






"""simple docstring"""






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


UpperCAmelCase_     :				Any         =				logging.get_logger(__name__)

UpperCAmelCase_     :				int         =				{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"nllb-moe"
      __UpperCamelCase							=						["past_key_values"]
      __UpperCamelCase							=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
      def __init__(		self  :						Dict		,			lowercase_  :						Optional[Any]=128112		,			lowercase_  :						Optional[Any]=1024		,			lowercase_  :						List[Any]=12		,			lowercase_  :						List[Any]=4096		,			lowercase_  :						str=16		,			lowercase_  :						Union[str, Any]=12		,			lowercase_  :						Union[str, Any]=4096		,			lowercase_  :						Optional[int]=16		,			lowercase_  :						int=0.05		,			lowercase_  :						str=0.05		,			lowercase_  :						Union[str, Any]=True		,			lowercase_  :						List[str]=True		,			lowercase_  :						List[Any]="relu"		,			lowercase_  :						Any=1024		,			lowercase_  :						str=0.1		,			lowercase_  :						Tuple=0.1		,			lowercase_  :						Dict=0.0		,			lowercase_  :						List[str]=0.02		,			lowercase_  :						Union[str, Any]=2		,			lowercase_  :						List[Any]=True		,			lowercase_  :						int=False		,			lowercase_  :						str="float32"		,			lowercase_  :						Optional[Any]=False		,			lowercase_  :						Optional[Any]=128		,			lowercase_  :						Optional[Any]=64		,			lowercase_  :						Union[str, Any]=4		,			lowercase_  :						List[Any]=4		,			lowercase_  :						str=0.0_01		,			lowercase_  :						Tuple=0.0_01		,			lowercase_  :						int="all"		,			lowercase_  :						Optional[Any]=False		,			lowercase_  :						Tuple=False		,			lowercase_  :						Tuple=1.0		,			lowercase_  :						str=0.2		,			lowercase_  :						Optional[int]=1		,			lowercase_  :						str=0		,			lowercase_  :						Union[str, Any]=2		,			lowercase_  :						Dict=False		,			**lowercase_  :						Tuple		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Dict							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	d_model
          SCREAMING_SNAKE_CASE_   :						List[str]							=	encoder_ffn_dim
          SCREAMING_SNAKE_CASE_   :						int							=	encoder_layers
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	encoder_attention_heads
          SCREAMING_SNAKE_CASE_   :						int							=	decoder_ffn_dim
          SCREAMING_SNAKE_CASE_   :						Any							=	decoder_layers
          SCREAMING_SNAKE_CASE_   :						Any							=	decoder_attention_heads
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	dropout
          SCREAMING_SNAKE_CASE_   :						str							=	attention_dropout
          SCREAMING_SNAKE_CASE_   :						int							=	activation_dropout
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	activation_function
          SCREAMING_SNAKE_CASE_   :						List[str]							=	init_std
          SCREAMING_SNAKE_CASE_   :						List[str]							=	encoder_layerdrop
          SCREAMING_SNAKE_CASE_   :						str							=	decoder_layerdrop
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	use_cache
          SCREAMING_SNAKE_CASE_   :						Tuple							=	encoder_layers
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	scale_embedding  # scale factor will be sqrt(d_model) if True
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	router_z_loss_coef
          SCREAMING_SNAKE_CASE_   :						Dict							=	router_aux_loss_coef
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	decoder_sparse_step
          SCREAMING_SNAKE_CASE_   :						int							=	encoder_sparse_step
          SCREAMING_SNAKE_CASE_   :						Any							=	num_experts
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	expert_capacity
          SCREAMING_SNAKE_CASE_   :						int							=	router_bias
          if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
          SCREAMING_SNAKE_CASE_   :						Any							=	router_dtype

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	router_ignore_padding_tokens
          SCREAMING_SNAKE_CASE_   :						Dict							=	batch_prioritized_routing
          SCREAMING_SNAKE_CASE_   :						str							=	second_expert_policy
          SCREAMING_SNAKE_CASE_   :						str							=	normalize_router_prob_before_dropping
          SCREAMING_SNAKE_CASE_   :						Tuple							=	moe_eval_capacity_token_fraction
          SCREAMING_SNAKE_CASE_   :						str							=	moe_token_dropout
          SCREAMING_SNAKE_CASE_   :						Dict							=	output_router_logits
          super().__init__(
              pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			is_encoder_decoder=lowercase_		,			decoder_start_token_id=lowercase_		,			**lowercase_		,			)










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


