



"""simple docstring"""


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_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							["""image_processor""", """tokenizer"""]
						__UpperCamelCase		    =							"""LayoutLMv3ImageProcessor"""
						__UpperCamelCase		    =							("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")






						def __init__( self			:Union[str, Any]       ,  lowercase_			:Optional[Any]=None       ,  lowercase_			:List[str]=None       ,  **lowercase_			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			None
												if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'       ,  lowercase_       ,  )
																		UpperCAmelCase					=			kwargs.pop('feature_extractor' )

												UpperCAmelCase					=			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__(lowercase_       ,  lowercase_ )






						def __call__( self			:Tuple       ,  lowercase_			:List[str]       ,  lowercase_			:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,  lowercase_			:Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,  lowercase_			:Union[List[List[int]], List[List[List[int]]]] = None       ,  lowercase_			:Optional[Union[List[int], List[List[int]]]] = None       ,  lowercase_			:bool = True       ,  lowercase_			:Union[bool, str, PaddingStrategy] = False       ,  lowercase_			:Union[bool, str, TruncationStrategy] = None       ,  lowercase_			:Optional[int] = None       ,  lowercase_			:int = 0       ,  lowercase_			:Optional[int] = None       ,  lowercase_			:Optional[bool] = None       ,  lowercase_			:Optional[bool] = None       ,  lowercase_			:bool = False       ,  lowercase_			:bool = False       ,  lowercase_			:bool = False       ,  lowercase_			:bool = False       ,  lowercase_			:bool = True       ,  lowercase_			:Optional[Union[str, TensorType]] = None       ,  **lowercase_			:Tuple       ,  )    ->      BatchEncoding:
												# verify input
												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.' )

												# first, apply the image processor
												UpperCAmelCase					=			self.image_processor(images=lowercase_       ,  return_tensors=lowercase_ )

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																		if isinstance(lowercase_       ,  lowercase_ ):
																								UpperCAmelCase					=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																		UpperCAmelCase					=			features['words']

												UpperCAmelCase					=			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=lowercase_       ,  add_special_tokens=lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_       ,  max_length=lowercase_       ,  stride=lowercase_       ,  pad_to_multiple_of=lowercase_       ,  return_token_type_ids=lowercase_       ,  return_attention_mask=lowercase_       ,  return_overflowing_tokens=lowercase_       ,  return_special_tokens_mask=lowercase_       ,  return_offsets_mapping=lowercase_       ,  return_length=lowercase_       ,  verbose=lowercase_       ,  return_tensors=lowercase_       ,  **lowercase_       ,  )

												# add pixel values
												UpperCAmelCase					=			features.pop('pixel_values' )
												if return_overflowing_tokens is True:
																		UpperCAmelCase					=			self.get_overflowing_images(lowercase_       ,  encoded_inputs['overflow_to_sample_mapping'] )
												UpperCAmelCase					=			images

												return encoded_inputs






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:List[Any]       ,  lowercase_			:Dict )    ->      Union[str, Any]:
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												UpperCAmelCase					=			[]
												for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx] )

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

												return images_with_overflow






						def      UpperCAmelCase__					( self			:Dict       ,  *lowercase_			:List[Any]       ,  **lowercase_			:Optional[int] )    ->      Tuple:
												return self.tokenizer.batch_decode(*lowercase_       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:List[str]       ,  *lowercase_			:Tuple       ,  **lowercase_			:Optional[int] )    ->      Tuple:
												return self.tokenizer.decode(*lowercase_       ,  **lowercase_ )






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Optional[Any]:
												return ["input_ids", "bbox", "attention_mask", "pixel_values"]






						@property
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												warnings.warn(
												    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,  lowercase_       ,  )
												return self.image_processor_class






						@property
						def      UpperCAmelCase__					( self			:List[str] )    ->      Optional[int]:
												warnings.warn(
												    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,  lowercase_       ,  )
												return self.image_processor










"""simple docstring"""


import math

import flax.linen as nn
import jax.numpy as jnp

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_ = 1		,     lowercase_ = 1		,     lowercase_ = 1.0e4		,     lowercase_ = False		,     lowercase_ = 1.0		,     ):
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
						UpperCAmelCase					=			float(embedding_dim // 2						)
						UpperCAmelCase					=			math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						UpperCAmelCase					=			min_timescale * jnp.exp(jnp.arange(lowercase_		,     dtype=jnp.floataa						) * -log_timescale_increment						)
						UpperCAmelCase					=			jnp.expand_dims(lowercase_		,     1						) * jnp.expand_dims(lowercase_		,     0						)

						# scale embeddings
						UpperCAmelCase					=			scale * emb

						if flip_sin_to_cos:
												UpperCAmelCase					=			jnp.concatenate([jnp.cos(lowercase_						), jnp.sin(lowercase_						)]		,     axis=1						)
						else:
												UpperCAmelCase					=			jnp.concatenate([jnp.sin(lowercase_						), jnp.cos(lowercase_						)]		,     axis=1						)
						UpperCAmelCase					=			jnp.reshape(lowercase_		,     [jnp.shape(lowercase_						)[0], embedding_dim]						)
						return signal







class 				A_      (					nn.Module    ):



						"""simple docstring"""
						__UpperCamelCase		    =							32
						__UpperCamelCase		    =							jnp.floataa






						@nn.compact
						def __call__( self			:Union[str, Any]       ,  lowercase_			:Tuple )    ->      str:
												UpperCAmelCase					=			nn.Dense(self.time_embed_dim       ,  dtype=self.dtype       ,  name='linear_1' )(lowercase_ )
												UpperCAmelCase					=			nn.silu(lowercase_ )
												UpperCAmelCase					=			nn.Dense(self.time_embed_dim       ,  dtype=self.dtype       ,  name='linear_2' )(lowercase_ )
												return temb







class 				A_      (					nn.Module    ):



						"""simple docstring"""
						__UpperCamelCase		    =							32
						__UpperCamelCase		    =							False
						__UpperCamelCase		    =							1






						@nn.compact
						def __call__( self			:Any       ,  lowercase_			:int )    ->      Union[str, Any]:
												return get_sinusoidal_embeddings(
												    lowercase_       ,  embedding_dim=self.dim       ,  flip_sin_to_cos=self.flip_sin_to_cos       ,  freq_shift=self.freq_shift )





