




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__         =      logging.get_logger()
@dataclass
class       a__   :
						_a							:   nn.Module
						_a							:   List[nn.Module] =   field(default_factory=snake_case__				)
						_a							:   list =   field(default_factory=snake_case__				)






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	len(list(m.modules()      )      ) == 1 or isinstance(_A		,      nn.Convad      ) or isinstance(_A		,      nn.BatchNormad      )
											if has_not_submodules:
																self.traced.append(_A      )






						def __call__(       self		,      _A      ):




											"""simple docstring"""



											for m in self.module.modules():
																self.handles.append(m.register_forward_hook(self._forward_hook      )      )
											self.module(_A      )
											[x.remove() for x in self.handles]
											return self






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return list(filter(lambda _A      : len(list(x.state_dict().keys()      )      ) > 0		,      self.traced      )      )






@dataclass
class       a__   :
						_a							:   nn.Module
						_a							:   nn.Module
						_a							:   int =   1
						_a							:   List =   field(default_factory=snake_case__				)
						_a							:   List =   field(default_factory=snake_case__				)
						_a							:   bool =   True






						def __call__(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	Tracker(self.dest      )(_A      ).parametrized
											__lowerCAmelCase           =	Tracker(self.src      )(_A      ).parametrized

											__lowerCAmelCase           =	list(filter(lambda _A      : type(_A      ) not in self.src_skip		,      _A      )      )
											__lowerCAmelCase           =	list(filter(lambda _A      : type(_A      ) not in self.dest_skip		,      _A      )      )

											if len(_A      ) != len(_A      ) and self.raise_if_mismatch:
																raise Exception(
																    f"""Numbers of operations are different. Source module has {len(_A      )} operations while"""
																    f""" destination module has {len(_A      )}."""      )

											for dest_m, src_m in zip(_A		,      _A      ):
																dest_m.load_state_dict(src_m.state_dict()      )
																if self.verbose == 1:
																					print(f"""Transfered from={src_m} to={dest_m}"""      )






class       a__   ( nn.Module				):






						def __init__(       self		,      _A      ):




											"""simple docstring"""



											super().__init__()

											__lowerCAmelCase           =	[]
											# - get the stem
											feature_blocks.append(("conv1", model.stem)      )
											# - get all the feature blocks
											for k, v in model.trunk_output.named_children():
																assert k.startswith("block"      ), f"""Unexpected layer name {k}"""
																__lowerCAmelCase           =	len(_A      ) + 1
																feature_blocks.append((f"""res{block_index}""", v)      )

											__lowerCAmelCase           =	nn.ModuleDict(_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											return get_trunk_forward_outputs(
											    _A		,      out_feat_keys=_A		,      feature_blocks=self._feature_blocks		,      )






class       a__   ( snake_case__				):






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	x.split("-"      )
											return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )






						def __getitem__(       self		,      _A      ):




											"""simple docstring"""



											if x not in self:
																__lowerCAmelCase           =	self.convert_name_to_timm(_A      )
																__lowerCAmelCase           =	partial(lambda: (timm.create_model(_A		,      pretrained=_A      ).eval(), None)      )

											else:
																__lowerCAmelCase           =	super().__getitem__(_A      )

											return val






class       a__   ( snake_case__				):






						def __getitem__(       self		,      _A      ):




											"""simple docstring"""



											if "seer" in x and "in1k" not in x:
																__lowerCAmelCase           =	RegNetModel
											else:
																__lowerCAmelCase           =	RegNetForImageClassification
											return val







def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[Tuple[str, str]]					):
					for from_key, to_key in keys:
										__lowerCAmelCase           =	from_state_dict[from_key].clone()
										print(F"""Copied key={from_key} to={to_key}"""					)
					return to_state_dict







