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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       __A					(					lowerCAmelCase				):

			'''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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=None							):



				'''simple docstring'''







				lowerCamelCase__              =	self.pre_processor(text=__lowerCAmelCase ,						return_tensors='''pt''' ,						truncation=__lowerCAmelCase							)

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

					lowerCamelCase__              =	load_dataset('''Matthijs/cmu-arctic-xvectors''' ,						split='''validation'''							)
					lowerCamelCase__              =	torch.tensor(embeddings_dataset[7_3_0_5]['''xvector''']							).unsqueeze(0							)

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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







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










import string
from math import logaa





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	document.translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							).replace('''\n''' ,''''''							)
	lowerCamelCase__              =	document_without_punctuation.split(''' '''							)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   tuple[int, int]:







	'''simple docstring'''







	lowerCamelCase__              =	corpus.lower().translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							)  # strip all punctuation and replace it with ''
	lowerCamelCase__              =	corpus_without_punctuation.split('''\n'''							)
	lowerCamelCase__              =	term.lower()
	return (len([doc for doc in docs if term in doc]							), len(__snake_case							))





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=False							)       ->   float:







	'''simple docstring'''







	if smoothing:
		if n == 0:
			raise ValueError('''log10(0) is undefined.'''							)
		return round(1 + logaa(n / (1 + df)							) ,3							)

	if df == 0:
		raise ZeroDivisionError('''df must be > 0'''							)
	elif n == 0:
		raise ValueError('''log10(0) is undefined.'''							)
	return round(logaa(n / df							) ,3							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   float:







	'''simple docstring'''







	return round(tf * idf ,3							)



