"""simple docstring"""






# Copyright 2021 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							data







					def __iter__(				self   :	List[Any]			):
									for element in self.data:
													yield element






def   a_						(					lowerCamelCase=True       ):
				UpperCAmelCase__             =							Accelerator(even_batches=lowerCamelCase       )
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase = False       ):
				if iterable:
								UpperCAmelCase__             =							DummyIterableDataset(torch.as_tensor(range(lowerCamelCase       )       )       )
				else:
								UpperCAmelCase__             =							TensorDataset(torch.as_tensor(range(lowerCamelCase       )       )       )

				UpperCAmelCase__             =							DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase       )
				UpperCAmelCase__             =							accelerator.prepare(lowerCamelCase       )

				return dl



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			):
				UpperCAmelCase__             =							create_dataloader(accelerator=lowerCamelCase					,			dataset_size=lowerCamelCase					,			batch_size=lowerCamelCase       )

				UpperCAmelCase__             =							[len(batch[0]       ) for batch in dl]

				if accelerator.process_index == 0:
								assert batch_sizes == process_0_expected_batch_sizes
				elif accelerator.process_index == 1:
								assert batch_sizes == process_1_expected_batch_sizes



def   a_						(					):
				UpperCAmelCase__             =							create_accelerator()

				# without padding, we would expect a different number of batches
				verify_dataloader_batch_sizes(
				    lowerCamelCase					,			dataset_size=3					,			batch_size=1					,			process_0_expected_batch_sizes=[1, 1]					,			process_1_expected_batch_sizes=[1, 1]					,			)

				# without padding, we would expect the same number of batches, but different sizes
				verify_dataloader_batch_sizes(
				    lowerCamelCase					,			dataset_size=7					,			batch_size=2					,			process_0_expected_batch_sizes=[2, 2]					,			process_1_expected_batch_sizes=[2, 2]					,			)



def   a_						(					):
				UpperCAmelCase__             =							create_accelerator(even_batches=lowerCamelCase       )

				verify_dataloader_batch_sizes(
				    lowerCamelCase					,			dataset_size=3					,			batch_size=1					,			process_0_expected_batch_sizes=[1, 1]					,			process_1_expected_batch_sizes=[1]					,			)

				verify_dataloader_batch_sizes(
				    lowerCamelCase					,			dataset_size=7					,			batch_size=2					,			process_0_expected_batch_sizes=[2, 2]					,			process_1_expected_batch_sizes=[2, 1]					,			)



def   a_						(					):
				UpperCAmelCase__             =							create_accelerator(even_batches=lowerCamelCase       )

				UpperCAmelCase__             =							torch.nn.Linear(1					,			1       )
				UpperCAmelCase__             =							accelerator.prepare(lowerCamelCase       )

				UpperCAmelCase__             =							create_dataloader(lowerCamelCase					,			dataset_size=3					,			batch_size=1       )

				UpperCAmelCase__             =							[]
				with accelerator.join_uneven_inputs([ddp_model]       ):
								for batch_idx, batch in enumerate(lowerCamelCase       ):
												UpperCAmelCase__             =							ddp_model(batch[0].float()       )
												UpperCAmelCase__             =							output.sum()
												loss.backward()
												batch_idxs.append(lowerCamelCase       )

				accelerator.wait_for_everyone()

				if accelerator.process_index == 0:
								assert batch_idxs == [0, 1]
				elif accelerator.process_index == 1:
								assert batch_idxs == [0]



def   a_						(					lowerCamelCase       ):
				with warnings.catch_warnings(record=lowerCamelCase       ) as w:
								with accelerator.join_uneven_inputs([Mock()]       ):
												pass

								assert issubclass(w[-1].category					,			lowerCamelCase       )
								assert "only supported for multi-GPU" in str(w[-1].message       )



def   a_						(					):
				UpperCAmelCase__             =							True
				UpperCAmelCase__             =							False
				UpperCAmelCase__             =							create_accelerator(even_batches=lowerCamelCase       )
				UpperCAmelCase__             =							torch.nn.Linear(1					,			1       )
				UpperCAmelCase__             =							accelerator.prepare(lowerCamelCase       )
				UpperCAmelCase__             =							create_dataloader(lowerCamelCase					,			dataset_size=3					,			batch_size=1       )
				UpperCAmelCase__             =							create_dataloader(lowerCamelCase					,			dataset_size=3					,			batch_size=1       )

				with accelerator.join_uneven_inputs([ddp_model]					,			even_batches=lowerCamelCase       ):
								UpperCAmelCase__             =							train_dl.batch_sampler.even_batches
								UpperCAmelCase__             =							valid_dl.batch_sampler.even_batches

				assert train_dl_overridden_value == overridden_even_batches
				assert valid_dl_overridden_value == overridden_even_batches
				assert train_dl.batch_sampler.even_batches == default_even_batches
				assert valid_dl.batch_sampler.even_batches == default_even_batches



