
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowercase  :					List[Any]						    =							{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase  :					List[str]						    =							{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase  :					List[Any]						    =							{
    """unc-nlp/lxmert-base-uncased""": 512,
}

lowercase  :					Dict						    =							{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						List[Any]=				VOCAB_FILES_NAMES
    _a		:						Optional[int]=				PRETRAINED_VOCAB_FILES_MAP
    _a		:						List[Any]=				PRETRAINED_INIT_CONFIGURATION
    _a		:						Dict=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						List[Any]=				LxmertTokenizer
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=True		,snake_case="[UNK]"		,snake_case="[SEP]"		,snake_case="[PAD]"		,snake_case="[CLS]"		,snake_case="[MASK]"		,snake_case=True		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               snake_case		,tokenizer_file=snake_case		,do_lower_case=snake_case		,unk_token=snake_case		,sep_token=snake_case		,pad_token=snake_case		,cls_token=snake_case		,mask_token=snake_case		,tokenize_chinese_chars=snake_case		,strip_accents=snake_case		,**snake_case		,)

           lowercase							:     int       	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
           if (
               normalizer_state.get("""lowercase"""		,snake_case      ) != do_lower_case
               or normalizer_state.get("""strip_accents"""		,snake_case      ) != strip_accents
               or normalizer_state.get("""handle_chinese_chars"""		,snake_case      ) != tokenize_chinese_chars
           ):
                  lowercase							:     Optional[Any]       	=      getattr(snake_case		,normalizer_state.pop("""type"""      )      )
                  lowercase							:     int       	=      do_lower_case
                  lowercase							:     Tuple       	=      strip_accents
                  lowercase							:     Union[str, Any]       	=      tokenize_chinese_chars
                  lowercase							:     str       	=      normalizer_class(**snake_case      )

           lowercase							:     Tuple       	=      do_lower_case
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      [self.sep_token_id]
           lowercase							:     int       	=      [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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self._tokenizer.model.save(snake_case		,name=snake_case      )
           return tuple(snake_case      )


# 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.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
  from datasets import load_dataset

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
        "text to read (in English) and returns a waveform object containing the sound."
    )
    _a		:						Dict=				"text_reader"
    _a		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

           if speaker_embeddings is None:
                  if not is_datasets_available():
                         raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""      )

                  lowercase							:     Tuple       	=      load_dataset("""Matthijs/cmu-arctic-xvectors"""		,split="""validation"""      )
                  lowercase							:     List[str]       	=      torch.tensor(embeddings_dataset[7305]["""xvector"""]      ).unsqueeze(0      )

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.post_processor(snake_case      ).cpu().detach()
