






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  (DEISMultistepScheduler,)
      __snake_case       =  (('''num_inference_steps''', 25),)







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					**__UpperCAmelCase      :						Union[str, Any]  )    ->int:




       """simple docstring"""


       a		  =      {
           '''num_train_timesteps''': 1_000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''solver_order''': 2,
       }

       config.update(**__UpperCAmelCase  )
       return config







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						List[str]=0 ,					**__UpperCAmelCase      :						Union[str, Any]  )    ->Optional[int]:




       """simple docstring"""


       a		  =      dict(self.forward_default_kwargs  )
       a		  =      kwargs.pop('''num_inference_steps''' ,					__UpperCAmelCase  )
       a		  =      self.dummy_sample
       a		  =      0.1 * sample
       a		  =      [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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







      def       __lowerCAmelCase					(      self      :						Dict  )    ->str:




       """simple docstring"""


       pass







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[str]=0 ,					**__UpperCAmelCase      :						str  )    ->int:




       """simple docstring"""


       a		  =      dict(self.forward_default_kwargs  )
       a		  =      kwargs.pop('''num_inference_steps''' ,					__UpperCAmelCase  )
       a		  =      self.dummy_sample
       a		  =      0.1 * sample
       a		  =      [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
        a		  =      self.get_scheduler_config()
        a		  =      scheduler_class(**__UpperCAmelCase  )
        scheduler.set_timesteps(__UpperCAmelCase  )

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

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

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

        a		  =      scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase  ).prev_sample
        a		  =      new_scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase  ).prev_sample

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







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						Dict=None ,					**__UpperCAmelCase      :						Dict  )    ->List[str]:




       """simple docstring"""


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

       a		  =      self.scheduler_classes[0]
       a		  =      self.get_scheduler_config(**__UpperCAmelCase  )
       a		  =      scheduler_class(**__UpperCAmelCase  )

       a		  =      10
       a		  =      self.dummy_model()
       a		  =      self.dummy_sample_deter
       scheduler.set_timesteps(__UpperCAmelCase  )

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

       return sample







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Dict:




       """simple docstring"""


       a		  =      dict(self.forward_default_kwargs  )

       a		  =      kwargs.pop('''num_inference_steps''' ,					__UpperCAmelCase  )

       for scheduler_class in self.scheduler_classes:
        a		  =      self.get_scheduler_config()
        a		  =      scheduler_class(**__UpperCAmelCase  )

        a		  =      self.dummy_sample
        a		  =      0.1 * sample

        if num_inference_steps is not None and hasattr(__UpperCAmelCase ,					'''set_timesteps'''  ):
         scheduler.set_timesteps(__UpperCAmelCase  )
        elif num_inference_steps is not None and not hasattr(__UpperCAmelCase ,					'''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 + 0.10]
        a		  =      dummy_past_residuals[: scheduler.config.solver_order]

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

        a		  =      scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase  ).prev_sample
        a		  =      scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase  ).prev_sample

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







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      DEISMultistepScheduler(**self.get_scheduler_config()  )
       a		  =      self.full_loop(scheduler=__UpperCAmelCase  )
       a		  =      torch.mean(torch.abs(__UpperCAmelCase  )  )

       assert abs(result_mean.item() - 0.23916  ) < 1e-3

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

       a		  =      self.full_loop(scheduler=__UpperCAmelCase  )
       a		  =      torch.mean(torch.abs(__UpperCAmelCase  )  )

       assert abs(result_mean.item() - 0.23916  ) < 1e-3







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Tuple:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->int:




       """simple docstring"""


       self.check_over_configs(thresholding=__UpperCAmelCase  )
       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=__UpperCAmelCase ,					prediction_type=__UpperCAmelCase ,					sample_max_value=__UpperCAmelCase ,					algorithm_type='''deis''' ,					solver_order=__UpperCAmelCase ,					solver_type=__UpperCAmelCase ,					)







      def       __lowerCAmelCase					(      self      :						int  )    ->Tuple:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """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=__UpperCAmelCase ,					solver_type=__UpperCAmelCase ,					prediction_type=__UpperCAmelCase ,					algorithm_type=__UpperCAmelCase ,					)
           a		  =      self.full_loop(
               solver_order=__UpperCAmelCase ,					solver_type=__UpperCAmelCase ,					prediction_type=__UpperCAmelCase ,					algorithm_type=__UpperCAmelCase ,					)
           assert not torch.isnan(__UpperCAmelCase  ).any(), "Samples have nan numbers"







