


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       """roberta"""






    def __init__(					self				:    Tuple				, __UpperCAmelCase				:    Tuple=50265				, __UpperCAmelCase				:    Dict=768				, __UpperCAmelCase				:    Optional[int]=12				, __UpperCAmelCase				:    Optional[int]=12				, __UpperCAmelCase				:    Any=3072				, __UpperCAmelCase				:    Optional[int]="gelu"				, __UpperCAmelCase				:    Optional[Any]=0.1				, __UpperCAmelCase				:    Union[str, Any]=0.1				, __UpperCAmelCase				:    Dict=512				, __UpperCAmelCase				:    Optional[int]=2				, __UpperCAmelCase				:    List[Any]=0.02				, __UpperCAmelCase				:    List[str]=1e-12				, __UpperCAmelCase				:    List[Any]=1				, __UpperCAmelCase				:    str=0				, __UpperCAmelCase				:    Optional[int]=2				, __UpperCAmelCase				:    Optional[int]="absolute"				, __UpperCAmelCase				:    List[str]=True				, __UpperCAmelCase				:    Dict=None				, **__UpperCAmelCase				:    str				, ):
      super().__init__(pad_token_id=__UpperCAmelCase				, bos_token_id=__UpperCAmelCase				, eos_token_id=__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		int      							=			vocab_size
      a						:		Optional[Any]      							=			hidden_size
      a						:		Any      							=			num_hidden_layers
      a						:		Dict      							=			num_attention_heads
      a						:		Dict      							=			hidden_act
      a						:		int      							=			intermediate_size
      a						:		str      							=			hidden_dropout_prob
      a						:		List[Any]      							=			attention_probs_dropout_prob
      a						:		Any      							=			max_position_embeddings
      a						:		str      							=			type_vocab_size
      a						:		Optional[int]      							=			initializer_range
      a						:		Optional[Any]      							=			layer_norm_eps
      a						:		Optional[Any]      							=			position_embedding_type
      a						:		Any      							=			use_cache
      a						:		List[str]      							=			classifier_dropout






class 						_A  (  _a  ):







    """simple docstring"""




    @property
    def 	__snake_case							(					self				:    int):
      if self.task == "multiple-choice":
        a						:		Optional[Any]      							=			{0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		Optional[Any]      							=			{0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ])










'''simple docstring'''



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			VQModel
    lowerCamelCase  :					Union[str, Any]							     =			'sample'







    @property
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=(32, 32)	)				->     Any:
         __lowerCamelCase   :	Tuple								=		4
         __lowerCamelCase   :	Union[str, Any]								=		3

         __lowerCamelCase   :	List[Any]								=		floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)

         return {"sample": image}







    @property
    def       lowercase_				(							self	)				->     Optional[int]:
         return (3, 32, 32)







    @property
    def       lowercase_				(							self	)				->     List[Any]:
         return (3, 32, 32)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Optional[Any]								=		{
             'block_out_channels': [32, 64],
             'in_channels': 3,
             'out_channels': 3,
             'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
             'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
             'latent_channels': 3,
         }
         __lowerCamelCase   :	Union[str, Any]								=		self.dummy_input
         return init_dict, inputs_dict







    def       lowercase_				(							self	)				->     str:
         pass







    def       lowercase_				(							self	)				->     Optional[int]:
         pass







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'			,     output_loading_info=SCREAMING_SNAKE_CASE_	)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(len(loading_info['missing_keys']	)			,     0	)

         model.to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		model(**self.dummy_input	)

         assert image is not None, "Make sure output is not None"







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	List[Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'	)
         model.to(SCREAMING_SNAKE_CASE_	).eval()

         torch.manual_seed(0	)
         if torch.cuda.is_available():
              torch.cuda.manual_seed_all(0	)

         __lowerCamelCase   :	List[str]								=		torch.randn(1			,     model.config.in_channels			,     model.config.sample_size			,     model.config.sample_size	)
         __lowerCamelCase   :	Optional[int]								=		image.to(SCREAMING_SNAKE_CASE_	)
         with torch.no_grad():
              __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_	).sample

         __lowerCamelCase   :	List[str]								=		output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         __lowerCamelCase   :	Union[str, Any]								=		torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]	)
         # fmt: on
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)






