





'''simple docstring'''






import csv

import tweepy

# Twitter API credentials
lowerCAmelCase       		=      """"""
lowerCAmelCase       		=      """"""
lowerCAmelCase       		=      """"""
lowerCAmelCase       		=      """"""






def      __A						(							a_						:  str	):
		# authorize twitter, initialize tweepy
		lowerCAmelCase     :    Tuple						=   tweepy.OAuthHandler(a_      ,a_	)
		auth.set_access_token(a_      ,a_	)
		lowerCAmelCase     :    Optional[int]						=   tweepy.API(a_	)

		# initialize a list to hold all the tweepy Tweets
		lowerCAmelCase     :    int						=   []

		# make initial request for most recent tweets (200 is the maximum allowed count)
		lowerCAmelCase     :    List[Any]						=   api.user_timeline(screen_name=a_      ,count=2_0_0	)

		# save most recent tweets
		alltweets.extend(a_	)

		# save the id of the oldest tweet less one
		lowerCAmelCase     :    Dict						=   alltweets[-1].id - 1

		# keep grabbing tweets until there are no tweets left to grab
		while len(a_	) > 0:
				print(f'''getting tweets before {oldest}'''	)

				# all subsequent requests use the max_id param to prevent duplicates
				lowerCAmelCase     :    Any						=   api.user_timeline(
				    screen_name=a_      ,count=2_0_0      ,max_id=a_	)

				# save most recent tweets
				alltweets.extend(a_	)

				# update the id of the oldest tweet less one
				lowerCAmelCase     :    Dict						=   alltweets[-1].id - 1

				print(f'''...{len(a_	)} tweets downloaded so far'''	)

		# transform the tweepy tweets into a 2D array that will populate the csv
		lowerCAmelCase     :    Union[str, Any]						=   [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

		# write the csv
		with open(f'''new_{screen_name}_tweets.csv'''      ,"w"	) as f:
				lowerCAmelCase     :    Dict						=   csv.writer(a_	)
				writer.writerow(["id", "created_at", "text"]	)
				writer.writerows(a_	)


if __name__ == "__main__":
					# pass in the username of the account you want to download
					get_all_tweets("""FirePing32""")










'''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 		lowerCamelCase  (      _A       ):
	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	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   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."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

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



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



