



'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


_lowerCamelCase	:       Dict      	=	logging.get_logger(__name__)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ["""pixel_values"""]






    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Union[int, float] = 1 / 2_5_5  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					bool = True  ,						**UpperCamelCase__      :					Tuple  ,						):


       """simple docstring"""







       super().__init__(**UpperCamelCase__ )
       UpperCamelCase					=  size if size is not None else {'shortest_edge': 2_2_4}
       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						default_to_square=UpperCamelCase__ )
       UpperCamelCase					=  crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						param_name='crop_size' )

       UpperCamelCase					=  do_resize
       UpperCamelCase					=  size
       UpperCamelCase					=  resample
       UpperCamelCase					=  do_rescale
       UpperCamelCase					=  rescale_factor
       UpperCamelCase					=  do_center_crop
       UpperCamelCase					=  crop_size
       UpperCamelCase					=  do_flip_channel_order






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Dict[str, int]  ,						UpperCamelCase__      :					PILImageResampling = PIL.Image.BILINEAR  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__      :					Union[str, Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						default_to_square=UpperCamelCase__ )
       if "shortest_edge" not in size:
          raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
       UpperCamelCase					=  get_resize_output_image_size(UpperCamelCase__  ,						size=size['shortest_edge']  ,						default_to_square=UpperCamelCase__ )
       return resize(UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Dict[str, int]  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__      :					int  ,						):


       """simple docstring"""







       UpperCamelCase					=  get_size_dict(UpperCamelCase__ )
       if "height" not in size or "width" not in size:
          raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
       return center_crop(UpperCamelCase__  ,						size=(size['height'], size['width'])  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Union[int, float]  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__      :					Any  ,						):


       """simple docstring"""







       return rescale(UpperCamelCase__  ,						scale=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None ):


       """simple docstring"""







       return flip_channel_order(UpperCamelCase__  ,						data_format=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					ImageInput  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					PILImageResampling = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					float = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Optional[Union[str, TensorType]] = None  ,						UpperCamelCase__      :					ChannelDimension = ChannelDimension.FIRST  ,						**UpperCamelCase__      :					Union[str, Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  do_resize if do_resize is not None else self.do_resize
       UpperCamelCase					=  resample if resample is not None else self.resample
       UpperCamelCase					=  do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase					=  rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase					=  do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase					=  (
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       UpperCamelCase					=  size if size is not None else self.size
       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						default_to_square=UpperCamelCase__ )
       UpperCamelCase					=  crop_size if crop_size is not None else self.crop_size
       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						param_name='crop_size' )

       UpperCamelCase					=  make_list_of_images(UpperCamelCase__ )

       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.' )

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

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

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

       # All transformations expect numpy arrays.
       UpperCamelCase					=  [to_numpy_array(UpperCamelCase__ ) for image in images]

       if do_resize:
          UpperCamelCase					=  [self.resize(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__ ) for image in images]

       if do_center_crop:
          UpperCamelCase					=  [self.center_crop(image=UpperCamelCase__  ,						size=UpperCamelCase__ ) for image in images]

       if do_rescale:
          UpperCamelCase					=  [self.rescale(image=UpperCamelCase__  ,						scale=UpperCamelCase__ ) for image in images]

       # the pretrained checkpoints assume images are BGR, not RGB
       if do_flip_channel_order:
          UpperCamelCase					=  [self.flip_channel_order(image=UpperCamelCase__ ) for image in images]

       UpperCamelCase					=  [to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__ ) for image in images]

       UpperCamelCase					=  {'pixel_values': images}
       return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__ )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					List[Tuple] = None ):


       """simple docstring"""







       UpperCamelCase					=  outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
          if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
             raise ValueError(
                 'Make sure that you pass in as many target sizes as the batch dimension of the logits' )

          if is_torch_tensor(UpperCamelCase__ ):
             UpperCamelCase					=  target_sizes.numpy()

          UpperCamelCase					=  []

          for idx in range(len(UpperCamelCase__ ) ):
             UpperCamelCase					=  torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0 )  ,						size=target_sizes[idx]  ,						mode='bilinear'  ,						align_corners=UpperCamelCase__ )
             UpperCamelCase					=  resized_logits[0].argmax(dim=0 )
             semantic_segmentation.append(UpperCamelCase__ )
       else:
          UpperCamelCase					=  logits.argmax(dim=1 )
          UpperCamelCase					=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

       return semantic_segmentation






'''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	:       Union[str, Any]      	=	"\\n\n"

_lowerCamelCase	:       List[str]      	=	"\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	:       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  SCREAMING_SNAKE_CASE (  datasets.Metric		):






    """simple docstring"""





    def  A       (  self      :					Tuple ):


       """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[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int = 1_6  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					List[Any]=None ):


       """simple docstring"""







       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(UpperCamelCase__ )
       UpperCamelCase					=  model.to(UpperCamelCase__ )

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

       UpperCamelCase					=  tokenizer(
           UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						return_tensors='pt'  ,						return_attention_mask=UpperCamelCase__  ,						).to(UpperCamelCase__ )

       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(UpperCamelCase__ )  ,						UpperCamelCase__ ) ):
          UpperCamelCase					=  min(start_index + batch_size  ,						len(UpperCamelCase__ ) )
          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(UpperCamelCase__ )
             UpperCamelCase					=  torch.cat([bos_tokens_tensor, encoded_batch]  ,						dim=1 )
             UpperCamelCase					=  torch.cat(
                 [torch.ones(bos_tokens_tensor.size()  ,						dtype=torch.intaa ).to(UpperCamelCase__ ), attn_mask]  ,						dim=1 )

          UpperCamelCase					=  encoded_batch

          with torch.no_grad():
             UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__ ).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 )  ,						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__ )}

