




"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)




class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	Any    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Union[str, Any]=1_3    ,   lowercase__					:	List[str]=7    ,   lowercase__					:	Any=True    ,   lowercase__					:	List[str]=True    ,   lowercase__					:	List[Any]=True    ,   lowercase__					:	Optional[Any]=True    ,   lowercase__					:	Dict=9_9    ,   lowercase__					:	int=3_2    ,   lowercase__					:	Tuple=2    ,   lowercase__					:	Optional[Any]=4    ,   lowercase__					:	List[str]=3_7    ,   lowercase__					:	int="gelu"    ,   lowercase__					:	Optional[Any]=0.1    ,   lowercase__					:	int=0.1    ,   lowercase__					:	int=5_1_2    ,   lowercase__					:	str=1_6    ,   lowercase__					:	Union[str, Any]=2    ,   lowercase__					:	Dict=0.0_2    ,   lowercase__					:	Dict=3    ,   lowercase__					:	Tuple=4    ,   lowercase__					:	Dict=None    ,   ):
						__lowercase				:		List[str]												=				parent
						__lowercase				:		Any												=				1_3
						__lowercase				:		Dict												=				7
						__lowercase				:		Optional[int]												=				True
						__lowercase				:		List[Any]												=				True
						__lowercase				:		Optional[int]												=				True
						__lowercase				:		List[Any]												=				True
						__lowercase				:		Tuple												=				9_9
						__lowercase				:		Tuple												=				3_2
						__lowercase				:		Optional[int]												=				2
						__lowercase				:		Union[str, Any]												=				4
						__lowercase				:		str												=				3_7
						__lowercase				:		Any												=				"gelu"
						__lowercase				:		List[Any]												=				0.1
						__lowercase				:		Optional[int]												=				0.1
						__lowercase				:		List[Any]												=				5_1_2
						__lowercase				:		Dict												=				1_6
						__lowercase				:		Union[str, Any]												=				2
						__lowercase				:		Any												=				0.0_2
						__lowercase				:		List[Any]												=				3
						__lowercase				:		int												=				4
						__lowercase				:		str												=				None



				def       snake_case    (     self					:	str						):
						__lowercase				:		Union[str, Any]												=				ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size						)

						__lowercase				:		Any												=				None
						if self.use_input_mask:
								__lowercase				:		Tuple												=				random_attention_mask([self.batch_size, self.seq_length]						)

						__lowercase				:		Any												=				None
						if self.use_token_type_ids:
								__lowercase				:		List[str]												=				ids_tensor([self.batch_size, self.seq_length]    ,   self.type_vocab_size						)

						__lowercase				:		List[str]												=				None
						__lowercase				:		int												=				None
						__lowercase				:		Dict												=				None
						if self.use_labels:
								__lowercase				:		Optional[Any]												=				ids_tensor([self.batch_size]    ,   self.type_sequence_label_size						)
								__lowercase				:		Tuple												=				ids_tensor([self.batch_size, self.seq_length]    ,   self.num_labels						)
								__lowercase				:		int												=				ids_tensor([self.batch_size]    ,   self.num_choices						)

						__lowercase				:		Union[str, Any]												=				RoFormerConfig(
						    vocab_size=self.vocab_size    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   type_vocab_size=self.type_vocab_size    ,   initializer_range=self.initializer_range    ,   return_dict=lowercase__    ,   )

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Dict    ,   lowercase__					:	List[Any]    ,   lowercase__					:	List[str]						):
						__lowercase				:		List[Any]												=				TFRoFormerModel(config=lowercase__						)
						__lowercase				:		List[str]												=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

						__lowercase				:		Tuple												=				[input_ids, input_mask]
						__lowercase				:		List[Any]												=				model(lowercase__						)

						__lowercase				:		Tuple												=				model(lowercase__						)

						self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Dict    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	str    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Dict    ,   lowercase__					:	List[str]						):
						__lowercase				:		str												=				True
						__lowercase				:		List[Any]												=				TFRoFormerForCausalLM(config=lowercase__						)
						__lowercase				:		Any												=				{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						__lowercase				:		List[str]												=				model(lowercase__						)["logits"]
						self.parent.assertListEqual(
						    list(prediction_scores.numpy().shape						)    ,   [self.batch_size, self.seq_length, self.vocab_size]						)



				def       snake_case    (     self					:	str    ,   lowercase__					:	Tuple    ,   lowercase__					:	Dict    ,   lowercase__					:	str    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Dict						):
						__lowercase				:		Tuple												=				TFRoFormerForMaskedLM(config=lowercase__						)
						__lowercase				:		List[Any]												=				{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						__lowercase				:		List[Any]												=				model(lowercase__						)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)						)



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	List[Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	int    ,   lowercase__					:	Dict    ,   lowercase__					:	int    ,   lowercase__					:	int    ,   lowercase__					:	Union[str, Any]						):
						__lowercase				:		Any												=				self.num_labels
						__lowercase				:		Dict												=				TFRoFormerForSequenceClassification(config=lowercase__						)
						__lowercase				:		Any												=				{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}

