


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(IPNDMScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 5_0),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{"""num_train_timesteps""": 1000}
					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Tuple:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Any:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					int:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					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)
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[Any]:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

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

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

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

					return sample

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					dict(self.forward_default_kwargs )

					A             =					kwargs.pop("""num_inference_steps"""					, a__ )

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

						A             =					self.dummy_sample
						A             =					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""" ):
							A             =					num_inference_steps

						# copy over dummy past residuals (must be done after set_timesteps)
						A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						A             =					dummy_past_residuals[:]

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [100, 1000]:
						self.check_over_configs(num_train_timesteps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100] ):
						self.check_over_forward(num_inference_steps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

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







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


_lowercase							:   List[Any]						=						logging.get_logger(__name__)

_lowercase							:   List[str]						=						{
    "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    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'mctct'

				def __init__(    self					, a__=8065					, a__=1536					, a__=36					, a__=6144					, a__=4					, a__=384					, a__=920					, a__=1e-5					, a__=0.3					, a__="relu"					, a__=0.02					, a__=0.3					, a__=0.3					, a__=1					, a__=0					, a__=2					, a__=1					, a__=0.3					, a__=1					, a__=(7,)					, a__=(3,)					, a__=80					, a__=1					, a__=None					, a__="sum"					, a__=False					, **a__					, )						->					Optional[Any]:
					super().__init__(**a__					, pad_token_id=a__					, bos_token_id=a__					, eos_token_id=a__ )
					A             =					vocab_size
					A             =					hidden_size
					A             =					num_hidden_layers
					A             =					intermediate_size
					A             =					num_attention_heads
					A             =					attention_head_dim
					A             =					max_position_embeddings
					A             =					layer_norm_eps
					A             =					layerdrop
					A             =					hidden_act
					A             =					initializer_range
					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					pad_token_id
					A             =					bos_token_id
					A             =					eos_token_id
					A             =					conv_glu_dim
					A             =					conv_dropout
					A             =					num_conv_layers
					A             =					input_feat_per_channel
					A             =					input_channels
					A             =					conv_channels
					A             =					ctc_loss_reduction
					A             =					ctc_zero_infinity

					# prevents config testing fail with exporting to json
					A             =					list(a__ )
					A             =					list(a__ )

					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}`.' )



