
from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
      from ..models.auto import AutoTokenizer



class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					Dict   ):
    raise NotImplementedError()







   def       lowerCAmelCase			(self :					Dict   ):
    raise NotImplementedError()





class 		UpperCamelCase__  (       __lowercase    ):
   def __init__(self :					Optional[int]					,				snake_case_ :					"AutoTokenizer"					,				snake_case_ :					bool = False					,				**snake_case_ :					int   ):
    __a :  List[Any]						=  tokenizer
    __a :  List[Any]						=  skip_prompt
    __a :  Optional[Any]						=  decode_kwargs

    # variables used in the streaming process
    __a :  Any						=  []
    __a :  Union[str, Any]						=  0
    __a :  List[Any]						=  True
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					List[str]   ):
    if len(value.shape   ) > 1 and value.shape[0] > 1:
     raise ValueError('''TextStreamer only supports batch size 1'''   )
    elif len(value.shape   ) > 1:
     __a :  Optional[int]						=  value[0]

    if self.skip_prompt and self.next_tokens_are_prompt:
     __a :  Optional[int]						=  False
     return

    # Add the new token to the cache and decodes the entire thing.
    self.token_cache.extend(value.tolist()   )
    __a :  List[Any]						=  self.tokenizer.decode(self.token_cache					,				**self.decode_kwargs   )

    # After the symbol for a new line, we flush the cache.
    if text.endswith('''\n'''   ):
     __a :  Tuple						=  text[self.print_len :]
     __a :  List[Any]						=  []
     __a :  List[Any]						=  0
    # If the last token is a CJK character, we print the characters.
    elif len(snake_case_   ) > 0 and self._is_chinese_char(ord(text[-1]   )   ):
     __a :  Any						=  text[self.print_len :]
     self.print_len += len(snake_case_   )
    # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
    # which may change with the subsequent token -- there are probably smarter ways to do this!)
    else:
     __a :  str						=  text[self.print_len : text.rfind(''' '''   ) + 1]
     self.print_len += len(snake_case_   )

    self.on_finalized_text(snake_case_   )
   def       lowerCAmelCase			(self :					Tuple   ):
    # Flush the cache, if it exists
    if len(self.token_cache   ) > 0:
     __a :  List[str]						=  self.tokenizer.decode(self.token_cache					,				**self.decode_kwargs   )
     __a :  str						=  text[self.print_len :]
     __a :  List[str]						=  []
     __a :  Optional[Any]						=  0
    else:
     __a :  str						=  ''''''

    __a :  int						=  True
    self.on_finalized_text(snake_case_					,				stream_end=snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					str					,				snake_case_ :					bool = False   ):
    print(snake_case_					,				flush=snake_case_					,				end='''''' if not stream_end else None   )







   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					List[str]   ):
    # This defines a "chinese character" as anything in the CJK Unicode block:
    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
    #
    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
    # despite its name. The modern Korean Hangul alphabet is a different block,
    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
    # space-separated words, so they are not treated specially and handled
    # like the all of the other languages.
    if (
        (cp >= 0X4e00 and cp <= 0X9fff)
        or (cp >= 0X3400 and cp <= 0X4dbf)  #
        or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
        or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
        or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
        or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
        or (cp >= 0Xf900 and cp <= 0Xfaff)
        or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
    ):  #
     return True

    return False





class 		UpperCamelCase__  (       __lowercase    ):
   def __init__(self :					Optional[Any]					,				snake_case_ :					"AutoTokenizer"					,				snake_case_ :					bool = False					,				snake_case_ :					Optional[float] = None					,				**snake_case_ :					Optional[int]   ):
    super().__init__(snake_case_					,				snake_case_					,				**snake_case_   )
    __a :  Optional[Any]						=  Queue()
    __a :  Optional[int]						=  None
    __a :  Tuple						=  timeout
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					str					,				snake_case_ :					bool = False   ):
    self.text_queue.put(snake_case_					,				timeout=self.timeout   )
    if stream_end:
     self.text_queue.put(self.stop_signal					,				timeout=self.timeout   )
   def __iter__(self :					Dict   ):
    return self







