




'''simple docstring'''





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

enable_full_determinism()

class        _a    (						__lowerCAmelCase , __lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Tuple       =						UNetaDModel
     SCREAMING_SNAKE_CASE_							:   str       =						"""sample"""






     @property
     def     _lowercase     (			self   )							->      int:
          _snake_case							=				4
          _snake_case							=				3
          _snake_case							=				(32, 32)

          _snake_case							=				floats_tensor((batch_size, num_channels) + sizes   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor([10]   ).to(_SCREAMING_SNAKE_CASE   )

          return {"sample": noise, "timestep": time_step}






     @property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return (3, 32, 32)






     @property
     def     _lowercase     (			self   )							->      Dict:
          return (3, 32, 32)






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				{
              "block_out_channels": (32, 64),
              "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
              "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
              "attention_head_dim": 3,
              "out_channels": 3,
              "in_channels": 3,
              "layers_per_block": 2,
              "sample_size": 32,
          }
          _snake_case							=				self.dummy_input
          return init_dict, inputs_dict







class        _a    (						__lowerCAmelCase , __lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						UNetaDModel
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""sample"""






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				4
          _snake_case							=				4
          _snake_case							=				(32, 32)

          _snake_case							=				floats_tensor((batch_size, num_channels) + sizes   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor([10]   ).to(_SCREAMING_SNAKE_CASE   )

          return {"sample": noise, "timestep": time_step}






     @property
     def     _lowercase     (			self   )							->      int:
          return (4, 32, 32)






     @property
     def     _lowercase     (			self   )							->      int:
          return (4, 32, 32)






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				{
              "sample_size": 32,
              "in_channels": 4,
              "out_channels": 4,
              "layers_per_block": 2,
              "block_out_channels": (32, 64),
              "attention_head_dim": 32,
              "down_block_types": ("DownBlock2D", "DownBlock2D"),
              "up_block_types": ("UpBlock2D", "UpBlock2D"),
          }
          _snake_case							=				self.dummy_input
          return init_dict, inputs_dict






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							, _snake_case							=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,output_loading_info=_SCREAMING_SNAKE_CASE   )

          self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )
          self.assertEqual(len(loading_info["missing_keys"]   ) ,0   )

          model.to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(**self.dummy_input   ).sample

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






     @unittest.skipIf(torch_device != "cuda" ,"This test is supposed to run on GPU"   )
     def     _lowercase     (			self   )							->      Any:
          _snake_case							, _snake_case							=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,output_loading_info=_SCREAMING_SNAKE_CASE   )
          model.to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(**self.dummy_input   ).sample

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






     @unittest.skipIf(torch_device != "cuda" ,"This test is supposed to run on GPU"   )
     def     _lowercase     (			self   )							->      Union[str, Any]:
          # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
          _snake_case							, _snake_case							=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,output_loading_info=_SCREAMING_SNAKE_CASE   )
          model_accelerate.to(_SCREAMING_SNAKE_CASE   )
          model_accelerate.eval()

          _snake_case							=				torch.randn(
              1 ,model_accelerate.config.in_channels ,model_accelerate.config.sample_size ,model_accelerate.config.sample_size ,generator=torch.manual_seed(0   ) ,)
          _snake_case							=				noise.to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor([10] * noise.shape[0]   ).to(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				model_accelerate(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )["sample"]

          # two models don't need to stay in the device at the same time
          del model_accelerate
          torch.cuda.empty_cache()
          gc.collect()

          _snake_case							, _snake_case							=				UNetaDModel.from_pretrained(
              "fusing/unet-ldm-dummy-update" ,output_loading_info=_SCREAMING_SNAKE_CASE ,low_cpu_mem_usage=_SCREAMING_SNAKE_CASE   )
          model_normal_load.to(_SCREAMING_SNAKE_CASE   )
          model_normal_load.eval()
          _snake_case							=				model_normal_load(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )["sample"]

          assert torch_all_close(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,rtol=1e-3   )






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"   )
          model.eval()
          model.to(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				torch.randn(
              1 ,model.config.in_channels ,model.config.sample_size ,model.config.sample_size ,generator=torch.manual_seed(0   ) ,)
          _snake_case							=				noise.to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor([10] * noise.shape[0]   ).to(_SCREAMING_SNAKE_CASE   )

          with torch.no_grad():
               _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).sample

          _snake_case							=				output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          _snake_case							=				torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]   )
          # fmt: on

          self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,rtol=1e-3   )   )







class        _a    (						__lowerCAmelCase , __lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						UNetaDModel
     SCREAMING_SNAKE_CASE_							:   Dict       =						"""sample"""






     @property
     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE=(32, 32)   )							->      List[Any]:
          _snake_case							=				4
          _snake_case							=				3

