


"""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 						snake_case_(				unittest.TestCase	):






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






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      FlaxStableDiffusionPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2'''    , revision='''bf16'''    , dtype=jnp.bfloataa    , )

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

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

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

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

           lowerCAmelCase    :		str				=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
           lowerCAmelCase    :		List[str]				=      images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

           lowerCAmelCase    :		Dict				=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
           lowerCAmelCase    :		List[str]				=      jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
           print(F'''output_slice: {output_slice}''' )
           assert jnp.abs(output_slice - expected_slice ).max() < 1E-2






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

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

           lowerCAmelCase    :		Dict				=      replicate(UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      shard(UpperCamelCase_ )

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

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

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

           lowerCAmelCase    :		Optional[int]				=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
           lowerCAmelCase    :		Tuple				=      jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
           print(F'''output_slice: {output_slice}''' )
           assert jnp.abs(output_slice - expected_slice ).max() < 1E-2



"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["VivitImageProcessor"]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VivitModel",
				    "VivitPreTrainedModel",
				    "VivitForVideoClassification",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

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





