"""simple docstring"""


import uuid
from typing import Any, Dict, List, Optional, Union

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

if is_torch_available():
    import torch


UpperCAmelCase__			=    logging.get_logger(__name__)


class 							a					:


    def __init__(       self						:	Tuple ,					__lowerCAmelCase						:	str = None ,					__lowerCAmelCase						:	uuid.UUID = None ,					__lowerCAmelCase						:	List[str]=None ,					__lowerCAmelCase						:	Dict=None       ):
      if not conversation_id:
        _UpperCAmelCase    			=   uuid.uuida()
      if past_user_inputs is None:
        _UpperCAmelCase    			=   []
      if generated_responses is None:
        _UpperCAmelCase    			=   []

      _UpperCAmelCase    			=   conversation_id
      _UpperCAmelCase    			=   past_user_inputs
      _UpperCAmelCase    			=   generated_responses
      _UpperCAmelCase    			=   text


    def __eq__(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Any       ):
      if not isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        return False
      if self.uuid == other.uuid:
        return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	bool = False       ):
      if self.new_user_input:
        if overwrite:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
              f'''with: "{text}".'''       )
          _UpperCAmelCase    			=   text
        else:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
              f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''       )
      else:
        _UpperCAmelCase    			=   text


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input       )
      _UpperCAmelCase    			=   None


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	str       ):
      self.generated_responses.append(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      for user_input, generated_response in zip(self.past_user_inputs ,					self.generated_responses       ):
        yield True, user_input
        yield False, generated_response
      if self.new_user_input:
        yield True, self.new_user_input


    def __repr__(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   f'''Conversation id: {self.uuid} \n'''
      for is_user, text in self.iter_texts():
        _UpperCAmelCase    			=   """user""" if is_user else """bot"""
        output += f'''{name} >> {text} \n'''
      return output



@add_end_docstrings(
    lowerCAmelCase_						,				R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '						,				)
class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	Union[str, Any] ,					*__lowerCAmelCase						:	Any ,					**__lowerCAmelCase						:	Tuple       ):
      super().__init__(*__lowerCAmelCase ,					**__lowerCAmelCase       )
      if self.tokenizer.pad_token_id is None:
        _UpperCAmelCase    			=   self.tokenizer.eos_token


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Tuple=None ,					__lowerCAmelCase						:	Optional[Any]=None ,					__lowerCAmelCase						:	str=None ,					**__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   {}
      _UpperCAmelCase    			=   {}
      _UpperCAmelCase    			=   {}

      if min_length_for_response is not None:
        _UpperCAmelCase    			=   min_length_for_response
      if minimum_tokens is not None:
        _UpperCAmelCase    			=   minimum_tokens

      if "max_length" in generate_kwargs:
        _UpperCAmelCase    			=   generate_kwargs["""max_length"""]
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
        _UpperCAmelCase    			=   clean_up_tokenization_spaces

      if generate_kwargs:
        forward_params.update(__lowerCAmelCase       )
      return preprocess_params, forward_params, postprocess_params


    def __call__(       self						:	Any ,					__lowerCAmelCase						:	Union[Conversation, List[Conversation]] ,					__lowerCAmelCase						:	List[Any]=0 ,					**__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   super().__call__(__lowerCAmelCase ,					num_workers=__lowerCAmelCase ,					**__lowerCAmelCase       )
      if isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) and len(__lowerCAmelCase       ) == 1:
        return outputs[0]
      return outputs


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Conversation ,					__lowerCAmelCase						:	Optional[int]=32       ):
      if not isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""       )
      if conversation.new_user_input is None:
        raise ValueError(
            f'''Conversation with UUID {type(conversation.uuid       )} does not contain new user input to process. '''
            """Add user inputs with the conversation's `add_user_input` method"""       )
      if hasattr(self.tokenizer ,					"""_build_conversation_input_ids"""       ):
        _UpperCAmelCase    			=   self.tokenizer._build_conversation_input_ids(__lowerCAmelCase       )
      else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        _UpperCAmelCase    			=   self._legacy_parse_and_tokenize(__lowerCAmelCase       )

      if self.framework == "pt":
        _UpperCAmelCase    			=   torch.LongTensor([input_ids]       )
      elif self.framework == "tf":
        _UpperCAmelCase    			=   tf.constant([input_ids]       )
      return {"input_ids": input_ids, "conversation": conversation}


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Tuple=10 ,					**__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   generate_kwargs.get("""max_length""" ,					self.model.config.max_length       )

      _UpperCAmelCase    			=   model_inputs["""input_ids"""].shape[1]
      if max_length - minimum_tokens < n:
        logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''       )
        _UpperCAmelCase    			=   max_length - minimum_tokens
        _UpperCAmelCase    			=   model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
          _UpperCAmelCase    			=   model_inputs["""attention_mask"""][:, -trim:]
      _UpperCAmelCase    			=   model_inputs.pop("""conversation"""       )
      _UpperCAmelCase    			=   max_length
      _UpperCAmelCase    			=   self.model.generate(**__lowerCAmelCase ,					**__lowerCAmelCase       )
      if self.model.config.is_encoder_decoder:
        _UpperCAmelCase    			=   1
      else:
        _UpperCAmelCase    			=   n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[Any]=True       ):
      _UpperCAmelCase    			=   model_outputs["""output_ids"""]
      _UpperCAmelCase    			=   self.tokenizer.decode(
          output_ids[0] ,					skip_special_tokens=__lowerCAmelCase ,					clean_up_tokenization_spaces=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__lowerCAmelCase       )
      return conversation


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	Conversation       ):
      _UpperCAmelCase    			=   self.tokenizer.eos_token_id
      _UpperCAmelCase    			=   []
      for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       ) + [eos_token_id]       )
        else:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       )       )

      if len(__lowerCAmelCase       ) > self.tokenizer.model_max_length:
        _UpperCAmelCase    			=   input_ids[-self.tokenizer.model_max_length :]
      return input_ids

"""simple docstring"""


from __future__ import annotations

import typing
from collections import Counter






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   Counter()
  for base in range(1 ,max_perimeter + 1		):
    for perpendicular in range(lowercase ,max_perimeter + 1		):
      _UpperCAmelCase    			=   (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(lowercase		):
        _UpperCAmelCase    			=   int(base + perpendicular + hypotenuse		)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets






def      __UpperCAmelCase       (      lowercase = 10_00		):







  """simple docstring"""
  _UpperCAmelCase    			=   pythagorean_triple(lowercase		)
  return triplets.most_common(1		)[0][0]


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