






"""simple docstring"""
import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowerCamelCase_					=      {
    '''E''': 12.70,
    '''T''': 9.06,
    '''A''': 8.17,
    '''O''': 7.51,
    '''I''': 6.97,
    '''N''': 6.75,
    '''S''': 6.33,
    '''H''': 6.09,
    '''R''': 5.99,
    '''D''': 4.25,
    '''L''': 4.03,
    '''C''': 2.78,
    '''U''': 2.76,
    '''M''': 2.41,
    '''W''': 2.36,
    '''F''': 2.23,
    '''G''': 2.02,
    '''Y''': 1.97,
    '''P''': 1.93,
    '''B''': 1.29,
    '''V''': 0.98,
    '''K''': 0.77,
    '''J''': 0.15,
    '''X''': 0.15,
    '''Q''': 0.10,
    '''Z''': 0.07,
}
lowerCamelCase_					=      '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
lowerCamelCase_					=      '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Optional[int]			     =					{letter: 0 for letter in string.ascii_uppercase}
			for letter in message.upper():
						if letter in LETTERS:
									letter_count[letter] += 1

			return letter_count

def 				snake_case      (   A__						):
			return x[0]

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Dict			     =					get_letter_count(A__						)
			UpperCAmelCase_     : dict[int, list[str]]			     =					{
			    freq: [] for letter, freq in letter_to_freq.items()
			}
			for letter in LETTERS:
						freq_to_letter[letter_to_freq[letter]].append(A__						)

			UpperCAmelCase_     : dict[int, str]			     =					{}

			for freq in freq_to_letter:
						freq_to_letter[freq].sort(key=ETAOIN.find	,reverse=A__						)
						UpperCAmelCase_     : int			     =					"".join(freq_to_letter[freq]						)

			UpperCAmelCase_     : str			     =					list(freq_to_letter_str.items()						)
			freq_pairs.sort(key=A__	,reverse=A__						)

			UpperCAmelCase_     : list[str]			     =					[freq_pair[1] for freq_pair in freq_pairs]

			return "".join(A__						)

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Optional[int]			     =					get_frequency_order(A__						)
			UpperCAmelCase_     : str			     =					0
			for common_letter in ETAOIN[:6]:
						if common_letter in freq_order[:6]:
									match_score += 1

			for uncommon_letter in ETAOIN[-6:]:
						if uncommon_letter in freq_order[-6:]:
									match_score += 1

			return match_score


if __name__ == "__main__":
			import doctest

			doctest.testmod()











"""simple docstring"""
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase_					=      logging.get_logger(__name__)







@add_end_docstrings(__A     )
class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: int	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: List[str]						)						->			Optional[Any]:
				super().__init__(*lowerCAmelCase_	,	**lowerCAmelCase_						)
				requires_backends(self	,	"vision"						)
				self.check_model_type(
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				    if self.framework == "tf"
				    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Optional[int]=None						)						->			List[Any]:
				UpperCAmelCase_     : str			     =					{}
				if top_k is not None:
							UpperCAmelCase_     : List[str]			     =					top_k
				return {}, {}, postprocess_params





	def __call__(      self					: str	,	lowerCAmelCase_					: Union[str, List[str], "Image.Image", List["Image.Image"]]	,	**lowerCAmelCase_					: Any						)						->			Tuple:
				return super().__call__(lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					load_image(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
				return model_inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Dict						)						->			str:
				UpperCAmelCase_     : Any			     =					self.model(**lowerCAmelCase_						)
				return model_outputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]=5						)						->			Any:
				if top_k > self.model.config.num_labels:
							UpperCAmelCase_     : int			     =					self.model.config.num_labels

				if self.framework == "pt":
							UpperCAmelCase_     : str			     =					model_outputs.logits.softmax(-1						)[0]
							UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					probs.topk(lowerCAmelCase_						)
				elif self.framework == "tf":
							UpperCAmelCase_     : str			     =					stable_softmax(model_outputs.logits	,	axis=-1						)[0]
							UpperCAmelCase_     : Union[str, Any]			     =					tf.math.top_k(lowerCAmelCase_	,	k=lowerCAmelCase_						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					topk.values.numpy(), topk.indices.numpy()
				else:
							raise ValueError(f"""Unsupported framework: {self.framework}"""						)

				UpperCAmelCase_     : int			     =					scores.tolist()
				UpperCAmelCase_     : Optional[Any]			     =					ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_	,	lowerCAmelCase_						)]



