
'''simple docstring'''
import functools
import math

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	int = 4096			)					->							Optional[Any]:





  """simple docstring"""

  __A				,       __A				,       __A		:  List[Any]       				=		key.shape[-3:]
  __A		:  Dict       				=		value.shape[-1]
  __A		:  List[str]       				=		min(__a      ,  __a			)
  __A		:  List[Any]       				=		query / jnp.sqrt(__a			)

  @functools.partial(jax.checkpoint      ,  prevent_cse=__a			)
  def summarize_chunk(__SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]			):
    __A		:  Dict       				=		jnp.einsum("""...qhd,...khd->...qhk"""      ,  __a      ,  __a      ,  precision=__a			)

    __A		:  Optional[Any]       				=		jnp.max(__a      ,  axis=-1      ,  keepdims=__a			)
    __A		:  str       				=		jax.lax.stop_gradient(__a			)
    __A		:  List[str]       				=		jnp.exp(attn_weights - max_score			)

    __A		:  Union[str, Any]       				=		jnp.einsum("""...vhf,...qhv->...qhf"""      ,  __a      ,  __a      ,  precision=__a			)
    __A		:  List[Any]       				=		jnp.einsum("""...qhk->...qh"""      ,  __a			)

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

  def chunk_scanner(__SCREAMING_SNAKE_CASE    :	str			):
    # julienne key array
    __A		:  int       				=		jax.lax.dynamic_slice(
        operand=__a      ,  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
    __A		:  str       				=		jax.lax.dynamic_slice(
        operand=__a      ,  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(__a      ,  __a      ,  __a			)

  __A				,       __A				,       __A		:  Optional[Any]       				=		jax.lax.map(f=__a      ,  xs=jnp.arange(0      ,  __a      ,  __a			)			)

  __A		:  int       				=		jnp.max(__a      ,  axis=0      ,  keepdims=__a			)
  __A		:  Optional[int]       				=		jnp.exp(chunk_max - global_max			)

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

  __A		:  Any       				=		chunk_values.sum(axis=0			)
  __A		:  int       				=		jnp.expand_dims(__a      ,  -1			).sum(axis=0			)

  return all_values / all_weights







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Any=jax.lax.Precision.HIGHEST      ,  __SCREAMING_SNAKE_CASE    :	int = 1024      ,  __SCREAMING_SNAKE_CASE    :	int = 4096			)					->							Any:





  """simple docstring"""

  __A				,       __A				,       __A		:  List[Any]       				=		query.shape[-3:]

  def chunk_scanner(__SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]			):
    # julienne query array
    __A		:  Union[str, Any]       				=		jax.lax.dynamic_slice(
        operand=__a      ,  start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]      ,  slice_sizes=list(query.shape[:-3]			) + [min(__a      ,  __a			), num_heads, q_features]      ,  )

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

  __A				,       __A		:  Optional[Any]       				=		jax.lax.scan(
      f=__a      ,  init=0      ,  xs=__a      ,  length=math.ceil(num_q / query_chunk_size			)			)

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







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=8
 lowerCamelCase												=64
 lowerCamelCase												=0.0
 lowerCamelCase												=False
 lowerCamelCase												=jnp.floataa
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  Optional[int]       				=		self.dim_head * self.heads
   __A		:  Dict       				=		self.dim_head**-0.5

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

   __A		:  List[Any]       				=		nn.Dense(self.query_dim					, dtype=self.dtype					, name="""to_out_0"""							)
   __A		:  Optional[int]       				=		nn.Dropout(rate=self.dropout							)
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     Dict							):



   """simple docstring"""





   __A				,       __A				,       __A		:  Optional[int]       				=		tensor.shape
   __A		:  List[str]       				=		self.heads
   __A		:  Dict       				=		tensor.reshape(lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, dim // head_size							)
   __A		:  Dict       				=		jnp.transpose(lowerCAmelCase_					, (0, 2, 1, 3)							)
   __A		:  str       				=		tensor.reshape(batch_size * head_size					, lowerCAmelCase_					, dim // head_size							)
   return tensor
 def 		lowercase_( self      :     str					, lowerCamelCase      :     Any							):



   """simple docstring"""





   __A				,       __A				,       __A		:  List[Any]       				=		tensor.shape
   __A		:  List[str]       				=		self.heads
   __A		:  Tuple       				=		tensor.reshape(batch_size // head_size					, lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_							)
   __A		:  str       				=		jnp.transpose(lowerCAmelCase_					, (0, 2, 1, 3)							)
   __A		:  str       				=		tensor.reshape(batch_size // head_size					, lowerCAmelCase_					, dim * head_size							)
   return tensor






 def __call__( self      :     Optional[int]					, lowerCamelCase      :     Optional[Any]					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     List[Any]=True							):



   """simple docstring"""





   __A		:  int       				=		hidden_states if context is None else context

   __A		:  List[Any]       				=		self.query(lowerCAmelCase_							)
   __A		:  Optional[Any]       				=		self.key(lowerCAmelCase_							)
   __A		:  Optional[int]       				=		self.value(lowerCAmelCase_							)

