






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

A_       :Tuple       =						{"""vocab_file""": """vocab.json"""}

A_       :List[str]       =						{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

A_       :str       =						{"""mgp-str""": 27}




class 				__A     (			__UpperCamelCase ):





				"""simple docstring"""







				UpperCamelCase__ :      List[Any]			 =VOCAB_FILES_NAMES
				UpperCamelCase__ :      List[str]			 =PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__ :      List[str]			 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__="[GO]"							,  lowerCamelCase__="[GO]"							,  lowerCamelCase__="[s]"							,  lowerCamelCase__="[GO]"							,  **lowerCamelCase__    ):




									"""simple docstring"""

									super().__init__(
									    unk_token=lowerCamelCase__							,  bos_token=lowerCamelCase__							,  eos_token=lowerCamelCase__							,  pad_token=lowerCamelCase__							,  **lowerCamelCase__							,  )

									with open(lowerCamelCase__							,  encoding='utf-8'    ) as vocab_handle:
														__UpperCamelCase      :					List[Any]       		=json.load(lowerCamelCase__    )
									__UpperCamelCase      :					Optional[int]       		={v: k for k, v in self.vocab.items()}


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return len(self.vocab    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									return dict(self.vocab							,  **self.added_tokens_encoder    )


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=[]
									for s in text:
														char_tokens.extend(lowerCamelCase__    )
									return char_tokens


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									return self.vocab.get(lowerCamelCase__							,  self.vocab.get(self.unk_token    )    )


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									return self.decoder.get(lowerCamelCase__    )







				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None    ):




									"""simple docstring"""

									if not os.path.isdir(lowerCamelCase__    ):
														logger.error('Vocabulary path ({}) should be a directory'.format(lowerCamelCase__    )    )
														return
									__UpperCamelCase      :					str       		=os.path.join(
									    lowerCamelCase__							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

									with open(lowerCamelCase__							,  'w'							,  encoding='utf-8'    ) as f:
														f.write(json.dumps(self.vocab							,  indent=2							,  sort_keys=lowerCamelCase__							,  ensure_ascii=lowerCamelCase__    ) + '\n'    )

									return (vocab_file,)








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

class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      Any	)			->	Any:
     lowerCamelCase__    :      str					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		hidden_states.shape
     lowerCamelCase__    :      Union[str, Any]					 =		jax.image.resize(
         UpperCAmelCase			,     shape=(batch, height * 2, width * 2, channels)			,     method='nearest'			,     )
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(2, 2)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[Any]:
     # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
     # hidden_states = jnp.pad(hidden_states, pad_width=pad)
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		0.0
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels if self.out_channels is None else self.out_channels

     lowerCamelCase__    :      Tuple					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      int					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		nn.Dense(UpperCAmelCase			,     dtype=self.dtype	)

     lowerCamelCase__    :      Union[str, Any]					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      List[Any]					 =		nn.Dropout(self.dropout_prob	)
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

     lowerCamelCase__    :      Union[str, Any]					 =		None
     if use_nin_shortcut:
         lowerCamelCase__    :      Dict					 =		nn.Conv(
             UpperCAmelCase			,     kernel_size=(1, 1)			,     strides=(1, 1)			,     padding='VALID'			,     dtype=self.dtype			,     )







 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int=True	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_states
     lowerCamelCase__    :      List[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.conva(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		self.time_emb_proj(nn.swish(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[str]					 =		jnp.expand_dims(jnp.expand_dims(UpperCAmelCase			,     1	)			,     1	)
     lowerCamelCase__    :      List[str]					 =		hidden_states + temb

     lowerCamelCase__    :      Optional[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.dropout(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.conva(UpperCAmelCase	)

     if self.conv_shortcut is not None:
         lowerCamelCase__    :      Dict					 =		self.conv_shortcut(UpperCAmelCase	)

     return hidden_states + residual





