


import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf





class  _a							(    enum.Enum):
	_a  :  str				   =							0
	_a  :  Dict				   =							1
	_a  :  Optional[int]				   =							2





@add_end_docstrings(_lowercase)
class  _a							(    _lowercase):
	_a  :  str				   =							'''
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    '''




	def __init__( self						:		Dict		, *_SCREAMING_SNAKE_CASE						:		Any		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Optional[Any]:
			super().__init__(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			self.check_model_type(
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING		)
			if "prefix" not in self._preprocess_params:
					# This is very specific. The logic is quite complex and needs to be done
					# as a "default".
					# It also defines both some preprocess_kwargs and generate_kwargs
					# which is why we cannot put them in their respective methods.
					lowerCAmelCase__       :      Any   					= None
					if self.model.config.prefix is not None:
							lowerCAmelCase__       :      int   					= self.model.config.prefix
					if prefix is None and self.model.__class__.__name__ in [
					    "XLNetLMHeadModel",
					    "TransfoXLLMHeadModel",
					    "TFXLNetLMHeadModel",
					    "TFTransfoXLLMHeadModel",
					]:
							# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
							lowerCAmelCase__       :      Dict   					= self.XL_PREFIX
					if prefix is not None:
							# Recalculate some generate_kwargs linked to prefix.
							lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= self._sanitize_parameters(prefix=_SCREAMING_SNAKE_CASE		, **self._forward_params		)
							lowerCAmelCase__       :      Union[str, Any]   					= {**self._preprocess_params, **preprocess_params}
							lowerCAmelCase__       :      Dict   					= {**self._forward_params, **forward_params}




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		int=None		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, _SCREAMING_SNAKE_CASE						:		List[str]=None		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, _SCREAMING_SNAKE_CASE						:		List[Any]=None		, _SCREAMING_SNAKE_CASE						:		Dict=None		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, **_SCREAMING_SNAKE_CASE						:		Dict		, )->	Union[str, Any]:
			lowerCAmelCase__       :      Tuple   					= {}
			if prefix is not None:
					lowerCAmelCase__       :      int   					= prefix
			if prefix:
					lowerCAmelCase__       :      Any   					= self.tokenizer(
					    _SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		, return_tensors=self.framework		)
					lowerCAmelCase__       :      Union[str, Any]   					= prefix_inputs['''input_ids'''].shape[-1]

			if handle_long_generation is not None:
					if handle_long_generation not in {"hole"}:
							raise ValueError(
							    F'{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'
							    ''' [None, \'hole\']'''		)
					lowerCAmelCase__       :      Optional[int]   					= handle_long_generation

			preprocess_params.update(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      str   					= generate_kwargs

			lowerCAmelCase__       :      Dict   					= {}
			if return_full_text is not None and return_type is None:
					if return_text is not None:
							raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''		)
					if return_tensors is not None:
							raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''		)
					lowerCAmelCase__       :      Optional[Any]   					= ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
			if return_tensors is not None and return_type is None:
					if return_text is not None:
							raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''		)
					lowerCAmelCase__       :      List[Any]   					= ReturnType.TENSORS
			if return_type is not None:
					lowerCAmelCase__       :      Optional[Any]   					= return_type
			if clean_up_tokenization_spaces is not None:
					lowerCAmelCase__       :      List[Any]   					= clean_up_tokenization_spaces

			if stop_sequence is not None:
					lowerCAmelCase__       :      Optional[int]   					= self.tokenizer.encode(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		)
					if len(_SCREAMING_SNAKE_CASE		) > 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.'''		)
					lowerCAmelCase__       :      Optional[int]   					= stop_sequence_ids[0]

			return preprocess_params, forward_params, postprocess_params




	def 							UpperCAmelCase__( self						:		List[str]		, *_SCREAMING_SNAKE_CASE						:		Union[str, Any]		, **_SCREAMING_SNAKE_CASE						:		int		)->	int:
			# Parse arguments
			if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
					kwargs.update({'''add_space_before_punct_symbol''': True}		)

			return super()._parse_and_tokenize(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def __call__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Union[str, Any]:
			return super().__call__(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		List[Any]=""		, _SCREAMING_SNAKE_CASE						:		List[Any]=None		, **_SCREAMING_SNAKE_CASE						:		List[Any]		)->	Optional[int]:
			lowerCAmelCase__       :      Union[str, Any]   					= self.tokenizer(
			    prefix + prompt_text		, padding=_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		, return_tensors=self.framework		)
			lowerCAmelCase__       :      List[Any]   					= prompt_text

			if handle_long_generation == "hole":
					lowerCAmelCase__       :      Tuple   					= inputs['''input_ids'''].shape[-1]
					if "max_new_tokens" in generate_kwargs:
							lowerCAmelCase__       :      str   					= generate_kwargs['''max_new_tokens''']
					else:
							lowerCAmelCase__       :      List[Any]   					= generate_kwargs.get('''max_length'''		, self.model.config.max_length		) - cur_len
							if new_tokens < 0:
									raise ValueError('''We cannot infer how many new tokens are expected'''		)
					if cur_len + new_tokens > self.tokenizer.model_max_length:
							lowerCAmelCase__       :      str   					= self.tokenizer.model_max_length - new_tokens
							if keep_length <= 0:
									raise ValueError(
									    '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
									    ''' models max length'''		)

