

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   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Optional[int]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Any=13   ,						lowerCAmelCase__  :     Optional[int]=7   ,						lowerCAmelCase__  :     Any=True   ,						lowerCAmelCase__  :     Union[str, Any]=True   ,						lowerCAmelCase__  :     Dict=True   ,						lowerCAmelCase__  :     Optional[Any]=True   ,						lowerCAmelCase__  :     List[Any]=99   ,						lowerCAmelCase__  :     Optional[int]=32   ,						lowerCAmelCase__  :     List[Any]=5   ,						lowerCAmelCase__  :     int=4   ,						lowerCAmelCase__  :     List[str]=4   ,						lowerCAmelCase__  :     Union[str, Any]="gelu"   ,						lowerCAmelCase__  :     List[Any]=0.0   ,						lowerCAmelCase__  :     Dict=0.1   ,						lowerCAmelCase__  :     str=True   ,						lowerCAmelCase__  :     Optional[Any]=512   ,						lowerCAmelCase__  :     int=16   ,						lowerCAmelCase__  :     Optional[Any]=2   ,						lowerCAmelCase__  :     List[Any]=0.02   ,						lowerCAmelCase__  :     Union[str, Any]=3   ,						lowerCAmelCase__  :     Optional[Any]=4   ,						lowerCAmelCase__  :     Tuple=None   ,						):
									SCREAMING_SNAKE_CASE_: str  		=       parent
									SCREAMING_SNAKE_CASE_: int  		=       batch_size
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       seq_length
									SCREAMING_SNAKE_CASE_: int  		=       is_training
									SCREAMING_SNAKE_CASE_: Any  		=       use_input_mask
									SCREAMING_SNAKE_CASE_: List[str]  		=       use_token_type_ids
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       use_labels
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       vocab_size
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       hidden_size
									SCREAMING_SNAKE_CASE_: str  		=       num_hidden_layers
									SCREAMING_SNAKE_CASE_: List[Any]  		=       num_attention_heads
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       intermediate_multiple_size
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hidden_act
									SCREAMING_SNAKE_CASE_: List[Any]  		=       hidden_dropout
									SCREAMING_SNAKE_CASE_: str  		=       attention_dropout
									SCREAMING_SNAKE_CASE_: Tuple  		=       weight_tying
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       max_position_embeddings
									SCREAMING_SNAKE_CASE_: Dict  		=       type_vocab_size
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       type_sequence_label_size
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       initializer_range
									SCREAMING_SNAKE_CASE_: Tuple  		=       num_labels
									SCREAMING_SNAKE_CASE_: int  		=       num_choices
									SCREAMING_SNAKE_CASE_: List[str]  		=       scope



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: List[str]  		=       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size)

									SCREAMING_SNAKE_CASE_: Dict  		=       None
									if self.use_input_mask:
															SCREAMING_SNAKE_CASE_: str  		=       random_attention_mask([self.batch_size, self.seq_length])

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

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_config()

									return config, input_ids, input_mask, token_labels



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									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=UpperCamelCase_   ,						initializer_range=self.initializer_range   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.prepare_config_and_inputs()

									SCREAMING_SNAKE_CASE_: str  		=       True

									return config, input_ids, input_mask, token_labels



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: str  		=       GPTNeoXJapaneseModel(config=UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()
									SCREAMING_SNAKE_CASE_: Dict  		=       model(UpperCamelCase_   ,						attention_mask=UpperCamelCase_)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       model(UpperCamelCase_)
									self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size))



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       True
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       GPTNeoXJapaneseModel(UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()
									SCREAMING_SNAKE_CASE_: str  		=       model(UpperCamelCase_   ,						attention_mask=UpperCamelCase_)
									self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size))



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       GPTNeoXJapaneseForCausalLM(config=UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(UpperCamelCase_   ,						attention_mask=UpperCamelCase_   ,						labels=UpperCamelCase_)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size))



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: Tuple  		=       True
									SCREAMING_SNAKE_CASE_: Tuple  		=       GPTNeoXJapaneseForCausalLM(config=UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()

									# first forward pass
									SCREAMING_SNAKE_CASE_: List[Any]  		=       model(UpperCamelCase_   ,						attention_mask=UpperCamelCase_   ,						use_cache=UpperCamelCase_)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       outputs.past_key_values

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

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

									SCREAMING_SNAKE_CASE_: str  		=       model(UpperCamelCase_   ,						attention_mask=UpperCamelCase_   ,						output_hidden_states=UpperCamelCase_)
									SCREAMING_SNAKE_CASE_: Any  		=       output_from_no_past["hidden_states"][0]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(
									    UpperCamelCase_   ,						attention_mask=UpperCamelCase_   ,						past_key_values=UpperCamelCase_   ,						output_hidden_states=UpperCamelCase_   ,						)["hidden_states"][0]

									# select random slice
									SCREAMING_SNAKE_CASE_: List[str]  		=       ids_tensor((1,)   ,						output_from_past.shape[-1]).item()
									SCREAMING_SNAKE_CASE_: List[str]  		=       output_from_no_past[:, -3:, random_slice_idx].detach()
									SCREAMING_SNAKE_CASE_: int  		=       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(UpperCamelCase_   ,						UpperCamelCase_   ,						atol=1E-3))




			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: List[Any]  		=       {"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict







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




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
			_UpperCAmelCase   :							Dict										=     (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
			_UpperCAmelCase   :							Dict										=     (
			    {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
			    if is_torch_available()
			    else {}
			)
			_UpperCAmelCase   :							Optional[int]										=     False
			_UpperCAmelCase   :							List[Any]										=     False
			_UpperCAmelCase   :							Tuple										=     False
			_UpperCAmelCase   :							Optional[int]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: int  		=       GPTNeoXJapaneseModelTester(self)
									SCREAMING_SNAKE_CASE_: Dict  		=       ConfigTester(self   ,						config_class=UpperCamelCase_   ,						hidden_size=37)



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



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									# This regression test was failing with PyTorch < 1.3
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self.model_tester.prepare_config_and_inputs_for_decoder()

									SCREAMING_SNAKE_CASE_: int  		=       None

									self.model_tester.create_and_check_model_as_decoder(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase_)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Dict  		=       "abeja/gpt-neox-japanese-2.7b"

									SCREAMING_SNAKE_CASE_: List[Any]  		=       ["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、"]

									SCREAMING_SNAKE_CASE_: 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。",
									]

									SCREAMING_SNAKE_CASE_: List[str]  		=       GPTNeoXJapaneseTokenizer.from_pretrained(UpperCamelCase_)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCamelCase_)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       []
									for prompt in prompts:
															SCREAMING_SNAKE_CASE_: Dict  		=       tokenizer(UpperCamelCase_   ,						return_tensors="pt").input_ids
															SCREAMING_SNAKE_CASE_: Dict  		=       model.generate(UpperCamelCase_   ,						max_length=50)
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tokenizer.batch_decode(UpperCamelCase_   ,						skip_special_tokens=UpperCamelCase_)
															predicted_outputs += generated_string
									self.assertListEqual(UpperCamelCase_   ,						UpperCamelCase_)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_									=	{
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['VisionEncoderDecoderModel']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['TFVisionEncoderDecoderModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
       from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
       import sys

       UpperCAmelCase_									=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

