"""simple docstring"""



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			Tuple           =		AutoencoderKL
 _snake_case   :			int           =		"""sample"""
 _snake_case   :			List[str]           =		1E-2


 @property
 def 		a__ (				self		):
        __a     				=  4
        __a     				=  3
        __a     				=  (32, 32)

        __a     				=  floats_tensor((batch_size, num_channels) + sizes		).to(lowerCamelCase		)

        return {"sample": image}


 @property
 def 		a__ (				self		):
        return (3, 32, 32)


 @property
 def 		a__ (				self		):
        return (3, 32, 32)


 def 		a__ (				self		):
        __a     				=  {
            "block_out_channels": [32, 64],
            "in_channels": 3,
            "out_channels": 3,
            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
            "latent_channels": 4,
        }
        __a     				=  self.dummy_input
        return init_dict, inputs_dict


 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        pass


 @unittest.skipIf(torch_device == "mps"  ,						"Gradient checkpointing skipped on MPS"		)
 def 		a__ (				self		):
        # enable deterministic behavior for gradient checkpointing
        __a				, __a     				=  self.prepare_init_args_and_inputs_for_common()
        __a     				=  self.model_class(**lowerCamelCase		)
        model.to(lowerCamelCase		)

        assert not model.is_gradient_checkpointing and model.training

        __a     				=  model(**lowerCamelCase		).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model.zero_grad()

        __a     				=  torch.randn_like(lowerCamelCase		)
        __a     				=  (out - labels).mean()
        loss.backward()

        # re-instantiate the model now enabling gradient checkpointing
        __a     				=  self.model_class(**lowerCamelCase		)
        # clone model
        model_a.load_state_dict(model.state_dict()		)
        model_a.to(lowerCamelCase		)
        model_a.enable_gradient_checkpointing()

        assert model_a.is_gradient_checkpointing and model_a.training

        __a     				=  model_a(**lowerCamelCase		).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model_a.zero_grad()
        __a     				=  (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1E-5		)
        __a     				=  dict(model.named_parameters()		)
        __a     				=  dict(model_a.named_parameters()		)
        for name, param in named_params.items():
               self.assertTrue(torch_all_close(param.grad.data  ,						named_params_a[name].grad.data  ,						atol=5E-5		)		)


 def 		a__ (				self		):
        __a				, __a     				=  AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"  ,						output_loading_info=lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)
        self.assertEqual(len(loading_info["missing_keys"]		)  ,						0		)

        model.to(lowerCamelCase		)
        __a     				=  model(**self.dummy_input		)

        assert image is not None, "Make sure output is not None"






 def 		a__ (				self		):
        __a     				=  AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		)
        __a     				=  model.to(lowerCamelCase		)
        model.eval()

        if torch_device == "mps":
               __a     				=  torch.manual_seed(0		)
        else:
               __a     				=  torch.Generator(device=lowerCamelCase		).manual_seed(0		)

        __a     				=  torch.randn(
            1  ,						model.config.in_channels  ,						model.config.sample_size  ,						model.config.sample_size  ,						generator=torch.manual_seed(0		)  ,						)
        __a     				=  image.to(lowerCamelCase		)
        with torch.no_grad():
               __a     				=  model(lowerCamelCase  ,						sample_posterior=lowerCamelCase  ,						generator=lowerCamelCase		).sample

        __a     				=  output[0, -1, -3:, -3:].flatten().cpu()

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        if torch_device == "mps":
               __a     				=  torch.tensor(
                   [
                       -4.0078E-01,
                       -3.8323E-04,
                       -1.2681E-01,
                       -1.1462E-01,
                       2.0095E-01,
                       1.0893E-01,
                       -8.8247E-02,
                       -3.0361E-01,
                       -9.8644E-03,
                   ]		)
        elif torch_device == "cpu":
               __a     				=  torch.tensor(
                   [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
        else:
               __a     				=  torch.tensor(
                   [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]		)

        self.assertTrue(torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						rtol=1E-2		)		)






@slow
class 				snake_case__     (   unittest.TestCase   ):


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase		) for s in shape]		)}.npy"


 def 		a__ (				self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 		a__ (				self  ,						lowerCamelCase=0  ,						lowerCamelCase=(4, 3, 512, 512)  ,						lowerCamelCase=False		):
        __a     				=  torch.floataa if fpaa else torch.floataa
        __a     				=  torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase  ,						lowerCamelCase		)		)		).to(lowerCamelCase		).to(lowerCamelCase		)
        return image


 def 		a__ (				self  ,						lowerCamelCase="CompVis/stable-diffusion-v1-4"  ,						lowerCamelCase=False		):
        __a     				=  "fp16" if fpaa else None
        __a     				=  torch.floataa if fpaa else torch.floataa

        __a     				=  AutoencoderKL.from_pretrained(
            lowerCamelCase  ,						subfolder="vae"  ,						torch_dtype=lowerCamelCase  ,						revision=lowerCamelCase  ,						)
        model.to(lowerCamelCase		).eval()

        return model


 def 		a__ (				self  ,						lowerCamelCase=0		):
        if torch_device == "mps":
               return torch.manual_seed(lowerCamelCase		)
        return torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)


