
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
							import jax
							import jax.numpy as jnp
							from flax.jax_utils import replicate
							from flax.training.common_utils import shard

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






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    Dict ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_,				UpperCamelCase_:							Union[str, Any]		=  FlaxStableDiffusionPipeline.from_pretrained(
							    """stabilityai/stable-diffusion-2"""  ,		revision="""bf16"""  ,		dtype=jnp.bfloataa  ,		)

							UpperCamelCase_:							Union[str, Any]		=  """A painting of a squirrel eating a burger"""
							UpperCamelCase_:							Optional[int]		=  jax.device_count()
							UpperCamelCase_:							Optional[Any]		=  num_samples * [prompt]
							UpperCamelCase_:							Tuple		=  sd_pipe.prepare_inputs(snake_case_ )

							UpperCamelCase_:							str		=  replicate(snake_case_ )
							UpperCamelCase_:							Optional[int]		=  shard(snake_case_ )

							UpperCamelCase_:							Union[str, Any]		=  jax.random.PRNGKey(0 )
							UpperCamelCase_:							Union[str, Any]		=  jax.random.split(snake_case_  ,		jax.device_count() )

							UpperCamelCase_:							str		=  sd_pipe(snake_case_  ,		snake_case_  ,		snake_case_  ,		num_inference_steps=25  ,		jit=snake_case_ )[0]
							assert images.shape == (jax.device_count(), 1, 768, 768, 3)

							UpperCamelCase_:							List[Any]		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
							UpperCamelCase_:							Optional[int]		=  images[0, 253:256, 253:256, -1]

							UpperCamelCase_:							Tuple		=  jnp.asarray(jax.device_get(image_slice.flatten() ) )
							UpperCamelCase_:							Union[str, Any]		=  jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512] )
							print(f'''output_slice: {output_slice}''' )
							assert jnp.abs(output_slice - expected_slice ).max() < 1e-2




			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Dict		=  """stabilityai/stable-diffusion-2"""
							UpperCamelCase_,				UpperCamelCase_:							int		=  FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case_  ,		subfolder="""scheduler""" )
							UpperCamelCase_,				UpperCamelCase_:							Any		=  FlaxStableDiffusionPipeline.from_pretrained(
							    snake_case_  ,		scheduler=snake_case_  ,		revision="""bf16"""  ,		dtype=jnp.bfloataa  ,		)
							UpperCamelCase_:							Optional[int]		=  scheduler_params

							UpperCamelCase_:							List[str]		=  """A painting of a squirrel eating a burger"""
							UpperCamelCase_:							str		=  jax.device_count()
							UpperCamelCase_:							Optional[int]		=  num_samples * [prompt]
							UpperCamelCase_:							Optional[Any]		=  sd_pipe.prepare_inputs(snake_case_ )

							UpperCamelCase_:							Union[str, Any]		=  replicate(snake_case_ )
							UpperCamelCase_:							Optional[int]		=  shard(snake_case_ )

							UpperCamelCase_:							List[Any]		=  jax.random.PRNGKey(0 )
							UpperCamelCase_:							Optional[int]		=  jax.random.split(snake_case_  ,		jax.device_count() )

							UpperCamelCase_:							Union[str, Any]		=  sd_pipe(snake_case_  ,		snake_case_  ,		snake_case_  ,		num_inference_steps=25  ,		jit=snake_case_ )[0]
							assert images.shape == (jax.device_count(), 1, 768, 768, 3)

							UpperCamelCase_:							Union[str, Any]		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
							UpperCamelCase_:							str		=  images[0, 253:256, 253:256, -1]

							UpperCamelCase_:							List[Any]		=  jnp.asarray(jax.device_get(image_slice.flatten() ) )
							UpperCamelCase_:							int		=  jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297] )
							print(f'''output_slice: {output_slice}''' )
							assert jnp.abs(output_slice - expected_slice ).max() < 1e-2








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_  :	Any 				=					5_00_00
lowerCamelCase_  :	Any 				=					50_00

lowerCamelCase_				,    lowerCamelCase_  :	Tuple 				=					os.path.split(__file__)
lowerCamelCase_  :	int 				=					os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(0       ,      len(lowerCamelCase  )       ,      lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  dataset[i : i + batch_size]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							List[str]		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(0       ,      lowerCamelCase       ,      lowerCamelCase  ):
												UpperCamelCase_:							Union[str, Any]		=  dataset[i : i + batch_size]







def     A__   (  )					->		Tuple:
				UpperCamelCase_:							int		=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:							Union[str, 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}),
				]

				UpperCamelCase_:							Tuple		=  [
				    (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"""  )
								UpperCamelCase_:							int		=  datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
								UpperCamelCase_:							Optional[int]		=  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  )  )
												UpperCamelCase_:							List[Any]		=  func(lowerCamelCase       ,      **lowerCamelCase  )

								print("""shuffling dataset"""  )
								UpperCamelCase_:							Dict		=  dataset.shuffle()
								print("""Second set of iterations (after shuffling"""  )
								for func, kwargs in functions_shuffled:
												print("""shuffled """       ,      func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							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()






