


import enum
import warnings

from ..tokenization_utils import TruncationStrategy
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

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_lowercase       :      int	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					enum.Enum				):

    """simple docstring"""







    lowerCAmelCase 		= 0
    lowerCAmelCase 		= 1






@add_end_docstrings(__A				)
class       _UpperCamelCase    (					__A				):

    """simple docstring"""







    lowerCAmelCase 		= """generated"""

    def __init__(						self				,   *a__				,   **a__   )	->				List[Any]:
     super().__init__(*UpperCamelCase__				,   **UpperCamelCase__   )

     self.check_model_type(
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
         if self.framework == """tf"""
         else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING   )

    def      _UpperCAmelCase					(						self				,   a__=None				,   a__=None				,   a__=None				,   a__=None				,   a__=None				,   a__=None				,   **a__				,   )	->				int:
     A								=				{}
     if truncation is not None:
      A								=				truncation

     A								=				generate_kwargs

     A								=				{}
     if return_tensors is not None and return_type is None:
      A								=				ReturnType.TENSORS if return_tensors else ReturnType.TEXT
     if return_type is not None:
      A								=				return_type

     if clean_up_tokenization_spaces is not None:
      A								=				clean_up_tokenization_spaces

     if stop_sequence is not None:
      A								=				self.tokenizer.encode(UpperCamelCase__				,   add_special_tokens=UpperCamelCase__   )
      if len(UpperCamelCase__   ) > 1:
       warnings.warn(
           """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
           """ the stop sequence will be used as the stop sequence string in the interim."""   )
      A								=				stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params

    def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				List[str]:
     return True

    def      _UpperCAmelCase					(						self				,   *a__				,   a__   )	->				Optional[Any]:
     A								=				self.model.config.prefix if self.model.config.prefix is not None else ''
     if isinstance(args[0]				,   UpperCamelCase__   ):
      if self.tokenizer.pad_token_id is None:
       raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""   )
      A								=				([prefix + arg for arg in args[0]],)
      A								=				True

     elif isinstance(args[0]				,   UpperCamelCase__   ):
      A								=				(prefix + args[0],)
      A								=				False
     else:
      raise ValueError(
          f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'   )
     A								=				self.tokenizer(*UpperCamelCase__				,   padding=UpperCamelCase__				,   truncation=UpperCamelCase__				,   return_tensors=self.framework   )
     # This is produced by tokenizers but is an invalid generate kwargs
     if "token_type_ids" in inputs:
      del inputs["token_type_ids"]
     return inputs

    def __call__(						self				,   *a__				,   **a__   )	->				Optional[Any]:
     A								=				super().__call__(*UpperCamelCase__				,   **UpperCamelCase__   )
     if (
         isinstance(args[0]				,   UpperCamelCase__   )
         and all(isinstance(UpperCamelCase__				,   UpperCamelCase__   ) for el in args[0]   )
         and all(len(UpperCamelCase__   ) == 1 for res in result   )
     ):
      return [res[0] for res in result]
     return result

    def      _UpperCAmelCase					(						self				,   a__				,   a__=TruncationStrategy.DO_NOT_TRUNCATE				,   **a__   )	->				Optional[int]:
     A								=				self._parse_and_tokenize(UpperCamelCase__				,   truncation=UpperCamelCase__				,   **UpperCamelCase__   )
     return inputs

    def      _UpperCAmelCase					(						self				,   a__				,   **a__   )	->				Union[str, Any]:
     if self.framework == "pt":
      A								=				model_inputs['input_ids'].shape
     elif self.framework == "tf":
      A								=				tf.shape(model_inputs["""input_ids"""]   ).numpy()

     A								=				generate_kwargs.get("""min_length"""				,   self.model.config.min_length   )
     A								=				generate_kwargs.get("""max_length"""				,   self.model.config.max_length   )
     self.check_inputs(UpperCamelCase__				,   generate_kwargs["""min_length"""]				,   generate_kwargs["""max_length"""]   )
     A								=				self.model.generate(**UpperCamelCase__				,   **UpperCamelCase__   )
     A								=				output_ids.shape[0]
     if self.framework == "pt":
      A								=				output_ids.reshape(UpperCamelCase__				,   out_b // in_b				,   *output_ids.shape[1:]   )
     elif self.framework == "tf":
      A								=				tf.reshape(UpperCamelCase__				,   (in_b, out_b // in_b, *output_ids.shape[1:])   )
     return {"output_ids": output_ids}

