



"""simple docstring"""


import functools
import math

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

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_ = 4096		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					key.shape[-3:]
						UpperCAmelCase			 =					value.shape[-1]
						UpperCAmelCase			 =					min(lowercase_						,      lowercase_		)
						UpperCAmelCase			 =					query / jnp.sqrt(lowercase_		)

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

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

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

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

						def chunk_scanner(lowercase_		):
												# julienne key array
												UpperCAmelCase			 =					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
												UpperCAmelCase			 =					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_		)

						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					jax.lax.map(f=lowercase_						,      xs=jnp.arange(0						,      lowercase_						,      lowercase_		)		)

						UpperCAmelCase			 =					jnp.max(lowercase_						,      axis=0						,      keepdims=lowercase_		)
						UpperCAmelCase			 =					jnp.exp(chunk_max - global_max		)

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

						UpperCAmelCase			 =					chunk_values.sum(axis=0		)
						UpperCAmelCase			 =					jnp.expand_dims(lowercase_						,      -1		).sum(axis=0		)

						return all_values / all_weights

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_=jax.lax.Precision.HIGHEST						,      lowercase_ = 1024						,      lowercase_ = 4096		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					query.shape[-3:]

						def chunk_scanner(lowercase_						,      lowercase_		):
												# julienne query array
												UpperCAmelCase			 =					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_		),
												)

						UpperCAmelCase ,       UpperCAmelCase			 =					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  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				8
						__UpperCamelCase													=				64
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Any   )							->     Dict:
												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       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:str   )							->     str:
												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       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Any   )							->     Optional[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					:Optional[int]  ,    lowercase_					:int  ,    lowercase_					:Tuple=None  ,    lowercase_					:List[Any]=True   )							->     Optional[int]:
												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=40_96 * 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"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Tuple:
												# 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					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Tuple=True   )							->     Tuple:
												# 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"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				1
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:int   )							->     Dict:
												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					:List[Any]  ,    lowercase_					:int  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[int]=True   )							->     int:
												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"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     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					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[Any]=True   )							->     Any:
												UpperCAmelCase			 =					self.net_a(lowercase_  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					self.net_a(lowercase_   )
												return hidden_states







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												UpperCAmelCase			 =					self.dim * 4
												UpperCAmelCase			 =					nn.Dense(inner_dim * 2  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def __call__(   self					:str  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Any=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_   )









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

_A			,	_A									=		os.path.split(__file__)
_A									=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=func(SCREAMING_SNAKE_CASE__							)

     with open(SCREAMING_SNAKE_CASE__     ,	'wb'							) as f:
          f.write(json.dumps(SCREAMING_SNAKE_CASE__							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()





