
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					(DDIMParallelScheduler,)
	_UpperCAmelCase       :		Tuple       =					(("eta", 0.0), ("num_inference_steps", 5_0))



	def  A							(  self	:    Optional[int]			,  **lowercase	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			{
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'clip_sample': True,
				}

				config.update(**lowercase	)
				return config



	def  A							(  self	:    List[str]			,  **lowercase	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config(**lowercase	)
				_snake_case		=			scheduler_class(**lowercase	)

				_snake_case	,					_snake_case		=			10, 0.0

				_snake_case		=			self.dummy_model()
				_snake_case		=			self.dummy_sample_deter

				scheduler.set_timesteps(lowercase	)

				for t in scheduler.timesteps:
							_snake_case		=			model(lowercase			,  lowercase	)
							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  lowercase	).prev_sample

				return sample



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				for timesteps in [100, 500, 1_000]:
							self.check_over_configs(num_train_timesteps=lowercase	)



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


				'''simple docstring'''


				for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=lowercase	)

				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config(steps_offset=1	)
				_snake_case		=			scheduler_class(**lowercase	)
				scheduler.set_timesteps(5	)
				assert torch.equal(scheduler.timesteps			,  torch.LongTensor([801, 601, 401, 201, 1]	)	)



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


				'''simple docstring'''


				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]			,  [0.002, 0.02, 0.2, 2]	):
							self.check_over_configs(beta_start=lowercase			,  beta_end=lowercase	)



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=lowercase	)



	def  A							(  self	:    int	):


				'''simple docstring'''


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



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


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



	def  A							(  self	:    str	):


				'''simple docstring'''


				for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=lowercase	)



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=lowercase	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				self.check_over_configs(thresholding=lowercase	)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(
										    thresholding=lowercase			,  prediction_type=lowercase			,  sample_max_value=lowercase			,  )



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				for t in [1, 10, 49]:
							self.check_over_forward(time_step=lowercase	)



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				for t, num_inference_steps in zip([1, 10, 50]			,  [10, 50, 500]	):
							self.check_over_forward(time_step=lowercase			,  num_inference_steps=lowercase	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				for t, eta in zip([1, 10, 49]			,  [0.0, 0.5, 1.0]	):
							self.check_over_forward(time_step=lowercase			,  eta=lowercase	)



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config()
				_snake_case		=			scheduler_class(**lowercase	)

				assert torch.sum(torch.abs(scheduler._get_variance(0			,  0	) - 0.0	)	) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(420			,  400	) - 0.14771	)	) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(980			,  960	) - 0.32460	)	) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(0			,  0	) - 0.0	)	) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(487			,  486	) - 0.00979	)	) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999			,  998	) - 0.02	)	) < 1E-5



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config()
				_snake_case		=			scheduler_class(**lowercase	)

				_snake_case	,					_snake_case		=			10, 0.0
				scheduler.set_timesteps(lowercase	)

				_snake_case		=			self.dummy_model()
				_snake_case		=			self.dummy_sample_deter
				_snake_case		=			self.dummy_sample_deter + 0.1
				_snake_case		=			self.dummy_sample_deter - 0.1

				_snake_case		=			samplea.shape[0]
				_snake_case		=			torch.stack([samplea, samplea, samplea]			,  dim=0	)
				_snake_case		=			torch.arange(lowercase	)[0:3, None].repeat(1			,  lowercase	)

				_snake_case		=			model(samples.flatten(0			,  1	)			,  timesteps.flatten(0			,  1	)	)
				_snake_case		=			scheduler.batch_step_no_noise(lowercase			,  timesteps.flatten(0			,  1	)			,  samples.flatten(0			,  1	)			,  lowercase	)

				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				assert abs(result_sum.item() - 1147.7904	) < 1E-2
				assert abs(result_mean.item() - 0.4982	) < 1E-3



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.full_loop()

				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				assert abs(result_sum.item() - 172.0067	) < 1E-2
				assert abs(result_mean.item() - 0.223967	) < 1E-3



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.full_loop(prediction_type='v_prediction'	)

				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				assert abs(result_sum.item() - 52.5302	) < 1E-2
				assert abs(result_mean.item() - 0.0684	) < 1E-3



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			self.full_loop(set_alpha_to_one=lowercase			,  beta_start=0.01	)
				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				assert abs(result_sum.item() - 149.8295	) < 1E-2
				assert abs(result_mean.item() - 0.1951	) < 1E-3





	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			self.full_loop(set_alpha_to_one=lowercase			,  beta_start=0.01	)
				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				assert abs(result_sum.item() - 149.0784	) < 1E-2
				assert abs(result_mean.item() - 0.1941	) < 1E-3

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


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

_lowerCamelCase       :				Union[str, Any]       =			{
    '''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    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"longformer"



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


				'''simple docstring'''


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

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




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    "PretrainedConfig"			,  lowercase	:    str = "default"			,  lowercase	:    "List[PatchingSpec]" = None	):


				'''simple docstring'''


				super().__init__(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			True



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


				'''simple docstring'''


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



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


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



	@property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return 1E-4



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return max(super().default_onnx_opset			,  14	)





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


				'''simple docstring'''


				_snake_case		=			super().generate_dummy_inputs(
				    preprocessor=lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)
				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
				_snake_case		=			torch.zeros_like(inputs['input_ids']	)
				# make every second token global
				_snake_case		=			1

				return inputs