							lowerCAmelCase__       :      Optional[int]   					= inputs['''input_ids'''][:, -keep_length:]
							if "attention_mask" in inputs:
									lowerCAmelCase__       :      List[str]   					= inputs['''attention_mask'''][:, -keep_length:]

			return inputs




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Dict:
			lowerCAmelCase__       :      str   					= model_inputs['''input_ids''']
			lowerCAmelCase__       :      Any   					= model_inputs.get('''attention_mask'''		, _SCREAMING_SNAKE_CASE		)
			# Allow empty prompts
			if input_ids.shape[1] == 0:
					lowerCAmelCase__       :      Optional[Any]   					= None
					lowerCAmelCase__       :      List[Any]   					= None
					lowerCAmelCase__       :      Tuple   					= 1
			else:
					lowerCAmelCase__       :      List[Any]   					= input_ids.shape[0]
			lowerCAmelCase__       :      Any   					= model_inputs.pop('''prompt_text'''		)

			# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
			# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
			lowerCAmelCase__       :      Any   					= generate_kwargs.pop('''prefix_length'''		, 0		)
			if prefix_length > 0:
					lowerCAmelCase__       :      List[str]   					= '''max_new_tokens''' in generate_kwargs or (
					    '''generation_config''' in generate_kwargs
					    and generate_kwargs['''generation_config'''].max_new_tokens is not None
					)
					if not has_max_new_tokens:
							lowerCAmelCase__       :      Optional[Any]   					= generate_kwargs.get('''max_length'''		) or self.model.config.max_length
							generate_kwargs["max_length"] += prefix_length
					lowerCAmelCase__       :      Optional[int]   					= '''min_new_tokens''' in generate_kwargs or (
					    '''generation_config''' in generate_kwargs
					    and generate_kwargs['''generation_config'''].min_new_tokens is not None
					)
					if not has_min_new_tokens and "min_length" in generate_kwargs:
							generate_kwargs["min_length"] += prefix_length

        # BS x SL
			lowerCAmelCase__       :      str   					= self.model.generate(input_ids=_SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= generated_sequence.shape[0]
			if self.framework == "pt":
					lowerCAmelCase__       :      Any   					= generated_sequence.reshape(_SCREAMING_SNAKE_CASE		, out_b // in_b		, *generated_sequence.shape[1:]		)
			elif self.framework == "tf":
					lowerCAmelCase__       :      List[Any]   					= tf.reshape(_SCREAMING_SNAKE_CASE		, (in_b, out_b // in_b, *generated_sequence.shape[1:])		)
			return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=ReturnType.FULL_TEXT		, _SCREAMING_SNAKE_CASE						:		Dict=True		)->	Tuple:
			lowerCAmelCase__       :      Any   					= model_outputs['''generated_sequence'''][0]
			lowerCAmelCase__       :      Union[str, Any]   					= model_outputs['''input_ids''']
			lowerCAmelCase__       :      str   					= model_outputs['''prompt_text''']
			lowerCAmelCase__       :      Optional[int]   					= generated_sequence.numpy().tolist()
			lowerCAmelCase__       :      int   					= []
			for sequence in generated_sequence:
					if return_type == ReturnType.TENSORS:
							lowerCAmelCase__       :      List[Any]   					= {'''generated_token_ids''': sequence}
					elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
							# Decode text
							lowerCAmelCase__       :      Any   					= self.tokenizer.decode(
							    _SCREAMING_SNAKE_CASE		, skip_special_tokens=_SCREAMING_SNAKE_CASE		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		, )

							# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
							if input_ids is None:
									lowerCAmelCase__       :      Tuple   					= 0
							else:
									lowerCAmelCase__       :      List[str]   					= len(
									    self.tokenizer.decode(
									        input_ids[0]		, skip_special_tokens=_SCREAMING_SNAKE_CASE		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		, )		)

							if return_type == ReturnType.FULL_TEXT:
									lowerCAmelCase__       :      List[str]   					= prompt_text + text[prompt_length:]
							else:
									lowerCAmelCase__       :      Union[str, Any]   					= text[prompt_length:]

							lowerCAmelCase__       :      int   					= {'''generated_text''': all_text}
					records.append(_SCREAMING_SNAKE_CASE		)

			return records








from collections.abc import Iterable
from typing import Any





class  _a							:




