






import os
import tempfile
import unittest

import numpy as np

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


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

					from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class     lowerCamelCase_					(   unittest.TestCase			):







				def       A						(    self  ):
								"""simple docstring"""
								with tempfile.TemporaryDirectory() as tmpdirname:
												# pipeline has Flax weights
												__magic_name__		:Optional[Any]   =   FlaxDiffusionPipeline.from_pretrained(
												    '''hf-internal-testing/tiny-stable-diffusion-pipe'''							,       safety_checker=__lowerCAmelCase							,       cache_dir=__lowerCAmelCase  )

												__magic_name__		:Dict   =   [t[-1] for t in os.walk(os.path.join(__lowerCAmelCase							,       os.listdir(__lowerCAmelCase  )[0]							,       '''snapshots'''  )  )]
												__magic_name__		:Tuple   =   [item for sublist in all_root_files for item in sublist]

												# None of the downloaded files should be a PyTorch file even if we have some here:
												# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
												assert not any(f.endswith('''.bin'''  ) for f in files  )







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







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:Tuple   =   FlaxStableDiffusionPipeline.from_pretrained(
								    '''hf-internal-testing/tiny-stable-diffusion-pipe'''							,       safety_checker=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   (
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								__magic_name__		:int   =   jax.random.PRNGKey(0  )
								__magic_name__		:Any   =   4

								__magic_name__		:str   =   jax.device_count()
								__magic_name__		:List[str]   =   num_samples * [prompt]
								__magic_name__		:str   =   pipeline.prepare_inputs(__lowerCAmelCase  )

								# shard inputs and rng
								__magic_name__		:List[Any]   =   replicate(__lowerCAmelCase  )
								__magic_name__		:int   =   jax.random.split(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:int   =   shard(__lowerCAmelCase  )

								__magic_name__		:Any   =   pipeline(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       jit=__lowerCAmelCase  ).images

								assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
								if jax.device_count() == 8:
												assert np.abs(np.abs(images[0, 0, :2, :2, -2:]							,       dtype=np.floataa  ).sum() - 4.1514745  ) < 1E-3
												assert np.abs(np.abs(__lowerCAmelCase							,       dtype=np.floataa  ).sum() - 49947.875  ) < 5E-1

								__magic_name__		:List[Any]   =   pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]  )  )  )
								assert len(__lowerCAmelCase  ) == num_samples







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:str   =   FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''							,       revision='''flax'''							,       safety_checker=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   (
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								__magic_name__		:List[str]   =   jax.random.PRNGKey(0  )
								__magic_name__		:str   =   5_0

								__magic_name__		:Tuple   =   jax.device_count()
								__magic_name__		:Any   =   num_samples * [prompt]
								__magic_name__		:Dict   =   pipeline.prepare_inputs(__lowerCAmelCase  )

								# shard inputs and rng
								__magic_name__		:List[Any]   =   replicate(__lowerCAmelCase  )
								__magic_name__		:List[str]   =   jax.random.split(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   shard(__lowerCAmelCase  )

								__magic_name__		:Optional[int]   =   pipeline(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       jit=__lowerCAmelCase  ).images

								assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
								if jax.device_count() == 8:
												assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,       dtype=np.floataa  ).sum() - 0.05652401)  ) < 1E-3
												assert np.abs((np.abs(__lowerCAmelCase							,       dtype=np.floataa  ).sum() - 2383808.2)  ) < 5E-1







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:Union[str, Any]   =   FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''							,       revision='''bf16'''							,       dtype=jnp.bfloataa							,       safety_checker=__lowerCAmelCase  )

								__magic_name__		:List[Any]   =   (
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								__magic_name__		:Tuple   =   jax.random.PRNGKey(0  )
								__magic_name__		:str   =   5_0

								__magic_name__		:Optional[Any]   =   jax.device_count()
								__magic_name__		:List[Any]   =   num_samples * [prompt]
								__magic_name__		:List[Any]   =   pipeline.prepare_inputs(__lowerCAmelCase  )

								# shard inputs and rng
								__magic_name__		:Dict   =   replicate(__lowerCAmelCase  )
								__magic_name__		:Tuple   =   jax.random.split(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   shard(__lowerCAmelCase  )

								__magic_name__		:List[str]   =   pipeline(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       jit=__lowerCAmelCase  ).images

								assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
								if jax.device_count() == 8:
												assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,       dtype=np.floataa  ).sum() - 0.04003906)  ) < 1E-3
												assert np.abs((np.abs(__lowerCAmelCase							,       dtype=np.floataa  ).sum() - 2373516.75)  ) < 5E-1







