





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	__snake_case  ):
      A_						:    Optional[int]							=   (PNDMScheduler,)
      A_						:    Optional[int]							=   (('num_inference_steps', 50),)


      def        a			(self	:				Optional[int]    ,      **a__	:				Any				):





         """simple docstring"""



         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**a__				)
         return config


      def        a			(self	:				List[str]    ,      a__	:				Optional[Any]=0    ,      **a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

            __snake_case								=       scheduler.step_prk(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step_prk(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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

            __snake_case								=       scheduler.step_plms(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step_plms(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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


      def        a			(self	:				Any				):





         """simple docstring"""



         pass


      def        a			(self	:				Any    ,      a__	:				Union[str, Any]=0    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)

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

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

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

            __snake_case								=       scheduler.step_prk(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step_prk(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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

            __snake_case								=       scheduler.step_plms(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step_plms(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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


      def        a			(self	:				Union[str, Any]    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(**a__				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.prk_timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step_prk(a__    ,      a__    ,      a__				).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step_plms(a__    ,      a__    ,      a__				).prev_sample

         return sample


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)

         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)

            __snake_case								=       self.dummy_sample
            __snake_case								=       0.1 * sample

            if num_inference_steps is not None and hasattr(a__    ,      '''set_timesteps'''				):
               scheduler.set_timesteps(a__				)
            elif num_inference_steps is not None and not hasattr(a__    ,      '''set_timesteps'''				):
               __snake_case								=       num_inference_steps

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

            __snake_case								=       scheduler.step_prk(a__    ,      0    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       scheduler.step_prk(a__    ,      1    ,      a__    ,      **a__				).prev_sample

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

            __snake_case								=       scheduler.step_plms(a__    ,      0    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       scheduler.step_plms(a__    ,      1    ,      a__    ,      **a__				).prev_sample

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


      def        a			(self	:				Tuple				):





         """simple docstring"""



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


      def        a			(self	:				Tuple				):





         """simple docstring"""



         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=a__				)

         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(steps_offset=1				)
         __snake_case								=       scheduler_class(**a__				)
         scheduler.set_timesteps(10				)
         assert torch.equal(
             scheduler.timesteps    ,      torch.LongTensor(
                 [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)    ,      )


      def        a			(self	:				Any				):





         """simple docstring"""



         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,      [0.0_0_2, 0.0_2]				):
            self.check_over_configs(beta_start=a__    ,      beta_end=a__				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=a__				)


      def        a			(self	:				int				):





         """simple docstring"""



         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=a__				)


      def        a			(self	:				Dict				):





         """simple docstring"""



         for t in [1, 5, 10]:
            self.check_over_forward(time_step=a__				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



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


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       27

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.dummy_sample
            __snake_case								=       0.1 * sample

            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)

            scheduler.set_timesteps(a__				)

            # before power of 3 fix, would error on first step, so we only need to do two
            for i, t in enumerate(scheduler.prk_timesteps[:2]				):
               __snake_case								=       scheduler.step_prk(a__    ,      a__    ,      a__				).prev_sample


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         with self.assertRaises(a__				):
            __snake_case								=       self.scheduler_classes[0]
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)

            scheduler.step_plms(self.dummy_sample    ,      1    ,      self.dummy_sample				).prev_sample


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop()
         __snake_case								=       torch.sum(torch.abs(a__				)				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_sum.item() - 1_9_8.1_3_1_8				) < 1E-2
         assert abs(result_mean.item() - 0.2_5_8_0				) < 1E-3


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
         __snake_case								=       torch.sum(torch.abs(a__				)				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_sum.item() - 6_7.3_9_8_6				) < 1E-2
         assert abs(result_mean.item() - 0.0_8_7_8				) < 1E-3


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       self.full_loop(set_alpha_to_one=a__    ,      beta_start=0.0_1				)
         __snake_case								=       torch.sum(torch.abs(a__				)				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_sum.item() - 2_3_0.0_3_9_9				) < 1E-2
         assert abs(result_mean.item() - 0.2_9_9_5				) < 1E-3


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       self.full_loop(set_alpha_to_one=a__    ,      beta_start=0.0_1				)
         __snake_case								=       torch.sum(torch.abs(a__				)				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_sum.item() - 1_8_6.9_4_8_2				) < 1E-2
         assert abs(result_mean.item() - 0.2_4_3_4				) < 1E-3








import functools
import operator

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


snake_case_           =      logging.get_logger(__name__)

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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    str							=   'sew-d'


      def __init__(self	:				str    ,      a__	:				List[Any]=32    ,      a__	:				Optional[int]=768    ,      a__	:				Union[str, Any]=12    ,      a__	:				Tuple=12    ,      a__	:				int=3072    ,      a__	:				List[Any]=2    ,      a__	:				Dict=512    ,      a__	:				Dict=256    ,      a__	:				List[str]=True    ,      a__	:				List[str]=True    ,      a__	:				str=("p2c", "c2p")    ,      a__	:				str="layer_norm"    ,      a__	:				str="gelu_python"    ,      a__	:				List[Any]=0.1    ,      a__	:				Dict=0.1    ,      a__	:				Optional[Any]=0.1    ,      a__	:				Union[str, Any]=0.0    ,      a__	:				Dict=0.1    ,      a__	:				int=0.0_2    ,      a__	:				str=1E-7    ,      a__	:				Union[str, Any]=1E-5    ,      a__	:				Union[str, Any]="group"    ,      a__	:				Optional[Any]="gelu"    ,      a__	:				List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,      a__	:				Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,      a__	:				Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,      a__	:				List[str]=False    ,      a__	:				Optional[Any]=128    ,      a__	:				Union[str, Any]=16    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=0.0_5    ,      a__	:				Tuple=10    ,      a__	:				str=2    ,      a__	:				int=0.0    ,      a__	:				Optional[Any]=10    ,      a__	:				List[Any]=0    ,      a__	:				Optional[int]="mean"    ,      a__	:				List[str]=False    ,      a__	:				Tuple=False    ,      a__	:				Optional[int]=256    ,      a__	:				str=0    ,      a__	:				List[Any]=1    ,      a__	:				int=2    ,      **a__	:				List[str]    ,      ):





         """simple docstring"""



         super().__init__(**a__    ,      pad_token_id=a__    ,      bos_token_id=a__    ,      eos_token_id=a__				)
         __snake_case								=       hidden_size
         __snake_case								=       feat_extract_norm
         __snake_case								=       feat_extract_activation
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __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								=       max_position_embeddings
         __snake_case								=       position_buckets
         __snake_case								=       share_att_key
         __snake_case								=       relative_attention
         __snake_case								=       norm_rel_ebd
         __snake_case								=       list(a__				)
         __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								=       layer_norm_eps
         __snake_case								=       feature_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        a			(self	:				List[Any]				):





         """simple docstring"""



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

