


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

SCREAMING_SNAKE_CASE       :      List[str]												= logging.get_logger(__name__)



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						List[List[ImageInput]]:
    if isinstance(lowerCamelCase_							, (list, tuple) ) and isinstance(videos[0]							, (list, tuple) ) and is_valid_image(videos[0][0] ):
        return videos

    elif isinstance(lowerCamelCase_							, (list, tuple) ) and is_valid_image(videos[0] ):
        return [videos]

    elif is_valid_image(lowerCamelCase_ ):
        return [[videos]]

    raise ValueError(F'''Could not make batched video from {videos}''' )






class _lowerCamelCase(						_a   ):
       lowercase_ :	Union[str, Any]     =			["""pixel_values"""]
       def __init__(					self,      lowerCamelCase = True,      lowerCamelCase = None,      lowerCamelCase = PILImageResampling.BILINEAR,      lowerCamelCase = True,      lowerCamelCase = None,      lowerCamelCase = True,      lowerCamelCase = 1 / 2_55,      lowerCamelCase = True,      lowerCamelCase = True,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase       :							str      		=		size if size is not None else {'shortest_edge': 2_56}
           _lowercase       :							Any      		=		get_size_dict(lowerCamelCase,      default_to_square=lowerCamelCase)
           _lowercase       :							List[str]      		=		crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
           _lowercase       :							Optional[Any]      		=		get_size_dict(lowerCamelCase,      param_name='crop_size')

           _lowercase       :							Optional[int]      		=		do_resize
           _lowercase       :							Tuple      		=		size
           _lowercase       :							Any      		=		do_center_crop
           _lowercase       :							Any      		=		crop_size
           _lowercase       :							int      		=		resample
           _lowercase       :							int      		=		do_rescale
           _lowercase       :							str      		=		rescale_factor
           _lowercase       :							Tuple      		=		offset
           _lowercase       :							List[str]      		=		do_normalize
           _lowercase       :							Tuple      		=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _lowercase       :							Any      		=		image_std if image_std is not None else IMAGENET_STANDARD_STD
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = PILImageResampling.BILINEAR,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   np.ndarray:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		get_size_dict(lowerCamelCase,      default_to_square=lowerCamelCase)
           if "shortest_edge" in size:
               _lowercase       :							Dict      		=		get_resize_output_image_size(lowerCamelCase,      size['shortest_edge'],      default_to_square=lowerCamelCase)
           elif "height" in size and "width" in size:
               _lowercase       :							List[Any]      		=		(size['height'], size['width'])
           else:
               raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
           return resize(lowerCamelCase,      size=lowerCamelCase,      resample=lowerCamelCase,      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   np.ndarray:




           """simple docstring"""

           _lowercase       :							Dict      		=		get_size_dict(lowerCamelCase)
           if "height" not in size or "width" not in size:
               raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
           return center_crop(lowerCamelCase,      size=(size['height'], size['width']),      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = True,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		image.astype(np.floataa)
           if offset:
               _lowercase       :							List[str]      		=		image - (scale / 2)
           return rescale(lowerCamelCase,      scale=lowerCamelCase,      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   np.ndarray:




           """simple docstring"""

           return normalize(lowerCamelCase,      mean=lowerCamelCase,      std=lowerCamelCase,      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = ChannelDimension.FIRST,      )       ->   np.ndarray:




           """simple docstring"""

           if do_resize and size is None or resample is None:
               raise ValueError('Size and resample must be specified if do_resize is True.')

           if do_center_crop and crop_size is None:
               raise ValueError('Crop size must be specified if do_center_crop is True.')

           if do_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.')

           if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('Image mean and std must be specified if do_normalize is True.')

           if offset and not do_rescale:
               raise ValueError('For offset, do_rescale must also be set to True.')

