





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 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
         # pipeline has Flax weights
         __A   :						List[str]									=					FlaxDiffusionPipeline.from_pretrained(
             '''hf-internal-testing/tiny-stable-diffusion-pipe''' ,						safety_checker=__lowercase ,						cache_dir=__lowercase  )

         __A   :						int									=					[t[-1] for t in os.walk(os.path.join(__lowercase ,						os.listdir(__lowercase  )[0] ,						'''snapshots'''  )  )]
         __A   :						Any									=					[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 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					FlaxStableDiffusionPipeline.from_pretrained(
         '''hf-internal-testing/tiny-stable-diffusion-pipe''' ,						safety_checker=__lowercase  )

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

     __A   :						Any									=					jax.random.PRNGKey(0  )
     __A   :						Dict									=					4

     __A   :						int									=					jax.device_count()
     __A   :						Any									=					num_samples * [prompt]
     __A   :						List[str]									=					pipeline.prepare_inputs(__lowercase  )

     # shard inputs and rng
     __A   :						Union[str, Any]									=					replicate(__lowercase  )
     __A   :						str									=					jax.random.split(__lowercase ,						__lowercase  )
     __A   :						int									=					shard(__lowercase  )

     __A   :						str									=					pipeline(__lowercase ,						__lowercase ,						__lowercase ,						__lowercase ,						jit=__lowercase  ).images

     assert images.shape == (num_samples, 1, 64, 64, 3)
     if jax.device_count() == 8:
         assert np.abs(np.abs(images[0, 0, :2, :2, -2:] ,						dtype=np.floataa  ).sum() - 4.1_5_1_4_7_4_5  ) < 1e-3
         assert np.abs(np.abs(__lowercase ,						dtype=np.floataa  ).sum() - 4_9947.875  ) < 5e-1

     __A   :						Union[str, Any]									=					pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]  )  )  )
     assert len(__lowercase  ) == num_samples

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					FlaxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4''' ,						revision='''flax''' ,						safety_checker=__lowercase  )

     __A   :						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'''
     )

     __A   :						Any									=					jax.random.PRNGKey(0  )
     __A   :						Union[str, Any]									=					50

     __A   :						Union[str, Any]									=					jax.device_count()
     __A   :						int									=					num_samples * [prompt]
     __A   :						Dict									=					pipeline.prepare_inputs(__lowercase  )

     # shard inputs and rng
     __A   :						Any									=					replicate(__lowercase  )
     __A   :						Optional[Any]									=					jax.random.split(__lowercase ,						__lowercase  )
     __A   :						str									=					shard(__lowercase  )

     __A   :						Optional[int]									=					pipeline(__lowercase ,						__lowercase ,						__lowercase ,						__lowercase ,						jit=__lowercase  ).images

     assert images.shape == (num_samples, 1, 512, 512, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,						dtype=np.floataa  ).sum() - 0.0_5_6_5_2_4_0_1)  ) < 1e-3
         assert np.abs((np.abs(__lowercase ,						dtype=np.floataa  ).sum() - 238_3808.2)  ) < 5e-1

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					FlaxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4''' ,						revision='''bf16''' ,						dtype=jnp.bfloataa ,						safety_checker=__lowercase  )

     __A   :						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'''
     )

     __A   :						Union[str, Any]									=					jax.random.PRNGKey(0  )
     __A   :						Optional[int]									=					50

     __A   :						Optional[int]									=					jax.device_count()
     __A   :						int									=					num_samples * [prompt]
     __A   :						str									=					pipeline.prepare_inputs(__lowercase  )

     # shard inputs and rng
     __A   :						Dict									=					replicate(__lowercase  )
     __A   :						Optional[int]									=					jax.random.split(__lowercase ,						__lowercase  )
     __A   :						List[str]									=					shard(__lowercase  )

     __A   :						int									=					pipeline(__lowercase ,						__lowercase ,						__lowercase ,						__lowercase ,						jit=__lowercase  ).images

     assert images.shape == (num_samples, 1, 512, 512, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,						dtype=np.floataa  ).sum() - 0.0_4_0_0_3_9_0_6)  ) < 1e-3
         assert np.abs((np.abs(__lowercase ,						dtype=np.floataa  ).sum() - 237_3516.75)  ) < 5e-1

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					FlaxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4''' ,						revision='''bf16''' ,						dtype=jnp.bfloataa  )

