code: 
  
  
  
  
  """simple docstring"""
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  __A 		=			logging.get_logger(__name__)
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Any								=     """mt5"""
  			a__ :      Optional[int]								=     ["""past_key_values"""]
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE=250_112				, SCREAMING_SNAKE_CASE=512				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=1_024				, SCREAMING_SNAKE_CASE=8				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=6				, SCREAMING_SNAKE_CASE=32				, SCREAMING_SNAKE_CASE=128				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE=1E-6				, SCREAMING_SNAKE_CASE=1.0				, SCREAMING_SNAKE_CASE="gated-gelu"				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE="T5Tokenizer"				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=0				, SCREAMING_SNAKE_CASE=1				, SCREAMING_SNAKE_CASE=0				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(
  					    is_encoder_decoder=SCREAMING_SNAKE_CASE				, tokenizer_class=SCREAMING_SNAKE_CASE				, tie_word_embeddings=SCREAMING_SNAKE_CASE				, pad_token_id=SCREAMING_SNAKE_CASE				, eos_token_id=SCREAMING_SNAKE_CASE				, decoder_start_token_id=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE				, )
  					snake_case		:						int								=				vocab_size
  					snake_case		:						Tuple								=				d_model
  					snake_case		:						Any								=				d_kv
  					snake_case		:						Union[str, Any]								=				d_ff
  					snake_case		:						Optional[int]								=				num_layers
  					snake_case		:						Dict								=				(
  					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
  					)  # default = symmetry
  					snake_case		:						Optional[Any]								=				num_heads
  					snake_case		:						Any								=				relative_attention_num_buckets
  					snake_case		:						List[str]								=				relative_attention_max_distance
  					snake_case		:						List[str]								=				dropout_rate
  					snake_case		:						List[Any]								=				layer_norm_epsilon
  					snake_case		:						Union[str, Any]								=				initializer_factor
  					snake_case		:						Dict								=				feed_forward_proj
  					snake_case		:						Tuple								=				use_cache
  
  					snake_case		:						Union[str, Any]								=				self.feed_forward_proj.split("-"							)
  					snake_case		:						Optional[Any]								=				act_info[-1]
  					snake_case		:						Optional[Any]								=				act_info[0] == "gated"
  
  					if len(SCREAMING_SNAKE_CASE							) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE							) > 2:
  							raise ValueError(
  							    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
  							    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
  							    "'gated-gelu' or 'relu'"							)
  
  					# for backwards compatibility
  					if feed_forward_proj == "gated-gelu":
  							snake_case		:						Tuple								=				"gelu_new"
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.d_model
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.num_heads
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.num_layers
  
  
  
  
  
  
  
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  
  
  			@property
  			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[str]								=				{
  					    "input_ids": {0: "batch", 1: "encoder_sequence"},
  					    "attention_mask": {0: "batch", 1: "encoder_sequence"},
  					}
  					if self.use_past:
  							snake_case		:						List[Any]								=				"past_encoder_sequence + sequence"
  							snake_case		:						Tuple								=				{0: "batch"}
  							snake_case		:						Any								=				{0: "batch", 1: "past_decoder_sequence + sequence"}
  					else:
  							snake_case		:						List[Any]								=				{0: "batch", 1: "decoder_sequence"}
  							snake_case		:						Union[str, Any]								=				{0: "batch", 1: "decoder_sequence"}
  
  					if self.use_past:
  							self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE				, direction="inputs"							)
  
  					return common_inputs
  
  
  			@property
  			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return 13
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return 5E-4
  
  
  
  
style_context: 
  
  
  
  
  """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
  
  