


'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase    :     Optional[int]					=						logging.get_logger(__name__)

lowerCAmelCase    :     List[Any]					=						{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "deit"







     def __init__( self      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=224      ,	snake_case__=16      ,	snake_case__=3      ,	snake_case__=True      ,	snake_case__=16      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       hidden_size
            _lowerCAmelCase    :							Union[str, Any]   			=       num_hidden_layers
            _lowerCAmelCase    :							Tuple   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       intermediate_size
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_act
            _lowerCAmelCase    :							Tuple   			=       hidden_dropout_prob
            _lowerCAmelCase    :							str   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							Optional[Any]   			=       initializer_range
            _lowerCAmelCase    :							int   			=       layer_norm_eps
            _lowerCAmelCase    :							Any   			=       image_size
            _lowerCAmelCase    :							List[Any]   			=       patch_size
            _lowerCAmelCase    :							List[Any]   			=       num_channels
            _lowerCAmelCase    :							int   			=       qkv_bias
            _lowerCAmelCase    :							List[Any]   			=       encoder_stride
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    version.parse("1.11"	)







     @property
     def a	( self   ):

            '''simple docstring'''

            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]   )







     @property
     def a	( self   ):

            '''simple docstring'''

            return 1E-4





'''simple docstring'''







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	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

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

