"""simple docstring"""






import unittest

import numpy as np
import PIL
import torch

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




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     Optional[Any]      ):
									UpperCAmelCase__					=							1
									UpperCAmelCase__					=							3
									UpperCAmelCase__					=							8
									UpperCAmelCase__					=							8

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

									return sample







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     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 		__lowerCAmelCase						(    self       :     Any      ):
									UpperCAmelCase__					=							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE__   ,do_normalize=SCREAMING_SNAKE_CASE__      )

									UpperCAmelCase__					=							self.dummy_sample
									UpperCAmelCase__					=							self.to_np(SCREAMING_SNAKE_CASE__      )

									for output_type in ["pt", "np", "pil"]:
													UpperCAmelCase__					=							image_processor.postprocess(
													    image_processor.preprocess(SCREAMING_SNAKE_CASE__      )   ,output_type=SCREAMING_SNAKE_CASE__   ,)
													UpperCAmelCase__					=							self.to_np(SCREAMING_SNAKE_CASE__      )
													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 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE__   ,do_normalize=SCREAMING_SNAKE_CASE__      )
									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(SCREAMING_SNAKE_CASE__      )   ,output_type=SCREAMING_SNAKE_CASE__      )

													UpperCAmelCase__					=							self.to_np(SCREAMING_SNAKE_CASE__      )
													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 		__lowerCAmelCase						(    self       :     Optional[Any]      ):
									UpperCAmelCase__					=							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE__   ,do_normalize=SCREAMING_SNAKE_CASE__      )

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

									for output_type in ["pt", "np", "pil"]:
													UpperCAmelCase__					=							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__      ):
																	UpperCAmelCase__					=							np.array(SCREAMING_SNAKE_CASE__      )
																	UpperCAmelCase__					=							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       :     Optional[int]      ):
									UpperCAmelCase__					=							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE__   ,do_normalize=SCREAMING_SNAKE_CASE__      )

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

									UpperCAmelCase__					=							image_processor.postprocess(
									    image_processor.preprocess(SCREAMING_SNAKE_CASE__      )   ,output_type='np'   ,)
									UpperCAmelCase__					=							image_processor.postprocess(
									    image_processor.preprocess(SCREAMING_SNAKE_CASE__      )   ,output_type='np'   ,)

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

									UpperCAmelCase__					=							image_processor.postprocess(
									    image_processor.preprocess(SCREAMING_SNAKE_CASE__      )   ,output_type='np'   ,)
									UpperCAmelCase__					=							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       :     Optional[int]      ):
									UpperCAmelCase__					=							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE__   ,do_normalize=SCREAMING_SNAKE_CASE__      )

									UpperCAmelCase__					=							self.dummy_sample
									UpperCAmelCase__					=							list(SCREAMING_SNAKE_CASE__      )

									UpperCAmelCase__					=							image_processor.postprocess(
									    image_processor.preprocess(SCREAMING_SNAKE_CASE__      )   ,output_type='np'   ,)

									UpperCAmelCase__					=							image_processor.postprocess(
									    image_processor.preprocess(SCREAMING_SNAKE_CASE__      )   ,output_type='np'   ,)

									UpperCAmelCase__					=							self.to_np(self.dummy_sample      )
									list(SCREAMING_SNAKE_CASE__      )

									UpperCAmelCase__					=							image_processor.postprocess(
									    image_processor.preprocess(SCREAMING_SNAKE_CASE__      )   ,output_type='np'   ,)

									UpperCAmelCase__					=							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




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