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		int | None = None		)->	Tuple:
			lowerCAmelCase__       :      Union[str, Any]   					= value
			lowerCAmelCase__       :      Node | None   					= None  # Added in order to delete a node easier
			lowerCAmelCase__       :      Node | None   					= None
			lowerCAmelCase__       :      Node | None   					= None




	def __repr__( self						:		List[Any]		)->	str:
			from pprint import pformat

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





class  _a							:




	def __init__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	int:
			lowerCAmelCase__       :      Dict   					= root




	def __str__( self						:		Tuple		)->	str:
			return str(self.root		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if new_children is not None:  # reset its kids
					lowerCAmelCase__       :      Union[str, Any]   					= node.parent
			if node.parent is not None:  # reset its parent
					if self.is_right(_SCREAMING_SNAKE_CASE		):  # If it is the right children
							lowerCAmelCase__       :      List[str]   					= new_children
					else:
							lowerCAmelCase__       :      Any   					= new_children
			else:
					lowerCAmelCase__       :      Optional[Any]   					= new_children




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Node		)->	bool:
			if node.parent and node.parent.right:
					return node == node.parent.right
			return False




	def 							UpperCAmelCase__( self						:		int		)->	bool:
			return self.root is None




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	None:
			lowerCAmelCase__       :      Any   					= Node(_SCREAMING_SNAKE_CASE		)  # create a new Node
			if self.empty():  # if Tree is empty
					lowerCAmelCase__       :      Optional[Any]   					= new_node  # set its root
			else:  # Tree is not empty
					lowerCAmelCase__       :      str   					= self.root  # from root
					if parent_node is None:
							return
					while True:  # While we don't get to a leaf
							if value < parent_node.value:  # We go left
									if parent_node.left is None:
											lowerCAmelCase__       :      Dict   					= new_node  # We insert the new node in a leaf
											break
									else:
											lowerCAmelCase__       :      List[Any]   					= parent_node.left
							else:
									if parent_node.right is None:
											lowerCAmelCase__       :      Tuple   					= new_node
											break
									else:
											lowerCAmelCase__       :      Union[str, Any]   					= parent_node.right
					lowerCAmelCase__       :      Dict   					= parent_node




	def 							UpperCAmelCase__( self						:		str		, *_SCREAMING_SNAKE_CASE						:		List[Any]		)->	None:
			for value in values:
					self.__insert(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Node | None:
			if self.empty():
					raise IndexError('''Warning: Tree is empty! please use another.'''		)
			else:
					lowerCAmelCase__       :      Union[str, Any]   					= self.root
					# use lazy evaluation here to avoid NoneType Attribute error
					while node is not None and node.value is not value:
							lowerCAmelCase__       :      List[str]   					= node.left if value < node.value else node.right
					return node




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					if self.root is None:
							return None
					lowerCAmelCase__       :      List[Any]   					= self.root

			if not self.empty():
					while node.right is not None:
							lowerCAmelCase__       :      str   					= node.right
			return node




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					lowerCAmelCase__       :      Any   					= self.root
			if self.root is None:
					return None
			if not self.empty():
					lowerCAmelCase__       :      Dict   					= self.root
					while node.left is not None:
							lowerCAmelCase__       :      Tuple   					= node.left
			return node




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			lowerCAmelCase__       :      Optional[Any]   					= self.search(_SCREAMING_SNAKE_CASE		)  # Look for the node with that label
			if node is not None:
					if node.left is None and node.right is None:  # If it has no children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					elif node.left is None:  # Has only right children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.right		)
					elif node.right is None:  # Has only left children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.left		)
					else:
							lowerCAmelCase__       :      Tuple   					= self.get_max(
							    node.left		)  # Gets the max value of the left branch
							self.remove(tmp_node.value		)  # type: ignore
							lowerCAmelCase__       :      Optional[Any]   					= (
							    tmp_node.value  # type: ignore
							)  # Assigns the value to the node to delete and keep tree structure




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	Iterable:
			if node is not None:
					yield node  # Preorder Traversal
					yield from self.preorder_traverse(node.left		)
					yield from self.preorder_traverse(node.right		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Tuple=None		)->	Any:
			if traversal_function is None:
					return self.preorder_traverse(self.root		)
			else:
					return traversal_function(self.root		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		list		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if node:
					self.inorder(_SCREAMING_SNAKE_CASE		, node.left		)
					arr.append(node.value		)
					self.inorder(_SCREAMING_SNAKE_CASE		, node.right		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Node		)->	int:
			lowerCAmelCase__       :      list[int]   					= []
			self.inorder(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)  # append all values to list using inorder traversal
			return arr[k - 1]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= []
		if curr_node is not None:
				lowerCAmelCase__       :      Optional[int]   					= postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
		return node_list






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= (8, 3, 6, 1, 10, 14, 13, 4, 7)
		lowerCAmelCase__       :      str   					= BinarySearchTree()
		for i in testlist:
				t.insert(_a	)

		# Prints all the elements of the list in order traversal
		print(_a	)

		if t.search(6	) is not None:
				print('''The value 6 exists'''	)
		else:
				print('''The value 6 doesn\'t exist'''	)

		if t.search(-1	) is not None:
				print('''The value -1 exists'''	)
		else:
				print('''The value -1 doesn\'t exist'''	)

		if not t.empty():
				print('''Max Value: '''     ,       t.get_max().value	)  # type: ignore
				print('''Min Value: '''     ,       t.get_min().value	)  # type: ignore

		for i in testlist:
				t.remove(_a	)
				print(_a	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)




