


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      )










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






