

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor






class 		__magic_name__ (				unittest.TestCase							):






     @property
     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		1
         UpperCamelCase_          =		3
         UpperCamelCase_          =		8
         UpperCamelCase_          =		8

         UpperCamelCase_          =		torch.rand((batch_size, num_channels, height, width)     )

         return sample






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			Dict:
         if isinstance(image[0]						,		PIL.Image.Image     ):
             return np.stack([np.array(_lowercase     ) for i in image]						,		axis=0     )
         elif isinstance(_lowercase						,		torch.Tensor     ):
             return image.cpu().numpy().transpose(0						,		2						,		3						,		1     )
         return image






     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		VaeImageProcessor(do_resize=_lowercase						,		do_normalize=_lowercase     )

         UpperCamelCase_          =		self.dummy_sample
         UpperCamelCase_          =		self.to_np(_lowercase     )

         for output_type in ["pt", "np", "pil"]:
             UpperCamelCase_          =		image_processor.postprocess(
                 image_processor.preprocess(_lowercase     )						,		output_type=_lowercase						,		)
             UpperCamelCase_          =		self.to_np(_lowercase     )
             UpperCamelCase_          =		(input_np * 255).round() if output_type == "pil" else input_np
             assert (
                 np.abs(in_np - out_np     ).max() < 1e-6
             ), F"decoded output does not match input for output_type {output_type}"






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		VaeImageProcessor(do_resize=_lowercase						,		do_normalize=_lowercase     )
         UpperCamelCase_          =		self.dummy_sample.cpu().numpy().transpose(0						,		2						,		3						,		1     )

         for output_type in ["pt", "np", "pil"]:
             UpperCamelCase_          =		image_processor.postprocess(image_processor.preprocess(_lowercase     )						,		output_type=_lowercase     )

             UpperCamelCase_          =		self.to_np(_lowercase     )
             UpperCamelCase_          =		(input_np * 255).round() if output_type == "pil" else input_np
             assert (
                 np.abs(in_np - out_np     ).max() < 1e-6
             ), F"decoded output does not match input for output_type {output_type}"






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_          =		VaeImageProcessor(do_resize=_lowercase						,		do_normalize=_lowercase     )

         UpperCamelCase_          =		self.dummy_sample.cpu().numpy().transpose(0						,		2						,		3						,		1     )
         UpperCamelCase_          =		image_processor.numpy_to_pil(_lowercase     )

         for output_type in ["pt", "np", "pil"]:
             UpperCamelCase_          =		image_processor.postprocess(image_processor.preprocess(_lowercase     )						,		output_type=_lowercase     )
             for i, o in zip(_lowercase						,		_lowercase     ):
                 UpperCamelCase_          =		np.array(_lowercase     )
                 UpperCamelCase_          =		self.to_np(_lowercase     ) if output_type == "pil" else (self.to_np(_lowercase     ) * 255).round()
                 assert (
                     np.abs(in_np - out_np     ).max() < 1e-6
                 ), F"decoded output does not match input for output_type {output_type}"






     def      UpperCAmelCase_							(							self     )->			Dict:
         UpperCamelCase_          =		VaeImageProcessor(do_resize=_lowercase						,		do_normalize=_lowercase     )

         UpperCamelCase_          =		self.dummy_sample
         UpperCamelCase_          =		input_pt_ad.squeeze(0     )

         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)
         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)

         UpperCamelCase_          =		self.to_np(self.dummy_sample     )
         UpperCamelCase_          =		input_np_ad.squeeze(0     )

         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)
         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)

         assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
         assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6






     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		VaeImageProcessor(do_resize=_lowercase						,		do_normalize=_lowercase     )

         UpperCamelCase_          =		self.dummy_sample
         UpperCamelCase_          =		list(_lowercase     )

         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)

         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)

         UpperCamelCase_          =		self.to_np(self.dummy_sample     )
         list(_lowercase     )

         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)

         UpperCamelCase_          =		image_processor.postprocess(
             image_processor.preprocess(_lowercase     )						,		output_type="np"						,		)

         assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
         assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6



from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					'rembert'







      def __init__(     self							,  lowerCamelCase=250300							,  lowerCamelCase=1152							,  lowerCamelCase=32							,  lowerCamelCase=18							,  lowerCamelCase=256							,  lowerCamelCase=1664							,  lowerCamelCase=4608							,  lowerCamelCase="gelu"							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.1							,  lowerCamelCase=512							,  lowerCamelCase=2							,  lowerCamelCase=0.02							,  lowerCamelCase=1e-12							,  lowerCamelCase=True							,  lowerCamelCase=0							,  lowerCamelCase=312							,  lowerCamelCase=313							,  **lowerCamelCase							,  )						->       Union[str, Any]:
        super().__init__(pad_token_id=lowerCamelCase							,  bos_token_id=lowerCamelCase							,  eos_token_id=lowerCamelCase							,  **lowerCamelCase   )

        snake_case_       						=      vocab_size
        snake_case_       						=      input_embedding_size
        snake_case_       						=      output_embedding_size
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      hidden_size
        snake_case_       						=      num_hidden_layers
        snake_case_       						=      num_attention_heads
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      classifier_dropout_prob
        snake_case_       						=      initializer_range
        snake_case_       						=      type_vocab_size
        snake_case_       						=      layer_norm_eps
        snake_case_       						=      use_cache
        snake_case_       						=      False







class  __lowerCamelCase				(	__snake_case	):







      @property
      def 	lowerCAmelCase_    (     self   )						->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          snake_case_       						=      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
          snake_case_       						=      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]   )







      @property
      def 	lowerCAmelCase_    (     self   )						->       float:
        return 1e-4