
'''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    _a				:





       """simple docstring"""




       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__=0.02       ,							A__=3       ,							A__=4       ,							A__=None       ,							)    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				parent
          _SCREAMING_SNAKE_CASE      =				batch_size
          _SCREAMING_SNAKE_CASE      =				seq_length
          _SCREAMING_SNAKE_CASE      =				is_training
          _SCREAMING_SNAKE_CASE      =				use_input_mask
          _SCREAMING_SNAKE_CASE      =				use_token_type_ids
          _SCREAMING_SNAKE_CASE      =				use_labels
          _SCREAMING_SNAKE_CASE      =				vocab_size
          _SCREAMING_SNAKE_CASE      =				hidden_size
          _SCREAMING_SNAKE_CASE      =				num_hidden_layers
          _SCREAMING_SNAKE_CASE      =				num_attention_heads
          _SCREAMING_SNAKE_CASE      =				intermediate_multiple_size
          _SCREAMING_SNAKE_CASE      =				hidden_act
          _SCREAMING_SNAKE_CASE      =				hidden_dropout
          _SCREAMING_SNAKE_CASE      =				attention_dropout
          _SCREAMING_SNAKE_CASE      =				weight_tying
          _SCREAMING_SNAKE_CASE      =				max_position_embeddings
          _SCREAMING_SNAKE_CASE      =				type_vocab_size
          _SCREAMING_SNAKE_CASE      =				type_sequence_label_size
          _SCREAMING_SNAKE_CASE      =				initializer_range
          _SCREAMING_SNAKE_CASE      =				num_labels
          _SCREAMING_SNAKE_CASE      =				num_choices
          _SCREAMING_SNAKE_CASE      =				scope






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size      )

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

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

          _SCREAMING_SNAKE_CASE      =				self.get_config()

          return config, input_ids, input_mask, token_labels






       def        UpperCamelCase				(							self      )    ->			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      )    ->			int:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.prepare_config_and_inputs()

          _SCREAMING_SNAKE_CASE      =				True

          return config, input_ids, input_mask, token_labels






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				GPTNeoXJapaneseModel(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__       ,							attention_mask=A__      )
          _SCREAMING_SNAKE_CASE      =				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__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				True
          _SCREAMING_SNAKE_CASE      =				GPTNeoXJapaneseModel(A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				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:
          _SCREAMING_SNAKE_CASE      =				GPTNeoXJapaneseForCausalLM(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				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[Any]:
          _SCREAMING_SNAKE_CASE      =				True
          _SCREAMING_SNAKE_CASE      =				GPTNeoXJapaneseForCausalLM(config=A__      )
          model.to(A__      )
          model.eval()

          # first forward pass
          _SCREAMING_SNAKE_CASE      =				model(A__       ,							attention_mask=A__       ,							use_cache=A__      )
          _SCREAMING_SNAKE_CASE      =				outputs.past_key_values

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

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

          _SCREAMING_SNAKE_CASE      =				model(A__       ,							attention_mask=A__       ,							output_hidden_states=A__      )
          _SCREAMING_SNAKE_CASE      =				output_from_no_past["""hidden_states"""][0]
          _SCREAMING_SNAKE_CASE      =				model(
              A__       ,							attention_mask=A__       ,							past_key_values=A__       ,							output_hidden_states=A__       ,							)["""hidden_states"""][0]

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






@require_torch
class    _a				(_lowerCamelCase  ,					_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       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        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				GPTNeoXJapaneseModelTester(self      )
          _SCREAMING_SNAKE_CASE      =				ConfigTester(self       ,							config_class=A__       ,							hidden_size=37      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          self.config_tester.run_common_tests()






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(A__       ,							A__       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(A__       ,							A__       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          # This regression test was failing with PyTorch < 1.3
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_decoder()

          _SCREAMING_SNAKE_CASE      =				None

          self.model_tester.create_and_check_model_as_decoder(A__       ,							A__       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				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]:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_causal_lm(*A__      )






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

          _SCREAMING_SNAKE_CASE      =				["""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、"""]

          _SCREAMING_SNAKE_CASE      =				[
              """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。""",
          ]

          _SCREAMING_SNAKE_CASE      =				GPTNeoXJapaneseTokenizer.from_pretrained(A__      )
          _SCREAMING_SNAKE_CASE      =				GPTNeoXJapaneseForCausalLM.from_pretrained(A__      )

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








'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ['image_processor', 'tokenizer']
       SCREAMING_SNAKE_CASE      =  'ChineseCLIPImageProcessor'
       SCREAMING_SNAKE_CASE      =  ('BertTokenizer', 'BertTokenizerFast')






       def __init__(							self       ,							A__=None       ,							A__=None       ,							**A__      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""       ,							A__       ,							)
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""feature_extractor"""      )

          _SCREAMING_SNAKE_CASE      =				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError("""You need to specify an `image_processor`."""      )
          if tokenizer is None:
             raise ValueError("""You need to specify a `tokenizer`."""      )

          super().__init__(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				self.image_processor






       def __call__(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:

          if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

          if text is not None:
             _SCREAMING_SNAKE_CASE      =				self.tokenizer(A__       ,							return_tensors=A__       ,							**A__      )

          if images is not None:
             _SCREAMING_SNAKE_CASE      =				self.image_processor(A__       ,							return_tensors=A__       ,							**A__      )

          if text is not None and images is not None:
             _SCREAMING_SNAKE_CASE      =				image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**A__      )       ,							tensor_type=A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Dict:
          return self.tokenizer.batch_decode(*A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Optional[Any]:
          return self.tokenizer.decode(*A__       ,							**A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.tokenizer.model_input_names
          _SCREAMING_SNAKE_CASE      =				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       @property
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							A__       ,							)
          return self.image_processor_class






