






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin




class 							_lowerCAmelCase(			_lowercase				,      _lowercase   ):





       """simple docstring"""







       @register_to_config
       def __init__(   self    ,     _lowerCamelCase = 1_2_8    ,     _lowerCamelCase = 2_5_6    ,     _lowerCamelCase = 2_0_0_0.0    ,     _lowerCamelCase = 7_6_8    ,     _lowerCamelCase = 1_2    ,     _lowerCamelCase = 1_2    ,     _lowerCamelCase = 6_4    ,     _lowerCamelCase = 2_0_4_8    ,     _lowerCamelCase = 0.1    ,     ):
         super().__init__()

         UpperCamelCase_:   Optional[int]        =							nn.Sequential(
             nn.Linear(A_    ,     d_model * 4    ,     bias=A_				)    ,     nn.SiLU()    ,     nn.Linear(d_model * 4    ,     d_model * 4    ,     bias=A_				)    ,     nn.SiLU()    ,     )

         UpperCamelCase_:   Tuple        =							nn.Embedding(A_    ,     A_				)
         UpperCamelCase_:   Tuple        =							False

         UpperCamelCase_:   Union[str, Any]        =							nn.Linear(A_    ,     A_    ,     bias=A_				)

         UpperCamelCase_:   Tuple        =							nn.Dropout(p=A_				)

         UpperCamelCase_:   Any        =							nn.ModuleList()
         for lyr_num in range(A_				):
           # FiLM conditional T5 decoder
           UpperCamelCase_:   Optional[Any]        =							DecoderLayer(d_model=A_    ,     d_kv=A_    ,     num_heads=A_    ,     d_ff=A_    ,     dropout_rate=A_				)
           self.decoders.append(A_				)

         UpperCamelCase_:   str        =							TaLayerNorm(A_				)

         UpperCamelCase_:   str        =							nn.Dropout(p=A_				)
         UpperCamelCase_:   Any        =							nn.Linear(A_    ,     A_    ,     bias=A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Tuple        =							torch.mul(query_input.unsqueeze(-1				)    ,     key_input.unsqueeze(-2				)				)
         return mask.unsqueeze(-3				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         UpperCamelCase_:   List[Any]        =							get_timestep_embedding(
             decoder_noise_time * self.config.max_decoder_noise_time    ,     embedding_dim=self.config.d_model    ,     max_period=self.config.max_decoder_noise_time    ,     ).to(dtype=self.dtype				)

         UpperCamelCase_:   int        =							self.conditioning_emb(A_				).unsqueeze(1				)

         assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

         UpperCamelCase_:   Optional[int]        =							decoder_input_tokens.shape[1]

         # If we want to use relative positions for audio context, we can just offset
         # this sequence by the length of encodings_and_masks.
         UpperCamelCase_:   Optional[Any]        =							torch.broadcast_to(
             torch.arange(A_    ,     device=decoder_input_tokens.device				)    ,     (batch, seq_length)    ,     )

         UpperCamelCase_:   List[str]        =							self.position_encoding(A_				)

         UpperCamelCase_:   Optional[int]        =							self.continuous_inputs_projection(A_				)
         inputs += position_encodings
         UpperCamelCase_:   int        =							self.dropout(A_				)

         # decoder: No padding present.
         UpperCamelCase_:   Dict        =							torch.ones(
             decoder_input_tokens.shape[:2]    ,     device=decoder_input_tokens.device    ,     dtype=inputs.dtype				)

         # Translate encoding masks to encoder-decoder masks.
         UpperCamelCase_:   int        =							[(x, self.encoder_decoder_mask(A_    ,     A_				)) for x, y in encodings_and_masks]

         # cross attend style: concat encodings
         UpperCamelCase_:   Optional[int]        =							torch.cat([x[0] for x in encodings_and_encdec_masks]    ,     dim=1				)
         UpperCamelCase_:   List[Any]        =							torch.cat([x[1] for x in encodings_and_encdec_masks]    ,     dim=-1				)

         for lyr in self.decoders:
           UpperCamelCase_:   Union[str, Any]        =							lyr(
               A_    ,     conditioning_emb=A_    ,     encoder_hidden_states=A_    ,     encoder_attention_mask=A_    ,     )[0]

