

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class 	snake_case_   (		nn.Module					):







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				int = 1_6							,   _UpperCamelCase :				int = 8_8							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				int = 1							,   _UpperCamelCase :				float = 0.0							,   _UpperCamelCase :				int = 3_2							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				str = "geglu"							,   _UpperCamelCase :				Optional[int] = None							,   )      ->Any:
									super().__init__()
									snake_case_   =	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
									snake_case_   =	0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									snake_case_   =	[7_7, 2_5_7]

									# 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])`
									snake_case_   =	[1, 0]







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[Any]=None							,   _UpperCamelCase :				List[Any]=None							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				bool = True							,   )      ->Optional[Any]:
									snake_case_   =	hidden_states

									snake_case_   =	[]
									snake_case_   =	0
									# attention_mask is not used yet
									for i in range(2							):
															# for each of the two transformers, pass the corresponding condition tokens
															snake_case_   =	encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
															snake_case_   =	self.transformer_index_for_condition[i]
															snake_case_   =	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]

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

									if not return_dict:
															return (output_states,)

									return TransformeraDModelOutput(sample=_UpperCamelCase							)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



