






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= '''gpt_neox_japanese'''




	def __init__(       self       ,			a=3_20_00       ,			a=25_60       ,			a=32       ,			a=32       ,			a=4       ,			a="gelu"       ,			a=1.00       ,			a=1_00_00       ,			a=20_48       ,			a=0.02       ,			a=1E-5       ,			a=True       ,			a=3_19_96       ,			a=3_19_99       ,			a=0.1       ,			a=0.0       ,			**a       ,			)       ->    int:
								super().__init__(bos_token_id=a       ,			eos_token_id=a       ,			**a					)
								snake_case_				=							vocab_size
								snake_case_				=							max_position_embeddings
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_multiple_size
								snake_case_				=							hidden_act
								snake_case_				=							rotary_pct
								snake_case_				=							rotary_emb_base
								snake_case_				=							initializer_range
								snake_case_				=							layer_norm_eps
								snake_case_				=							use_cache
								snake_case_				=							attention_dropout
								snake_case_				=							hidden_dropout













import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
	import torch

	from transformers import AutoModelForImageClassification

if is_vision_available():
	from transformers import AutoImageProcessor




@require_torch
@require_vision
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								snake_case_				=							AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								model.to(a					)

								from datasets import load_dataset

								snake_case_				=							load_dataset('nielsr/rvlcdip-demo'					)

								snake_case_				=							dataset['train'][0]['image'].convert('RGB'					)

								snake_case_				=							image_processor(a       ,			return_tensors='pt'					).to(a					)

								# forward pass
								with torch.no_grad():
															snake_case_				=							model(**a					)
															snake_case_				=							outputs.logits

								snake_case_				=							torch.Size((1, 16)					)
								self.assertEqual(logits.shape       ,			a					)

								snake_case_				=							torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347]       ,			device=a       ,			dtype=torch.float       ,			)
								self.assertTrue(torch.allclose(logits[0, :3]       ,			a       ,			atol=1E-4					)					)





