


import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel




class A    :






      def __init__(   self		:	str     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	Any=13     ,  lowercase_		:	int=7     ,  lowercase_		:	Any=True     ,  lowercase_		:	int=True     ,  lowercase_		:	Optional[Any]=True     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	List[str]=99     ,  lowercase_		:	Dict=32     ,  lowercase_		:	str=5     ,  lowercase_		:	int=4     ,  lowercase_		:	str=4     ,  lowercase_		:	Dict="gelu"     ,  lowercase_		:	str=0.0     ,  lowercase_		:	str=0.1     ,  lowercase_		:	Optional[Any]=True     ,  lowercase_		:	Optional[int]=512     ,  lowercase_		:	List[str]=16     ,  lowercase_		:	List[str]=2     ,  lowercase_		:	Any=0.02     ,  lowercase_		:	Tuple=3     ,  lowercase_		:	Optional[int]=4     ,  lowercase_		:	str=None     ,  )   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =seq_length
             _lowerCamelCase			:						Optional[int]            =is_training
             _lowerCamelCase			:						Any            =use_input_mask
             _lowerCamelCase			:						Any            =use_token_type_ids
             _lowerCamelCase			:						Tuple            =use_labels
             _lowerCamelCase			:						int            =vocab_size
             _lowerCamelCase			:						int            =hidden_size
             _lowerCamelCase			:						Dict            =num_hidden_layers
             _lowerCamelCase			:						Dict            =num_attention_heads
             _lowerCamelCase			:						str            =intermediate_multiple_size
             _lowerCamelCase			:						str            =hidden_act
             _lowerCamelCase			:						List[Any]            =hidden_dropout
             _lowerCamelCase			:						List[Any]            =attention_dropout
             _lowerCamelCase			:						Any            =weight_tying
             _lowerCamelCase			:						Optional[Any]            =max_position_embeddings
             _lowerCamelCase			:						List[Any]            =type_vocab_size
             _lowerCamelCase			:						Dict            =type_sequence_label_size
             _lowerCamelCase			:						str            =initializer_range
             _lowerCamelCase			:						List[Any]            =num_labels
             _lowerCamelCase			:						Optional[int]            =num_choices
             _lowerCamelCase			:						Optional[int]            =scope






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						str            =ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

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

             _lowerCamelCase			:						Union[str, Any]            =None
             if self.use_labels:
                    _lowerCamelCase			:						Optional[int]            =ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)

             _lowerCamelCase			:						Dict            =self.get_config()

             return config, input_ids, input_mask, token_labels






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Dict:
             """simple docstring"""




             return GPTNeoXJapaneseConfig(
                 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_multiple_size=self.intermediate_multiple_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout=self.hidden_dropout     ,  attention_dropout=self.attention_dropout     ,  weight_tying=self.weight_tying     ,  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		:	List[str]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self.prepare_config_and_inputs()

             _lowerCamelCase			:						Optional[int]            =True

             return config, input_ids, input_mask, token_labels






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	List[str]     ,  lowercase_		:	List[Any]     ,  lowercase_		:	str					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =GPTNeoXJapaneseModel(config=lowercase__					)
             model.to(lowercase__					)
             model.eval()
             _lowerCamelCase			:						Dict            =model(lowercase__     ,  attention_mask=lowercase__					)
             _lowerCamelCase			:						List[str]            =model(lowercase__					)
             self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	Optional[int]     ,  lowercase_		:	Dict     ,  lowercase_		:	Optional[Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =True
             _lowerCamelCase			:						Union[str, Any]            =GPTNeoXJapaneseModel(lowercase__					)
             model.to(lowercase__					)
             model.eval()
             _lowerCamelCase			:						Optional[Any]            =model(lowercase__     ,  attention_mask=lowercase__					)
             self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	Dict     ,  lowercase_		:	int     ,  lowercase_		:	List[Any]     ,  lowercase_		:	List[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =GPTNeoXJapaneseForCausalLM(config=lowercase__					)
             model.to(lowercase__					)
             model.eval()
             _lowerCamelCase			:						Dict            =model(lowercase__     ,  attention_mask=lowercase__     ,  labels=lowercase__					)
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	Tuple     ,  lowercase_		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						int            =True
             _lowerCamelCase			:						Optional[Any]            =GPTNeoXJapaneseForCausalLM(config=lowercase__					)
             model.to(lowercase__					)
             model.eval()

             # first forward pass
             _lowerCamelCase			:						Tuple            =model(lowercase__     ,  attention_mask=lowercase__     ,  use_cache=lowercase__					)
             _lowerCamelCase			:						Optional[Any]            =outputs.past_key_values

             # create hypothetical multiple next token and extent to next_input_ids
             _lowerCamelCase			:						Union[str, Any]            =ids_tensor((self.batch_size, 3)     ,  config.vocab_size					)
             _lowerCamelCase			:						Any            =ids_tensor((self.batch_size, 3)     ,  vocab_size=2					)

