
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      :



		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_=512       ,   A_=16       ,   A_=2       ,   A_=0.02       ,   A_=3       ,   A_=4       ,   A_=None       ,   )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  seq_length
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_input_mask
				UpperCamelCase						=  use_token_type_ids
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  intermediate_multiple_size
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  hidden_dropout
				UpperCamelCase						=  attention_dropout
				UpperCamelCase						=  weight_tying
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  type_sequence_label_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  num_labels
				UpperCamelCase						=  num_choices
				UpperCamelCase						=  scope



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size )

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

				UpperCamelCase						=  None
				if self.use_labels:
						UpperCamelCase						=  ids_tensor([self.batch_size, self.seq_length]       ,   self.num_labels )

				UpperCamelCase						=  self.get_config()

				return config, input_ids, input_mask, token_labels



		def __UpperCamelCase				(				self )						->      Any:
				"""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=A_       ,   initializer_range=self.initializer_range       ,   )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  self.prepare_config_and_inputs()

				UpperCamelCase						=  True

				return config, input_ids, input_mask, token_labels



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  GPTNeoXJapaneseModel(config=A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  model(A_       ,   attention_mask=A_ )
				UpperCamelCase						=  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_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  True
				UpperCamelCase						=  GPTNeoXJapaneseModel(A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  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_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  GPTNeoXJapaneseForCausalLM(config=A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  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_ )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  True
				UpperCamelCase						=  GPTNeoXJapaneseForCausalLM(config=A_ )
				model.to(A_ )
				model.eval()

				# first forward pass
				UpperCamelCase						=  model(A_       ,   attention_mask=A_       ,   use_cache=A_ )
				UpperCamelCase						=  outputs.past_key_values

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

				# append to next input_ids and
				UpperCamelCase						=  torch.cat([input_ids, next_tokens]       ,   dim=-1 )
				UpperCamelCase						=  torch.cat([input_mask, next_mask]       ,   dim=-1 )

				UpperCamelCase						=  model(A_       ,   attention_mask=A_       ,   output_hidden_states=A_ )
				UpperCamelCase						=  output_from_no_past['hidden_states'][0]
				UpperCamelCase						=  model(
				    A_       ,   attention_mask=A_       ,   past_key_values=A_       ,   output_hidden_states=A_       ,   )['hidden_states'][0]

				# select random slice
				UpperCamelCase						=  ids_tensor((1,)       ,   output_from_past.shape[-1] ).item()
				UpperCamelCase						=  output_from_no_past[:, -3:, random_slice_idx].detach()
				UpperCamelCase						=  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 )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  config_and_inputs
				UpperCamelCase						=  {'input_ids': input_ids, 'attention_mask': input_mask}
				return config, inputs_dict






@require_torch
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : int			  =       (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
		__lowercase : Any			  =       (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
		__lowercase : Union[str, Any]			  =       (
		    {"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
		    if is_torch_available()
		    else {}
		)
		__lowercase : str			  =       False
		__lowercase : Optional[Any]			  =       False
		__lowercase : Any			  =       False
		__lowercase : Optional[int]			  =       False



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  GPTNeoXJapaneseModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=A_       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(A_       ,   A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  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[int]:
				"""simple docstring"""
				# This regression test was failing with PyTorch < 1.3
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_decoder()

				UpperCamelCase						=  None

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



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  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[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_causal_lm(*A_ )



		@slow
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  'abeja/gpt-neox-japanese-2.7b'

				UpperCamelCase						=  ['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						=  [
				    '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						=  GPTNeoXJapaneseTokenizer.from_pretrained(A_ )
				UpperCamelCase						=  GPTNeoXJapaneseForCausalLM.from_pretrained(A_ )

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


from string import ascii_uppercase

_UpperCAmelCase      :				Dict          = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase      :				Tuple          = dict(enumerate(ascii_uppercase))


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  len(lowercase				)
		UpperCamelCase						=  0
		while True:
				if x == i:
						UpperCamelCase						=  0
				if len(lowercase				) == len(lowercase				):
						break
				key += key[i]
				i += 1
		return key


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in message:
				if letter == " ":
						cipher_text += " "
				else:
						UpperCamelCase						=  (dicta[letter] - dicta[key_new[i]]) % 26
						i += 1
						cipher_text += dicta[x]
		return cipher_text


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in cipher_text:
				if letter == " ":
						or_txt += " "
				else:
						UpperCamelCase						=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
						i += 1
						or_txt += dicta[x]
		return or_txt


def    A (      )     ->						None:
		'''simple docstring'''

		UpperCamelCase						=  'THE GERMAN ATTACK'
		UpperCamelCase						=  'SECRET'
		UpperCamelCase						=  generate_key(lowercase					, lowercase				)
		UpperCamelCase						=  cipher_text(lowercase					, lowercase				)
		print(f'''Encrypted Text = {s}'''				)
		print(f'''Original Text = {original_text(lowercase					, lowercase				)}'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