      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Any:




       """simple docstring"""


       self.check_over_configs(lower_order_final=__UpperCAmelCase  )
       self.check_over_configs(lower_order_final=__UpperCAmelCase  )







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




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      self.full_loop()
       a		  =      torch.mean(torch.abs(__UpperCAmelCase  )  )

       assert abs(result_mean.item() - 0.23916  ) < 1e-3







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->List[Any]:




       """simple docstring"""


       a		  =      self.full_loop(prediction_type='''v_prediction'''  )
       a		  =      torch.mean(torch.abs(__UpperCAmelCase  )  )

       assert abs(result_mean.item() - 0.091  ) < 1e-3







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Dict:




       """simple docstring"""


       a		  =      self.scheduler_classes[0]
       a		  =      self.get_scheduler_config(thresholding=__UpperCAmelCase ,					dynamic_thresholding_ratio=0  )
       a		  =      scheduler_class(**__UpperCAmelCase  )

       a		  =      10
       a		  =      self.dummy_model()
       a		  =      self.dummy_sample_deter.half()
       scheduler.set_timesteps(__UpperCAmelCase  )

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

       assert sample.dtype == torch.floataa










from typing import Mapping

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


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''t5'''
      __snake_case       =  ['''past_key_values''']
      __snake_case       =  {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Optional[Any]=32_128 ,					__UpperCAmelCase      :						List[Any]=512 ,					__UpperCAmelCase      :						Dict=64 ,					__UpperCAmelCase      :						Tuple=2_048 ,					__UpperCAmelCase      :						int=6 ,					__UpperCAmelCase      :						Optional[int]=None ,					__UpperCAmelCase      :						Optional[int]=8 ,					__UpperCAmelCase      :						str=32 ,					__UpperCAmelCase      :						Tuple=128 ,					__UpperCAmelCase      :						Optional[Any]=0.1 ,					__UpperCAmelCase      :						int=1e-6 ,					__UpperCAmelCase      :						int=1.0 ,					__UpperCAmelCase      :						List[str]="relu" ,					__UpperCAmelCase      :						int=True ,					__UpperCAmelCase      :						int=True ,					__UpperCAmelCase      :						List[Any]=0 ,					__UpperCAmelCase      :						int=1 ,					**__UpperCAmelCase      :						str ,					)    ->Optional[Any]:




       """simple docstring"""


       a		  =      vocab_size
       a		  =      d_model
       a		  =      d_kv
       a		  =      d_ff
       a		  =      num_layers
       a		  =      (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a		  =      num_heads
       a		  =      relative_attention_num_buckets
       a		  =      relative_attention_max_distance
       a		  =      dropout_rate
       a		  =      layer_norm_epsilon
       a		  =      initializer_factor
       a		  =      feed_forward_proj
       a		  =      use_cache

       a		  =      self.feed_forward_proj.split('''-'''  )
       a		  =      act_info[-1]
       a		  =      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\''''  )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
        a		  =      '''gelu_new'''

       super().__init__(
           pad_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					is_encoder_decoder=__UpperCAmelCase ,					**__UpperCAmelCase ,					)






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Mapping[str, Mapping[int, str]]:




       """simple docstring"""


       a		  =      {
           '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
           '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
       }
       if self.use_past:
        a		  =      '''past_encoder_sequence + sequence'''
        a		  =      {0: '''batch'''}
        a		  =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
        a		  =      {0: '''batch''', 1: '''decoder_sequence'''}
        a		  =      {0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
        self.fill_with_past_key_values_(__UpperCAmelCase ,					direction='''inputs'''  )

       return common_inputs







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




       """simple docstring"""


       return 13


