





"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_albert import AlbertTokenizer
else:
		a		:				Tuple  			=    None

a		:				Any  			=    logging.get_logger(__name__)
a		:				List[Any]  			=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				str  			=    {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

a		:				str  			=    {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

a		:				Union[str, Any]  			=    '''▁'''


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      List[Any]         =AlbertTokenizer






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[MASK]"   ,      **lowerCAmelCase__   ,      )			->				Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								a   :	Optional[int]			=       (
								    AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ,      normalized=lowerCAmelCase__   )
								    if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								    else mask_token
								)

								super().__init__(
								    lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      remove_space=lowerCAmelCase__   ,      keep_accents=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	Dict			=       do_lower_case
								a   :	Any			=       remove_space
								a   :	Optional[Any]			=       keep_accents
								a   :	List[str]			=       vocab_file
								a   :	Optional[Any]			=       False if not self.vocab_file else True






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."   )

								if not os.path.isdir(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	Dict			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase__   ):
									copyfile(self.vocab_file   ,      lowerCAmelCase__   )

								return (out_vocab_file,)









"""simple docstring"""




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


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Tuple:
								a   :	str			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a, a, a, a   :	List[str]			=       hidden_states.shape
								a   :	List[Any]			=       jax.image.resize(
								    lowerCAmelCase__   ,      shape=(batch, height * 2, width * 2, channels)   ,      method="nearest"   ,      )
								a   :	List[str]			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(2, 2)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Tuple:
								# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
								# hidden_states = jnp.pad(hidden_states, pad_width=pad)
								a   :	Tuple			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      int         =None
							lowerCamelCase     :      float         =0.0
							lowerCamelCase     :      bool         =None
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				int:
								a   :	Dict			=       self.in_channels if self.out_channels is None else self.out_channels

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	List[Any]			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	List[Any]			=       nn.Dense(lowerCAmelCase__   ,      dtype=self.dtype   )

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	Optional[int]			=       nn.Dropout(self.dropout_prob   )
								a   :	Dict			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	Union[str, Any]			=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

								a   :	List[str]			=       None
								if use_nin_shortcut:
									a   :	Optional[Any]			=       nn.Conv(
									    lowerCAmelCase__   ,      kernel_size=(1, 1)   ,      strides=(1, 1)   ,      padding="VALID"   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=True   )			->				str:
								a   :	int			=       hidden_states
								a   :	Tuple			=       self.norma(lowerCAmelCase__   )
								a   :	Any			=       nn.swish(lowerCAmelCase__   )
								a   :	int			=       self.conva(lowerCAmelCase__   )

								a   :	int			=       self.time_emb_proj(nn.swish(lowerCAmelCase__   )   )
								a   :	Tuple			=       jnp.expand_dims(jnp.expand_dims(lowerCAmelCase__   ,      1   )   ,      1   )
								a   :	Dict			=       hidden_states + temb

								a   :	str			=       self.norma(lowerCAmelCase__   )
								a   :	List[Any]			=       nn.swish(lowerCAmelCase__   )
								a   :	List[str]			=       self.dropout(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[str]			=       self.conva(lowerCAmelCase__   )

								if self.conv_shortcut is not None:
									a   :	Tuple			=       self.conv_shortcut(lowerCAmelCase__   )

								return hidden_states + residual


