





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class    lowerCAmelCase							:
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Union[str, Any]=99			,     UpperCAmelCase  :      str=13			,     UpperCAmelCase  :      List[str]=7			,     UpperCAmelCase  :      str=9			,     UpperCAmelCase  :      Optional[int]=True			,     UpperCAmelCase  :      str=True			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      Union[str, Any]=32			,     UpperCAmelCase  :      List[str]=5			,     UpperCAmelCase  :      Tuple=4			,     UpperCAmelCase  :      Union[str, Any]=37			,     UpperCAmelCase  :      int=8			,     UpperCAmelCase  :      List[str]=0.1			,     UpperCAmelCase  :      Any=0.0_0_2			,     UpperCAmelCase  :      Optional[Any]=1			,     UpperCAmelCase  :      List[Any]=0			,     UpperCAmelCase  :      Union[str, Any]=0			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      int					 =		parent
     lowerCamelCase__    :      Any					 =		batch_size
     lowerCamelCase__    :      Optional[int]					 =		encoder_seq_length
     lowerCamelCase__    :      int					 =		decoder_seq_length
     # For common tests
     lowerCamelCase__    :      List[str]					 =		self.decoder_seq_length
     lowerCamelCase__    :      Optional[int]					 =		is_training
     lowerCamelCase__    :      List[Any]					 =		use_attention_mask
     lowerCamelCase__    :      Optional[Any]					 =		use_labels
     lowerCamelCase__    :      Union[str, Any]					 =		vocab_size
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_size
     lowerCamelCase__    :      Optional[Any]					 =		num_hidden_layers
     lowerCamelCase__    :      Any					 =		num_attention_heads
     lowerCamelCase__    :      str					 =		d_ff
     lowerCamelCase__    :      Optional[Any]					 =		relative_attention_num_buckets
     lowerCamelCase__    :      Any					 =		dropout_rate
     lowerCamelCase__    :      Any					 =		initializer_factor
     lowerCamelCase__    :      Union[str, Any]					 =		eos_token_id
     lowerCamelCase__    :      List[str]					 =		pad_token_id
     lowerCamelCase__    :      List[str]					 =		decoder_start_token_id
     lowerCamelCase__    :      List[Any]					 =		None
     lowerCamelCase__    :      Optional[Any]					 =		decoder_layers
 def      A_	(      self  :      List[Any]	)			->	int:
     return TaConfig.from_pretrained('google/umt5-base'	)
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      List[str]=None			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      Optional[Any]=None			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	List[str]:
     if attention_mask is None:
         lowerCamelCase__    :      Optional[Any]					 =		input_ids.ne(config.pad_token_id	)
     if decoder_attention_mask is None:
         lowerCamelCase__    :      Optional[Any]					 =		decoder_input_ids.ne(config.pad_token_id	)
     if head_mask is None:
         lowerCamelCase__    :      int					 =		torch.ones(config.num_hidden_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     if decoder_head_mask is None:
         lowerCamelCase__    :      Dict					 =		torch.ones(config.num_decoder_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     if cross_attn_head_mask is None:
         lowerCamelCase__    :      Dict					 =		torch.ones(
             config.num_decoder_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }
 def      A_	(      self  :      str	)			->	List[str]:
     lowerCamelCase__    :      Any					 =		ids_tensor([self.batch_size, self.encoder_seq_length]			,     self.vocab_size	)
     lowerCamelCase__    :      Optional[Any]					 =		ids_tensor([self.batch_size, self.decoder_seq_length]			,     self.vocab_size	)

     # we need to clamp the input ids here to avoid having pad token in between
     # this is because for NllbMoe the position_ids are prepared such that
     # all pad tokens have pos id = 2 and rest are between 2..seq_length
     # and the seq_length here is seq_length - num_pad_tokens
     # but when using past, there is no way of knowing if the past input ids had
     # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
     # position_ids being off by num_pad_tokens in past input
     lowerCamelCase__    :      List[str]					 =		input_ids.clamp(self.pad_token_id + 1	)
     lowerCamelCase__    :      Union[str, Any]					 =		decoder_input_ids.clamp(self.pad_token_id + 1	)

