
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE    :      Dict          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"ibert"





	def __init__(			self				,       UpperCamelCase__=3_0522				,       UpperCamelCase__=768				,       UpperCamelCase__=12				,       UpperCamelCase__=12				,       UpperCamelCase__=3072				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=512				,       UpperCamelCase__=2				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1e-12				,       UpperCamelCase__=1				,       UpperCamelCase__=0				,       UpperCamelCase__=2				,       UpperCamelCase__="absolute"				,       UpperCamelCase__=False				,       UpperCamelCase__="none"				,       **UpperCamelCase__				,       ):
					super().__init__(pad_token_id=UpperCamelCase__				,       bos_token_id=UpperCamelCase__				,       eos_token_id=UpperCamelCase__				,       **UpperCamelCase__		)

					A__  :	Optional[Any]         =							vocab_size
					A__  :	Optional[int]         =							hidden_size
					A__  :	Optional[Any]         =							num_hidden_layers
					A__  :	Any         =							num_attention_heads
					A__  :	str         =							hidden_act
					A__  :	Tuple         =							intermediate_size
					A__  :	int         =							hidden_dropout_prob
					A__  :	int         =							attention_probs_dropout_prob
					A__  :	List[str]         =							max_position_embeddings
					A__  :	Union[str, Any]         =							type_vocab_size
					A__  :	Union[str, Any]         =							initializer_range
					A__  :	Any         =							layer_norm_eps
					A__  :	List[Any]         =							position_embedding_type
					A__  :	List[str]         =							quant_mode
					A__  :	Tuple         =							force_dequant







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	@property
	def   __snake_case				(			self		):
					if self.task == "multiple-choice":
									A__  :	List[Any]         =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
									A__  :	List[Any]         =							{0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]		)

# 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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"microsoft/speecht5_tts"
	_lowerCAmelCase							       =				(
	    "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."
	)
	_lowerCAmelCase							       =				"text_reader"
	_lowerCAmelCase							       =				SpeechTaProcessor
	_lowerCAmelCase							       =				SpeechTaForTextToSpeech
	_lowerCAmelCase							       =				SpeechTaHifiGan

	_lowerCAmelCase							       =				["text"]
	_lowerCAmelCase							       =				["audio"]





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





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	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.'''		)

									A__  :	List[Any]         =							load_dataset('''Matthijs/cmu-arctic-xvectors'''				,       split='''validation'''		)
									A__  :	Dict         =							torch.tensor(embeddings_dataset[7305]['''xvector''']		).unsqueeze(0		)

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





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.model.generate_speech(**UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.post_processor(UpperCamelCase__		).cpu().detach()