
import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	@property
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      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 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.dummy_uncond_unet
				_snake_case          =      KarrasVeScheduler()

				_snake_case          =      KarrasVePipeline(unet=_UpperCAmelCase      ,  scheduler=_UpperCAmelCase     )
				pipe.to(_UpperCAmelCase     )
				pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pipe(num_inference_steps=2      ,  generator=_UpperCAmelCase      ,  output_type='numpy'     ).images

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pipe(num_inference_steps=2      ,  generator=_UpperCAmelCase      ,  output_type='numpy'      ,  return_dict=_UpperCAmelCase     )[0]

				_snake_case          =      image[0, -3:, -3:, -1]
				_snake_case          =      image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				_snake_case          =      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        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      'google/ncsnpp-celebahq-256'
				_snake_case          =      UNetaDModel.from_pretrained(_UpperCAmelCase     )
				_snake_case          =      KarrasVeScheduler()

				_snake_case          =      KarrasVePipeline(unet=_UpperCAmelCase      ,  scheduler=_UpperCAmelCase     )
				pipe.to(_UpperCAmelCase     )
				pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pipe(num_inference_steps=20      ,  generator=_UpperCAmelCase      ,  output_type='numpy'     ).images

				_snake_case          =      image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				_snake_case          =      np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

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

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


_lowerCamelCase	:    Tuple          =      logging.get_logger(__name__)

_lowerCamelCase	:    Union[str, Any]          =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "canine"



	def __init__(  self     :   int      ,  lowercase     :   Optional[int]=768      ,  lowercase     :   Tuple=12      ,  lowercase     :   Union[str, Any]=12      ,  lowercase     :   Optional[int]=3_072      ,  lowercase     :   Tuple="gelu"      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   Tuple=0.1      ,  lowercase     :   int=16_384      ,  lowercase     :   Optional[int]=16      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   Optional[Any]=1E-12      ,  lowercase     :   Optional[Any]=0      ,  lowercase     :   Dict=0xE000      ,  lowercase     :   Optional[Any]=0xE001      ,  lowercase     :   Union[str, Any]=4      ,  lowercase     :   str=4      ,  lowercase     :   Optional[int]=8      ,  lowercase     :   List[str]=16_384      ,  lowercase     :   Union[str, Any]=128      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase      ,  bos_token_id=lowercase      ,  eos_token_id=lowercase      ,  **lowercase     )

				_snake_case          =      max_position_embeddings
				_snake_case          =      hidden_size
				_snake_case          =      num_hidden_layers
				_snake_case          =      num_attention_heads
				_snake_case          =      intermediate_size
				_snake_case          =      hidden_act
				_snake_case          =      hidden_dropout_prob
				_snake_case          =      attention_probs_dropout_prob
				_snake_case          =      initializer_range
				_snake_case          =      type_vocab_size
				_snake_case          =      layer_norm_eps

				# Character config:
				_snake_case          =      downsampling_rate
				_snake_case          =      upsampling_kernel_size
				_snake_case          =      num_hash_functions
				_snake_case          =      num_hash_buckets
				_snake_case          =      local_transformer_stride