     lowerCamelCase__    :      Dict					 =		self.get_config()
     lowerCamelCase__    :      Tuple					 =		config.num_attention_heads
     lowerCamelCase__    :      Any					 =		self.prepare_inputs_dict(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     return config, input_dict
 def      A_	(      self  :      Tuple	)			->	Union[str, Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		self.prepare_config_and_inputs()
     return config, inputs_dict
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     return TaConfig(
         vocab_size=166			,     d_model=self.hidden_size			,     d_ff=self.d_ff			,     d_kv=self.hidden_size // self.num_attention_heads			,     num_layers=self.num_hidden_layers			,     num_decoder_layers=self.decoder_layers			,     num_heads=self.num_attention_heads			,     relative_attention_num_buckets=self.relative_attention_num_buckets			,     dropout_rate=self.dropout_rate			,     initializer_factor=self.initializer_factor			,     eos_token_id=self.eos_token_id			,     bos_token_id=self.pad_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.decoder_start_token_id			,     )
 def      A_	(      self  :      Union[str, Any]	)			->	Dict:
     return TaConfig(
         vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     d_ff=self.d_ff			,     d_kv=self.hidden_size // self.num_attention_heads			,     num_layers=self.num_hidden_layers			,     num_decoder_layers=self.decoder_layers			,     num_heads=self.num_attention_heads			,     relative_attention_num_buckets=self.relative_attention_num_buckets			,     dropout_rate=self.dropout_rate			,     initializer_factor=self.initializer_factor			,     eos_token_id=self.eos_token_id			,     bos_token_id=self.pad_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.decoder_start_token_id			,     )
 def      A_	(      self  :      Any			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Dict			,     )			->	str:
     lowerCamelCase__    :      Dict					 =		UMTaModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Optional[int]					 =		model(
         input_ids=UpperCAmelCase			,     decoder_input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase			,     decoder_attention_mask=UpperCAmelCase			,     )
     lowerCamelCase__    :      Any					 =		model(input_ids=UpperCAmelCase			,     decoder_input_ids=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		result.last_hidden_state
     lowerCamelCase__    :      Any					 =		result.past_key_values
     lowerCamelCase__    :      List[Any]					 =		result.encoder_last_hidden_state

     self.parent.assertEqual(encoder_output.size()			,     (self.batch_size, self.encoder_seq_length, self.hidden_size)	)
     self.parent.assertEqual(decoder_output.size()			,     (self.batch_size, self.decoder_seq_length, self.hidden_size)	)
     # There should be `num_layers` key value embeddings stored in decoder_past
     self.parent.assertEqual(len(UpperCAmelCase	)			,     config.num_layers	)
     # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
     self.parent.assertEqual(len(decoder_past[0]	)			,     4	)
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Optional[Any]			,     )			->	Optional[int]:
     lowerCamelCase__    :      List[Any]					 =		UMTaModel(config=UpperCAmelCase	).get_decoder().to(UpperCAmelCase	).eval()
     # first forward pass
     lowerCamelCase__    :      Tuple					 =		model(UpperCAmelCase			,     use_cache=UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		model(UpperCAmelCase	)
     lowerCamelCase__    :      int					 =		model(UpperCAmelCase			,     use_cache=UpperCAmelCase	)

     self.parent.assertTrue(len(UpperCAmelCase	) == len(UpperCAmelCase	)	)
     self.parent.assertTrue(len(UpperCAmelCase	) == len(UpperCAmelCase	) + 1	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     lowerCamelCase__    :      Optional[int]					 =		ids_tensor((self.batch_size, 1)			,     config.vocab_size	)

     # append to next input_ids and
     lowerCamelCase__    :      List[Any]					 =		torch.cat([input_ids, next_tokens]			,     dim=-1	)

     lowerCamelCase__    :      List[str]					 =		model(UpperCAmelCase	)['last_hidden_state']
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase			,     past_key_values=UpperCAmelCase	)['last_hidden_state']

     # select random slice
     lowerCamelCase__    :      List[str]					 =		ids_tensor((1,)			,     output_from_past.shape[-1]	).item()
     lowerCamelCase__    :      Tuple					 =		output_from_no_past[:, -1, random_slice_idx].detach()
     lowerCamelCase__    :      List[str]					 =		output_from_past[:, 0, random_slice_idx].detach()

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      List[str]			,     )			->	Tuple:
     lowerCamelCase__    :      Union[str, Any]					 =		UMTaModel(config=UpperCAmelCase	).to(UpperCAmelCase	).half().eval()
     lowerCamelCase__    :      Optional[int]					 =		model(**UpperCAmelCase	)['last_hidden_state']
     self.parent.assertFalse(torch.isnan(UpperCAmelCase	).any().item()	)