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Callable[[], nn.Module]				,				SCREAMING_SNAKE_CASE_							:    Callable[[], nn.Module]				,				SCREAMING_SNAKE_CASE_							:    RegNetConfig				,				SCREAMING_SNAKE_CASE_							:    Path				,				SCREAMING_SNAKE_CASE_							:    bool = True				,				):
					print(F"""Converting {name}..."""					)
					with torch.no_grad():
										__lowerCAmelCase       ,							__lowerCAmelCase           =	from_model_func()
										__lowerCAmelCase           =	our_model_func(SCREAMING_SNAKE_CASE_					).eval()
										__lowerCAmelCase           =	ModuleTransfer(src=SCREAMING_SNAKE_CASE_				,				dest=SCREAMING_SNAKE_CASE_				,				raise_if_mismatch=SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	torch.randn((1, 3, 2_24, 2_24)					)
										module_transfer(SCREAMING_SNAKE_CASE_					)

					if from_state_dict is not None:
										__lowerCAmelCase           =	[]
										# for seer - in1k finetuned we have to manually copy the head
										if "seer" in name and "in1k" in name:
															__lowerCAmelCase           =	[("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
										__lowerCAmelCase           =	manually_copy_vissl_head(SCREAMING_SNAKE_CASE_				,				our_model.state_dict()				,				SCREAMING_SNAKE_CASE_					)
										our_model.load_state_dict(SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	our_model(SCREAMING_SNAKE_CASE_				,				output_hidden_states=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	(
					    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					) else our_outputs.last_hidden_state
					)

					__lowerCAmelCase           =	from_model(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	from_output[-1] if type(SCREAMING_SNAKE_CASE_					) is list else from_output

					# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
					if "seer" in name and "in1k" in name:
										__lowerCAmelCase           =	our_outputs.hidden_states[-1]

					assert torch.allclose(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					), "The model logits don't match the original one."

					if push_to_hub:
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / name				,				commit_message="Add model"				,				use_temp_dir=SCREAMING_SNAKE_CASE_				,				)

										__lowerCAmelCase           =	2_24 if "seer" not in name else 3_84
										# we can use the convnext one
										__lowerCAmelCase           =	AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"				,				size=SCREAMING_SNAKE_CASE_					)
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / name				,				commit_message="Add image processor"				,				use_temp_dir=SCREAMING_SNAKE_CASE_				,				)

										print(F"""Pushed {name}"""					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Path				,				SCREAMING_SNAKE_CASE_							:    str = None				,				SCREAMING_SNAKE_CASE_							:    bool = True					):
					__lowerCAmelCase           =	"imagenet-1k-id2label.json"
					__lowerCAmelCase           =	10_00
					__lowerCAmelCase           =	(1, num_labels)

					__lowerCAmelCase           =	"huggingface/label-files"
					__lowerCAmelCase           =	num_labels
					__lowerCAmelCase           =	json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				repo_type="dataset"					)					)				,				"r"					)					)
					__lowerCAmelCase           =	{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}

