




'''simple docstring'''







import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase = 4_096							):
     snake_case :			str					   = key.shape[-3:]
     snake_case :			str					   = value.shape[-1]
     snake_case :			Dict					   = min(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE							)
     snake_case :			Optional[int]					   = query / jnp.sqrt(_SCREAMING_SNAKE_CASE							)

     @functools.partial(jax.checkpoint    ,      prevent_cse=_SCREAMING_SNAKE_CASE							)
     def summarize_chunk(__lowercase    ,      __lowercase    ,      __lowercase							):
          snake_case :			Tuple					   = jnp.einsum("""...qhd,...khd->...qhk"""    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      precision=_SCREAMING_SNAKE_CASE							)

          snake_case :			Dict					   = jnp.max(_SCREAMING_SNAKE_CASE    ,      axis=-1    ,      keepdims=_SCREAMING_SNAKE_CASE							)
          snake_case :			int					   = jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE							)
          snake_case :			str					   = jnp.exp(attn_weights - max_score							)

          snake_case :			Dict					   = jnp.einsum("""...vhf,...qhv->...qhf"""    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      precision=_SCREAMING_SNAKE_CASE							)
          snake_case :			Tuple					   = jnp.einsum("""...qhk->...qh"""    ,      _SCREAMING_SNAKE_CASE							)

          return (exp_values, exp_weights.sum(axis=-1							), max_score)

     def chunk_scanner(__lowercase							):
          # julienne key array
          snake_case :			Dict					   = jax.lax.dynamic_slice(
              operand=_SCREAMING_SNAKE_CASE    ,      start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]    ,      slice_sizes=list(key.shape[:-3]							) + [key_chunk_size, num_heads, k_features]    ,      )

          # julienne value array
          snake_case :			List[Any]					   = jax.lax.dynamic_slice(
              operand=_SCREAMING_SNAKE_CASE    ,      start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]    ,      slice_sizes=list(value.shape[:-3]							) + [key_chunk_size, num_heads, v_features]    ,      )

          return summarize_chunk(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE							)

     snake_case :			List[Any]					   = jax.lax.map(f=_SCREAMING_SNAKE_CASE    ,      xs=jnp.arange(0    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE							)							)

     snake_case :			Dict					   = jnp.max(_SCREAMING_SNAKE_CASE    ,      axis=0    ,      keepdims=_SCREAMING_SNAKE_CASE							)
     snake_case :			str					   = jnp.exp(chunk_max - global_max							)

     chunk_values *= jnp.expand_dims(_SCREAMING_SNAKE_CASE    ,      axis=-1							)
     chunk_weights *= max_diffs

     snake_case :			List[Any]					   = chunk_values.sum(axis=0							)
     snake_case :			Tuple					   = jnp.expand_dims(_SCREAMING_SNAKE_CASE    ,      -1							).sum(axis=0							)

     return all_values / all_weights



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=jax.lax.Precision.HIGHEST    ,      __lowercase = 1_024    ,      __lowercase = 4_096							):
     snake_case :			Dict					   = query.shape[-3:]

     def chunk_scanner(__lowercase    ,      __lowercase							):
          # julienne query array
          snake_case :			Any					   = jax.lax.dynamic_slice(
              operand=_SCREAMING_SNAKE_CASE    ,      start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]    ,      slice_sizes=list(query.shape[:-3]							) + [min(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE							), num_heads, q_features]    ,      )

          return (
              chunk_idx + query_chunk_size,  # unused ignore it
              _query_chunk_attention(
                  query=_SCREAMING_SNAKE_CASE    ,      key=_SCREAMING_SNAKE_CASE    ,      value=_SCREAMING_SNAKE_CASE    ,      precision=_SCREAMING_SNAKE_CASE    ,      key_chunk_size=_SCREAMING_SNAKE_CASE							),
          )

     snake_case :			Union[str, Any]					   = jax.lax.scan(
         f=_SCREAMING_SNAKE_CASE    ,      init=0    ,      xs=_SCREAMING_SNAKE_CASE    ,      length=math.ceil(num_q / query_chunk_size							)							)

     return jnp.concatenate(_SCREAMING_SNAKE_CASE    ,      axis=-3							)  # fuse the chunked result back







class 				_a   (nn.Module  ):





