



"""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     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE  ):



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



     """simple docstring"""
     super().__init__()

     _snake_case   	=      nn.Sequential(
         nn.Linear(__lowerCamelCase							,       d_model * 4							,       bias=__lowerCamelCase				)							,       nn.SiLU()							,       nn.Linear(d_model * 4							,       d_model * 4							,       bias=__lowerCamelCase				)							,       nn.SiLU()							,       )

     _snake_case   	=      nn.Embedding(__lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      False

     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)

     _snake_case   	=      nn.Dropout(p=__lowerCamelCase				)

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

     _snake_case   	=      TaLayerNorm(__lowerCamelCase				)

     _snake_case   	=      nn.Dropout(p=__lowerCamelCase				)
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      torch.mul(query_input.unsqueeze(-1				)							,       key_input.unsqueeze(-2				)				)
     return mask.unsqueeze(-3				)





   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case	,     _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(__lowerCamelCase				).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(__lowerCamelCase							,       device=decoder_input_tokens.device				)							,       (batch, seq_length)							,       )

     _snake_case   	=      self.position_encoding(__lowerCamelCase				)

     _snake_case   	=      self.continuous_inputs_projection(__lowerCamelCase				)
     inputs += position_encodings
     _snake_case   	=      self.dropout(__lowerCamelCase				)

     # 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(__lowerCamelCase							,       __lowerCamelCase				)) 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(
           __lowerCamelCase							,       conditioning_emb=__lowerCamelCase							,       encoder_hidden_states=__lowerCamelCase							,       encoder_attention_mask=__lowerCamelCase							,       )[0]

     _snake_case   	=      self.decoder_norm(__lowerCamelCase				)
     _snake_case   	=      self.post_dropout(__lowerCamelCase				)

     _snake_case   	=      self.spec_out(__lowerCamelCase				)
     return spec_out



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	int							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	str=1E-6				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.ModuleList()

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

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

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





   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	int=None							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	Dict=None							,       __lowerCamelCase    :	Optional[Any]=None							,       ):



     """simple docstring"""
     _snake_case   	=      self.layer[0](
         __lowerCamelCase							,       conditioning_emb=__lowerCamelCase							,       attention_mask=__lowerCamelCase							,       )

     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](
           __lowerCamelCase							,       key_value_states=__lowerCamelCase							,       attention_mask=__lowerCamelCase							,       )

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

     return (hidden_states,)



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      TaLayerNorm(__lowerCamelCase				)
     _snake_case   	=      TaFiLMLayer(in_features=d_model * 4							,       out_features=__lowerCamelCase				)
     _snake_case   	=      Attention(query_dim=__lowerCamelCase							,       heads=__lowerCamelCase							,       dim_head=__lowerCamelCase							,       out_bias=__lowerCamelCase							,       scale_qk=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Dict=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       ):



     """simple docstring"""
     # pre_self_attention_layer_norm
     _snake_case   	=      self.layer_norm(__lowerCamelCase				)

     if conditioning_emb is not None:
       _snake_case   	=      self.FiLMLayer(__lowerCamelCase							,       __lowerCamelCase				)

     # Self-attention block
     _snake_case   	=      self.attention(__lowerCamelCase				)

     _snake_case   	=      hidden_states + self.dropout(__lowerCamelCase				)

     return hidden_states



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	int							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      Attention(query_dim=__lowerCamelCase							,       heads=__lowerCamelCase							,       dim_head=__lowerCamelCase							,       out_bias=__lowerCamelCase							,       scale_qk=__lowerCamelCase				)
     _snake_case   	=      TaLayerNorm(__lowerCamelCase							,       eps=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	str=None							,       __lowerCamelCase    :	Optional[int]=None							,       ):



     """simple docstring"""
     _snake_case   	=      self.layer_norm(__lowerCamelCase				)
     _snake_case   	=      self.attention(
         __lowerCamelCase							,       encoder_hidden_states=__lowerCamelCase							,       attention_mask=attention_mask.squeeze(1				)							,       )
     _snake_case   	=      hidden_states + self.dropout(__lowerCamelCase				)
     return layer_output



