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

import datasets
from datasets import logging


snake_case :		int							      =  "\\n\n"

snake_case :		str							      =  "\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"

snake_case :		Optional[Any]							      =  "\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       _snake_case					(     datasets.Metric				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a = 16						,   _a = True						,   _a=None		):
				if device is not None:
					assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
					if device == "gpu":
						__magic_name__		:		Any	   =     "cuda"
				else:
					__magic_name__		:		Tuple	   =     "cuda" if torch.cuda.is_available() else "cpu"

				__magic_name__		:		Union[str, Any]	   =     AutoModelForCausalLM.from_pretrained(_a		)
				__magic_name__		:		List[str]	   =     model.to(_a		)

				__magic_name__		:		Dict	   =     AutoTokenizer.from_pretrained(_a		)

				# 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:
					__magic_name__		:		Union[str, Any]	   =     list(tokenizer.special_tokens_map_extended.values()		)
					# check that the model already has at least one special token defined
					assert (
					    len(_a		) > 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"
					__magic_name__		:		List[Any]	   =     model.config.max_length - 1
				else:
					__magic_name__		:		List[Any]	   =     model.config.max_length

				__magic_name__		:		List[Any]	   =     tokenizer(
				    _a						,   add_special_tokens=_a						,   padding=_a						,   truncation=_a						,   max_length=_a						,   return_tensors="pt"						,   return_attention_mask=_a						,   ).to(_a		)

				__magic_name__		:		Dict	   =     encodings["input_ids"]
				__magic_name__		:		int	   =     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."

				__magic_name__		:		Tuple	   =     []
				__magic_name__		:		int	   =     CrossEntropyLoss(reduction="none"		)

				for start_index in logging.tqdm(range(0						,   len(_a		)						,   _a		)		):
					__magic_name__		:		Any	   =     min(start_index + batch_size						,   len(_a		)		)
					__magic_name__		:		Optional[int]	   =     encoded_texts[start_index:end_index]
					__magic_name__		:		Union[str, Any]	   =     attn_masks[start_index:end_index]

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

					__magic_name__		:		str	   =     encoded_batch

					with torch.no_grad():
						__magic_name__		:		Union[str, Any]	   =     model(_a						,   attention_mask=_a		).logits

					__magic_name__		:		Union[str, Any]	   =     out_logits[..., :-1, :].contiguous()
					__magic_name__		:		List[str]	   =     labels[..., 1:].contiguous()
					__magic_name__		:		Dict	   =     attn_mask[..., 1:].contiguous()

					__magic_name__		:		List[str]	   =     torch.expa(
					    (loss_fct(shift_logits.transpose(1						,   2		)						,   _a		) * shift_attention_mask_batch).sum(1		)
					    / shift_attention_mask_batch.sum(1		)		)

					ppls += perplexity_batch.tolist()

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


def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  str   )				->		bool:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     len(_snake_case   ) + 1
	__magic_name__		:		List[str]	   =     len(_snake_case   ) + 1

	# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
	# length i of input_string matches with prefix string of length j of
	# given pattern.
	# "dp" stands for dynamic programming.
	__magic_name__		:		str	   =     [[0 for i in range(_snake_case   )] for j in range(_snake_case   )]

	# since string of zero length match pattern of zero length
	__magic_name__		:		Optional[int]	   =     1

	# since pattern of zero length will never match with string of non-zero length
	for i in range(1      ,   _snake_case   ):
		__magic_name__		:		Optional[int]	   =     0

	# since string of zero length will match with pattern where there
	# is at least one * alternatively
	for j in range(1      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     dp[0][j - 2] if pattern[j - 1] == "*" else 0

	# now using bottom-up approach to find for all remaining lengths
	for i in range(1      ,   _snake_case   ):
		for j in range(1      ,   _snake_case   ):
			if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
				__magic_name__		:		Optional[int]	   =     dp[i - 1][j - 1]

			elif pattern[j - 1] == "*":
				if dp[i][j - 2] == 1:
					__magic_name__		:		Optional[Any]	   =     1
				elif pattern[j - 2] in (input_string[i - 1], "."):
					__magic_name__		:		List[Any]	   =     dp[i - 1][j]
				else:
					__magic_name__		:		Union[str, Any]	   =     0
			else:
				__magic_name__		:		Dict	   =     0

	return bool(dp[-1][-1]   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				snake_case :		Optional[Any]							      =  "aab"
				snake_case :		List[str]							      =  "c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F"{input_string} matches the given pattern {pattern}")
				else:
								print(F"{input_string} does not match with the given pattern {pattern}")
