

'''simple docstring'''
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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ):







 @register_to_config
 def __init__(       self  , snake_case_ = 1_28  , snake_case_ = 2_56  , snake_case_ = 20_00.0  , snake_case_ = 7_68  , snake_case_ = 12  , snake_case_ = 12  , snake_case_ = 64  , snake_case_ = 20_48  , snake_case_ = 0.1  , ):
   super().__init__()

   lowercase									=nn.Sequential(
       nn.Linear(snake_case_  , d_model * 4  , bias=snake_case_   )  , nn.SiLU()  , nn.Linear(d_model * 4  , d_model * 4  , bias=snake_case_   )  , nn.SiLU()  , )

   lowercase									=nn.Embedding(snake_case_  , snake_case_   )
   lowercase									=False

   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )

   lowercase									=nn.Dropout(p=snake_case_   )

   lowercase									=nn.ModuleList()
   for lyr_num in range(snake_case_   ):
     # FiLM conditional T5 decoder
     lowercase									=DecoderLayer(d_model=snake_case_  , d_kv=snake_case_  , num_heads=snake_case_  , d_ff=snake_case_  , dropout_rate=snake_case_   )
     self.decoders.append(snake_case_   )

   lowercase									=TaLayerNorm(snake_case_   )

   lowercase									=nn.Dropout(p=snake_case_   )
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_   ):
   lowercase									=torch.mul(query_input.unsqueeze(-1   )  , key_input.unsqueeze(-2   )   )
   return mask.unsqueeze(-3   )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase							, lowercase							, lowercase									=decoder_input_tokens.shape
   assert decoder_noise_time.shape == (batch,)

   # decoder_noise_time is in [0, 1), so rescale to expected timing range.
   lowercase									=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   )

   lowercase									=self.conditioning_emb(snake_case_   ).unsqueeze(1   )

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

   lowercase									=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.
   lowercase									=torch.broadcast_to(
       torch.arange(snake_case_  , device=decoder_input_tokens.device   )  , (batch, seq_length)  , )

   lowercase									=self.position_encoding(snake_case_   )

   lowercase									=self.continuous_inputs_projection(snake_case_   )
   inputs += position_encodings
   lowercase									=self.dropout(snake_case_   )

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

   # Translate encoding masks to encoder-decoder masks.
   lowercase									=[(x, self.encoder_decoder_mask(snake_case_  , snake_case_   )) for x, y in encodings_and_masks]

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

   for lyr in self.decoders:
     lowercase									=lyr(
         snake_case_  , conditioning_emb=snake_case_  , encoder_hidden_states=snake_case_  , encoder_attention_mask=snake_case_  , )[0]

   lowercase									=self.decoder_norm(snake_case_   )
   lowercase									=self.post_dropout(snake_case_   )

   lowercase									=self.spec_out(snake_case_   )
   return spec_out

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_=1E-6   ):
   super().__init__()
   lowercase									=nn.ModuleList()

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

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

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







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=None  , snake_case_=None  , snake_case_=None  , snake_case_=None  , ):
   lowercase									=self.layer[0](
       snake_case_  , conditioning_emb=snake_case_  , attention_mask=snake_case_  , )

   if encoder_hidden_states is not None:
     lowercase									=torch.where(encoder_attention_mask > 0  , 0  , -1E10   ).to(
         encoder_hidden_states.dtype   )

     lowercase									=self.layer[1](
         snake_case_  , key_value_states=snake_case_  , attention_mask=snake_case_  , )

   # Apply Film Conditional Feed Forward layer
   lowercase									=self.layer[-1](snake_case_  , snake_case_   )

   return (hidden_states,)

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=TaLayerNorm(snake_case_   )
   lowercase									=TaFiLMLayer(in_features=d_model * 4  , out_features=snake_case_   )
   lowercase									=Attention(query_dim=snake_case_  , heads=snake_case_  , dim_head=snake_case_  , out_bias=snake_case_  , scale_qk=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=None  , ):
   # pre_self_attention_layer_norm
   lowercase									=self.layer_norm(snake_case_   )

   if conditioning_emb is not None:
     lowercase									=self.FiLMLayer(snake_case_  , snake_case_   )

   # Self-attention block
   lowercase									=self.attention(snake_case_   )

   lowercase									=hidden_states + self.dropout(snake_case_   )

   return hidden_states

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=Attention(query_dim=snake_case_  , heads=snake_case_  , dim_head=snake_case_  , out_bias=snake_case_  , scale_qk=snake_case_   )
   lowercase									=TaLayerNorm(snake_case_  , eps=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=None  , ):
   lowercase									=self.layer_norm(snake_case_   )
   lowercase									=self.attention(
       snake_case_  , encoder_hidden_states=snake_case_  , attention_mask=attention_mask.squeeze(1   )  , )
   lowercase									=hidden_states + self.dropout(snake_case_   )
   return layer_output

