

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


UpperCamelCase__	:   str								=     logging.get_logger(__name__)

UpperCamelCase__	:   Dict								=     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   Tuple								=     {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

UpperCamelCase__	:   List[Any]								=     {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

UpperCamelCase__	:   Union[str, Any]								=     {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}



class    __snake_case	(     _UpperCAmelCase							):
  __lowerCAmelCase  :						Dict    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						Dict    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						str    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						Tuple    			=	["""input_ids""", """attention_mask""", """token_type_ids"""]
  __lowerCAmelCase  :						List[Any]    			=	DebertaTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A=None    ,		_A="replace"    ,		_A="[CLS]"    ,		_A="[SEP]"    ,		_A="[SEP]"    ,		_A="[CLS]"    ,		_A="[UNK]"    ,		_A="[PAD]"    ,		_A="[MASK]"    ,		_A=False    ,		**_A    ,		):
      super().__init__(
          _A    ,		_A    ,		tokenizer_file=_A    ,		errors=_A    ,		bos_token=_A    ,		eos_token=_A    ,		unk_token=_A    ,		sep_token=_A    ,		cls_token=_A    ,		pad_token=_A    ,		mask_token=_A    ,		add_prefix_space=_A    ,		**_A    ,		)
      SCREAMING_SNAKE_CASE_						=    kwargs.pop('add_bos_token'    ,		_A)

      SCREAMING_SNAKE_CASE_						=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
      if pre_tok_state.get('add_prefix_space'    ,		_A) != add_prefix_space:
          SCREAMING_SNAKE_CASE_						=    getattr(_A    ,		pre_tok_state.pop('type'))
          SCREAMING_SNAKE_CASE_						=    add_prefix_space
          SCREAMING_SNAKE_CASE_						=    pre_tok_class(**_A)

      SCREAMING_SNAKE_CASE_						=    add_prefix_space

  @property
  def     lowerCAmelCase__  (       self):







      if self._mask_token is None:
          if self.verbose:
              logger.error('Using mask_token, but it is not set yet.')
          return None
      return str(self._mask_token)

  @mask_token.setter
  def     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    AddedToken(_A    ,		lstrip=_A    ,		rstrip=_A) if isinstance(_A    ,		_A) else value
      SCREAMING_SNAKE_CASE_						=    value

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None):







      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      SCREAMING_SNAKE_CASE_						=    [self.cls_token_id]
      SCREAMING_SNAKE_CASE_						=    [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None):
      SCREAMING_SNAKE_CASE_						=    [self.sep_token_id]
      SCREAMING_SNAKE_CASE_						=    [self.cls_token_id]

      if token_ids_a is None:
          return len(cls + token_ids_a + sep) * [0]
      return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]

  def     lowerCAmelCase__  (       self    ,		*_A    ,		**_A):
      SCREAMING_SNAKE_CASE_						=    kwargs.get('is_split_into_words'    ,		_A)
      assert self.add_prefix_space or not is_split_into_words, (
          f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		*_A    ,		**_A):
      SCREAMING_SNAKE_CASE_						=    kwargs.get('is_split_into_words'    ,		_A)

      assert self.add_prefix_space or not is_split_into_words, (
          f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None):
      SCREAMING_SNAKE_CASE_						=    self._tokenizer.model.save(_A    ,		name=_A)
      return tuple(_A)


  def     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    []
      for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(_A    ,		add_special_tokens=_A) + [self.eos_token_id])

      if len(_A) > self.model_max_length:
          SCREAMING_SNAKE_CASE_						=    input_ids[-self.model_max_length :]
      return input_ids







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__			=    {
    "agents": ["Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent"],
    "base": ["PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__			=    ["DocumentQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageCaptioningTool"]
			UpperCamelCase__			=    ["ImageQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageSegmentationTool"]
			UpperCamelCase__			=    ["SpeechToTextTool"]
			UpperCamelCase__			=    ["TextClassificationTool"]
			UpperCamelCase__			=    ["TextQuestionAnsweringTool"]
			UpperCamelCase__			=    ["TextSummarizationTool"]
			UpperCamelCase__			=    ["TextToSpeechTool"]
			UpperCamelCase__			=    ["TranslationTool"]

if TYPE_CHECKING:
			from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
			from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .document_question_answering import DocumentQuestionAnsweringTool
						from .image_captioning import ImageCaptioningTool
						from .image_question_answering import ImageQuestionAnsweringTool
						from .image_segmentation import ImageSegmentationTool
						from .speech_to_text import SpeechToTextTool
						from .text_classification import TextClassificationTool
						from .text_question_answering import TextQuestionAnsweringTool
						from .text_summarization import TextSummarizationTool
						from .text_to_speech import TextToSpeechTool
						from .translation import TranslationTool
else:
			import sys

			UpperCamelCase__			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





