





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 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					FlaxStableDiffusionPipeline.from_pretrained(
									    '''stabilityai/stable-diffusion-2'''		,     revision='''bf16'''		,     dtype=jnp.bfloataa		,     )

									__a	:  Union[str, Any]         =					'''A painting of a squirrel eating a burger'''
									__a	:  int         =					jax.device_count()
									__a	:  List[Any]         =					num_samples * [prompt]
									__a	:  Union[str, Any]         =					sd_pipe.prepare_inputs(_UpperCAmelCase       )

									__a	:  Optional[int]         =					replicate(_UpperCAmelCase       )
									__a	:  List[str]         =					shard(_UpperCAmelCase       )

									__a	:  Dict         =					jax.random.PRNGKey(0       )
									__a	:  List[str]         =					jax.random.split(_UpperCAmelCase		,     jax.device_count()       )

									__a	:  Tuple         =					sd_pipe(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     num_inference_steps=25		,     jit=_UpperCAmelCase       )[0]
									assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

									__a	:  Optional[int]         =					jnp.asarray(jax.device_get(image_slice.flatten()       )       )
									__a	:  Tuple         =					jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]       )
									print(f"""output_slice: {output_slice}"""       )
									assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					'''stabilityai/stable-diffusion-2'''
									__a	:  Optional[int]         =					FlaxDPMSolverMultistepScheduler.from_pretrained(_UpperCAmelCase		,     subfolder='''scheduler'''       )
									__a	:  int         =					FlaxStableDiffusionPipeline.from_pretrained(
									    _UpperCAmelCase		,     scheduler=_UpperCAmelCase		,     revision='''bf16'''		,     dtype=jnp.bfloataa		,     )
									__a	:  Dict         =					scheduler_params

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

									__a	:  List[str]         =					replicate(_UpperCAmelCase       )
									__a	:  Dict         =					shard(_UpperCAmelCase       )

									__a	:  Dict         =					jax.random.PRNGKey(0       )
									__a	:  Optional[int]         =					jax.random.split(_UpperCAmelCase		,     jax.device_count()       )

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

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

									__a	:  List[Any]         =					jnp.asarray(jax.device_get(image_slice.flatten()       )       )
									__a	:  Any         =					jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]       )
									print(f"""output_slice: {output_slice}"""       )
									assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	     =					{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InstructBlipQFormerModel''',
		    '''InstructBlipPreTrainedModel''',
		    '''InstructBlipForConditionalGeneration''',
		    '''InstructBlipVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    InstructBlipConfig,
		    InstructBlipQFormerConfig,
		    InstructBlipVisionConfig,
		)
		from .processing_instructblip import InstructBlipProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InstructBlipForConditionalGeneration,
				    InstructBlipPreTrainedModel,
				    InstructBlipQFormerModel,
				    InstructBlipVisionModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)