 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase		)
        __a     				=  self.get_generator(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model(lowerCamelCase  ,						generator=lowerCamelCase  ,						sample_posterior=lowerCamelCase		).sample

        assert sample.shape == image.shape

        __a     				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
        __a     				=  torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=3E-3		)


 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
         [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model(fpaa=lowerCamelCase		)
        __a     				=  self.get_sd_image(lowerCamelCase  ,						fpaa=lowerCamelCase		)
        __a     				=  self.get_generator(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model(lowerCamelCase  ,						generator=lowerCamelCase  ,						sample_posterior=lowerCamelCase		).sample

        assert sample.shape == image.shape

        __a     				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-2		)


 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model(lowerCamelCase		).sample

        assert sample.shape == image.shape

        __a     				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
        __a     				=  torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=3E-3		)


 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
         [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        __a     				=  sample[-1, -2:, :2, -2:].flatten().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-3		)


 @parameterized.expand(
     [
         # fmt: off
         [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
         [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model(fpaa=lowerCamelCase		)
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)  ,						fpaa=lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        __a     				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=5E-3		)


 @parameterized.expand([(13,), (16,), (27,)]		)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()  ,						reason="xformers is not required when using PyTorch 2.0."		)
 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model(fpaa=lowerCamelCase		)
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)  ,						fpaa=lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-1		)


 @parameterized.expand([(13,), (16,), (37,)]		)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()  ,						reason="xformers is not required when using PyTorch 2.0."		)
 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-2		)






 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
         [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
         # fmt: on
     ]		)
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase		)
        __a     				=  self.get_generator(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model.encode(lowerCamelCase		).latent_dist
               __a     				=  dist.sample(generator=lowerCamelCase		)

        assert list(sample.shape		) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

        __a     				=  sample[0, -1, -3:, -3:].flatten().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        __a     				=  3E-3 if torch_device != "mps" else 1E-2
        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=lowerCamelCase		)

"""simple docstring"""



import functools
import operator

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


SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:str			=	{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		"""sew-d"""


 def __init__(				self  ,						lowerCamelCase=32  ,						lowerCamelCase=768  ,						lowerCamelCase=12  ,						lowerCamelCase=12  ,						lowerCamelCase=3072  ,						lowerCamelCase=2  ,						lowerCamelCase=512  ,						lowerCamelCase=256  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=("p2c", "c2p")  ,						lowerCamelCase="layer_norm"  ,						lowerCamelCase="gelu_python"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-7  ,						lowerCamelCase=1E-5  ,						lowerCamelCase="group"  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,						lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,						lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,						lowerCamelCase=False  ,						lowerCamelCase=128  ,						lowerCamelCase=16  ,						lowerCamelCase=True  ,						lowerCamelCase=0.05  ,						lowerCamelCase=10  ,						lowerCamelCase=2  ,						lowerCamelCase=0.0  ,						lowerCamelCase=10  ,						lowerCamelCase=0  ,						lowerCamelCase="mean"  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=256  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase  ,						pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase		)
        __a     				=  hidden_size
        __a     				=  feat_extract_norm
        __a     				=  feat_extract_activation
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  conv_bias
        __a     				=  num_conv_pos_embeddings
        __a     				=  num_conv_pos_embedding_groups
        __a     				=  len(self.conv_dim		)
        __a     				=  num_hidden_layers
        __a     				=  intermediate_size
        __a     				=  squeeze_factor
        __a     				=  max_position_embeddings
        __a     				=  position_buckets
        __a     				=  share_att_key
        __a     				=  relative_attention
        __a     				=  norm_rel_ebd
        __a     				=  list(lowerCamelCase		)
        __a     				=  hidden_act
        __a     				=  num_attention_heads
        __a     				=  hidden_dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  feat_proj_dropout
        __a     				=  final_dropout
        __a     				=  layer_norm_eps
        __a     				=  feature_layer_norm_eps
        __a     				=  initializer_range
        __a     				=  vocab_size

        if (
            (len(self.conv_stride		) != self.num_feat_extract_layers)
            or (len(self.conv_kernel		) != self.num_feat_extract_layers)
            or (len(self.conv_dim		) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a     				=  apply_spec_augment
        __a     				=  mask_time_prob
        __a     				=  mask_time_length
        __a     				=  mask_time_min_masks
        __a     				=  mask_feature_prob
        __a     				=  mask_feature_length
        __a     				=  mask_feature_min_masks

        # ctc loss
        __a     				=  ctc_loss_reduction
        __a     				=  ctc_zero_infinity

        # sequence classification
        __a     				=  use_weighted_layer_sum
        __a     				=  classifier_proj_size






 @property
 def 		a__ (				self		):
        return functools.reduce(operator.mul  ,						self.conv_stride  ,						1		)
