



"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Dict=0.999,	UpperCAmelCase_ :       Tuple="cosine",	)	-> List[str]:






	"""simple docstring"""





	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCAmelCase_ :       Tuple     ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCAmelCase_ :       Optional[Any]     ):
			return math.exp(t * -12.0     )

	else:
		raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

	A__											=					[]
	for i in range(UpperCAmelCase_     ):
		A__											=					i / num_diffusion_timesteps
		A__											=					(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_     ) / alpha_bar_fn(UpperCAmelCase_     ),	UpperCAmelCase_     )     )
	return torch.tensor(UpperCAmelCase_,	dtype=torch.floataa     )






class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[Any]							  =     [e.name for e in KarrasDiffusionSchedulers]
			A__	:					str							  =     2


			@register_to_config
			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = 1000						,	SCREAMING_SNAKE_CASE__ = 0.0_0_0_8_5						,	SCREAMING_SNAKE_CASE__ = 0.0_1_2						,	SCREAMING_SNAKE_CASE__ = "linear"						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = "epsilon"						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = 1.0						,	SCREAMING_SNAKE_CASE__ = "linspace"						,	SCREAMING_SNAKE_CASE__ = 0						,	)      ->	Optional[int]:
				if trained_betas is not None:
					A__											=					torch.tensor(SCREAMING_SNAKE_CASE__						,	dtype=torch.floataa		)
				elif beta_schedule == "linear":
					A__											=					torch.linspace(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=torch.floataa		)
				elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					A__											=					(
					    torch.linspace(beta_start**0.5						,	beta_end**0.5						,	SCREAMING_SNAKE_CASE__						,	dtype=torch.floataa		) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					A__											=					betas_for_alpha_bar(SCREAMING_SNAKE_CASE__						,	alpha_transform_type="cosine"		)
				elif beta_schedule == "exp":
					A__											=					betas_for_alpha_bar(SCREAMING_SNAKE_CASE__						,	alpha_transform_type="exp"		)
				else:
					raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""		)

				A__											=					1.0 - self.betas
				A__											=					torch.cumprod(self.alphas						,	dim=0		)

				#  set all values
				self.set_timesteps(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					use_karras_sigmas


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None		)      ->	Optional[int]:
				if schedule_timesteps is None:
					A__											=					self.timesteps

				A__											=					(schedule_timesteps == timestep).nonzero()

				# The sigma index that is taken for the **very** first `step`
				# is always the second index (or the last index if there is only 1)
				# This way we can ensure we don't accidentally skip a sigma in
				# case we start in the middle of the denoising schedule (e.g. for image-to-image)
				if len(self._index_counter		) == 0:
					A__											=					1 if len(SCREAMING_SNAKE_CASE__		) > 1 else 0
				else:
					A__											=					timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE__		) else timestep
					A__											=					self._index_counter[timestep_int]

				return indices[pos].item()


			@property
			def   snake_case__	(      self		)      ->	Any:
				# standard deviation of the initial noise distribution
				if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	)      ->	torch.FloatTensor:
				A__											=					self.index_for_timestep(SCREAMING_SNAKE_CASE__		)

				A__											=					self.sigmas[step_index]
				A__											=					sample / ((sigma**2 + 1) ** 0.5)
				return sample


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	)      ->	Dict:
				A__											=					num_inference_steps

				A__											=					num_train_timesteps or self.config.num_train_timesteps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
					A__											=					np.linspace(0						,	num_train_timesteps - 1						,	SCREAMING_SNAKE_CASE__						,	dtype=SCREAMING_SNAKE_CASE__		)[::-1].copy()
				elif self.config.timestep_spacing == "leading":
					A__											=					num_train_timesteps // self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					A__											=					(np.arange(0						,	SCREAMING_SNAKE_CASE__		) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE__		)
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					A__											=					num_train_timesteps / self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					A__											=					(np.arange(SCREAMING_SNAKE_CASE__						,	0						,	-step_ratio		)).round().copy().astype(SCREAMING_SNAKE_CASE__		)
					timesteps -= 1
				else:
					raise ValueError(
					    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""		)

				A__											=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
				A__											=					np.log(SCREAMING_SNAKE_CASE__		)
				A__											=					np.interp(SCREAMING_SNAKE_CASE__						,	np.arange(0						,	len(SCREAMING_SNAKE_CASE__		)		)						,	SCREAMING_SNAKE_CASE__		)

				if self.config.use_karras_sigmas:
					A__											=					self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE__						,	num_inference_steps=self.num_inference_steps		)
					A__											=					np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) for sigma in sigmas]		)

				A__											=					np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
				A__											=					torch.from_numpy(SCREAMING_SNAKE_CASE__		).to(device=SCREAMING_SNAKE_CASE__		)
				A__											=					torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

				A__											=					torch.from_numpy(SCREAMING_SNAKE_CASE__		)
				A__											=					torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

				if str(SCREAMING_SNAKE_CASE__		).startswith("mps"		):
					# mps does not support float64
					A__											=					timesteps.to(SCREAMING_SNAKE_CASE__						,	dtype=torch.floataa		)
				else:
					A__											=					timesteps.to(device=SCREAMING_SNAKE_CASE__		)

				# empty dt and derivative
				A__											=					None
				A__											=					None

				# for exp beta schedules, such as the one for `pipeline_shap_e.py`
				# we need an index counter
				A__											=					defaultdict(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				# get log sigma
				A__											=					np.log(SCREAMING_SNAKE_CASE__		)

