



"""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

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      logging.get_logger(__name__)


class 							lowerCamelCase_(   enum.Enum    ):





					'''simple docstring'''



					lowercase__   :       Dict										= 0
					lowercase__   :       Union[str, Any]										= 1


@add_end_docstrings(A__    )
class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       Optional[Any]										= 'generated'

					def __init__(    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									super().__init__(*lowerCamelCase__				,       **lowerCamelCase__   )

									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 			snake_case__      (    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       **lowerCamelCase__				,       ):
									_lowerCamelCase        =				{}
									if truncation is not None:
													_lowerCamelCase        =				truncation

									_lowerCamelCase        =				generate_kwargs

									_lowerCamelCase        =				{}
									if return_tensors is not None and return_type is None:
													_lowerCamelCase        =				ReturnType.TENSORS if return_tensors else ReturnType.TEXT
									if return_type is not None:
													_lowerCamelCase        =				return_type

									if clean_up_tokenization_spaces is not None:
													_lowerCamelCase        =				clean_up_tokenization_spaces

									if stop_sequence is not None:
													_lowerCamelCase        =				self.tokenizer.encode(lowerCamelCase__				,       add_special_tokens=lowerCamelCase__   )
													if len(lowerCamelCase__   ) > 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        =				stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									return True

					def 			snake_case__      (    self				,       *lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.model.config.prefix if self.model.config.prefix is not None else ''''''
									if isinstance(args[0]				,       lowerCamelCase__   ):
													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        =				([prefix + arg for arg in args[0]],)
													_lowerCamelCase        =				True

									elif isinstance(args[0]				,       lowerCamelCase__   ):
													_lowerCamelCase        =				(prefix + args[0],)
													_lowerCamelCase        =				False
									else:
													raise ValueError(
													    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""   )
									_lowerCamelCase        =				self.tokenizer(*lowerCamelCase__				,       padding=lowerCamelCase__				,       truncation=lowerCamelCase__				,       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				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				super().__call__(*lowerCamelCase__				,       **lowerCamelCase__   )
									if (
									    isinstance(args[0]				,       lowerCamelCase__   )
									    and all(isinstance(lowerCamelCase__				,       lowerCamelCase__   ) for el in args[0]   )
									    and all(len(lowerCamelCase__   ) == 1 for res in result   )
									):
													return [res[0] for res in result]
									return result

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=TruncationStrategy.DO_NOT_TRUNCATE				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				self._parse_and_tokenize(lowerCamelCase__				,       truncation=lowerCamelCase__				,       **lowerCamelCase__   )
									return inputs

					def 			snake_case__      (    self				,       lowerCamelCase__				,       **lowerCamelCase__   ):
									if self.framework == "pt":
													_lowerCamelCase  ,     _lowerCamelCase        =				model_inputs['''input_ids'''].shape
									elif self.framework == "tf":
													_lowerCamelCase  ,     _lowerCamelCase        =				tf.shape(model_inputs['''input_ids''']   ).numpy()

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





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=ReturnType.TEXT				,       lowerCamelCase__=False   ):
									_lowerCamelCase        =				[]
									for output_ids in model_outputs["output_ids"][0]:
													if return_type == ReturnType.TENSORS:
																	_lowerCamelCase        =				{F"""{self.return_name}_token_ids""": output_ids}
													elif return_type == ReturnType.TEXT:
																	_lowerCamelCase        =				{
																	    F"""{self.return_name}_text""": self.tokenizer.decode(
																	        lowerCamelCase__				,       skip_special_tokens=lowerCamelCase__				,       clean_up_tokenization_spaces=lowerCamelCase__				,       )
																	}
													records.append(lowerCamelCase__   )
									return records


@add_end_docstrings(A__    )
class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= 'summary'

					def __call__(    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									return super().__call__(*lowerCamelCase__				,       **lowerCamelCase__   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									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(A__    )
class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       List[Any]										= 'translation'

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

					def 			snake_case__      (    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				super()._sanitize_parameters(**lowerCamelCase__   )
									if src_lang is not None:
													_lowerCamelCase        =				src_lang
									if tgt_lang is not None:
													_lowerCamelCase        =				tgt_lang
									if src_lang is None and tgt_lang is None:
													# Backward compatibility, direct arguments use is preferred.
													_lowerCamelCase        =				kwargs.get('''task'''				,       self.task   )
													_lowerCamelCase        =				task.split('''_'''   )
													if task and len(lowerCamelCase__   ) == 4:
																	# translation, XX, to YY
																	_lowerCamelCase        =				items[1]
																	_lowerCamelCase        =				items[3]
									return preprocess_params, forward_params, postprocess_params





					def __call__(    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									return super().__call__(*lowerCamelCase__				,       **lowerCamelCase__   )





"""simple docstring"""
from __future__ import annotations

from math import pow, sqrt



def        lowerCAmelCase_(   lowercase_     :							float						,   lowercase_     :							float						,   lowercase_     :							float							)	->       dict[str, float]:
				if (resistance, reactance, impedance).count(0							) != 1:
								raise ValueError('''One and only one argument must be 0'''							)
				if resistance == 0:
								return {"resistance": sqrt(pow(lowercase_						,   2							) - pow(lowercase_						,   2							)							)}
				elif reactance == 0:
								return {"reactance": sqrt(pow(lowercase_						,   2							) - pow(lowercase_						,   2							)							)}
				elif impedance == 0:
								return {"impedance": sqrt(pow(lowercase_						,   2							) + pow(lowercase_						,   2							)							)}
				else:
								raise ValueError('''Exactly one argument must be 0'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
