"""simple docstring"""






import functools
import operator

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


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

__lowerCAmelCase					: List[Any]        ={
    """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 				_A		( lowerCAmelCase			):
      snake_case__    :     List[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=0.0_2	, __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=0.0_5	, __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	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase	, pad_token_id=__lowerCAmelCase	, bos_token_id=__lowerCAmelCase	, eos_token_id=__lowerCAmelCase		)
            lowercase														=			hidden_size
            lowercase														=			feat_extract_norm
            lowercase														=			feat_extract_activation
            lowercase														=			list(__lowerCAmelCase		)
            lowercase														=			list(__lowerCAmelCase		)
            lowercase														=			list(__lowerCAmelCase		)
            lowercase														=			conv_bias
            lowercase														=			num_conv_pos_embeddings
            lowercase														=			num_conv_pos_embedding_groups
            lowercase														=			len(self.conv_dim		)
            lowercase														=			num_hidden_layers
            lowercase														=			intermediate_size
            lowercase														=			squeeze_factor
            lowercase														=			max_position_embeddings
            lowercase														=			position_buckets
            lowercase														=			share_att_key
            lowercase														=			relative_attention
            lowercase														=			norm_rel_ebd
            lowercase														=			list(__lowerCAmelCase		)
            lowercase														=			hidden_act
            lowercase														=			num_attention_heads
            lowercase														=			hidden_dropout
            lowercase														=			attention_dropout
            lowercase														=			activation_dropout
            lowercase														=			feat_proj_dropout
            lowercase														=			final_dropout
            lowercase														=			layer_norm_eps
            lowercase														=			feature_layer_norm_eps
            lowercase														=			initializer_range
            lowercase														=			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
            lowercase														=			apply_spec_augment
            lowercase														=			mask_time_prob
            lowercase														=			mask_time_length
            lowercase														=			mask_time_min_masks
            lowercase														=			mask_feature_prob
            lowercase														=			mask_feature_length
            lowercase														=			mask_feature_min_masks

            # ctc loss
            lowercase														=			ctc_loss_reduction
            lowercase														=			ctc_zero_infinity

            # sequence classification
            lowercase														=			use_weighted_layer_sum
            lowercase														=			classifier_proj_size







      @property
      def        A__ (		self		):







            """simple docstring"""



            return functools.reduce(operator.mul	, self.conv_stride	, 1		)




"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Union[str, Any]													=			(IPNDMScheduler,)
      snake_case__    :     List[str]													=			(('num_inference_steps', 50),)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			{"""num_train_timesteps""": 1000}
            config.update(**__lowerCAmelCase		)
            return config

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowercase														=			dummy_past_residuals[:]

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			scheduler_class.from_pretrained(__lowerCAmelCase		)
                        new_scheduler.set_timesteps(__lowerCAmelCase		)
                        # copy over dummy past residuals
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

      def        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)

                  # copy over dummy past residuals (must be after setting timesteps)
                  lowercase														=			dummy_past_residuals[:]

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			scheduler_class.from_pretrained(__lowerCAmelCase		)
                        # copy over dummy past residuals
                        new_scheduler.set_timesteps(__lowerCAmelCase		)

                        # copy over dummy past residual (must be after setting timesteps)
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scheduler_classes[0]
            lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
            lowercase														=			scheduler_class(**__lowerCAmelCase		)

            lowercase														=			10
            lowercase														=			self.dummy_model()
            lowercase														=			self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCAmelCase		)

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            return sample

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)

            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)

                  lowercase														=			self.dummy_sample
                  lowercase														=			0.1 * sample

                  if num_inference_steps is not None and hasattr(__lowerCAmelCase	, """set_timesteps"""		):
                        scheduler.set_timesteps(__lowerCAmelCase		)
                  elif num_inference_steps is not None and not hasattr(__lowerCAmelCase	, """set_timesteps"""		):
                        lowercase														=			num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                  lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.timesteps[5]
                  lowercase														=			scheduler.timesteps[6]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  self.assertEqual(output_a.shape	, sample.shape		)
                  self.assertEqual(output_a.shape	, output_a.shape		)

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  self.assertEqual(output_a.shape	, sample.shape		)
                  self.assertEqual(output_a.shape	, output_a.shape		)

      def        A__ (		self		):







            """simple docstring"""



            for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            for t, num_inference_steps in zip([1, 5, 10]	, [10, 50, 100]		):
                  self.check_over_forward(num_inference_steps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.full_loop()
            lowercase														=			torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_mean.item() - 254_0529		) < 10



