"""simple docstring"""





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 	UpperCAmelCase_      :


      def __init__(  self						,     a						,     a=1_3						,     a=7						,     a=True						,     a=True						,     a=True						,     a=True						,     a=9_9						,     a=3_2						,     a=5						,     a=4						,     a=4						,     a="gelu"						,     a=0.0						,     a=0.1						,     a=True						,     a=5_1_2						,     a=1_6						,     a=2						,     a=0.02						,     a=3						,     a=4						,     a=None						,     )			->   Tuple:
             lowercase__     :						Tuple         =      parent
             lowercase__     :						Any         =      batch_size
             lowercase__     :						Dict         =      seq_length
             lowercase__     :						str         =      is_training
             lowercase__     :						Optional[int]         =      use_input_mask
             lowercase__     :						Optional[Any]         =      use_token_type_ids
             lowercase__     :						List[str]         =      use_labels
             lowercase__     :						Optional[Any]         =      vocab_size
             lowercase__     :						Tuple         =      hidden_size
             lowercase__     :						Union[str, Any]         =      num_hidden_layers
             lowercase__     :						str         =      num_attention_heads
             lowercase__     :						List[str]         =      intermediate_multiple_size
             lowercase__     :						List[Any]         =      hidden_act
             lowercase__     :						Union[str, Any]         =      hidden_dropout
             lowercase__     :						Any         =      attention_dropout
             lowercase__     :						str         =      weight_tying
             lowercase__     :						Any         =      max_position_embeddings
             lowercase__     :						int         =      type_vocab_size
             lowercase__     :						Any         =      type_sequence_label_size
             lowercase__     :						Union[str, Any]         =      initializer_range
             lowercase__     :						Any         =      num_labels
             lowercase__     :						List[Any]         =      num_choices
             lowercase__     :						List[str]         =      scope


      def 						_UpperCAmelCase			(  self				)			->   Optional[Any]:
             lowercase__     :						int         =      ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size				)

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

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

             lowercase__     :						List[Any]         =      self.get_config()

             return config, input_ids, input_mask, token_labels


      def 						_UpperCAmelCase			(  self				)			->   List[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=a						,     initializer_range=self.initializer_range						,     )


      def 						_UpperCAmelCase			(  self				)			->   Any:
             lowercase__     :						Optional[Any]         =      self.prepare_config_and_inputs()

             lowercase__     :						Optional[int]         =      True

             return config, input_ids, input_mask, token_labels


      def 						_UpperCAmelCase			(  self						,     a						,     a						,     a				)			->   Optional[Any]:
             lowercase__     :						List[str]         =      GPTNeoXJapaneseModel(config=a				)
             model.to(a				)
             model.eval()
             lowercase__     :						str         =      model(a						,     attention_mask=a				)
             lowercase__     :						Union[str, Any]         =      model(a				)
             self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)				)


      def 						_UpperCAmelCase			(  self						,     a						,     a						,     a				)			->   List[Any]:
             lowercase__     :						int         =      True
             lowercase__     :						Dict         =      GPTNeoXJapaneseModel(a				)
             model.to(a				)
             model.eval()
             lowercase__     :						List[Any]         =      model(a						,     attention_mask=a				)
             self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)				)


      def 						_UpperCAmelCase			(  self						,     a						,     a						,     a						,     a				)			->   Tuple:
             lowercase__     :						int         =      GPTNeoXJapaneseForCausalLM(config=a				)
             model.to(a				)
             model.eval()
             lowercase__     :						Optional[int]         =      model(a						,     attention_mask=a						,     labels=a				)
             self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.vocab_size)				)


      def 						_UpperCAmelCase			(  self						,     a						,     a						,     a				)			->   Optional[int]:
             lowercase__     :						Optional[Any]         =      True
             lowercase__     :						str         =      GPTNeoXJapaneseForCausalLM(config=a				)
             model.to(a				)
             model.eval()

             # first forward pass
             lowercase__     :						List[str]         =      model(a						,     attention_mask=a						,     use_cache=a				)
             lowercase__     :						Any         =      outputs.past_key_values