         UpperCamelCase_:   List[Any]        =							self.decoder_norm(A_				)
         UpperCamelCase_:   List[Any]        =							self.post_dropout(A_				)

         UpperCamelCase_:   Optional[Any]        =							self.spec_out(A_				)
         return spec_out







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=1e-6				):
         super().__init__()
         UpperCamelCase_:   Optional[int]        =							nn.ModuleList()

         # cond self attention: layer 0
         self.layer.append(
             TaLayerSelfAttentionCond(d_model=A_    ,     d_kv=A_    ,     num_heads=A_    ,     dropout_rate=A_				)				)

         # cross attention: layer 1
         self.layer.append(
             TaLayerCrossAttention(
                 d_model=A_    ,     d_kv=A_    ,     num_heads=A_    ,     dropout_rate=A_    ,     layer_norm_epsilon=A_    ,     )				)

         # Film Cond MLP + dropout: last layer
         self.layer.append(
             TaLayerFFCond(d_model=A_    ,     d_ff=A_    ,     dropout_rate=A_    ,     layer_norm_epsilon=A_				)				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     ):
         UpperCamelCase_:   Dict        =							self.layer[0](
             A_    ,     conditioning_emb=A_    ,     attention_mask=A_    ,     )

         if encoder_hidden_states is not None:
           UpperCamelCase_:   int        =							torch.where(encoder_attention_mask > 0    ,     0    ,     -1e10				).to(
               encoder_hidden_states.dtype				)

           UpperCamelCase_:   List[str]        =							self.layer[1](
               A_    ,     key_value_states=A_    ,     attention_mask=A_    ,     )

         # Apply Film Conditional Feed Forward layer
         UpperCamelCase_:   List[str]        =							self.layer[-1](A_    ,     A_				)

         return (hidden_states,)







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         super().__init__()
         UpperCamelCase_:   str        =							TaLayerNorm(A_				)
         UpperCamelCase_:   Optional[Any]        =							TaFiLMLayer(in_features=d_model * 4    ,     out_features=A_				)
         UpperCamelCase_:   List[str]        =							Attention(query_dim=A_    ,     heads=A_    ,     dim_head=A_    ,     out_bias=A_    ,     scale_qk=A_				)
         UpperCamelCase_:   Union[str, Any]        =							nn.Dropout(A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     ):
         # pre_self_attention_layer_norm
         UpperCamelCase_:   List[str]        =							self.layer_norm(A_				)

         if conditioning_emb is not None:
           UpperCamelCase_:   List[str]        =							self.FiLMLayer(A_    ,     A_				)

         # Self-attention block
         UpperCamelCase_:   int        =							self.attention(A_				)

         UpperCamelCase_:   List[str]        =							hidden_states + self.dropout(A_				)

         return hidden_states







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         super().__init__()
         UpperCamelCase_:   Union[str, Any]        =							Attention(query_dim=A_    ,     heads=A_    ,     dim_head=A_    ,     out_bias=A_    ,     scale_qk=A_				)
         UpperCamelCase_:   str        =							TaLayerNorm(A_    ,     eps=A_				)
         UpperCamelCase_:   List[str]        =							nn.Dropout(A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     ):
         UpperCamelCase_:   int        =							self.layer_norm(A_				)
         UpperCamelCase_:   List[Any]        =							self.attention(
             A_    ,     encoder_hidden_states=A_    ,     attention_mask=attention_mask.squeeze(1				)    ,     )
         UpperCamelCase_:   Optional[int]        =							hidden_states + self.dropout(A_				)
         return layer_output







