
"""simple docstring"""





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

import datasets
from datasets import logging


_a								=						"\\n\n"

_a								=						"\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"

_a								=						"\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        _UpperCAmelCase(       datasets.Metric      ):



		def 		UpperCAmelCase				(   self)			->			Any:






							'''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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = 16							,						__a = True							,						__a=None)			->			List[Any]:






							'''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(__a)
							_UpperCamelCase									=    model.to(__a)

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

							_UpperCamelCase									=    tokenizer(
							    __a							,						add_special_tokens=__a							,						padding=__a							,						truncation=__a							,						max_length=__a							,						return_tensors='''pt'''							,						return_attention_mask=__a							,						).to(__a)

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

												_UpperCamelCase									=    encoded_batch

												with torch.no_grad():
																	_UpperCamelCase									=    model(__a							,						attention_mask=__a).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)							,						__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)}












from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
