





"""simple docstring"""





import functools
import math

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




def 		lowerCAmelCase_     (  lowercase_       :							List[str]			,				lowercase_       :							List[str]			,				lowercase_       :							List[str]			,				lowercase_       :							int			,				lowercase_       :							int = 4096		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[str] 							=	key.shape[-3:]
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	value.shape[-1]
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	min(lowercase_			,				lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	query / jnp.sqrt(lowercase_		)

 @functools.partial(jax.checkpoint			,				prevent_cse=lowercase_		)
 def summarize_chunk(lowercase_       :							Dict			,				lowercase_       :							int			,				lowercase_       :							int		):
  __SCREAMING_SNAKE_CASE			:      str 							=	jnp.einsum('''...qhd,...khd->...qhk'''			,				lowercase_			,				lowercase_			,				precision=lowercase_		)

  __SCREAMING_SNAKE_CASE			:      str 							=	jnp.max(lowercase_			,				axis=-1			,				keepdims=lowercase_		)
  __SCREAMING_SNAKE_CASE			:      str 							=	jax.lax.stop_gradient(lowercase_		)
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	jnp.exp(attn_weights - max_score		)

  __SCREAMING_SNAKE_CASE			:      Any 							=	jnp.einsum('''...vhf,...qhv->...qhf'''			,				lowercase_			,				lowercase_			,				precision=lowercase_		)
  __SCREAMING_SNAKE_CASE			:      Any 							=	jnp.einsum('''...qhk->...qh'''			,				lowercase_		)

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

 def chunk_scanner(lowercase_       :							Union[str, Any]		):
  # julienne key array
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	jax.lax.dynamic_slice(
      operand=lowercase_			,				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
  __SCREAMING_SNAKE_CASE			:      int 							=	jax.lax.dynamic_slice(
      operand=lowercase_			,				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(lowercase_			,				lowercase_			,				lowercase_		)

 __SCREAMING_SNAKE_CASE			:      int 							=	jax.lax.map(f=lowercase_			,				xs=jnp.arange(0			,				lowercase_			,				lowercase_		)		)

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	jnp.max(lowercase_			,				axis=0			,				keepdims=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	jnp.exp(chunk_max - global_max		)

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

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	chunk_values.sum(axis=0		)
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	jnp.expand_dims(lowercase_			,				-1		).sum(axis=0		)

 return all_values / all_weights




def 		lowerCAmelCase_     (  lowercase_       :							Any			,				lowercase_       :							List[Any]			,				lowercase_       :							Optional[Any]			,				lowercase_       :							str=jax.lax.Precision.HIGHEST			,				lowercase_       :							int = 1024			,				lowercase_       :							int = 4096		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	query.shape[-3:]

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

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

 __SCREAMING_SNAKE_CASE			:      Any 							=	jax.lax.scan(
     f=lowercase_			,				init=0			,				xs=lowercase_			,				length=math.ceil(num_q / query_chunk_size		)		)

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





class 					snake_case		(       nn.Module       ):
      lowerCamelCase__      =			42
      lowerCamelCase__      =			8
      lowerCamelCase__      =			64
      lowerCamelCase__      =			0.0
      lowerCamelCase__      =			False
      lowerCamelCase__      =			jnp.floataa

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.dim_head * self.heads
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.dim_head**-0.5

       # Weights were exported with old names {to_q, to_k, to_v, to_out}
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	nn.Dense(_lowerCamelCase						,							use_bias=_lowerCamelCase						,							dtype=self.dtype						,							name='''to_q'''						)
       __SCREAMING_SNAKE_CASE			:      int 							=	nn.Dense(_lowerCamelCase						,							use_bias=_lowerCamelCase						,							dtype=self.dtype						,							name='''to_k'''						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	nn.Dense(_lowerCamelCase						,							use_bias=_lowerCamelCase						,							dtype=self.dtype						,							name='''to_v'''						)

       __SCREAMING_SNAKE_CASE			:      Any 							=	nn.Dense(self.query_dim						,							dtype=self.dtype						,							name='''to_out_0'''						)
       __SCREAMING_SNAKE_CASE			:      int 							=	nn.Dropout(rate=self.dropout						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Any						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	tensor.shape
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.heads
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	tensor.reshape(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							dim // head_size						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	jnp.transpose(_lowerCamelCase						,							(0, 2, 1, 3)						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tensor.reshape(batch_size * head_size						,							_lowerCamelCase						,							dim // head_size						)
       return tensor