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Optional[Any]						=  self.text_queue.get(timeout=self.timeout   )
    if value == self.stop_signal:
     raise StopIteration()
    else:
     return value


from typing import Dict, List, Optional, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase__       =logging.get_logger(__name__)



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      ["pixel_values"]
   def __init__(self :					str					,				snake_case_ :					bool = True					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = PILImageResampling.BILINEAR					,				snake_case_ :					bool = True					,				snake_case_ :					Union[int, float] = 1 / 2_5_5					,				snake_case_ :					bool = True					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				**snake_case_ :					Tuple					,				):
    super().__init__(**snake_case_   )
    __a :  Optional[Any]						=  size if size is not None else {'''shortest_edge''': 3_8_4}
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  Tuple						=  do_resize
    __a :  List[str]						=  size
    # Default value set here for backwards compatibility where the value in config is None
    __a :  List[str]						=  crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
    __a :  Tuple						=  resample
    __a :  Any						=  do_rescale
    __a :  Optional[Any]						=  rescale_factor
    __a :  int						=  do_normalize
    __a :  List[str]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __a :  Union[str, Any]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					np.ndarray					,				snake_case_ :					Dict[str, int]					,				snake_case_ :					float					,				snake_case_ :					PILImageResampling = PILImageResampling.BICUBIC					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					str					,				):
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )
    if "shortest_edge" not in size:
     raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"   )
    __a :  Any						=  size['''shortest_edge''']

    if shortest_edge < 3_8_4:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     __a :  Union[str, Any]						=  int(shortest_edge / crop_pct   )
     __a :  Tuple						=  get_resize_output_image_size(snake_case_					,				size=snake_case_					,				default_to_square=snake_case_   )
     __a :  List[str]						=  resize(image=snake_case_					,				size=snake_case_					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=snake_case_					,				size=(shortest_edge, shortest_edge)					,				data_format=snake_case_					,				**snake_case_   )
    else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         snake_case_					,				size=(shortest_edge, shortest_edge)					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[int, float]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return rescale(snake_case_					,				scale=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return normalize(snake_case_					,				mean=snake_case_					,				std=snake_case_					,				data_format=snake_case_					,				**snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					ImageInput					,				snake_case_ :					bool = None					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = None					,				snake_case_ :					bool = None					,				snake_case_ :					float = None					,				snake_case_ :					bool = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[str, TensorType]] = None					,				snake_case_ :					ChannelDimension = ChannelDimension.FIRST					,				**snake_case_ :					Dict					,				):
    __a :  List[Any]						=  do_resize if do_resize is not None else self.do_resize
    __a :  Dict						=  crop_pct if crop_pct is not None else self.crop_pct
    __a :  Tuple						=  resample if resample is not None else self.resample
    __a :  Optional[int]						=  do_rescale if do_rescale is not None else self.do_rescale
    __a :  Optional[int]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a :  int						=  do_normalize if do_normalize is not None else self.do_normalize
    __a :  Optional[int]						=  image_mean if image_mean is not None else self.image_mean
    __a :  Optional[int]						=  image_std if image_std is not None else self.image_std

    __a :  Dict						=  size if size is not None else self.size
    __a :  List[str]						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  List[str]						=  make_list_of_images(snake_case_   )

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

    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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
     raise ValueError('''crop_pct must be specified if size < 384.'''   )

    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.'''   )

    # All transformations expect numpy arrays.
    __a :  List[Any]						=  [to_numpy_array(snake_case_   ) for image in images]

    if do_resize:
     __a :  List[str]						=  [self.resize(image=snake_case_					,				size=snake_case_					,				crop_pct=snake_case_					,				resample=snake_case_   ) for image in images]

    if do_rescale:
     __a :  List[Any]						=  [self.rescale(image=snake_case_					,				scale=snake_case_   ) for image in images]

    if do_normalize:
     __a :  Optional[int]						=  [self.normalize(image=snake_case_					,				mean=snake_case_					,				std=snake_case_   ) for image in images]

    __a :  str						=  [to_channel_dimension_format(snake_case_					,				snake_case_   ) for image in images]

    __a :  Tuple						=  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_					,				tensor_type=snake_case_   )