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 UpperCAmelCase__													=		(UMTaForConditionalGeneration,) if is_torch_available() else ()
 UpperCAmelCase__													=		(
     {
         """conversational""": UMTaForConditionalGeneration,
         """feature-extraction""": UMTaModel,
         """summarization""": UMTaForConditionalGeneration,
         """text2text-generation""": UMTaForConditionalGeneration,
         """translation""": UMTaForConditionalGeneration,
         """question-answering""": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 UpperCAmelCase__													=		[0.8, 0.9]
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		UMTaModelTester(self	)
 @unittest.skip('Test has a segmentation fault on torch 1.8.0'	)
 def      A_	(      self  :      Tuple	)			->	int:
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__    :      Tuple					 =		UMTaModel(config_and_inputs[0]	).to(UpperCAmelCase	)
     with tempfile.TemporaryDirectory() as tmpdirname:
         torch.onnx.export(
             UpperCAmelCase			,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,     F"""{tmpdirname}/t5_test.onnx"""			,     export_params=UpperCAmelCase			,     opset_version=9			,     input_names=['input_ids', 'decoder_input_ids']			,     )
 @unittest.skipIf(torch_device == 'cpu'			,     'Cant do half precision'	)
 def      A_	(      self  :      Tuple	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	str:
     lowerCamelCase__    :      int					 =		['encoder_attentions', 'decoder_attentions', 'cross_attentions']
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__    :      Any					 =		config_and_inputs[0]
     lowerCamelCase__    :      Any					 =		UMTaForConditionalGeneration(UpperCAmelCase	).eval()
     model.to(UpperCAmelCase	)

     lowerCamelCase__    :      Tuple					 =		{
         'head_mask': torch.zeros(config.num_layers			,     config.num_heads			,     device=UpperCAmelCase	),
         'decoder_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	),
         'cross_attn_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	),
     }

     for attn_name, (name, mask) in zip(UpperCAmelCase			,     head_masking.items()	):
         lowerCamelCase__    :      Union[str, Any]					 =		{name: mask}
         # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
         if name == "head_mask":
             lowerCamelCase__    :      Union[str, Any]					 =		torch.ones(
                 config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	)

         lowerCamelCase__    :      Tuple					 =		model.generate(
             config_and_inputs[1]['input_ids']			,     num_beams=1			,     max_length=3			,     output_attentions=UpperCAmelCase			,     return_dict_in_generate=UpperCAmelCase			,     **UpperCAmelCase			,     )
         # We check the state of decoder_attentions and cross_attentions just from the last step
         lowerCamelCase__    :      Union[str, Any]					 =		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
         self.assertEqual(sum([w.sum().item() for w in attn_weights]	)			,     0.0	)







 @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'	)
 def      A_	(      self  :      Optional[Any]	)			->	Optional[Any]:
     pass


@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase							(					unittest.TestCase			):
 @slow
 @unittest.skip(
     'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'	)
 def      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      Optional[Any]					 =		UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,     return_dict=UpperCAmelCase	).to(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		AutoTokenizer.from_pretrained('google/umt5-small'			,     use_fast=UpperCAmelCase			,     legacy=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		[
         'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
         'No se como puedo <extra_id_0>.',
         'This is the reason why we <extra_id_0> them.',
         'The <extra_id_0> walks in <extra_id_1>, seats',
         'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
     ]
     lowerCamelCase__    :      Tuple					 =		tokenizer(UpperCAmelCase			,     return_tensors='pt'			,     padding=UpperCAmelCase	).input_ids
     # fmt: off
     lowerCamelCase__    :      Any					 =		torch.tensor(
         [
             [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
         ]	)
     # fmt: on
     torch.testing.assert_allclose(UpperCAmelCase			,     UpperCAmelCase	)

     lowerCamelCase__    :      Optional[int]					 =		model.generate(input_ids.to(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[Any]					 =		[
         '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
         '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
     ]
     lowerCamelCase__    :      Union[str, Any]					 =		tokenizer.batch_decode(UpperCAmelCase	)
     self.assertEqual(UpperCAmelCase			,     UpperCAmelCase	)









"""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_					)}