     '''simple docstring'''



     lowerCAmelCase_     :    str									=				42
     lowerCAmelCase_     :    Tuple									=				8
     lowerCAmelCase_     :    Union[str, Any]									=				64
     lowerCAmelCase_     :    Optional[int]									=				0.0
     lowerCAmelCase_     :    int									=				False
     lowerCAmelCase_     :    Optional[Any]									=				jnp.floataa







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			Dict					   = self.dim_head * self.heads
          snake_case :			Any					   = self.dim_head**-0.5

          # Weights were exported with old names {to_q, to_k, to_v, to_out}
          snake_case :			List[str]					   = nn.Dense(__a       ,use_bias=__a       ,dtype=self.dtype       ,name="""to_q"""       )
          snake_case :			Optional[Any]					   = nn.Dense(__a       ,use_bias=__a       ,dtype=self.dtype       ,name="""to_k"""       )
          snake_case :			Dict					   = nn.Dense(__a       ,use_bias=__a       ,dtype=self.dtype       ,name="""to_v"""       )

          snake_case :			Union[str, Any]					   = nn.Dense(self.query_dim       ,dtype=self.dtype       ,name="""to_out_0"""       )
          snake_case :			Optional[int]					   = nn.Dropout(rate=self.dropout       )







     def  snake_case_			(							self       ,__a       )			->							List[Any]:
          snake_case :			List[str]					   = tensor.shape
          snake_case :			str					   = self.heads
          snake_case :			int					   = tensor.reshape(__a       ,__a       ,__a       ,dim // head_size       )
          snake_case :			int					   = jnp.transpose(__a       ,(0, 2, 1, 3)       )
          snake_case :			Union[str, Any]					   = tensor.reshape(batch_size * head_size       ,__a       ,dim // head_size       )
          return tensor







     def  snake_case_			(							self       ,__a       )			->							List[Any]:
          snake_case :			int					   = tensor.shape
          snake_case :			Any					   = self.heads
          snake_case :			Union[str, Any]					   = tensor.reshape(batch_size // head_size       ,__a       ,__a       ,__a       )
          snake_case :			Optional[int]					   = jnp.transpose(__a       ,(0, 2, 1, 3)       )
          snake_case :			int					   = tensor.reshape(batch_size // head_size       ,__a       ,dim * head_size       )
          return tensor







     def __call__(							self       ,__a       ,__a=None       ,__a=True       )			->							Union[str, Any]:
          snake_case :			str					   = hidden_states if context is None else context

          snake_case :			List[str]					   = self.query(__a       )
          snake_case :			Union[str, Any]					   = self.key(__a       )
          snake_case :			List[Any]					   = self.value(__a       )

          snake_case :			Union[str, Any]					   = self.reshape_heads_to_batch_dim(__a       )
          snake_case :			List[str]					   = self.reshape_heads_to_batch_dim(__a       )
          snake_case :			Tuple					   = self.reshape_heads_to_batch_dim(__a       )

          if self.use_memory_efficient_attention:
               snake_case :			Union[str, Any]					   = query_states.transpose(1       ,0       ,2       )
               snake_case :			Dict					   = key_states.transpose(1       ,0       ,2       )
               snake_case :			Union[str, Any]					   = value_states.transpose(1       ,0       ,2       )