						__lowercase				:		Any												=				model(lowercase__						)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)						)



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	List[str]    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Optional[int]						):
						__lowercase				:		Union[str, Any]												=				self.num_choices
						__lowercase				:		int												=				TFRoFormerForMultipleChoice(config=lowercase__						)
						__lowercase				:		Any												=				tf.tile(tf.expand_dims(lowercase__    ,   1						)    ,   (1, self.num_choices, 1)						)
						__lowercase				:		Dict												=				tf.tile(tf.expand_dims(lowercase__    ,   1						)    ,   (1, self.num_choices, 1)						)
						__lowercase				:		Any												=				tf.tile(tf.expand_dims(lowercase__    ,   1						)    ,   (1, self.num_choices, 1)						)
						__lowercase				:		Any												=				{
						    "input_ids": multiple_choice_inputs_ids,
						    "attention_mask": multiple_choice_input_mask,
						    "token_type_ids": multiple_choice_token_type_ids,
						}
						__lowercase				:		List[Any]												=				model(lowercase__						)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_choices)						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Dict    ,   lowercase__					:	int    ,   lowercase__					:	str    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	str						):
						__lowercase				:		List[str]												=				self.num_labels
						__lowercase				:		Optional[int]												=				TFRoFormerForTokenClassification(config=lowercase__						)
						__lowercase				:		Union[str, Any]												=				{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						__lowercase				:		int												=				model(lowercase__						)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)						)



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Tuple    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Tuple    ,   lowercase__					:	Tuple    ,   lowercase__					:	str    ,   lowercase__					:	int    ,   lowercase__					:	Optional[int]						):
						__lowercase				:		List[str]												=				TFRoFormerForQuestionAnswering(config=lowercase__						)
						__lowercase				:		List[str]												=				{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}

						__lowercase				:		Tuple												=				model(lowercase__						)
						self.parent.assertEqual(result.start_logits.shape    ,   (self.batch_size, self.seq_length)						)
						self.parent.assertEqual(result.end_logits.shape    ,   (self.batch_size, self.seq_length)						)



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Dict												=				self.prepare_config_and_inputs()
						(
						    __lowercase
						)				:		Union[str, Any]												=				config_and_inputs
						__lowercase				:		Tuple												=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict





@require_tf
class       lowerCAmelCase__       (			lowerCAmelCase_  ,			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     List[str]       =      (
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__UpperCAmelCase :     List[str]       =      (
				    {
				        "feature-extraction": TFRoFormerModel,
				        "fill-mask": TFRoFormerForMaskedLM,
				        "question-answering": TFRoFormerForQuestionAnswering,
				        "text-classification": TFRoFormerForSequenceClassification,
				        "text-generation": TFRoFormerForCausalLM,
				        "token-classification": TFRoFormerForTokenClassification,
				        "zero-shot": TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				__UpperCAmelCase :     int       =      False
				__UpperCAmelCase :     Any       =      False



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Tuple    ,   lowercase__					:	int    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Dict						):
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
								return True

						return False



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		Tuple												=				TFRoFormerModelTester(self						)
						__lowercase				:		Any												=				ConfigTester(self    ,   config_class=lowercase__    ,   hidden_size=3_7						)



				def       snake_case    (     self					:	Optional[Any]						):
						self.config_tester.run_common_tests()



				def       snake_case    (     self					:	int						):
						__lowercase				:		List[Any]												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase__						)



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		Union[str, Any]												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowercase__						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		Tuple												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head(*lowercase__						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		List[Any]												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*lowercase__						)



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		int												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowercase__						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Any												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowercase__						)



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		List[str]												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*lowercase__						)



				@slow
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Dict												=				TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"						)
						self.assertIsNotNone(lowercase__						)





@require_tf
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				@slow
				def       snake_case    (     self					:	str						):
						__lowercase				:		List[Any]												=				TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"						)
						__lowercase				:		List[Any]												=				tf.constant([[0, 1, 2, 3, 4, 5]]						)
						__lowercase				:		Dict												=				model(lowercase__						)[0]

						# TODO Replace vocab size
						__lowercase				:		Any												=				5_0_0_0_0

						__lowercase				:		Optional[int]												=				[1, 6, vocab_size]
						self.assertEqual(output.shape    ,   lowercase__						)

						print(output[:, :3, :3]						)

						# TODO Replace values below with what was printed above.
						__lowercase				:		str												=				tf.constant(
						    [
						        [
						            [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
						            [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
						            [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
						        ]
						    ]						)
						tf.debugging.assert_near(output[:, :3, :3]    ,   lowercase__    ,   atol=1e-4						)





@require_tf
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Any       =      1E-4



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		str												=				tf.constant([[4, 1_0]]						)
						__lowercase				:		str												=				TFRoFormerSinusoidalPositionalEmbedding(num_positions=6    ,   embedding_dim=6						)

