
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_A     =  "\\n\n"

_A     =  "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

_A     =  "\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,   _KWARGS_DESCRIPTION )
class 			_lowerCAmelCase      (			datasets.Metric ):




				def   __a		(      self  )       ->   Optional[Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,       citation=_CITATION       ,       inputs_description=_KWARGS_DESCRIPTION       ,       features=datasets.Features(
							        {
							            "input_texts": datasets.Value("string"  ),
							        }  )       ,       reference_urls=["https://huggingface.co/docs/transformers/perplexity"]       ,       )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = 16       ,       _UpperCamelCase = True       ,       _UpperCamelCase=None  )       ->   Optional[int]:
							if device is not None:
										assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
										if device == "gpu":
													lowerCAmelCase_      			=			"cuda"
							else:
										lowerCAmelCase_      			=			"cuda" if torch.cuda.is_available() else "cpu"

							lowerCAmelCase_      			=			AutoModelForCausalLM.from_pretrained(_UpperCamelCase  )
							lowerCAmelCase_      			=			model.to(_UpperCamelCase  )

							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )

							# if batch_size > 1 (which generally leads to padding being required), and
							# if there is not an already assigned pad_token, assign an existing
							# special token to also be the padding token
							if tokenizer.pad_token is None and batch_size > 1:
										lowerCAmelCase_      			=			list(tokenizer.special_tokens_map_extended.values()  )
										# check that the model already has at least one special token defined
										assert (
										    len(_UpperCamelCase  ) > 0
										), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
										# assign one of the special tokens to also be the pad token
										tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}  )

							if add_start_token:
										# leave room for <BOS> token to be added:
										assert (
										    tokenizer.bos_token is not None
										), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
										lowerCAmelCase_      			=			model.config.max_length - 1
							else:
										lowerCAmelCase_      			=			model.config.max_length

							lowerCAmelCase_      			=			tokenizer(
							    _UpperCamelCase       ,       add_special_tokens=_UpperCamelCase       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase       ,       max_length=_UpperCamelCase       ,       return_tensors="pt"       ,       return_attention_mask=_UpperCamelCase       ,       ).to(_UpperCamelCase  )

							lowerCAmelCase_      			=			encodings["input_ids"]
							lowerCAmelCase_      			=			encodings["attention_mask"]

							# check that each input is long enough:
							if add_start_token:
										assert torch.all(torch.ge(attn_masks.sum(1  )       ,       1  )  ), "Each input text must be at least one token long."
							else:
										assert torch.all(
										    torch.ge(attn_masks.sum(1  )       ,       2  )  ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			CrossEntropyLoss(reduction="none"  )

							for start_index in logging.tqdm(range(0       ,       len(_UpperCamelCase  )       ,       _UpperCamelCase  )  ):
										lowerCAmelCase_      			=			min(start_index + batch_size       ,       len(_UpperCamelCase  )  )
										lowerCAmelCase_      			=			encoded_texts[start_index:end_index]
										lowerCAmelCase_      			=			attn_masks[start_index:end_index]

										if add_start_token:
													lowerCAmelCase_      			=			torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0  )  ).to(_UpperCamelCase  )
													lowerCAmelCase_      			=			torch.cat([bos_tokens_tensor, encoded_batch]       ,       dim=1  )
													lowerCAmelCase_      			=			torch.cat(
													    [torch.ones(bos_tokens_tensor.size()       ,       dtype=torch.intaa  ).to(_UpperCamelCase  ), attn_mask]       ,       dim=1  )

										lowerCAmelCase_      			=			encoded_batch

										with torch.no_grad():
													lowerCAmelCase_      			=			model(_UpperCamelCase       ,       attention_mask=_UpperCamelCase  ).logits

										lowerCAmelCase_      			=			out_logits[..., :-1, :].contiguous()
										lowerCAmelCase_      			=			labels[..., 1:].contiguous()
										lowerCAmelCase_      			=			attn_mask[..., 1:].contiguous()

										lowerCAmelCase_      			=			torch.expa(
										    (loss_fct(shift_logits.transpose(1       ,       2  )       ,       _UpperCamelCase  ) * shift_attention_mask_batch).sum(1  )
										    / shift_attention_mask_batch.sum(1  )  )

										ppls += perplexity_batch.tolist()

							return {"perplexities": ppls, "mean_perplexity": np.mean(_UpperCamelCase  )}





from collections.abc import Iterable
from typing import Any





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase = None  )       ->   str:
							lowerCAmelCase_      			=			value
							lowerCAmelCase_      			=			None  # Added in order to delete a node easier
							lowerCAmelCase_      			=			None
							lowerCAmelCase_      			=			None




				def __repr__(      self  )       ->   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 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase = None  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			root