    def      _UpperCAmelCase					(						self				,   a__				,   a__=ReturnType.TEXT				,   a__=False   )	->				int:
     A								=				[]
     for output_ids in model_outputs["output_ids"][0]:
      if return_type == ReturnType.TENSORS:
       A								=				{f'{self.return_name}_token_ids': output_ids}
      elif return_type == ReturnType.TEXT:
       A								=				{
           f'{self.return_name}_text': self.tokenizer.decode(
               UpperCamelCase__				,   skip_special_tokens=UpperCamelCase__				,   clean_up_tokenization_spaces=UpperCamelCase__				,   )
       }
      records.append(UpperCamelCase__   )
     return records






@add_end_docstrings(__A				)
class       _UpperCamelCase    (					__A				):

    """simple docstring"""







    lowerCAmelCase 		= """summary"""

    def __call__(						self				,   *a__				,   **a__   )	->				Tuple:
     return super().__call__(*UpperCamelCase__				,   **UpperCamelCase__   )

    def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				Any:
     if max_length < min_length:
      logger.warning(f'Your min_length={min_length} must be inferior than your max_length={max_length}.'   )

     if input_length < max_length:
      logger.warning(
          f'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
          """a summarization task, where outputs shorter than the input are typically wanted, you might """
          f'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'   )






@add_end_docstrings(__A				)
class       _UpperCamelCase    (					__A				):

    """simple docstring"""







    lowerCAmelCase 		= """translation"""

    def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				Tuple:
     if input_length > 0.9 * max_length:
      logger.warning(
          f'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
          """increasing your max_length manually, e.g. translator(\'...\', max_length=400)"""   )
     return True

    def      _UpperCAmelCase					(						self				,   *a__				,   a__=TruncationStrategy.DO_NOT_TRUNCATE				,   a__=None				,   a__=None   )	->				Optional[int]:
     if getattr(self.tokenizer				,   """_build_translation_inputs"""				,   UpperCamelCase__   ):
      return self.tokenizer._build_translation_inputs(
          *UpperCamelCase__				,   return_tensors=self.framework				,   truncation=UpperCamelCase__				,   src_lang=UpperCamelCase__				,   tgt_lang=UpperCamelCase__   )
     else:
      return super()._parse_and_tokenize(*UpperCamelCase__				,   truncation=UpperCamelCase__   )

    def      _UpperCAmelCase					(						self				,   a__=None				,   a__=None				,   **a__   )	->				Union[str, Any]:
     A								=				super()._sanitize_parameters(**UpperCamelCase__   )
     if src_lang is not None:
      A								=				src_lang
     if tgt_lang is not None:
      A								=				tgt_lang
     if src_lang is None and tgt_lang is None:
      # Backward compatibility, direct arguments use is preferred.
      A								=				kwargs.get("""task"""				,   self.task   )
      A								=				task.split("""_"""   )
      if task and len(UpperCamelCase__   ) == 4:
       # translation, XX, to YY
       A								=				items[1]
       A								=				items[3]
     return preprocess_params, forward_params, postprocess_params

    def __call__(						self				,   *a__				,   **a__   )	->				List[Any]:
     return super().__call__(*UpperCamelCase__				,   **UpperCamelCase__   )







import qiskit





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 2						) ->   qiskit.result.counts.Counts:





	"""simple docstring"""







	A								=				qubits

	# Using Aer's simulator
	A								=				qiskit.Aer.get_backend("""aer_simulator"""						)

	# Creating a Quantum Circuit acting on the q register
	A								=				qiskit.QuantumCircuit(UpperCamelCase__							,					UpperCamelCase__						)

	# Adding a H gate on qubit 0 (now q0 in superposition)
	circuit.h(0						)

	for i in range(1							,					UpperCamelCase__						):
		# Adding CX (CNOT) gate
		circuit.cx(i - 1							,					UpperCamelCase__						)

	# Mapping the quantum measurement to the classical bits
	circuit.measure(list(range(UpperCamelCase__						)						)							,					list(range(UpperCamelCase__						)						)						)

	# Now measuring any one qubit would affect other qubits to collapse
	# their super position and have same state as the measured one.

	# Executing the circuit on the simulator
	A								=				qiskit.execute(UpperCamelCase__							,					UpperCamelCase__							,					shots=10_00						)

	return job.result().get_counts(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''Total count for various states are: {quantum_entanglement(3)}''')



