

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 _UpperCamelCase							(							enum.Enum   ):
			'''simple docstring'''







			lowerCamelCase__           =0
			lowerCamelCase__           =1
			lowerCamelCase__           =2






@add_end_docstrings(_UpperCAmelCase   )
class _UpperCamelCase							(							_UpperCAmelCase   ):
			'''simple docstring'''







			lowerCamelCase__           ='''
    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							: Optional[Any]      ,      *a							: Dict      ,      **a							: List[Any]	)							->       Any:

				"""simple docstring"""

				super().__init__(*A_      ,      **A_	)
				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.
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
					if self.model.config.prefix is not None:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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.
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.XL_PREFIX
					if prefix is not None:
						# Recalculate some generate_kwargs linked to prefix.
						SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       self._sanitize_parameters(prefix=A_      ,      **self._forward_params	)
						SCREAMING_SNAKE_CASE   :      str				     =       {**self._preprocess_params, **preprocess_params}
						SCREAMING_SNAKE_CASE   :      List[str]				     =       {**self._forward_params, **forward_params}
			def 							__UpperCamelCase (			self							: str      ,      a							: Tuple=None      ,      a							: int=None      ,      a							: Union[str, Any]=None      ,      a							: List[str]=None      ,      a							: str=None      ,      a							: Dict=None      ,      a							: str=None      ,      a							: Optional[int]=None      ,      **a							: Any      ,      )							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       {}
				if prefix is not None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       prefix
				if prefix:
					SCREAMING_SNAKE_CASE   :      str				     =       self.tokenizer(
					    A_      ,      padding=A_      ,      add_special_tokens=A_      ,      return_tensors=self.framework	)
					SCREAMING_SNAKE_CASE   :      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\']"	)
					SCREAMING_SNAKE_CASE   :      Any				     =       handle_long_generation

				preprocess_params.update(A_	)
				SCREAMING_SNAKE_CASE   :      str				     =       generate_kwargs

				SCREAMING_SNAKE_CASE   :      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`"	)
					SCREAMING_SNAKE_CASE   :      List[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`"	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       ReturnType.TENSORS
				if return_type is not None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       return_type
				if clean_up_tokenization_spaces is not None:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       clean_up_tokenization_spaces

				if stop_sequence is not None:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.tokenizer.encode(A_      ,      add_special_tokens=A_	)
					if len(A_	) > 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."	)
					SCREAMING_SNAKE_CASE   :      str				     =       stop_sequence_ids[0]

				return preprocess_params, forward_params, postprocess_params
			def 							__UpperCamelCase (			self							: Any      ,      *a							: List[Any]      ,      **a							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
					kwargs.update({"add_space_before_punct_symbol": True}	)

				return super()._parse_and_tokenize(*A_      ,      **A_	)
			def __call__(			self							: Tuple      ,      a							: str      ,      **a							: Optional[int]	)							->       str:

				"""simple docstring"""

				return super().__call__(A_      ,      **A_	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: List[Any]      ,      a							: Union[str, Any]=""      ,      a							: int=None      ,      **a							: Union[str, Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.tokenizer(
				    prefix + prompt_text      ,      padding=A_      ,      add_special_tokens=A_      ,      return_tensors=self.framework	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       prompt_text

				if handle_long_generation == "hole":
					SCREAMING_SNAKE_CASE   :      Any				     =       inputs["input_ids"].shape[-1]
					if "max_new_tokens" in generate_kwargs:
						SCREAMING_SNAKE_CASE   :      Dict				     =       generate_kwargs["max_new_tokens"]
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       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:
						SCREAMING_SNAKE_CASE   :      int				     =       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"	)

