




"""simple docstring"""

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


__A 		=			logging.get_logger(__name__)

__A 		=			{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}
class 			lowerCamelCase__    (  _SCREAMING_SNAKE_CASE			):
			a__ :      Union[str, Any]								=     """qdqbert"""


			def __init__(     self				, SCREAMING_SNAKE_CASE=30_522				, SCREAMING_SNAKE_CASE=768				, SCREAMING_SNAKE_CASE=12				, SCREAMING_SNAKE_CASE=12				, SCREAMING_SNAKE_CASE=3_072				, SCREAMING_SNAKE_CASE="gelu"				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE=512				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=0.02				, SCREAMING_SNAKE_CASE=1E-12				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=1				, SCREAMING_SNAKE_CASE=0				, SCREAMING_SNAKE_CASE=2				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					super().__init__(pad_token_id=__A				, bos_token_id=__A				, eos_token_id=__A				, **__A							)

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









"""simple docstring"""

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class 			lowerCamelCase__    (  unittest.TestCase			):


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				1
					snake_case		:						Dict								=				3
					snake_case		:						int								=				8
					snake_case		:						int								=				8

					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)

					return sample


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(image[0]				, PIL.Image.Image							):
							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					return image


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				self.dummy_sample
					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Optional[Any]								=				image_processor.postprocess(
							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Any								=				(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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)

							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Tuple								=				(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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				self.dummy_sample
					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, 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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Union[str, Any]								=				self.dummy_sample
					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
					list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, 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