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      int 							=	tensor.shape
       __SCREAMING_SNAKE_CASE			:      int 							=	self.heads
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	tensor.reshape(batch_size // head_size						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	jnp.transpose(_lowerCamelCase						,							(0, 2, 1, 3)						)
       __SCREAMING_SNAKE_CASE			:      int 							=	tensor.reshape(batch_size // head_size						,							_lowerCamelCase						,							dim * head_size						)
       return tensor



      def __call__(      self       :List[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Optional[int]=None						,							_lowerCamelCase       :str=True						):
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	hidden_states if context is None else context

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.query(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.key(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.value(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      str 							=	self.reshape_heads_to_batch_dim(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      str 							=	self.reshape_heads_to_batch_dim(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.reshape_heads_to_batch_dim(_lowerCamelCase						)

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

        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	query_states.shape[-3]
        if flatten_latent_dim % 6_4 == 0:
         __SCREAMING_SNAKE_CASE			:      str 							=	int(flatten_latent_dim / 6_4						)
        elif flatten_latent_dim % 1_6 == 0:
         __SCREAMING_SNAKE_CASE			:      List[str] 							=	int(flatten_latent_dim / 1_6						)
        elif flatten_latent_dim % 4 == 0:
         __SCREAMING_SNAKE_CASE			:      Any 							=	int(flatten_latent_dim / 4						)
        else:
         __SCREAMING_SNAKE_CASE			:      Any 							=	int(_lowerCamelCase						)

        __SCREAMING_SNAKE_CASE			:      Tuple 							=	jax_memory_efficient_attention(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							query_chunk_size=_lowerCamelCase						,							key_chunk_size=4_0_9_6 * 4						)

        __SCREAMING_SNAKE_CASE			:      List[str] 							=	hidden_states.transpose(1						,							0						,							2						)
       else:
        # compute attentions
        __SCREAMING_SNAKE_CASE			:      Dict 							=	jnp.einsum('''b i d, b j d->b i j'''						,							_lowerCamelCase						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	attention_scores * self.scale
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	nn.softmax(_lowerCamelCase						,							axis=2						)

        # attend to values
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	jnp.einsum('''b i j, b j d -> b i d'''						,							_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.reshape_batch_dim_to_heads(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.proj_attn(_lowerCamelCase						)
       return self.dropout_layer(_lowerCamelCase						,							deterministic=_lowerCamelCase						)







class 					snake_case		(       nn.Module       ):
      lowerCamelCase__      =			42
      lowerCamelCase__      =			42
      lowerCamelCase__      =			42
      lowerCamelCase__      =			0.0
      lowerCamelCase__      =			False
      lowerCamelCase__      =			jnp.floataa
      lowerCamelCase__      =			False

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       # self attention (or cross_attention if only_cross_attention is True)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	FlaxAttention(
           self.dim						,							self.n_heads						,							self.d_head						,							self.dropout						,							self.use_memory_efficient_attention						,							dtype=self.dtype						)
       # cross attention
       __SCREAMING_SNAKE_CASE			:      Any 							=	FlaxAttention(
           self.dim						,							self.n_heads						,							self.d_head						,							self.dropout						,							self.use_memory_efficient_attention						,							dtype=self.dtype						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	FlaxFeedForward(dim=self.dim						,							dropout=self.dropout						,							dtype=self.dtype						)
       __SCREAMING_SNAKE_CASE			:      int 							=	nn.LayerNorm(epsilon=1e-5						,							dtype=self.dtype						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	nn.LayerNorm(epsilon=1e-5						,							dtype=self.dtype						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	nn.LayerNorm(epsilon=1e-5						,							dtype=self.dtype						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	nn.Dropout(rate=self.dropout						)



      def __call__(      self       :Optional[Any]						,							_lowerCamelCase       :List[Any]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]=True						):
       # self attention
       __SCREAMING_SNAKE_CASE			:      int 							=	hidden_states
       if self.only_cross_attention:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.attna(self.norma(_lowerCamelCase						)						,							_lowerCamelCase						,							deterministic=_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.attna(self.norma(_lowerCamelCase						)						,							deterministic=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	hidden_states + residual

