
"""simple docstring"""




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


A__ :       Dict				= logging.get_logger(__name__)


A__ :       Optional[Any]				= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A__ :       Optional[Any]				= {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

A__ :       Optional[int]				= {
    'allenai/led-base-16384': 1_6_3_8_4,
}


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		VOCAB_FILES_NAMES
       UpperCamelCase_ =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_ =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_ =		LEDTokenizer
       UpperCamelCase_ =		['''input_ids''', '''attention_mask''']
       def __init__(  self			,					A_=None			,					A_=None			,					A_=None			,					A_="replace"			,					A_="<s>"			,					A_="</s>"			,					A_="</s>"			,					A_="<s>"			,					A_="<unk>"			,					A_="<pad>"			,					A_="<mask>"			,					A_=False			,					A_=True			,					**A_			,					)  ->						Dict:





         """simple docstring"""




         super().__init__(
             A_			,					A_			,					tokenizer_file=A_			,					errors=A_			,					bos_token=A_			,					eos_token=A_			,					sep_token=A_			,					cls_token=A_			,					unk_token=A_			,					pad_token=A_			,					mask_token=A_			,					add_prefix_space=A_			,					trim_offsets=A_			,					**A_			,					)

         _lowercase:      str   			=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
         if pre_tok_state.get('''add_prefix_space'''			,					A_							) != add_prefix_space:
           _lowercase:      Optional[Any]   			=  getattr(A_			,					pre_tok_state.pop('''type'''							)							)
           _lowercase:      int   			=  add_prefix_space
           _lowercase:      Optional[int]   			=  pre_tok_class(**A_							)

         _lowercase:      str   			=  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         _lowercase:      int   			=  '''post_processor'''
         _lowercase:      List[Any]   			=  getattr(self.backend_tokenizer			,					A_			,					A_							)
         if tokenizer_component_instance:
           _lowercase:      Any   			=  json.loads(tokenizer_component_instance.__getstate__()							)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             _lowercase:      Optional[Any]   			=  tuple(state['''sep''']							)
           if "cls" in state:
             _lowercase:      List[Any]   			=  tuple(state['''cls''']							)

           _lowercase:      Tuple   			=  False

           if state.get('''add_prefix_space'''			,					A_							) != add_prefix_space:
             _lowercase:      List[str]   			=  add_prefix_space
             _lowercase:      Dict   			=  True

           if state.get('''trim_offsets'''			,					A_							) != trim_offsets:
             _lowercase:      Tuple   			=  trim_offsets
             _lowercase:      Dict   			=  True

           if changes_to_apply:
             _lowercase:      Dict   			=  getattr(A_			,					state.pop('''type'''							)							)
             _lowercase:      Union[str, Any]   			=  component_class(**A_							)
             setattr(self.backend_tokenizer			,					A_			,					A_							)
       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         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 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      int   			=  AddedToken(A_			,					lstrip=A_			,					rstrip=A_							) if isinstance(A_			,					A_							) else value
         _lowercase:      Union[str, Any]   			=  value
       def 				lowercase_				(  self			,					*A_			,					**A_							)  ->						BatchEncoding:





         """simple docstring"""




         _lowercase:      str   			=  kwargs.get('''is_split_into_words'''			,					A_							)

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               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 				lowercase_				(  self			,					*A_			,					**A_							)  ->						BatchEncoding:





         """simple docstring"""




         _lowercase:      str   			=  kwargs.get('''is_split_into_words'''			,					A_							)

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               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 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						Tuple[str]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self._tokenizer.model.save(A_			,					name=A_							)
         return tuple(A_							)
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
           return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						List[int]:





         """simple docstring"""




         _lowercase:      Any   			=  [self.sep_token_id]
         _lowercase:      Tuple   			=  [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 + sep + token_ids_a + sep							) * [0]



       def 				lowercase_				(  self			,					A_			,					A_ = None			,					A_ = PaddingStrategy.DO_NOT_PAD			,					A_ = None			,					A_ = None			,					)  ->						dict:





         """simple docstring"""




         _lowercase:      List[Any]   			=  super()._pad(
             encoded_inputs=A_			,					max_length=A_			,					padding_strategy=A_			,					pad_to_multiple_of=A_			,					return_attention_mask=A_			,					)

         # Load from model defaults
         if return_attention_mask is None:
           _lowercase:      Optional[int]   			=  '''attention_mask''' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
           _lowercase:      Optional[int]   			=  encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           _lowercase:      Union[str, Any]   			=  len(encoded_inputs['''global_attention_mask''']							) != len(A_							)

           if needs_to_be_padded:
             _lowercase:      List[Any]   			=  len(A_							) - len(encoded_inputs['''global_attention_mask''']							)

             if self.padding_side == "right":
               # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
               _lowercase:      Optional[int]   			=  (
                   encoded_inputs['''global_attention_mask'''] + [-1] * difference
               )
             elif self.padding_side == "left":
               _lowercase:      Optional[Any]   			=  [-1] * difference + encoded_inputs[
                   '''global_attention_mask'''
               ]
             else:
               raise ValueError('''Invalid padding strategy:''' + str(self.padding_side							)							)

         return encoded_inputs


"""simple docstring"""




# 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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''microsoft/speecht5_tts'''
       UpperCamelCase_ =		(
           '''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.'''
       )
       UpperCamelCase_ =		'''text_reader'''
       UpperCamelCase_ =		SpeechTaProcessor
       UpperCamelCase_ =		SpeechTaForTextToSpeech
       UpperCamelCase_ =		SpeechTaHifiGan

       UpperCamelCase_ =		['''text''']
       UpperCamelCase_ =		['''audio''']
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.post_processor is None:
           _lowercase:      Tuple   			=  '''microsoft/speecht5_hifigan'''
         super().setup()
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.pre_processor(text=A_			,					return_tensors='''pt'''			,					truncation=A_							)

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

           _lowercase:      Union[str, Any]   			=  load_dataset('''Matthijs/cmu-arctic-xvectors'''			,					split='''validation'''							)
           _lowercase:      Optional[int]   			=  torch.tensor(embeddings_dataset[7305]['''xvector''']							).unsqueeze(0							)

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         with torch.no_grad():
           return self.model.generate_speech(**A_							)



       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




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