






'''simple docstring'''



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

import datasets
from datasets import logging


SCREAMING_SNAKE_CASE						=	'\\n\n'

SCREAMING_SNAKE_CASE						=	'\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

SCREAMING_SNAKE_CASE						=	'\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,       _KWARGS_DESCRIPTION				)
class UpperCAmelCase_					(    datasets.Metric				):




      """simple docstring"""




      def 				A__      ( self			:		int		)  ->  int:


       '''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 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any] = 16     ,      UpperCAmelCase			:		Any = True     ,      UpperCAmelCase			:		Dict=None		)  ->  Tuple:


       '''simple docstring'''

       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					:   Tuple				  ="cuda" if torch.cuda.is_available() else "cpu"

       lowercase					:   Tuple				  =AutoModelForCausalLM.from_pretrained(a_		)
       lowercase					:   List[str]				  =model.to(a_		)

       lowercase					:   Any				  =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:
        lowercase					:   str				  =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"
        lowercase					:   List[str]				  =model.config.max_length - 1
       else:
        lowercase					:   int				  =model.config.max_length

       lowercase					:   Any				  =tokenizer(
           a_     ,      add_special_tokens=a_     ,      padding=a_     ,      truncation=a_     ,      max_length=a_     ,      return_tensors='''pt'''     ,      return_attention_mask=a_     ,      ).to(a_		)

       lowercase					:   Dict				  =encodings["input_ids"]
       lowercase					:   Union[str, Any]				  =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					:   str				  =[]
       lowercase					:   Union[str, Any]				  =CrossEntropyLoss(reduction='''none'''		)

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

        if add_start_token:
         lowercase					:   Any				  =torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0		)		).to(a_		)
         lowercase					:   List[str]				  =torch.cat([bos_tokens_tensor, encoded_batch]     ,      dim=1		)
         lowercase					:   Tuple				  =torch.cat(
             [torch.ones(bos_tokens_tensor.size()     ,      dtype=torch.intaa		).to(a_		), attn_mask]     ,      dim=1		)

        lowercase					:   Tuple				  =encoded_batch

        with torch.no_grad():
         lowercase					:   Dict				  =model(a_     ,      attention_mask=a_		).logits

        lowercase					:   List[str]				  =out_logits[..., :-1, :].contiguous()
        lowercase					:   Any				  =labels[..., 1:].contiguous()
        lowercase					:   int				  =attn_mask[..., 1:].contiguous()

        lowercase					:   Optional[int]				  =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_		)}











'''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 UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Union[str, Any]=13     ,      UpperCAmelCase			:		int=7     ,      UpperCAmelCase			:		Union[str, Any]=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		Any=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		Any=99     ,      UpperCAmelCase			:		List[Any]=32     ,      UpperCAmelCase			:		str=2     ,      UpperCAmelCase			:		str=4     ,      UpperCAmelCase			:		List[Any]=37     ,      UpperCAmelCase			:		str="gelu"     ,      UpperCAmelCase			:		List[Any]=0.1     ,      UpperCAmelCase			:		int=0.1     ,      UpperCAmelCase			:		Dict=512     ,      UpperCAmelCase			:		List[Any]=16     ,      UpperCAmelCase			:		List[str]=2     ,      UpperCAmelCase			:		Any=0.0_2     ,      UpperCAmelCase			:		List[str]=3     ,      UpperCAmelCase			:		Dict=4     ,      UpperCAmelCase			:		Optional[int]=None     ,      )  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =parent
       lowercase					:   Tuple				  =13
       lowercase					:   Any				  =7
       lowercase					:   Union[str, Any]				  =True
       lowercase					:   Any				  =True
       lowercase					:   Optional[int]				  =True
       lowercase					:   List[str]				  =True
       lowercase					:   Tuple				  =99
       lowercase					:   str				  =32
       lowercase					:   Union[str, Any]				  =2
       lowercase					:   Dict				  =4
       lowercase					:   Union[str, Any]				  =37
       lowercase					:   Union[str, Any]				  ='''gelu'''
       lowercase					:   Any				  =0.1
       lowercase					:   Dict				  =0.1
       lowercase					:   Dict				  =512
       lowercase					:   List[str]				  =16
       lowercase					:   Dict				  =2
       lowercase					:   int				  =0.0_2
       lowercase					:   List[Any]				  =3
       lowercase					:   List[str]				  =4
       lowercase					:   Optional[Any]				  =None






      def 				A__      ( self			:		Union[str, Any]		)  ->  int:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size		)

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

       lowercase					:   List[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=UpperCAmelCase     ,      )

