
'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowercase :  Tuple    							=       logging.get_logger(__name__)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[tf.Tensor, np.ndarray]							):



						"""simple docstring"""





						if isinstance(__SCREAMING_SNAKE_CASE      ,      np.ndarray							):
												return list(tensor.shape							)

						lowercase_	:     str													=       tf.shape(__SCREAMING_SNAKE_CASE							)

						if tensor.shape == tf.TensorShape(__SCREAMING_SNAKE_CASE							):
												return dynamic

						lowercase_	:     Dict													=       tensor.shape.as_list()

						return [dynamic[i] if s is None else s for i, s in enumerate(__SCREAMING_SNAKE_CASE							)]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: tf.Tensor      ,      __SCREAMING_SNAKE_CASE					: Optional[int] = None      ,      __SCREAMING_SNAKE_CASE					: Optional[str] = None							):



						"""simple docstring"""





						return tf.nn.softmax(logits=logits + 1E-9      ,      axis=__SCREAMING_SNAKE_CASE      ,      name=__SCREAMING_SNAKE_CASE							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]=1E-5      ,      __SCREAMING_SNAKE_CASE					: Dict=-1							):



						"""simple docstring"""





						if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
												raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''							)

						# Get mean and variance on the axis to be normalized
						lowercase_						, lowercase_	:     Any													=       tf.nn.moments(__SCREAMING_SNAKE_CASE      ,      axes=[axis]      ,      keepdims=__SCREAMING_SNAKE_CASE							)

						if axis != -1:
												# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
												# on every dimension except axis
												lowercase_	:     Union[str, Any]													=       [1] * inputs.shape.rank
												lowercase_	:     Dict													=       shape_list(__SCREAMING_SNAKE_CASE							)[axis]
												lowercase_	:     List[Any]													=       tf.reshape(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
												lowercase_	:     str													=       tf.reshape(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						# Compute layer normalization using the batch_normalization
						# function.
						lowercase_	:     List[Any]													=       tf.nn.batch_normalization(
						    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      offset=__SCREAMING_SNAKE_CASE      ,      scale=__SCREAMING_SNAKE_CASE      ,      variance_epsilon=__SCREAMING_SNAKE_CASE      ,      )
						return outputs
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: int=0      ,      __SCREAMING_SNAKE_CASE					: List[str]=-1							):



						"""simple docstring"""





						if end_dim < 0:
												end_dim += input.shape.rank
						if start_dim < 0:
												start_dim += input.shape.rank

						if start_dim == end_dim:
												return input

						lowercase_	:     int													=       tf.shape(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     List[str]													=       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]							)
						lowercase_	:     List[Any]													=       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]      ,      axis=0							)
						return tf.reshape(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: tf.Tensor							):



						"""simple docstring"""





						if not isinstance(__SCREAMING_SNAKE_CASE      ,      tf.Tensor							):
												lowercase_	:     str													=       tf.convert_to_tensor(__SCREAMING_SNAKE_CASE							)  # Catches stray NumPy inputs
						if encoder_attention_mask.shape.rank == 3:
												lowercase_	:     Optional[int]													=       encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.shape.rank == 2:
												lowercase_	:     Optional[int]													=       encoder_attention_mask[:, None, None, :]
						# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
						# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
						# /transformer/transformer_layers.py#L270
						# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
						# encoder_extended_attention_mask.transpose(-1, -2))
						lowercase_	:     Dict													=       (
						    tf.cast(1      ,      encoder_attention_mask.dtype							) - encoder_extended_attention_mask
						) * encoder_extended_attention_mask.dtype.min

						return encoder_extended_attention_mask
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: tf.Tensor      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: str = "input_ids"							):



						"""simple docstring"""





						tf.debugging.assert_less(
						    __SCREAMING_SNAKE_CASE      ,      tf.cast(__SCREAMING_SNAKE_CASE      ,      dtype=tensor.dtype							)      ,      message=(
						        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(__SCREAMING_SNAKE_CASE							)}) must be smaller than the embedding '''
						        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
						    )      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     List[str]													=       64512
						# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
						# because in that case even chunking the array would not make the saving
						# possible.
						lowercase_	:     List[str]													=       [x for x in data if len(__SCREAMING_SNAKE_CASE							) > HDF5_OBJECT_HEADER_LIMIT]

						# Expecting this to never be true.
						if bad_attributes:
												raise RuntimeError(
												    '''The following attributes cannot be saved to HDF5 file because '''
												    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
												    F'''bytes: {bad_attributes}'''							)

						lowercase_	:     Dict													=       np.asarray(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Tuple													=       1
						lowercase_	:     Optional[Any]													=       np.array_split(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						# This will never loop forever thanks to the test above.
						while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data							):
												num_chunks += 1
												lowercase_	:     Optional[int]													=       np.array_split(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						if num_chunks > 1:
												for chunk_id, chunk_data in enumerate(__SCREAMING_SNAKE_CASE							):
																		lowercase_	:     Tuple													=       chunk_data
						else:
												lowercase_	:     int													=       data
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						if name in group.attrs:
												lowercase_	:     List[Any]													=       [n.decode('''utf8'''							) if hasattr(__SCREAMING_SNAKE_CASE      ,      '''decode'''							) else n for n in group.attrs[name]]
						else:
												lowercase_	:     Dict													=       []
												lowercase_	:     Dict													=       0
												while "%s%d" % (name, chunk_id) in group.attrs:
																		data.extend(
																		    [n.decode('''utf8'''							) if hasattr(__SCREAMING_SNAKE_CASE      ,      '''decode'''							) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]							)
																		chunk_id += 1
						return data




def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Tuple							):



						"""simple docstring"""





						def _expand_single_ad_tensor(__SCREAMING_SNAKE_CASE					: Any							):
												if isinstance(__SCREAMING_SNAKE_CASE      ,      tf.Tensor							) and t.shape.rank == 1:
																		return tf.expand_dims(__SCREAMING_SNAKE_CASE      ,      axis=-1							)
												return t

						return tf.nest.map_structure(_expand_single_ad_tensor      ,      __SCREAMING_SNAKE_CASE							)





"""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_fnet import FNetTokenizer
else:
    _a		       =							None

_a		       =							logging.get_logger(__name__)
_a		       =							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_a		       =							{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_a		       =							{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

_a		       =							'▁'


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[str]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Tuple       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Any       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		["""input_ids""", """token_type_ids"""]
  SCREAMING_SNAKE_CASE__			:						Tuple       =		FNetTokenizer







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=False				,   lowercase_=True				,   lowercase_=True				,   lowercase_="<unk>"				,   lowercase_="[SEP]"				,   lowercase_="<pad>"				,   lowercase_="[CLS]"				,   lowercase_="[MASK]"				,   **lowercase_				,   ):

     """simple docstring"""
     # 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.
     UpperCAmelCase_					:					int			     =    (
         AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_				,   normalized=lowercase_  )
         if isinstance(lowercase_				,   lowercase_  )
         else mask_token
     )

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

     UpperCAmelCase_					:					Any			     =    do_lower_case
     UpperCAmelCase_					:					Tuple			     =    remove_space
     UpperCAmelCase_					:					str			     =    keep_accents
     UpperCAmelCase_					:					Any			     =    vocab_file
     UpperCAmelCase_					:					List[Any]			     =    False if not self.vocab_file else True







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     if not os.path.isdir(lowercase_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
     UpperCAmelCase_					:					List[str]			     =    os.path.join(
         lowercase_				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

     return (out_vocab_file,)


