


"""simple docstring"""


import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class   _a      (      unittest.TestCase ):
    @property
    def      _UpperCamelCase       ( self							:					str				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.dummy_uncond_unet
          lowerCamelCase__           =		PNDMScheduler()

          lowerCamelCase__           =		PNDMPipeline(unet=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__				)
          pndm.to(SCREAMING_SNAKE_CASE__				)
          pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.manual_seed(0				)
          lowerCamelCase__           =		pndm(generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=20							,					output_type='numpy'				).images

          lowerCamelCase__           =		torch.manual_seed(0				)
          lowerCamelCase__           =		pndm(generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=20							,					output_type='numpy'							,					return_dict=SCREAMING_SNAKE_CASE__				)[0]

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

          assert image.shape == (1, 32, 32, 3)
          lowerCamelCase__           =		np.array([1.0, 1.0, 0.0, 1.0, 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   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		'google/ddpm-cifar10-32'

          lowerCamelCase__           =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		PNDMScheduler()

          lowerCamelCase__           =		PNDMPipeline(unet=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__				)
          pndm.to(SCREAMING_SNAKE_CASE__				)
          pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		torch.manual_seed(0				)
          lowerCamelCase__           =		pndm(generator=SCREAMING_SNAKE_CASE__							,					output_type='numpy'				).images

          lowerCamelCase__           =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowerCamelCase__           =		np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]				)

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







"""simple docstring"""


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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



