


"""simple docstring"""



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    (KDPMaDiscreteScheduler,)
   __UpperCAmelCase					:			List[Any]				    =    10







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : List[str]    =       {
           "num_train_timesteps": 1100,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**UpperCamelCase__		)
       return config







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,      [0.0002, 0.002, 0.02]		):
           self.check_over_configs(beta_start=UpperCamelCase__		,      beta_end=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : str    =       self.scheduler_classes[0]
       snake_case       : str    =       self.get_scheduler_config(prediction_type="v_prediction"		)
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : List[str]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : Optional[Any]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : int    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : int    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Optional[Any]    =       output.prev_sample

       snake_case       : Optional[Any]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Any    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.6934e-07		) < 1e-2
           assert abs(result_mean.item() - 6.1112e-10		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 4.693_4286_5017_0972e-07		) < 1e-2
           assert abs(result_mean.item() - 0.0002		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Optional[Any]    =       self.scheduler_classes[0]
       snake_case       : Optional[int]    =       self.get_scheduler_config()
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : Union[str, Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : List[str]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : List[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : int    =       output.prev_sample

       snake_case       : int    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Union[str, Any]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : Any    =       self.get_scheduler_config()
       snake_case       : Union[str, Any]    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		,      device=UpperCamelCase__		)

       snake_case       : Optional[Any]    =       self.dummy_model()
       snake_case       : Optional[int]    =       self.dummy_sample_deter.to(UpperCamelCase__		) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
           snake_case       : Optional[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Any    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : List[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Dict    =       output.prev_sample

       snake_case       : Dict    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : str    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if str(UpperCamelCase__		).startswith("cpu"		):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3









"""simple docstring"""



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    '''umt5'''
   __UpperCAmelCase					:			str				    =    ['''past_key_values''']







   def __init__(      self		,      UpperCamelCase__=25_0112		,      UpperCamelCase__=512		,      UpperCamelCase__=64		,      UpperCamelCase__=1024		,      UpperCamelCase__=8		,      UpperCamelCase__=None		,      UpperCamelCase__=6		,      UpperCamelCase__=32		,      UpperCamelCase__=128		,      UpperCamelCase__=0.1		,      UpperCamelCase__=1e-6		,      UpperCamelCase__=1.0		,      UpperCamelCase__="gated-gelu"		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__="T5Tokenizer"		,      UpperCamelCase__=True		,      UpperCamelCase__=0		,      UpperCamelCase__=1		,      UpperCamelCase__=0		,      **UpperCamelCase__		,      )      ->						Dict:

       '''simple docstring'''




       super().__init__(
           is_encoder_decoder=UpperCamelCase__		,      tokenizer_class=UpperCamelCase__		,      tie_word_embeddings=UpperCamelCase__		,      pad_token_id=UpperCamelCase__		,      eos_token_id=UpperCamelCase__		,      decoder_start_token_id=UpperCamelCase__		,      **UpperCamelCase__		,      )
       snake_case       : Optional[int]    =       vocab_size
       snake_case       : Any    =       d_model
       snake_case       : Optional[Any]    =       d_kv
       snake_case       : Any    =       d_ff
       snake_case       : Optional[Any]    =       num_layers
       snake_case       : Optional[Any]    =       (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       snake_case       : Optional[Any]    =       num_heads
       snake_case       : Optional[Any]    =       relative_attention_num_buckets
       snake_case       : Dict    =       relative_attention_max_distance
       snake_case       : Tuple    =       dropout_rate
       snake_case       : List[Any]    =       layer_norm_epsilon
       snake_case       : Any    =       initializer_factor
       snake_case       : Optional[Any]    =       feed_forward_proj
       snake_case       : Tuple    =       use_cache

       snake_case       : Any    =       self.feed_forward_proj.split("-"		)
       snake_case       : Tuple    =       act_info[-1]
       snake_case       : Optional[int]    =       act_info[0] == "gated"

       if len(UpperCamelCase__		) > 1 and act_info[0] != "gated" or len(UpperCamelCase__		) > 2:
           raise ValueError(
               F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"		)

       if feed_forward_proj == "gated-gelu":
           snake_case       : List[str]    =       "gelu_new"







   @property
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       return self.d_model







   @property
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return self.num_heads







   @property
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.num_layers







class _lowerCAmelCase							( snake_case_      ):







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       {
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
           snake_case       : Tuple    =       "past_encoder_sequence + sequence"
           snake_case       : int    =       {0: "batch"}
           snake_case       : int    =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
           snake_case       : List[Any]    =       {0: "batch", 1: "decoder_sequence"}
           snake_case       : Any    =       {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
           self.fill_with_past_key_values_(UpperCamelCase__		,      direction="inputs"		)

       return common_inputs







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return 13







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 5e-4





