



'''simple docstring'''

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 						a__  (					nn.Module						):


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

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE	:    Tuple          =			nn.ModuleList(
					    [
					        TransformeraDModel(
					            num_attention_heads=__UpperCAmelCase,       attention_head_dim=__UpperCAmelCase,       in_channels=__UpperCAmelCase,       num_layers=__UpperCAmelCase,       dropout=__UpperCAmelCase,       norm_num_groups=__UpperCAmelCase,       cross_attention_dim=__UpperCAmelCase,       attention_bias=__UpperCAmelCase,       sample_size=__UpperCAmelCase,       num_vector_embeds=__UpperCAmelCase,       activation_fn=__UpperCAmelCase,       num_embeds_ada_norm=__UpperCAmelCase,       )
					        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
					SCREAMING_SNAKE_CASE	:    int          =			0.5

					# The shape of `encoder_hidden_states` is expected to be
					# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
					SCREAMING_SNAKE_CASE	:    Dict          =			[77, 257]

					# 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])`
					SCREAMING_SNAKE_CASE	:    int          =			[1, 0]






	def 					lowercase__	(self			:			Optional[Any],       __UpperCAmelCase			:			int,       __UpperCAmelCase			:			Dict,       __UpperCAmelCase			:			List[Any]=None,       __UpperCAmelCase			:			Optional[int]=None,       __UpperCAmelCase			:			str=None,       __UpperCAmelCase			:			bool = True,       )  ->      Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Any          =			hidden_states

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

					SCREAMING_SNAKE_CASE	:    Tuple          =			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
					SCREAMING_SNAKE_CASE	:    str          =			output_states + input_states

					if not return_dict:
									return (output_states,)

					return TransformeraDModelOutput(sample=__UpperCAmelCase   )












"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase            =	{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				_lowerCAmelCase            =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
