {text}'

                         # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                         #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                         # )
                         # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                         # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                         # self.assertEqual(
                         #     encoding.offset_mapping[1],
                         #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                         # )

                         _A					:     Any     = self.rust_tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)
                         _A					:     List[str]     = tokenizer_r(SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(encoding.offset_mapping[0]				,							(1, 1 + len(SCREAMING_SNAKE_CASE)))
                         self.assertEqual(
                             encoding.offset_mapping[1]				,							(1 + len(SCREAMING_SNAKE_CASE) + 1, 1 + len(SCREAMING_SNAKE_CASE) + 1 + len(SCREAMING_SNAKE_CASE))				,							)

                         _A					:     Union[str, Any]     = self.rust_tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)
                         _A					:     Optional[int]     = tokenizer_r(SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(SCREAMING_SNAKE_CASE)))
                         self.assertEqual(
                             encoding.offset_mapping[1]				,							(1 + len(SCREAMING_SNAKE_CASE), 1 + len(SCREAMING_SNAKE_CASE) + 1 + len(SCREAMING_SNAKE_CASE))				,							)

                         _A					:     Tuple     = self.rust_tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							use_fast=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							trim_offsets=SCREAMING_SNAKE_CASE)
                         _A					:     Optional[int]     = tokenizer_r(SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(SCREAMING_SNAKE_CASE)))
                         self.assertEqual(
                             encoding.offset_mapping[1]				,							(1 + len(SCREAMING_SNAKE_CASE), 1 + len(SCREAMING_SNAKE_CASE) + 1 + len(SCREAMING_SNAKE_CASE))				,							)










'''simple docstring'''







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

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


if is_tf_available():
   import tensorflow as tf

if is_torch_available():
   import torch


A				:					Any    						=			logging.get_logger(__name__)


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str = None				,							SCREAMING_SNAKE_CASE      :							uuid.UUID = None				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None):
           if not conversation_id:
                  _A					:     str     = uuid.uuida()
           if past_user_inputs is None:
                  _A					:     List[Any]     = []
           if generated_responses is None:
                  _A					:     Union[str, Any]     = []

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


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           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        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							bool = 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}".')
                         _A					:     Optional[Any]     = 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:
                  _A					:     Optional[Any]     = text


    def        A   (  self      :							Optional[int]):
           if self.new_user_input:
                  self.past_user_inputs.append(self.new_user_input)
           _A					:     Optional[Any]     = None


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str):
           self.generated_responses.append(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           for user_input, generated_response in zip(self.past_user_inputs				,							self.generated_responses):
                  yield True, user_input
                  yield False, generated_response
           if self.new_user_input:
                  yield True, self.new_user_input


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




@add_end_docstrings(
    a_    , R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "    , )
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							str):
           super().__init__(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if self.tokenizer.pad_token_id is None:
                  _A					:     Any     = self.tokenizer.eos_token


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							**SCREAMING_SNAKE_CASE      :							str):
           _A					:     str     = {}
           _A					:     Union[str, Any]     = {}
           _A					:     List[str]     = {}

           if min_length_for_response is not None:
                  _A					:     Optional[Any]     = min_length_for_response
           if minimum_tokens is not None:
                  _A					:     Tuple     = minimum_tokens

           if "max_length" in generate_kwargs:
                  _A					:     List[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:
                  _A					:     List[str]     = clean_up_tokenization_spaces

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


    def __call__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[Conversation, List[Conversation]]				,							SCREAMING_SNAKE_CASE      :							Any=0				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Any     = 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        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Conversation				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=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'):
                  _A					:     Optional[Any]     = self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE)
           else:
                  # If the tokenizer cannot handle conversations, we default to only the old version
                  _A					:     Dict     = self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE)

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


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=10				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     str     = generate_kwargs.get('max_length'				,							self.model.config.max_length)

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


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							List[Any]=True):
           _A					:     Optional[Any]     = model_outputs['output_ids']
           _A					:     Optional[int]     = self.tokenizer.decode(
               output_ids[0]				,							skip_special_tokens=SCREAMING_SNAKE_CASE				,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE				,							)
           _A					:     Any     = model_outputs['conversation']
           conversation.mark_processed()
           conversation.append_response(SCREAMING_SNAKE_CASE)
           return conversation


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Conversation):
           _A					:     Optional[Any]     = self.tokenizer.eos_token_id
           _A					:     List[Any]     = []
           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:
                  _A					:     Dict     = input_ids[-self.tokenizer.model_max_length :]
           return input_ids






