



"""simple docstring"""
import math
from typing import Optional

import numpy as np

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


_UpperCAmelCase     =			logging.get_logger(__name__)

_UpperCAmelCase     =			{
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Union[str, Any]     						=					'encodec'



  def __init__(						self :					Optional[Any]       ,		lowerCAmelCase :					int=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]       ,		lowerCAmelCase :					Optional[int]=2_4000       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					Tuple=False       ,		lowerCAmelCase :					Optional[int]=None       ,		lowerCAmelCase :					str=None       ,		lowerCAmelCase :					Optional[int]=128       ,		lowerCAmelCase :					Optional[Any]=32       ,		lowerCAmelCase :					str=1       ,		lowerCAmelCase :					Optional[Any]=[8, 5, 4, 2]       ,		lowerCAmelCase :					Tuple="weight_norm"       ,		lowerCAmelCase :					int=7       ,		lowerCAmelCase :					Tuple=7       ,		lowerCAmelCase :					str=3       ,		lowerCAmelCase :					Union[str, Any]=2       ,		lowerCAmelCase :					Union[str, Any]=True       ,		lowerCAmelCase :					Union[str, Any]="reflect"       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					Union[str, Any]=2       ,		lowerCAmelCase :					Any=1.0       ,		lowerCAmelCase :					Tuple=1024       ,		lowerCAmelCase :					Optional[int]=None       ,		lowerCAmelCase :					List[Any]=True       ,		**lowerCAmelCase :					Optional[int]       ,		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=target_bandwidths
     SCREAMING_SNAKE_CASE_: str     			=sampling_rate
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=audio_channels
     SCREAMING_SNAKE_CASE_: int     			=normalize
     SCREAMING_SNAKE_CASE_: List[str]     			=chunk_length_s
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=overlap
     SCREAMING_SNAKE_CASE_: List[Any]     			=hidden_size
     SCREAMING_SNAKE_CASE_: str     			=num_filters
     SCREAMING_SNAKE_CASE_: Dict     			=num_residual_layers
     SCREAMING_SNAKE_CASE_: int     			=upsampling_ratios
     SCREAMING_SNAKE_CASE_: Optional[int]     			=norm_type
     SCREAMING_SNAKE_CASE_: List[str]     			=kernel_size
     SCREAMING_SNAKE_CASE_: int     			=last_kernel_size
     SCREAMING_SNAKE_CASE_: List[str]     			=residual_kernel_size
     SCREAMING_SNAKE_CASE_: Dict     			=dilation_growth_rate
     SCREAMING_SNAKE_CASE_: Tuple     			=use_causal_conv
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=pad_mode
     SCREAMING_SNAKE_CASE_: List[str]     			=compress
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_lstm_layers
     SCREAMING_SNAKE_CASE_: Tuple     			=trim_right_ratio
     SCREAMING_SNAKE_CASE_: Tuple     			=codebook_size
     SCREAMING_SNAKE_CASE_: Tuple     			=codebook_dim if codebook_dim is not None else hidden_size
     SCREAMING_SNAKE_CASE_: int     			=use_conv_shortcut

     if self.norm_type not in ["weight_norm", "time_group_norm"]:
        raise ValueError(
            f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''		)

     super().__init__(**lowerCAmelCase		)



  @property
  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[int]:


     '''simple docstring'''




     if self.chunk_length_s is None:
        return None
     else:
        return int(self.chunk_length_s * self.sampling_rate		)



  @property
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Optional[int]:


     '''simple docstring'''




     if self.chunk_length_s is None or self.overlap is None:
        return None
     else:
        return max(1       ,		int((1.0 - self.overlap) * self.chunk_length		)		)



  @property
  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=np.prod(self.upsampling_ratios		)
     return math.ceil(self.sampling_rate / hop_length		)





  @property
  def 	lowerCamelCase__       (						self :					Any		)     ->					int:


     '''simple docstring'''




     return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)		)








"""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 					a	(  unittest.TestCase       ):



  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     super().tearDown()
     gc.collect()



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=FlaxStableDiffusionPipeline.from_pretrained(
         """stabilityai/stable-diffusion-2"""       ,		revision="""bf16"""       ,		dtype=jnp.bfloataa       ,		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Any     			=jax.device_count()
     SCREAMING_SNAKE_CASE_: Dict     			=num_samples * [prompt]
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe.prepare_inputs(lowerCAmelCase		)

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

     SCREAMING_SNAKE_CASE_: Dict     			=jax.random.PRNGKey(0		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=jax.random.split(lowerCAmelCase       ,		jax.device_count()		)

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

     SCREAMING_SNAKE_CASE_: Dict     			=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
     SCREAMING_SNAKE_CASE_: Dict     			=images[0, 253:256, 253:256, -1]

     SCREAMING_SNAKE_CASE_: Optional[int]     			=jnp.asarray(jax.device_get(image_slice.flatten()		)		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=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 :					List[str]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			="""stabilityai/stable-diffusion-2"""
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase       ,		subfolder="""scheduler"""		)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=FlaxStableDiffusionPipeline.from_pretrained(
         lowerCAmelCase       ,		scheduler=lowerCAmelCase       ,		revision="""bf16"""       ,		dtype=jnp.bfloataa       ,		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=scheduler_params

     SCREAMING_SNAKE_CASE_: Tuple     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=jax.device_count()
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_samples * [prompt]
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe.prepare_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=replicate(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=shard(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Any     			=jax.random.PRNGKey(0		)
     SCREAMING_SNAKE_CASE_: Any     			=jax.random.split(lowerCAmelCase       ,		jax.device_count()		)

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

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

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=jnp.asarray(jax.device_get(image_slice.flatten()		)		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=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