       # cross attention
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hidden_states
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.attna(self.norma(_lowerCamelCase						)						,							_lowerCamelCase						,							deterministic=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	hidden_states + residual

       # feed forward
       __SCREAMING_SNAKE_CASE			:      str 							=	hidden_states
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.ff(self.norma(_lowerCamelCase						)						,							deterministic=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	hidden_states + residual

       return self.dropout_layer(_lowerCamelCase						,							deterministic=_lowerCamelCase						)







class 					snake_case		(       nn.Module       ):
      lowerCamelCase__      =			42
      lowerCamelCase__      =			42
      lowerCamelCase__      =			42
      lowerCamelCase__      =			1
      lowerCamelCase__      =			0.0
      lowerCamelCase__      =			False
      lowerCamelCase__      =			False
      lowerCamelCase__      =			jnp.floataa
      lowerCamelCase__      =			False

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	nn.GroupNorm(num_groups=3_2						,							epsilon=1e-5						)

       __SCREAMING_SNAKE_CASE			:      int 							=	self.n_heads * self.d_head
       if self.use_linear_projection:
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	nn.Dense(_lowerCamelCase						,							dtype=self.dtype						)
       else:
        __SCREAMING_SNAKE_CASE			:      str 							=	nn.Conv(
            _lowerCamelCase						,							kernel_size=(1, 1)						,							strides=(1, 1)						,							padding='''VALID'''						,							dtype=self.dtype						,							)

       __SCREAMING_SNAKE_CASE			:      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:
        __SCREAMING_SNAKE_CASE			:      int 							=	nn.Dense(_lowerCamelCase						,							dtype=self.dtype						)
       else:
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	nn.Conv(
            _lowerCamelCase						,							kernel_size=(1, 1)						,							strides=(1, 1)						,							padding='''VALID'''						,							dtype=self.dtype						,							)

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



      def __call__(      self       :Any						,							_lowerCamelCase       :str						,							_lowerCamelCase       :int						,							_lowerCamelCase       :List[str]=True						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	hidden_states.shape
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	hidden_states
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.norm(_lowerCamelCase						)
       if self.use_linear_projection:
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hidden_states.reshape(_lowerCamelCase						,							height * width						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Dict 							=	self.proj_in(_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.proj_in(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hidden_states.reshape(_lowerCamelCase						,							height * width						,							_lowerCamelCase						)

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

       if self.use_linear_projection:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.proj_out(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Dict 							=	hidden_states.reshape(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	hidden_states.reshape(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Dict 							=	self.proj_out(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      str 							=	hidden_states + residual
       return self.dropout_layer(_lowerCamelCase						,							deterministic=_lowerCamelCase						)







class 					snake_case		(       nn.Module       ):
      lowerCamelCase__      =			42
      lowerCamelCase__      =			0.0
      lowerCamelCase__      =			jnp.floataa

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       # The second linear layer needs to be called
       # net_2 for now to match the index of the Sequential layer
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	FlaxGEGLU(self.dim						,							self.dropout						,							self.dtype						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	nn.Dense(self.dim						,							dtype=self.dtype						)



      def __call__(      self       :Optional[Any]						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :Any=True						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.net_a(_lowerCamelCase						,							deterministic=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.net_a(_lowerCamelCase						)
       return hidden_states







class 					snake_case		(       nn.Module       ):
      lowerCamelCase__      =			42
      lowerCamelCase__      =			0.0
      lowerCamelCase__      =			jnp.floataa

      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.dim * 4
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	nn.Dense(inner_dim * 2						,							dtype=self.dtype						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	nn.Dropout(rate=self.dropout						)



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








"""simple docstring"""





#
# Sort large text files in a minimum amount of memory
#
import argparse
import os





class       snake_case		:
      lowerCamelCase__      =			'''block_{0}.dat'''

      def __init__(      self       :int						,							_lowerCamelCase       :Dict						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	filename
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	[]

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.BLOCK_FILENAME_FORMAT.format(_lowerCamelCase						)
       with open(_lowerCamelCase						,							'''w'''						) as file:
        file.write(_lowerCamelCase						)
       self.block_filenames.append(_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       return self.block_filenames

