"""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__      						=  "\\n\n"

SCREAMING_SNAKE_CASE__      						=  "\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"

SCREAMING_SNAKE_CASE__      						=  "\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   lowerCAmelCase_    (			datasets.Metric					):



				"""simple docstring"""

				def 		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 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = 16		,    lowerCAmelCase = True		,    lowerCAmelCase=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(lowerCAmelCase    )
										snake_case								=			model.to(lowerCAmelCase    )

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

										snake_case								=			tokenizer(
										    lowerCAmelCase		,    add_special_tokens=lowerCAmelCase		,    padding=lowerCAmelCase		,    truncation=lowerCAmelCase		,    max_length=lowerCAmelCase		,    return_tensors='pt'		,    return_attention_mask=lowerCAmelCase		,    ).to(lowerCAmelCase    )

										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(lowerCAmelCase    )		,    lowerCAmelCase    )    ):
																snake_case								=			min(start_index + batch_size		,    len(lowerCAmelCase    )    )
																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(lowerCAmelCase    )
																						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(lowerCAmelCase    ), attn_mask]		,    dim=1    )

																snake_case								=			encoded_batch

																with torch.no_grad():
																						snake_case								=			model(lowerCAmelCase		,    attention_mask=lowerCAmelCase    ).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    )		,    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    )}



"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    vocab_size=lowerCAmelCase		,    hidden_size=lowerCAmelCase		,    num_hidden_layers=lowerCAmelCase		,    num_attention_heads=lowerCAmelCase		,    intermediate_size=lowerCAmelCase		,    hidden_act=lowerCAmelCase		,    hidden_dropout_prob=lowerCAmelCase		,    attention_probs_dropout_prob=lowerCAmelCase		,    max_position_embeddings=lowerCAmelCase		,    type_vocab_size=lowerCAmelCase		,    initializer_range=lowerCAmelCase		,    layer_norm_eps=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.task in ["question-answering", "sequence-classification"]:
																return OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'sequence'}),
																        ('bbox', {0: 'batch', 1: 'sequence'}),
																        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
																    ]    )
										else:
																return OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'sequence'}),
																        ('bbox', {0: 'batch', 1: 'sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'sequence'}),
																        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
																    ]    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

										setattr(processor.image_processor		,    'apply_ocr'		,    lowerCAmelCase    )

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_batch		,    num_token_to_add=0    )
										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										snake_case								=			processor.tokenizer.num_special_tokens_to_add(lowerCAmelCase    )
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_sequence		,    num_token_to_add=lowerCAmelCase    )
										# Generate dummy inputs according to compute batch and sequence
										snake_case								=			[[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * batch_size

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
										snake_case								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