             # create hypothetical multiple next token and extent to next_input_ids
             lowercase__     :						Dict         =      ids_tensor((self.batch_size, 3)						,     config.vocab_size				)
             lowercase__     :						str         =      ids_tensor((self.batch_size, 3)						,     vocab_size=2				)

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

             lowercase__     :						List[Any]         =      model(a						,     attention_mask=a						,     output_hidden_states=a				)
             lowercase__     :						Any         =      output_from_no_past["hidden_states"][0]
             lowercase__     :						Dict         =      model(
                 a						,     attention_mask=a						,     past_key_values=a						,     output_hidden_states=a						,     )["hidden_states"][0]

             # select random slice
             lowercase__     :						Optional[int]         =      ids_tensor((1,)						,     output_from_past.shape[-1]				).item()
             lowercase__     :						List[Any]         =      output_from_no_past[:, -3:, random_slice_idx].detach()
             lowercase__     :						List[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 						_UpperCAmelCase			(  self				)			->   Union[str, Any]:
             lowercase__     :						Any         =      self.prepare_config_and_inputs()
             lowercase__     :						Optional[int]         =      config_and_inputs
             lowercase__     :						Any         =      {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict





@require_torch
class 	UpperCAmelCase_      (       __A					,				__A					,				unittest.TestCase):
      lowerCamelCase__						:       Any					    =    (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
      lowerCamelCase__						:       Any					    =    (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
      lowerCamelCase__						:       Tuple					    =    (
          {"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
          if is_torch_available()
          else {}
      )
      lowerCamelCase__						:       Optional[int]					    =    False
      lowerCamelCase__						:       List[Any]					    =    False
      lowerCamelCase__						:       Any					    =    False
      lowerCamelCase__						:       str					    =    False


      def 						_UpperCAmelCase			(  self				)			->   Any:
             lowercase__     :						Union[str, Any]         =      GPTNeoXJapaneseModelTester(self				)
             lowercase__     :						List[str]         =      ConfigTester(self						,     config_class=a						,     hidden_size=3_7				)


      def 						_UpperCAmelCase			(  self				)			->   Optional[Any]:
             self.config_tester.run_common_tests()


      def 						_UpperCAmelCase			(  self				)			->   int:
             lowercase__     :						str         =      self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(a						,     a						,     a				)


      def 						_UpperCAmelCase			(  self				)			->   Dict:
             lowercase__     :						str         =      self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(a						,     a						,     a				)


      def 						_UpperCAmelCase			(  self				)			->   Optional[Any]:
             lowercase__     :						List[str]         =      self.model_tester.prepare_config_and_inputs_for_decoder()

             lowercase__     :						Dict         =      None

             self.model_tester.create_and_check_model_as_decoder(a						,     a						,     a				)


      def 						_UpperCAmelCase			(  self				)			->   str:
             lowercase__     :						Union[str, Any]         =      self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(a						,     a						,     a				)


      def 						_UpperCAmelCase			(  self				)			->   List[Any]:
             lowercase__     :						List[Any]         =      self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_causal_lm(*a				)


      @slow
      def 						_UpperCAmelCase			(  self				)			->   Dict:
             lowercase__     :						List[Any]         =      "abeja/gpt-neox-japanese-2.7b"

             lowercase__     :						Optional[int]         =      ["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、"]

             lowercase__     :						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。",
             ]

             lowercase__     :						List[str]         =      GPTNeoXJapaneseTokenizer.from_pretrained(a				)
             lowercase__     :						Tuple         =      GPTNeoXJapaneseForCausalLM.from_pretrained(a				)

             lowercase__     :						Optional[Any]         =      []
             for prompt in prompts:
                    lowercase__     :						str         =      tokenizer(a						,     return_tensors='pt'				).input_ids
                    lowercase__     :						Any         =      model.generate(a						,     max_length=5_0				)
                    lowercase__     :						Tuple         =      tokenizer.batch_decode(a						,     skip_special_tokens=a				)
                    predicted_outputs += generated_string
             self.assertListEqual(a						,     a				)




def 			lowerCamelCase__			(       _a):
	if not isinstance(_a				,      _a):
		SCREAMING_SNAKE_CASE :   Tuple					  =							f"Input value of [number={number}] must be an integer"
		raise TypeError(_a)
	if number < 0:
		return False
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()