

from __future__ import annotations

import sys
class 					A_   :


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__  )     ->      str:
        __UpperCAmelCase									=			letter
        __UpperCAmelCase									=			freq
        __UpperCAmelCase									=			{}






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






class 					A_   :


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Optional[Any]:
        __UpperCAmelCase									=			freq
        __UpperCAmelCase									=			left
        __UpperCAmelCase									=			right






def       __a       (   SCREAMING_SNAKE_CASE      )      ->     list[Letter]:






    '''simple docstring'''
    __UpperCAmelCase									=			{}
    with open(SCREAMING_SNAKE_CASE      ) as f:
        while True:
            __UpperCAmelCase									=			f.read(1      )
            if not c:
                break
            __UpperCAmelCase									=			chars[c] + 1 if c in chars else 1
    return sorted((Letter(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ) for c, f in chars.items())       ,   key=lambda SCREAMING_SNAKE_CASE      : x.freq      )


def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Letter | TreeNode:






    '''simple docstring'''
    __UpperCAmelCase									=			letters  # type: ignore
    while len(SCREAMING_SNAKE_CASE      ) > 1:
        __UpperCAmelCase									=			response.pop(0      )
        __UpperCAmelCase									=			response.pop(0      )
        __UpperCAmelCase									=			left.freq + right.freq
        __UpperCAmelCase									=			TreeNode(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
        response.append(SCREAMING_SNAKE_CASE      )
        response.sort(key=lambda SCREAMING_SNAKE_CASE      : x.freq      )
    return response[0]


def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     list[Letter]:






    '''simple docstring'''
    if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
        __UpperCAmelCase									=			bitstring
        return [root]
    __UpperCAmelCase									=			root  # type: ignore
    __UpperCAmelCase									=			[]
    letters += traverse_tree(treenode.left       ,   bitstring + '''0'''      )
    letters += traverse_tree(treenode.right       ,   bitstring + '''1'''      )
    return letters


def       __a       (   SCREAMING_SNAKE_CASE      )      ->     None:






    '''simple docstring'''
    __UpperCAmelCase									=			parse_file(SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			build_tree(SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			{
        k: v for letter in traverse_tree(SCREAMING_SNAKE_CASE       ,   ''''''      ) for k, v in letter.bitstring.items()
    }
    print(f'''Huffman Coding  of {file_path}: '''      )
    with open(SCREAMING_SNAKE_CASE      ) as f:
        while True:
            __UpperCAmelCase									=			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])







import functools
import math

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


def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE = 4_0_9_6      )      ->     Any:






    '''simple docstring'''
    __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			key.shape[-3:]
    __UpperCAmelCase									=			value.shape[-1]
    __UpperCAmelCase									=			min(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			query / jnp.sqrt(SCREAMING_SNAKE_CASE      )

    @functools.partial(jax.checkpoint       ,   prevent_cse=SCREAMING_SNAKE_CASE      )
    def summarize_chunk(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
        __UpperCAmelCase									=			jnp.einsum('''...qhd,...khd->...qhk'''       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   precision=SCREAMING_SNAKE_CASE      )

        __UpperCAmelCase									=			jnp.max(SCREAMING_SNAKE_CASE       ,   axis=-1       ,   keepdims=SCREAMING_SNAKE_CASE      )
        __UpperCAmelCase									=			jax.lax.stop_gradient(SCREAMING_SNAKE_CASE      )
        __UpperCAmelCase									=			jnp.exp(attn_weights - max_score      )

        __UpperCAmelCase									=			jnp.einsum('''...vhf,...qhv->...qhf'''       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   precision=SCREAMING_SNAKE_CASE      )
        __UpperCAmelCase									=			jnp.einsum('''...qhk->...qh'''       ,   SCREAMING_SNAKE_CASE      )

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

    def chunk_scanner(SCREAMING_SNAKE_CASE      ):
        # julienne key array
        __UpperCAmelCase									=			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
        __UpperCAmelCase									=			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      )

    __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			jax.lax.map(f=SCREAMING_SNAKE_CASE       ,   xs=jnp.arange(0       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      )

