import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase = 4_096						)       ->       str:

						'''simple docstring'''



						_snake_case						:     Dict				=    key.shape[-3:]
						_snake_case						:     Union[str, Any]				=    value.shape[-1]
						_snake_case						:     Union[str, Any]				=    min(lowerCamelCase_					,			lowerCamelCase_						)
						_snake_case						:     Tuple				=    query / jnp.sqrt(lowerCamelCase_						)

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

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

												_snake_case						:     str				=    jnp.einsum("...vhf,...qhv->...qhf"					,			lowerCamelCase_					,			lowerCamelCase_					,			precision=lowerCamelCase_						)
												_snake_case						:     List[str]				=    jnp.einsum("...qhk->...qh"					,			lowerCamelCase_						)

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

						def chunk_scanner(__lowercase						):
												# julienne key array
												_snake_case						:     Any				=    jax.lax.dynamic_slice(
												    operand=lowerCamelCase_					,			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						:     Union[str, Any]				=    jax.lax.dynamic_slice(
												    operand=lowerCamelCase_					,			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(lowerCamelCase_					,			lowerCamelCase_					,			lowerCamelCase_						)

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

						_snake_case						:     List[str]				=    jnp.max(lowerCamelCase_					,			axis=0					,			keepdims=lowerCamelCase_						)
						_snake_case						:     str				=    jnp.exp(chunk_max - global_max						)

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

						_snake_case						:     List[Any]				=    chunk_values.sum(axis=0						)
						_snake_case						:     Optional[int]				=    jnp.expand_dims(lowerCamelCase_					,			-1						).sum(axis=0						)

						return all_values / all_weights
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase=jax.lax.Precision.HIGHEST					,			__lowercase = 1_024					,			__lowercase = 4_096						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     Any				=    query.shape[-3:]

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

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

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

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



class    lowercase_    (     nn.Module		):
							_lowerCamelCase						=		42
							_lowerCamelCase						=		8
							_lowerCamelCase						=		64
							_lowerCamelCase						=		0.0
							_lowerCamelCase						=		False
							_lowerCamelCase						=		jnp.floataa



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.dim_head * self.heads
													_snake_case						:     Optional[int]				=    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(snake_case_      ,				use_bias=snake_case_      ,				dtype=self.dtype      ,				name="to_q"				)
													_snake_case						:     List[Any]				=    nn.Dense(snake_case_      ,				use_bias=snake_case_      ,				dtype=self.dtype      ,				name="to_k"				)
													_snake_case						:     List[Any]				=    nn.Dense(snake_case_      ,				use_bias=snake_case_      ,				dtype=self.dtype      ,				name="to_v"				)

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



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     str				=    tensor.shape
													_snake_case						:     Optional[int]				=    self.heads
													_snake_case						:     Tuple				=    tensor.reshape(snake_case_      ,				snake_case_      ,				snake_case_      ,				dim // head_size				)
													_snake_case						:     List[str]				=    jnp.transpose(snake_case_      ,				(0, 2, 1, 3)				)
													_snake_case						:     Tuple				=    tensor.reshape(batch_size * head_size      ,				snake_case_      ,				dim // head_size				)
													return tensor



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     Optional[Any]				=    tensor.shape
													_snake_case						:     Union[str, Any]				=    self.heads
													_snake_case						:     Union[str, Any]				=    tensor.reshape(batch_size // head_size      ,				snake_case_      ,				snake_case_      ,				snake_case_				)
													_snake_case						:     Optional[Any]				=    jnp.transpose(snake_case_      ,				(0, 2, 1, 3)				)
													_snake_case						:     Union[str, Any]				=    tensor.reshape(batch_size // head_size      ,				snake_case_      ,				dim * head_size				)
													return tensor



							def __call__(						self      ,				lowercase_      ,				lowercase_=None      ,				lowercase_=True				):
													_snake_case						:     Dict				=    hidden_states if context is None else context

													_snake_case						:     str				=    self.query(snake_case_				)
													_snake_case						:     Optional[int]				=    self.key(snake_case_				)
													_snake_case						:     Optional[int]				=    self.value(snake_case_				)

													_snake_case						:     Dict				=    self.reshape_heads_to_batch_dim(snake_case_				)
													_snake_case						:     str				=    self.reshape_heads_to_batch_dim(snake_case_				)
													_snake_case						:     Tuple				=    self.reshape_heads_to_batch_dim(snake_case_				)

													if self.use_memory_efficient_attention:
																			_snake_case						:     Dict				=    query_states.transpose(1      ,				0      ,				2				)
																			_snake_case						:     List[str]				=    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						:     List[str]				=    query_states.shape[-3]
																			if flatten_latent_dim % 64 == 0:
																									_snake_case						:     Dict				=    int(flatten_latent_dim / 64				)
																			elif flatten_latent_dim % 16 == 0:
																									_snake_case						:     List[Any]				=    int(flatten_latent_dim / 16				)
																			elif flatten_latent_dim % 4 == 0:
																									_snake_case						:     int				=    int(flatten_latent_dim / 4				)
																			else:
																									_snake_case						:     List[str]				=    int(snake_case_				)

																			_snake_case						:     List[Any]				=    jax_memory_efficient_attention(
																			    snake_case_      ,				snake_case_      ,				snake_case_      ,				query_chunk_size=snake_case_      ,				key_chunk_size=4_096 * 4				)

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

																			# attend to values
																			_snake_case						:     Optional[Any]				=    jnp.einsum("b i j, b j d -> b i d"      ,				snake_case_      ,				snake_case_				)