      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Optional[Any]=None						):
       __SCREAMING_SNAKE_CASE			:      int 							=	0
       with open(self.filename						) as file:
        while True:
         __SCREAMING_SNAKE_CASE			:      Any 							=	file.readlines(_lowerCamelCase						)

         if lines == []:
          break

         if sort_key is None:
          lines.sort()
         else:
          lines.sort(key=_lowerCamelCase						)

         self.write_block(''''''.join(_lowerCamelCase						)						,							_lowerCamelCase						)
         i += 1



      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						):
       map(os.remove						,							self.block_filenames						)







class       snake_case		:

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	-1
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	None

       for i in range(len(_lowerCamelCase						)						):
        if min_str is None or choices[i] < min_str:
         __SCREAMING_SNAKE_CASE			:      Tuple 							=	i

       return min_index







class       snake_case		:

      def __init__(      self       :str						,							_lowerCamelCase       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	files
       __SCREAMING_SNAKE_CASE			:      str 							=	set()
       __SCREAMING_SNAKE_CASE			:      Dict 							=	len(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      str 							=	{i: None for i in range(self.num_buffers						)}

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       return {
           i: self.buffers[i] for i in range(self.num_buffers						) if i not in self.empty
       }

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       for i in range(self.num_buffers						):
        if self.buffers[i] is None and i not in self.empty:
         __SCREAMING_SNAKE_CASE			:      int 							=	self.files[i].readline()

         if self.buffers[i] == "":
          self.empty.add(_lowerCamelCase						)
          self.files[i].close()

       if len(self.empty						) == self.num_buffers:
        return False

       return True



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.buffers[index]
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	None

       return value







class       snake_case		:

      def __init__(      self       :List[Any]						,							_lowerCamelCase       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	merge_strategy

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :str						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	FilesArray(self.get_file_handles(_lowerCamelCase						,							_lowerCamelCase						)						)
       with open(_lowerCamelCase						,							'''w'''						,							_lowerCamelCase						) as outfile:
        while buffers.refresh():
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.merge_strategy.select(buffers.get_dict()						)
         outfile.write(buffers.unshift(_lowerCamelCase						)						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :int						,							_lowerCamelCase       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{}

       for i in range(len(_lowerCamelCase						)						):
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	open(filenames[i]						,							'''r'''						,							_lowerCamelCase						)  # noqa: UP015

       return files







class       snake_case		:

      def __init__(      self       :Dict						,							_lowerCamelCase       :str						):
       __SCREAMING_SNAKE_CASE			:      int 							=	block_size

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Dict=None						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.get_number_blocks(_lowerCamelCase						,							self.block_size						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	FileSplitter(_lowerCamelCase						)
       splitter.split(self.block_size						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Any 							=	FileMerger(NWayMerge()						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.block_size / (num_blocks + 1)
       merger.merge(splitter.get_block_filenames()						,							filename + '''.out'''						,							_lowerCamelCase						)

       splitter.cleanup()



      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						,							_lowerCamelCase       :str						,							_lowerCamelCase       :List[Any]						):
       return (os.stat(_lowerCamelCase						).st_size / block_size) + 1







def 		lowerCAmelCase_     (  lowercase_       :							str		):

 '''simple docstring'''






 if string[-1].lower() == "k":
  return int(string[:-1]		) * 1024
 elif string[-1].lower() == "m":
  return int(string[:-1]		) * 1024 * 1024
 elif string[-1].lower() == "g":
  return int(string[:-1]		) * 1024 * 1024 * 1024
 else:
  return int(lowercase_		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	argparse.ArgumentParser()
 parser.add_argument(
     '''-m'''			,				'''--mem'''			,				help='''amount of memory to use for sorting'''			,				default='''100M'''		)
 parser.add_argument(
     '''filename'''			,				metavar='''<filename>'''			,				nargs=1			,				help='''name of file to sort'''		)
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	parser.parse_args()

 __SCREAMING_SNAKE_CASE			:      List[str] 							=	ExternalSort(parse_memory(args.mem		)		)
 sorter.sort(args.filename[0]		)


if __name__ == "__main__":
    main()

