


"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					True
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					List[Any]   =		[]
      _lowerCamelCase	:					List[Any]   =		[]

      for i in range(self.num_layers):
         _lowerCamelCase	:					Dict   =		self.in_channels if i == 0 else self.out_channels

         _lowerCamelCase	:					Optional[Any]   =		FlaxResnetBlockaD(
             in_channels=SCREAMING_SNAKE_CASE      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					str   =		FlaxTransformeraDModel(
             in_channels=self.out_channels      ,   n_heads=self.num_attention_heads      ,   d_head=self.out_channels // self.num_attention_heads      ,   depth=1      ,   use_linear_projection=self.use_linear_projection      ,   only_cross_attention=self.only_cross_attention      ,   use_memory_efficient_attention=self.use_memory_efficient_attention      ,   dtype=self.dtype      ,   )
         attentions.append(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		resnets
      _lowerCamelCase	:					Tuple   =		attentions

      if self.add_downsample:
         _lowerCamelCase	:					int   =		FlaxDownsampleaD(self.out_channels      ,   dtype=self.dtype)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  Optional[int]:
      _lowerCamelCase	:					Tuple   =		()

      for resnet, attn in zip(self.resnets      ,   self.attentions):
         _lowerCamelCase	:					List[str]   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Any   =		attn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         output_states += (hidden_states,)

      if self.add_downsample:
         _lowerCamelCase	:					int   =		self.downsamplers_a(SCREAMING_SNAKE_CASE)
         output_states += (hidden_states,)

      return hidden_states, output_states







class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					True
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[int]   =		[]

      for i in range(self.num_layers):
         _lowerCamelCase	:					Tuple   =		self.in_channels if i == 0 else self.out_channels

         _lowerCamelCase	:					Optional[Any]   =		FlaxResnetBlockaD(
             in_channels=SCREAMING_SNAKE_CASE      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		resnets

      if self.add_downsample:
         _lowerCamelCase	:					Optional[int]   =		FlaxDownsampleaD(self.out_channels      ,   dtype=self.dtype)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  Tuple:
      _lowerCamelCase	:					Optional[int]   =		()

      for resnet in self.resnets:
         _lowerCamelCase	:					Dict   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         output_states += (hidden_states,)

      if self.add_downsample:
         _lowerCamelCase	:					List[Any]   =		self.downsamplers_a(SCREAMING_SNAKE_CASE)
         output_states += (hidden_states,)

      return hidden_states, output_states







class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					True
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		[]
      _lowerCamelCase	:					List[str]   =		[]

      for i in range(self.num_layers):
         _lowerCamelCase	:					Union[str, Any]   =		self.in_channels if (i == self.num_layers - 1) else self.out_channels
         _lowerCamelCase	:					Optional[Any]   =		self.prev_output_channel if i == 0 else self.out_channels

         _lowerCamelCase	:					Optional[int]   =		FlaxResnetBlockaD(
             in_channels=resnet_in_channels + res_skip_channels      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Tuple   =		FlaxTransformeraDModel(
             in_channels=self.out_channels      ,   n_heads=self.num_attention_heads      ,   d_head=self.out_channels // self.num_attention_heads      ,   depth=1      ,   use_linear_projection=self.use_linear_projection      ,   only_cross_attention=self.only_cross_attention      ,   use_memory_efficient_attention=self.use_memory_efficient_attention      ,   dtype=self.dtype      ,   )
         attentions.append(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		resnets
      _lowerCamelCase	:					Optional[Any]   =		attentions

      if self.add_upsample:
         _lowerCamelCase	:					List[Any]   =		FlaxUpsampleaD(self.out_channels      ,   dtype=self.dtype)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  int:
      for resnet, attn in zip(self.resnets      ,   self.attentions):
         # pop res hidden states
         _lowerCamelCase	:					Any   =		res_hidden_states_tuple[-1]
         _lowerCamelCase	:					Tuple   =		res_hidden_states_tuple[:-1]
         _lowerCamelCase	:					List[Any]   =		jnp.concatenate((hidden_states, res_hidden_states)      ,   axis=-1)

         _lowerCamelCase	:					str   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Any   =		attn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)

      if self.add_upsample:
         _lowerCamelCase	:					Any   =		self.upsamplers_a(SCREAMING_SNAKE_CASE)

      return hidden_states







class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					True
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					str   =		[]

      for i in range(self.num_layers):
         _lowerCamelCase	:					Union[str, Any]   =		self.in_channels if (i == self.num_layers - 1) else self.out_channels
         _lowerCamelCase	:					Optional[Any]   =		self.prev_output_channel if i == 0 else self.out_channels

         _lowerCamelCase	:					Dict   =		FlaxResnetBlockaD(
             in_channels=resnet_in_channels + res_skip_channels      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		resnets

      if self.add_upsample:
         _lowerCamelCase	:					Dict   =		FlaxUpsampleaD(self.out_channels      ,   dtype=self.dtype)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  int:
      for resnet in self.resnets:
         # pop res hidden states
         _lowerCamelCase	:					Union[str, Any]   =		res_hidden_states_tuple[-1]
         _lowerCamelCase	:					str   =		res_hidden_states_tuple[:-1]
         _lowerCamelCase	:					str   =		jnp.concatenate((hidden_states, res_hidden_states)      ,   axis=-1)

         _lowerCamelCase	:					int   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)

      if self.add_upsample:
         _lowerCamelCase	:					List[str]   =		self.upsamplers_a(SCREAMING_SNAKE_CASE)

      return hidden_states







class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      # there is always at least one resnet
      _lowerCamelCase	:					Optional[int]   =		[
          FlaxResnetBlockaD(
              in_channels=self.in_channels      ,   out_channels=self.in_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
      ]

      _lowerCamelCase	:					str   =		[]

      for _ in range(self.num_layers):
         _lowerCamelCase	:					Dict   =		FlaxTransformeraDModel(
             in_channels=self.in_channels      ,   n_heads=self.num_attention_heads      ,   d_head=self.in_channels // self.num_attention_heads      ,   depth=1      ,   use_linear_projection=self.use_linear_projection      ,   use_memory_efficient_attention=self.use_memory_efficient_attention      ,   dtype=self.dtype      ,   )
         attentions.append(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					List[Any]   =		FlaxResnetBlockaD(
             in_channels=self.in_channels      ,   out_channels=self.in_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		resnets
      _lowerCamelCase	:					List[str]   =		attentions





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		self.resnets[0](SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
      for attn, resnet in zip(self.attentions      ,   self.resnets[1:]):
         _lowerCamelCase	:					Dict   =		attn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)

      return hidden_states








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

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


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