






'''simple docstring'''
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			):




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Dict           =     FlaxStableDiffusionPipeline.from_pretrained(
           "stabilityai/stable-diffusion-2"  ,     revision="bf16"  ,     dtype=jnp.bfloataa  ,     )

       lowercase_	:    int           =     "A painting of a squirrel eating a burger"
       lowercase_	:    Optional[Any]           =     jax.device_count()
       lowercase_	:    Tuple           =     num_samples * [prompt]
       lowercase_	:    Dict           =     sd_pipe.prepare_inputs(a			)

       lowercase_	:    List[Any]           =     replicate(a			)
       lowercase_	:    str           =     shard(a			)

       lowercase_	:    Any           =     jax.random.PRNGKey(0			)
       lowercase_	:    Any           =     jax.random.split(a  ,     jax.device_count()			)

       lowercase_	:    Tuple           =     sd_pipe(a  ,     a  ,     a  ,     num_inference_steps=2_5  ,     jit=a			)[0]
       assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

       lowercase_	:    Optional[Any]           =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
       lowercase_	:    Optional[int]           =     images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       lowercase_	:    List[str]           =     jnp.asarray(jax.device_get(image_slice.flatten()			)			)
       lowercase_	:    Dict           =     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]			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     "stabilityai/stable-diffusion-2"
       lowercase_    ,		lowercase_	:    Optional[int]           =     FlaxDPMSolverMultistepScheduler.from_pretrained(a  ,     subfolder="scheduler"			)
       lowercase_    ,		lowercase_	:    List[str]           =     FlaxStableDiffusionPipeline.from_pretrained(
           a  ,     scheduler=a  ,     revision="bf16"  ,     dtype=jnp.bfloataa  ,     )
       lowercase_	:    Union[str, Any]           =     scheduler_params

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

       lowercase_	:    int           =     replicate(a			)
       lowercase_	:    int           =     shard(a			)

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

       lowercase_	:    List[str]           =     sd_pipe(a  ,     a  ,     a  ,     num_inference_steps=2_5  ,     jit=a			)[0]
       assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

       lowercase_	:    int           =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
       lowercase_	:    Optional[Any]           =     images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       lowercase_	:    Optional[int]           =     jnp.asarray(jax.device_get(image_slice.flatten()			)			)
       lowercase_	:    Tuple           =     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












'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




