
"""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 __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@slow
			def   lowerCamelCase__  (					self    :       str						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"						)
								A_								=    AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"						)
								model.to(_snake_case						)

								from datasets import load_dataset

								A_								=    load_dataset("nielsr/rvlcdip-demo"						)

								A_								=    dataset["train"][0]["image"].convert("RGB"						)

								A_								=    image_processor(_snake_case							, return_tensors="pt"						).to(_snake_case						)

								# forward pass
								with torch.no_grad():
													A_								=    model(**_snake_case						)
													A_								=    outputs.logits

								A_								=    torch.Size((1, 16)						)
								self.assertEqual(logits.shape							, _snake_case						)

								A_								=    torch.tensor(
								    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]							, device=_snake_case							, dtype=torch.float							, )
								self.assertTrue(torch.allclose(logits[0, :3]							, _snake_case							, atol=1e-4						)						)






"""simple docstring"""





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


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# TODO: upload to AWS
UpperCamelCase_       :    List[str]						      =			{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"retribert"




			def __init__(					self    :       str							, _snake_case    :       List[str]=30_522							, _snake_case    :       List[Any]=768							, _snake_case    :       List[Any]=8							, _snake_case    :       Union[str, Any]=12							, _snake_case    :       Optional[Any]=3_072							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       Dict=0.1							, _snake_case    :       str=512							, _snake_case    :       Tuple=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Optional[Any]=1e-12							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=128							, _snake_case    :       Union[str, Any]=0							, **_snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								super().__init__(pad_token_id=_snake_case							, **_snake_case						)

								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    hidden_act
								A_								=    intermediate_size
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    max_position_embeddings
								A_								=    type_vocab_size
								A_								=    initializer_range
								A_								=    layer_norm_eps
								A_								=    share_encoders
								A_								=    projection_dim




