


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase_      : Tuple  					=       5_0000
UpperCAmelCase_      : str  					=       5000

UpperCAmelCase_					,   UpperCAmelCase_      : List[Any]  					=       os.path.split(__file__)
UpperCAmelCase_      : Any  					=       os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))







@get_duration
def        SCREAMING_SNAKE_CASE_						(			__A :			datasets.Dataset  ,    __A :			Any				)	->		Optional[Any]:

       """simple docstring"""

       for i in range(__A				):
              a_					:		List[Any]							=  dataset[i]







@get_duration
def        SCREAMING_SNAKE_CASE_						(			__A :			datasets.Dataset  ,    __A :			Tuple  ,    __A :			List[Any]				)	->		str:

       """simple docstring"""

       for i in range(0  ,    len(__A				)  ,    __A				):
              a_					:		Tuple							=  dataset[i : i + batch_size]







@get_duration
def        SCREAMING_SNAKE_CASE_						(			__A :			datasets.Dataset  ,    __A :			int  ,    __A :			Optional[Any]				)	->		Optional[int]:

       """simple docstring"""

       with dataset.formatted_as(type=__A				):
              for i in range(__A				):
                     a_					:		int							=  dataset[i]







@get_duration
def        SCREAMING_SNAKE_CASE_						(			__A :			datasets.Dataset  ,    __A :			Tuple  ,    __A :			Any  ,    __A :			Optional[Any]				)	->		int:

       """simple docstring"""

       with dataset.formatted_as(type=__A				):
              for i in range(0  ,    __A  ,    __A				):
                     a_					:		str							=  dataset[i : i + batch_size]







def        SCREAMING_SNAKE_CASE_						(			)	->		List[str]:

       """simple docstring"""

       a_					:		Any							=  {'num examples': SPEED_TEST_N_EXAMPLES}
       a_					:		Optional[int]							=  [
           (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}),
       ]

       a_					:		Dict							=  [
           (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'				)
              a_					:		Optional[int]							=  datasets.Features(
                  {'list': datasets.Sequence(datasets.Value('float32'				)				), 'numbers': datasets.Value('float32'				)}				)
              a_					:		str							=  generate_example_dataset(
                  os.path.join(__A  ,    'dataset.arrow'				)  ,    __A  ,    num_examples=__A  ,    seq_shapes={'list': (1_00,)}  ,    )
              print('first set of iterations'				)
              for func, kwargs in functions:
                     print(func.__name__  ,    str(__A				)				)
                     a_					:		Any							=  func(__A  ,    **__A				)

              print('shuffling dataset'				)
              a_					:		int							=  dataset.shuffle()
              print('Second set of iterations (after shuffling'				)
              for func, kwargs in functions_shuffled:
                     print('shuffled '  ,    func.__name__  ,    str(__A				)				)
                     a_					:		Any							=  func(
                         __A  ,    **__A				)

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


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






import math

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







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


