






"""simple docstring"""




import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)



class 				__UpperCAmelCase		(nn.Module   ):







			def __init__(     self:  Optional[Any]					):







									'''simple docstring'''




									super().__init__()
									_SCREAMING_SNAKE_CASE     				=  nn.Linear(3				,     4					)
									_SCREAMING_SNAKE_CASE     				=  nn.BatchNormad(4					)
									_SCREAMING_SNAKE_CASE     				=  nn.Linear(4				,     5					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase_					)					)					)



class 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  ModelForTest()
									with TemporaryDirectory() as tmp_dir:
															offload_state_dict(UpperCAmelCase_				,     model.state_dict()					)
															_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     """index.json"""					)
															self.assertTrue(os.path.isfile(UpperCAmelCase_					)					)
															# TODO: add tests on what is inside the index

															for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
																					_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     F'{key}.dat'					)
																					self.assertTrue(os.path.isfile(UpperCAmelCase_					)					)
																					# TODO: add tests on the fact weights are properly loaded







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  [torch.floataa, torch.floataa, torch.bfloataa]

									for dtype in dtypes:
															_SCREAMING_SNAKE_CASE     				=  torch.randn(2				,     3				,     dtype=UpperCAmelCase_					)
															with TemporaryDirectory() as tmp_dir:
																					_SCREAMING_SNAKE_CASE     				=  offload_weight(UpperCAmelCase_				,     """weight"""				,     UpperCAmelCase_				,     {}					)
																					_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     """weight.dat"""					)
																					self.assertTrue(os.path.isfile(UpperCAmelCase_					)					)
																					self.assertDictEqual(UpperCAmelCase_				,     {"""weight""": {"""shape""": [2, 3], """dtype""": str(UpperCAmelCase_					).split("""."""					)[1]}}					)

																					_SCREAMING_SNAKE_CASE     				=  load_offloaded_weight(UpperCAmelCase_				,     index["""weight"""]					)
																					self.assertTrue(torch.equal(UpperCAmelCase_				,     UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  ModelForTest()
									_SCREAMING_SNAKE_CASE     				=  model.state_dict()
									_SCREAMING_SNAKE_CASE     				=  {k: v for k, v in state_dict.items() if """linear2""" not in k}
									_SCREAMING_SNAKE_CASE     				=  {k: v for k, v in state_dict.items() if """linear2""" in k}

									with TemporaryDirectory() as tmp_dir:
															offload_state_dict(UpperCAmelCase_				,     UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  OffloadedWeightsLoader(state_dict=UpperCAmelCase_				,     save_folder=UpperCAmelCase_					)

															# Every key is there with the right value
															self.assertEqual(sorted(UpperCAmelCase_					)				,     sorted(state_dict.keys()					)					)
															for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(UpperCAmelCase_				,     weight_map[key]					)					)

									_SCREAMING_SNAKE_CASE     				=  {k: v for k, v in state_dict.items() if """weight""" in k}
									_SCREAMING_SNAKE_CASE     				=  {k: v for k, v in state_dict.items() if """weight""" not in k}

									with TemporaryDirectory() as tmp_dir:
															offload_state_dict(UpperCAmelCase_				,     UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  OffloadedWeightsLoader(state_dict=UpperCAmelCase_				,     save_folder=UpperCAmelCase_					)

															# Every key is there with the right value
															self.assertEqual(sorted(UpperCAmelCase_					)				,     sorted(state_dict.keys()					)					)
															for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(UpperCAmelCase_				,     weight_map[key]					)					)

									with TemporaryDirectory() as tmp_dir:
															offload_state_dict(UpperCAmelCase_				,     UpperCAmelCase_					)
															# Duplicates are removed
															_SCREAMING_SNAKE_CASE     				=  OffloadedWeightsLoader(state_dict=UpperCAmelCase_				,     save_folder=UpperCAmelCase_					)

															# Every key is there with the right value
															self.assertEqual(sorted(UpperCAmelCase_					)				,     sorted(state_dict.keys()					)					)
															for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(UpperCAmelCase_				,     weight_map[key]					)					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
									_SCREAMING_SNAKE_CASE     				=  extract_submodules_state_dict(UpperCAmelCase_				,     ["""a.1""", """a.2"""]					)
									self.assertDictEqual(UpperCAmelCase_				,     {"""a.1""": 0, """a.2""": 2}					)

									_SCREAMING_SNAKE_CASE     				=  {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
									_SCREAMING_SNAKE_CASE     				=  extract_submodules_state_dict(UpperCAmelCase_				,     ["""a.1""", """a.2"""]					)
									self.assertDictEqual(UpperCAmelCase_				,     {"""a.1.a""": 0, """a.2.a""": 2}					)









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_camembert import CamembertTokenizer
else:
		UpperCamelCase			    =     None


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase			    =     {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase			    =     {
    '''camembert-base''': 512,
}

UpperCamelCase			    =     '''▁'''



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				int									=							VOCAB_FILES_NAMES
			__snake_case    :				Any									=							PRETRAINED_VOCAB_FILES_MAP
			__snake_case    :				Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case    :				Dict									=							["input_ids", "attention_mask"]
			__snake_case    :				Tuple									=							CamembertTokenizer







			def __init__(     self:  List[Any]				,     UpperCAmelCase_:  Optional[int]=None				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  str="<s>"				,     UpperCAmelCase_:  List[str]="</s>"				,     UpperCAmelCase_:  Dict="</s>"				,     UpperCAmelCase_:  List[Any]="<s>"				,     UpperCAmelCase_:  Dict="<unk>"				,     UpperCAmelCase_:  Any="<pad>"				,     UpperCAmelCase_:  Tuple="<mask>"				,     UpperCAmelCase_:  str=["<s>NOTUSED", "</s>NOTUSED"]				,     **UpperCAmelCase_:  Optional[Any]				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  AddedToken(UpperCAmelCase_				,     lstrip=UpperCAmelCase_				,     rstrip=UpperCAmelCase_					) if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) else mask_token

									super().__init__(
									    UpperCAmelCase_				,     tokenizer_file=UpperCAmelCase_				,     bos_token=UpperCAmelCase_				,     eos_token=UpperCAmelCase_				,     sep_token=UpperCAmelCase_				,     cls_token=UpperCAmelCase_				,     unk_token=UpperCAmelCase_				,     pad_token=UpperCAmelCase_				,     mask_token=UpperCAmelCase_				,     additional_special_tokens=UpperCAmelCase_				,     **UpperCAmelCase_				,     )

									_SCREAMING_SNAKE_CASE     				=  vocab_file
									_SCREAMING_SNAKE_CASE     				=  False if not self.vocab_file else True







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.cls_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]
									_SCREAMING_SNAKE_CASE     				=  [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 + sep + token_ids_a + sep					) * [0]







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  Optional[str] = None					):







									'''simple docstring'''




									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(UpperCAmelCase_					):
															logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
															return
									_SCREAMING_SNAKE_CASE     				=  os.path.join(
									    UpperCAmelCase_				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

									return (out_vocab_file,)