               # this if statement create a chunk size for each layer of the unet
               # the chunk size is equal to the query_length dimension of the deepest layer of the unet

               snake_case :			Optional[Any]					   = query_states.shape[-3]
               if flatten_latent_dim % 64 == 0:
                    snake_case :			Optional[Any]					   = int(flatten_latent_dim / 64       )
               elif flatten_latent_dim % 16 == 0:
                    snake_case :			Optional[Any]					   = int(flatten_latent_dim / 16       )
               elif flatten_latent_dim % 4 == 0:
                    snake_case :			Tuple					   = int(flatten_latent_dim / 4       )
               else:
                    snake_case :			Any					   = int(__a       )

               snake_case :			int					   = jax_memory_efficient_attention(
                   __a       ,__a       ,__a       ,query_chunk_size=__a       ,key_chunk_size=4_096 * 4       )

               snake_case :			int					   = hidden_states.transpose(1       ,0       ,2       )
          else:
               # compute attentions
               snake_case :			Optional[Any]					   = jnp.einsum("""b i d, b j d->b i j"""       ,__a       ,__a       )
               snake_case :			Any					   = attention_scores * self.scale
               snake_case :			List[Any]					   = nn.softmax(__a       ,axis=2       )

               # attend to values
               snake_case :			List[str]					   = jnp.einsum("""b i j, b j d -> b i d"""       ,__a       ,__a       )

          snake_case :			List[str]					   = self.reshape_batch_dim_to_heads(__a       )
          snake_case :			Union[str, Any]					   = self.proj_attn(__a       )
          return self.dropout_layer(__a       ,deterministic=__a       )







class 				_a   (nn.Module  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				42
     lowerCAmelCase_     :    Dict									=				42
     lowerCAmelCase_     :    List[Any]									=				42
     lowerCAmelCase_     :    List[Any]									=				0.0
     lowerCAmelCase_     :    Optional[int]									=				False
     lowerCAmelCase_     :    Dict									=				jnp.floataa
     lowerCAmelCase_     :    Dict									=				False







     def  snake_case_			(							self       )			->							List[str]:
          snake_case :			Any					   = FlaxAttention(
              self.dim       ,self.n_heads       ,self.d_head       ,self.dropout       ,self.use_memory_efficient_attention       ,dtype=self.dtype       )
          # cross attention
          snake_case :			Dict					   = FlaxAttention(
              self.dim       ,self.n_heads       ,self.d_head       ,self.dropout       ,self.use_memory_efficient_attention       ,dtype=self.dtype       )
          snake_case :			Any					   = FlaxFeedForward(dim=self.dim       ,dropout=self.dropout       ,dtype=self.dtype       )
          snake_case :			str					   = nn.LayerNorm(epsilon=1E-5       ,dtype=self.dtype       )
          snake_case :			Optional[Any]					   = nn.LayerNorm(epsilon=1E-5       ,dtype=self.dtype       )
          snake_case :			Any					   = nn.LayerNorm(epsilon=1E-5       ,dtype=self.dtype       )
          snake_case :			Optional[Any]					   = nn.Dropout(rate=self.dropout       )







     def __call__(							self       ,__a       ,__a       ,__a=True       )			->							int:
          snake_case :			Optional[Any]					   = hidden_states
          if self.only_cross_attention:
               snake_case :			List[str]					   = self.attna(self.norma(__a       )       ,__a       ,deterministic=__a       )
          else:
               snake_case :			str					   = self.attna(self.norma(__a       )       ,deterministic=__a       )
          snake_case :			Tuple					   = hidden_states + residual

          # cross attention
          snake_case :			Dict					   = hidden_states
          snake_case :			List[str]					   = self.attna(self.norma(__a       )       ,__a       ,deterministic=__a       )
          snake_case :			Union[str, Any]					   = hidden_states + residual