    __UpperCAmelCase									=			jnp.max(SCREAMING_SNAKE_CASE       ,   axis=0       ,   keepdims=SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			jnp.exp(chunk_max - global_max      )

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

    __UpperCAmelCase									=			chunk_values.sum(axis=0      )
    __UpperCAmelCase									=			jnp.expand_dims(SCREAMING_SNAKE_CASE       ,   -1      ).sum(axis=0      )

    return all_values / all_weights


def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE=jax.lax.Precision.HIGHEST       ,   SCREAMING_SNAKE_CASE = 1_0_2_4       ,   SCREAMING_SNAKE_CASE = 4_0_9_6      )      ->     int:






    '''simple docstring'''
    __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			query.shape[-3:]

    def chunk_scanner(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
        # julienne query array
        __UpperCAmelCase									=			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      ),
        )

    __UpperCAmelCase					,     __UpperCAmelCase									=			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'''






    a__     							=       42
    a__     							=       8
    a__     							=       64
    a__     							=       0.0
    a__     							=       False
    a__     							=       jnp.floataa


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        __UpperCAmelCase									=			self.dim_head * self.heads
        __UpperCAmelCase									=			self.dim_head**-0.5

        # Weights were exported with old names {to_q, to_k, to_v, to_out}
        __UpperCAmelCase									=			nn.Dense(lowercase__     ,      use_bias=lowercase__     ,      dtype=self.dtype     ,      name='''to_q'''  )
        __UpperCAmelCase									=			nn.Dense(lowercase__     ,      use_bias=lowercase__     ,      dtype=self.dtype     ,      name='''to_k'''  )
        __UpperCAmelCase									=			nn.Dense(lowercase__     ,      use_bias=lowercase__     ,      dtype=self.dtype     ,      name='''to_v'''  )

        __UpperCAmelCase									=			nn.Dense(self.query_dim     ,      dtype=self.dtype     ,      name='''to_out_0'''  )
        __UpperCAmelCase									=			nn.Dropout(rate=self.dropout  )


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      Optional[int]:
        __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			tensor.shape
        __UpperCAmelCase									=			self.heads
        __UpperCAmelCase									=			tensor.reshape(lowercase__     ,      lowercase__     ,      lowercase__     ,      dim // head_size  )
        __UpperCAmelCase									=			jnp.transpose(lowercase__     ,      (0, 2, 1, 3)  )
        __UpperCAmelCase									=			tensor.reshape(batch_size * head_size     ,      lowercase__     ,      dim // head_size  )
        return tensor


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      Any:
        __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			tensor.shape
        __UpperCAmelCase									=			self.heads
        __UpperCAmelCase									=			tensor.reshape(batch_size // head_size     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
        __UpperCAmelCase									=			jnp.transpose(lowercase__     ,      (0, 2, 1, 3)  )
        __UpperCAmelCase									=			tensor.reshape(batch_size // head_size     ,      lowercase__     ,      dim * head_size  )
        return tensor






    def __call__(self     ,      lowercase__     ,      lowercase__=None     ,      lowercase__=True  )     ->      Optional[Any]:
        __UpperCAmelCase									=			hidden_states if context is None else context

        __UpperCAmelCase									=			self.query(lowercase__  )
        __UpperCAmelCase									=			self.key(lowercase__  )
        __UpperCAmelCase									=			self.value(lowercase__  )

        __UpperCAmelCase									=			self.reshape_heads_to_batch_dim(lowercase__  )
        __UpperCAmelCase									=			self.reshape_heads_to_batch_dim(lowercase__  )
        __UpperCAmelCase									=			self.reshape_heads_to_batch_dim(lowercase__  )

        if self.use_memory_efficient_attention:
            __UpperCAmelCase									=			query_states.transpose(1     ,      0     ,      2  )
            __UpperCAmelCase									=			key_states.transpose(1     ,      0     ,      2  )
            __UpperCAmelCase									=			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

            __UpperCAmelCase									=			query_states.shape[-3]
            if flatten_latent_dim % 64 == 0:
                __UpperCAmelCase									=			int(flatten_latent_dim / 64  )
            elif flatten_latent_dim % 16 == 0:
                __UpperCAmelCase									=			int(flatten_latent_dim / 16  )
            elif flatten_latent_dim % 4 == 0:
                __UpperCAmelCase									=			int(flatten_latent_dim / 4  )
            else:
                __UpperCAmelCase									=			int(lowercase__  )

