



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer



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




							def __init__(       self     :       str				,				A_     :       Dict[str, int]				,				A_     :       List[str]				,				A_     :       int = None				,				A_     :       int = None):
									super().__init__()
									lowerCAmelCase_			:     Tuple									= pad_token_id
									lowerCAmelCase_			:     List[str]									= max_length
									lowerCAmelCase_			:     Optional[Any]									= vocab
									lowerCAmelCase_			:     Optional[int]									= merges
									lowerCAmelCase_			:     str									= BytePairTokenizer(A_				,				A_				,				sequence_length=A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       Optional[int]				,				A_     :       GPTaTokenizer				,				*A_     :       Any				,				**A_     :       List[str]):
									lowerCAmelCase_			:     List[str]									= [''' '''.join(A_) for m in tokenizer.bpe_ranks.keys()]
									lowerCAmelCase_			:     Optional[Any]									= tokenizer.get_vocab()
									return cls(A_				,				A_				,				*A_				,				**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       Union[str, Any]				,				A_     :       Union[str, os.PathLike]				,				*A_     :       Optional[Any]				,				**A_     :       Union[str, Any]):
									lowerCAmelCase_			:     List[Any]									= GPTaTokenizer.from_pretrained(A_				,				*A_				,				**A_)
									return cls.from_tokenizer(A_				,				*A_				,				**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       Any				,				A_     :       Dict):
									return cls(**A_)




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									return {
									    "vocab": self.vocab,
									    "merges": self.merges,
									    "max_length": self.max_length,
									    "pad_token_id": self.pad_token_id,
									}







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       int				,				A_     :       int = None):
									lowerCAmelCase_			:     Optional[int]									= self.tf_tokenizer(A_)
									lowerCAmelCase_			:     Any									= tf.ones_like(A_)

									if self.pad_token_id is not None:
											# pad the tokens up to max length
											lowerCAmelCase_			:     Union[str, Any]									= max_length if max_length is not None else self.max_length

											if max_length is not None:
													lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pad_model_inputs(
													    A_				,				max_seq_length=A_				,				pad_value=self.pad_token_id)

									return {"attention_mask": attention_mask, "input_ids": input_ids}










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__  :  List[str]								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''maskformer-swin'''

							_a			      = {
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(       self     :       Union[str, Any]				,				A_     :       Dict=2_2_4				,				A_     :       Optional[Any]=4				,				A_     :       List[str]=3				,				A_     :       str=9_6				,				A_     :       Optional[Any]=[2, 2, 6, 2]				,				A_     :       Tuple=[3, 6, 1_2, 2_4]				,				A_     :       List[Any]=7				,				A_     :       List[Any]=4.0				,				A_     :       List[str]=True				,				A_     :       Dict=0.0				,				A_     :       int=0.0				,				A_     :       str=0.1				,				A_     :       Optional[int]="gelu"				,				A_     :       List[Any]=False				,				A_     :       int=0.02				,				A_     :       int=1e-5				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				**A_     :       List[Any]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     Dict									= image_size
									lowerCAmelCase_			:     Optional[Any]									= patch_size
									lowerCAmelCase_			:     Tuple									= num_channels
									lowerCAmelCase_			:     Any									= embed_dim
									lowerCAmelCase_			:     List[str]									= depths
									lowerCAmelCase_			:     Union[str, Any]									= len(A_)
									lowerCAmelCase_			:     List[str]									= num_heads
									lowerCAmelCase_			:     Dict									= window_size
									lowerCAmelCase_			:     Optional[int]									= mlp_ratio
									lowerCAmelCase_			:     Dict									= qkv_bias
									lowerCAmelCase_			:     str									= hidden_dropout_prob
									lowerCAmelCase_			:     List[str]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[int]									= drop_path_rate
									lowerCAmelCase_			:     Any									= hidden_act
									lowerCAmelCase_			:     str									= use_absolute_embeddings
									lowerCAmelCase_			:     List[str]									= layer_norm_eps
									lowerCAmelCase_			:     int									= initializer_range
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCAmelCase_			:     str									= int(embed_dim * 2 ** (len(A_) - 1))
									lowerCAmelCase_			:     Optional[Any]									= ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,				len(A_) + 1)]
									lowerCAmelCase_					,  lowerCAmelCase_			:     int									= get_aligned_output_features_output_indices(
									    out_features=A_				,				out_indices=A_				,				stage_names=self.stage_names)