						SCREAMING_SNAKE_CASE   :      Dict				     =       inputs["input_ids"][:, -keep_length:]
						if "attention_mask" in inputs:
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       inputs["attention_mask"][:, -keep_length:]

				return inputs
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Union[str, Any]      ,      **a							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_inputs["input_ids"]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       model_inputs.get("attention_mask"      ,      A_	)
				# Allow empty prompts
				if input_ids.shape[1] == 0:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       None
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       None
					SCREAMING_SNAKE_CASE   :      List[str]				     =       1
				else:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       input_ids.shape[0]
				SCREAMING_SNAKE_CASE   :      str				     =       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.
				SCREAMING_SNAKE_CASE   :      Tuple				     =       generate_kwargs.pop("prefix_length"      ,      0	)
				if prefix_length > 0:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "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:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       generate_kwargs.get("max_length"	) or self.model.config.max_length
						generate_kwargs["max_length"] += prefix_length
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       "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
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model.generate(input_ids=A_      ,      attention_mask=A_      ,      **A_	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       generated_sequence.shape[0]
				if self.framework == "pt":
					SCREAMING_SNAKE_CASE   :      Tuple				     =       generated_sequence.reshape(A_      ,      out_b // in_b      ,      *generated_sequence.shape[1:]	)
				elif self.framework == "tf":
					SCREAMING_SNAKE_CASE   :      str				     =       tf.reshape(A_      ,      (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							: int      ,      a							: int      ,      a							: Optional[Any]=ReturnType.FULL_TEXT      ,      a							: str=True	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       model_outputs["generated_sequence"][0]
				SCREAMING_SNAKE_CASE   :      Any				     =       model_outputs["input_ids"]
				SCREAMING_SNAKE_CASE   :      int				     =       model_outputs["prompt_text"]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       generated_sequence.numpy().tolist()
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
				for sequence in generated_sequence:
					if return_type == ReturnType.TENSORS:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       {"generated_token_ids": sequence}
					elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
						# Decode text
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.tokenizer.decode(
						    A_      ,      skip_special_tokens=A_      ,      clean_up_tokenization_spaces=A_      ,      )

						# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
						if input_ids is None:
							SCREAMING_SNAKE_CASE   :      int				     =       0
						else:
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(
							    self.tokenizer.decode(
							        input_ids[0]      ,      skip_special_tokens=A_      ,      clean_up_tokenization_spaces=A_      ,      )	)

						if return_type == ReturnType.FULL_TEXT:
							SCREAMING_SNAKE_CASE   :      Any				     =       prompt_text + text[prompt_length:]
						else:
							SCREAMING_SNAKE_CASE   :      str				     =       text[prompt_length:]

						SCREAMING_SNAKE_CASE   :      int				     =       {"generated_text": all_text}
					records.append(A_	)

				return records


from __future__ import annotations

import math
import random
from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Union[str, Any]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      list[Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       0
				SCREAMING_SNAKE_CASE   :      int				     =       0
			def 							__UpperCamelCase (			self							: List[Any]	)							->       bool:

				"""simple docstring"""

				return self.head == self.tail
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				self.data.append(a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.tail + 1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.data[self.head]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.head + 1
				return ret
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				return self.tail - self.head






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       None:

				"""simple docstring"""

				print(self.data	)
				print("**************"	)
				print(self.data[self.head : self.tail]	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[str]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       data
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      int				     =       1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				return self.data
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       MyNode | None:

				"""simple docstring"""

				return self.left
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       MyNode | None:

				"""simple docstring"""

				return self.right
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				return self.height
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       data
			def 							__UpperCamelCase (			self							: Any      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       node
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       node






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       height

def  lowerCamelCase__ (					_a):
	if node is None:
		return 0
	return node.get_height()

def  lowerCamelCase__ (					_a							,			_a):
	if a > b:
		return a
	return b

def  lowerCamelCase__ (					_a):
	print("left rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      List[str]				     =       node.get_left()
	assert ret is not None
	node.set_left(ret.get_right())
	ret.set_right(_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	print("right rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      Dict				     =       node.get_right()
	assert ret is not None
	node.set_right(ret.get_left())
	ret.set_left(_a)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       node.get_left()
	assert left_child is not None
	node.set_left(left_rotation(_a))
	return right_rotation(_a)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       node.get_right()
	assert right_child is not None
	node.set_right(right_rotation(_a))
	return left_rotation(_a)

def  lowerCamelCase__ (					_a							,			_a):
	if node is None:
		return MyNode(_a)
	if data < node.get_data():
		node.set_left(insert_node(node.get_left()							,			_a))
		if (
		    get_height(node.get_left()) - get_height(node.get_right()) == 2
		):  # an unbalance detected
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       node.get_left()
			assert left_child is not None
			if (
			    data < left_child.get_data()
			):  # new node is the left child of the left child
				SCREAMING_SNAKE_CASE   :      Tuple				     =       right_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       lr_rotation(_a)
	else:
		node.set_right(insert_node(node.get_right()							,			_a))
		if get_height(node.get_right()) - get_height(node.get_left()) == 2:
			SCREAMING_SNAKE_CASE   :      Any				     =       node.get_right()
			assert right_child is not None
			if data < right_child.get_data():
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rl_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       left_rotation(_a)
	SCREAMING_SNAKE_CASE   :      str				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	return node

