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#
# 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 _lowercase			(       SCREAMING_SNAKE_CASE_	):
							a          = """microsoft/speecht5_tts"""
							a          = (
							    """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          = """text_reader"""
							a          = SpeechTaProcessor
							a          = SpeechTaForTextToSpeech
							a          = SpeechTaHifiGan

							a          = ["""text"""]
							a          = ["""audio"""]




							def 	lowerCamelCase_       (							self:       int  ):
														if self.post_processor is None:
																					lowerCamelCase__      :			int 						= '''microsoft/speecht5_hifigan'''
														super().setup()




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str=None  ):
														lowerCamelCase__      :			List[Any] 						= self.pre_processor(text=UpperCamelCase__    ,							return_tensors="""pt"""    ,							truncation=UpperCamelCase__  )

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

																					lowerCamelCase__      :			List[Any] 						= load_dataset("""Matthijs/cmu-arctic-xvectors"""    ,							split="""validation"""  )
																					lowerCamelCase__      :			Dict 						= torch.tensor(embeddings_dataset[7_305]["""xvector"""]  ).unsqueeze(0  )

														return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       List[Any]  ):
														with torch.no_grad():
																					return self.model.generate_speech(**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       Optional[int]  ):
														with torch.no_grad():
																					return self.post_processor(UpperCamelCase__  ).cpu().detach()









'''simple docstring'''







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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Dict					  ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:    Tuple					  ={
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_A			:    List[Any]					  ={
    '''gpt-neox-20b''': 2_048,
}

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """attention_mask"""]




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Union[str, Any]="<|endoftext|>"    ,							UpperCamelCase__:       Tuple=False    ,							**UpperCamelCase__:       str    ,							):
														super().__init__(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							bos_token=UpperCamelCase__    ,							eos_token=UpperCamelCase__    ,							add_prefix_space=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

														lowerCamelCase__      :			Dict 						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""    ,							UpperCamelCase__  ) != add_prefix_space:
																					lowerCamelCase__      :			Any 						= getattr(UpperCamelCase__    ,							pre_tok_state.pop("""type"""  )  )
																					lowerCamelCase__      :			Dict 						= add_prefix_space
																					lowerCamelCase__      :			Optional[int] 						= pre_tok_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= add_prefix_space




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= self._tokenizer.model.save(UpperCamelCase__    ,							name=UpperCamelCase__  )
														return tuple(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       "Conversation"  ):
														lowerCamelCase__      :			str 						= []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  ) + [self.eos_token_id]  )

														if len(UpperCamelCase__  ) > self.model_max_length:
																					lowerCamelCase__      :			int 						= input_ids[-self.model_max_length :]
														return input_ids

