code: 
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  lowerCAmelCase					 =	{
      '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							Dict ='''mbart'''
  							UpperCamelCase_     :							Any =['''past_key_values''']
  							UpperCamelCase_     :							Union[str, Any] ={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  							def __init__(self   ,       lowerCAmelCase=5_0_2_6_5   ,       lowerCAmelCase=1_0_2_4   ,       lowerCAmelCase=1_2   ,       lowerCAmelCase=4_0_9_6   ,       lowerCAmelCase=1_6   ,       lowerCAmelCase=1_2   ,       lowerCAmelCase=4_0_9_6   ,       lowerCAmelCase=1_6   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=True   ,       lowerCAmelCase=True   ,       lowerCAmelCase="gelu"   ,       lowerCAmelCase=1_0_2_4   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.02   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=False   ,       lowerCAmelCase=1   ,       lowerCAmelCase=0   ,       lowerCAmelCase=2   ,       lowerCAmelCase=2   ,       **lowerCAmelCase   ,       ):
  									__lowercase=    vocab_size
  									__lowercase=    max_position_embeddings
  									__lowercase=    d_model
  									__lowercase=    encoder_ffn_dim
  									__lowercase=    encoder_layers
  									__lowercase=    encoder_attention_heads
  									__lowercase=    decoder_ffn_dim
  									__lowercase=    decoder_layers
  									__lowercase=    decoder_attention_heads
  									__lowercase=    dropout
  									__lowercase=    attention_dropout
  									__lowercase=    activation_dropout
  									__lowercase=    activation_function
  									__lowercase=    init_std
  									__lowercase=    encoder_layerdrop
  									__lowercase=    decoder_layerdrop
  									__lowercase=    classifier_dropout
  									__lowercase=    use_cache
  									__lowercase=    encoder_layers
  									__lowercase=    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    A					(     A_				):
  
  
  							@property
  							def     _A (self      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											    ]      )
  
  											if self.use_past:
  													__lowercase=    {0: 'batch'}
  													__lowercase=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
  											else:
  													__lowercase=    {0: 'batch', 1: 'decoder_sequence'}
  													__lowercase=    {0: 'batch', 1: 'decoder_sequence'}
  
  											if self.use_past:
  													self.fill_with_past_key_values_(lowerCAmelCase   ,       direction='inputs'      )
  									elif self.task == "causal-lm":
  											# TODO: figure this case out.
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											    ]      )
  											if self.use_past:
  													__lowercase,							__lowercase=    self.num_layers
  													for i in range(lowerCAmelCase      ):
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  									else:
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
  											        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
  											    ]      )
  
  									return common_inputs
  
  
  							@property
  							def     _A (self      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    super().outputs
  									else:
  											__lowercase=    super(lowerCAmelCase   ,       self      ).outputs
  											if self.use_past:
  													__lowercase,							__lowercase=    self.num_layers
  													for i in range(lowerCAmelCase      ):
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  									return common_outputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  									# Generate decoder inputs
  									__lowercase=    seq_length if not self.use_past else 1
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									__lowercase=    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
  									__lowercase=    dict(**lowerCAmelCase   ,       **lowerCAmelCase      )
  
  									if self.use_past:
  											if not is_torch_available():
  													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
  											else:
  													import torch
  											__lowercase,							__lowercase=    common_inputs['input_ids'].shape
  											__lowercase=    common_inputs['decoder_input_ids'].shape[1]
  											__lowercase,							__lowercase=    self.num_attention_heads
  											__lowercase=    (
  											    batch,
  											    num_encoder_attention_heads,
  											    encoder_seq_length,
  											    self._config.hidden_size // num_encoder_attention_heads,
  											)
  											__lowercase=    decoder_seq_length + 3
  											__lowercase=    (
  											    batch,
  											    num_decoder_attention_heads,
  											    decoder_past_length,
  											    self._config.hidden_size // num_decoder_attention_heads,
  											)
  
  											__lowercase=    torch.cat(
  											    [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase   ,       lowerCAmelCase      )]   ,       dim=1      )
  
  											__lowercase=    []
  											# If the number of encoder and decoder layers are present in the model configuration, both are considered
  											__lowercase,							__lowercase=    self.num_layers
  											__lowercase=    min(lowerCAmelCase   ,       lowerCAmelCase      )
  											__lowercase=    max(lowerCAmelCase   ,       lowerCAmelCase      ) - min_num_layers
  											__lowercase=    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
  
  											for _ in range(lowerCAmelCase      ):
  													common_inputs["past_key_values"].append(
  													    (
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													    )      )
  											# TODO: test this.
  											__lowercase=    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
  											for _ in range(lowerCAmelCase   ,       lowerCAmelCase      ):
  													common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase      ), torch.zeros(lowerCAmelCase      ))      )
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  									if self.use_past:
  											if not is_torch_available():
  													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
  											else:
  													import torch
  											__lowercase,							__lowercase=    common_inputs['input_ids'].shape
  											# Not using the same length for past_key_values
  											__lowercase=    seqlen + 2
  											__lowercase,							__lowercase=    self.num_layers
  											__lowercase,							__lowercase=    self.num_attention_heads
  											__lowercase=    (
  											    batch,
  											    num_encoder_attention_heads,
  											    past_key_values_length,
  											    self._config.hidden_size // num_encoder_attention_heads,
  											)
  
  											__lowercase=    common_inputs['attention_mask'].dtype
  											__lowercase=    torch.cat(
  											    [common_inputs['attention_mask'], torch.ones(lowerCAmelCase   ,       lowerCAmelCase   ,       dtype=lowerCAmelCase      )]   ,       dim=1      )
  											__lowercase=    [
  											    (torch.zeros(lowerCAmelCase      ), torch.zeros(lowerCAmelCase      )) for _ in range(lowerCAmelCase      )
  											]
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									# Copied from OnnxConfig.generate_dummy_inputs
  									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
  									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
  									__lowercase=    compute_effective_axis_dimension(
  									    lowerCAmelCase   ,       fixed_dimension=OnnxConfig.default_fixed_batch   ,       num_token_to_add=0      )
  
  									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  									__lowercase=    tokenizer.num_special_tokens_to_add(lowerCAmelCase      )
  									__lowercase=    compute_effective_axis_dimension(
  									    lowerCAmelCase   ,       fixed_dimension=OnnxConfig.default_fixed_sequence   ,       num_token_to_add=lowerCAmelCase      )
  
  									# Generate dummy inputs according to compute batch and sequence
  									__lowercase=    [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
  									__lowercase=    dict(tokenizer(lowerCAmelCase   ,       return_tensors=lowerCAmelCase      )      )
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  
  									elif self.task == "causal-lm":
  											__lowercase=    self._generate_dummy_inputs_for_causal_lm(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  									else:
  											__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  
  									return common_inputs
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    super()._flatten_past_key_values_(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									else:
  											__lowercase=    super(lowerCAmelCase   ,       self      )._flatten_past_key_values_(
  											    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  