

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["image_processor", "tokenizer"]
						_lowercase    						=			"LayoutLMv2ImageProcessor"
						_lowercase    						=			("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


						def __init__(       self						,					A_=None						,					A_=None						,					**A_	):







										'''simple docstring'''


										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."						,					A_						,					)
														_UpperCAmelCase       :		List[Any] 		=    kwargs.pop("feature_extractor"	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."	)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."	)

										super().__init__(A_						,					A_	)


						def __call__(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = True						,					A_ = False						,					A_ = None						,					A_ = None						,					A_ = 0						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = True						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										if self.image_processor.apply_ocr and (boxes is not None):
														raise ValueError(
														    "You cannot provide bounding boxes "
														    "if you initialized the image processor with apply_ocr set to True."	)

										if self.image_processor.apply_ocr and (word_labels is not None):
														raise ValueError(
														    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."	)

										if return_overflowing_tokens is True and return_offsets_mapping is False:
														raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."	)

										# first, apply the image processor
										_UpperCAmelCase       :		str 		=    self.image_processor(images=A_						,					return_tensors=A_	)

										# second, apply the tokenizer
										if text is not None and self.image_processor.apply_ocr and text_pair is None:
														if isinstance(A_						,					A_	):
																		_UpperCAmelCase       :		Optional[int] 		=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
														_UpperCAmelCase       :		Tuple 		=    features["words"]

										_UpperCAmelCase       :		Optional[Any] 		=    self.tokenizer(
										    text=text if text is not None else features["words"]						,					text_pair=text_pair if text_pair is not None else None						,					boxes=boxes if boxes is not None else features["boxes"]						,					word_labels=A_						,					add_special_tokens=A_						,					padding=A_						,					truncation=A_						,					max_length=A_						,					stride=A_						,					pad_to_multiple_of=A_						,					return_token_type_ids=A_						,					return_attention_mask=A_						,					return_overflowing_tokens=A_						,					return_special_tokens_mask=A_						,					return_offsets_mapping=A_						,					return_length=A_						,					verbose=A_						,					return_tensors=A_						,					**A_						,					)

										# add pixel values
										_UpperCAmelCase       :		List[Any] 		=    features.pop("pixel_values"	)
										if return_overflowing_tokens is True:
														_UpperCAmelCase       :		Optional[int] 		=    self.get_overflowing_images(A_						,					encoded_inputs["overflow_to_sample_mapping"]	)
										_UpperCAmelCase       :		List[Any] 		=    images

										return encoded_inputs


						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    []
										for sample_idx in overflow_to_sample_mapping:
														images_with_overflow.append(images[sample_idx]	)

										if len(A_	) != len(A_	):
														raise ValueError(
														    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
														    f' {len(A_	)} and {len(A_	)}'	)

										return images_with_overflow


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										return self.tokenizer.decode(*A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return ["input_ids", "bbox", "attention_mask", "image"]


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,					A_						,					)
										return self.image_processor_class





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,					A_						,					)
										return self.image_processor





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


