





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__ = 4096)  ->       Optional[int]:
  __snake_case:		List[str]					    =		key.shape[-3:]
  __snake_case:		List[str]					    =		value.shape[-1]
  __snake_case:		str					    =		min(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  __snake_case:		Union[str, Any]					    =		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__):
    __snake_case:		Optional[Any]					    =		jnp.einsum("""...qhd,...khd->...qhk"""   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   precision=SCREAMING_SNAKE_CASE__)

    __snake_case:		Any					    =		jnp.max(SCREAMING_SNAKE_CASE__   ,   axis=-1   ,   keepdims=SCREAMING_SNAKE_CASE__)
    __snake_case:		Union[str, Any]					    =		jax.lax.stop_gradient(SCREAMING_SNAKE_CASE__)
    __snake_case:		str					    =		jnp.exp(attn_weights - max_score)

    __snake_case:		Any					    =		jnp.einsum("""...vhf,...qhv->...qhf"""   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   precision=SCREAMING_SNAKE_CASE__)
    __snake_case:		Any					    =		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
    __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:		Union[str, 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:		str					    =		jax.lax.map(f=SCREAMING_SNAKE_CASE__   ,   xs=jnp.arange(0   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))

  __snake_case:		Union[str, Any]					    =		jnp.max(SCREAMING_SNAKE_CASE__   ,   axis=0   ,   keepdims=SCREAMING_SNAKE_CASE__)
  __snake_case:		Union[str, Any]					    =		jnp.exp(chunk_max - global_max)

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

  __snake_case:		Union[str, Any]					    =		chunk_values.sum(axis=0)
  __snake_case:		Dict					    =		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__ = 1024   ,   SCREAMING_SNAKE_CASE__ = 4096)  ->       str:
  __snake_case:		Union[str, Any]					    =		query.shape[-3:]

  def chunk_scanner(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__):
    # julienne query array
    __snake_case:		Dict					    =		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:		Optional[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        __snake_case			(				nn.Module     ):





       '''simple docstring'''







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




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Tuple					    =		self.dim_head * self.heads
         __snake_case:		Optional[Any]					    =		self.dim_head**-0.5

         # Weights were exported with old names {to_q, to_k, to_v, to_out}
         __snake_case:		Union[str, Any]					    =		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:		Union[str, Any]					    =		nn.Dense(A				,							use_bias=A				,							dtype=self.dtype				,							name="""to_v"""						)

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




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




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       List[str]						):
         __snake_case:		Optional[Any]					    =		tensor.shape
         __snake_case:		List[Any]					    =		self.heads
         __snake_case:		List[Any]					    =		tensor.reshape(batch_size // head_size				,							A				,							A				,							A						)
         __snake_case:		Any					    =		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					:       str				,							A					:       Dict				,							A					:       List[Any]=None				,							A					:       Optional[Any]=True						):
         __snake_case:		Any					    =		hidden_states if context is None else context

         __snake_case:		Union[str, Any]					    =		self.query(A						)
         __snake_case:		Tuple					    =		self.key(A						)
         __snake_case:		Optional[int]					    =		self.value(A						)

         __snake_case:		Tuple					    =		self.reshape_heads_to_batch_dim(A						)
         __snake_case:		Dict					    =		self.reshape_heads_to_batch_dim(A						)
         __snake_case:		Dict					    =		self.reshape_heads_to_batch_dim(A						)

         if self.use_memory_efficient_attention:
           __snake_case:		Dict					    =		query_states.transpose(1				,							0				,							2						)
           __snake_case:		int					    =		key_states.transpose(1				,							0				,							2						)
           __snake_case:		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

           __snake_case:		Dict					    =		query_states.shape[-3]
           if flatten_latent_dim % 64 == 0:
             __snake_case:		str					    =		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:		Union[str, Any]					    =		int(flatten_latent_dim / 4						)
           else:
             __snake_case:		Optional[int]					    =		int(A						)

           __snake_case:		List[Any]					    =		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[int]					    =		jnp.einsum("""b i d, b j d->b i j"""				,							A				,							A						)
           __snake_case:		Optional[Any]					    =		attention_scores * self.scale
           __snake_case:		Optional[int]					    =		nn.softmax(A				,							axis=2						)

           # attend to values
           __snake_case:		Any					    =		jnp.einsum("""b i j, b j d -> b i d"""				,							A				,							A						)

         __snake_case:		Dict					    =		self.reshape_batch_dim_to_heads(A						)
         __snake_case:		List[str]					    =		self.proj_attn(A						)
         return self.dropout_layer(A				,							deterministic=A						)







class        __snake_case			(				nn.Module     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	42
       lowerCAmelCase__ 						=	42
       lowerCAmelCase__ 						=	42
       lowerCAmelCase__ 						=	0.0
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	jnp.floataa
       lowerCAmelCase__ 						=	False




