


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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:int									=			FlaxStableDiffusionPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2'''   ,    revision='''bf16'''   ,    dtype=jnp.bfloataa   ,    )

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

       a		:List[str]									=			replicate(_lowerCamelCase       )
       a		:Dict									=			shard(_lowerCamelCase       )

       a		:Dict									=			jax.random.PRNGKey(0       )
       a		:Optional[Any]									=			jax.random.split(_lowerCamelCase   ,    jax.device_count()       )

       a		:Dict									=			sd_pipe(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    num_inference_steps=25   ,    jit=_lowerCamelCase       )[0]
       assert images.shape == (jax.device_count(), 1, 768, 768, 3)

       a		:List[str]									=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
       a		:Union[str, Any]									=			images[0, 253:256, 253:256, -1]

       a		:List[Any]									=			jnp.asarray(jax.device_get(image_slice.flatten()       )       )
       a		:str									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			'''stabilityai/stable-diffusion-2'''
       a				,							a		:Dict									=			FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase   ,    subfolder='''scheduler'''       )
       a				,							a		:str									=			FlaxStableDiffusionPipeline.from_pretrained(
           _lowerCamelCase   ,    scheduler=_lowerCamelCase   ,    revision='''bf16'''   ,    dtype=jnp.bfloataa   ,    )
       a		:Optional[int]									=			scheduler_params

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

       a		:Union[str, Any]									=			replicate(_lowerCamelCase       )
       a		:Tuple									=			shard(_lowerCamelCase       )

       a		:Tuple									=			jax.random.PRNGKey(0       )
       a		:str									=			jax.random.split(_lowerCamelCase   ,    jax.device_count()       )

       a		:List[str]									=			sd_pipe(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    num_inference_steps=25   ,    jit=_lowerCamelCase       )[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		:List[str]									=			images[0, 253:256, 253:256, -1]

       a		:Union[str, Any]									=			jnp.asarray(jax.device_get(image_slice.flatten()       )       )
       a		:int									=			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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


snake_case							:       Any										=	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTNeoForCausalLM''',
         '''GPTNeoForQuestionAnswering''',
         '''GPTNeoForSequenceClassification''',
         '''GPTNeoForTokenClassification''',
         '''GPTNeoModel''',
         '''GPTNeoPreTrainedModel''',
         '''load_tf_weights_in_gpt_neo''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       Any										=	[
         '''FlaxGPTNeoForCausalLM''',
         '''FlaxGPTNeoModel''',
         '''FlaxGPTNeoPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

     snake_case							:       Optional[int]										=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





