






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      __SCREAMING_SNAKE_CASE(		UpperCAmelCase__			,     UpperCAmelCase__		):
							@register_to_config
							def __init__(   self:					Any				,		UpperCamelCase:					Optional[int] = 1_28				,		UpperCamelCase:					Optional[Any] = 2_56				,		UpperCamelCase:					Optional[int] = 20_00.0				,		UpperCamelCase:					str = 7_68				,		UpperCamelCase:					int = 12				,		UpperCamelCase:					List[Any] = 12				,		UpperCamelCase:					Union[str, Any] = 64				,		UpperCamelCase:					List[Any] = 20_48				,		UpperCamelCase:					int = 0.1				,		)						->						Tuple:
											super().__init__()

											snake_case__													=   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()				,		)

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

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

											snake_case__													=   nn.Dropout(p=snake_case_  )

											snake_case__													=   nn.ModuleList()
											for lyr_num in range(snake_case_  ):
															# FiLM conditional T5 decoder
															snake_case__													=   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_  )

											snake_case__													=   TaLayerNorm(snake_case_  )

											snake_case__													=   nn.Dropout(p=snake_case_  )
											snake_case__													=   nn.Linear(snake_case_				,		snake_case_				,		bias=snake_case_  )
							def lowerCAmelCase_       (   self:					Dict				,		UpperCamelCase:					Union[str, Any]				,		UpperCamelCase:					Any  )						->						List[Any]:
											snake_case__													=   torch.mul(query_input.unsqueeze(-1  )				,		key_input.unsqueeze(-2  )  )
											return mask.unsqueeze(-3  )


							def lowerCAmelCase_       (   self:					List[Any]				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					Dict				,		UpperCamelCase:					Tuple  )						->						List[Any]:
											snake_case__													=   decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

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

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

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

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

											snake_case__													=   self.position_encoding(snake_case_  )

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

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

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

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

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

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

											snake_case__													=   self.spec_out(snake_case_  )
											return spec_out


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(   self:					List[str]				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					str				,		UpperCamelCase:					Optional[int]				,		UpperCamelCase:					Optional[int]				,		UpperCamelCase:					Dict				,		UpperCamelCase:					Any=1e-6  )						->						Optional[int]:
											super().__init__()
											snake_case__													=   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 lowerCAmelCase_       (   self:					Optional[Any]				,		UpperCamelCase:					int				,		UpperCamelCase:					Union[str, Any]=None				,		UpperCamelCase:					str=None				,		UpperCamelCase:					Any=None				,		UpperCamelCase:					Tuple=None				,		UpperCamelCase:					Dict=None				,		)						->						List[Any]:
											snake_case__													=   self.layer[0](
											    snake_case_				,		conditioning_emb=snake_case_				,		attention_mask=snake_case_				,		)

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

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

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

											return (hidden_states,)


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(   self:					Dict				,		UpperCamelCase:					Tuple				,		UpperCamelCase:					int				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					List[Any]  )						->						Union[str, Any]:
											super().__init__()
											snake_case__													=   TaLayerNorm(snake_case_  )
											snake_case__													=   TaFiLMLayer(in_features=d_model * 4				,		out_features=snake_case_  )
											snake_case__													=   Attention(query_dim=snake_case_				,		heads=snake_case_				,		dim_head=snake_case_				,		out_bias=snake_case_				,		scale_qk=snake_case_  )
											snake_case__													=   nn.Dropout(snake_case_  )


							def lowerCAmelCase_       (   self:					Any				,		UpperCamelCase:					Optional[int]				,		UpperCamelCase:					List[str]=None				,		UpperCamelCase:					Tuple=None				,		)						->						Optional[int]:
											snake_case__													=   self.layer_norm(snake_case_  )

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

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

											snake_case__													=   hidden_states + self.dropout(snake_case_  )

											return hidden_states


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(   self:					Optional[int]				,		UpperCamelCase:					Optional[int]				,		UpperCamelCase:					Union[str, Any]				,		UpperCamelCase:					int				,		UpperCamelCase:					str				,		UpperCamelCase:					str  )						->						List[Any]:
											super().__init__()
											snake_case__													=   Attention(query_dim=snake_case_				,		heads=snake_case_				,		dim_head=snake_case_				,		out_bias=snake_case_				,		scale_qk=snake_case_  )
											snake_case__													=   TaLayerNorm(snake_case_				,		eps=snake_case_  )
											snake_case__													=   nn.Dropout(snake_case_  )


							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					Tuple=None				,		UpperCamelCase:					List[str]=None				,		)						->						List[Any]:
											snake_case__													=   self.layer_norm(snake_case_  )
											snake_case__													=   self.attention(
											    snake_case_				,		encoder_hidden_states=snake_case_				,		attention_mask=attention_mask.squeeze(1  )				,		)
											snake_case__													=   hidden_states + self.dropout(snake_case_  )
											return layer_output