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






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

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

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

         return self.dropout_layer(A				,							deterministic=A						)







class        __snake_case			(				nn.Module     ):





       '''simple docstring'''







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




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		Dict					    =		nn.GroupNorm(num_groups=32				,							epsilon=1E-5						)

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

         __snake_case:		Tuple					    =		[
             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:		Any					    =		nn.Dense(A				,							dtype=self.dtype						)
         else:
           __snake_case:		Dict					    =		nn.Conv(
               A				,							kernel_size=(1, 1)				,							strides=(1, 1)				,							padding="""VALID"""				,							dtype=self.dtype				,							)

         __snake_case:		List[str]					    =		nn.Dropout(rate=self.dropout						)






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

         for transformer_block in self.transformer_blocks:
           __snake_case:		Optional[Any]					    =		transformer_block(A				,							A				,							deterministic=A						)

         if self.use_linear_projection:
           __snake_case:		Optional[int]					    =		self.proj_out(A						)
           __snake_case:		int					    =		hidden_states.reshape(A				,							A				,							A				,							A						)
         else:
           __snake_case:		Dict					    =		hidden_states.reshape(A				,							A				,							A				,							A						)
           __snake_case:		List[Any]					    =		self.proj_out(A						)

         __snake_case:		int					    =		hidden_states + residual
         return self.dropout_layer(A				,							deterministic=A						)







class        __snake_case			(				nn.Module     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	42
       lowerCAmelCase__ 						=	0.0
       lowerCAmelCase__ 						=	jnp.floataa




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






       def __call__(    self					:       Union[str, Any]				,							A					:       List[Any]				,							A					:       Dict=True						):
         __snake_case:		Dict					    =		self.net_a(A				,							deterministic=A						)
         __snake_case:		Tuple					    =		self.net_a(A						)
         return hidden_states







class        __snake_case			(				nn.Module     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	42
       lowerCAmelCase__ 						=	0.0
       lowerCAmelCase__ 						=	jnp.floataa




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Tuple					    =		self.dim * 4
         __snake_case:		Optional[int]					    =		nn.Dense(inner_dim * 2				,							dtype=self.dtype						)
         __snake_case:		Tuple					    =		nn.Dropout(rate=self.dropout						)






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








import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    copy.deepcopy(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[Any] 		=    sorted(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    [bisect.bisect_right(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							) for y in x]
			return quantized




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    np.arange(start=3     ,						stop=SCREAMING_SNAKE_CASE     ,						step=4							).tolist() if k > 0 else [10]
			UpperCAmelCase__		:List[Any] 		=    _quantize(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
			# count number of elements per group
			UpperCAmelCase__		:Dict 		=    np.unique(SCREAMING_SNAKE_CASE     ,						return_counts=SCREAMING_SNAKE_CASE							)[1]
			UpperCAmelCase__		:Any 		=    [0] + bins + [np.inf]
			logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE							)							)
			logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE							)							)
			return groups




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A  , A  , A	)  ->Union[str, Any]:
									if not isinstance(A  , A	):
												raise ValueError(
												    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(A	)	)
									UpperCAmelCase__		:Tuple 		=    sampler
									UpperCAmelCase__		:Tuple 		=    group_ids
									UpperCAmelCase__		:str 		=    batch_size


						def __iter__(  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)

									UpperCAmelCase__		:Any 		=    0
									for idx in self.sampler:
												UpperCAmelCase__		:List[Any] 		=    self.group_ids[idx]
												buffer_per_group[group_id].append(A	)
												samples_per_group[group_id].append(A	)
												if len(buffer_per_group[group_id]	) == self.batch_size:
															yield buffer_per_group[group_id]  # TODO
															num_batches += 1
															del buffer_per_group[group_id]
												assert len(buffer_per_group[group_id]	) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									UpperCAmelCase__		:Union[str, Any] 		=    len(self	)
									UpperCAmelCase__		:Tuple 		=    expected_num_batches - num_batches
									if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												UpperCAmelCase__		:str 		=    []
												for group_id, idxs in sorted(buffer_per_group.items()  , key=lambda A	: x[0]	):
															batch_idx.extend(A	)
															if len(A	) >= self.batch_size:
																		yield batch_idx[: self.batch_size]
																		UpperCAmelCase__		:int 		=    batch_idx[self.batch_size :]
																		num_remaining -= 1
												if len(A	) > 0:
															yield batch_idx
															num_remaining -= 1
									assert num_remaining == 0


						def __len__(  self	)  ->Union[str, Any]:
									return (len(self.sampler	) + self.batch_size - 1) // self.batch_size





