
'''simple docstring'''






from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


A			      =      logging.get_logger(__name__)




class     __snake_case		(   a__):
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if isinstance(A,    A      ):
													lowerCamelCase					:			Dict									= [label.strip() for label in labels.split(','      ) if label.strip()]
									return labels




					def __call__(     self,    A,    A,    A      ):

									"""simple docstring"""


									if len(A      ) == 0 or len(A      ) == 0:
													raise ValueError('You must include at least one label and at least one sequence.'      )
									if hypothesis_template.format(labels[0]      ) == hypothesis_template:
													raise ValueError(
													    (
													        'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
													        'Make sure the passed template includes formatting syntax such as {{}} where the label should go.'
													    ).format(A      )      )

									if isinstance(A,    A      ):
													lowerCamelCase					:			Dict									= [sequences]

									lowerCamelCase					:			str									= []
									for sequence in sequences:
													sequence_pairs.extend([[sequence, hypothesis_template.format(A      )] for label in labels]      )

									return sequence_pairs, sequences





@add_end_docstrings(a__)
class     __snake_case		(   a__):
					def __init__(     self,    A=ZeroShotClassificationArgumentHandler(),    *A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= args_parser
									super().__init__(*A,    **A      )
									if self.entailment_id == -1:
													logger.warning(
													    'Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '
													    '-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for label, ind in self.model.config.labelaid.items():
													if label.lower().startswith('entail'      ):
																	return ind
									return -1
					def     UpperCAmelCase_							(     self,    A,    A=True,    A=True,    A=TruncationStrategy.ONLY_FIRST,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.framework
									if self.tokenizer.pad_token is None:
													# Override for tokenizers not supporting padding
													logger.error(
													    'Tokenizer was not supporting padding necessary for zero-shot, attempting to use '
													    ' `pad_token=eos_token`'      )
													lowerCamelCase					:			Union[str, Any]									= self.tokenizer.eos_token
									try:
													lowerCamelCase					:			List[str]									= self.tokenizer(
													    A,    add_special_tokens=A,    return_tensors=A,    padding=A,    truncation=A,    )
									except Exception as e:
													if "too short" in str(A      ):
																	# tokenizers might yell that we want to truncate
																	# to a value that is not even reached by the input.
																	# In that case we don't want to truncate.
																	# It seems there's not a really better way to catch that
																	# exception.

																	lowerCamelCase					:			List[Any]									= self.tokenizer(
																	    A,    add_special_tokens=A,    return_tensors=A,    padding=A,    truncation=TruncationStrategy.DO_NOT_TRUNCATE,    )
													else:
																	raise e

									return inputs
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									if kwargs.get('multi_class',    A      ) is not None:
													lowerCamelCase					:			Tuple									= kwargs['multi_class']
													logger.warning(
													    'The `multi_class` argument has been deprecated and renamed to `multi_label`. '
													    '`multi_class` will be removed in a future version of Transformers.'      )
									lowerCamelCase					:			Any									= {}
									if "candidate_labels" in kwargs:
													lowerCamelCase					:			Union[str, Any]									= self._args_parser._parse_labels(kwargs['candidate_labels']      )
									if "hypothesis_template" in kwargs:
													lowerCamelCase					:			str									= kwargs['hypothesis_template']

									lowerCamelCase					:			Dict									= {}
									if "multi_label" in kwargs:
													lowerCamelCase					:			Any									= kwargs['multi_label']
									return preprocess_params, {}, postprocess_params
					def __call__(     self,    A,    *A,    **A,    ):

									"""simple docstring"""


									if len(A      ) == 0:
													pass
									elif len(A      ) == 1 and "candidate_labels" not in kwargs:
													lowerCamelCase					:			Dict									= args[0]
									else:
													raise ValueError(F'''Unable to understand extra arguments {args}'''      )

									return super().__call__(A,    **A      )
					def     UpperCAmelCase_							(     self,    A,    A=None,    A="This example is {}."      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			List[str]									= self._args_parser(A,    A,    A      )

									for i, (candidate_label, sequence_pair) in enumerate(zip(A,    A      )      ):
													lowerCamelCase					:			Optional[int]									= self._parse_and_tokenize([sequence_pair]      )

													yield {
													    "candidate_label": candidate_label,
													    "sequence": sequences[0],
													    "is_last": i == len(A      ) - 1,
													    **model_input,
													}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= inputs['candidate_label']
									lowerCamelCase					:			List[str]									= inputs['sequence']
									lowerCamelCase					:			int									= {k: inputs[k] for k in self.tokenizer.model_input_names}
									lowerCamelCase					:			Union[str, Any]									= self.model(**A      )

									lowerCamelCase					:			Tuple									= {
									    'candidate_label': candidate_label,
									    'sequence': sequence,
									    'is_last': inputs['is_last'],
									    **outputs,
									}
									return model_outputs




					def     UpperCAmelCase_							(     self,    A,    A=False      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= [outputs['candidate_label'] for outputs in model_outputs]
									lowerCamelCase					:			Dict									= [outputs['sequence'] for outputs in model_outputs]
									lowerCamelCase					:			List[str]									= np.concatenate([output['logits'].numpy() for output in model_outputs]      )
									lowerCamelCase					:			Union[str, Any]									= logits.shape[0]
									lowerCamelCase					:			Union[str, Any]									= len(A      )
									lowerCamelCase					:			str									= N // n
									lowerCamelCase					:			List[Any]									= logits.reshape((num_sequences, n, -1)      )

									if multi_label or len(A      ) == 1:
													# softmax over the entailment vs. contradiction dim for each label independently
													lowerCamelCase					:			Tuple									= self.entailment_id
													lowerCamelCase					:			Tuple									= -1 if entailment_id == 0 else 0
													lowerCamelCase					:			Tuple									= reshaped_outputs[..., [contradiction_id, entailment_id]]
													lowerCamelCase					:			Any									= np.exp(A      ) / np.exp(A      ).sum(-1,    keepdims=A      )
													lowerCamelCase					:			Any									= scores[..., 1]
									else:
													# softmax the "entailment" logits over all candidate labels
													lowerCamelCase					:			Optional[Any]									= reshaped_outputs[..., self.entailment_id]
													lowerCamelCase					:			str									= np.exp(A      ) / np.exp(A      ).sum(-1,    keepdims=A      )

									lowerCamelCase					:			Any									= list(reversed(scores[0].argsort()      )      )
									return {
									    "sequence": sequences[0],
									    "labels": [candidate_labels[i] for i in top_inds],
									    "scores": scores[0, top_inds].tolist(),
									}


'''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()