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:Dict   =   FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''							,       revision='''bf16'''							,       dtype=jnp.bfloataa  )

								__magic_name__		:Union[str, Any]   =   (
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								__magic_name__		:Any   =   jax.random.PRNGKey(0  )
								__magic_name__		:Any   =   5_0

								__magic_name__		:Optional[Any]   =   jax.device_count()
								__magic_name__		:Optional[Any]   =   num_samples * [prompt]
								__magic_name__		:Optional[Any]   =   pipeline.prepare_inputs(__lowerCAmelCase  )

								# shard inputs and rng
								__magic_name__		:Tuple   =   replicate(__lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   jax.random.split(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:Tuple   =   shard(__lowerCAmelCase  )

								__magic_name__		:Tuple   =   pipeline(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       jit=__lowerCAmelCase  ).images

								assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
								if jax.device_count() == 8:
												assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,       dtype=np.floataa  ).sum() - 0.04003906)  ) < 1E-3
												assert np.abs((np.abs(__lowerCAmelCase							,       dtype=np.floataa  ).sum() - 2373516.75)  ) < 5E-1







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   FlaxDDIMScheduler(
								    beta_start=0.00085							,       beta_end=0.012							,       beta_schedule='''scaled_linear'''							,       set_alpha_to_one=__lowerCAmelCase							,       steps_offset=1							,       )

								__magic_name__  ,					__magic_name__		:Optional[Any]   =   FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''							,       revision='''bf16'''							,       dtype=jnp.bfloataa							,       scheduler=__lowerCAmelCase							,       safety_checker=__lowerCAmelCase							,       )
								__magic_name__		:Any   =   scheduler.create_state()

								__magic_name__		:Optional[int]   =   scheduler_state

								__magic_name__		:int   =   (
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								__magic_name__		:Tuple   =   jax.random.PRNGKey(0  )
								__magic_name__		:Any   =   5_0

								__magic_name__		:Dict   =   jax.device_count()
								__magic_name__		:List[Any]   =   num_samples * [prompt]
								__magic_name__		:Optional[int]   =   pipeline.prepare_inputs(__lowerCAmelCase  )

								# shard inputs and rng
								__magic_name__		:List[str]   =   replicate(__lowerCAmelCase  )
								__magic_name__		:Tuple   =   jax.random.split(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:int   =   shard(__lowerCAmelCase  )

								__magic_name__		:Tuple   =   pipeline(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       jit=__lowerCAmelCase  ).images

								assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
								if jax.device_count() == 8:
												assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,       dtype=np.floataa  ).sum() - 0.045043945)  ) < 1E-3
												assert np.abs((np.abs(__lowerCAmelCase							,       dtype=np.floataa  ).sum() - 2347693.5)  ) < 5E-1







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   (
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								__magic_name__		:str   =   jax.device_count()
								__magic_name__		:str   =   num_samples * [prompt]
								__magic_name__		:Dict   =   jax.random.split(jax.random.PRNGKey(0  )							,       __lowerCAmelCase  )

								__magic_name__  ,					__magic_name__		:Union[str, Any]   =   FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''							,       revision='''bf16'''							,       dtype=jnp.bfloataa							,       safety_checker=__lowerCAmelCase							,       )

								__magic_name__		:Any   =   replicate(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   pipeline.prepare_inputs(__lowerCAmelCase  )
								__magic_name__		:Any   =   shard(__lowerCAmelCase  )
								__magic_name__		:int   =   pipeline(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       jit=__lowerCAmelCase  ).images
								assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
								__magic_name__		:List[Any]   =   images[2, 0, 2_5_6, 1_0:1_7, 1]

								# With memory efficient attention
								__magic_name__  ,					__magic_name__		:Any   =   FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''							,       revision='''bf16'''							,       dtype=jnp.bfloataa							,       safety_checker=__lowerCAmelCase							,       use_memory_efficient_attention=__lowerCAmelCase							,       )

								__magic_name__		:List[Any]   =   replicate(__lowerCAmelCase  )
								__magic_name__		:Any   =   pipeline.prepare_inputs(__lowerCAmelCase  )
								__magic_name__		:Any   =   shard(__lowerCAmelCase  )
								__magic_name__		:Any   =   pipeline(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       jit=__lowerCAmelCase  ).images
								assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
								__magic_name__		:List[Any]   =   images[2, 0, 2_5_6, 1_0:1_7, 1]

								# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
								# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
								assert abs(slice_eff - slice  ).max() < 1E-2












'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