           # All transformations expect numpy arrays.
           _lowercase       :							Tuple      		=		to_numpy_array(lowerCamelCase)

           if do_resize:
               _lowercase       :							Optional[Any]      		=		self.resize(image=lowerCamelCase,      size=lowerCamelCase,      resample=lowerCamelCase)

           if do_center_crop:
               _lowercase       :							Dict      		=		self.center_crop(lowerCamelCase,      size=lowerCamelCase)

           if do_rescale:
               _lowercase       :							Dict      		=		self.rescale(image=lowerCamelCase,      scale=lowerCamelCase,      offset=lowerCamelCase)

           if do_normalize:
               _lowercase       :							Optional[Any]      		=		self.normalize(image=lowerCamelCase,      mean=lowerCamelCase,      std=lowerCamelCase)

           _lowercase       :							Tuple      		=		to_channel_dimension_format(lowerCamelCase,      lowerCamelCase)
           return image




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = ChannelDimension.FIRST,      **lowerCamelCase,      )       ->   PIL.Image.Image:




           """simple docstring"""

           _lowercase       :							Any      		=		do_resize if do_resize is not None else self.do_resize
           _lowercase       :							Optional[int]      		=		resample if resample is not None else self.resample
           _lowercase       :							Dict      		=		do_center_crop if do_center_crop is not None else self.do_center_crop
           _lowercase       :							Optional[int]      		=		do_rescale if do_rescale is not None else self.do_rescale
           _lowercase       :							Dict      		=		rescale_factor if rescale_factor is not None else self.rescale_factor
           _lowercase       :							Optional[int]      		=		offset if offset is not None else self.offset
           _lowercase       :							List[Any]      		=		do_normalize if do_normalize is not None else self.do_normalize
           _lowercase       :							Any      		=		image_mean if image_mean is not None else self.image_mean
           _lowercase       :							Union[str, Any]      		=		image_std if image_std is not None else self.image_std

           _lowercase       :							Union[str, Any]      		=		size if size is not None else self.size
           _lowercase       :							Any      		=		get_size_dict(lowerCamelCase,      default_to_square=lowerCamelCase)
           _lowercase       :							List[str]      		=		crop_size if crop_size is not None else self.crop_size
           _lowercase       :							Any      		=		get_size_dict(lowerCamelCase,      param_name='crop_size')

           if not valid_images(lowerCamelCase):
               raise ValueError(
                   'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                   'torch.Tensor, tf.Tensor or jax.ndarray.')

           _lowercase       :							List[str]      		=		make_batched(lowerCamelCase)

           _lowercase       :							Any      		=		[
               [
                   self._preprocess_image(
                       image=lowerCamelCase,      do_resize=lowerCamelCase,      size=lowerCamelCase,      resample=lowerCamelCase,      do_center_crop=lowerCamelCase,      crop_size=lowerCamelCase,      do_rescale=lowerCamelCase,      rescale_factor=lowerCamelCase,      offset=lowerCamelCase,      do_normalize=lowerCamelCase,      image_mean=lowerCamelCase,      image_std=lowerCamelCase,      data_format=lowerCamelCase,      )
                   for img in video
               ]
               for video in videos
           ]

           _lowercase       :							Dict      		=		{'pixel_values': videos}
           return BatchFeature(data=lowerCamelCase,      tensor_type=lowerCamelCase)






'''simple docstring'''
import csv

import tweepy

# Twitter API credentials
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''


def 			a      (			__a   )  ->	None:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= tweepy.OAuthHandler(__a ,					__a   )
					auth.set_access_token(__a ,					__a   )
					UpperCamelCase__   :List[str]												= tweepy.API(__a   )

					# initialize a list to hold all the tweepy Tweets
					UpperCamelCase__   :Dict												= []

					# make initial request for most recent tweets (200 is the maximum allowed count)
					UpperCamelCase__   :Tuple												= api.user_timeline(screen_name=__a ,					count=200   )

					# save most recent tweets
					alltweets.extend(__a   )

					# save the id of the oldest tweet less one
					UpperCamelCase__   :Union[str, Any]												= 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
										UpperCamelCase__   :Union[str, Any]												= api.user_timeline(
										    screen_name=__a ,					count=200 ,					max_id=__a   )

										# save most recent tweets
										alltweets.extend(__a   )

										# update the id of the oldest tweet less one
										UpperCamelCase__   :Tuple												= 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
					UpperCamelCase__   :int												= [[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:
										UpperCamelCase__   :Tuple												= 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''')