



"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				     =       logging.get_logger(__name__)

snake_case_				     =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class       A_			(     __snake_case				):



						"""simple docstring"""
						__UpperCamelCase						=							"""donut-swin"""

						__UpperCamelCase						=							{
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}






						def __init__(					self :str   ,  lowercase_ :Optional[Any]=2_24   ,  lowercase_ :Optional[Any]=4   ,  lowercase_ :List[str]=3   ,  lowercase_ :List[Any]=96   ,  lowercase_ :List[Any]=[2, 2, 6, 2]   ,  lowercase_ :Dict=[3, 6, 12, 24]   ,  lowercase_ :int=7   ,  lowercase_ :Union[str, Any]=4.0   ,  lowercase_ :List[Any]=True   ,  lowercase_ :List[Any]=0.0   ,  lowercase_ :str=0.0   ,  lowercase_ :Union[str, Any]=0.1   ,  lowercase_ :str="gelu"   ,  lowercase_ :Tuple=False   ,  lowercase_ :str=0.02   ,  lowercase_ :List[Any]=1E-5   ,  **lowercase_ :Dict   ,  )				->			int:
												super().__init__(**a_					)

												UpperCAmelCase             = image_size
												UpperCAmelCase             = patch_size
												UpperCAmelCase             = num_channels
												UpperCAmelCase             = embed_dim
												UpperCAmelCase             = depths
												UpperCAmelCase             = len(a_					)
												UpperCAmelCase             = num_heads
												UpperCAmelCase             = window_size
												UpperCAmelCase             = mlp_ratio
												UpperCAmelCase             = qkv_bias
												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob
												UpperCAmelCase             = drop_path_rate
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = use_absolute_embeddings
												UpperCAmelCase             = layer_norm_eps
												UpperCAmelCase             = initializer_range
												# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												UpperCAmelCase             = int(embed_dim * 2 ** (len(a_					) - 1)					)









"""simple docstring"""
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 :    Union[str, Any]									=				"""\

"""

SCREAMING_SNAKE_CASE :    Any									=				"""
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
"""

SCREAMING_SNAKE_CASE :    Dict									=				"""
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 			_UpperCAmelCase (							datasets.Metric				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''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 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = 16				,       a_ = True				,       a_=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	:						Optional[Any]						=				'''cuda'''
			else:
				__snake_case	:						Tuple						=				'''cuda''' if torch.cuda.is_available() else '''cpu'''

			__snake_case	:						int						=				AutoModelForCausalLM.from_pretrained(a_					)
			__snake_case	:						Optional[int]						=				model.to(a_					)

			__snake_case	:						Optional[int]						=				AutoTokenizer.from_pretrained(a_					)

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

			__snake_case	:						Tuple						=				tokenizer(
			    a_				,       add_special_tokens=a_				,       padding=a_				,       truncation=a_				,       max_length=a_				,       return_tensors='''pt'''				,       return_attention_mask=a_				,       ).to(a_					)

			__snake_case	:						List[Any]						=				encodings['''input_ids''']
			__snake_case	:						str						=				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	:						Union[str, Any]						=				[]
			__snake_case	:						str						=				CrossEntropyLoss(reduction='''none'''					)

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

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

				__snake_case	:						List[Any]						=				encoded_batch

				with torch.no_grad():
					__snake_case	:						List[str]						=				model(a_				,       attention_mask=a_					).logits

				__snake_case	:						List[str]						=				out_logits[..., :-1, :].contiguous()
				__snake_case	:						int						=				labels[..., 1:].contiguous()
				__snake_case	:						int						=				attn_mask[..., 1:].contiguous()

				__snake_case	:						Tuple						=				torch.expa(
				    (loss_fct(shift_logits.transpose(1				,       2					)				,       a_					) * shift_attention_mask_batch).sum(1					)
				    / shift_attention_mask_batch.sum(1					)					)

				ppls += perplexity_batch.tolist()

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





