

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

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


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     lowercase_			=  None


lowercase_			=  logging.get_logger(__name__)


lowercase_			=  "▁"

lowercase_			=  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase_			=  {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

lowercase_			=  {
    "google/pegasus-xsum": 512,
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase		     =							PegasusTokenizer
    lowerCamelCase		     =							['input_ids', 'attention_mask']


    def __init__(				self			:	Any,lowercase_			:	Optional[int]=None,lowercase_			:	Union[str, Any]=None,lowercase_			:	Union[str, Any]="<pad>",lowercase_			:	List[str]="</s>",lowercase_			:	str="<unk>",lowercase_			:	Union[str, Any]="<mask_2>",lowercase_			:	int="<mask_1>",lowercase_			:	Any=None,lowercase_			:	Optional[int]=1_0_3,**lowercase_			:	Tuple,)->   str:





        '''simple docstring'''







        A__   				=       offset

        if additional_special_tokens is not None:
            if not isinstance(lowercase_,lowercase_		):
                raise TypeError(
                    F'additional_special_tokens should be of type {type(lowercase_		)}, but is'
                    F' {type(lowercase_		)}'		)

            A__   				=       (
                ([mask_token_sent] + additional_special_tokens)
                if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                else additional_special_tokens
            )
            # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
            additional_special_tokens_extended += [
                F'<unk_{i}>' for i in range(len(lowercase_		),self.offset - 1		)
            ]

            if len(set(lowercase_		)		) != len(lowercase_		):
                raise ValueError(
                    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'		)
            A__   				=       additional_special_tokens_extended
        else:
            A__   				=       [mask_token_sent] if mask_token_sent is not None else []
            additional_special_tokens += [F'<unk_{i}>' for i in range(2,self.offset		)]

        super().__init__(
            lowercase_,tokenizer_file=lowercase_,pad_token=lowercase_,eos_token=lowercase_,unk_token=lowercase_,mask_token=lowercase_,mask_token_sent=lowercase_,offset=lowercase_,additional_special_tokens=lowercase_,**lowercase_,)
        A__   				=       vocab_file
        A__   				=       False if not self.vocab_file else True


    def 						snake_case__					(				self			:	str,lowercase_			:	List[str]		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       set(self.all_special_ids		)  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

        if all_special_ids != set(range(len(self.additional_special_tokens		) + 3		)		):
            raise ValueError(
                'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
                F' {len(self.additional_special_tokens		)} additional_special_tokens, but got {all_special_ids}'		)

        return [1 if x in all_special_ids else 0 for x in seq]


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	List,lowercase_			:	Optional[List] = None,lowercase_			:	bool = False		)->   List[int]:





        '''simple docstring'''







        if already_has_special_tokens:
            return self._special_token_mask(lowercase_		)
        elif token_ids_a is None:
            return self._special_token_mask(lowercase_		) + [1]
        else:
            return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Union[str, Any],lowercase_			:	Union[str, Any]=None		)->   List[int]:





        '''simple docstring'''







        if token_ids_a is None:
            return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]


    def 						snake_case__					(				self			:	Dict,lowercase_			:	str,lowercase_			:	Optional[str] = None		)->   Tuple[str]:





        '''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(lowercase_		):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
            return
        A__   				=       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 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 			A						(							nn.Module			):







    """simple docstring"""





    def __init__(				self			:	Union[str, Any]		)->   List[str]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self.lineara(self.batchnorm(self.lineara(lowercase_		)		)		)




class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







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

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


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







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

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

                A__   				=       load_offloaded_weight(lowercase_,index['weight']		)
                self.assertTrue(torch.equal(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Optional[Any]:





        '''simple docstring'''







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

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

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

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

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

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

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

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


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       {'a.1': 0, 'a.10': 1, 'a.2': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1': 0, 'a.2': 2}		)

        A__   				=       {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1.a': 0, 'a.2.a': 2}		)