					__lowerCAmelCase           =	idalabel
					__lowerCAmelCase           =	{v: k for k, v in idalabel.items()}

					__lowerCAmelCase           =	partial(SCREAMING_SNAKE_CASE_				,				num_labels=SCREAMING_SNAKE_CASE_				,				idalabel=SCREAMING_SNAKE_CASE_				,				labelaid=SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	{
					    "regnet-x-002": ImageNetPreTrainedConfig(
					        depths=[1, 1, 4, 7]				,				hidden_sizes=[24, 56, 1_52, 3_68]				,				groups_width=8				,				layer_type="x"					),
					    "regnet-x-004": ImageNetPreTrainedConfig(
					        depths=[1, 2, 7, 12]				,				hidden_sizes=[32, 64, 1_60, 3_84]				,				groups_width=16				,				layer_type="x"					),
					    "regnet-x-006": ImageNetPreTrainedConfig(
					        depths=[1, 3, 5, 7]				,				hidden_sizes=[48, 96, 2_40, 5_28]				,				groups_width=24				,				layer_type="x"					),
					    "regnet-x-008": ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 5]				,				hidden_sizes=[64, 1_28, 2_88, 6_72]				,				groups_width=16				,				layer_type="x"					),
					    "regnet-x-016": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 2]				,				hidden_sizes=[72, 1_68, 4_08, 9_12]				,				groups_width=24				,				layer_type="x"					),
					    "regnet-x-032": ImageNetPreTrainedConfig(
					        depths=[2, 6, 15, 2]				,				hidden_sizes=[96, 1_92, 4_32, 10_08]				,				groups_width=48				,				layer_type="x"					),
					    "regnet-x-040": ImageNetPreTrainedConfig(
					        depths=[2, 5, 14, 2]				,				hidden_sizes=[80, 2_40, 5_60, 13_60]				,				groups_width=40				,				layer_type="x"					),
					    "regnet-x-064": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]				,				hidden_sizes=[1_68, 3_92, 7_84, 16_24]				,				groups_width=56				,				layer_type="x"					),
					    "regnet-x-080": ImageNetPreTrainedConfig(
					        depths=[2, 5, 15, 1]				,				hidden_sizes=[80, 2_40, 7_20, 19_20]				,				groups_width=1_20				,				layer_type="x"					),
					    "regnet-x-120": ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]				,				hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,				groups_width=1_12				,				layer_type="x"					),
					    "regnet-x-160": ImageNetPreTrainedConfig(
					        depths=[2, 6, 13, 1]				,				hidden_sizes=[2_56, 5_12, 8_96, 20_48]				,				groups_width=1_28				,				layer_type="x"					),
					    "regnet-x-320": ImageNetPreTrainedConfig(
					        depths=[2, 7, 13, 1]				,				hidden_sizes=[3_36, 6_72, 13_44, 25_20]				,				groups_width=1_68				,				layer_type="x"					),
					    # y variant
					    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,				hidden_sizes=[24, 56, 1_52, 3_68]				,				groups_width=8					),
					    "regnet-y-004": ImageNetPreTrainedConfig(
					        depths=[1, 3, 6, 6]				,				hidden_sizes=[48, 1_04, 2_08, 4_40]				,				groups_width=8					),
					    "regnet-y-006": ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 4]				,				hidden_sizes=[48, 1_12, 2_56, 6_08]				,				groups_width=16					),
					    "regnet-y-008": ImageNetPreTrainedConfig(
					        depths=[1, 3, 8, 2]				,				hidden_sizes=[64, 1_28, 3_20, 7_68]				,				groups_width=16					),
					    "regnet-y-016": ImageNetPreTrainedConfig(
					        depths=[2, 6, 17, 2]				,				hidden_sizes=[48, 1_20, 3_36, 8_88]				,				groups_width=24					),
					    "regnet-y-032": ImageNetPreTrainedConfig(
					        depths=[2, 5, 13, 1]				,				hidden_sizes=[72, 2_16, 5_76, 15_12]				,				groups_width=24					),
					    "regnet-y-040": ImageNetPreTrainedConfig(
					        depths=[2, 6, 12, 2]				,				hidden_sizes=[1_28, 1_92, 5_12, 10_88]				,				groups_width=64					),
					    "regnet-y-064": ImageNetPreTrainedConfig(
					        depths=[2, 7, 14, 2]				,				hidden_sizes=[1_44, 2_88, 5_76, 12_96]				,				groups_width=72					),
					    "regnet-y-080": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]				,				hidden_sizes=[1_68, 4_48, 8_96, 20_16]				,				groups_width=56					),
					    "regnet-y-120": ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]				,				hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,				groups_width=1_12					),
					    "regnet-y-160": ImageNetPreTrainedConfig(
					        depths=[2, 4, 11, 1]				,				hidden_sizes=[2_24, 4_48, 12_32, 30_24]				,				groups_width=1_12					),
					    "regnet-y-320": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]				,				hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,				groups_width=2_32					),
					    # models created by SEER -> https://arxiv.org/abs/2202.08360
					    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]				,				hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,				groups_width=2_32					),
					    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]				,				hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,				groups_width=3_28					),
					    "regnet-y-1280-seer": RegNetConfig(
					        depths=[2, 7, 17, 1]				,				hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,				groups_width=2_64					),
					    "regnet-y-2560-seer": RegNetConfig(
					        depths=[3, 7, 16, 1]				,				hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,				groups_width=6_40					),
					    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]				,				hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,				groups_width=10_10					),
					    # finetuned on imagenet
					    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]				,				hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,				groups_width=2_32					),
					    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]				,				hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,				groups_width=3_28					),
					    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]				,				hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,				groups_width=2_64					),
					    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[3, 7, 16, 1]				,				hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,				groups_width=6_40					),
					    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]				,				hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,				groups_width=10_10					),
					}

