




'''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_      :  Optional[int]				     =  '\\n\n'

lowerCAmelCase_      :  int				     =  '\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_      :  Optional[int]				     =  '\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      SCREAMING_SNAKE_CASE    (						datasets.Metric				):






      def 							lowercase_						( self   :  Optional[int]       ):


           '''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 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  List[str]						,						lowercase__   :  Optional[int]						,						lowercase__   :  Dict = 16						,						lowercase__   :  List[Any] = True						,						lowercase__   :  str=None       ):


           '''simple docstring'''

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

           a_     :							Any     						=  AutoModelForCausalLM.from_pretrained(__UpperCAmelCase       )
           a_     :							str     						=  model.to(__UpperCAmelCase       )

           a_     :							Union[str, Any]     						=  AutoTokenizer.from_pretrained(__UpperCAmelCase       )

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

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

           a_     :							Tuple     						=  encodings["input_ids"]
           a_     :							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."

           a_     :							List[Any]     						=  []
           a_     :							Tuple     						=  CrossEntropyLoss(reduction="""none"""       )

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

                if add_start_token:
                     a_     :							str     						=  torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0       )       ).to(__UpperCAmelCase       )
                     a_     :							str     						=  torch.cat([bos_tokens_tensor, encoded_batch]						,						dim=1       )
                     a_     :							Union[str, Any]     						=  torch.cat(
                         [torch.ones(bos_tokens_tensor.size()						,						dtype=torch.intaa       ).to(__UpperCAmelCase       ), attn_mask]						,						dim=1       )

                a_     :							Any     						=  encoded_batch

                with torch.no_grad():
                     a_     :							int     						=  model(__UpperCAmelCase						,						attention_mask=__UpperCAmelCase       ).logits

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

                a_     :							Optional[Any]     						=  torch.expa(
                    (loss_fct(shift_logits.transpose(1						,						2       )						,						__UpperCAmelCase       ) * shift_attention_mask_batch).sum(1       )
                    / shift_attention_mask_batch.sum(1       )       )

                ppls += perplexity_batch.tolist()

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





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[List[ImageInput]]:
				if isinstance(__UpperCamelCase	,				(list, tuple) ) and isinstance(videos[0]	,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(__UpperCamelCase	,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(__UpperCamelCase ):
								return [[videos]]

				raise ValueError(F'Could not make batched video from {videos}' )






class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:List[str] =       ["pixel_values"]



							def __init__(							self ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = 1 / 255 ,					__UpperCAmelCase = True ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											super().__init__(**__UpperCAmelCase				)
											a__	:							int      						= size if size is not None else {"shortest_edge": 256}
											a__	:							str      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= crop_size if crop_size is not None else {"height": 224, "width": 224}
											a__	:							int      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

											a__	:							str      						= do_resize
											a__	:							Union[str, Any]      						= size
											a__	:							Any      						= do_center_crop
											a__	:							Optional[int]      						= crop_size
											a__	:							Optional[int]      						= resample
											a__	:							Optional[Any]      						= do_rescale
											a__	:							Union[str, Any]      						= rescale_factor
											a__	:							Dict      						= offset
											a__	:							List[str]      						= do_normalize
											a__	:							Dict      						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__	:							Tuple      						= image_std if image_std is not None else IMAGENET_STANDARD_STD



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											if "shortest_edge" in size:
															a__	:							Optional[Any]      						= get_resize_output_image_size(__UpperCAmelCase ,					size["shortest_edge"] ,					default_to_square=__UpperCAmelCase				)
											elif "height" in size and "width" in size:
															a__	:							Union[str, Any]      						= (size["height"], size["width"])
											else:
															raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'				)
											return resize(__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= get_size_dict(__UpperCAmelCase				)
											if "height" not in size or "width" not in size:
															raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'				)
											return center_crop(__UpperCAmelCase ,					size=(size["height"], size["width"]) ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= image.astype(np.floataa				)
											if offset:
															a__	:							Dict      						= image - (scale / 2)
											return rescale(__UpperCAmelCase ,					scale=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											return normalize(__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					):







											"""simple docstring"""



											if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."				)

											if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True."				)

											if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."				)

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True."				)

											if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."				)

											# All transformations expect numpy arrays.
											a__	:							int      						= to_numpy_array(__UpperCAmelCase				)

											if do_resize:
															a__	:							List[Any]      						= self.resize(image=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase				)

											if do_center_crop:
															a__	:							str      						= self.center_crop(__UpperCAmelCase ,					size=__UpperCAmelCase				)

											if do_rescale:
															a__	:							Optional[Any]      						= self.rescale(image=__UpperCAmelCase ,					scale=__UpperCAmelCase ,					offset=__UpperCAmelCase				)

											if do_normalize:
															a__	:							Union[str, Any]      						= self.normalize(image=__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase				)

											a__	:							Optional[int]      						= to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase				)
											return image




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							List[Any]      						= do_resize if do_resize is not None else self.do_resize
											a__	:							Dict      						= resample if resample is not None else self.resample
											a__	:							Tuple      						= do_center_crop if do_center_crop is not None else self.do_center_crop
											a__	:							Optional[Any]      						= do_rescale if do_rescale is not None else self.do_rescale
											a__	:							Optional[int]      						= rescale_factor if rescale_factor is not None else self.rescale_factor
											a__	:							List[Any]      						= offset if offset is not None else self.offset
											a__	:							Optional[Any]      						= do_normalize if do_normalize is not None else self.do_normalize
											a__	:							int      						= image_mean if image_mean is not None else self.image_mean
											a__	:							List[Any]      						= image_std if image_std is not None else self.image_std

											a__	:							int      						= size if size is not None else self.size
											a__	:							Tuple      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							List[Any]      						= crop_size if crop_size is not None else self.crop_size
											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

											if not valid_images(__UpperCAmelCase				):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray."				)

											a__	:							Union[str, Any]      						= make_batched(__UpperCAmelCase				)

											a__	:							Optional[Any]      						= [
											    [
											        self._preprocess_image(
											            image=__UpperCAmelCase ,					do_resize=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					do_center_crop=__UpperCAmelCase ,					crop_size=__UpperCAmelCase ,					do_rescale=__UpperCAmelCase ,					rescale_factor=__UpperCAmelCase ,					offset=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					image_mean=__UpperCAmelCase ,					image_std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					)
											        for img in video
											    ]
											    for video in videos
											]

											a__	:							Any      						= {"pixel_values": videos}
											return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase				)


