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

import datasets
from datasets import logging


_lowerCamelCase       :				Tuple       =			'''\

'''

_lowerCamelCase       :				List[str]       =			'''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

_lowerCamelCase       :				Any       =			'''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''




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





	'''simple docstring'''



	def  A							(  self	:    Union[str, 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	:    Optional[int]			,  lowercase	:    Optional[Any]			,  lowercase	:    Any			,  lowercase	:    int = 16			,  lowercase	:    bool = True			,  lowercase	:    int=None	):


				'''simple docstring'''


				if device is not None:
							assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
							if device == "gpu":
										_snake_case		=			'cuda'
				else:
							_snake_case		=			'cuda' if torch.cuda.is_available() else 'cpu'

				_snake_case		=			AutoModelForCausalLM.from_pretrained(lowercase	)
				_snake_case		=			model.to(lowercase	)

				_snake_case		=			AutoTokenizer.from_pretrained(lowercase	)

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

				_snake_case		=			tokenizer(
				    lowercase			,  add_special_tokens=lowercase			,  padding=lowercase			,  truncation=lowercase			,  max_length=lowercase			,  return_tensors='pt'			,  return_attention_mask=lowercase			,  ).to(lowercase	)

				_snake_case		=			encodings['input_ids']
				_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."

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

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

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

							_snake_case		=			encoded_batch

							with torch.no_grad():
										_snake_case		=			model(lowercase			,  attention_mask=lowercase	).logits

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

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

							ppls += perplexity_batch.tolist()

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

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)