            __UpperCAmelCase									=			jax_memory_efficient_attention(
                lowercase__     ,      lowercase__     ,      lowercase__     ,      query_chunk_size=lowercase__     ,      key_chunk_size=4_096 * 4  )

            __UpperCAmelCase									=			hidden_states.transpose(1     ,      0     ,      2  )
        else:
            # compute attentions
            __UpperCAmelCase									=			jnp.einsum('''b i d, b j d->b i j'''     ,      lowercase__     ,      lowercase__  )
            __UpperCAmelCase									=			attention_scores * self.scale
            __UpperCAmelCase									=			nn.softmax(lowercase__     ,      axis=2  )

            # attend to values
            __UpperCAmelCase									=			jnp.einsum('''b i j, b j d -> b i d'''     ,      lowercase__     ,      lowercase__  )

        __UpperCAmelCase									=			self.reshape_batch_dim_to_heads(lowercase__  )
        __UpperCAmelCase									=			self.proj_attn(lowercase__  )
        return self.dropout_layer(lowercase__     ,      deterministic=lowercase__  )






class 					A_   (      nn.Module						):


    '''simple docstring'''






    a__     							=       42
    a__     							=       42
    a__     							=       42
    a__     							=       0.0
    a__     							=       False
    a__     							=       jnp.floataa
    a__     							=       False


    def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
        # self attention (or cross_attention if only_cross_attention is True)
        __UpperCAmelCase									=			FlaxAttention(
            self.dim     ,      self.n_heads     ,      self.d_head     ,      self.dropout     ,      self.use_memory_efficient_attention     ,      dtype=self.dtype  )
        # cross attention
        __UpperCAmelCase									=			FlaxAttention(
            self.dim     ,      self.n_heads     ,      self.d_head     ,      self.dropout     ,      self.use_memory_efficient_attention     ,      dtype=self.dtype  )
        __UpperCAmelCase									=			FlaxFeedForward(dim=self.dim     ,      dropout=self.dropout     ,      dtype=self.dtype  )
        __UpperCAmelCase									=			nn.LayerNorm(epsilon=1E-5     ,      dtype=self.dtype  )
        __UpperCAmelCase									=			nn.LayerNorm(epsilon=1E-5     ,      dtype=self.dtype  )
        __UpperCAmelCase									=			nn.LayerNorm(epsilon=1E-5     ,      dtype=self.dtype  )
        __UpperCAmelCase									=			nn.Dropout(rate=self.dropout  )






    def __call__(self     ,      lowercase__     ,      lowercase__     ,      lowercase__=True  )     ->      Optional[int]:
        # self attention
        __UpperCAmelCase									=			hidden_states
        if self.only_cross_attention:
            __UpperCAmelCase									=			self.attna(self.norma(lowercase__  )     ,      lowercase__     ,      deterministic=lowercase__  )
        else:
            __UpperCAmelCase									=			self.attna(self.norma(lowercase__  )     ,      deterministic=lowercase__  )
        __UpperCAmelCase									=			hidden_states + residual

        # cross attention
        __UpperCAmelCase									=			hidden_states
        __UpperCAmelCase									=			self.attna(self.norma(lowercase__  )     ,      lowercase__     ,      deterministic=lowercase__  )
        __UpperCAmelCase									=			hidden_states + residual

        # feed forward
        __UpperCAmelCase									=			hidden_states
        __UpperCAmelCase									=			self.ff(self.norma(lowercase__  )     ,      deterministic=lowercase__  )
        __UpperCAmelCase									=			hidden_states + residual

        return self.dropout_layer(lowercase__     ,      deterministic=lowercase__  )






class 					A_   (      nn.Module						):


    '''simple docstring'''






    a__     							=       42
    a__     							=       42
    a__     							=       42
    a__     							=       1
    a__     							=       0.0
    a__     							=       False
    a__     							=       False
    a__     							=       jnp.floataa
    a__     							=       False


    def      lowerCAmelCase_						(self  )     ->      int:
        __UpperCAmelCase									=			nn.GroupNorm(num_groups=32     ,      epsilon=1E-5  )