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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							if new_children is not None:  # reset its kids
										lowerCAmelCase_      			=			node.parent
							if node.parent is not None:  # reset its parent
										if self.is_right(_UpperCamelCase  ):  # If it is the right children
													lowerCAmelCase_      			=			new_children
										else:
													lowerCAmelCase_      			=			new_children
							else:
										lowerCAmelCase_      			=			new_children




				def   __a		(      self       ,       _UpperCamelCase  )       ->   bool:
							if node.parent and node.parent.right:
										return node == node.parent.right
							return False




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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			Node(_UpperCamelCase  )  # create a new Node
							if self.empty():  # if Tree is empty
										lowerCAmelCase_      			=			new_node  # set its root
							else:  # Tree is not empty
										lowerCAmelCase_      			=			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_      			=			new_node  # We insert the new node in a leaf
																			break
																else:
																			lowerCAmelCase_      			=			parent_node.left
													else:
																if parent_node.right is None:
																			lowerCAmelCase_      			=			new_node
																			break
																else:
																			lowerCAmelCase_      			=			parent_node.right
										lowerCAmelCase_      			=			parent_node




				def   __a		(      self       ,       *_UpperCamelCase  )       ->   None:
							for value in values:
										self.__insert(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Node | None:
							if self.empty():
										raise IndexError("Warning: Tree is empty! please use another."  )
							else:
										lowerCAmelCase_      			=			self.root
										# use lazy evaluation here to avoid NoneType Attribute error
										while node is not None and node.value is not value:
													lowerCAmelCase_      			=			node.left if value < node.value else node.right
										return node




				def   __a		(      self       ,       _UpperCamelCase = None  )       ->   Node | None:
							if node is None:
										if self.root is None:
													return None
										lowerCAmelCase_      			=			self.root

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




				def   __a		(      self       ,       _UpperCamelCase = None  )       ->   Node | None:
							if node is None:
										lowerCAmelCase_      			=			self.root
							if self.root is None:
										return None
							if not self.empty():
										lowerCAmelCase_      			=			self.root
										while node.left is not None:
													lowerCAmelCase_      			=			node.left
							return node




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.search(_UpperCamelCase  )  # 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(_UpperCamelCase       ,       _UpperCamelCase  )
										elif node.left is None:  # Has only right children
													self.__reassign_nodes(_UpperCamelCase       ,       node.right  )
										elif node.right is None:  # Has only left children
													self.__reassign_nodes(_UpperCamelCase       ,       node.left  )
										else:
													lowerCAmelCase_      			=			self.get_max(
													    node.left  )  # Gets the max value of the left branch
													self.remove(tmp_node.value  )  # type: ignore
													lowerCAmelCase_      			=			(
													    tmp_node.value  # type: ignore
													)  # Assigns the value to the node to delete and keep tree structure




				def   __a		(      self       ,       _UpperCamelCase  )       ->   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   __a		(      self       ,       _UpperCamelCase=None  )       ->   Any:
							if traversal_function is None:
										return self.preorder_traverse(self.root  )
							else:
										return traversal_function(self.root  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							if node:
										self.inorder(_UpperCamelCase       ,       node.left  )
										arr.append(node.value  )
										self.inorder(_UpperCamelCase       ,       node.right  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			[]
							self.inorder(_UpperCamelCase       ,       _UpperCamelCase  )  # append all values to list using inorder traversal
							return arr[k - 1]






def      lowerCamelCase__       (       __lowerCAmelCase						:						Node | None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			if curr_node is not None:
						lowerCAmelCase_      			=			postorder(curr_node.left       ) + postorder(curr_node.right       ) + [curr_node]
			return node_list






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			(8, 3, 6, 1, 10, 14, 13, 4, 7)
			lowerCAmelCase_      			=			BinarySearchTree()
			for i in testlist:
						t.insert(__lowerCAmelCase       )

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

			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(__lowerCAmelCase       )
						print(__lowerCAmelCase       )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)