						__lowercase				:		List[Any]												=				emba(input_ids.shape						)
						__lowercase				:		List[Any]												=				tf.constant(
						    [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]						)

						tf.debugging.assert_near(lowercase__    ,   lowercase__    ,   atol=self.tolerance						)



				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		Optional[int]												=				tf.constant(
						    [
						        [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
						        [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
						        [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
						    ]						)
						__lowercase				:		List[str]												=				TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2    ,   embedding_dim=5_1_2						)
						emba([2, 1_6, 5_1_2]						)
						__lowercase				:		Optional[int]												=				emba.weight[:3, :5]

						tf.debugging.assert_near(lowercase__    ,   lowercase__    ,   atol=self.tolerance						)





@require_tf
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[int]       =      1E-4



				def       snake_case    (     self					:	Tuple						):
						# 2,12,16,64
						__lowercase				:		List[str]												=				tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4    ,   dtype=tf.floataa						)    ,   shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

						__lowercase				:		List[Any]												=				-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4    ,   dtype=tf.floataa						)    ,   shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

						__lowercase				:		Optional[Any]												=				TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2    ,   embedding_dim=6_4						)
						__lowercase				:		List[str]												=				embed_positions([2, 1_6, 7_6_8]						)[None, None, :, :]

						__lowercase				:		Any												=				TFRoFormerSelfAttention.apply_rotary_position_embeddings(
						    lowercase__    ,   lowercase__    ,   lowercase__						)

						__lowercase				:		Optional[int]												=				tf.constant(
						    [
						        [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
						        [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
						        [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
						        [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
						        [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
						        [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
						    ]						)
						__lowercase				:		str												=				tf.constant(
						    [
						        [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
						        [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
						        [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
						        [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
						        [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
						        [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
						    ]						)

						tf.debugging.assert_near(query_layer[0, 0, :6, :8]    ,   lowercase__    ,   atol=self.tolerance						)
						tf.debugging.assert_near(key_layer[0, 0, :6, :8]    ,   lowercase__    ,   atol=self.tolerance						)






"""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   :  str					=      '\\n\n'

__A   :  int					=      '\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   :  Optional[Any]					=      '\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					:	Dict						):
						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					:	Optional[Any]    ,   lowercase__					:	int    ,   lowercase__					:	str    ,   lowercase__					:	int = 1_6    ,   lowercase__					:	bool = True    ,   lowercase__					:	List[Any]=None						):
						if device is not None:
								assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
								if device == "gpu":
										__lowercase				:		Optional[Any]												=				"cuda"
						else:
								__lowercase				:		Optional[Any]												=				"cuda" if torch.cuda.is_available() else "cpu"

						__lowercase				:		List[str]												=				AutoModelForCausalLM.from_pretrained(lowercase__						)
						__lowercase				:		str												=				model.to(lowercase__						)

						__lowercase				:		Any												=				AutoTokenizer.from_pretrained(lowercase__						)

						# 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:
								__lowercase				:		Any												=				list(tokenizer.special_tokens_map_extended.values()						)
								# check that the model already has at least one special token defined
								assert (
								    len(lowercase__						) > 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"
								__lowercase				:		Any												=				model.config.max_length - 1
						else:
								__lowercase				:		Union[str, Any]												=				model.config.max_length

						__lowercase				:		List[Any]												=				tokenizer(
						    lowercase__    ,   add_special_tokens=lowercase__    ,   padding=lowercase__    ,   truncation=lowercase__    ,   max_length=lowercase__    ,   return_tensors="pt"    ,   return_attention_mask=lowercase__    ,   ).to(lowercase__						)

						__lowercase				:		Tuple												=				encodings["input_ids"]
						__lowercase				:		Tuple												=				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."

						__lowercase				:		Optional[int]												=				[]
						__lowercase				:		List[Any]												=				CrossEntropyLoss(reduction="none"						)

						for start_index in logging.tqdm(range(0    ,   len(lowercase__						)    ,   lowercase__						)						):
								__lowercase				:		List[str]												=				min(start_index + batch_size    ,   len(lowercase__						)						)
								__lowercase				:		Any												=				encoded_texts[start_index:end_index]
								__lowercase				:		Dict												=				attn_masks[start_index:end_index]

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

								__lowercase				:		Optional[int]												=				encoded_batch

								with torch.no_grad():
										__lowercase				:		Any												=				model(lowercase__    ,   attention_mask=lowercase__						).logits

								__lowercase				:		Any												=				out_logits[..., :-1, :].contiguous()
								__lowercase				:		Tuple												=				labels[..., 1:].contiguous()
								__lowercase				:		str												=				attn_mask[..., 1:].contiguous()

								__lowercase				:		Optional[int]												=				torch.expa(
								    (loss_fct(shift_logits.transpose(1    ,   2						)    ,   lowercase__						) * shift_attention_mask_batch).sum(1						)
								    / shift_attention_mask_batch.sum(1						)						)

								ppls += perplexity_batch.tolist()

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