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

     __A   :						Any									=					jax.random.PRNGKey(0  )
     __A   :						Dict									=					50

     __A   :						Union[str, Any]									=					jax.device_count()
     __A   :						str									=					num_samples * [prompt]
     __A   :						Tuple									=					pipeline.prepare_inputs(__lowercase  )

     # shard inputs and rng
     __A   :						Optional[int]									=					replicate(__lowercase  )
     __A   :						int									=					jax.random.split(__lowercase ,						__lowercase  )
     __A   :						List[str]									=					shard(__lowercase  )

     __A   :						int									=					pipeline(__lowercase ,						__lowercase ,						__lowercase ,						__lowercase ,						jit=__lowercase  ).images

     assert images.shape == (num_samples, 1, 512, 512, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,						dtype=np.floataa  ).sum() - 0.0_4_0_0_3_9_0_6)  ) < 1e-3
         assert np.abs((np.abs(__lowercase ,						dtype=np.floataa  ).sum() - 237_3516.75)  ) < 5e-1

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					FlaxDDIMScheduler(
         beta_start=0.0_0_0_8_5 ,						beta_end=0.0_1_2 ,						beta_schedule='''scaled_linear''' ,						set_alpha_to_one=__lowercase ,						steps_offset=1 ,						)

     __A   :						List[str]									=					FlaxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4''' ,						revision='''bf16''' ,						dtype=jnp.bfloataa ,						scheduler=__lowercase ,						safety_checker=__lowercase ,						)
     __A   :						Dict									=					scheduler.create_state()

     __A   :						Optional[Any]									=					scheduler_state

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

     __A   :						int									=					jax.random.PRNGKey(0  )
     __A   :						List[Any]									=					50

     __A   :						Optional[int]									=					jax.device_count()
     __A   :						int									=					num_samples * [prompt]
     __A   :						Dict									=					pipeline.prepare_inputs(__lowercase  )

     # shard inputs and rng
     __A   :						Dict									=					replicate(__lowercase  )
     __A   :						Optional[int]									=					jax.random.split(__lowercase ,						__lowercase  )
     __A   :						List[str]									=					shard(__lowercase  )

     __A   :						Optional[Any]									=					pipeline(__lowercase ,						__lowercase ,						__lowercase ,						__lowercase ,						jit=__lowercase  ).images

     assert images.shape == (num_samples, 1, 512, 512, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,						dtype=np.floataa  ).sum() - 0.0_4_5_0_4_3_9_4_5)  ) < 1e-3
         assert np.abs((np.abs(__lowercase ,						dtype=np.floataa  ).sum() - 234_7693.5)  ) < 5e-1







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

     __A   :						Any									=					jax.device_count()
     __A   :						Dict									=					num_samples * [prompt]
     __A   :						List[Any]									=					jax.random.split(jax.random.PRNGKey(0  ) ,						__lowercase  )

     __A   :						List[Any]									=					FlaxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4''' ,						revision='''bf16''' ,						dtype=jnp.bfloataa ,						safety_checker=__lowercase ,						)

     __A   :						str									=					replicate(__lowercase  )
     __A   :						str									=					pipeline.prepare_inputs(__lowercase  )
     __A   :						Union[str, Any]									=					shard(__lowercase  )
     __A   :						Union[str, Any]									=					pipeline(__lowercase ,						__lowercase ,						__lowercase ,						jit=__lowercase  ).images
     assert images.shape == (num_samples, 1, 512, 512, 3)
     __A   :						List[Any]									=					images[2, 0, 256, 10:17, 1]

     # With memory efficient attention
     __A   :						Union[str, Any]									=					FlaxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4''' ,						revision='''bf16''' ,						dtype=jnp.bfloataa ,						safety_checker=__lowercase ,						use_memory_efficient_attention=__lowercase ,						)

     __A   :						Optional[int]									=					replicate(__lowercase  )
     __A   :						List[str]									=					pipeline.prepare_inputs(__lowercase  )
     __A   :						Any									=					shard(__lowercase  )
     __A   :						Any									=					pipeline(__lowercase ,						__lowercase ,						__lowercase ,						jit=__lowercase  ).images
     assert images_eff.shape == (num_samples, 1, 512, 512, 3)
     __A   :						List[str]									=					images[2, 0, 256, 10:17, 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"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