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=TaDenseGatedActDense(d_model=snake_case_  , d_ff=snake_case_  , dropout_rate=snake_case_   )
   lowercase									=TaFiLMLayer(in_features=d_model * 4  , out_features=snake_case_   )
   lowercase									=TaLayerNorm(snake_case_  , eps=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None   ):
   lowercase									=self.layer_norm(snake_case_   )
   if conditioning_emb is not None:
     lowercase									=self.film(snake_case_  , snake_case_   )

   lowercase									=self.DenseReluDense(snake_case_   )
   lowercase									=hidden_states + self.dropout(snake_case_   )
   return hidden_states

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )
   lowercase									=NewGELUActivation()







 def 						_A(       self  , snake_case_   ):
   lowercase									=self.act(self.wi_a(snake_case_   )   )
   lowercase									=self.wi_a(snake_case_   )
   lowercase									=hidden_gelu * hidden_linear
   lowercase									=self.dropout(snake_case_   )

   lowercase									=self.wo(snake_case_   )
   return hidden_states

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_=1E-6   ):
   super().__init__()
   lowercase									=nn.Parameter(torch.ones(snake_case_   )   )
   lowercase									=eps







 def 						_A(       self  , snake_case_   ):
   # 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

   lowercase									=hidden_states.to(torch.floataa   ).pow(2   ).mean(-1  , keepdim=snake_case_   )
   lowercase									=hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

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

   return self.weight * hidden_states

class 						__magic_name__   (							nn.Module ):







 def 						_A(       self  , snake_case_   ):
   return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_47_15 * torch.pow(snake_case_  , 3.0   ))   ))

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=nn.Linear(snake_case_  , out_features * 2  , bias=snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_   ):
   lowercase									=self.scale_bias(snake_case_   )
   lowercase							, lowercase									=torch.chunk(snake_case_  , 2  , -1   )
   lowercase									=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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Union[str, Any]    =						'beit'






       def __init__(   self			,					snake_case=8_192			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.0			,					snake_case=0.0			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=224			,					snake_case=16			,					snake_case=3			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=True			,					snake_case=[3, 5, 7, 11]			,					snake_case=[1, 2, 3, 6]			,					snake_case=True			,					snake_case=0.4			,					snake_case=256			,					snake_case=1			,					snake_case=False			,					snake_case=255			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				str 			=				num_hidden_layers
        lowerCAmelCase__   :				Any 			=				num_attention_heads
        lowerCAmelCase__   :				List[str] 			=				intermediate_size
        lowerCAmelCase__   :				int 			=				hidden_act
        lowerCAmelCase__   :				Optional[Any] 			=				hidden_dropout_prob
        lowerCAmelCase__   :				str 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				Optional[int] 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps

        lowerCAmelCase__   :				int 			=				image_size
        lowerCAmelCase__   :				Union[str, Any] 			=				patch_size
        lowerCAmelCase__   :				Dict 			=				num_channels
        lowerCAmelCase__   :				Optional[Any] 			=				use_mask_token
        lowerCAmelCase__   :				Dict 			=				use_absolute_position_embeddings
        lowerCAmelCase__   :				Any 			=				use_relative_position_bias
        lowerCAmelCase__   :				List[Any] 			=				use_shared_relative_position_bias
        lowerCAmelCase__   :				Dict 			=				layer_scale_init_value
        lowerCAmelCase__   :				Optional[int] 			=				drop_path_rate
        lowerCAmelCase__   :				Optional[Any] 			=				use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowerCAmelCase__   :				Optional[int] 			=				out_indices
        lowerCAmelCase__   :				List[Any] 			=				pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowerCAmelCase__   :				List[Any] 			=				use_auxiliary_head
        lowerCAmelCase__   :				Optional[int] 			=				auxiliary_loss_weight
        lowerCAmelCase__   :				List[str] 			=				auxiliary_channels
        lowerCAmelCase__   :				Optional[Any] 			=				auxiliary_num_convs
        lowerCAmelCase__   :				Union[str, Any] 			=				auxiliary_concat_input
        lowerCAmelCase__   :				List[str] 			=				semantic_loss_ignore_index






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						version.parse('1.11'       )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return 1e-4