          # feed forward
          snake_case :			str					   = hidden_states
          snake_case :			Any					   = self.ff(self.norma(__a       )       ,deterministic=__a       )
          snake_case :			str					   = hidden_states + residual

          return self.dropout_layer(__a       ,deterministic=__a       )







class 				_a   (nn.Module  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Any									=				42
     lowerCAmelCase_     :    Dict									=				42
     lowerCAmelCase_     :    str									=				42
     lowerCAmelCase_     :    Dict									=				1
     lowerCAmelCase_     :    List[Any]									=				0.0
     lowerCAmelCase_     :    Dict									=				False
     lowerCAmelCase_     :    str									=				False
     lowerCAmelCase_     :    List[str]									=				jnp.floataa
     lowerCAmelCase_     :    Optional[Any]									=				False







     def  snake_case_			(							self       )			->							List[Any]:
          snake_case :			int					   = nn.GroupNorm(num_groups=32       ,epsilon=1E-5       )

          snake_case :			int					   = self.n_heads * self.d_head
          if self.use_linear_projection:
               snake_case :			Optional[int]					   = nn.Dense(__a       ,dtype=self.dtype       )
          else:
               snake_case :			int					   = nn.Conv(
                   __a       ,kernel_size=(1, 1)       ,strides=(1, 1)       ,padding="""VALID"""       ,dtype=self.dtype       ,)

          snake_case :			Optional[int]					   = [
              FlaxBasicTransformerBlock(
                  __a       ,self.n_heads       ,self.d_head       ,dropout=self.dropout       ,only_cross_attention=self.only_cross_attention       ,dtype=self.dtype       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,)
              for _ in range(self.depth       )
          ]

          if self.use_linear_projection:
               snake_case :			str					   = nn.Dense(__a       ,dtype=self.dtype       )
          else:
               snake_case :			str					   = nn.Conv(
                   __a       ,kernel_size=(1, 1)       ,strides=(1, 1)       ,padding="""VALID"""       ,dtype=self.dtype       ,)

          snake_case :			Tuple					   = nn.Dropout(rate=self.dropout       )







     def __call__(							self       ,__a       ,__a       ,__a=True       )			->							Tuple:
          snake_case :			List[str]					   = hidden_states.shape
          snake_case :			Optional[int]					   = hidden_states
          snake_case :			List[str]					   = self.norm(__a       )
          if self.use_linear_projection:
               snake_case :			Union[str, Any]					   = hidden_states.reshape(__a       ,height * width       ,__a       )
               snake_case :			Optional[int]					   = self.proj_in(__a       )
          else:
               snake_case :			List[str]					   = self.proj_in(__a       )
               snake_case :			Union[str, Any]					   = hidden_states.reshape(__a       ,height * width       ,__a       )

          for transformer_block in self.transformer_blocks:
               snake_case :			List[str]					   = transformer_block(__a       ,__a       ,deterministic=__a       )

          if self.use_linear_projection:
               snake_case :			int					   = self.proj_out(__a       )
               snake_case :			Any					   = hidden_states.reshape(__a       ,__a       ,__a       ,__a       )
          else:
               snake_case :			str					   = hidden_states.reshape(__a       ,__a       ,__a       ,__a       )
               snake_case :			str					   = self.proj_out(__a       )

          snake_case :			List[Any]					   = hidden_states + residual
          return self.dropout_layer(__a       ,deterministic=__a       )







class 				_a   (nn.Module  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Any									=				42
     lowerCAmelCase_     :    List[str]									=				0.0
     lowerCAmelCase_     :    Union[str, Any]									=				jnp.floataa







     def  snake_case_			(							self       )			->							str:
          snake_case :			int					   = FlaxGEGLU(self.dim       ,self.dropout       ,self.dtype       )
          snake_case :			int					   = nn.Dense(self.dim       ,dtype=self.dtype       )







     def __call__(							self       ,__a       ,__a=True       )			->							Optional[int]:
          snake_case :			Union[str, Any]					   = self.net_a(__a       ,deterministic=__a       )
          snake_case :			List[Any]					   = self.net_a(__a       )
          return hidden_states







class 				_a   (nn.Module  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[Any]									=				42
     lowerCAmelCase_     :    List[Any]									=				0.0
     lowerCAmelCase_     :    int									=				jnp.floataa







