
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__lowercase							:   Optional[Any]					=     logging.get_logger(__name__)


__lowercase							:   str					=     r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''

class 	__lowercase				(  _lowercase    ):

			@add_start_docstrings(A      )
			def __call__(self			,   A			,   A			,   **A      ):
								raise NotImplementedError('''StoppingCriteria needs to be subclassed'''      )

class 	__lowercase				(  _lowercase    ):

			def __init__(self			,   A			,   A = None      ):
								lowerCamelCase_					:      str     =		max_length
								lowerCamelCase_					:      str     =		max_position_embeddings






			@add_start_docstrings(A      )
			def __call__(self			,   A			,   A			,   **A      ):
								lowerCamelCase_					:      List[str]     =		input_ids.shape[-1]
								lowerCamelCase_					:      Union[str, Any]     =		cur_len >= self.max_length
								if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
													logger.warning_once(
													    '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
													    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
													    '''exceptions, performance degradation, or nothing at all.'''      )
								return is_done

class 	__lowercase				(  _lowercase    ):

			def __init__(self			,   A			,   A      ):
								warnings.warn(
								    '''The class `MaxNewTokensCriteria` is deprecated. '''
								    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
								    '''with `max_length = start_length + max_new_tokens` instead.'''			,   A			,   )
								lowerCamelCase_					:      str     =		start_length
								lowerCamelCase_					:      List[Any]     =		max_new_tokens
								lowerCamelCase_					:      Any     =		start_length + max_new_tokens






			@add_start_docstrings(A      )
			def __call__(self			,   A			,   A			,   **A      ):
								return input_ids.shape[-1] >= self.max_length

class 	__lowercase				(  _lowercase    ):

			def __init__(self			,   A			,   A = None      ):
								lowerCamelCase_					:      List[str]     =		max_time
								lowerCamelCase_					:      Optional[int]     =		time.time() if initial_timestamp is None else initial_timestamp






			@add_start_docstrings(A      )
			def __call__(self			,   A			,   A			,   **A      ):
								return time.time() - self.initial_timestamp > self.max_time

class 	__lowercase				(  _lowercase    ):

			@add_start_docstrings(A      )
			def __call__(self			,   A			,   A			,   **A      ):
								return any(criteria(A			,   A      ) for criteria in self      )






			@property
			def        UpperCAmelCase__ (self      ):
								for stopping_criterium in self:
													if isinstance(A			,   A      ):
																		return stopping_criterium.max_length
													elif isinstance(A			,   A      ):
																		return stopping_criterium.max_length
								return None




def lowercase_	(   _lowercase			,       _lowercase				)     ->							StoppingCriteriaList:
					'''simple docstring'''

					lowerCamelCase_					:      int     =		stopping_criteria.max_length
					lowerCamelCase_					:      List[Any]     =		deepcopy(_lowercase				)
					if stopping_max_length is not None and stopping_max_length != max_length:
										warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter'''			,       _lowercase				)
					elif stopping_max_length is None:
										new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowercase				)				)
					return new_stopping_criteria




'''simple docstring'''


import qiskit




def lowercase_	(   _lowercase			,       _lowercase				)     ->							qiskit.result.counts.Counts:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		qiskit.Aer.get_backend('''aer_simulator'''				)

					# Create a Quantum Circuit acting on the q register
					lowerCamelCase_					:      Optional[int]     =		qiskit.QuantumCircuit(_lowercase			,       _lowercase				)

					# Apply X (NOT) Gate to Qubits 0 & 1
					circuit.x(0				)
					circuit.x(1				)

					# Map the quantum measurement to the classical bits
					circuit.measure([0, 1]			,       [0, 1]				)

					# Execute the circuit on the qasm simulator
					lowerCamelCase_					:      Optional[int]     =		qiskit.execute(_lowercase			,       _lowercase			,       shots=1_000				)

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


if __name__ == "__main__":
					__lowercase							:   List[Any]					=     single_qubit_measure(2, 2)
					print(f'Total count for various states are: {counts}')


