



'''simple docstring'''




import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
    )
    from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST



class    lowerCamelCase__				:






      """simple docstring"""




      def __init__(       self							:     Dict			,a__							:     Dict			,a__							:     Optional[Any]=13			,a__							:     Any=7			,a__							:     Tuple=True			,a__							:     Dict=True			,a__							:     Any=True			,a__							:     Any=True			,a__							:     Optional[Any]=99			,a__							:     Optional[Any]=32			,a__							:     List[Any]=5			,a__							:     Optional[int]=4			,a__							:     List[Any]=37			,a__							:     str="gelu"			,a__							:     Dict=0.1			,a__							:     int=0.1			,a__							:     Tuple=1_28			,a__							:     Any=32			,a__							:     Any=16			,a__							:     List[Any]=2			,a__							:     int=0.02			,a__							:     Tuple=3			,a__							:     Tuple=4			,a__							:     str=None			,):
             a__								= parent
             a__								= batch_size
             a__								= seq_length
             a__								= is_training
             a__								= use_input_mask
             a__								= use_token_type_ids
             a__								= use_labels
             a__								= vocab_size
             a__								= hidden_size
             a__								= num_hidden_layers
             a__								= num_attention_heads
             a__								= intermediate_size
             a__								= hidden_act
             a__								= hidden_dropout_prob
             a__								= attention_probs_dropout_prob
             a__								= max_position_embeddings
             a__								= type_vocab_size
             a__								= type_sequence_label_size
             a__								= initializer_range
             a__								= num_labels
             a__								= num_choices
             a__								= scope




      def lowerCAmelCase_					(       self							:     Any   ):
             a__								= ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size   )

             a__								= None
             if self.use_input_mask:
                    a__								= random_attention_mask([self.batch_size, self.seq_length]   )

             a__								= None
             if self.use_token_type_ids:
                    a__								= ids_tensor([self.batch_size, self.seq_length]			,self.type_vocab_size   )

             a__								= None
             a__								= None
             a__								= None
             if self.use_labels:
                    a__								= ids_tensor([self.batch_size]			,self.type_sequence_label_size   )
                    a__								= ids_tensor([self.batch_size, self.seq_length]			,self.num_labels   )
                    a__								= ids_tensor([self.batch_size]			,self.num_choices   )

             a__								= self.get_config()

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




      def lowerCAmelCase_					(       self							:     int   ):
             return NezhaConfig(
                 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			,is_decoder=__lowercase			,initializer_range=self.initializer_range			,)




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             (
                 (
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					  
             )								= self.prepare_config_and_inputs()

             a__								= True
             a__								= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
             a__								= ids_tensor([self.batch_size, self.seq_length]			,vocab_size=2   )

             return (
                 config,
                 input_ids,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
                 choice_labels,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )




      def lowerCAmelCase_					(       self							:     int			,a__							:     Dict			,a__							:     Dict			,a__							:     Tuple			,a__							:     Dict			,a__							:     Optional[Any]			,a__							:     List[str]			,a__							:     str   ):
             a__								= NezhaModel(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase   )
             a__								= model(__lowercase			,token_type_ids=__lowercase   )
             a__								= model(__lowercase   )
             self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)   )
             self.parent.assertEqual(result.pooler_output.shape			,(self.batch_size, self.hidden_size)   )




      def lowerCAmelCase_					(       self							:     List[str]			,a__							:     Optional[int]			,a__							:     str			,a__							:     Union[str, Any]			,a__							:     Union[str, Any]			,a__							:     Any			,a__							:     int			,a__							:     Any			,a__							:     Union[str, Any]			,a__							:     int			,):
             a__								= True
             a__								= NezhaModel(__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,encoder_hidden_states=__lowercase			,encoder_attention_mask=__lowercase			,)
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,encoder_hidden_states=__lowercase			,)
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase   )
             self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)   )
             self.parent.assertEqual(result.pooler_output.shape			,(self.batch_size, self.hidden_size)   )




      def lowerCAmelCase_					(       self							:     Any			,a__							:     List[str]			,a__							:     int			,a__							:     int			,a__							:     Dict			,a__							:     Union[str, Any]			,a__							:     Tuple			,a__							:     Union[str, Any]   ):
             a__								= NezhaForMaskedLM(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase   )
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.seq_length, self.vocab_size)   )




      def lowerCAmelCase_					(       self							:     Tuple			,a__							:     Dict			,a__							:     Any			,a__							:     Tuple			,a__							:     List[Any]			,a__							:     List[Any]			,a__							:     Optional[Any]			,a__							:     List[str]   ):
             a__								= NezhaForNextSentencePrediction(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase			,)
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, 2)   )




