


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        lowerCamelCase      :


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


	def    A(						self):
							__UpperCAmelCase :       int  						=  ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size)

							__UpperCAmelCase :       Union[str, Any]  						=  None
							if self.use_input_mask:
													__UpperCAmelCase :       int  						=  random_attention_mask([self.batch_size, self.seq_length])

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

							__UpperCAmelCase :       Union[str, Any]  						=  self.get_config()

							return config, input_ids, input_mask, token_labels


	def    A(						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=lowercase__							,    initializer_range=self.initializer_range							,    )


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       int  						=  self.prepare_config_and_inputs()

							__UpperCAmelCase :       Optional[Any]  						=  True

							return config, input_ids, input_mask, token_labels


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Dict  						=  GPTNeoXJapaneseModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       Optional[Any]  						=  model(lowercase__							,    attention_mask=lowercase__)
							__UpperCAmelCase :       List[str]  						=  model(lowercase__)
							self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size))


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Dict  						=  True
							__UpperCAmelCase :       Union[str, Any]  						=  GPTNeoXJapaneseModel(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       List[str]  						=  model(lowercase__							,    attention_mask=lowercase__)
							self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size))


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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Optional[Any]  						=  True
							__UpperCAmelCase :       List[Any]  						=  GPTNeoXJapaneseForCausalLM(config=lowercase__)
							model.to(lowercase__)
							model.eval()

							# first forward pass
							__UpperCAmelCase :       Dict  						=  model(lowercase__							,    attention_mask=lowercase__							,    use_cache=lowercase__)
							__UpperCAmelCase :       Tuple  						=  outputs.past_key_values

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

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

							__UpperCAmelCase :       int  						=  model(lowercase__							,    attention_mask=lowercase__							,    output_hidden_states=lowercase__)
							__UpperCAmelCase :       Optional[Any]  						=  output_from_no_past['''hidden_states'''][0]
							__UpperCAmelCase :       int  						=  model(
							    lowercase__							,    attention_mask=lowercase__							,    past_key_values=lowercase__							,    output_hidden_states=lowercase__							,    )['''hidden_states'''][0]

							# select random slice
							__UpperCAmelCase :       Tuple  						=  ids_tensor((1,)							,    output_from_past.shape[-1]).item()
							__UpperCAmelCase :       str  						=  output_from_no_past[:, -3:, random_slice_idx].detach()
							__UpperCAmelCase :       Tuple  						=  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(lowercase__							,    lowercase__							,    atol=1e-3))


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.prepare_config_and_inputs()
							__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       int  						=  config_and_inputs
							__UpperCAmelCase :       Any  						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class        lowerCamelCase      (     _UpperCamelCase       ,	_UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Union[str, Any]        =	(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
	_lowerCAmelCase    :		Union[str, Any]        =	(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
	_lowerCAmelCase    :		List[str]        =	(
	    {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
	    if is_torch_available()
	    else {}
	)
	_lowerCAmelCase    :		Union[str, Any]        =	False
	_lowerCAmelCase    :		Dict        =	False
	_lowerCAmelCase    :		Union[str, Any]        =	False
	_lowerCAmelCase    :		Optional[Any]        =	False


	def    A(						self):
							__UpperCAmelCase :       Any  						=  GPTNeoXJapaneseModelTester(self)
							__UpperCAmelCase :       List[Any]  						=  ConfigTester(self							,    config_class=lowercase__							,    hidden_size=3_7)


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


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       List[Any]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(lowercase__							,    lowercase__							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[int]  						=  self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(lowercase__							,    lowercase__							,    lowercase__)


	def    A(						self):
							# This regression test was failing with PyTorch < 1.3
							__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  self.model_tester.prepare_config_and_inputs_for_decoder()

							__UpperCAmelCase :       str  						=  None

							self.model_tester.create_and_check_model_as_decoder(lowercase__							,    lowercase__							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase__							,    lowercase__							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Dict  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*lowercase__)


	@slow
	def    A(						self):
							__UpperCAmelCase :       List[str]  						=  '''abeja/gpt-neox-japanese-2.7b'''

							__UpperCAmelCase :       Tuple  						=  ['''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[str]  						=  [
							    '''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 :       List[Any]  						=  GPTNeoXJapaneseTokenizer.from_pretrained(lowercase__)
							__UpperCAmelCase :       List[Any]  						=  GPTNeoXJapaneseForCausalLM.from_pretrained(lowercase__)

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





lowerCAmelCase							=						256
# Modulus to hash a string
lowerCAmelCase							=						1_000_003




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    bool:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  len(lowercase_			)
						__UpperCAmelCase :       Tuple  						=  len(lowercase_			)
						if p_len > t_len:
												return False

						__UpperCAmelCase :       Any  						=  0
						__UpperCAmelCase :       List[Any]  						=  0
						__UpperCAmelCase :       List[Any]  						=  1

						# Calculating the hash of pattern and substring of text
						for i in range(lowercase_			):
												__UpperCAmelCase :       List[str]  						=  (ord(pattern[i]			) + p_hash * alphabet_size) % modulus
												__UpperCAmelCase :       List[Any]  						=  (ord(text[i]			) + text_hash * alphabet_size) % modulus
												if i == p_len - 1:
																		continue
												__UpperCAmelCase :       Any  						=  (modulus_power * alphabet_size) % modulus

						for i in range(0			,					t_len - p_len + 1			):
												if text_hash == p_hash and text[i : i + p_len] == pattern:
																		return True
												if i == t_len - p_len:
																		continue
												# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
												__UpperCAmelCase :       int  						=  (
												    (text_hash - ord(text[i]			) * modulus_power) * alphabet_size
												    + ord(text[i + p_len]			)
												) % modulus
						return False




def 						__SCREAMING_SNAKE_CASE       (					)		->    None:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  '''abc1abc12'''
						__UpperCAmelCase :       List[str]  						=  '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
						__UpperCAmelCase :       Any  						=  '''alskfjaldsk23adsfabcabc'''
						assert rabin_karp(lowercase_			,					lowercase_			) and not rabin_karp(lowercase_			,					lowercase_			)

						# Test 2)
						__UpperCAmelCase :       Union[str, Any]  						=  '''ABABX'''
						__UpperCAmelCase :       List[Any]  						=  '''ABABZABABYABABX'''
						assert rabin_karp(lowercase_			,					lowercase_			)

						# Test 3)
						__UpperCAmelCase :       str  						=  '''AAAB'''
						__UpperCAmelCase :       List[Any]  						=  '''ABAAAAAB'''
						assert rabin_karp(lowercase_			,					lowercase_			)

						# Test 4)
						__UpperCAmelCase :       Optional[Any]  						=  '''abcdabcy'''
						__UpperCAmelCase :       Any  						=  '''abcxabcdabxabcdabcdabcy'''
						assert rabin_karp(lowercase_			,					lowercase_			)

						# Test 5)
						__UpperCAmelCase :       Any  						=  '''Lü'''
						__UpperCAmelCase :       Optional[int]  						=  '''Lüsai'''
						assert rabin_karp(lowercase_			,					lowercase_			)
						__UpperCAmelCase :       List[Any]  						=  '''Lue'''
						assert not rabin_karp(lowercase_			,					lowercase_			)
						print('''Success.'''			)


if __name__ == "__main__":
				test_rabin_karp()

