




"""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

lowerCAmelCase__							=				logging.get_logger(__name__)





class  snake_case     (							enum.Enum   ):
							UpperCAmelCase__						     =      0
							UpperCAmelCase__						     =      1







@add_end_docstrings(__lowercase   )
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''generated'''

							def __init__(self       ,       *SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(*SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

									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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {}
									if truncation is not None:
											SCREAMING_SNAKE_CASE_						 = truncation

									SCREAMING_SNAKE_CASE_						 = generate_kwargs

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

									if clean_up_tokenization_spaces is not None:
											SCREAMING_SNAKE_CASE_						 = clean_up_tokenization_spaces

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

									return preprocess_params, forward_params, postprocess_params

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return True

							def _lowercase					(self       ,       *SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

									elif isinstance(args[0]       ,       SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = (prefix + args[0],)
											SCREAMING_SNAKE_CASE_						 = False
									else:
											raise ValueError(
											    f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'    )
									SCREAMING_SNAKE_CASE_						 = self.tokenizer(*SCREAMING_SNAKE_CASE_       ,       padding=SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_       ,       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       ,       *SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = super().__call__(*SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									if (
									    isinstance(args[0]       ,       SCREAMING_SNAKE_CASE_    )
									    and all(isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) for el in args[0]    )
									    and all(len(SCREAMING_SNAKE_CASE_    ) == 1 for res in result    )
									):
											return [res[0] for res in result]
									return result

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=TruncationStrategy.DO_NOT_TRUNCATE       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self._parse_and_tokenize(SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									return inputs

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if self.framework == "pt":
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = model_inputs['''input_ids'''].shape
									elif self.framework == "tf":
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = tf.shape(model_inputs['''input_ids''']    ).numpy()

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







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=ReturnType.TEXT       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = []
									for output_ids in model_outputs["output_ids"][0]:
											if return_type == ReturnType.TENSORS:
													SCREAMING_SNAKE_CASE_						 = {f'{self.return_name}_token_ids': output_ids}
											elif return_type == ReturnType.TEXT:
													SCREAMING_SNAKE_CASE_						 = {
													    f'{self.return_name}_text': self.tokenizer.decode(
													        SCREAMING_SNAKE_CASE_       ,       skip_special_tokens=SCREAMING_SNAKE_CASE_       ,       clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       ,       )
													}
											records.append(SCREAMING_SNAKE_CASE_    )
									return records







@add_end_docstrings(__lowercase   )
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''summary'''

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

									"""simple docstring"""







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







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""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(__lowercase   )
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''translation'''

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""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 _lowercase					(self       ,       *SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=TruncationStrategy.DO_NOT_TRUNCATE       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									if getattr(self.tokenizer       ,       '''_build_translation_inputs'''       ,       SCREAMING_SNAKE_CASE_    ):
											return self.tokenizer._build_translation_inputs(
											    *SCREAMING_SNAKE_CASE_       ,       return_tensors=self.framework       ,       truncation=SCREAMING_SNAKE_CASE_       ,       src_lang=SCREAMING_SNAKE_CASE_       ,       tgt_lang=SCREAMING_SNAKE_CASE_    )
									else:
											return super()._parse_and_tokenize(*SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







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

									"""simple docstring"""







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

"""simple docstring"""




from __future__ import annotations

import typing
from collections import Counter



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = Counter()
		for base in range(1,							max_perimeter + 1    ):
				for perpendicular in range(__a,							max_perimeter + 1    ):
						SCREAMING_SNAKE_CASE_						 = (base * base + perpendicular * perpendicular) ** 0.5
						if hypotenuse == int(__a    ):
								SCREAMING_SNAKE_CASE_						 = int(base + perpendicular + hypotenuse    )
								if perimeter > max_perimeter:
										continue
								triplets[perimeter] += 1
		return triplets



def        _lowerCamelCase ( __a = 1_000    ):
		SCREAMING_SNAKE_CASE_						 = pythagorean_triple(__a    )
		return triplets.most_common(1    )[0][0]


if __name__ == "__main__":
						print(f'''Perimeter {solution()} has maximum solutions''')