






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__		:





     """simple docstring"""






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



     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   Dict     =						ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size )

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

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

      snake_case__				:   Any     =						self.get_config()

      return config, input_ids, input_mask, token_labels



     def  _lowercase  (      self  :						Optional[int] ):
      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  _lowercase  (      self  :						int ):
      snake_case__				:   Optional[Any]     =						self.prepare_config_and_inputs()

      snake_case__				:   Dict     =						True

      return config, input_ids, input_mask, token_labels



     def  _lowercase  (      self  :						Tuple							,	__A  :						int							,	__A  :						Dict							,	__A  :						List[Any] ):
      snake_case__				:   Tuple     =						GPTNeoXJapaneseModel(config=__A )
      model.to(__A )
      model.eval()
      snake_case__				:   Dict     =						model(__A							,	attention_mask=__A )
      snake_case__				:   Dict     =						model(__A )
      self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size) )



     def  _lowercase  (      self  :						List[Any]							,	__A  :						int							,	__A  :						List[str]							,	__A  :						Optional[Any] ):
      snake_case__				:   Optional[int]     =						True
      snake_case__				:   Optional[Any]     =						GPTNeoXJapaneseModel(__A )
      model.to(__A )
      model.eval()
      snake_case__				:   Dict     =						model(__A							,	attention_mask=__A )
      self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size) )



     def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[str]							,	__A  :						List[Any]							,	__A  :						Union[str, Any]							,	__A  :						int ):
      snake_case__				:   Any     =						GPTNeoXJapaneseForCausalLM(config=__A )
      model.to(__A )
      model.eval()
      snake_case__				:   Union[str, Any]     =						model(__A							,	attention_mask=__A							,	labels=__A )
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size) )



     def  _lowercase  (      self  :						List[str]							,	__A  :						List[Any]							,	__A  :						str							,	__A  :						List[Any] ):
      snake_case__				:   Union[str, Any]     =						True
      snake_case__				:   Tuple     =						GPTNeoXJapaneseForCausalLM(config=__A )
      model.to(__A )
      model.eval()

      # first forward pass
      snake_case__				:   List[str]     =						model(__A							,	attention_mask=__A							,	use_cache=__A )
      snake_case__				:   Optional[int]     =						outputs.past_key_values

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

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

      snake_case__				:   Any     =						model(__A							,	attention_mask=__A							,	output_hidden_states=__A )
      snake_case__				:   Optional[int]     =						output_from_no_past["hidden_states"][0]
      snake_case__				:   Dict     =						model(
          __A							,	attention_mask=__A							,	past_key_values=__A							,	output_hidden_states=__A							,	)["hidden_states"][0]

      # select random slice
      snake_case__				:   Dict     =						ids_tensor((1,)							,	output_from_past.shape[-1] ).item()
      snake_case__				:   Union[str, Any]     =						output_from_no_past[:, -3:, random_slice_idx].detach()
      snake_case__				:   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  _lowercase  (      self  :						Dict ):
      snake_case__				:   Dict     =						self.prepare_config_and_inputs()
      snake_case__				:   Any     =						config_and_inputs
      snake_case__				:   Tuple     =						{"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





     """simple docstring"""






     a_			=	(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
     a_			=	(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
     a_			=	(
         {"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
         if is_torch_available()
         else {}
     )
     a_			=	False
     a_			=	False
     a_			=	False
     a_			=	False



     def  _lowercase  (      self  :						int ):
      snake_case__				:   Dict     =						GPTNeoXJapaneseModelTester(self )
      snake_case__				:   Optional[int]     =						ConfigTester(self							,	config_class=__A							,	hidden_size=3_7 )



     def  _lowercase  (      self  :						Optional[int] ):
      self.config_tester.run_common_tests()



     def  _lowercase  (      self  :						int ):
      snake_case__				:   Union[str, Any]     =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(__A							,	__A							,	__A )



     def  _lowercase  (      self  :						List[Any] ):
      snake_case__				:   Any     =						self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(__A							,	__A							,	__A )



     def  _lowercase  (      self  :						Tuple ):
      # This regression test was failing with PyTorch < 1.3
      snake_case__				:   str     =						self.model_tester.prepare_config_and_inputs_for_decoder()

      snake_case__				:   List[Any]     =						None

      self.model_tester.create_and_check_model_as_decoder(__A							,	__A							,	__A )



     def  _lowercase  (      self  :						Any ):
      snake_case__				:   List[str]     =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(__A							,	__A							,	__A )



     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   Dict     =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*__A )






     @slow
     def  _lowercase  (      self  :						Union[str, Any] ):
      snake_case__				:   Optional[Any]     =						"abeja/gpt-neox-japanese-2.7b"

      snake_case__				:   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、"]

      snake_case__				:   Optional[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。",
      ]

      snake_case__				:   Dict     =						GPTNeoXJapaneseTokenizer.from_pretrained(__A )
      snake_case__				:   Dict     =						GPTNeoXJapaneseForCausalLM.from_pretrained(__A )

      snake_case__				:   Dict     =						[]
      for prompt in prompts:
       snake_case__				:   Tuple     =						tokenizer(__A							,	return_tensors="pt" ).input_ids
       snake_case__				:   List[str]     =						model.generate(__A							,	max_length=5_0 )
       snake_case__				:   Optional[int]     =						tokenizer.batch_decode(__A							,	skip_special_tokens=__A )
       predicted_outputs += generated_string
      self.assertListEqual(__A							,	__A )











# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCamelCase      :    Dict   		=   abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	from diffusers.utils.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(snake_case_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any     ):
	from diffusers.utils.testing_utils import pytest_terminal_summary_main

	snake_case__				:   Optional[int]     =						terminalreporter.config.getoption("--make-reports"     )
	if make_reports:
		pytest_terminal_summary_main(snake_case_  ,						id=snake_case_     )



