





'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__							:							Tuple =logging.get_logger(__name__)

a__							:							str ={
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					int					="rwkv"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					={"max_position_embeddings": "context_length"}






 def __init__(       self       :							str							,	__A       :							Optional[int]=5_0_2_7_7							,	__A       :							Any=1_0_2_4							,	__A       :							Optional[int]=4_0_9_6							,	__A       :							Optional[Any]=3_2							,	__A       :							str=None							,	__A       :							Dict=None							,	__A       :							List[str]=1e-5							,	__A       :							Dict=0							,	__A       :							List[Any]=0							,	__A       :							Dict=6							,	__A       :							Optional[Any]=False							,	__A       :							Union[str, Any]=True							,	**__A       :							Tuple							,	):
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  context_length
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  attention_hidden_size if attention_hidden_size is not None else hidden_size
        __UpperCamelCase       =  intermediate_size if intermediate_size is not None else 4 * hidden_size
        __UpperCamelCase       =  layer_norm_epsilon
        __UpperCamelCase       =  rescale_every
        __UpperCamelCase       =  use_cache

        __UpperCamelCase       =  bos_token_id
        __UpperCamelCase       =  eos_token_id

        super().__init__(
            tie_word_embeddings=__A							,	bos_token_id=__A							,	eos_token_id=__A							,	**__A	)


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 		UpperCamelCase__  (       unittest.TestCase    ):
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
   def       lowerCAmelCase			(self :					int   ):
    __a				,       __a :  Optional[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        '''stabilityai/stable-diffusion-2'''					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)

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

    __a :  Optional[Any]						=  replicate(snake_case_   )
    __a :  Optional[int]						=  shard(snake_case_   )

    __a :  int						=  jax.random.PRNGKey(0   )
    __a :  str						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  int						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  Optional[int]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  List[Any]						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

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

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

    __a :  Tuple						=  replicate(snake_case_   )
    __a :  Dict						=  shard(snake_case_   )

    __a :  Dict						=  jax.random.PRNGKey(0   )
    __a :  Dict						=  jax.random.split(snake_case_					,				jax.device_count()   )

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

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

    __a :  List[Any]						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  List[Any]						=  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