        __UpperCAmelCase									=			self.n_heads * self.d_head
        if self.use_linear_projection:
            __UpperCAmelCase									=			nn.Dense(lowercase__     ,      dtype=self.dtype  )
        else:
            __UpperCAmelCase									=			nn.Conv(
                lowercase__     ,      kernel_size=(1, 1)     ,      strides=(1, 1)     ,      padding='''VALID'''     ,      dtype=self.dtype     ,      )

        __UpperCAmelCase									=			[
            FlaxBasicTransformerBlock(
                lowercase__     ,      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:
            __UpperCAmelCase									=			nn.Dense(lowercase__     ,      dtype=self.dtype  )
        else:
            __UpperCAmelCase									=			nn.Conv(
                lowercase__     ,      kernel_size=(1, 1)     ,      strides=(1, 1)     ,      padding='''VALID'''     ,      dtype=self.dtype     ,      )

        __UpperCAmelCase									=			nn.Dropout(rate=self.dropout  )






    def __call__(self     ,      lowercase__     ,      lowercase__     ,      lowercase__=True  )     ->      Dict:
        __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			hidden_states.shape
        __UpperCAmelCase									=			hidden_states
        __UpperCAmelCase									=			self.norm(lowercase__  )
        if self.use_linear_projection:
            __UpperCAmelCase									=			hidden_states.reshape(lowercase__     ,      height * width     ,      lowercase__  )
            __UpperCAmelCase									=			self.proj_in(lowercase__  )
        else:
            __UpperCAmelCase									=			self.proj_in(lowercase__  )
            __UpperCAmelCase									=			hidden_states.reshape(lowercase__     ,      height * width     ,      lowercase__  )

        for transformer_block in self.transformer_blocks:
            __UpperCAmelCase									=			transformer_block(lowercase__     ,      lowercase__     ,      deterministic=lowercase__  )

        if self.use_linear_projection:
            __UpperCAmelCase									=			self.proj_out(lowercase__  )
            __UpperCAmelCase									=			hidden_states.reshape(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
        else:
            __UpperCAmelCase									=			hidden_states.reshape(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
            __UpperCAmelCase									=			self.proj_out(lowercase__  )

        __UpperCAmelCase									=			hidden_states + residual
        return self.dropout_layer(lowercase__     ,      deterministic=lowercase__  )






class 					A_   (      nn.Module						):


    '''simple docstring'''






    a__     							=       42
    a__     							=       0.0
    a__     							=       jnp.floataa


    def      lowerCAmelCase_						(self  )     ->      int:
        # The second linear layer needs to be called
        # net_2 for now to match the index of the Sequential layer
        __UpperCAmelCase									=			FlaxGEGLU(self.dim     ,      self.dropout     ,      self.dtype  )
        __UpperCAmelCase									=			nn.Dense(self.dim     ,      dtype=self.dtype  )






    def __call__(self     ,      lowercase__     ,      lowercase__=True  )     ->      Dict:
        __UpperCAmelCase									=			self.net_a(lowercase__     ,      deterministic=lowercase__  )
        __UpperCAmelCase									=			self.net_a(lowercase__  )
        return hidden_states






class 					A_   (      nn.Module						):


    '''simple docstring'''






    a__     							=       42
    a__     							=       0.0
    a__     							=       jnp.floataa


    def      lowerCAmelCase_						(self  )     ->      Optional[int]:
        __UpperCAmelCase									=			self.dim * 4
        __UpperCAmelCase									=			nn.Dense(inner_dim * 2     ,      dtype=self.dtype  )
        __UpperCAmelCase									=			nn.Dropout(rate=self.dropout  )






    def __call__(self     ,      lowercase__     ,      lowercase__=True  )     ->      Union[str, Any]:
        __UpperCAmelCase									=			self.proj(lowercase__  )
        __UpperCAmelCase					,     __UpperCAmelCase									=			jnp.split(lowercase__     ,      2     ,      axis=2  )
        return self.dropout_layer(hidden_linear * nn.gelu(lowercase__  )     ,      deterministic=lowercase__  )




