


'''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


__A   :    Dict											= logging.get_logger(__name__)






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    int       ,		lowerCamelCase       :    str = None       ,		lowerCamelCase       :    uuid.UUID = None       ,		lowerCamelCase       :    Union[str, Any]=None       ,		lowerCamelCase       :    Union[str, Any]=None						)  ->    List[str]:
      if not conversation_id:
        lowerCAmelCase_     : str     =  uuid.uuida()
      if past_user_inputs is None:
        lowerCAmelCase_     : Tuple     =  []
      if generated_responses is None:
        lowerCAmelCase_     : str     =  []

      lowerCAmelCase_     : uuid.UUID     =  conversation_id
      lowerCAmelCase_     : List[str]     =  past_user_inputs
      lowerCAmelCase_     : List[str]     =  generated_responses
      lowerCAmelCase_     : Optional[str]     =  text

    def __eq__(  self       :    Tuple       ,		lowerCamelCase       :    List[Any]						)  ->    List[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 __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    bool = False						)  ->    Optional[int]:
      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}".'						)
          lowerCAmelCase_     : List[str]     =  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:
        lowerCAmelCase_     : str     =  text

    def __lowercase  (  self       :    Tuple						)  ->    List[str]:
      if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input						)
      lowerCAmelCase_     : str     =  None

    def __lowercase  (  self       :    Dict       ,		lowerCamelCase       :    str						)  ->    Dict:
      self.generated_responses.append(lowerCamelCase						)

    def __lowercase  (  self       :    str						)  ->    int:
      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       :    int						)  ->    Optional[Any]:
      lowerCAmelCase_     : str     =  F'Conversation id: {self.uuid} \n'
      for is_user, text in self.iter_texts():
        lowerCAmelCase_     : Optional[int]     =  """user""" if is_user else """bot"""
        output += F'{name} >> {text} \n'
      return output







@add_end_docstrings(
    _SCREAMING_SNAKE_CASE      ,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   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(  self       :    List[Any]       ,		*lowerCamelCase       :    str       ,		**lowerCamelCase       :    Dict						)  ->    Optional[Any]:
      super().__init__(*lowerCamelCase       ,		**lowerCamelCase						)
      if self.tokenizer.pad_token_id is None:
        lowerCAmelCase_     : Optional[Any]     =  self.tokenizer.eos_token

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int=None       ,		lowerCamelCase       :    int=None       ,		lowerCamelCase       :    Optional[int]=None       ,		**lowerCamelCase       :    List[str]						)  ->    int:
      lowerCAmelCase_     : Union[str, Any]     =  {}
      lowerCAmelCase_     : List[Any]     =  {}
      lowerCAmelCase_     : Dict     =  {}

      if min_length_for_response is not None:
        lowerCAmelCase_     : List[str]     =  min_length_for_response
      if minimum_tokens is not None:
        lowerCAmelCase_     : Tuple     =  minimum_tokens

      if "max_length" in generate_kwargs:
        lowerCAmelCase_     : str     =  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:
        lowerCAmelCase_     : Union[str, Any]     =  clean_up_tokenization_spaces

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

    def __call__(  self       :    Optional[int]       ,		lowerCamelCase       :    Union[Conversation, List[Conversation]]       ,		lowerCamelCase       :    Dict=0       ,		**lowerCamelCase       :    Optional[Any]						)  ->    List[Any]:
      lowerCAmelCase_     : int     =  super().__call__(lowerCamelCase       ,		num_workers=lowerCamelCase       ,		**lowerCamelCase						)
      if isinstance(lowerCamelCase       ,		lowerCamelCase						) and len(lowerCamelCase						) == 1:
        return outputs[0]
      return outputs

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    Conversation       ,		lowerCamelCase       :    Tuple=32						)  ->    Dict[str, Any]:
      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"""						):
        lowerCAmelCase_     : List[str]     =  self.tokenizer._build_conversation_input_ids(lowerCamelCase						)
      else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        lowerCAmelCase_     : Optional[Any]     =  self._legacy_parse_and_tokenize(lowerCamelCase						)

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

    def __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Optional[int]=10       ,		**lowerCamelCase       :    int						)  ->    str:
      lowerCAmelCase_     : Dict     =  generate_kwargs.get("""max_length"""       ,		self.model.config.max_length						)

      lowerCAmelCase_     : Union[str, Any]     =  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})'						)
        lowerCAmelCase_     : Any     =  max_length - minimum_tokens
        lowerCAmelCase_     : List[str]     =  model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
          lowerCAmelCase_     : Tuple     =  model_inputs["""attention_mask"""][:, -trim:]
      lowerCAmelCase_     : Union[str, Any]     =  model_inputs.pop("""conversation"""						)
      lowerCAmelCase_     : Optional[int]     =  max_length
      lowerCAmelCase_     : Optional[Any]     =  self.model.generate(**lowerCamelCase       ,		**lowerCamelCase						)
      if self.model.config.is_encoder_decoder:
        lowerCAmelCase_     : Optional[int]     =  1
      else:
        lowerCAmelCase_     : Dict     =  n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Tuple=True						)  ->    int:
      lowerCAmelCase_     : str     =  model_outputs["""output_ids"""]
      lowerCAmelCase_     : int     =  self.tokenizer.decode(
          output_ids[0]       ,		skip_special_tokens=lowerCamelCase       ,		clean_up_tokenization_spaces=lowerCamelCase       ,		)
      lowerCAmelCase_     : List[str]     =  model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(lowerCamelCase						)
      return conversation





    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    Conversation						)  ->    Dict:
      lowerCAmelCase_     : Any     =  self.tokenizer.eos_token_id
      lowerCAmelCase_     : List[Any]     =  []
      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:
        lowerCAmelCase_     : Tuple     =  input_ids[-self.tokenizer.model_max_length :]
      return input_ids







'''simple docstring'''






import math







def 							UpperCamelCase_     (       A__   :  float				,       A__   :  float   ):
  '''simple docstring'''





  if initial_intensity < 0:
    raise ValueError("""The value of intensity cannot be negative"""   )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 3_60:
    raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees"""   )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(A__   )   ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



