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


lowerCAmelCase			:							int     =logging.get_logger(__name__)




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				str    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				uuid.UUID = None    ,  _UpperCamelCase     :				Any=None    ,  _UpperCamelCase     :				List[Any]=None)				->str:


          """simple docstring"""


          if not conversation_id:
              _lowerCamelCase     :		Dict 						=						uuid.uuida()
          if past_user_inputs is None:
              _lowerCamelCase     :		Dict 						=						[]
          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     :				Union[str, Any]    ,  _UpperCamelCase     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          if not isinstance(_UpperCamelCase    ,  _UpperCamelCase):
              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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				bool = False)				->Tuple:


          """simple docstring"""


          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     :		Optional[int] 						=						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     :		Union[str, Any] 						=						text



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          if self.new_user_input:
              self.past_user_inputs.append(self.new_user_input)
          _lowerCamelCase     :		Optional[int] 						=						None



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				str)				->str:


          """simple docstring"""


          self.generated_responses.append(_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


          """simple docstring"""


          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     :				Union[str, Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						F"""Conversation id: {self.uuid} \n"""
          for is_user, text in self.iter_texts():
              _lowerCamelCase     :		str 						=						"""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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Tuple    ,  *_UpperCamelCase     :				str    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(*_UpperCamelCase    ,  **_UpperCamelCase)
          if self.tokenizer.pad_token_id is None:
              _lowerCamelCase     :		Any 						=						self.tokenizer.eos_token



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				Optional[Any]=None    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  **_UpperCamelCase     :				Union[str, Any])				->int:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						{}
          _lowerCamelCase     :		Union[str, Any] 						=						{}
          _lowerCamelCase     :		List[Any] 						=						{}

          if min_length_for_response is not None:
              _lowerCamelCase     :		Dict 						=						min_length_for_response
          if minimum_tokens is not None:
              _lowerCamelCase     :		Dict 						=						minimum_tokens

          if "max_length" in generate_kwargs:
              _lowerCamelCase     :		Any 						=						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     :		int 						=						clean_up_tokenization_spaces

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



      def __call__(					self     :				Any    ,  _UpperCamelCase     :				Union[Conversation, List[Conversation]]    ,  _UpperCamelCase     :				Optional[Any]=0    ,  **_UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						super().__call__(_UpperCamelCase    ,  num_workers=_UpperCamelCase    ,  **_UpperCamelCase)
          if isinstance(_UpperCamelCase    ,  _UpperCamelCase) and len(_UpperCamelCase) == 1:
              return outputs[0]
          return outputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Conversation    ,  _UpperCamelCase     :				Tuple=32)				->Dict[str, Any]:


          """simple docstring"""


          if not isinstance(_UpperCamelCase    ,  _UpperCamelCase):
              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(_UpperCamelCase)
          else:
              # If the tokenizer cannot handle conversations, we default to only the old version
              _lowerCamelCase     :		Optional[int] 						=						self._legacy_parse_and_tokenize(_UpperCamelCase)

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Tuple=10    ,  **_UpperCamelCase     :				Optional[Any])				->Tuple:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				str=True)				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						model_outputs["""output_ids"""]
          _lowerCamelCase     :		Dict 						=						self.tokenizer.decode(
              output_ids[0]    ,  skip_special_tokens=_UpperCamelCase    ,  clean_up_tokenization_spaces=_UpperCamelCase    ,  )
          _lowerCamelCase     :		Tuple 						=						model_outputs["""conversation"""]
          conversation.mark_processed()
          conversation.append_response(_UpperCamelCase)
          return conversation




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Conversation)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.tokenizer.eos_token_id
          _lowerCamelCase     :		int 						=						[]
          for is_user, text in conversation.iter_texts():
              if eos_token_id is not None:
                  input_ids.extend(self.tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase) + [eos_token_id])
              else:
                  input_ids.extend(self.tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase))

          if len(_UpperCamelCase) > self.tokenizer.model_max_length:
              _lowerCamelCase     :		List[str] 						=						input_ids[-self.tokenizer.model_max_length :]
          return input_ids





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