             # append to next input_ids and
             _lowerCamelCase			:						Union[str, Any]            =torch.cat([input_ids, next_tokens]     ,  dim=-1					)
             _lowerCamelCase			:						int            =torch.cat([input_mask, next_mask]     ,  dim=-1					)

             _lowerCamelCase			:						Tuple            =model(lowercase__     ,  attention_mask=lowercase__     ,  output_hidden_states=lowercase__					)
             _lowerCamelCase			:						Optional[int]            =output_from_no_past['hidden_states'][0]
             _lowerCamelCase			:						Optional[Any]            =model(
                 lowercase__     ,  attention_mask=lowercase__     ,  past_key_values=lowercase__     ,  output_hidden_states=lowercase__     ,  )['hidden_states'][0]

             # select random slice
             _lowerCamelCase			:						Optional[Any]            =ids_tensor((1,)     ,  output_from_past.shape[-1]					).item()
             _lowerCamelCase			:						str            =output_from_no_past[:, -3:, random_slice_idx].detach()
             _lowerCamelCase			:						str            =output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

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






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




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




@require_torch
class A    (  UpperCamelCase_       ,				UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    int =(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
      UpperCamelCase__				:    List[str] =(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
      UpperCamelCase__				:    Optional[Any] =(
          {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
          if is_torch_available()
          else {}
      )
      UpperCamelCase__				:    List[str] =False
      UpperCamelCase__				:    str =False
      UpperCamelCase__				:    Tuple =False
      UpperCamelCase__				:    Dict =False






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =GPTNeoXJapaneseModelTester(self					)
             _lowerCamelCase			:						List[str]            =ConfigTester(self     ,  config_class=lowercase__     ,  hidden_size=37					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(lowercase__     ,  lowercase__     ,  lowercase__					)






      def 			lowerCamelCase			(   self		:	Any					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(lowercase__     ,  lowercase__     ,  lowercase__					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self.model_tester.prepare_config_and_inputs_for_decoder()

             _lowerCamelCase			:						Optional[int]            =None

             self.model_tester.create_and_check_model_as_decoder(lowercase__     ,  lowercase__     ,  lowercase__					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase__     ,  lowercase__     ,  lowercase__					)






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_causal_lm(*lowercase__					)






      @slow
      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            ='abeja/gpt-neox-japanese-2.7b'

             _lowerCamelCase			:						Dict            =['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

             _lowerCamelCase			:						int            =[
                 'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
                 '100年後に必要とされる会社は、「人」が中心の会社です。',
                 'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
                 '国境の長いトンネルを抜けると、そこは雪国だった。',
                 '美味しい日本食といえば、やっぱりお寿司ですよね。',
             ]

             _lowerCamelCase			:						Optional[int]            =GPTNeoXJapaneseTokenizer.from_pretrained(lowercase__					)
             _lowerCamelCase			:						Dict            =GPTNeoXJapaneseForCausalLM.from_pretrained(lowercase__					)

             _lowerCamelCase			:						Optional[Any]            =[]
             for prompt in prompts:
                    _lowerCamelCase			:						Optional[int]            =tokenizer(lowercase__     ,  return_tensors='pt'					).input_ids
                    _lowerCamelCase			:						Tuple            =model.generate(lowercase__     ,  max_length=50					)
                    _lowerCamelCase			:						Optional[Any]            =tokenizer.batch_decode(lowercase__     ,  skip_special_tokens=lowercase__					)
                    predicted_outputs += generated_string
             self.assertListEqual(lowercase__     ,  lowercase__					)



from collections import OrderedDict
from typing import Mapping

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


_lowercase:	Optional[Any]       			=		logging.get_logger(__name__)

_lowercase:	Tuple       			=		{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="roberta-prelayernorm"





			def __init__(       self	:	int  ,  lowercase__	:	int=5_02_65  ,  lowercase__	:	str=7_68  ,  lowercase__	:	List[Any]=12  ,  lowercase__	:	Any=12  ,  lowercase__	:	Optional[Any]=30_72  ,  lowercase__	:	List[Any]="gelu"  ,  lowercase__	:	str=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Tuple=5_12  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Dict=0.0_2  ,  lowercase__	:	Tuple=1e-12  ,  lowercase__	:	Optional[int]=1  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Any="absolute"  ,  lowercase__	:	Any=True  ,  lowercase__	:	Any=None  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(pad_token_id=lowercase__  ,  bos_token_id=lowercase__  ,  eos_token_id=lowercase__  ,  **lowercase__						)

										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							position_embedding_type
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							classifier_dropout







class        lowerCamelCase__				(   UpperCAmelCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										if self.task == "multiple-choice":
																	_lowerCAmelCase							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	_lowerCAmelCase							=							{0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]						)






