

"""simple docstring"""


import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class 			lowerCAmelCase_			( tf.keras.layers.Layer      ):



						'''simple docstring'''


						def __init__(	self       : Union[str, Any]					,A_       : List					,A_       : bool					,A_       : int = None					,A_       : int = None					,A_       : int = None					,A_       : str = "longest"					,A_       : bool = True					,A_       : int = 512					,A_       : int = None					,A_       : bool = True					,A_       : bool = True					,A_       : bool = True					,**A_       : List[Any]					,)       ->				Dict:
							super().__init__()
							if use_fast_bert_tokenizer:
								A  				=				FastBertTokenizer(
								    A_					,token_out_type=tf.intaa					,lower_case_nfd_strip_accents=A_					,**A_ )
							else:
								A  				=				tf.lookup.StaticVocabularyTable(
								    tf.lookup.KeyValueTensorInitializer(
								        keys=A_					,key_dtype=tf.string					,values=tf.range(tf.size(A_					,out_type=tf.intaa )					,dtype=tf.intaa )					,value_dtype=tf.intaa					,)					,num_oov_buckets=1					,)
								A  				=				BertTokenizerLayer(
								    A_					,token_out_type=tf.intaa					,lower_case=A_					,**A_ )

							A  				=				vocab_list
							A  				=				do_lower_case
							A  				=				cls_token_id or vocab_list.index('[CLS]' )
							A  				=				sep_token_id or vocab_list.index('[SEP]' )
							A  				=				pad_token_id or vocab_list.index('[PAD]' )
							A  				=				ShrinkLongestTrimmer(max_length - 3					,axis=1 )  # Allow room for special tokens
							A  				=				max_length
							A  				=				padding
							A  				=				truncation
							A  				=				pad_to_multiple_of
							A  				=				return_token_type_ids
							A  				=				return_attention_mask


						@classmethod
						def   _SCREAMING_SNAKE_CASE (	cls       : Tuple					,A_       : "PreTrainedTokenizerBase"					,**A_       : Any )       ->				Optional[int]:  # noqa: F821
							A  				=				kwargs.pop('do_lower_case'					,A_ )
							A  				=				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
							A  				=				kwargs.pop('cls_token_id'					,A_ )
							A  				=				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
							A  				=				kwargs.pop('sep_token_id'					,A_ )
							A  				=				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
							A  				=				kwargs.pop('pad_token_id'					,A_ )
							A  				=				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

							A  				=				tokenizer.get_vocab()
							A  				=				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]					,key=lambda A_ : x[1] )
							A  				=				[entry[0] for entry in vocab]
							return cls(
							    vocab_list=A_					,do_lower_case=A_					,cls_token_id=A_					,sep_token_id=A_					,pad_token_id=A_					,**A_					,)


						@classmethod
						def   _SCREAMING_SNAKE_CASE (	cls       : Dict					,A_       : Union[str, os.PathLike]					,*A_       : List[Any]					,**A_       : Optional[Any] )       ->				List[Any]:
							try:
								A  				=				BertTokenizer.from_pretrained(A_					,*A_					,**A_ )
							except:  # noqa: E722
								from .tokenization_bert_fast import BertTokenizerFast

								A  				=				BertTokenizerFast.from_pretrained(A_					,*A_					,**A_ )
							return cls.from_tokenizer(A_					,**A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : Optional[int] )       ->				Optional[Any]:
							if self.do_lower_case:
								A  				=				case_fold_utfa(A_ )
							A  				=				self.tf_tokenizer.tokenize(A_ )
							return tokens.merge_dims(1					,-1 )


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Optional[int]					,A_       : Union[str, Any]=None					,A_       : List[Any]=None					,A_       : List[Any]=None					,A_       : List[str]=None					,A_       : List[Any]=None					,A_       : Union[str, Any]=None					,A_       : List[str]=None					,)       ->				Tuple:
							if padding is None:
								A  				=				self.padding
							if padding not in ("longest", "max_length"):
								raise ValueError('Padding must be either \'longest\' or \'max_length\'!' )
							if max_length is not None and text_pair is not None:
								# Because we have to instantiate a Trimmer to do it properly
								raise ValueError('max_length cannot be overridden at call time when truncating paired texts!' )
							if max_length is None:
								A  				=				self.max_length
							if truncation is None:
								A  				=				self.truncation
							if pad_to_multiple_of is None:
								A  				=				self.pad_to_multiple_of
							if return_token_type_ids is None:
								A  				=				self.return_token_type_ids
							if return_attention_mask is None:
								A  				=				self.return_attention_mask
							if not isinstance(A_					,tf.Tensor ):
								A  				=				tf.convert_to_tensor(A_ )
							if text_pair is not None and not isinstance(A_					,tf.Tensor ):
								A  				=				tf.convert_to_tensor(A_ )
							if text_pair is not None:
								if text.shape.rank > 1:
									raise ValueError('text argument should not be multidimensional when a text pair is supplied!' )
								if text_pair.shape.rank > 1:
									raise ValueError('text_pair should not be multidimensional!' )
							if text.shape.rank == 2:
								A				, A  				=				text[:, 0], text[:, 1]
							A  				=				self.unpaired_tokenize(A_ )
							if text_pair is None:  # Unpaired text
								if truncation:
									A  				=				text[:, : max_length - 2]  # Allow room for special tokens
								A				, A  				=				combine_segments(
								    (text,)					,start_of_sequence_id=self.cls_token_id					,end_of_segment_id=self.sep_token_id )
							else:  # Paired text
								A  				=				self.unpaired_tokenize(A_ )
								if truncation:
									A				, A  				=				self.paired_trimmer.trim([text, text_pair] )
								A				, A  				=				combine_segments(
								    (text, text_pair)					,start_of_sequence_id=self.cls_token_id					,end_of_segment_id=self.sep_token_id )
							if padding == "longest":
								A  				=				input_ids.bounding_shape(axis=1 )
								if pad_to_multiple_of is not None:
									# No ceiling division in tensorflow, so we negate floordiv instead
									A  				=				pad_to_multiple_of * (-tf.math.floordiv(-pad_length					,A_ ))
							else:
								A  				=				max_length

							A				, A  				=				pad_model_inputs(A_					,max_seq_length=A_					,pad_value=self.pad_token_id )
							A  				=				{'input_ids': input_ids}
							if return_attention_mask:
								A  				=				attention_mask
							if return_token_type_ids:
								A				, A  				=				pad_model_inputs(
								    A_					,max_seq_length=A_					,pad_value=self.pad_token_id )
								A  				=				token_type_ids
							return output




						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				Optional[Any]:
							return {
							    "vocab_list": self.vocab_list,
							    "do_lower_case": self.do_lower_case,
							    "cls_token_id": self.cls_token_id,
							    "sep_token_id": self.sep_token_id,
							    "pad_token_id": self.pad_token_id,
							}

"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .image_encoder import PaintByExampleImageEncoder
		from .pipeline_paint_by_example import PaintByExamplePipeline