



'''simple docstring'''





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						(UniPCMultistepScheduler,)
    lowercase   =						(("num_inference_steps", 25),)




    def lowerCamelCase			(       self   :	str					,	**snake_case_   :	List[str]		):
          snake_case__    :       Tuple       		= {
              """num_train_timesteps""": 1_000,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """solver_order""": 2,
              """solver_type""": """bh2""",
          }

          config.update(**snake_case_		)
          return config




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	str=0					,	**snake_case_   :	Any		):
          snake_case__    :       Tuple       		= dict(self.forward_default_kwargs		)
          snake_case__    :       Optional[int]       		= kwargs.pop("""num_inference_steps"""					,	snake_case_		)
          snake_case__    :       Union[str, Any]       		= self.dummy_sample
          snake_case__    :       Tuple       		= 0.1 * sample
          snake_case__    :       List[Any]       		= [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                snake_case__    :       Tuple       		= self.get_scheduler_config(**snake_case_		)
                snake_case__    :       str       		= scheduler_class(**snake_case_		)
                scheduler.set_timesteps(snake_case_		)
                # copy over dummy past residuals
                snake_case__    :       Optional[Any]       		= dummy_past_residuals[: scheduler.config.solver_order]

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

                snake_case__		,							snake_case__    :       Union[str, Any]       		= sample, sample
                for t in range(snake_case_					,	time_step + scheduler.config.solver_order + 1		):
                      snake_case__    :       Union[str, Any]       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample
                      snake_case__    :       Optional[Any]       		= new_scheduler.step(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample

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




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Dict=0					,	**snake_case_   :	Tuple		):
          snake_case__    :       List[Any]       		= dict(self.forward_default_kwargs		)
          snake_case__    :       Any       		= kwargs.pop("""num_inference_steps"""					,	snake_case_		)
          snake_case__    :       Tuple       		= self.dummy_sample
          snake_case__    :       Union[str, Any]       		= 0.1 * sample
          snake_case__    :       Optional[int]       		= [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                snake_case__    :       Union[str, Any]       		= self.get_scheduler_config()
                snake_case__    :       Tuple       		= scheduler_class(**snake_case_		)
                scheduler.set_timesteps(snake_case_		)

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

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

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

                snake_case__    :       Any       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample
                snake_case__    :       str       		= new_scheduler.step(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample

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




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Dict=None					,	**snake_case_   :	Any		):
          if scheduler is None:
                snake_case__    :       Dict       		= self.scheduler_classes[0]
                snake_case__    :       Any       		= self.get_scheduler_config(**snake_case_		)
                snake_case__    :       Union[str, Any]       		= scheduler_class(**snake_case_		)

          snake_case__    :       Any       		= self.scheduler_classes[0]
          snake_case__    :       Optional[Any]       		= self.get_scheduler_config(**snake_case_		)
          snake_case__    :       Any       		= scheduler_class(**snake_case_		)

          snake_case__    :       List[str]       		= 10
          snake_case__    :       str       		= self.dummy_model()
          snake_case__    :       Tuple       		= self.dummy_sample_deter
          scheduler.set_timesteps(snake_case_		)

          for i, t in enumerate(scheduler.timesteps		):
                snake_case__    :       Optional[int]       		= model(snake_case_					,	snake_case_		)
                snake_case__    :       Optional[Any]       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_		).prev_sample

          return sample




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Dict       		= dict(self.forward_default_kwargs		)

          snake_case__    :       Tuple       		= kwargs.pop("""num_inference_steps"""					,	snake_case_		)

          for scheduler_class in self.scheduler_classes:
                snake_case__    :       Any       		= self.get_scheduler_config()
                snake_case__    :       Union[str, Any]       		= scheduler_class(**snake_case_		)

                snake_case__    :       str       		= self.dummy_sample
                snake_case__    :       List[str]       		= 0.1 * sample

                if num_inference_steps is not None and hasattr(snake_case_					,	"""set_timesteps"""		):
                      scheduler.set_timesteps(snake_case_		)
                elif num_inference_steps is not None and not hasattr(snake_case_					,	"""set_timesteps"""		):
                      snake_case__    :       Any       		= num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                snake_case__    :       Any       		= [residual + 0.2, residual + 0.15, residual + 0.10]
                snake_case__    :       int       		= dummy_past_residuals[: scheduler.config.solver_order]

                snake_case__    :       str       		= scheduler.timesteps[5]
                snake_case__    :       List[Any]       		= scheduler.timesteps[6]

                snake_case__    :       Union[str, Any]       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample
                snake_case__    :       List[str]       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample

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




    def lowerCamelCase			(       self   :	List[Any]		):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          snake_case__    :       Union[str, Any]       		= UniPCMultistepScheduler(**self.get_scheduler_config()		)
          snake_case__    :       Dict       		= self.full_loop(scheduler=snake_case_		)
          snake_case__    :       List[Any]       		= torch.mean(torch.abs(snake_case_		)		)

          assert abs(result_mean.item() - 0.2464		) < 1E-3

          snake_case__    :       str       		= DPMSolverSinglestepScheduler.from_config(scheduler.config		)
          snake_case__    :       Any       		= DEISMultistepScheduler.from_config(scheduler.config		)
          snake_case__    :       str       		= DPMSolverMultistepScheduler.from_config(scheduler.config		)
          snake_case__    :       str       		= UniPCMultistepScheduler.from_config(scheduler.config		)

          snake_case__    :       Optional[int]       		= self.full_loop(scheduler=snake_case_		)
          snake_case__    :       Dict       		= torch.mean(torch.abs(snake_case_		)		)

          assert abs(result_mean.item() - 0.2464		) < 1E-3




    def lowerCamelCase			(       self   :	List[str]		):
          for timesteps in [25, 50, 100, 999, 1_000]:
                self.check_over_configs(num_train_timesteps=snake_case_		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          self.check_over_configs(thresholding=snake_case_		)
          for order in [1, 2, 3]:
                for solver_type in ["bh1", "bh2"]:
                      for threshold in [0.5, 1.0, 2.0]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      thresholding=snake_case_					,	prediction_type=snake_case_					,	sample_max_value=snake_case_					,	solver_order=snake_case_					,	solver_type=snake_case_					,	)




    def lowerCamelCase			(       self   :	Tuple		):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          for solver_type in ["bh1", "bh2"]:
                for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                            self.check_over_configs(
                                solver_order=snake_case_					,	solver_type=snake_case_					,	prediction_type=snake_case_					,	)
                            snake_case__    :       Optional[Any]       		= self.full_loop(
                                solver_order=snake_case_					,	solver_type=snake_case_					,	prediction_type=snake_case_					,	)
                            assert not torch.isnan(snake_case_		).any(), "Samples have nan numbers"




    def lowerCamelCase			(       self   :	int		):
          self.check_over_configs(lower_order_final=snake_case_		)
          self.check_over_configs(lower_order_final=snake_case_		)




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




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Dict       		= self.full_loop()
          snake_case__    :       str       		= torch.mean(torch.abs(snake_case_		)		)

          assert abs(result_mean.item() - 0.2464		) < 1E-3




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Any       		= self.full_loop(prediction_type="""v_prediction"""		)
          snake_case__    :       Optional[int]       		= torch.mean(torch.abs(snake_case_		)		)

          assert abs(result_mean.item() - 0.1014		) < 1E-3




    def lowerCamelCase			(       self   :	str		):
          snake_case__    :       Tuple       		= self.scheduler_classes[0]
          snake_case__    :       Any       		= self.get_scheduler_config(thresholding=snake_case_					,	dynamic_thresholding_ratio=0		)
          snake_case__    :       List[str]       		= scheduler_class(**snake_case_		)

          snake_case__    :       Dict       		= 10
          snake_case__    :       Dict       		= self.dummy_model()
          snake_case__    :       Any       		= self.dummy_sample_deter.half()
          scheduler.set_timesteps(snake_case_		)

          for i, t in enumerate(scheduler.timesteps		):
                snake_case__    :       Any       		= model(snake_case_					,	snake_case_		)
                snake_case__    :       Optional[int]       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_		).prev_sample

          assert sample.dtype == torch.floataa




    def lowerCamelCase			(       self   :	Optional[int]					,	**snake_case_   :	Optional[Any]		):
          for scheduler_class in self.scheduler_classes:
                snake_case__    :       Any       		= self.get_scheduler_config(**snake_case_		)
                snake_case__    :       Optional[Any]       		= scheduler_class(**snake_case_		)

                scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
                assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps





