






'''simple docstring'''




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


lowerCAmelCase__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}







class       __lowercase					(_UpperCAmelCase    ):
 _lowerCamelCase              =  '''dpr'''

 def __init__(      self   :						Optional[int]  ,					UpperCAmelCase_   :						Dict=30_522  ,					UpperCAmelCase_   :						Any=768  ,					UpperCAmelCase_   :						List[Any]=12  ,					UpperCAmelCase_   :						str=12  ,					UpperCAmelCase_   :						Union[str, Any]=3_072  ,					UpperCAmelCase_   :						List[Any]="gelu"  ,					UpperCAmelCase_   :						str=0.1  ,					UpperCAmelCase_   :						Any=0.1  ,					UpperCAmelCase_   :						List[str]=512  ,					UpperCAmelCase_   :						Optional[Any]=2  ,					UpperCAmelCase_   :						str=0.02  ,					UpperCAmelCase_   :						Optional[Any]=1e-12  ,					UpperCAmelCase_   :						List[str]=0  ,					UpperCAmelCase_   :						Any="absolute"  ,					UpperCAmelCase_   :						int = 0  ,					**UpperCAmelCase_   :						Tuple  ,					):
   super().__init__(pad_token_id=lowerCamelCase_  ,					**lowerCamelCase_)

   UpperCamelCase__				:					Union[str, Any]					=					vocab_size
   UpperCamelCase__				:					Any					=					hidden_size
   UpperCamelCase__				:					int					=					num_hidden_layers
   UpperCamelCase__				:					str					=					num_attention_heads
   UpperCamelCase__				:					Optional[Any]					=					hidden_act
   UpperCamelCase__				:					Dict					=					intermediate_size
   UpperCamelCase__				:					int					=					hidden_dropout_prob
   UpperCamelCase__				:					Optional[int]					=					attention_probs_dropout_prob
   UpperCamelCase__				:					List[str]					=					max_position_embeddings
   UpperCamelCase__				:					Optional[int]					=					type_vocab_size
   UpperCamelCase__				:					Any					=					initializer_range
   UpperCamelCase__				:					List[str]					=					layer_norm_eps
   UpperCamelCase__				:					List[Any]					=					projection_dim
   UpperCamelCase__				:					List[Any]					=					position_embedding_type












'''simple docstring'''




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       __lowercase					(unittest.TestCase    ):

	@slow
	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			UpperCamelCase__				:					List[str]					=					AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			model.to(UpperCAmelCase_)

			from datasets import load_dataset

			UpperCamelCase__				:					Optional[Any]					=					load_dataset('nielsr/rvlcdip-demo')

			UpperCamelCase__				:					int					=					dataset['train'][0]['image'].convert('RGB')

			UpperCamelCase__				:					Union[str, Any]					=					image_processor(UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Optional[Any]					=					model(**UpperCAmelCase_)
					UpperCamelCase__				:					Tuple					=					outputs.logits

			UpperCamelCase__				:					str					=					torch.Size((1, 16))
			self.assertEqual(logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					torch.tensor(
			    [-0.41_58, -0.40_92, -0.43_47]  ,					device=UpperCAmelCase_  ,					dtype=torch.float  ,					)
			self.assertTrue(torch.allclose(logits[0, :3]  ,					UpperCAmelCase_  ,					atol=1e-4))