				# get distribution
				A__											=					log_sigma - log_sigmas[:, np.newaxis]

				# get sigmas range
				A__											=					np.cumsum((dists >= 0)						,	axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
				A__											=					low_idx + 1

				A__											=					log_sigmas[low_idx]
				A__											=					log_sigmas[high_idx]

				# interpolate sigmas
				A__											=					(low - log_sigma) / (low - high)
				A__											=					np.clip(SCREAMING_SNAKE_CASE__						,	0						,	1		)

				# transform interpolation to time range
				A__											=					(1 - w) * low_idx + w * high_idx
				A__											=					t.reshape(sigma.shape		)
				return t


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	torch.FloatTensor:
				A__											=					in_sigmas[-1].item()
				A__											=					in_sigmas[0].item()

				A__											=					7.0  # 7.0 is the value used in the paper
				A__											=					np.linspace(0						,	1						,	SCREAMING_SNAKE_CASE__		)
				A__											=					sigma_min ** (1 / rho)
				A__											=					sigma_max ** (1 / rho)
				A__											=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas


			@property
			def   snake_case__	(      self		)      ->	Optional[Any]:
				return self.dt is None


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = True						,	)      ->	Union[SchedulerOutput, Tuple]:
				A__											=					self.index_for_timestep(SCREAMING_SNAKE_CASE__		)

				# advance index counter by 1
				A__											=					timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE__		) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
					A__											=					self.sigmas[step_index]
					A__											=					self.sigmas[step_index + 1]
				else:
					# 2nd order / Heun's method
					A__											=					self.sigmas[step_index - 1]
					A__											=					self.sigmas[step_index]

				# currently only gamma=0 is supported. This usually works best anyways.
				# We can support gamma in the future but then need to scale the timestep before
				# passing it to the model which requires a change in API
				A__											=					0
				A__											=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					A__											=					sigma_hat if self.state_in_first_order else sigma_next
					A__											=					sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
					A__											=					sigma_hat if self.state_in_first_order else sigma_next
					A__											=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
					    sample / (sigma_input**2 + 1)
					)
				elif self.config.prediction_type == "sample":
					A__											=					model_output
				else:
					raise ValueError(
					    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""		)

				if self.config.clip_sample:
					A__											=					pred_original_sample.clamp(
					    -self.config.clip_sample_range						,	self.config.clip_sample_range		)

				if self.state_in_first_order:
					# 2. Convert to an ODE derivative for 1st order
					A__											=					(sample - pred_original_sample) / sigma_hat
					# 3. delta timestep
					A__											=					sigma_next - sigma_hat

					# store for 2nd order step
					A__											=					derivative
					A__											=					dt
					A__											=					sample
				else:
					# 2. 2nd order / Heun's method
					A__											=					(sample - pred_original_sample) / sigma_next
					A__											=					(self.prev_derivative + derivative) / 2

					# 3. take prev timestep & sample
					A__											=					self.dt
					A__											=					self.sample

					# free dt and derivative
					# Note, this puts the scheduler in "first order mode"
					A__											=					None
					A__											=					None
					A__											=					None

				A__											=					sample + derivative * dt

				if not return_dict:
					return (prev_sample,)

				return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	)      ->	torch.FloatTensor:
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				A__											=					self.sigmas.to(device=original_samples.device						,	dtype=original_samples.dtype		)
				if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE__		):
					# mps does not support float64
					A__											=					self.timesteps.to(original_samples.device						,	dtype=torch.floataa		)
					A__											=					timesteps.to(original_samples.device						,	dtype=torch.floataa		)
				else:
					A__											=					self.timesteps.to(original_samples.device		)
					A__											=					timesteps.to(original_samples.device		)

				A__											=					[self.index_for_timestep(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) for t in timesteps]

				A__											=					sigmas[step_indices].flatten()
				while len(sigma.shape		) < len(original_samples.shape		):
					A__											=					sigma.unsqueeze(-1		)

				A__											=					original_samples + noise * sigma
				return noisy_samples







			def __len__(      self		)      ->	Tuple:
				return self.config.num_train_timesteps









"""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


UpperCamelCase     =							logging.get_logger(__name__)

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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "layoutlmv3"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=50265						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=224						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Any:
				super().__init__(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	hidden_size=SCREAMING_SNAKE_CASE__						,	num_hidden_layers=SCREAMING_SNAKE_CASE__						,	num_attention_heads=SCREAMING_SNAKE_CASE__						,	intermediate_size=SCREAMING_SNAKE_CASE__						,	hidden_act=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__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__						,	)
				A__											=					max_ad_position_embeddings
				A__											=					coordinate_size
				A__											=					shape_size
				A__											=					has_relative_attention_bias
				A__											=					rel_pos_bins
				A__											=					max_rel_pos
				A__											=					has_spatial_attention_bias
				A__											=					rel_ad_pos_bins
				A__											=					max_rel_ad_pos
				A__											=					text_embed
				A__											=					visual_embed
				A__											=					input_size
				A__											=					num_channels
				A__											=					patch_size
				A__											=					classifier_dropout






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     version.parse("1.12"      )


			@property
			def   snake_case__	(      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   snake_case__	(      self		)      ->	float:
				return 1e-5


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







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

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

				# Generate dummy bounding boxes
				A__											=					[[[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)
				A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					dict(
				    processor(
				        SCREAMING_SNAKE_CASE__						,	text=SCREAMING_SNAKE_CASE__						,	boxes=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__						,	)		)

				return inputs




