


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 		_SCREAMING_SNAKE_CASE     :


     def __init__(  self							,					A_							,					A_=13							,					A_=7							,					A_=True							,					A_=True							,					A_=True							,					A_=True							,					A_=99							,					A_=32							,					A_=5							,					A_=4							,					A_=4							,					A_="gelu"							,					A_=0.0							,					A_=0.1							,					A_=True							,					A_=5_12							,					A_=16							,					A_=2							,					A_=0.0_2							,					A_=3							,					A_=4							,					A_=None							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							Optional[int]							=     batch_size
       _UpperCAmelCase		:							List[str]							=     seq_length
       _UpperCAmelCase		:							List[Any]							=     is_training
       _UpperCAmelCase		:							List[str]							=     use_input_mask
       _UpperCAmelCase		:							Optional[Any]							=     use_token_type_ids
       _UpperCAmelCase		:							Any							=     use_labels
       _UpperCAmelCase		:							str							=     vocab_size
       _UpperCAmelCase		:							Union[str, Any]							=     hidden_size
       _UpperCAmelCase		:							Tuple							=     num_hidden_layers
       _UpperCAmelCase		:							str							=     num_attention_heads
       _UpperCAmelCase		:							Any							=     intermediate_multiple_size
       _UpperCAmelCase		:							Union[str, Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout
       _UpperCAmelCase		:							str							=     attention_dropout
       _UpperCAmelCase		:							Any							=     weight_tying
       _UpperCAmelCase		:							Dict							=     max_position_embeddings
       _UpperCAmelCase		:							List[Any]							=     type_vocab_size
       _UpperCAmelCase		:							List[str]							=     type_sequence_label_size
       _UpperCAmelCase		:							Union[str, Any]							=     initializer_range
       _UpperCAmelCase		:							Tuple							=     num_labels
       _UpperCAmelCase		:							List[Any]							=     num_choices
       _UpperCAmelCase		:							Optional[Any]							=     scope


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

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

       _UpperCAmelCase		:							Any							=     None
       if self.use_labels:
         _UpperCAmelCase		:							List[str]							=     ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels    )

       _UpperCAmelCase		:							Optional[Any]							=     self.get_config()

       return config, input_ids, input_mask, token_labels


     def        __snake_case(  self    ):
       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=A_							,					initializer_range=self.initializer_range							,					)


     def        __snake_case(  self    ):
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							int							=     self.prepare_config_and_inputs()

       _UpperCAmelCase		:							Tuple							=     True

       return config, input_ids, input_mask, token_labels


     def        __snake_case(  self							,					A_							,					A_							,					A_    ):
       _UpperCAmelCase		:							List[Any]							=     GPTNeoXJapaneseModel(config=A_    )
       model.to(A_    )
       model.eval()
       _UpperCAmelCase		:							str							=     model(A_							,					attention_mask=A_    )
       _UpperCAmelCase		:							List[str]							=     model(A_    )
       self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)    )


     def        __snake_case(  self							,					A_							,					A_							,					A_    ):
       _UpperCAmelCase		:							Any							=     True
       _UpperCAmelCase		:							Optional[Any]							=     GPTNeoXJapaneseModel(A_    )
       model.to(A_    )
       model.eval()
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_    )
       self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)    )


     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       _UpperCAmelCase		:							int							=     GPTNeoXJapaneseForCausalLM(config=A_    )
       model.to(A_    )
       model.eval()
       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_							,					labels=A_    )
       self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )


     def        __snake_case(  self							,					A_							,					A_							,					A_    ):
       _UpperCAmelCase		:							Any							=     True
       _UpperCAmelCase		:							Any							=     GPTNeoXJapaneseForCausalLM(config=A_    )
       model.to(A_    )
       model.eval()

       # first forward pass
       _UpperCAmelCase		:							Dict							=     model(A_							,					attention_mask=A_							,					use_cache=A_    )
       _UpperCAmelCase		:							Dict							=     outputs.past_key_values

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

       # append to next input_ids and
       _UpperCAmelCase		:							Dict							=     torch.cat([input_ids, next_tokens]							,					dim=-1    )
       _UpperCAmelCase		:							Optional[Any]							=     torch.cat([input_mask, next_mask]							,					dim=-1    )

       _UpperCAmelCase		:							str							=     model(A_							,					attention_mask=A_							,					output_hidden_states=A_    )
       _UpperCAmelCase		:							Dict							=     output_from_no_past["""hidden_states"""][0]
       _UpperCAmelCase		:							Dict							=     model(
           A_							,					attention_mask=A_							,					past_key_values=A_							,					output_hidden_states=A_							,					)["""hidden_states"""][0]

       # select random slice
       _UpperCAmelCase		:							str							=     ids_tensor((1,)							,					output_from_past.shape[-1]    ).item()
       _UpperCAmelCase		:							List[Any]							=     output_from_no_past[:, -3:, random_slice_idx].detach()
       _UpperCAmelCase		:							Union[str, Any]							=     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(A_							,					A_							,					atol=1e-3    )    )




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


@require_torch
class 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (
         {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
         if is_torch_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     GPTNeoXJapaneseModelTester(self    )
       _UpperCAmelCase		:							Optional[int]							=     ConfigTester(self							,					config_class=A_							,					hidden_size=37    )


     def        __snake_case(  self    ):
       self.config_tester.run_common_tests()


     def        __snake_case(  self    ):
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							Any							=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(A_							,					A_							,					A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							int							=     self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(A_							,					A_							,					A_    )


     def        __snake_case(  self    ):
       # This regression test was failing with PyTorch < 1.3
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							Tuple							=     self.model_tester.prepare_config_and_inputs_for_decoder()

       _UpperCAmelCase		:							Tuple							=     None

       self.model_tester.create_and_check_model_as_decoder(A_							,					A_							,					A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							List[Any]							=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(A_							,					A_							,					A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Dict							=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*A_    )




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     """abeja/gpt-neox-japanese-2.7b"""

       _UpperCAmelCase		:							Union[str, 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、"""]

       _UpperCAmelCase		:							List[Any]							=     [
           """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。""",
       ]

       _UpperCAmelCase		:							str							=     GPTNeoXJapaneseTokenizer.from_pretrained(A_    )
       _UpperCAmelCase		:							Optional[Any]							=     GPTNeoXJapaneseForCausalLM.from_pretrained(A_    )

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









SCREAMING_SNAKE_CASE__					:     dict[str, float]       =     {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}




def    a__    (		snake_case__					:							str						,   snake_case__					:							str						,   snake_case__					:							float     ):
  if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
    _UpperCAmelCase		:							Optional[Any]							=     (
        f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
        f'''Valid values are: {", ".join(snake_case__     )}'''
    )
    raise ValueError(snake_case__     )
  return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





