



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

UpperCAmelCase_							:      List[Any]      =  logging.get_logger(__name__)






class      UpperCamelCase    (						enum.Enum     ):
	lowerCAmelCase	:   int				=	0
	lowerCAmelCase	:   Optional[int]				=	1






@add_end_docstrings(_UpperCAmelCase     )
class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Dict				=	"""generated"""



	def __init__(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							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 			__A							(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							return True



	def 			__A							(       self			,	*UpperCAmelCase__			,	UpperCAmelCase__		):
							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			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=TruncationStrategy.DO_NOT_TRUNCATE			,	**UpperCAmelCase__		):
							A__							=       self._parse_and_tokenize(UpperCAmelCase__			,	truncation=UpperCAmelCase__			,	**UpperCAmelCase__		)
							return inputs



	def 			__A							(       self			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							if self.framework == "pt":
													A__						,       A__							=       model_inputs["input_ids"].shape
							elif self.framework == "tf":
													A__						,       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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=ReturnType.TEXT			,	UpperCAmelCase__=False		):
							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(_UpperCAmelCase     )
class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Union[str, Any]				=	"""summary"""



	def __call__(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return super().__call__(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							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(_UpperCAmelCase     )
class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""translation"""



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							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 			__A							(       self			,	*UpperCAmelCase__			,	UpperCAmelCase__=TruncationStrategy.DO_NOT_TRUNCATE			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None		):
							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 			__A							(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	**UpperCAmelCase__		):
							A__						,       A__						,       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			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return super().__call__(*UpperCAmelCase__			,	**UpperCAmelCase__		)










from __future__ import annotations

from random import random






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__ = None		):
							A__							=       value
							A__							=       random()
							A__							=       None
							A__							=       None



	def __repr__(       self		):
							from pprint import pformat

							if self.left is None and self.right is None:
													return F"""'{self.value}: {self.prior:.5}'"""
							else:
													return pformat(
													    {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}			,	indent=1		)



	def __str__(       self		):
							A__							=       str(self.value		) + " "
							A__							=       str(self.left or ""		)
							A__							=       str(self.right or ""		)
							return value + left + right





def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       tuple[Node | None, Node | None]:





						"""simple docstring"""





						if root is None:  # None tree is split into 2 Nones
												return None, None
						elif root.value is None:
												return None, None
						else:
												if value < root.value:
																		A__						,       A__							=       split(root.left			,							_A    )
																		return left, root
												else:
																		A__						,       A__							=       split(root.right			,							_A    )
																		return root, right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				Node | None    )->       Node | None:





						"""simple docstring"""





						if (not left) or (not right):  # If one node is None, return the other
												return left or right
						elif left.prior < right.prior:
												A__							=       merge(left.right			,							_A    )
												return left
						else:
												A__							=       merge(_A			,							right.left    )
												return right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__							=       Node(_A    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(merge(_A			,							_A    )			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__						,       A__							=       split(_A			,							value - 1    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(_A			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None    )->       None:





						"""simple docstring"""





						if not root:  # None
												return
						else:
												inorder(root.left    )
												print(root.value			,							end=","    )
												inorder(root.right    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				str    )->       Node | None:





						"""simple docstring"""





						for arg in args.split():
												if arg[0] == "+":
																		A__							=       insert(_A			,							int(arg[1:]    )    )

												elif arg[0] == "-":
																		A__							=       erase(_A			,							int(arg[1:]    )    )

												else:
																		print("Unknown command"    )

						return root
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       None
						print(
						    "enter numbers to create a tree, + value to add value into treap, "
						    "- value to erase all nodes with value. 'q' to quit. "    )

						A__							=       input()
						while args != "q":
												A__							=       interact_treap(_A			,							_A    )
												print(_A    )
												A__							=       input()

						print("good by!"    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