   __A		:  Tuple       				=		self.reshape_heads_to_batch_dim(lowerCAmelCase_							)
   __A		:  List[Any]       				=		self.reshape_heads_to_batch_dim(lowerCAmelCase_							)
   __A		:  Optional[Any]       				=		self.reshape_heads_to_batch_dim(lowerCAmelCase_							)

   if self.use_memory_efficient_attention:
     __A		:  str       				=		query_states.transpose(1					, 0					, 2							)
     __A		:  int       				=		key_states.transpose(1					, 0					, 2							)
     __A		:  List[str]       				=		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

     __A		:  List[str]       				=		query_states.shape[-3]
     if flatten_latent_dim % 64 == 0:
       __A		:  List[str]       				=		int(flatten_latent_dim / 64							)
     elif flatten_latent_dim % 16 == 0:
       __A		:  Any       				=		int(flatten_latent_dim / 16							)
     elif flatten_latent_dim % 4 == 0:
       __A		:  Any       				=		int(flatten_latent_dim / 4							)
     else:
       __A		:  Union[str, Any]       				=		int(lowerCAmelCase_							)

     __A		:  Any       				=		jax_memory_efficient_attention(
         lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, query_chunk_size=lowerCAmelCase_					, key_chunk_size=40_96 * 4							)

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

     # attend to values
     __A		:  str       				=		jnp.einsum("""b i j, b j d -> b i d"""					, lowerCAmelCase_					, lowerCAmelCase_							)

   __A		:  int       				=		self.reshape_batch_dim_to_heads(lowerCAmelCase_							)
   __A		:  Optional[Any]       				=		self.proj_attn(lowerCAmelCase_							)
   return self.dropout_layer(lowerCAmelCase_					, deterministic=lowerCAmelCase_							)







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=0.0
 lowerCamelCase												=False
 lowerCamelCase												=jnp.floataa
 lowerCamelCase												=False
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  str       				=		FlaxAttention(
       self.dim					, self.n_heads					, self.d_head					, self.dropout					, self.use_memory_efficient_attention					, dtype=self.dtype							)
   # cross attention
   __A		:  Any       				=		FlaxAttention(
       self.dim					, self.n_heads					, self.d_head					, self.dropout					, self.use_memory_efficient_attention					, dtype=self.dtype							)
   __A		:  Optional[int]       				=		FlaxFeedForward(dim=self.dim					, dropout=self.dropout					, dtype=self.dtype							)
   __A		:  Optional[Any]       				=		nn.LayerNorm(epsilon=1e-5					, dtype=self.dtype							)
   __A		:  Dict       				=		nn.LayerNorm(epsilon=1e-5					, dtype=self.dtype							)
   __A		:  Optional[Any]       				=		nn.LayerNorm(epsilon=1e-5					, dtype=self.dtype							)
   __A		:  str       				=		nn.Dropout(rate=self.dropout							)






 def __call__( self      :     str					, lowerCamelCase      :     Any					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Any=True							):



   """simple docstring"""





   __A		:  Dict       				=		hidden_states
   if self.only_cross_attention:
     __A		:  List[str]       				=		self.attna(self.norma(lowerCAmelCase_							)					, lowerCAmelCase_					, deterministic=lowerCAmelCase_							)
   else:
     __A		:  Optional[int]       				=		self.attna(self.norma(lowerCAmelCase_							)					, deterministic=lowerCAmelCase_							)
   __A		:  List[Any]       				=		hidden_states + residual

   # cross attention
   __A		:  int       				=		hidden_states
   __A		:  str       				=		self.attna(self.norma(lowerCAmelCase_							)					, lowerCAmelCase_					, deterministic=lowerCAmelCase_							)
   __A		:  Optional[Any]       				=		hidden_states + residual

   # feed forward
   __A		:  Tuple       				=		hidden_states
   __A		:  Optional[Any]       				=		self.ff(self.norma(lowerCAmelCase_							)					, deterministic=lowerCAmelCase_							)
   __A		:  List[str]       				=		hidden_states + residual

   return self.dropout_layer(lowerCAmelCase_					, deterministic=lowerCAmelCase_							)







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=1
 lowerCamelCase												=0.0
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=jnp.floataa
 lowerCamelCase												=False
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Any       				=		nn.GroupNorm(num_groups=32					, epsilon=1e-5							)

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

   __A		:  Optional[int]       				=		[
       FlaxBasicTransformerBlock(
           lowerCAmelCase_					, 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:
     __A		:  List[str]       				=		nn.Dense(lowerCAmelCase_					, dtype=self.dtype							)
   else:
     __A		:  Union[str, Any]       				=		nn.Conv(
         lowerCAmelCase_					, kernel_size=(1, 1)					, strides=(1, 1)					, padding="""VALID"""					, dtype=self.dtype					, )

   __A		:  str       				=		nn.Dropout(rate=self.dropout							)






 def __call__( self      :     Dict					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     int					, lowerCamelCase      :     str=True							):