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
		if right_child is None:
			break
		SCREAMING_SNAKE_CASE   :      str				     =       right_child
	return root.get_data()

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       root.get_left()
		if left_child is None:
			break
		SCREAMING_SNAKE_CASE   :      List[str]				     =       left_child
	return root.get_data()

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       root.get_left()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
	if root.get_data() == data:
		if left_child is not None and right_child is not None:
			SCREAMING_SNAKE_CASE   :      Any				     =       get_left_most(_a)
			root.set_data(_a)
			root.set_right(del_node(_a							,			_a))
		elif left_child is not None:
			SCREAMING_SNAKE_CASE   :      Dict				     =       left_child
		elif right_child is not None:
			SCREAMING_SNAKE_CASE   :      str				     =       right_child
		else:
			return None
	elif root.get_data() > data:
		if left_child is None:
			print("No such data")
			return root
		else:
			root.set_left(del_node(_a							,			_a))
	else:  # root.get_data() < data
		if right_child is None:
			return root
		else:
			root.set_right(del_node(_a							,			_a))

	if get_height(_a) - get_height(_a) == 2:
		assert right_child is not None
		if get_height(right_child.get_right()) > get_height(right_child.get_left()):
			SCREAMING_SNAKE_CASE   :      List[str]				     =       left_rotation(_a)
		else:
			SCREAMING_SNAKE_CASE   :      int				     =       rl_rotation(_a)
	elif get_height(_a) - get_height(_a) == -2:
		assert left_child is not None
		if get_height(left_child.get_left()) > get_height(left_child.get_right()):
			SCREAMING_SNAKE_CASE   :      str				     =       right_rotation(_a)
		else:
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       lr_rotation(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       my_max(get_height(root.get_right())							,			get_height(root.get_left())) + 1
	root.set_height(_a)
	return root






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: str	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return get_height(self.root	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("insert:" + str(a	)	)
				SCREAMING_SNAKE_CASE   :      Any				     =       insert_node(self.root      ,      a	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("delete:" + str(a	)	)
				if self.root is None:
					print("Tree is empty!"	)
					return
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       del_node(self.root      ,      a	)






			def __str__(			self							: Optional[int]      ,      )							->       str:  # a level traversale, gives a more intuitive look on the tree

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       ""
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MyQueue()
				q.push(self.root	)
				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_height()
				if layer == 0:
					return output
				SCREAMING_SNAKE_CASE   :      Dict				     =       0
				while not q.is_empty():
					SCREAMING_SNAKE_CASE   :      Dict				     =       q.pop()
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       " " * int(math.pow(2      ,      layer - 1	)	)
					output += space
					if node is None:
						output += "*"
						q.push(a	)
						q.push(a	)
					else:
						output += str(node.get_data()	)
						q.push(node.get_left()	)
						q.push(node.get_right()	)
					output += space
					SCREAMING_SNAKE_CASE   :      List[str]				     =       cnt + 1
					for i in range(100	):
						if cnt == math.pow(2      ,      a	) - 1:
							SCREAMING_SNAKE_CASE   :      List[str]				     =       layer - 1
							if layer == 0:
								output += "\n*************************************"
								return output
							output += "\n"
							break
				output += "\n*************************************"
				return output

def  lowerCamelCase__ (					):
	import doctest

	doctest.testmod()


if __name__ == "__main__":
			_test()
			a_						     =    AVLtree()
			a_						     =    list(range(10))
			random.shuffle(lst)
			for i in lst:
						t.insert(i)
						print(str(t))
			random.shuffle(lst)
			for i in lst:
						t.del_node(i)
						print(str(t))