

# 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_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       "microsoft/speecht5_tts"
							UpperCAmelCase__			:		int             =       (
							    "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__			:		List[Any]             =       "text_reader"
							UpperCAmelCase__			:		Optional[Any]             =       SpeechTaProcessor
							UpperCAmelCase__			:		Union[str, Any]             =       SpeechTaForTextToSpeech
							UpperCAmelCase__			:		Union[str, Any]             =       SpeechTaHifiGan

							UpperCAmelCase__			:		Dict             =       ["text"]
							UpperCAmelCase__			:		str             =       ["audio"]


							def 							__lowercase							(     self			)       ->       Tuple:
												if self.post_processor is None:
																	_a      :       str   =						'''microsoft/speecht5_hifigan'''
												super().setup()


							def 							__lowercase							(     self			,				_a			,				_a=None			)       ->       Optional[Any]:
												_a      :       Union[str, Any]   =						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.'''			)

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

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


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												with torch.no_grad():
																	return self.model.generate_speech(**_a			)




							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												with torch.no_grad():
																	return self.post_processor(_a			).cpu().detach()






import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

a__												=						'''Usage of script: script_name <size_of_canvas:int>'''

a__												=						[0] * 100 + [1] * 10
random.shuffle(choice)



def   __UpperCAmelCase					(      __a :     int      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       int   =						[[False for i in range(__a      )] for j in range(__a      )]
					return canvas



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	None:

					"""simple docstring"""

					for i, row in enumerate(__a      ):
										for j, _ in enumerate(__a      ):
															_a      :       Optional[int]   =						bool(random.getrandbits(1      )      )



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       Any   =						np.array(__a      )
					_a      :       Optional[int]   =						np.array(create_canvas(current_canvas.shape[0]      )      )
					for r, row in enumerate(__a      ):
										for c, pt in enumerate(__a      ):
															_a      :       Tuple   =						__judge_point(
															    __a						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]      )

					_a      :       List[str]   =						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					_a      :       list[list[bool]]   =						current_canvas.tolist()
					return return_canvas



def   __UpperCAmelCase					(      __a :     bool						,__a :     list[list[bool]]      )     ->	bool:

					"""simple docstring"""

					_a      :       Optional[Any]   =						0
					_a      :       str   =						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					_a      :       Optional[int]   =						pt
					if pt:
										if alive < 2:
															_a      :       Dict   =						False
										elif alive == 2 or alive == 3:
															_a      :       Optional[Any]   =						True
										elif alive > 3:
															_a      :       str   =						False
					else:
										if alive == 3:
															_a      :       int   =						True

					return state


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise Exception(usage_doc)

			a__												=						int(sys.argv[1])
			# main working structure of this module.
			a__												=						create_canvas(canvas_size)
			seed(c)
			a__						,     a__												=						plt.subplots()
			fig.show()
			a__												=						ListedColormap(['''w''', '''k'''])
			try:
						while True:
									a__												=						run(c)
									ax.matshow(c, cmap=cmap)
									fig.canvas.draw()
									ax.cla()
			except KeyboardInterrupt:
						# do nothing.
						pass