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      TaDenseGatedActDense(d_model=__lowerCamelCase							,       d_ff=__lowerCamelCase							,       dropout_rate=__lowerCamelCase				)
     _snake_case   	=      TaFiLMLayer(in_features=d_model * 4							,       out_features=__lowerCamelCase				)
     _snake_case   	=      TaLayerNorm(__lowerCamelCase							,       eps=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	int=None				):



     """simple docstring"""
     _snake_case   	=      self.layer_norm(__lowerCamelCase				)
     if conditioning_emb is not None:
       _snake_case   	=      self.film(__lowerCamelCase							,       __lowerCamelCase				)

     _snake_case   	=      self.DenseReluDense(__lowerCamelCase				)
     _snake_case   	=      hidden_states + self.dropout(__lowerCamelCase				)
     return hidden_states



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	str							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)
     _snake_case   	=      NewGELUActivation()





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      self.act(self.wi_a(__lowerCamelCase				)				)
     _snake_case   	=      self.wi_a(__lowerCamelCase				)
     _snake_case   	=      hidden_gelu * hidden_linear
     _snake_case   	=      self.dropout(__lowerCamelCase				)

     _snake_case   	=      self.wo(__lowerCamelCase				)
     return hidden_states



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Union[str, Any]=1E-6				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.Parameter(torch.ones(__lowerCamelCase				)				)
     _snake_case   	=      eps





   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     # 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

     _snake_case   	=      hidden_states.to(torch.floataa				).pow(2				).mean(-1							,       keepdim=__lowerCamelCase				)
     _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     UpperCAmelCase    (	nn.Module  ):



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	torch.Tensor				):



     """simple docstring"""
     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(__lowerCamelCase							,       3.0				))				))



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       out_features * 2							,       bias=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      self.scale_bias(__lowerCamelCase				)
     _snake_case	,     _snake_case   	=      torch.chunk(__lowerCamelCase							,       2							,       -1				)
     _snake_case   	=      x * (1 + scale) + shift
     return x






'''simple docstring'''

import math
import sys



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      ''''''
   try:
      with open(__snake_case	,   '''rb''') as binary_file:
         lowerCAmelCase_			=      binary_file.read()
      for dat in data:
         lowerCAmelCase_			=      F'''{dat:08b}'''
         result += curr_byte
      return result
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      {'''0''': '''0''', '''1''': '''1'''}
   lowerCAmelCase_	,lowerCAmelCase_			=      '''''', ''''''
   lowerCAmelCase_			=      len(__snake_case)

   for i in range(len(__snake_case)):
      curr_string += data_bits[i]
      if curr_string not in lexicon:
         continue

      lowerCAmelCase_			=      lexicon[curr_string]
      result += last_match_id
      lowerCAmelCase_			=      last_match_id + '''0'''

      if math.loga(__snake_case).is_integer():
         lowerCAmelCase_			=      {}
         for curr_key in list(__snake_case):
            lowerCAmelCase_			=      lexicon.pop(__snake_case)
         lowerCAmelCase_			=      new_lex

      lowerCAmelCase_			=      last_match_id + '''1'''
      index += 1
      lowerCAmelCase_			=      ''''''
   return result



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      8
   try:
      with open(__snake_case	,   '''wb''') as opened_file:
         lowerCAmelCase_			=      [
             to_write[i : i + byte_length]
             for i in range(0	,   len(__snake_case)	,   __snake_case)
         ]

         if len(result_byte_array[-1]) % byte_length == 0:
            result_byte_array.append('''10000000''')
         else:
            result_byte_array[-1] += "1" + "0" * (
                byte_length - len(result_byte_array[-1]) - 1
            )

         for elem in result_byte_array[:-1]:
            opened_file.write(int(__snake_case	,   2).to_bytes(1	,   byteorder='''big'''))
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      0
   for letter in data_bits:
      if letter == "1":
         break
      counter += 1

   lowerCAmelCase_			=      data_bits[counter:]
   lowerCAmelCase_			=      data_bits[counter + 1 :]
   return data_bits



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      read_file_binary(__snake_case)
   lowerCAmelCase_			=      remove_prefix(__snake_case)
   lowerCAmelCase_			=      decompress_data(__snake_case)
   write_file_binary(__snake_case	,   __snake_case)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])