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(   self:					Any				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					Any				,		UpperCamelCase:					Dict				,		UpperCamelCase:					str  )						->						Optional[Any]:
											super().__init__()
											snake_case__													=   TaDenseGatedActDense(d_model=snake_case_				,		d_ff=snake_case_				,		dropout_rate=snake_case_  )
											snake_case__													=   TaFiLMLayer(in_features=d_model * 4				,		out_features=snake_case_  )
											snake_case__													=   TaLayerNorm(snake_case_				,		eps=snake_case_  )
											snake_case__													=   nn.Dropout(snake_case_  )


							def lowerCAmelCase_       (   self:					Any				,		UpperCamelCase:					Optional[int]				,		UpperCamelCase:					List[str]=None  )						->						Tuple:
											snake_case__													=   self.layer_norm(snake_case_  )
											if conditioning_emb is not None:
															snake_case__													=   self.film(snake_case_				,		snake_case_  )

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


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(   self:					List[str]				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					Union[str, Any]				,		UpperCamelCase:					Any  )						->						Dict:
											super().__init__()
											snake_case__													=   nn.Linear(snake_case_				,		snake_case_				,		bias=snake_case_  )
											snake_case__													=   nn.Linear(snake_case_				,		snake_case_				,		bias=snake_case_  )
											snake_case__													=   nn.Linear(snake_case_				,		snake_case_				,		bias=snake_case_  )
											snake_case__													=   nn.Dropout(snake_case_  )
											snake_case__													=   NewGELUActivation()


							def lowerCAmelCase_       (   self:					Tuple				,		UpperCamelCase:					int  )						->						Optional[int]:
											snake_case__													=   self.act(self.wi_a(snake_case_  )  )
											snake_case__													=   self.wi_a(snake_case_  )
											snake_case__													=   hidden_gelu * hidden_linear
											snake_case__													=   self.dropout(snake_case_  )

											snake_case__													=   self.wo(snake_case_  )
											return hidden_states


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(   self:					int				,		UpperCamelCase:					Dict				,		UpperCamelCase:					Optional[int]=1e-6  )						->						List[str]:
											super().__init__()
											snake_case__													=   nn.Parameter(torch.ones(snake_case_  )  )
											snake_case__													=   eps


							def lowerCAmelCase_       (   self:					str				,		UpperCamelCase:					List[str]  )						->						str:
											snake_case__													=   hidden_states.to(torch.floataa  ).pow(2  ).mean(-1				,		keepdim=snake_case_  )
											snake_case__													=   hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

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

											return self.weight * hidden_states


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def lowerCAmelCase_       (   self:					List[str]				,		UpperCamelCase:					Union[str, Any]  )						->						Dict:
											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.044_715 * torch.pow(snake_case_				,		3.0  ))  ))


class      __SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(   self:					List[str]				,		UpperCamelCase:					List[Any]				,		UpperCamelCase:					Optional[int]  )						->						int:
											super().__init__()
											snake_case__													=   nn.Linear(snake_case_				,		out_features * 2				,		bias=snake_case_  )


							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					List[Any]				,		UpperCamelCase:					int  )						->						Optional[int]:
											snake_case__													=   self.scale_bias(snake_case_  )
											snake_case__													=   torch.chunk(snake_case_				,		2				,		-1  )
											snake_case__													=   x * (1 + scale) + shift
											return x











"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        super().__init__(*snake_case_					,      **snake_case_  )
        A_  :       Tuple										=  {}

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       str										=  super().add_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
        if num_added_tokens == 0:
          raise ValueError(
              F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
              ' `placeholder_token` that is not already in the tokenizer.'  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=1					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  []
        if num_vec_per_token == 1:
          self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
          output.append(snake_case_  )
        else:
          A_  :       Tuple										=  []
          for i in range(snake_case_  ):
            A_  :       List[str]										=  placeholder_token + F"""_{i}"""
            self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
            output.append(snake_case_  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
        for token in self.token_map:
          if token in placeholder_token:
            raise ValueError(
                F"""The tokenizer already has placeholder token {token} that can get confused with"""
                F""" {placeholder_token}keep placeholder tokens independent"""  )
        A_  :       Any										=  output

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False					,      snake_case_=1.0  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[Any]										=  []
          for i in range(len(snake_case_  )  ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,      vector_shuffle=snake_case_  )  )
          return output
        for placeholder_token in self.token_map:
          if placeholder_token in text:
            A_  :       List[Any]										=  self.token_map[placeholder_token]
            A_  :       Optional[int]										=  tokens[: 1 + int(len(snake_case_  ) * prop_tokens_to_load  )]
            if vector_shuffle:
              A_  :       Optional[Any]										=  copy.copy(snake_case_  )
              random.shuffle(snake_case_  )
            A_  :       List[str]										=  text.replace(snake_case_					,      ' '.join(snake_case_  )  )
        return text

      def __call__(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().encode(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )