

"""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:
							__UpperCamelCase  :      str							      =		None

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

__UpperCamelCase  :      Optional[Any]							      =		{
    '''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''',
    },
}

__UpperCamelCase  :      Dict							      =		{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__UpperCamelCase  :      Optional[Any]							      =		'''▁'''







class 				SCREAMING_SNAKE_CASE					(		A_       ):






					"""simple docstring"""



					lowercase__				 =       VOCAB_FILES_NAMES
					lowercase__				 =       PRETRAINED_VOCAB_FILES_MAP
					lowercase__				 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__				 =       AlbertTokenizer







					def __init__(       self			:				Any   ,lowercase_			:				Any=None   ,lowercase_			:				Dict=None   ,lowercase_			:				Dict=True   ,lowercase_			:				int=True   ,lowercase_			:				Union[str, Any]=False   ,lowercase_			:				Optional[Any]="[CLS]"   ,lowercase_			:				Dict="[SEP]"   ,lowercase_			:				Dict="<unk>"   ,lowercase_			:				int="[SEP]"   ,lowercase_			:				str="<pad>"   ,lowercase_			:				int="[CLS]"   ,lowercase_			:				str="[MASK]"   ,**lowercase_			:				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.
						lowerCAmelCase__	:						Union[str, Any]  				=	(
						    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_   ,bos_token=lowercase_   ,eos_token=lowercase_   ,unk_token=lowercase_   ,sep_token=lowercase_   ,pad_token=lowercase_   ,cls_token=lowercase_   ,mask_token=lowercase_   ,**lowercase_   ,)

						lowerCAmelCase__	:						Tuple  				=	do_lower_case
						lowerCAmelCase__	:						Tuple  				=	remove_space
						lowerCAmelCase__	:						Optional[Any]  				=	keep_accents
						lowerCAmelCase__	:						Any  				=	vocab_file
						lowerCAmelCase__	:						Dict  				=	False if not self.vocab_file else True







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				str = None		):
						lowerCAmelCase__	:						Tuple  				=	[self.sep_token_id]
						lowerCAmelCase__	:						List[str]  				=	[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        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Tuple   ,lowercase_			:				List[str] = None		):
						lowerCAmelCase__	:						Dict  				=	[self.sep_token_id]
						lowerCAmelCase__	:						List[str]  				=	[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        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[str]   ,lowercase_			:				List[Any] = None		):
						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(lowercase_		):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
							return
						lowerCAmelCase__	:						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,)








import os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		for i, input_name in enumerate(node_proto.input	):
				if input_name == name:
						node_proto.input.insert(lowercase__   , lowercase__	)
						node_proto.input.pop(i + 1	)

		if node_proto.op_type == "If":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)
				_graph_replace_input_with(node_proto.attribute[1].g   , lowercase__   , lowercase__	)
		if node_proto.op_type == "Loop":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		for n in graph_proto.node:
				_node_replace_input_with(lowercase__   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			list(model_without_ext.graph.initializer	)
		for i, ref_i in ind_to_replace:
				assert inits_with_data[i].name == inits[i].name
				assert inits_with_data[ref_i].name == inits[ref_i].name
				assert i > ref_i

				__lowercase=			inits[i].name
				__lowercase=			inits[ref_i].name

				model_without_ext.graph.initializer.remove(inits[i]	)

				# for n in model.graph.node:
				_graph_replace_input_with(model_without_ext.graph   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

		__lowercase=			set()
		__lowercase=			{}
		__lowercase=			[]

		__lowercase=			0

		for i in range(len(lowercase__	)	):
				if i in dup_set:
						continue

				for j in range(i + 1   , len(lowercase__	)	):
						if j in dup_set:
								continue
						if _is_equal_tensor_proto(inits[i]   , inits[j]	):
								dup_set.add(lowercase__	)
								dup_set.add(lowercase__	)

								__lowercase=			inits[j].data_type
								__lowercase=			numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('unexpected data type: '   , lowercase__	)
								total_reduced_size += mem_size

								__lowercase=			inits[i].name
								__lowercase=			inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(lowercase__	)
								else:
										__lowercase=			[name_j]
								ind_to_replace.append((j, i)	)

		print('total reduced size: '   , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4   , 'GB'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