'''simple docstring'''





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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"layoutlmv3"




    def __init__(       self   :	Any					,	snake_case_   :	Dict=50_265					,	snake_case_   :	Union[str, Any]=768					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	Optional[int]=12					,	snake_case_   :	Union[str, Any]=3_072					,	snake_case_   :	List[str]="gelu"					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=512					,	snake_case_   :	List[str]=2					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	List[Any]=1E-5					,	snake_case_   :	Any=1					,	snake_case_   :	Any=0					,	snake_case_   :	int=2					,	snake_case_   :	Optional[Any]=1_024					,	snake_case_   :	int=128					,	snake_case_   :	Union[str, Any]=128					,	snake_case_   :	int=True					,	snake_case_   :	Tuple=32					,	snake_case_   :	str=128					,	snake_case_   :	Optional[int]=64					,	snake_case_   :	Optional[Any]=256					,	snake_case_   :	List[str]=True					,	snake_case_   :	str=True					,	snake_case_   :	Dict=True					,	snake_case_   :	int=224					,	snake_case_   :	Any=3					,	snake_case_   :	int=16					,	snake_case_   :	Tuple=None					,	**snake_case_   :	Optional[Any]					,	):
          super().__init__(
              vocab_size=snake_case_					,	hidden_size=snake_case_					,	num_hidden_layers=snake_case_					,	num_attention_heads=snake_case_					,	intermediate_size=snake_case_					,	hidden_act=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_					,	pad_token_id=snake_case_					,	bos_token_id=snake_case_					,	eos_token_id=snake_case_					,	**snake_case_					,	)
          snake_case__    :       List[Any]       		= max_ad_position_embeddings
          snake_case__    :       Optional[int]       		= coordinate_size
          snake_case__    :       int       		= shape_size
          snake_case__    :       Optional[int]       		= has_relative_attention_bias
          snake_case__    :       Dict       		= rel_pos_bins
          snake_case__    :       List[Any]       		= max_rel_pos
          snake_case__    :       str       		= has_spatial_attention_bias
          snake_case__    :       Optional[Any]       		= rel_ad_pos_bins
          snake_case__    :       Optional[int]       		= max_rel_ad_pos
          snake_case__    :       Union[str, Any]       		= text_embed
          snake_case__    :       List[Any]       		= visual_embed
          snake_case__    :       Union[str, Any]       		= input_size
          snake_case__    :       int       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Dict       		= classifier_dropout







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          # The order of inputs is different for question answering and sequence classification
          if self.task in ["question-answering", "sequence-classification"]:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ]		)
          else:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
                    ]		)




    @property
    def lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	"ProcessorMixin"					,	snake_case_   :	int = -1					,	snake_case_   :	int = -1					,	snake_case_   :	bool = False					,	snake_case_   :	Optional["TensorType"] = None					,	snake_case_   :	int = 3					,	snake_case_   :	int = 40					,	snake_case_   :	int = 40					,	):
          setattr(processor.image_processor					,	"""apply_ocr"""					,	snake_case_		)

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          snake_case__    :       Optional[Any]       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case__    :       List[Any]       		= processor.tokenizer.num_special_tokens_to_add(snake_case_		)
          snake_case__    :       Tuple       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=snake_case_		)
          # Generate dummy inputs according to compute batch and sequence
          snake_case__    :       int       		= [[""" """.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          snake_case__    :       int       		= [[[48, 84, 73, 128]]] * batch_size

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
          snake_case__    :       Optional[int]       		= self._generate_dummy_images(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= dict(
              processor(
                  snake_case_					,	text=snake_case_					,	boxes=snake_case_					,	return_tensors=snake_case_					,	)		)

          return inputs
