
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class       a		(   UpperCamelCase_					):
       lowercase_		:						Union[str, Any]   							=  (DEISMultistepScheduler,)
       lowercase_		:						List[str]   							=  (('num_inference_steps', 25),)







       def       UpperCAmelCase__					(						self  :  List[str]      ,				**snake_case__  :  Tuple ):
        """simple docstring"""







        __lowerCAmelCase         =							{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.0_0_0_1,
            '''beta_end''': 0.0_2,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
        }

        config.update(**snake_case__ )
        return config







       def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  str=0      ,				**snake_case__  :  List[Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							dict(self.forward_default_kwargs )
        __lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
        __lowerCAmelCase         =							self.dummy_sample
        __lowerCAmelCase         =							0.1 * sample
        __lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

         __lowerCAmelCase         =							sample, sample
         for t in range(snake_case__      ,				time_step + scheduler.config.solver_order + 1 ):
          __lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
          __lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

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







       def       UpperCAmelCase__					(						self  :  str ):
        """simple docstring"""







        pass







       def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  List[str]=0      ,				**snake_case__  :  Dict ):
        """simple docstring"""







        __lowerCAmelCase         =							dict(self.forward_default_kwargs )
        __lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
        __lowerCAmelCase         =							self.dummy_sample
        __lowerCAmelCase         =							0.1 * sample
        __lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

         # copy over dummy past residuals (must be after setting timesteps)
         __lowerCAmelCase         =							dummy_past_residuals[: scheduler.config.solver_order]

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

          # copy over dummy past residual (must be after setting timesteps)
          __lowerCAmelCase         =							dummy_past_residuals[: new_scheduler.config.solver_order]

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

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







       def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Optional[Any]=None      ,				**snake_case__  :  List[Any] ):
        """simple docstring"""







        if scheduler is None:
         __lowerCAmelCase         =							self.scheduler_classes[0]
         __lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
         __lowerCAmelCase         =							scheduler_class(**snake_case__ )

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

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

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

        return sample







       def       UpperCAmelCase__					(						self  :  List[Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							dict(self.forward_default_kwargs )

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

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

         __lowerCAmelCase         =							self.dummy_sample
         __lowerCAmelCase         =							0.1 * sample

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

         # copy over dummy past residuals (must be done after set_timesteps)
         __lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
         __lowerCAmelCase         =							dummy_past_residuals[: scheduler.config.solver_order]

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

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

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







       def       UpperCAmelCase__					(						self  :  Dict ):
        """simple docstring"""







        __lowerCAmelCase         =							DEISMultistepScheduler(**self.get_scheduler_config() )
        __lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
        __lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

        assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3

        __lowerCAmelCase         =							DPMSolverSinglestepScheduler.from_config(scheduler.config )
        __lowerCAmelCase         =							DPMSolverMultistepScheduler.from_config(scheduler.config )
        __lowerCAmelCase         =							UniPCMultistepScheduler.from_config(scheduler.config )
        __lowerCAmelCase         =							DEISMultistepScheduler.from_config(scheduler.config )

        __lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
        __lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

        assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3







       def       UpperCAmelCase__					(						self  :  Optional[Any] ):
        """simple docstring"""







        for timesteps in [25, 50, 100, 999, 1_000]:
         self.check_over_configs(num_train_timesteps=snake_case__ )







       def       UpperCAmelCase__					(						self  :  Any ):
        """simple docstring"""







        self.check_over_configs(thresholding=snake_case__ )
        for order in [1, 2, 3]:
         for solver_type in ["logrho"]:
          for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(
                thresholding=snake_case__      ,				prediction_type=snake_case__      ,				sample_max_value=snake_case__      ,				algorithm_type="deis"      ,				solver_order=snake_case__      ,				solver_type=snake_case__      ,				)







       def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
        """simple docstring"""







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







       def       UpperCAmelCase__					(						self  :  List[Any] ):
        """simple docstring"""







        for algorithm_type in ["deis"]:
         for solver_type in ["logrho"]:
          for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(
                solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
            __lowerCAmelCase         =							self.full_loop(
                solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
            assert not torch.isnan(snake_case__ ).any(), "Samples have nan numbers"







       def       UpperCAmelCase__					(						self  :  Any ):
        """simple docstring"""







        self.check_over_configs(lower_order_final=snake_case__ )
        self.check_over_configs(lower_order_final=snake_case__ )







       def       UpperCAmelCase__					(						self  :  int ):
        """simple docstring"""







        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
         self.check_over_forward(num_inference_steps=snake_case__      ,				time_step=0 )







       def       UpperCAmelCase__					(						self  :  Dict ):
        """simple docstring"""







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

        assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3







       def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction" )
        __lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

        assert abs(result_mean.item() - 0.0_9_1 ) < 1E-3







       def       UpperCAmelCase__					(						self  :  Tuple ):
        """simple docstring"""







        __lowerCAmelCase         =							self.scheduler_classes[0]
        __lowerCAmelCase         =							self.get_scheduler_config(thresholding=snake_case__      ,				dynamic_thresholding_ratio=0 )
        __lowerCAmelCase         =							scheduler_class(**snake_case__ )

        __lowerCAmelCase         =							10
        __lowerCAmelCase         =							self.dummy_model()
        __lowerCAmelCase         =							self.dummy_sample_deter.half()
        scheduler.set_timesteps(snake_case__ )

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

        assert sample.dtype == torch.floataa








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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'mctct'







							def __init__(						self  :  Dict      ,				snake_case__  :  Any=8_065      ,				snake_case__  :  Tuple=1_536      ,				snake_case__  :  Union[str, Any]=36      ,				snake_case__  :  List[Any]=6_144      ,				snake_case__  :  Tuple=4      ,				snake_case__  :  List[str]=384      ,				snake_case__  :  List[Any]=920      ,				snake_case__  :  Any=1E-5      ,				snake_case__  :  List[Any]=0.3      ,				snake_case__  :  List[str]="relu"      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Optional[Any]=0.3      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  List[Any]=1      ,				snake_case__  :  Optional[int]=0      ,				snake_case__  :  Any=2      ,				snake_case__  :  Any=1      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  Tuple=1      ,				snake_case__  :  Union[str, Any]=(7,)      ,				snake_case__  :  List[Any]=(3,)      ,				snake_case__  :  List[Any]=80      ,				snake_case__  :  Optional[int]=1      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  int="sum"      ,				snake_case__  :  Dict=False      ,				**snake_case__  :  List[str]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__      ,				pad_token_id=snake_case__      ,				bos_token_id=snake_case__      ,				eos_token_id=snake_case__ )
								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							num_hidden_layers
								__lowerCAmelCase         =							intermediate_size
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							attention_head_dim
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							layerdrop
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							pad_token_id
								__lowerCAmelCase         =							bos_token_id
								__lowerCAmelCase         =							eos_token_id
								__lowerCAmelCase         =							conv_glu_dim
								__lowerCAmelCase         =							conv_dropout
								__lowerCAmelCase         =							num_conv_layers
								__lowerCAmelCase         =							input_feat_per_channel
								__lowerCAmelCase         =							input_channels
								__lowerCAmelCase         =							conv_channels
								__lowerCAmelCase         =							ctc_loss_reduction
								__lowerCAmelCase         =							ctc_zero_infinity

								# prevents config testing fail with exporting to json
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(snake_case__ )

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