													_snake_case						:     Optional[Any]				=    self.reshape_batch_dim_to_heads(snake_case_				)
													_snake_case						:     List[Any]				=    self.proj_attn(snake_case_				)
													return self.dropout_layer(snake_case_      ,				deterministic=snake_case_				)




class    lowercase_    (     nn.Module		):
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		0.0
							_lowerCamelCase						=		False
							_lowerCamelCase						=		jnp.floataa
							_lowerCamelCase						=		False



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



							def __call__(						self      ,				lowercase_      ,				lowercase_      ,				lowercase_=True				):
													_snake_case						:     List[str]				=    hidden_states
													if self.only_cross_attention:
																			_snake_case						:     Optional[int]				=    self.attna(self.norma(snake_case_				)      ,				snake_case_      ,				deterministic=snake_case_				)
													else:
																			_snake_case						:     Union[str, Any]				=    self.attna(self.norma(snake_case_				)      ,				deterministic=snake_case_				)
													_snake_case						:     Dict				=    hidden_states + residual

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

													# feed forward
													_snake_case						:     Dict				=    hidden_states
													_snake_case						:     Tuple				=    self.ff(self.norma(snake_case_				)      ,				deterministic=snake_case_				)
													_snake_case						:     Optional[Any]				=    hidden_states + residual

													return self.dropout_layer(snake_case_      ,				deterministic=snake_case_				)




class    lowercase_    (     nn.Module		):
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		1
							_lowerCamelCase						=		0.0
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		jnp.floataa
							_lowerCamelCase						=		False



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    nn.GroupNorm(num_groups=32      ,				epsilon=1e-5				)

													_snake_case						:     Dict				=    self.n_heads * self.d_head
													if self.use_linear_projection:
																			_snake_case						:     List[str]				=    nn.Dense(snake_case_      ,				dtype=self.dtype				)
													else:
																			_snake_case						:     List[Any]				=    nn.Conv(
																			    snake_case_      ,				kernel_size=(1, 1)      ,				strides=(1, 1)      ,				padding="VALID"      ,				dtype=self.dtype      ,				)

													_snake_case						:     int				=    [
													    FlaxBasicTransformerBlock(
													        snake_case_      ,				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						:     int				=    nn.Dense(snake_case_      ,				dtype=self.dtype				)
													else:
																			_snake_case						:     str				=    nn.Conv(
																			    snake_case_      ,				kernel_size=(1, 1)      ,				strides=(1, 1)      ,				padding="VALID"      ,				dtype=self.dtype      ,				)

													_snake_case						:     Union[str, Any]				=    nn.Dropout(rate=self.dropout				)



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

													for transformer_block in self.transformer_blocks:
																			_snake_case						:     List[Any]				=    transformer_block(snake_case_      ,				snake_case_      ,				deterministic=snake_case_				)

													if self.use_linear_projection:
																			_snake_case						:     int				=    self.proj_out(snake_case_				)
																			_snake_case						:     Optional[int]				=    hidden_states.reshape(snake_case_      ,				snake_case_      ,				snake_case_      ,				snake_case_				)
													else:
																			_snake_case						:     Optional[Any]				=    hidden_states.reshape(snake_case_      ,				snake_case_      ,				snake_case_      ,				snake_case_				)
																			_snake_case						:     List[str]				=    self.proj_out(snake_case_				)

													_snake_case						:     Any				=    hidden_states + residual
													return self.dropout_layer(snake_case_      ,				deterministic=snake_case_				)




class    lowercase_    (     nn.Module		):
							_lowerCamelCase						=		42
							_lowerCamelCase						=		0.0
							_lowerCamelCase						=		jnp.floataa



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    FlaxGEGLU(self.dim      ,				self.dropout      ,				self.dtype				)
													_snake_case						:     Union[str, Any]				=    nn.Dense(self.dim      ,				dtype=self.dtype				)



							def __call__(						self      ,				lowercase_      ,				lowercase_=True				):
													_snake_case						:     Optional[Any]				=    self.net_a(snake_case_      ,				deterministic=snake_case_				)
													_snake_case						:     int				=    self.net_a(snake_case_				)
													return hidden_states




class    lowercase_    (     nn.Module		):
							_lowerCamelCase						=		42
							_lowerCamelCase						=		0.0
							_lowerCamelCase						=		jnp.floataa



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.dim * 4
													_snake_case						:     Dict				=    nn.Dense(inner_dim * 2      ,				dtype=self.dtype				)
													_snake_case						:     List[Any]				=    nn.Dropout(rate=self.dropout				)



							def __call__(						self      ,				lowercase_      ,				lowercase_=True				):
													_snake_case						:     Optional[Any]				=    self.proj(snake_case_				)
													_snake_case						:     int				=    jnp.split(snake_case_      ,				2      ,				axis=2				)
													return self.dropout_layer(hidden_linear * nn.gelu(snake_case_				)      ,				deterministic=snake_case_				)
'''simple docstring'''


def 					_lowerCamelCase    (      lowerCamelCase_					:							str   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      str(lowerCamelCase_   ).strip()
     if not oct_string:
          raise ValueError('Empty string was passed to the function'   )
     UpperCAmelCase_							:   Tuple   =      oct_string[0] == '-'
     if is_negative:
          UpperCAmelCase_							:   Tuple   =      oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_   ) <= 7 for char in oct_string   ):
          raise ValueError('Non-octal value was passed to the function'   )
     UpperCAmelCase_							:   str   =      0
     for char in oct_string:
          UpperCAmelCase_							:   List[Any]   =      8 * decimal_number + int(lowerCamelCase_   )
     if is_negative:
          UpperCAmelCase_							:   List[Any]   =      -decimal_number
     return decimal_number


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

 testmod()






