from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[Any]      =			'swinv2'

   __UpperCAmelCase : str      =			{
       'num_attention_heads': 'num_heads',
       'num_hidden_layers': 'num_layers',
   }


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						str=2_2_4       ,  __UpperCAmelCase     :						List[Any]=4       ,  __UpperCAmelCase     :						Tuple=3       ,  __UpperCAmelCase     :						List[str]=9_6       ,  __UpperCAmelCase     :						Union[str, Any]=[2, 2, 6, 2]       ,  __UpperCAmelCase     :						Tuple=[3, 6, 1_2, 2_4]       ,  __UpperCAmelCase     :						Dict=7       ,  __UpperCAmelCase     :						Union[str, Any]=4.0       ,  __UpperCAmelCase     :						Optional[int]=True       ,  __UpperCAmelCase     :						List[Any]=0.0       ,  __UpperCAmelCase     :						str=0.0       ,  __UpperCAmelCase     :						int=0.1       ,  __UpperCAmelCase     :						List[Any]="gelu"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						Optional[Any]=0.02       ,  __UpperCAmelCase     :						List[Any]=1E-5       ,  __UpperCAmelCase     :						List[str]=3_2       ,  **__UpperCAmelCase     :						List[str]       ,  )						->				Tuple:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)

         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  patch_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  embed_dim
         UpperCAmelCase__								=  depths
         UpperCAmelCase__								=  len(__UpperCAmelCase		)
         UpperCAmelCase__								=  num_heads
         UpperCAmelCase__								=  window_size
         UpperCAmelCase__								=  mlp_ratio
         UpperCAmelCase__								=  qkv_bias
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  drop_path_rate
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  use_absolute_embeddings
         UpperCAmelCase__								=  layer_norm_eps
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  encoder_stride
         # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         UpperCAmelCase__								=  int(embed_dim * 2 ** (len(__UpperCAmelCase		) - 1)		)
         UpperCAmelCase__								=  (0, 0, 0, 0)







"""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


