



'''simple docstring'''






import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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

A			      =      logging.get_logger(__name__)




class     __snake_case		(   enum.Enum):
					_lowerCAmelCase			     =	0
					_lowerCAmelCase			     =	1





@add_end_docstrings(_UpperCamelCase)
class     __snake_case		(   _UpperCamelCase):
					_lowerCAmelCase			     =	'''generated'''
					def __init__(     self,    *A,    **A      ):

									"""simple docstring"""


									super().__init__(*A,    **A      )

									self.check_model_type(
									    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
									    if self.framework == 'tf'
									    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING      )
					def     UpperCAmelCase_							(     self,    A=None,    A=None,    A=None,    A=None,    A=None,    A=None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= {}
									if truncation is not None:
													lowerCamelCase					:			Union[str, Any]									= truncation

									lowerCamelCase					:			Any									= generate_kwargs

									lowerCamelCase					:			Any									= {}
									if return_tensors is not None and return_type is None:
													lowerCamelCase					:			Optional[int]									= ReturnType.TENSORS if return_tensors else ReturnType.TEXT
									if return_type is not None:
													lowerCamelCase					:			Optional[Any]									= return_type

									if clean_up_tokenization_spaces is not None:
													lowerCamelCase					:			Dict									= clean_up_tokenization_spaces

									if stop_sequence is not None:
													lowerCamelCase					:			Any									= self.tokenizer.encode(A,    add_special_tokens=A      )
													if len(A      ) > 1:
																	warnings.warn(
																	    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																	    ' the stop sequence will be used as the stop sequence string in the interim.'      )
													lowerCamelCase					:			List[str]									= stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model.config.prefix if self.model.config.prefix is not None else ""
									if isinstance(args[0],    A      ):
													if self.tokenizer.pad_token_id is None:
																	raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'      )
													lowerCamelCase					:			Union[str, Any]									= ([prefix + arg for arg in args[0]],)
													lowerCamelCase					:			int									= True

									elif isinstance(args[0],    A      ):
													lowerCamelCase					:			str									= (prefix + args[0],)
													lowerCamelCase					:			Optional[int]									= False
									else:
													raise ValueError(
													    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''      )
									lowerCamelCase					:			int									= self.tokenizer(*A,    padding=A,    truncation=A,    return_tensors=self.framework      )
									# This is produced by tokenizers but is an invalid generate kwargs
									if "token_type_ids" in inputs:
													del inputs["token_type_ids"]
									return inputs
					def __call__(     self,    *A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= super().__call__(*A,    **A      )
									if (
									    isinstance(args[0],    A      )
									    and all(isinstance(A,    A      ) for el in args[0]      )
									    and all(len(A      ) == 1 for res in result      )
									):
													return [res[0] for res in result]
									return result
					def     UpperCAmelCase_							(     self,    A,    A=TruncationStrategy.DO_NOT_TRUNCATE,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self._parse_and_tokenize(A,    truncation=A,    **A      )
									return inputs
					def     UpperCAmelCase_							(     self,    A,    **A      ):

									"""simple docstring"""


									if self.framework == "pt":
													lowerCamelCase					:			Optional[int]									= model_inputs["input_ids"].shape
									elif self.framework == "tf":
													lowerCamelCase					:			Optional[Any]									= tf.shape(model_inputs['input_ids']      ).numpy()

									lowerCamelCase					:			int									= generate_kwargs.get('min_length',    self.model.config.min_length      )
									lowerCamelCase					:			Union[str, Any]									= generate_kwargs.get('max_length',    self.model.config.max_length      )
									self.check_inputs(A,    generate_kwargs['min_length'],    generate_kwargs['max_length']      )
									lowerCamelCase					:			Any									= self.model.generate(**A,    **A      )
									lowerCamelCase					:			List[str]									= output_ids.shape[0]
									if self.framework == "pt":
													lowerCamelCase					:			str									= output_ids.reshape(A,    out_b // in_b,    *output_ids.shape[1:]      )
									elif self.framework == "tf":
													lowerCamelCase					:			List[str]									= tf.reshape(A,    (in_b, out_b // in_b, *output_ids.shape[1:])      )
									return {"output_ids": output_ids}




					def     UpperCAmelCase_							(     self,    A,    A=ReturnType.TEXT,    A=False      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= []
									for output_ids in model_outputs["output_ids"][0]:
													if return_type == ReturnType.TENSORS:
																	lowerCamelCase					:			Union[str, Any]									= {F'''{self.return_name}_token_ids''': output_ids}
													elif return_type == ReturnType.TEXT:
																	lowerCamelCase					:			Union[str, Any]									= {
																	    F'''{self.return_name}_text''': self.tokenizer.decode(
																	        A,    skip_special_tokens=A,    clean_up_tokenization_spaces=A,    )
																	}
													records.append(A      )
									return records





@add_end_docstrings(_UpperCamelCase)
class     __snake_case		(   _UpperCamelCase):
					_lowerCAmelCase			     =	'''summary'''
					def __call__(     self,    *A,    **A      ):

									"""simple docstring"""


									return super().__call__(*A,    **A      )




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

									"""simple docstring"""


									if max_length < min_length:
													logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''      )

									if input_length < max_length:
													logger.warning(
													    F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
													    'a summarization task, where outputs shorter than the input are typically wanted, you might '
													    F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''      )





@add_end_docstrings(_UpperCamelCase)
class     __snake_case		(   _UpperCamelCase):
					_lowerCAmelCase			     =	'''translation'''
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									if input_length > 0.9 * max_length:
													logger.warning(
													    F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
													    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'      )
									return True
					def     UpperCAmelCase_							(     self,    *A,    A=TruncationStrategy.DO_NOT_TRUNCATE,    A=None,    A=None      ):

									"""simple docstring"""


									if getattr(self.tokenizer,    '_build_translation_inputs',    A      ):
													return self.tokenizer._build_translation_inputs(
													    *A,    return_tensors=self.framework,    truncation=A,    src_lang=A,    tgt_lang=A      )
									else:
													return super()._parse_and_tokenize(*A,    truncation=A      )
					def     UpperCAmelCase_							(     self,    A=None,    A=None,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= super()._sanitize_parameters(**A      )
									if src_lang is not None:
													lowerCamelCase					:			Any									= src_lang
									if tgt_lang is not None:
													lowerCamelCase					:			Optional[Any]									= tgt_lang
									if src_lang is None and tgt_lang is None:
													# Backward compatibility, direct arguments use is preferred.
													lowerCamelCase					:			Dict									= kwargs.get('task',    self.task      )
													lowerCamelCase					:			List[str]									= task.split('_'      )
													if task and len(A      ) == 4:
																	# translation, XX, to YY
																	lowerCamelCase					:			str									= items[1]
																	lowerCamelCase					:			Tuple									= items[3]
									return preprocess_params, forward_params, postprocess_params




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

									"""simple docstring"""


									return super().__call__(*A,    **A      )


'''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()
