



'''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


lowerCAmelCase	:		Union[str, Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Union[str, Any]				=   {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Any        =							"""layoutlmv3"""



					def __init__(    self			,				UpperCamelCase=5_0265			,				UpperCamelCase=768			,				UpperCamelCase=12			,				UpperCamelCase=12			,				UpperCamelCase=3072			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=512			,				UpperCamelCase=2			,				UpperCamelCase=0.02			,				UpperCamelCase=1E-5			,				UpperCamelCase=1			,				UpperCamelCase=0			,				UpperCamelCase=2			,				UpperCamelCase=1024			,				UpperCamelCase=128			,				UpperCamelCase=128			,				UpperCamelCase=True			,				UpperCamelCase=32			,				UpperCamelCase=128			,				UpperCamelCase=64			,				UpperCamelCase=256			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=224			,				UpperCamelCase=3			,				UpperCamelCase=16			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			Optional[int]:
						super().__init__(
						    vocab_size=UpperCamelCase			,				hidden_size=UpperCamelCase			,				num_hidden_layers=UpperCamelCase			,				num_attention_heads=UpperCamelCase			,				intermediate_size=UpperCamelCase			,				hidden_act=UpperCamelCase			,				hidden_dropout_prob=UpperCamelCase			,				attention_probs_dropout_prob=UpperCamelCase			,				max_position_embeddings=UpperCamelCase			,				type_vocab_size=UpperCamelCase			,				initializer_range=UpperCamelCase			,				layer_norm_eps=UpperCamelCase			,				pad_token_id=UpperCamelCase			,				bos_token_id=UpperCamelCase			,				eos_token_id=UpperCamelCase			,				**UpperCamelCase			,				)
						__lowerCAmelCase												=  max_ad_position_embeddings
						__lowerCAmelCase												=  coordinate_size
						__lowerCAmelCase												=  shape_size
						__lowerCAmelCase												=  has_relative_attention_bias
						__lowerCAmelCase												=  rel_pos_bins
						__lowerCAmelCase												=  max_rel_pos
						__lowerCAmelCase												=  has_spatial_attention_bias
						__lowerCAmelCase												=  rel_ad_pos_bins
						__lowerCAmelCase												=  max_rel_ad_pos
						__lowerCAmelCase												=  text_embed
						__lowerCAmelCase												=  visual_embed
						__lowerCAmelCase												=  input_size
						__lowerCAmelCase												=  num_channels
						__lowerCAmelCase												=  patch_size
						__lowerCAmelCase												=  classifier_dropout






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[Any]        =							version.parse("""1.12""" )



					@property
					def        UpperCAmelCase_ (    self     )     ->			Mapping[str, Mapping[int, str]]:
						# 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        UpperCAmelCase_ (    self     )     ->			float:
						return 1E-5



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 12



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = -1			,				UpperCamelCase = -1			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = 3			,				UpperCamelCase = 40			,				UpperCamelCase = 40			,				)     ->			Mapping[str, Any]:
						setattr(processor.image_processor			,				"apply_ocr"			,				UpperCamelCase     )

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__lowerCAmelCase												=  compute_effective_axis_dimension(
						    UpperCamelCase			,				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
						__lowerCAmelCase												=  processor.tokenizer.num_special_tokens_to_add(UpperCamelCase     )
						__lowerCAmelCase												=  compute_effective_axis_dimension(
						    UpperCamelCase			,				fixed_dimension=OnnxConfig.default_fixed_sequence			,				num_token_to_add=UpperCamelCase     )
						# Generate dummy inputs according to compute batch and sequence
						__lowerCAmelCase												=  [[" ".join([processor.tokenizer.unk_token]     ) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						__lowerCAmelCase												=  [[[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)
						__lowerCAmelCase												=  self._generate_dummy_images(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  dict(
						    processor(
						        UpperCamelCase			,				text=UpperCamelCase			,				boxes=UpperCamelCase			,				return_tensors=UpperCamelCase			,				)     )

						return inputs




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

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



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

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

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

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

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )