


"""simple docstring"""




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

import datasets
from datasets import logging


__UpperCamelCase						=      "\\n\n"

__UpperCamelCase						=      "\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"

__UpperCamelCase						=      "\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    ):




		'''simple docstring'''







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

								SCREAMING_SNAKE_CASE													=					AutoModelForCausalLM.from_pretrained(_a				)
								SCREAMING_SNAKE_CASE													=					model.to(_a				)

								SCREAMING_SNAKE_CASE													=					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:
														SCREAMING_SNAKE_CASE													=					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"
														SCREAMING_SNAKE_CASE													=					model.config.max_length - 1
								else:
														SCREAMING_SNAKE_CASE													=					model.config.max_length

								SCREAMING_SNAKE_CASE													=					tokenizer(
								    _a						,       add_special_tokens=_a						,       padding=_a						,       truncation=_a						,       max_length=_a						,       return_tensors='pt'						,       return_attention_mask=_a						,       ).to(_a				)

								SCREAMING_SNAKE_CASE													=					encodings["input_ids"]
								SCREAMING_SNAKE_CASE													=					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."

								SCREAMING_SNAKE_CASE													=					[]
								SCREAMING_SNAKE_CASE													=					CrossEntropyLoss(reduction='none'				)

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

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

														SCREAMING_SNAKE_CASE													=					encoded_batch

														with torch.no_grad():
																				SCREAMING_SNAKE_CASE													=					model(_a						,       attention_mask=_a				).logits

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

														SCREAMING_SNAKE_CASE													=					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				)}




snake_case :		Optional[int]							      =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"






def   lowerCAmelCase_						(					_snake_case      :  bytes   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Tuple	   =     F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
		raise TypeError(_snake_case   )

	__magic_name__		:		Optional[int]	   =     "".join(bin(_snake_case   )[2:].zfill(8   ) for byte in data   )

	__magic_name__		:		List[Any]	   =     len(_snake_case   ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		__magic_name__		:		List[str]	   =     B"=" * ((6 - len(_snake_case   ) % 6) // 2)

		# Append binary_stream with arbitrary binary digits (0's by default) to make its
		# length a multiple of 6.
		binary_stream += "0" * (6 - len(_snake_case   ) % 6)
	else:
		__magic_name__		:		List[str]	   =     B""

	# Encode every 6 binary digits to their corresponding Base64 character
	return (
	    "".join(
	        B64_CHARSET[int(binary_stream[index : index + 6]      ,   2   )]
	        for index in range(0      ,   len(_snake_case   )      ,   6   )   ).encode()
	    + padding
	)






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ) and not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		List[str]	   =     (
		    "argument should be a bytes-like object or ASCII string, "
		    F'''not \'{encoded_data.__class__.__name__}\''''
		)
		raise TypeError(_snake_case   )

	# In case encoded_data is a bytes-like object, make sure it contains only
	# ASCII characters so we convert it to a string object
	if isinstance(_snake_case      ,   _snake_case   ):
		try:
			__magic_name__		:		List[Any]	   =     encoded_data.decode("utf-8"   )
		except UnicodeDecodeError:
			raise ValueError("base64 encoded data should only contain ASCII characters"   )

	__magic_name__		:		List[str]	   =     encoded_data.count("="   )

	# Check if the encoded string contains non base64 characters
	if padding:
		assert all(
		    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
	else:
		assert all(
		    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

	# Check the padding
	assert len(_snake_case   ) % 4 == 0 and padding < 3, "Incorrect padding"

	if padding:
		# Remove padding if there is one
		__magic_name__		:		Optional[int]	   =     encoded_data[:-padding]

		__magic_name__		:		Dict	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
	else:
		__magic_name__		:		Union[str, Any]	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )

	__magic_name__		:		List[Any]	   =     [
	    int(binary_stream[index : index + 8]      ,   2   )
	    for index in range(0      ,   len(_snake_case   )      ,   8   )
	]

	return bytes(_snake_case   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
