






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
   __snake_case    :				Any									=							"longformer"







   def __init__(     self:  str				,     UpperCAmelCase_:  Union[List[int], int] = 512				,     UpperCAmelCase_:  int = 2				,     UpperCAmelCase_:  int = 1				,     UpperCAmelCase_:  int = 0				,     UpperCAmelCase_:  int = 2				,     UpperCAmelCase_:  int = 30_522				,     UpperCAmelCase_:  int = 768				,     UpperCAmelCase_:  int = 12				,     UpperCAmelCase_:  int = 12				,     UpperCAmelCase_:  int = 3_072				,     UpperCAmelCase_:  str = "gelu"				,     UpperCAmelCase_:  float = 0.1				,     UpperCAmelCase_:  float = 0.1				,     UpperCAmelCase_:  int = 512				,     UpperCAmelCase_:  int = 2				,     UpperCAmelCase_:  float = 0.02				,     UpperCAmelCase_:  float = 1E-12				,     UpperCAmelCase_:  bool = False				,     **UpperCAmelCase_:  Dict				,     ):







         '''simple docstring'''




         super().__init__(pad_token_id=UpperCAmelCase_				,     **UpperCAmelCase_					)

         _SCREAMING_SNAKE_CASE     				=  attention_window
         _SCREAMING_SNAKE_CASE     				=  sep_token_id
         _SCREAMING_SNAKE_CASE     				=  bos_token_id
         _SCREAMING_SNAKE_CASE     				=  eos_token_id
         _SCREAMING_SNAKE_CASE     				=  vocab_size
         _SCREAMING_SNAKE_CASE     				=  hidden_size
         _SCREAMING_SNAKE_CASE     				=  num_hidden_layers
         _SCREAMING_SNAKE_CASE     				=  num_attention_heads
         _SCREAMING_SNAKE_CASE     				=  hidden_act
         _SCREAMING_SNAKE_CASE     				=  intermediate_size
         _SCREAMING_SNAKE_CASE     				=  hidden_dropout_prob
         _SCREAMING_SNAKE_CASE     				=  attention_probs_dropout_prob
         _SCREAMING_SNAKE_CASE     				=  max_position_embeddings
         _SCREAMING_SNAKE_CASE     				=  type_vocab_size
         _SCREAMING_SNAKE_CASE     				=  initializer_range
         _SCREAMING_SNAKE_CASE     				=  layer_norm_eps
         _SCREAMING_SNAKE_CASE     				=  onnx_export



class 				__UpperCAmelCase		(_UpperCAmelCase   ):







   def __init__(     self:  Union[str, Any]				,     UpperCAmelCase_:  "PretrainedConfig"				,     UpperCAmelCase_:  str = "default"				,     UpperCAmelCase_:  "List[PatchingSpec]" = None					):







         '''simple docstring'''




         super().__init__(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_					)
         _SCREAMING_SNAKE_CASE     				=  True







   @property
   def 							UpperCamelCase				(     self:  Any					):







         '''simple docstring'''




         if self.task == "multiple-choice":
               _SCREAMING_SNAKE_CASE     				=  {0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
               _SCREAMING_SNAKE_CASE     				=  {0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
                 ("""global_attention_mask""", dynamic_axis),
             ]					)







   @property
   def 							UpperCamelCase				(     self:  Union[str, Any]					):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  super().outputs
         if self.task == "default":
               _SCREAMING_SNAKE_CASE     				=  {0: """batch"""}
         return outputs







   @property
   def 							UpperCamelCase				(     self:  Any					):







         '''simple docstring'''




         return 1E-4







   @property
   def 							UpperCamelCase				(     self:  Optional[int]					):







         '''simple docstring'''




         return max(super().default_onnx_opset				,     14					)







   def 							UpperCamelCase				(     self:  str				,     UpperCAmelCase_:  "PreTrainedTokenizerBase"				,     UpperCAmelCase_:  int = -1				,     UpperCAmelCase_:  int = -1				,     UpperCAmelCase_:  bool = False				,     UpperCAmelCase_:  Optional[TensorType] = None				,     ):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  super().generate_dummy_inputs(
             preprocessor=UpperCAmelCase_				,     batch_size=UpperCAmelCase_				,     seq_length=UpperCAmelCase_				,     is_pair=UpperCAmelCase_				,     framework=UpperCAmelCase_					)
         import torch

         # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
         # makes the export fail randomly
         _SCREAMING_SNAKE_CASE     				=  torch.zeros_like(inputs["""input_ids"""]					)
         # make every second token global
         _SCREAMING_SNAKE_CASE     				=  1

         return inputs









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																					if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																					self.check_over_forward(time_step=UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