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         super().__init__()
         UpperCamelCase_:   Tuple        =							TaDenseGatedActDense(d_model=A_    ,     d_ff=A_    ,     dropout_rate=A_				)
         UpperCamelCase_:   Optional[int]        =							TaFiLMLayer(in_features=d_model * 4    ,     out_features=A_				)
         UpperCamelCase_:   int        =							TaLayerNorm(A_    ,     eps=A_				)
         UpperCamelCase_:   Dict        =							nn.Dropout(A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   str        =							self.layer_norm(A_				)
         if conditioning_emb is not None:
           UpperCamelCase_:   Tuple        =							self.film(A_    ,     A_				)

         UpperCamelCase_:   Tuple        =							self.DenseReluDense(A_				)
         UpperCamelCase_:   Tuple        =							hidden_states + self.dropout(A_				)
         return hidden_states







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         super().__init__()
         UpperCamelCase_:   List[Any]        =							nn.Linear(A_    ,     A_    ,     bias=A_				)
         UpperCamelCase_:   Tuple        =							nn.Linear(A_    ,     A_    ,     bias=A_				)
         UpperCamelCase_:   Any        =							nn.Linear(A_    ,     A_    ,     bias=A_				)
         UpperCamelCase_:   str        =							nn.Dropout(A_				)
         UpperCamelCase_:   Tuple        =							NewGELUActivation()





       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   str        =							self.act(self.wi_a(A_				)				)
         UpperCamelCase_:   Any        =							self.wi_a(A_				)
         UpperCamelCase_:   Union[str, Any]        =							hidden_gelu * hidden_linear
         UpperCamelCase_:   List[str]        =							self.dropout(A_				)

         UpperCamelCase_:   Tuple        =							self.wo(A_				)
         return hidden_states







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase=1e-6				):
         super().__init__()
         UpperCamelCase_:   List[str]        =							nn.Parameter(torch.ones(A_				)				)
         UpperCamelCase_:   Optional[Any]        =							eps





       def      _a       (   self    ,     _lowerCamelCase				):
         # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
         # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32

         UpperCamelCase_:   Dict        =							hidden_states.to(torch.floataa				).pow(2				).mean(-1    ,     keepdim=A_				)
         UpperCamelCase_:   Optional[Any]        =							hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

         # convert into half-precision if necessary
         if self.weight.dtype in [torch.floataa, torch.bfloataa]:
           UpperCamelCase_:   Any        =							hidden_states.to(self.weight.dtype				)

         return self.weight * hidden_states







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def      _a       (   self    ,     _lowerCamelCase				):
         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(A_    ,     3.0				))				))







class 							_lowerCAmelCase(			nn.Module   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         super().__init__()
         UpperCamelCase_:   Union[str, Any]        =							nn.Linear(A_    ,     out_features * 2    ,     bias=A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   str        =							self.scale_bias(A_				)
         UpperCamelCase_ ,UpperCamelCase_:   List[Any]        =							torch.chunk(A_    ,     2    ,     -1				)
         UpperCamelCase_:   Optional[int]        =							x * (1 + scale) + shift
         return x

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''mobilenet_v1'''


						def __init__(			self  :   Optional[int]	,A_  :   Optional[int]=3	,A_  :   Any=224	,A_  :   List[Any]=1.0	,A_  :   Union[str, Any]=8	,A_  :   Union[str, Any]="relu6"	,A_  :   Optional[Any]=True	,A_  :   List[str]=0.9_99	,A_  :   int=0.02	,A_  :   int=0.0_01	,**A_  :   Union[str, Any]	,)  -> Dict:
							super().__init__(**A_   )

							if depth_multiplier <= 0:
								raise ValueError('depth_multiplier must be greater than zero.'   )

							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = hidden_act
							A     = tf_padding
							A     = classifier_dropout_prob
							A     = initializer_range
							A     = layer_norm_eps







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     version.parse('''1.11'''     )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict([('pixel_values', {0: 'batch'})]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							if self.task == "image-classification":
								return OrderedDict([('logits', {0: 'batch'})]   )
							else:
								return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> float:
							return 1e-4