





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 			UpperCamelCase_		(  tf.keras.layers.Layer					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None  )		->							Dict:
										super().__init__()
										_snake_case									=				pad_token_id
										_snake_case									=				max_length
										_snake_case									=				vocab
										_snake_case									=				merges
										_snake_case									=				BytePairTokenizer(lowerCAmelCase_				,						lowerCAmelCase_				,						sequence_length=lowerCAmelCase_  )




					@classmethod
					def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Union[str, Any]:
										_snake_case									=				[' '.join(lowerCAmelCase_  ) for m in tokenizer.bpe_ranks.keys()]
										_snake_case									=				tokenizer.get_vocab()
										return cls(lowerCAmelCase_				,						lowerCAmelCase_				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )




					@classmethod
					def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Any:
										_snake_case									=				GPTaTokenizer.from_pretrained(lowerCAmelCase_				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )
										return cls.from_tokenizer(lowerCAmelCase_				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )




					@classmethod
					def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_  )		->							str:
										return cls(**lowerCAmelCase_  )




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






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None  )		->							List[Any]:
										_snake_case									=				self.tf_tokenizer(lowerCAmelCase_  )
										_snake_case									=				tf.ones_like(lowerCAmelCase_  )

										if self.pad_token_id is not None:
															# pad the tokens up to max length
															_snake_case									=				max_length if max_length is not None else self.max_length

															if max_length is not None:
																				_snake_case							,     _snake_case									=				pad_model_inputs(
																				    lowerCAmelCase_				,						max_seq_length=lowerCAmelCase_				,						pad_value=self.pad_token_id  )

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''vit_msn'''




					def __init__(		self				,						lowerCAmelCase_=768				,						lowerCAmelCase_=12				,						lowerCAmelCase_=12				,						lowerCAmelCase_=3072				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=1E-06				,						lowerCAmelCase_=224				,						lowerCAmelCase_=16				,						lowerCAmelCase_=3				,						lowerCAmelCase_=True				,						**lowerCAmelCase_				,						)		->							str:
										super().__init__(**lowerCAmelCase_  )

										_snake_case									=				hidden_size
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				intermediate_size
										_snake_case									=				hidden_act
										_snake_case									=				hidden_dropout_prob
										_snake_case									=				attention_probs_dropout_prob
										_snake_case									=				initializer_range
										_snake_case									=				layer_norm_eps
										_snake_case									=				image_size
										_snake_case									=				patch_size
										_snake_case									=				num_channels
										_snake_case									=				qkv_bias



