


'''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 			_lowercase :
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      str = None       ,   SCREAMING_SNAKE_CASE_       :      uuid.UUID = None       ,   SCREAMING_SNAKE_CASE_       :      List[str]=None       ,   SCREAMING_SNAKE_CASE_       :      str=None      ) ->							int:
        if not conversation_id:
             __snake_case           =						uuid.uuida()
        if past_user_inputs is None:
             __snake_case           =						[]
        if generated_responses is None:
             __snake_case           =						[]

        __snake_case           =						conversation_id
        __snake_case           =						past_user_inputs
        __snake_case           =						generated_responses
        __snake_case           =						text
   def __eq__(						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							List[Any]:
        if not isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ):
             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 a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      bool = False      ) ->							Union[str, Any]:
        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}".'      )
                  __snake_case           =						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:
             __snake_case           =						text
   def a  (						self       :      Tuple      ) ->							Optional[Any]:
        if self.new_user_input:
             self.past_user_inputs.append(self.new_user_input      )
        __snake_case           =						None
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							Tuple:
        self.generated_responses.append(SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Dict      ) ->							Tuple:
        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       :      Optional[int]      ) ->							List[Any]:
        __snake_case           =						f'Conversation id: {self.uuid} \n'
        for is_user, text in self.iter_texts():
             __snake_case           =						'user' if is_user else 'bot'
             output += f'{name} >> {text} \n'
        return output




@add_end_docstrings(
    __lowercase						,					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 			_lowercase (	__lowercase							):
   def __init__(						self       :      str       ,   *SCREAMING_SNAKE_CASE_       :      Tuple       ,   **SCREAMING_SNAKE_CASE_       :      Dict      ) ->							str:
        super().__init__(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
        if self.tokenizer.pad_token_id is None:
             __snake_case           =						self.tokenizer.eos_token
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      int=None       ,   SCREAMING_SNAKE_CASE_       :      Any=None       ,   SCREAMING_SNAKE_CASE_       :      str=None       ,   **SCREAMING_SNAKE_CASE_       :      int      ) ->							Optional[Any]:
        __snake_case           =						{}
        __snake_case           =						{}
        __snake_case           =						{}

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

        if "max_length" in generate_kwargs:
             __snake_case           =						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:
             __snake_case           =						clean_up_tokenization_spaces

        if generate_kwargs:
             forward_params.update(SCREAMING_SNAKE_CASE_      )
        return preprocess_params, forward_params, postprocess_params
   def __call__(						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      Union[Conversation, List[Conversation]]       ,   SCREAMING_SNAKE_CASE_       :      Dict=0       ,   **SCREAMING_SNAKE_CASE_       :      Optional[Any]      ) ->							Optional[int]:
        __snake_case           =						super().__call__(SCREAMING_SNAKE_CASE_       ,   num_workers=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
        if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) and len(SCREAMING_SNAKE_CASE_      ) == 1:
             return outputs[0]
        return outputs
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Conversation       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=32      ) ->							Dict[str, Any]:
        if not isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ):
             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'      ):
             __snake_case           =						self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE_      )
        else:
             # If the tokenizer cannot handle conversations, we default to only the old version
             __snake_case           =						self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE_      )

        if self.framework == "pt":
             __snake_case           =						torch.LongTensor([input_ids]      )
        elif self.framework == "tf":
             __snake_case           =						tf.constant([input_ids]      )
        return {"input_ids": input_ids, "conversation": conversation}
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=10       ,   **SCREAMING_SNAKE_CASE_       :      str      ) ->							List[str]:
        __snake_case           =						generate_kwargs.get('max_length'       ,   self.model.config.max_length      )

        __snake_case           =						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})'      )
             __snake_case           =						max_length - minimum_tokens
             __snake_case           =						model_inputs['input_ids'][:, -trim:]
             if "attention_mask" in model_inputs:
                  __snake_case           =						model_inputs['attention_mask'][:, -trim:]
        __snake_case           =						model_inputs.pop('conversation'      )
        __snake_case           =						max_length
        __snake_case           =						self.model.generate(**SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
        if self.model.config.is_encoder_decoder:
             __snake_case           =						1
        else:
             __snake_case           =						n
        return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
   def a  (						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Any=True      ) ->							List[str]:
        __snake_case           =						model_outputs['output_ids']
        __snake_case           =						self.tokenizer.decode(
            output_ids[0]       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_       ,   clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       ,   )
        __snake_case           =						model_outputs['conversation']
        conversation.mark_processed()
        conversation.append_response(SCREAMING_SNAKE_CASE_      )
        return conversation





   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Conversation      ) ->							Dict:
        __snake_case           =						self.tokenizer.eos_token_id
        __snake_case           =						[]
        for is_user, text in conversation.iter_texts():
             if eos_token_id is not None:
                  input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_       ,   add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [eos_token_id]      )
             else:
                  input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_       ,   add_special_tokens=SCREAMING_SNAKE_CASE_      )      )

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











import qiskit





def  __magic_name__  (				lowercase				,       lowercase				)				-> qiskit.result.counts.Counts:

							"""simple docstring"""



							lowercase_	:   Dict  				=     qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							lowercase_	:   str  				=     qiskit.QuantumCircuit(lowercase				,       lowercase				)

							# Map the quantum measurement to the classical bits
							circuit.measure([0]				,       [0]				)

							# Execute the circuit on the simulator
							lowercase_	:   List[Any]  				=     qiskit.execute(lowercase				,       lowercase				,       shots=1000				)

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(lowercase				)


if __name__ == "__main__":
							print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')