					__lowerCAmelCase           =	NameToOurModelFuncMap()
					__lowerCAmelCase           =	NameToFromModelFuncMap()
					# add seer weights logic

					def load_using_classy_vision(SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Callable[[], nn.Module]					) -> Tuple[nn.Module, Dict]:
										__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,				model_dir=str(SCREAMING_SNAKE_CASE_					)				,				map_location="cpu"					)
										__lowerCAmelCase           =	model_func()
										# check if we have a head, if yes add it
										__lowerCAmelCase           =	files["classy_state_dict"]["base_model"]["model"]
										__lowerCAmelCase           =	model_state_dict["trunk"]
										model.load_state_dict(SCREAMING_SNAKE_CASE_					)
										return model.eval(), model_state_dict["heads"]

					# pretrained
					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"				,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)				,				)

					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"				,				lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=27				,				group_width=10_10				,				w_a=17_44				,				w_a=6_20.83				,				w_m=2.52					)					)					)				,				)

					# IN1K finetuned
					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"				,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)				,				)

					__lowerCAmelCase           =	partial(
					    SCREAMING_SNAKE_CASE_				,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"				,				lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=27				,				group_width=10_10				,				w_a=17_44				,				w_a=6_20.83				,				w_m=2.52					)					)					)				,				)

					if model_name:
										convert_weight_and_push(
										    SCREAMING_SNAKE_CASE_				,				names_to_from_model_map[model_name]				,				names_to_ours_model_map[model_name]				,				names_to_config[model_name]				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				)
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(
															    SCREAMING_SNAKE_CASE_				,				names_to_from_model_map[model_name]				,				names_to_ours_model_map[model_name]				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				)
					return config, expected_shape


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
					        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					UpperCamelCase__         =      parser.parse_args()

					UpperCamelCase__         =      args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










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

import datasets
from datasets import logging


UpperCamelCase__         =      """\

"""

UpperCamelCase__         =      """
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
"""

UpperCamelCase__         =      """
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       a__   ( datasets.Metric				):






						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 = 1_6		,      _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":
																					__lowerCAmelCase           =	"cuda"
											else:
																__lowerCAmelCase           =	"cuda" if torch.cuda.is_available() else "cpu"

											__lowerCAmelCase           =	AutoModelForCausalLM.from_pretrained(_A      )
											__lowerCAmelCase           =	model.to(_A      )

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

											__lowerCAmelCase           =	tokenizer(
											    _A		,      add_special_tokens=_A		,      padding=_A		,      truncation=_A		,      max_length=_A		,      return_tensors="pt"		,      return_attention_mask=_A		,      ).to(_A      )

											__lowerCAmelCase           =	encodings["input_ids"]
											__lowerCAmelCase           =	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."

											__lowerCAmelCase           =	[]
											__lowerCAmelCase           =	CrossEntropyLoss(reduction="none"      )

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

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

																__lowerCAmelCase           =	encoded_batch

																with torch.no_grad():
																					__lowerCAmelCase           =	model(_A		,      attention_mask=_A      ).logits

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

																__lowerCAmelCase           =	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      )}




