

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 			_SCREAMING_SNAKE_CASE      (  unittest.TestCase):







						@property
						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								=1
													lowerCamelCase_								=3
													lowerCamelCase_								=8
													lowerCamelCase_								=8

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

													return sample







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[Any]:
													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        _snake_case				(			self							)->							List[Any]:
													lowerCamelCase_								=VaeImageProcessor(do_resize=_SCREAMING_SNAKE_CASE     ,						do_normalize=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=self.dummy_sample
													lowerCamelCase_								=self.to_np(_SCREAMING_SNAKE_CASE							)

													for output_type in ["pt", "np", "pil"]:
																				lowerCamelCase_								=image_processor.postprocess(
																				    image_processor.preprocess(_SCREAMING_SNAKE_CASE							)     ,						output_type=_SCREAMING_SNAKE_CASE     ,						)
																				lowerCamelCase_								=self.to_np(_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=(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        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_								=VaeImageProcessor(do_resize=_SCREAMING_SNAKE_CASE     ,						do_normalize=_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=self.dummy_sample.cpu().numpy().transpose(0     ,						2     ,						3     ,						1							)

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

																				lowerCamelCase_								=self.to_np(_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=(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        _snake_case				(			self							)->							List[Any]:
													lowerCamelCase_								=VaeImageProcessor(do_resize=_SCREAMING_SNAKE_CASE     ,						do_normalize=_SCREAMING_SNAKE_CASE							)

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

													for output_type in ["pt", "np", "pil"]:
																				lowerCamelCase_								=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							):
																											lowerCamelCase_								=np.array(_SCREAMING_SNAKE_CASE							)
																											lowerCamelCase_								=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        _snake_case				(			self							)->							int:
													lowerCamelCase_								=VaeImageProcessor(do_resize=_SCREAMING_SNAKE_CASE     ,						do_normalize=_SCREAMING_SNAKE_CASE							)

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

													lowerCamelCase_								=image_processor.postprocess(
													    image_processor.preprocess(_SCREAMING_SNAKE_CASE							)     ,						output_type="""np"""     ,						)
													lowerCamelCase_								=image_processor.postprocess(
													    image_processor.preprocess(_SCREAMING_SNAKE_CASE							)     ,						output_type="""np"""     ,						)

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

													lowerCamelCase_								=image_processor.postprocess(
													    image_processor.preprocess(_SCREAMING_SNAKE_CASE							)     ,						output_type="""np"""     ,						)
													lowerCamelCase_								=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        _snake_case				(			self							)->							Any:
													lowerCamelCase_								=VaeImageProcessor(do_resize=_SCREAMING_SNAKE_CASE     ,						do_normalize=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=self.dummy_sample
													lowerCamelCase_								=list(_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=image_processor.postprocess(
													    image_processor.preprocess(_SCREAMING_SNAKE_CASE							)     ,						output_type="""np"""     ,						)

													lowerCamelCase_								=image_processor.postprocess(
													    image_processor.preprocess(_SCREAMING_SNAKE_CASE							)     ,						output_type="""np"""     ,						)

													lowerCamelCase_								=self.to_np(self.dummy_sample							)
													list(_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=image_processor.postprocess(
													    image_processor.preprocess(_SCREAMING_SNAKE_CASE							)     ,						output_type="""np"""     ,						)

													lowerCamelCase_								=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



from collections import OrderedDict
from typing import Mapping

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


__A	:   List[Any]	=					logging.get_logger(__name__)

__A	:   Tuple	=					{
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Tuple	=		"electra"







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=3_0522     ,						_SCREAMING_SNAKE_CASE=128     ,						_SCREAMING_SNAKE_CASE=256     ,						_SCREAMING_SNAKE_CASE=12     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=1024     ,						_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=0.0_2     ,						_SCREAMING_SNAKE_CASE=1E-12     ,						_SCREAMING_SNAKE_CASE="first"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE="absolute"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[int]:
													super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=vocab_size
													lowerCamelCase_								=embedding_size
													lowerCamelCase_								=hidden_size
													lowerCamelCase_								=num_hidden_layers
													lowerCamelCase_								=num_attention_heads
													lowerCamelCase_								=intermediate_size
													lowerCamelCase_								=hidden_act
													lowerCamelCase_								=hidden_dropout_prob
													lowerCamelCase_								=attention_probs_dropout_prob
													lowerCamelCase_								=max_position_embeddings
													lowerCamelCase_								=type_vocab_size
													lowerCamelCase_								=initializer_range
													lowerCamelCase_								=layer_norm_eps

													lowerCamelCase_								=summary_type
													lowerCamelCase_								=summary_use_proj
													lowerCamelCase_								=summary_activation
													lowerCamelCase_								=summary_last_dropout
													lowerCamelCase_								=position_embedding_type
													lowerCamelCase_								=use_cache
													lowerCamelCase_								=classifier_dropout


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):







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