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






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerModel(config=UpperCAmelCase		)
       lowercase					:   Optional[int]				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

       lowercase					:   Tuple				  =[input_ids, input_mask]
       lowercase					:   str				  =model(UpperCAmelCase		)

       lowercase					:   Dict				  =model(UpperCAmelCase		)

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






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[str]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Dict				  =True
       lowercase					:   List[Any]				  =TFRoFormerForCausalLM(config=UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       lowercase					:   Optional[Any]				  =model(UpperCAmelCase		)['''logits''']
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape		)     ,      [self.batch_size, self.seq_length, self.vocab_size]		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Tuple		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerForMaskedLM(config=UpperCAmelCase		)
       lowercase					:   List[str]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       lowercase					:   Dict				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)		)






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.num_labels
       lowercase					:   Optional[int]				  =TFRoFormerForSequenceClassification(config=UpperCAmelCase		)
       lowercase					:   Optional[int]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       lowercase					:   Optional[Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)		)






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple		)  ->  Union[str, Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		int		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.num_labels
       lowercase					:   Union[str, Any]				  =TFRoFormerForTokenClassification(config=UpperCAmelCase		)
       lowercase					:   Tuple				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       lowercase					:   List[str]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		str		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Tuple				  =TFRoFormerForQuestionAnswering(config=UpperCAmelCase		)
       lowercase					:   List[str]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       lowercase					:   List[str]				  =model(UpperCAmelCase		)
       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 				A__      ( self			:		List[Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.prepare_config_and_inputs()
       (
           (
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		
       )					:   Optional[int]				  =config_and_inputs
       lowercase					:   str				  ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict





@require_tf
class UpperCAmelCase_					(    __A				,       __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase_								=			(
          {
              '''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_								=			False
      UpperCamelCase_								=			False






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		str		)  ->  Tuple:


       '''simple docstring'''

       if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

       return False






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerModelTester(self		)
       lowercase					:   Union[str, Any]				  =ConfigTester(self     ,      config_class=UpperCAmelCase     ,      hidden_size=37		)






      def 				A__      ( self			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase		)






      def 				A__      ( self			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Tuple				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase		)






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*UpperCAmelCase		)






      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase		)






      def 				A__      ( self			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Dict				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase		)






      def 				A__      ( self			:		Dict		)  ->  Any:


       '''simple docstring'''

       lowercase					:   int				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase		)






      def 				A__      ( self			:		List[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase		)






      @slow
      def 				A__      ( self			:		str		)  ->  str:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''		)
       self.assertIsNotNone(UpperCAmelCase		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      @slow
      def 				A__      ( self			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Any				  =TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''		)
       lowercase					:   Optional[Any]				  =tf.constant([[0, 1, 2, 3, 4, 5]]		)
       lowercase					:   List[str]				  =model(UpperCAmelCase		)[0]

       # TODO Replace vocab size
       lowercase					:   Tuple				  =5_0000

       lowercase					:   List[str]				  =[1, 6, vocab_size]
       self.assertEqual(output.shape     ,      UpperCAmelCase		)

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

       # TODO Replace values below with what was printed above.
       lowercase					:   Dict				  =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]     ,      UpperCAmelCase     ,      atol=1e-4		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			1e-4






      def 				A__      ( self			:		int		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =tf.constant([[4, 10]]		)
       lowercase					:   List[Any]				  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,      embedding_dim=6		)

       lowercase					:   Any				  =emba(input_ids.shape		)
       lowercase					:   List[str]				  =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(UpperCAmelCase     ,      UpperCAmelCase     ,      atol=self.tolerance		)






      def 				A__      ( self			:		Optional[Any]		)  ->  int:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =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					:   Tuple				  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=512     ,      embedding_dim=512		)
       emba([2, 16, 512]		)
       lowercase					:   str				  =emba.weight[:3, :5]

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





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			1e-4






      def 				A__      ( self			:		Dict		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   str				  =tf.reshape(tf.range(2 * 12 * 16 * 64     ,      dtype=tf.floataa		)     ,      shape=(2, 12, 16, 64)		) / 100

       lowercase					:   Any				  =-tf.reshape(tf.range(2 * 12 * 16 * 64     ,      dtype=tf.floataa		)     ,      shape=(2, 12, 16, 64)		) / 100

       lowercase					:   Any				  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=32     ,      embedding_dim=64		)
       lowercase					:   Optional[Any]				  =embed_positions([2, 16, 768]		)[None, None, :, :]

       lowercase  ,		lowercase					:   Optional[int]				  =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

       lowercase					:   Any				  =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					:   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],
           ]		)

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



