
"""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   _UpperCAmelCase				(		unittest.TestCase				):




      @property
      def      a_   (				self				)    ->       List[str]:
          torch.manual_seed(0				)
          UpperCAmelCase          =       UNetaDModel(
              block_out_channels=(3_2, 6_4) ,							layers_per_block=2 ,							sample_size=3_2 ,							in_channels=3 ,							out_channels=3 ,							down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,							up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,							)
          return model




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       self.dummy_uncond_unet
          UpperCAmelCase          =       ScoreSdeVeScheduler()

          UpperCAmelCase          =       ScoreSdeVePipeline(unet=lowercase_ ,							scheduler=lowercase_				)
          sde_ve.to(lowercase_				)
          sde_ve.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       torch.manual_seed(0				)
          UpperCAmelCase          =       sde_ve(num_inference_steps=2 ,							output_type='numpy' ,							generator=lowercase_				).images

          UpperCAmelCase          =       torch.manual_seed(0				)
          UpperCAmelCase          =       sde_ve(num_inference_steps=2 ,							output_type='numpy' ,							generator=lowercase_ ,							return_dict=lowercase_				)[
              0
          ]

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

          assert image.shape == (1, 3_2, 3_2, 3)
          UpperCAmelCase          =       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   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       'google/ncsnpp-church-256'
          UpperCAmelCase          =       UNetaDModel.from_pretrained(lowercase_				)

          UpperCAmelCase          =       ScoreSdeVeScheduler.from_pretrained(lowercase_				)

          UpperCAmelCase          =       ScoreSdeVePipeline(unet=lowercase_ ,							scheduler=lowercase_				)
          sde_ve.to(lowercase_				)
          sde_ve.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       torch.manual_seed(0				)
          UpperCAmelCase          =       sde_ve(num_inference_steps=1_0 ,							output_type='numpy' ,							generator=lowercase_				).images

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

          assert image.shape == (1, 2_5_6, 2_5_6, 3)

          UpperCAmelCase          =       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





"""simple docstring"""

import functools
import operator

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


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[str]				   =						"sew-d"




      def __init__(				self ,							lowercase_=3_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_=2 ,							lowercase_=5_1_2 ,							lowercase_=2_5_6 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=("p2c", "c2p") ,							lowercase_="layer_norm" ,							lowercase_="gelu_python" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=0.0 ,							lowercase_=0.1 ,							lowercase_=0.0_2 ,							lowercase_=1E-7 ,							lowercase_=1E-5 ,							lowercase_="group" ,							lowercase_="gelu" ,							lowercase_=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,							lowercase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,							lowercase_=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,							lowercase_=False ,							lowercase_=1_2_8 ,							lowercase_=1_6 ,							lowercase_=True ,							lowercase_=0.0_5 ,							lowercase_=1_0 ,							lowercase_=2 ,							lowercase_=0.0 ,							lowercase_=1_0 ,							lowercase_=0 ,							lowercase_="mean" ,							lowercase_=False ,							lowercase_=False ,							lowercase_=2_5_6 ,							lowercase_=0 ,							lowercase_=1 ,							lowercase_=2 ,							**lowercase_ ,							)    ->       Dict:
          super().__init__(**lowercase_ ,							pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_				)
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       feat_extract_norm
          UpperCAmelCase          =       feat_extract_activation
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       conv_bias
          UpperCAmelCase          =       num_conv_pos_embeddings
          UpperCAmelCase          =       num_conv_pos_embedding_groups
          UpperCAmelCase          =       len(self.conv_dim				)
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       squeeze_factor
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       position_buckets
          UpperCAmelCase          =       share_att_key
          UpperCAmelCase          =       relative_attention
          UpperCAmelCase          =       norm_rel_ebd
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_dropout
          UpperCAmelCase          =       attention_dropout
          UpperCAmelCase          =       activation_dropout
          UpperCAmelCase          =       feat_proj_dropout
          UpperCAmelCase          =       final_dropout
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       feature_layer_norm_eps
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       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
          UpperCAmelCase          =       apply_spec_augment
          UpperCAmelCase          =       mask_time_prob
          UpperCAmelCase          =       mask_time_length
          UpperCAmelCase          =       mask_time_min_masks
          UpperCAmelCase          =       mask_feature_prob
          UpperCAmelCase          =       mask_feature_length
          UpperCAmelCase          =       mask_feature_min_masks

          # ctc loss
          UpperCAmelCase          =       ctc_loss_reduction
          UpperCAmelCase          =       ctc_zero_infinity

          # sequence classification
          UpperCAmelCase          =       use_weighted_layer_sum
          UpperCAmelCase          =       classifier_proj_size




      @property
      def      a_   (				self				)    ->       Tuple:
          return functools.reduce(operator.mul ,							self.conv_stride ,							1				)