      def lowerCAmelCase_					(       self							:     List[str]			,a__							:     int			,a__							:     Tuple			,a__							:     List[str]			,a__							:     Optional[int]			,a__							:     List[Any]			,a__							:     Any			,a__							:     str   ):
             a__								= NezhaForPreTraining(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase			,next_sentence_label=__lowercase			,)
             self.parent.assertEqual(result.prediction_logits.shape			,(self.batch_size, self.seq_length, self.vocab_size)   )
             self.parent.assertEqual(result.seq_relationship_logits.shape			,(self.batch_size, 2)   )




      def lowerCAmelCase_					(       self							:     Optional[int]			,a__							:     Union[str, Any]			,a__							:     int			,a__							:     Dict			,a__							:     Union[str, Any]			,a__							:     Dict			,a__							:     Optional[Any]			,a__							:     Any   ):
             a__								= NezhaForQuestionAnswering(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,start_positions=__lowercase			,end_positions=__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 lowerCAmelCase_					(       self							:     str			,a__							:     List[str]			,a__							:     str			,a__							:     Optional[Any]			,a__							:     List[str]			,a__							:     Union[str, Any]			,a__							:     int			,a__							:     str   ):
             a__								= self.num_labels
             a__								= NezhaForSequenceClassification(__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase   )
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)   )




      def lowerCAmelCase_					(       self							:     Optional[Any]			,a__							:     List[str]			,a__							:     Tuple			,a__							:     Optional[Any]			,a__							:     Union[str, Any]			,a__							:     Dict			,a__							:     Dict			,a__							:     Any   ):
             a__								= self.num_labels
             a__								= NezhaForTokenClassification(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase   )
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.seq_length, self.num_labels)   )




      def lowerCAmelCase_					(       self							:     Optional[Any]			,a__							:     int			,a__							:     List[Any]			,a__							:     Any			,a__							:     str			,a__							:     List[Any]			,a__							:     Optional[int]			,a__							:     str   ):
             a__								= self.num_choices
             a__								= NezhaForMultipleChoice(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= input_ids.unsqueeze(1   ).expand(-1			,self.num_choices			,-1   ).contiguous()
             a__								= token_type_ids.unsqueeze(1   ).expand(-1			,self.num_choices			,-1   ).contiguous()
             a__								= input_mask.unsqueeze(1   ).expand(-1			,self.num_choices			,-1   ).contiguous()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase			,)
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_choices)   )




      def lowerCAmelCase_					(       self							:     List[Any]   ):
             a__								= self.prepare_config_and_inputs()
             (
                 (
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					  
             )								= config_and_inputs
             a__								= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict



@require_torch
class    lowerCamelCase__				(			__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     (
          (
              NezhaModel,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase__			 =     (
          {
              '''feature-extraction''': NezhaModel,
              '''fill-mask''': NezhaForMaskedLM,
              '''question-answering''': NezhaForQuestionAnswering,
              '''text-classification''': NezhaForSequenceClassification,
              '''token-classification''': NezhaForTokenClassification,
              '''zero-shot''': NezhaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCamelCase__			 =     True




      def lowerCAmelCase_					(       self							:     List[Any]			,a__							:     int			,a__							:     Optional[Any]			,a__							:     Tuple=False   ):
             a__								= super()._prepare_for_class(__lowercase			,__lowercase			,return_labels=__lowercase   )

             if return_labels:
                    if model_class in get_values(__lowercase   ):
                           a__								= torch.zeros(
                               (self.model_tester.batch_size, self.model_tester.seq_length)			,dtype=torch.long			,device=__lowercase   )
                           a__								= torch.zeros(
                               self.model_tester.batch_size			,dtype=torch.long			,device=__lowercase   )
             return inputs_dict




      def lowerCAmelCase_					(       self							:     List[str]   ):
             a__								= NezhaModelTester(self   )
             a__								= ConfigTester(self			,config_class=__lowercase			,hidden_size=37   )




      def lowerCAmelCase_					(       self							:     List[str]   ):
             self.config_tester.run_common_tests()




      def lowerCAmelCase_					(       self							:     Dict   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(*__lowercase   )




      def lowerCAmelCase_					(       self							:     int   ):
             (
                 (
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					  
             )								= self.model_tester.prepare_config_and_inputs_for_decoder()

             a__								= None

             self.model_tester.create_and_check_model_as_decoder(
                 __lowercase			,__lowercase			,__lowercase			,__lowercase			,__lowercase			,__lowercase			,__lowercase			,__lowercase			,__lowercase			,)




      def lowerCAmelCase_					(       self							:     List[str]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Any   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*__lowercase   )




      def lowerCAmelCase_					(       self							:     int   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_next_sequence_prediction(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Dict   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__lowercase   )