   """simple docstring"""





   __A				,       __A				,       __A				,       __A		:  Union[str, Any]       				=		hidden_states.shape
   __A		:  Dict       				=		hidden_states
   __A		:  List[str]       				=		self.norm(lowerCAmelCase_							)
   if self.use_linear_projection:
     __A		:  Union[str, Any]       				=		hidden_states.reshape(lowerCAmelCase_					, height * width					, lowerCAmelCase_							)
     __A		:  int       				=		self.proj_in(lowerCAmelCase_							)
   else:
     __A		:  Union[str, Any]       				=		self.proj_in(lowerCAmelCase_							)
     __A		:  List[str]       				=		hidden_states.reshape(lowerCAmelCase_					, height * width					, lowerCAmelCase_							)

   for transformer_block in self.transformer_blocks:
     __A		:  str       				=		transformer_block(lowerCAmelCase_					, lowerCAmelCase_					, deterministic=lowerCAmelCase_							)

   if self.use_linear_projection:
     __A		:  Any       				=		self.proj_out(lowerCAmelCase_							)
     __A		:  str       				=		hidden_states.reshape(lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_							)
   else:
     __A		:  str       				=		hidden_states.reshape(lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_							)
     __A		:  Optional[int]       				=		self.proj_out(lowerCAmelCase_							)

   __A		:  List[str]       				=		hidden_states + residual
   return self.dropout_layer(lowerCAmelCase_					, deterministic=lowerCAmelCase_							)







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=0.0
 lowerCamelCase												=jnp.floataa
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  int       				=		FlaxGEGLU(self.dim					, self.dropout					, self.dtype							)
   __A		:  str       				=		nn.Dense(self.dim					, dtype=self.dtype							)






 def __call__( self      :     Dict					, lowerCamelCase      :     str					, lowerCamelCase      :     int=True							):



   """simple docstring"""





   __A		:  str       				=		self.net_a(lowerCAmelCase_					, deterministic=lowerCAmelCase_							)
   __A		:  Tuple       				=		self.net_a(lowerCAmelCase_							)
   return hidden_states







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=0.0
 lowerCamelCase												=jnp.floataa
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  List[str]       				=		self.dim * 4
   __A		:  Optional[Any]       				=		nn.Dense(inner_dim * 2					, dtype=self.dtype							)
   __A		:  Dict       				=		nn.Dropout(rate=self.dropout							)






 def __call__( self      :     Any					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     Union[str, Any]=True							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		self.proj(lowerCAmelCase_							)
   __A				,       __A		:  Optional[int]       				=		jnp.split(lowerCAmelCase_					, 2					, axis=2							)
   return self.dropout_layer(hidden_linear * nn.gelu(lowerCAmelCase_							)					, deterministic=lowerCAmelCase_							)




from __future__ import annotations

import sys
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =letter
		_lowercase   =freq
		_lowercase   ={}


	def __repr__(   self       ):
		return F'''{self.letter}:{self.freq}'''

class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =freq
		_lowercase   =left
		_lowercase   =right

def 	__lowerCamelCase							(				__a    : str		)      ->       list[Letter]:
	_lowercase   ={}
	with open(__a		) as f:
		while True:
			_lowercase   =f.read(1		)
			if not c:
				break
			_lowercase   =chars[c] + 1 if c in chars else 1
	return sorted((Letter(__a      ,							__a		) for c, f in chars.items())      ,							key=lambda __a		: x.freq		)

def 	__lowerCamelCase							(				__a    : list[Letter]		)      ->       Letter | TreeNode:
	_lowercase   =letters  # type: ignore
	while len(__a		) > 1:
		_lowercase   =response.pop(0		)
		_lowercase   =response.pop(0		)
		_lowercase   =left.freq + right.freq
		_lowercase   =TreeNode(__a      ,							__a      ,							__a		)
		response.append(__a		)
		response.sort(key=lambda __a		: x.freq		)
	return response[0]

def 	__lowerCamelCase							(				__a    : Letter | TreeNode      ,							__a    : str		)      ->       list[Letter]:
	if isinstance(__a      ,							__a		):
		_lowercase   =bitstring
		return [root]
	_lowercase   =root  # type: ignore
	_lowercase   =[]
	letters += traverse_tree(treenode.left      ,							bitstring + "0"		)
	letters += traverse_tree(treenode.right      ,							bitstring + "1"		)
	return letters

def 	__lowerCamelCase							(				__a    : str		)      ->       None:
	_lowercase   =parse_file(__a		)
	_lowercase   =build_tree(__a		)
	_lowercase   ={
	    k: v for letter in traverse_tree(__a      ,							""		) for k, v in letter.bitstring.items()
	}
	print(f'''Huffman Coding  of {file_path}: '''		)
	with open(__a		) as f:
		while True:
			_lowercase   =f.read(1		)
			if not c:
				break
			print(letters[c]      ,							end=" "		)
	print()


if __name__ == "__main__":
						# pass the file path to the huffman function
						huffman(sys.argv[1])




