



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


A_		:     Union[str, Any]        =     logging.get_logger(__name__)

A_		:     Optional[int]        =     {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''switch_transformers'''
   _UpperCAmelCase     	=			['''past_key_values''']
   _UpperCAmelCase     	=			{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

   def __init__(      self			:       str							,   __lowerCAmelCase			:       int=3_2128							,   __lowerCAmelCase			:       Optional[Any]=768							,   __lowerCAmelCase			:       Optional[Any]=64							,   __lowerCAmelCase			:       Optional[int]=2048							,   __lowerCAmelCase			:       List[str]=64							,   __lowerCAmelCase			:       int=12							,   __lowerCAmelCase			:       List[Any]=3							,   __lowerCAmelCase			:       Union[str, Any]=12							,   __lowerCAmelCase			:       Optional[int]=3							,   __lowerCAmelCase			:       str=12							,   __lowerCAmelCase			:       Optional[int]=8							,   __lowerCAmelCase			:       List[str]=False							,   __lowerCAmelCase			:       Any=0.0_1							,   __lowerCAmelCase			:       Union[str, Any]="float32"							,   __lowerCAmelCase			:       List[str]=False							,   __lowerCAmelCase			:       int=32							,   __lowerCAmelCase			:       Tuple=128							,   __lowerCAmelCase			:       List[str]=0.1							,   __lowerCAmelCase			:       Union[str, Any]=1E-6							,   __lowerCAmelCase			:       Union[str, Any]=0.0_0_1							,   __lowerCAmelCase			:       Any=0.0_0_1							,   __lowerCAmelCase			:       Dict=1.0							,   __lowerCAmelCase			:       Optional[int]="relu"							,   __lowerCAmelCase			:       List[str]=True							,   __lowerCAmelCase			:       List[str]=False							,   __lowerCAmelCase			:       int=True							,   __lowerCAmelCase			:       Tuple=0							,   __lowerCAmelCase			:       Optional[Any]=1							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				int:


    """simple docstring"""






    a						=		vocab_size
    a						=		d_model
    a						=		d_kv
    a						=		d_ff

    a						=		num_sparse_encoder_layers

    a						=		num_layers
    a						=		(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    a						=		num_sparse_decoder_layers

    # This tells us, each how many encoder layer we'll have to set a sparse layer.
    if self.num_sparse_encoder_layers > 0:
     a						=		self.num_layers // self.num_sparse_encoder_layers
    else:
     a						=		self.num_layers  # HACK: this will create 0 sparse layers

    # This tells us, each how many encoder layer we'll have to set a sparse layer.
    if self.num_sparse_decoder_layers > 0:
     a						=		self.num_decoder_layers // self.num_sparse_decoder_layers
    else:
     a						=		self.num_decoder_layers  # HACK: this will create 0 sparse layers

    a						=		num_heads
    a						=		num_experts
    a						=		expert_capacity
    a						=		router_bias
    a						=		router_jitter_noise
    if router_dtype not in ["float32", "float16", "bfloat16"]:
     raise ValueError(f"""`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}"""							)
    a						=		router_dtype

    a						=		router_ignore_padding_tokens
    a						=		relative_attention_num_buckets
    a						=		relative_attention_max_distance

    a						=		dropout_rate
    a						=		layer_norm_epsilon
    a						=		initializer_factor
    a						=		feed_forward_proj
    a						=		use_cache
    a						=		add_router_probs

    a						=		router_z_loss_coef
    a						=		router_aux_loss_coef

    a						=		self.feed_forward_proj.split("-"							)
    a						=		act_info[-1]
    a						=		act_info[0] == "gated"

    if len(__UpperCamelCase							) > 1 and act_info[0] != "gated" or len(__UpperCamelCase							) > 2:
     raise ValueError(
         f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
         "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
         "'gated-gelu' or 'relu'"							)

    # for backwards compatibility
    if feed_forward_proj == "gated-gelu":
     a						=		"gelu_new"

    super().__init__(
        pad_token_id=__UpperCamelCase							,   eos_token_id=__UpperCamelCase							,   is_encoder_decoder=__UpperCamelCase							,   **__UpperCamelCase							,   )






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       UNetaDModel							,   __lowerCAmelCase			:       ScoreSdeVeScheduler							)       ->				str:


    """simple docstring"""






    super().__init__()
    self.register_modules(unet=__lowerCAmelCase							,   scheduler=__lowerCAmelCase							)




   @torch.no_grad()
   def __call__(      self			:       int							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   __lowerCAmelCase			:       Optional[str] = "pil"							,   __lowerCAmelCase			:       bool = True							,   **__lowerCAmelCase			:       Any							,   )       ->				Union[ImagePipelineOutput, Tuple]:


    """simple docstring"""






    a						=		self.unet.config.sample_size
    a						=		(batch_size, 3, img_size, img_size)

    a						=		self.unet

    a						=		randn_tensor(__lowerCAmelCase							,   generator=__lowerCAmelCase							) * self.scheduler.init_noise_sigma
    a						=		sample.to(self.device							)

    self.scheduler.set_timesteps(__lowerCAmelCase							)
    self.scheduler.set_sigmas(__lowerCAmelCase							)

    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
     a						=		self.scheduler.sigmas[i] * torch.ones(shape[0]							,   device=self.device							)

     # correction step
     for _ in range(self.scheduler.config.correct_steps							):
      a						=		self.unet(__lowerCAmelCase							,   __lowerCAmelCase							).sample
      a						=		self.scheduler.step_correct(__lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							).prev_sample

     # prediction step
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							).sample
     a						=		self.scheduler.step_pred(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)

     a		,  a						=		output.prev_sample, output.prev_sample_mean

    a						=		sample_mean.clamp(0							,   1							)
    a						=		sample.cpu().permute(0							,   2							,   3							,   1							).numpy()
    if output_type == "pil":
     a						=		self.numpy_to_pil(__lowerCAmelCase							)

    if not return_dict:
     return (sample,)

    return ImagePipelineOutput(images=__lowerCAmelCase							)

