
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCamelCase							=       5_00_00
__lowerCamelCase							=       50_00

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


@get_duration
def 			UpperCamelCase					(    __lowerCamelCase     : datasets.Dataset				,					__lowerCamelCase     : Tuple    ):
       for i in range(__lowerCamelCase    ):
              snake_case       :   Optional[int]							=      dataset[i]


@get_duration
def 			UpperCamelCase					(    __lowerCamelCase     : datasets.Dataset				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]    ):
       for i in range(0				,					len(__lowerCamelCase    )				,					__lowerCamelCase    ):
              snake_case       :   str							=      dataset[i : i + batch_size]


@get_duration
def 			UpperCamelCase					(    __lowerCamelCase     : datasets.Dataset				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]    ):
       with dataset.formatted_as(type=__lowerCamelCase    ):
              for i in range(__lowerCamelCase    ):
                     snake_case       :   Dict							=      dataset[i]


@get_duration
def 			UpperCamelCase					(    __lowerCamelCase     : datasets.Dataset				,					__lowerCamelCase     : List[Any]				,					__lowerCamelCase     : List[Any]				,					__lowerCamelCase     : str    ):
       with dataset.formatted_as(type=__lowerCamelCase    ):
              for i in range(0				,					__lowerCamelCase				,					__lowerCamelCase    ):
                     snake_case       :   int							=      dataset[i : i + batch_size]


def 			UpperCamelCase					(    ):
       snake_case       :   Optional[Any]							=      {"num examples": SPEED_TEST_N_EXAMPLES}
       snake_case       :   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": 100}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
           (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": 1000}),
       ]

       snake_case       :   List[str]							=      [
           (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": 100}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
           (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": 1000}),
       ]
       with tempfile.TemporaryDirectory() as tmp_dir:
              print("generating dataset"    )
              snake_case       :   List[Any]							=      datasets.Features(
                  {"list": datasets.Sequence(datasets.Value("float32"    )    ), "numbers": datasets.Value("float32"    )}    )
              snake_case       :   Dict							=      generate_example_dataset(
                  os.path.join(__lowerCamelCase				,					"dataset.arrow"    )				,					__lowerCamelCase				,					num_examples=__lowerCamelCase				,					seq_shapes={"list": (100,)}				,					)
              print("first set of iterations"    )
              for func, kwargs in functions:
                     print(func.__name__				,					str(__lowerCamelCase    )    )
                     snake_case       :   List[str]							=      func(__lowerCamelCase				,					**__lowerCamelCase    )

              print("shuffling dataset"    )
              snake_case       :   Union[str, Any]							=      dataset.shuffle()
              print("Second set of iterations (after shuffling"    )
              for func, kwargs in functions_shuffled:
                     print("shuffled "				,					func.__name__				,					str(__lowerCamelCase    )    )
                     snake_case       :   List[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()







import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str] )		->	List[str]:


          '''simple docstring'''


          return f"""gaussian_noise_s={seed}_shape={'_'.join([str(snake_case__ ) for s in shape] )}.npy"""



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          super().tearDown()
          gc.collect()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Optional[Any]=0    ,						snake_case__		:					Any=(4, 4, 64, 64)    ,						snake_case__		:					List[Any]=False )		->	int:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Optional[int]							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return image



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					List[Any]="CompVis/stable-diffusion-v1-4" )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   str							=      "bf16" if fpaa else None

          snake_case				,					snake_case       :   Optional[int]							=      FlaxUNetaDConditionModel.from_pretrained(
              snake_case__    ,						subfolder="unet"    ,						dtype=snake_case__    ,						revision=snake_case__ )
          return model, params



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Union[str, Any]=0    ,						snake_case__		:					Union[str, Any]=(4, 77, 7_68)    ,						snake_case__		:					Dict=False )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Any							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Any							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return hidden_states



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[str]							=      self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_latents(snake_case__    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_encoder_hidden_states(snake_case__    ,						fpaa=snake_case__ )

          snake_case       :   Dict							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   Optional[Any]							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Optional[int]							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					str    ,						snake_case__		:					Tuple )		->	str:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[Any]							=      self.get_unet_model(model_id="stabilityai/stable-diffusion-2"    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_latents(snake_case__    ,						shape=(4, 4, 96, 96)    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_encoder_hidden_states(snake_case__    ,						shape=(4, 77, 10_24)    ,						fpaa=snake_case__ )

          snake_case       :   Optional[int]							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   int							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Dict							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )





