"""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        snake_case (				UpperCamelCase__       ):




					"""simple docstring"""

					snake_case__       					=				"microsoft/speecht5_tts"
					snake_case__       					=				(
					    "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."
					)
					snake_case__       					=				"text_reader"
					snake_case__       					=				SpeechTaProcessor
					snake_case__       					=				SpeechTaForTextToSpeech
					snake_case__       					=				SpeechTaHifiGan

					snake_case__       					=				["text"]
					snake_case__       					=				["audio"]







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									if self.post_processor is None:
													UpperCAmelCase__    =							"""microsoft/speecht5_hifigan"""
									super().setup()







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			List[Any]=None    ):
									UpperCAmelCase__    =							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.'    )

													UpperCAmelCase__    =							load_dataset('Matthijs/cmu-arctic-xvectors'					,split='validation'    )
													UpperCAmelCase__    =							torch.tensor(embeddings_dataset[7_305]['xvector']    ).unsqueeze(0    )

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







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Optional[Any]    ):
									with torch.no_grad():
													return self.model.generate_speech(**_a    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Any    ):
									with torch.no_grad():
													return self.post_processor(_a    ).cpu().detach()




"""simple docstring"""






lowerCAmelCase__						:						Tuple		  =			range(2, 20 + 1)
lowerCAmelCase__						:						Optional[Any]		  =			[10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__						:						dict[int, dict[int, list[list[int]]]]		  =			{}



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							sum(a_i[j] for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ) )
				UpperCAmelCase__    =							sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase )      ,    lowerCamelCase ) ) )

				UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0
				UpperCAmelCase__    =							n - i

				UpperCAmelCase__    =							memo.get(lowerCamelCase )

				if sub_memo is not None:
								UpperCAmelCase__    =							sub_memo.get(lowerCamelCase )

								if jumps is not None and len(lowerCamelCase ) > 0:
												# find and make the largest jump without going over
												UpperCAmelCase__    =							-1
												for _k in range(len(lowerCamelCase ) - 1      ,    -1      ,    -1 ):
																if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																				UpperCAmelCase__    =							_k
																				break

												if max_jump >= 0:
																UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							jumps[max_jump]
																# since the difference between jumps is cached, add c
																UpperCAmelCase__    =							diff + c
																for j in range(min(lowerCamelCase      ,    len(lowerCamelCase ) ) ):
																				UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
																if new_c > 0:
																				add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )

								else:
												UpperCAmelCase__    =							[]
				else:
								UpperCAmelCase__    =							{c: []}
								UpperCAmelCase__    =							sub_memo

				if dn >= max_dn or c + diff >= base[k]:
								return diff, dn

				if k > ks[0]:
								while True:
												# keep doing smaller jumps
												UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    k - 1      ,    i + dn      ,    lowerCamelCase )
												diff += _diff
												dn += terms_jumped

												if dn >= max_dn or c + diff >= base[k]:
																break
				else:
								# would be too small a jump, just compute sequential terms instead
								UpperCAmelCase__   ,			UpperCAmelCase__    =							compute(lowerCamelCase      ,    lowerCamelCase      ,    i + dn      ,    lowerCamelCase )
								diff += _diff
								dn += terms_jumped

				UpperCAmelCase__    =							sub_memo[c]

				# keep jumps sorted by # of terms skipped
				UpperCAmelCase__    =							0
				while j < len(lowerCamelCase ):
								if jumps[j][1] > dn:
												break
								j += 1

				# cache the jump for this value digitsum(b) and c
				sub_memo[c].insert(lowerCamelCase      ,    (diff, dn, k) )
				return (diff, dn)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				if i >= n:
								return 0, i
				if k > len(lowerCamelCase ):
								a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )

				# note: a_i -> b * 10^k + c
				# ds_b -> digitsum(b)
				# ds_c -> digitsum(c)
				UpperCAmelCase__    =							i
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0, 0
				for j in range(len(lowerCamelCase ) ):
								if j >= k:
												ds_b += a_i[j]
								else:
												ds_c += a_i[j]

				while i < n:
								i += 1
								UpperCAmelCase__    =							ds_c + ds_b
								diff += addend
								UpperCAmelCase__    =							0
								for j in range(lowerCamelCase ):
												UpperCAmelCase__    =							a_i[j] + addend
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )

												ds_c += a_i[j]

								if addend > 0:
												break

				if addend > 0:
								add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )
				return diff, i - start_i



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ):
								UpperCAmelCase__    =							digits[j] + addend
								if s >= 1_0:
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
												UpperCAmelCase__    =							addend // 1_0 + quotient
								else:
												UpperCAmelCase__    =							s
												UpperCAmelCase__    =							addend // 1_0

								if addend == 0:
												break

				while addend > 0:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
								digits.append(lowerCamelCase )



def 						a_		(      lowerCamelCase = 1_0**1_5 ):
				UpperCAmelCase__    =							[1]
				UpperCAmelCase__    =							1
				UpperCAmelCase__    =							0
				while True:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    2_0      ,    i + dn      ,    lowerCamelCase )
								dn += terms_jumped
								if dn == n - i:
												break

				UpperCAmelCase__    =							0
				for j in range(len(lowerCamelCase ) ):
								a_n += digits[j] * 1_0**j
				return a_n


if __name__ == "__main__":
						print(F"""{solution() = }""")



