


'''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


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_=None					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if not conversation_id:
												UpperCamelCase__     :Union[str, Any]							      =				uuid.uuida()
							if past_user_inputs is None:
												UpperCamelCase__     :Any							      =				[]
							if generated_responses is None:
												UpperCamelCase__     :str							      =				[]

							UpperCamelCase__     :int							      =				conversation_id
							UpperCamelCase__     :Tuple							      =				past_user_inputs
							UpperCamelCase__     :Union[str, Any]							      =				generated_responses
							UpperCamelCase__     :Tuple							      =				text




		def __eq__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = False							):

							'''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}\".'''							)
																	UpperCamelCase__     :List[Any]							      =				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__     :Dict							      =				text




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if self.new_user_input:
												self.past_user_inputs.append(self.new_user_input							)
							UpperCamelCase__     :Tuple							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							self.generated_responses.append(_SCREAMING_SNAKE_CASE							)




		def 					lowerCAmelCase__       (	self							):

							'''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							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				F'''Conversation id: {self.uuid} \n'''
							for is_user, text in self.iter_texts():
												UpperCamelCase__     :List[str]							      =				'''user''' if is_user else '''bot'''
												output += F'''{name} >> {text} \n'''
							return output



@add_end_docstrings(
    A__			,	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			(							A__  ):

		"""simple docstring"""

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

							'''simple docstring'''


							super().__init__(*_SCREAMING_SNAKE_CASE					,     **_SCREAMING_SNAKE_CASE							)
							if self.tokenizer.pad_token_id is None:
												UpperCamelCase__     :Dict							      =				self.tokenizer.eos_token




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				{}
							UpperCamelCase__     :Any							      =				{}
							UpperCamelCase__     :Tuple							      =				{}

							if min_length_for_response is not None:
												UpperCamelCase__     :Tuple							      =				min_length_for_response
							if minimum_tokens is not None:
												UpperCamelCase__     :int							      =				minimum_tokens

							if "max_length" in generate_kwargs:
												UpperCamelCase__     :List[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:
												UpperCamelCase__     :Any							      =				clean_up_tokenization_spaces

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




		def __call__(	self					,     UpperCamelCase_					,     UpperCamelCase_=0					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=32							):

							'''simple docstring'''


							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'''							):
												UpperCamelCase__     :str							      =				self.tokenizer._build_conversation_input_ids(_SCREAMING_SNAKE_CASE							)
							else:
												# If the tokenizer cannot handle conversations, we default to only the old version
												UpperCamelCase__     :Any							      =				self._legacy_parse_and_tokenize(_SCREAMING_SNAKE_CASE							)

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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=10					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				generate_kwargs.get('''max_length'''					,     self.model.config.max_length							)

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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=True							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				model_outputs['''output_ids''']
							UpperCamelCase__     :Any							      =				self.tokenizer.decode(
							    output_ids[0]					,     skip_special_tokens=_SCREAMING_SNAKE_CASE					,     clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE					,     )
							UpperCamelCase__     :List[Any]							      =				model_outputs['''conversation''']
							conversation.mark_processed()
							conversation.append_response(_SCREAMING_SNAKE_CASE							)
							return conversation




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.tokenizer.eos_token_id
							UpperCamelCase__     :Any							      =				[]
							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:
												UpperCamelCase__     :Optional[int]							      =				input_ids[-self.tokenizer.model_max_length :]
							return input_ids



'''simple docstring'''
import qiskit


def     a   (    __a				,      __a     )						->			qiskit.result.counts.Counts:



					'''simple docstring'''






					UpperCamelCase__     :int							      =				qiskit.Aer.get_backend('''aer_simulator'''     )

					# Create a Quantum Circuit acting on the q register
					UpperCamelCase__     :Any							      =				qiskit.QuantumCircuit(__a				,      __a     )

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

					# Execute the circuit on the simulator
					UpperCamelCase__     :Optional[int]							      =				qiskit.execute(__a				,      __a				,      shots=1000     )

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


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