


from typing import Mapping

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


UpperCamelCase			:					Any         =							logging.get_logger(__name__)

UpperCamelCase			:					Dict         =							{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}

class      __snake_case				(   _lowerCamelCase):
					SCREAMING_SNAKE_CASE_		:       Dict								=			"longt5"
					SCREAMING_SNAKE_CASE_		:       Dict								=			["past_key_values"]
					SCREAMING_SNAKE_CASE_		:       int								=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
					def __init__(						self       :						Tuple	,      _lowerCAmelCase       :						Union[str, Any]=3_2_1_2_8	,      _lowerCAmelCase       :						Union[str, Any]=5_1_2	,      _lowerCAmelCase       :						List[str]=6_4	,      _lowerCAmelCase       :						str=2_0_4_8	,      _lowerCAmelCase       :						int=6	,      _lowerCAmelCase       :						Any=None	,      _lowerCAmelCase       :						List[str]=8	,      _lowerCAmelCase       :						int=1_2_7	,      _lowerCAmelCase       :						List[Any]=1_6	,      _lowerCAmelCase       :						Optional[int]=3_2	,      _lowerCAmelCase       :						List[Any]=1_2_8	,      _lowerCAmelCase       :						Tuple=0.1	,      _lowerCAmelCase       :						Optional[Any]=1E-6	,      _lowerCAmelCase       :						int=1.0	,      _lowerCAmelCase       :						int="relu"	,      _lowerCAmelCase       :						Optional[int]=True	,      _lowerCAmelCase       :						Union[str, Any]="local"	,      _lowerCAmelCase       :						Dict=True	,      _lowerCAmelCase       :						Tuple=0	,      _lowerCAmelCase       :						str=1	,      **_lowerCAmelCase       :						Optional[int]	,      )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      vocab_size
											__magic_name__					=      d_model
											__magic_name__					=      d_kv
											__magic_name__					=      d_ff
											__magic_name__					=      num_layers
											# default = symmetry
											__magic_name__					=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__magic_name__					=      num_heads
											__magic_name__					=      local_radius
											__magic_name__					=      global_block_size
											__magic_name__					=      relative_attention_num_buckets
											__magic_name__					=      relative_attention_max_distance
											__magic_name__					=      dropout_rate
											__magic_name__					=      layer_norm_epsilon
											__magic_name__					=      initializer_factor
											__magic_name__					=      feed_forward_proj
											__magic_name__					=      encoder_attention_type
											__magic_name__					=      use_cache

											__magic_name__					=      self.feed_forward_proj.split('-'  )
											__magic_name__					=      act_info[-1]
											__magic_name__					=      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":
																	__magic_name__					=      'gelu_new'

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







class      __snake_case				(   _lowerCamelCase):
					@property
					def 	a			(						self       :						Any  )							->					Mapping[str, Mapping[int, str]]:



											'''simple docstring'''





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

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

											return common_inputs







					@property
					def 	a			(						self       :						str  )							->					int:



											'''simple docstring'''





											return 1_3








import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					(LMSDiscreteScheduler,)
   lowerCAmelCase__  	=					10




   def _lowerCAmelCase (self      :List[str]       ,  **_UpperCamelCase      :Optional[int]					)->   List[str]:
         __A			  =							{
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**_UpperCamelCase					)
         return config




   def _lowerCAmelCase (self      :List[str]					)->   Tuple:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Dict					)->   Optional[int]:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
               self.check_over_configs(beta_start=_UpperCamelCase       ,  beta_end=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[int]					)->   Any:
         for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=_UpperCamelCase					)




   def _lowerCAmelCase (self      :List[str]					)->   str:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_UpperCamelCase					)




   def _lowerCAmelCase (self      :str					)->   List[str]:
         for t in [0, 500, 800]:
               self.check_over_forward(time_step=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   Tuple:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[int]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config(prediction_type='''v_prediction'''					)
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 0.0_0_1_7					) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06					) < 1e-3




   def _lowerCAmelCase (self      :List[Any]					)->   List[Any]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

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

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A			  =							sample.to(_UpperCamelCase					)

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3






   def _lowerCAmelCase (self      :List[str]					)->   int:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase       ,  use_karras_sigmas=_UpperCamelCase					)

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

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter.to(_UpperCamelCase					) * scheduler.init_noise_sigma
         __A			  =							sample.to(_UpperCamelCase					)

         for t in scheduler.timesteps:
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 3_8_1_2.9_9_2_7					) < 2e-2
         assert abs(result_mean.item() - 4.9_6_4_8					) < 1e-3





