




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

import datasets
from datasets import logging


SCREAMING_SNAKE_CASE__     :     Tuple		    =       "\\n\n"

SCREAMING_SNAKE_CASE__     :     Any		    =       "\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"

SCREAMING_SNAKE_CASE__     :     Dict		    =       "\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       a_       (     datasets.Metric     ):





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				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      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = 16      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE=None      )  ->					List[Any]:




				"""simple docstring"""



				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(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						model.to(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )

				# 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(SCREAMING_SNAKE_CASE      ) > 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(
				    SCREAMING_SNAKE_CASE      ,     add_special_tokens=SCREAMING_SNAKE_CASE      ,     padding=SCREAMING_SNAKE_CASE      ,     truncation=SCREAMING_SNAKE_CASE      ,     max_length=SCREAMING_SNAKE_CASE      ,     return_tensors='pt'      ,     return_attention_mask=SCREAMING_SNAKE_CASE      ,     ).to(SCREAMING_SNAKE_CASE      )

				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(SCREAMING_SNAKE_CASE      )      ,     SCREAMING_SNAKE_CASE      )      ):
							SCREAMING_SNAKE_CASE_								=						min(start_index + batch_size      ,     len(SCREAMING_SNAKE_CASE      )      )
							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(SCREAMING_SNAKE_CASE      )
										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(SCREAMING_SNAKE_CASE      ), attn_mask]      ,     dim=1      )

							SCREAMING_SNAKE_CASE_								=						encoded_batch

							with torch.no_grad():
										SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ).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      )      ,     SCREAMING_SNAKE_CASE      ) * shift_attention_mask_batch).sum(1      )
							    / shift_attention_mask_batch.sum(1      )      )

							ppls += perplexity_batch.tolist()

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						gather(SCREAMING_SNAKE_CASE )
			assert gathered_tensor.tolist() == list(range(1       ,    state.num_processes**2 + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[state.process_index]
			SCREAMING_SNAKE_CASE_								=						gather_object(SCREAMING_SNAKE_CASE )
			assert len(SCREAMING_SNAKE_CASE ) == state.num_processes, F'{gathered_obj}, {len(SCREAMING_SNAKE_CASE )} != {state.num_processes}'
			assert gathered_obj == list(range(state.num_processes ) ), F'{gathered_obj} != {list(range(state.num_processes ) )}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						broadcast(SCREAMING_SNAKE_CASE )
			assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
			assert broadcasted_tensor.tolist() == list(range(1       ,    state.num_processes + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if state.is_main_process:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes + 1 ).to(state.device )
			else:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes ).to(state.device )
			SCREAMING_SNAKE_CASE_								=						pad_across_processes(SCREAMING_SNAKE_CASE )
			assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
			if not state.is_main_process:
						assert padded_tensor.tolist() == list(range(0       ,    state.num_processes ) ) + [0]

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'sum' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([4.0, 6] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'mean' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([2.0, 3] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			main()

def lowercase    (  )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						PartialState()
			state.print(F'State: {state}' )
			state.print('testing gather' )
			test_gather(SCREAMING_SNAKE_CASE )
			state.print('testing gather_object' )
			test_gather_object(SCREAMING_SNAKE_CASE )
			state.print('testing broadcast' )
			test_broadcast(SCREAMING_SNAKE_CASE )
			state.print('testing pad_across_processes' )
			test_pad_across_processes(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_sum' )
			test_reduce_sum(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_mean' )
			test_reduce_mean(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				main()