          _snake_case							=				floats_tensor((batch_size, num_channels) + sizes   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor(batch_size * [10]   ).to(dtype=torch.intaa ,device=_SCREAMING_SNAKE_CASE   )

          return {"sample": noise, "timestep": time_step}






     @property
     def     _lowercase     (			self   )							->      Union[str, Any]:
          return (3, 32, 32)






     @property
     def     _lowercase     (			self   )							->      List[Any]:
          return (3, 32, 32)






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				{
              "block_out_channels": [32, 64, 64, 64],
              "in_channels": 3,
              "layers_per_block": 1,
              "out_channels": 3,
              "time_embedding_type": "fourier",
              "norm_eps": 1e-6,
              "mid_block_scale_factor": math.sqrt(2.0   ),
              "norm_num_groups": None,
              "down_block_types": [
                  "SkipDownBlock2D",
                  "AttnSkipDownBlock2D",
                  "SkipDownBlock2D",
                  "SkipDownBlock2D",
              ],
              "up_block_types": [
                  "SkipUpBlock2D",
                  "SkipUpBlock2D",
                  "AttnSkipUpBlock2D",
                  "SkipUpBlock2D",
              ],
          }
          _snake_case							=				self.dummy_input
          return init_dict, inputs_dict






     @slow
     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							, _snake_case							=				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" ,output_loading_info=_SCREAMING_SNAKE_CASE   )
          self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )
          self.assertEqual(len(loading_info["missing_keys"]   ) ,0   )

          model.to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.dummy_input
          _snake_case							=				floats_tensor((4, 3) + (256, 256)   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				noise
          _snake_case							=				model(**_SCREAMING_SNAKE_CASE   )

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






     @slow
     def     _lowercase     (			self   )							->      str:
          _snake_case							=				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"   )
          model.to(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				4
          _snake_case							=				3
          _snake_case							=				(256, 256)

          _snake_case							=				torch.ones((batch_size, num_channels) + sizes   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor(batch_size * [1e-4]   ).to(_SCREAMING_SNAKE_CASE   )

          with torch.no_grad():
               _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).sample

          _snake_case							=				output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          _snake_case							=				torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]   )
          # fmt: on

          self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,rtol=1e-2   )   )






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"   )
          model.to(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				4
          _snake_case							=				3
          _snake_case							=				(32, 32)

          _snake_case							=				torch.ones((batch_size, num_channels) + sizes   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor(batch_size * [1e-4]   ).to(_SCREAMING_SNAKE_CASE   )

          with torch.no_grad():
               _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).sample

          _snake_case							=				output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          _snake_case							=				torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]   )
          # fmt: on

          self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,rtol=1e-2   )   )






     def     _lowercase     (			self   )							->      List[Any]:
          # not required for this model
          pass








'''simple docstring'''





import functools
import operator

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


UpperCamelCase_						:      str       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[Any]       =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						"""sew"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-5 ,_SCREAMING_SNAKE_CASE="group" ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=0.0_5 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE="mean" ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=256 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=2 ,**_SCREAMING_SNAKE_CASE ,)							->      str:
          super().__init__(**_SCREAMING_SNAKE_CASE ,pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				hidden_size
          _snake_case							=				feat_extract_norm
          _snake_case							=				feat_extract_activation
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				conv_bias
          _snake_case							=				num_conv_pos_embeddings
          _snake_case							=				num_conv_pos_embedding_groups
          _snake_case							=				len(self.conv_dim   )
          _snake_case							=				num_hidden_layers
          _snake_case							=				intermediate_size
          _snake_case							=				squeeze_factor
          _snake_case							=				hidden_act
          _snake_case							=				num_attention_heads
          _snake_case							=				hidden_dropout
          _snake_case							=				attention_dropout
          _snake_case							=				activation_dropout
          _snake_case							=				feat_proj_dropout
          _snake_case							=				final_dropout
          _snake_case							=				layerdrop
          _snake_case							=				layer_norm_eps
          _snake_case							=				initializer_range
          _snake_case							=				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
          _snake_case							=				apply_spec_augment
          _snake_case							=				mask_time_prob
          _snake_case							=				mask_time_length
          _snake_case							=				mask_time_min_masks
          _snake_case							=				mask_feature_prob
          _snake_case							=				mask_feature_length
          _snake_case							=				mask_feature_min_masks

          # ctc loss
          _snake_case							=				ctc_loss_reduction
          _snake_case							=				ctc_zero_infinity

          # sequence classification
          _snake_case							=				use_weighted_layer_sum
          _snake_case							=				classifier_proj_size






     @property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return functools.reduce(operator.mul ,self.conv_stride ,1   )


