






"""simple docstring"""



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


__A		:       Dict  				=		logging.get_logger(__name__)

__A		:       Optional[int]  				=		{
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}





class 					_UpperCAmelCase       ( __UpperCAmelCase							):
					SCREAMING_SNAKE_CASE_		:				str							    =   "open-llama"




					def __init__(				self     :						Optional[int]    ,						A     :						Optional[Any]=10_00_00    ,						A     :						List[Any]=40_96    ,						A     :						Any=1_10_08    ,						A     :						int=32    ,						A     :						Dict=32    ,						A     :						Optional[Any]="silu"    ,						A     :						Union[str, Any]=20_48    ,						A     :						Optional[Any]=0.02    ,						A     :						Union[str, Any]=1e-6    ,						A     :						Optional[Any]=True    ,						A     :						str=0    ,						A     :						Tuple=1    ,						A     :						List[Any]=2    ,						A     :						Tuple=False    ,						A     :						List[Any]=True    ,						A     :						Union[str, Any]=0.1    ,						A     :						Tuple=0.1    ,						A     :						str=True    ,						A     :						List[str]=True    ,						A     :						str=None    ,						**A     :						int    ,						)  ->  Dict:
							lowercase_ : Tuple							       =   vocab_size
							lowercase_ : Optional[int]							       =   max_position_embeddings
							lowercase_ : Tuple							       =   hidden_size
							lowercase_ : Optional[int]							       =   intermediate_size
							lowercase_ : List[str]							       =   num_hidden_layers
							lowercase_ : Dict							       =   num_attention_heads
							lowercase_ : List[Any]							       =   hidden_act
							lowercase_ : Any							       =   initializer_range
							lowercase_ : Union[str, Any]							       =   rms_norm_eps
							lowercase_ : Any							       =   use_cache
							lowercase_ : List[Any]							       =   kwargs.pop(
							    '''use_memorry_efficient_attention'''    ,						UpperCamelCase__   )
							lowercase_ : List[Any]							       =   hidden_dropout_prob
							lowercase_ : int							       =   attention_dropout_prob
							lowercase_ : Optional[Any]							       =   use_stable_embedding
							lowercase_ : Optional[Any]							       =   shared_input_output_embedding
							lowercase_ : Dict							       =   rope_scaling
							self._rope_scaling_validation()

							super().__init__(
							    pad_token_id=UpperCamelCase__    ,						bos_token_id=UpperCamelCase__    ,						eos_token_id=UpperCamelCase__    ,						tie_word_embeddings=UpperCamelCase__    ,						**UpperCamelCase__    ,						)






					def   A  (				self     :						Any   )  ->  List[str]:



							if self.rope_scaling is None:
									return

							if not isinstance(self.rope_scaling    ,						UpperCamelCase__   ) or len(self.rope_scaling   ) != 2:
									raise ValueError(
									    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
									    F'''got {self.rope_scaling}'''   )
							lowercase_ : Any							       =   self.rope_scaling.get('''type'''    ,						UpperCamelCase__   )
							lowercase_ : str							       =   self.rope_scaling.get('''factor'''    ,						UpperCamelCase__   )
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
									raise ValueError(
									    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''   )
							if rope_scaling_factor is None or not isinstance(UpperCamelCase__    ,						UpperCamelCase__   ) or rope_scaling_factor <= 1.0:
									raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''   )









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  A		(  unittest.TestCase     ):



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

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

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

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

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




