
'''simple docstring'''






import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]):
				return 1.0 / (1.0 + np.exp(-_outputs))





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]):
				lowerCamelCase					:			int									= np.max(_outputs       ,							axis=-1       ,							keepdims=UpperCAmelCase__)
				lowerCamelCase					:			Any									= np.exp(_outputs - maxes)
				return shifted_exp / shifted_exp.sum(axis=-1       ,							keepdims=UpperCAmelCase__)




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''sigmoid'''
					_lowerCAmelCase			     =	'''softmax'''
					_lowerCAmelCase			     =	'''none'''





@add_end_docstrings(
    a__    ,   R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''    ,   )
class     __snake_case		(   a__):
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	ClassificationFunction.NONE
					def __init__(     self,    **A      ):

									"""simple docstring"""


									super().__init__(**A      )

									self.check_model_type(
									    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
									    if self.framework == 'tf'
									    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )
					def     UpperCAmelCase_							(     self,    A=None,    A=None,    A="",    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= tokenizer_kwargs

									lowerCamelCase					:			Any									= {}
									if hasattr(self.model.config,    'return_all_scores'      ) and return_all_scores is None:
													lowerCamelCase					:			List[str]									= self.model.config.return_all_scores

									if isinstance(A,    A      ) or top_k is None:
													lowerCamelCase					:			Any									= top_k
													lowerCamelCase					:			str									= False
									elif return_all_scores is not None:
													warnings.warn(
													    '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
													    ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.',    A,    )
													if return_all_scores:
																	lowerCamelCase					:			List[str]									= None
													else:
																	lowerCamelCase					:			List[str]									= 1

									if isinstance(A,    A      ):
													lowerCamelCase					:			Optional[int]									= ClassificationFunction[function_to_apply.upper()]

									if function_to_apply is not None:
													lowerCamelCase					:			List[Any]									= function_to_apply
									return preprocess_params, {}, postprocess_params
					def __call__(     self,    *A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= super().__call__(*A,    **A      )
									# TODO try and retrieve it in a nicer way from _sanitize_parameters.
									lowerCamelCase					:			Tuple									= 'top_k' not in kwargs
									if isinstance(args[0],    A      ) and _legacy:
													# This pipeline is odd, and return a list when single item is run
													return [result]
									else:
													return result
					def     UpperCAmelCase_							(     self,    A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.framework
									if isinstance(A,    A      ):
													return self.tokenizer(**A,    return_tensors=A,    **A      )
									elif isinstance(A,    A      ) and len(A      ) == 1 and isinstance(inputs[0],    A      ) and len(inputs[0]      ) == 2:
													# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
													return self.tokenizer(
													    text=inputs[0][0],    text_pair=inputs[0][1],    return_tensors=A,    **A      )
									elif isinstance(A,    A      ):
													# This is likely an invalid usage of the pipeline attempting to pass text pairs.
													raise ValueError(
													    'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
													    ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'      )
									return self.tokenizer(A,    return_tensors=A,    **A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.model(**A      )




					def     UpperCAmelCase_							(     self,    A,    A=None,    A=1,    A=True      ):

									"""simple docstring"""


									if function_to_apply is None:
													if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																	lowerCamelCase					:			str									= ClassificationFunction.SIGMOID
													elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																	lowerCamelCase					:			List[str]									= ClassificationFunction.SOFTMAX
													elif hasattr(self.model.config,    'function_to_apply'      ) and function_to_apply is None:
																	lowerCamelCase					:			Optional[Any]									= self.model.config.function_to_apply
													else:
																	lowerCamelCase					:			Optional[Any]									= ClassificationFunction.NONE

									lowerCamelCase					:			List[str]									= model_outputs['logits'][0]
									lowerCamelCase					:			str									= outputs.numpy()

									if function_to_apply == ClassificationFunction.SIGMOID:
													lowerCamelCase					:			Optional[int]									= sigmoid(A      )
									elif function_to_apply == ClassificationFunction.SOFTMAX:
													lowerCamelCase					:			Optional[int]									= softmax(A      )
									elif function_to_apply == ClassificationFunction.NONE:
													lowerCamelCase					:			List[Any]									= outputs
									else:
													raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}'''      )

									if top_k == 1 and _legacy:
													return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

									lowerCamelCase					:			List[Any]									= [
									    {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(A      )
									]
									if not _legacy:
													dict_scores.sort(key=lambda A      : x["score"],    reverse=A      )
													if top_k is not None:
																	lowerCamelCase					:			Union[str, Any]									= dict_scores[:top_k]
									return dict_scores


'''simple docstring'''






import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
A			      =      {
    '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,
}
A			      =      'ETAOINSHRDLCUMWFGYPBVKJXQZ'
A			      =      'ABCDEFGHIJKLMNOPQRSTUVWXYZ'





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= {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    UpperCAmelCase							(  UpperCAmelCase__							:							tuple):
				return x[0]





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_letter_count(UpperCAmelCase__)
				lowerCamelCase					:			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(UpperCAmelCase__)

				lowerCamelCase					:			dict[int, str]									= {}

				for freq in freq_to_letter:
								freq_to_letter[freq].sort(key=ETAOIN.find       ,							reverse=UpperCAmelCase__)
								lowerCamelCase					:			int									= ''.join(freq_to_letter[freq])

				lowerCamelCase					:			Any									= list(freq_to_letter_str.items())
				freq_pairs.sort(key=UpperCAmelCase__       ,							reverse=UpperCAmelCase__)

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

				return "".join(UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_frequency_order(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= 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()
