



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       __lowerCAmelCase   (      nn.Module						):



			def __init__(						self       :  Tuple			,		A       :  int = 16			,		A       :  int = 88			,		A       :  Optional[int] = None			,		A       :  int = 1			,		A       :  float = 0.0			,		A       :  int = 32			,		A       :  Optional[int] = None			,		A       :  bool = False			,		A       :  Optional[int] = None			,		A       :  Optional[int] = None			,		A       :  str = "geglu"			,		A       :  Optional[int] = None			,		)       ->   Optional[Any]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase			=       nn.ModuleList(
						    [
						        TransformeraDModel(
						            num_attention_heads=A			,		attention_head_dim=A			,		in_channels=A			,		num_layers=A			,		dropout=A			,		norm_num_groups=A			,		cross_attention_dim=A			,		attention_bias=A			,		sample_size=A			,		num_vector_embeds=A			,		activation_fn=A			,		num_embeds_ada_norm=A			,		)
						        for _ in range(2)
						    ])

						# Variables that can be set by a pipeline:

						# The ratio of transformer1 to transformer2's output states to be combined during inference
						_UpperCAmelCase			=       0.5

						# The shape of `encoder_hidden_states` is expected to be
						# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
						_UpperCAmelCase			=       [77, 2_57]

						# Which transformer to use to encode which condition.
						# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
						_UpperCAmelCase			=       [1, 0]



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Tuple			,		A       :  Tuple			,		A       :  List[str]=None			,		A       :  Optional[int]=None			,		A       :  Dict=None			,		A       :  bool = True			,		)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       hidden_states

						_UpperCAmelCase			=       []
						_UpperCAmelCase			=       0
						# attention_mask is not used yet
						for i in range(2):
									# for each of the two transformers, pass the corresponding condition tokens
									_UpperCAmelCase			=       encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
									_UpperCAmelCase			=       self.transformer_index_for_condition[i]
									_UpperCAmelCase			=       self.transformers[transformer_index](
									    A			,		encoder_hidden_states=A			,		timestep=A			,		cross_attention_kwargs=A			,		return_dict=A			,		)[0]
									encoded_states.append(encoded_state - input_states)
									tokens_start += self.condition_lengths[i]

						_UpperCAmelCase			=       encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
						_UpperCAmelCase			=       output_states + input_states

						if not return_dict:
									return (output_states,)

						return TransformeraDModelOutput(sample=A)





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''xmod'''



			def __init__(						self       :  Optional[int]			,		A       :  int=3_05_22			,		A       :  Tuple=7_68			,		A       :  Optional[Any]=12			,		A       :  Tuple=12			,		A       :  str=30_72			,		A       :  List[str]="gelu"			,		A       :  Any=0.1			,		A       :  int=0.1			,		A       :  Dict=5_12			,		A       :  Optional[Any]=2			,		A       :  Optional[Any]=0.0_2			,		A       :  List[Any]=1E-12			,		A       :  int=1			,		A       :  Tuple=0			,		A       :  Optional[Any]=2			,		A       :  int="absolute"			,		A       :  Union[str, Any]=True			,		A       :  List[Any]=None			,		A       :  Optional[Any]=False			,		A       :  List[str]=2			,		A       :  int=False			,		A       :  str=True			,		A       :  Optional[Any]=True			,		A       :  Tuple=("en_XX",)			,		A       :  Optional[int]=None			,		**A       :  List[str]			,		)       ->   Dict:



						"""simple docstring"""






						super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A)

						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       layer_norm_eps
						_UpperCAmelCase			=       position_embedding_type
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       classifier_dropout
						_UpperCAmelCase			=       pre_norm
						_UpperCAmelCase			=       adapter_reduction_factor
						_UpperCAmelCase			=       adapter_layer_norm
						_UpperCAmelCase			=       adapter_reuse_layer_norm
						_UpperCAmelCase			=       ln_before_adapter
						_UpperCAmelCase			=       list(A)
						_UpperCAmelCase			=       default_language







class       __lowerCAmelCase   (      A						):



			@property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Mapping[str, Mapping[int, str]]:



						"""simple docstring"""






						if self.task == "multiple-choice":
									_UpperCAmelCase			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									_UpperCAmelCase			=       {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ])
