



'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  (IPNDMScheduler,)
 UpperCAmelCase							=  (("""num_inference_steps""", 50),)







 def _snake_case (							self  ,**a_							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = {"""num_train_timesteps""": 1_000}
        config.update(**a_							)
        return config







 def _snake_case (							self  ,a_=0  ,**a_							)		->   List[str]:
        _UpperCAmelCase  :						Any			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Any			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = self.dummy_sample
        _UpperCAmelCase  :						Union[str, Any]			     = 0.1 * sample
        _UpperCAmelCase  :						List[str]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						str			     = self.get_scheduler_config(**a_							)
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)
               # copy over dummy past residuals
               _UpperCAmelCase  :						str			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						List[str]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						List[str]			     = scheduler_class.from_pretrained(a_							)
                      new_scheduler.set_timesteps(a_							)
                      # copy over dummy past residuals
                      _UpperCAmelCase  :						Tuple			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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







 def _snake_case (							self							)		->   Any:
        pass







 def _snake_case (							self  ,a_=0  ,**a_							)		->   Dict:
        _UpperCAmelCase  :						str			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Union[str, Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
        _UpperCAmelCase  :						Tuple			     = 0.1 * sample
        _UpperCAmelCase  :						Optional[Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						Optional[Any]			     = self.get_scheduler_config()
               _UpperCAmelCase  :						Dict			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)

               # copy over dummy past residuals (must be after setting timesteps)
               _UpperCAmelCase  :						Any			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						Optional[int]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

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

                      # copy over dummy past residual (must be after setting timesteps)
                      _UpperCAmelCase  :						Dict			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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







 def _snake_case (							self  ,**a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[str]			     = self.get_scheduler_config(**a_							)
        _UpperCAmelCase  :						List[Any]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						List[Any]			     = 10
        _UpperCAmelCase  :						Dict			     = self.dummy_model()
        _UpperCAmelCase  :						Dict			     = self.dummy_sample_deter
        scheduler.set_timesteps(a_							)

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						Optional[Any]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						List[str]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						int			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        return sample







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						int			     = dict(self.forward_default_kwargs							)

        _UpperCAmelCase  :						Optional[Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						int			     = self.get_scheduler_config()
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)

               _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
               _UpperCAmelCase  :						Tuple			     = 0.1 * sample

               if num_inference_steps is not None and hasattr(a_  ,"""set_timesteps"""							):
                      scheduler.set_timesteps(a_							)
               elif num_inference_steps is not None and not hasattr(a_  ,"""set_timesteps"""							):
                      _UpperCAmelCase  :						Optional[Any]			     = num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               _UpperCAmelCase  :						Union[str, Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               _UpperCAmelCase  :						Optional[int]			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.timesteps[5]
               _UpperCAmelCase  :						Optional[Any]			     = scheduler.timesteps[6]

               _UpperCAmelCase  :						Optional[Any]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Optional[int]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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







 def _snake_case (							self							)		->   List[Any]:
        for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   int:
        for t, num_inference_steps in zip([1, 5, 10]  ,[10, 50, 100]							):
               self.check_over_forward(num_inference_steps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = self.full_loop()
        _UpperCAmelCase  :						Any			     = torch.mean(torch.abs(a_							)							)

        assert abs(result_mean.item() - 2_540_529							) < 10








'''simple docstring'''






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


A_							: Any						 =	logging.get_logger(__name__)

A_							: Optional[int]						 =	{
    """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 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """longformer"""







 def __init__(							self  ,a_ = 512  ,a_ = 2  ,a_ = 1  ,a_ = 0  ,a_ = 2  ,a_ = 30_522  ,a_ = 768  ,a_ = 12  ,a_ = 12  ,a_ = 3_072  ,a_ = "gelu"  ,a_ = 0.1  ,a_ = 0.1  ,a_ = 512  ,a_ = 2  ,a_ = 0.02  ,a_ = 1E-1_2  ,a_ = False  ,**a_  ,)		->   List[Any]:
        super().__init__(pad_token_id=a_  ,**a_							)

        _UpperCAmelCase  :						List[Any]			     = attention_window
        _UpperCAmelCase  :						Any			     = sep_token_id
        _UpperCAmelCase  :						Dict			     = bos_token_id
        _UpperCAmelCase  :						Tuple			     = eos_token_id
        _UpperCAmelCase  :						Tuple			     = vocab_size
        _UpperCAmelCase  :						Optional[Any]			     = hidden_size
        _UpperCAmelCase  :						Optional[int]			     = num_hidden_layers
        _UpperCAmelCase  :						Union[str, Any]			     = num_attention_heads
        _UpperCAmelCase  :						Optional[int]			     = hidden_act
        _UpperCAmelCase  :						str			     = intermediate_size
        _UpperCAmelCase  :						List[Any]			     = hidden_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						List[str]			     = max_position_embeddings
        _UpperCAmelCase  :						Optional[int]			     = type_vocab_size
        _UpperCAmelCase  :						Any			     = initializer_range
        _UpperCAmelCase  :						Optional[int]			     = layer_norm_eps
        _UpperCAmelCase  :						Union[str, Any]			     = onnx_export







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_ = "default"  ,a_ = None							)		->   int:
        super().__init__(a_  ,a_  ,a_							)
        _UpperCAmelCase  :						Tuple			     = True







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCAmelCase  :						Optional[int]			     = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               _UpperCAmelCase  :						Tuple			     = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""global_attention_mask""", dynamic_axis),
            ]							)







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        _UpperCAmelCase  :						str			     = super().outputs
        if self.task == "default":
               _UpperCAmelCase  :						int			     = {0: """batch"""}
        return outputs







 @property
 def _snake_case (							self							)		->   float:
        return 1E-4







 @property
 def _snake_case (							self							)		->   int:
        # needs to be >= 14 to support tril operator
        return max(super().default_onnx_opset  ,14							)







 def _snake_case (							self  ,a_  ,a_ = -1  ,a_ = -1  ,a_ = False  ,a_ = None  ,)		->   Mapping[str, Any]:
        _UpperCAmelCase  :						List[str]			     = super().generate_dummy_inputs(
            preprocessor=a_  ,batch_size=a_  ,seq_length=a_  ,is_pair=a_  ,framework=a_							)
        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
        _UpperCAmelCase  :						int			     = torch.zeros_like(inputs["""input_ids"""]							)
        # make every second token global
        _UpperCAmelCase  :						List[str]			     = 1

        return inputs