def   a_						(					):
				UpperCAmelCase__             =							True
				UpperCAmelCase__             =							False
				UpperCAmelCase__             =							create_accelerator(even_batches=lowerCamelCase       )
				UpperCAmelCase__             =							torch.nn.Linear(1					,			1       )
				UpperCAmelCase__             =							accelerator.prepare(lowerCamelCase       )
				create_dataloader(lowerCamelCase					,			dataset_size=3					,			batch_size=1					,			iterable=lowerCamelCase       )
				UpperCAmelCase__             =							create_dataloader(lowerCamelCase					,			dataset_size=3					,			batch_size=1       )

				with warnings.catch_warnings():
								warnings.filterwarnings('ignore'       )
								try:
												with accelerator.join_uneven_inputs([ddp_model]					,			even_batches=lowerCamelCase       ):
																UpperCAmelCase__             =							batch_dl.batch_sampler.even_batches
								except AttributeError:
												# ensure attribute error is not raised when processing iterable dl
												raise AssertionError

				assert batch_dl_overridden_value == overridden_even_batches
				assert batch_dl.batch_sampler.even_batches == default_even_batches



def   a_						(					):
				UpperCAmelCase__             =							create_accelerator()
				UpperCAmelCase__             =							torch.nn.Linear(1					,			1       )
				UpperCAmelCase__             =							accelerator.prepare(lowerCamelCase       )
				create_dataloader(lowerCamelCase					,			dataset_size=3					,			batch_size=1					,			iterable=lowerCamelCase       )

				with warnings.catch_warnings(record=lowerCamelCase       ) as w:
								with accelerator.join_uneven_inputs([ddp_model]					,			even_batches=lowerCamelCase       ):
												pass

								assert issubclass(w[-1].category					,			lowerCamelCase       )
								assert "only supported for map-style datasets" in str(w[-1].message       )



def   a_						(					):
				UpperCAmelCase__             =							create_accelerator()

				accelerator.print('Test that even_batches variable ensures uniform batches across processes'       )
				test_default_ensures_even_batch_sizes()

				accelerator.print('Run tests with even_batches disabled'       )
				test_can_disable_even_batches()

				accelerator.print('Test joining uneven inputs'       )
				test_can_join_uneven_inputs()

				accelerator.print('Test overriding even_batches when joining uneven inputs'       )
				test_join_can_override_even_batches()

				accelerator.print('Test overriding even_batches for mixed dataloader types'       )
				test_join_can_override_for_mixed_type_dataloaders()

				accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders'       )
				test_join_raises_warning_for_iterable_when_overriding_even_batches()

				accelerator.print('Test join with non DDP distributed raises warning'       )
				UpperCAmelCase__             =							accelerator.state.distributed_type
				UpperCAmelCase__             =							DistributedType.FSDP
				test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase       )
				UpperCAmelCase__             =							original_state


if __name__ == "__main__":
						main()




"""simple docstring"""






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

import datasets
from datasets import logging


lowerCAmelCase__  :  List[Any]						     =	'\\n\n'

lowerCAmelCase__  :  Tuple						     =	'\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'

lowerCAmelCase__  :  str						     =	'\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				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	str			):
									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 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	int = 16    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	List[str]=None			):
									if device is not None:
													assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
													if device == "gpu":
																	UpperCAmelCase__             =							'cuda'
									else:
													UpperCAmelCase__             =							'cuda' if torch.cuda.is_available() else 'cpu'

									UpperCAmelCase__             =							AutoModelForCausalLM.from_pretrained(lowerCamelCase__			)
									UpperCAmelCase__             =							model.to(lowerCamelCase__			)

									UpperCAmelCase__             =							AutoTokenizer.from_pretrained(lowerCamelCase__			)

									# 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:
													UpperCAmelCase__             =							list(tokenizer.special_tokens_map_extended.values()			)
													# check that the model already has at least one special token defined
													assert (
													    len(lowerCamelCase__			) > 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"
													UpperCAmelCase__             =							model.config.max_length - 1
									else:
													UpperCAmelCase__             =							model.config.max_length

									UpperCAmelCase__             =							tokenizer(
									    lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__    ,max_length=lowerCamelCase__    ,return_tensors='pt'    ,return_attention_mask=lowerCamelCase__    ,).to(lowerCamelCase__			)

									UpperCAmelCase__             =							encodings['input_ids']
									UpperCAmelCase__             =							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."

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

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

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

													UpperCAmelCase__             =							encoded_batch

													with torch.no_grad():
																	UpperCAmelCase__             =							model(lowerCamelCase__    ,attention_mask=lowerCamelCase__			).logits

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

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

													ppls += perplexity_batch.tolist()

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