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			int					   = self.dim * 4
          snake_case :			Optional[Any]					   = nn.Dense(inner_dim * 2       ,dtype=self.dtype       )
          snake_case :			Any					   = nn.Dropout(rate=self.dropout       )







     def __call__(							self       ,__a       ,__a=True       )			->							Dict:
          snake_case :			Optional[Any]					   = self.proj(__a       )
          snake_case :			List[Any]					   = jnp.split(__a       ,2       ,axis=2       )
          return self.dropout_layer(hidden_linear * nn.gelu(__a       )       ,deterministic=__a       )









'''simple docstring'''







from dataclasses import dataclass

lowercase			:      Tuple			=					"""0.1"""
lowercase			:      Optional[Any]			=					"""Lucia Harcekova"""







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    str







     def __repr__(							self       )			->							str:
          return F'''({self.offset}, {self.length}, {self.indicator})'''







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a = 13       ,__a = 6       )			->							None:
          snake_case :			Dict					   = window_size
          snake_case :			Any					   = lookahead_buffer_size
          snake_case :			str					   = self.window_size - self.lookahead_buffer_size







     def  snake_case_			(							self       ,__a       )			->							list[Token]:
          snake_case :			Tuple					   = []
          snake_case :			Optional[int]					   = """"""

          # while there are still characters in text to compress
          while text:
               # find the next encoding phrase
               # - triplet with offset, length, indicator (the next encoding character)
               snake_case :			str					   = self._find_encoding_token(__a       ,__a       )

               # update the search buffer:
               # - add new characters from text into it
               # - check if size exceed the max search buffer size, if so, drop the
               #   oldest elements
               search_buffer += text[: token.length + 1]
               if len(__a       ) > self.search_buffer_size:
                    snake_case :			List[Any]					   = search_buffer[-self.search_buffer_size :]

               # update the text
               snake_case :			str					   = text[token.length + 1 :]

               # append the token to output
               output.append(__a       )

          return output







     def  snake_case_			(							self       ,__a       )			->							str:
          snake_case :			Union[str, Any]					   = """"""

          for token in tokens:
               for _ in range(token.length       ):
                    output += output[-token.offset]
               output += token.indicator

          return output







     def  snake_case_			(							self       ,__a       ,__a       )			->							Token:

          if not text:
               raise ValueError("""We need some text to work with."""       )

          # Initialise result parameters to default values
          snake_case   ,		snake_case :			int					   = 0, 0

          if not search_buffer:
               return Token(__a       ,__a       ,text[length]       )

          for i, character in enumerate(__a       ):
               snake_case :			Tuple					   = len(__a       ) - i
               if character == text[0]:
                    snake_case :			List[Any]					   = self._match_length_from_index(__a       ,__a       ,0       ,__a       )
                    # if the found length is bigger than the current or if it's equal,
                    # which means it's offset is smaller: update offset and length
                    if found_length >= length:
                         snake_case   ,		snake_case :			Optional[Any]					   = found_offset, found_length

          return Token(__a       ,__a       ,text[length]       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       )			->							int:
          if not text or text[text_index] != window[window_index]:
               return 0
          return 1 + self._match_length_from_index(
              __a       ,window + text[text_index]       ,text_index + 1       ,window_index + 1       )


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      # Initialize compressor class
      lowercase			:      int			=					LZaaCompressor(window_size=13, lookahead_buffer_size=6)

      # Example
      lowercase			:      int			=					"""cabracadabrarrarrad"""
      lowercase			:      str			=					lzaa_compressor.compress(TEXT)
      print(lzaa_compressor.compress("""ababcbababaa"""))
      lowercase			:      List[Any]			=					lzaa_compressor.decompress(compressed_text)
      assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."