      @slow
      def lowerCAmelCase_					(       self							:     List[Any]   ):







             for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a__								= NezhaModel.from_pretrained(__lowercase   )
                    self.assertIsNotNone(__lowercase   )




      @slow
      @require_torch_gpu
      def lowerCAmelCase_					(       self							:     int   ):
             a__						,					a__								= self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    # NezhaForMultipleChoice behaves incorrectly in JIT environments.
                    if model_class == NezhaForMultipleChoice:
                           return

                    a__								= True
                    a__								= model_class(config=__lowercase   )

                    a__								= self._prepare_for_class(__lowercase			,__lowercase   )
                    a__								= torch.jit.trace(
                        __lowercase			,(inputs_dict["input_ids"].to("cpu"   ), inputs_dict["attention_mask"].to("cpu"   ))   )

                    with tempfile.TemporaryDirectory() as tmp:
                           torch.jit.save(__lowercase			,os.path.join(__lowercase			,"bert.pt"   )   )
                           a__								= torch.jit.load(os.path.join(__lowercase			,"bert.pt"   )			,map_location=__lowercase   )
                           loaded(inputs_dict["input_ids"].to(__lowercase   )			,inputs_dict["attention_mask"].to(__lowercase   )   )



@require_torch
class    lowerCamelCase__				(			unittest.TestCase						):






      """simple docstring"""




      @slow
      def lowerCAmelCase_					(       self							:     int   ):
             a__								= NezhaModel.from_pretrained("sijunhe/nezha-cn-base"   )
             a__								= torch.tensor([[0, 1, 2, 3, 4, 5]]   )
             a__								= torch.tensor([[0, 1, 1, 1, 1, 1]]   )
             with torch.no_grad():
                    a__								= model(__lowercase			,attention_mask=__lowercase   )[0]
             a__								= torch.Size((1, 6, 7_68)   )
             self.assertEqual(output.shape			,__lowercase   )
             a__								= torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]   )

             self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,__lowercase			,atol=1e-4   )   )




      @slow
      def lowerCAmelCase_					(       self							:     List[Any]   ):
             a__								= NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"   )
             a__								= torch.tensor([[0, 1, 2, 3, 4, 5]]   )
             a__								= torch.tensor([[1, 1, 1, 1, 1, 1]]   )
             with torch.no_grad():
                    a__								= model(__lowercase			,attention_mask=__lowercase   )[0]
             a__								= torch.Size((1, 6, 2_11_28)   )
             self.assertEqual(output.shape			,__lowercase   )
             a__								= torch.tensor(
                 [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]   )

             self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,__lowercase			,atol=1e-4   )   )



'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase					 =	{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase					 =	{
    '''gpt-neox-20b''': 2048,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[int]="<|endoftext|>"    ,   __lowercase      :			List[Any]="<|endoftext|>"    ,   __lowercase      :			Tuple="<|endoftext|>"    ,   __lowercase      :			Optional[int]=False    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            super().__init__(
                __lowercase    ,   __lowercase    ,   tokenizer_file=__lowercase    ,   unk_token=__lowercase    ,   bos_token=__lowercase    ,   eos_token=__lowercase    ,   add_prefix_space=__lowercase    ,   **__lowercase    ,   )

            __lowercase      		=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
            if pre_tok_state.get('add_prefix_space'    ,   __lowercase   ) != add_prefix_space:
                 __lowercase      		=getattr(__lowercase    ,   pre_tok_state.pop('type'   )   )
                 __lowercase      		=add_prefix_space
                 __lowercase      		=pre_tok_class(**__lowercase   )

            __lowercase      		=add_prefix_space







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







            __lowercase      		=self._tokenizer.model.save(__lowercase    ,   name=__lowercase   )
            return tuple(__lowercase   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			"Conversation"   ):







            """simple docstring"""







            __lowercase      		=[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(__lowercase    ,   add_special_tokens=__lowercase   ) + [self.eos_token_id]   )

            if len(__lowercase   ) > self.model_max_length:
                 __lowercase      		=input_ids[-self.model_max_length :]
            return input_ids


