





"""simple docstring"""




import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class 					_UpperCAmelCase    (  __snake_case     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					List[str]             =  RealmTokenizer
      lowercase_ :					int             =  RealmTokenizerFast
      lowercase_ :					Dict             =  True
      lowercase_ :					Optional[Any]             =  True
      lowercase_ :					Dict             =  filter_non_english

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        super().setUp()

        A_    :			List[str]									=    [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        A_    :			str									=    os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES['vocab_file']    )
        with open(self.vocab_file   , 'w'   , encoding='utf-8'    ) as vocab_writer:
          vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    'UNwant\u00E9d,running'
        A_    :			Tuple									=    'unwanted, running'
        return input_text, output_text

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    self.tokenizer_class(self.vocab_file    )

        A_    :			int									=    tokenizer.tokenize('UNwant\u00E9d,running'    )
        self.assertListEqual(__UpperCamelCase   , ['un', '##want', '##ed', ',', 'runn', '##ing']    )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase    )   , [9, 6, 7, 1_2, 1_0, 1_1]    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if not self.test_rust_tokenizer:
          return

        A_    :			Optional[int]									=    self.get_tokenizer()
        A_    :			List[Any]									=    self.get_rust_tokenizer()

        A_    :			Dict									=    'UNwant\u00E9d,running'

        A_    :			Optional[int]									=    tokenizer.tokenize(__UpperCamelCase    )
        A_    :			List[Any]									=    rust_tokenizer.tokenize(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase   , __UpperCamelCase    )

        A_    :			Optional[Any]									=    tokenizer.encode(__UpperCamelCase   , add_special_tokens=__UpperCamelCase    )
        A_    :			str									=    rust_tokenizer.encode(__UpperCamelCase   , add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase   , __UpperCamelCase    )

        A_    :			str									=    self.get_rust_tokenizer()
        A_    :			int									=    tokenizer.encode(__UpperCamelCase    )
        A_    :			Optional[int]									=    rust_tokenizer.encode(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase   , __UpperCamelCase    )

        # With lower casing
        A_    :			str									=    self.get_tokenizer(do_lower_case=__UpperCamelCase    )
        A_    :			Dict									=    self.get_rust_tokenizer(do_lower_case=__UpperCamelCase    )

        A_    :			int									=    'UNwant\u00E9d,running'

        A_    :			int									=    tokenizer.tokenize(__UpperCamelCase    )
        A_    :			int									=    rust_tokenizer.tokenize(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase   , __UpperCamelCase    )

        A_    :			Optional[Any]									=    tokenizer.encode(__UpperCamelCase   , add_special_tokens=__UpperCamelCase    )
        A_    :			Union[str, Any]									=    rust_tokenizer.encode(__UpperCamelCase   , add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase   , __UpperCamelCase    )

        A_    :			Optional[Any]									=    self.get_rust_tokenizer()
        A_    :			List[str]									=    tokenizer.encode(__UpperCamelCase    )
        A_    :			Optional[int]									=    rust_tokenizer.encode(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase   , __UpperCamelCase    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'    )   , ['ah', '\u535A', '\u63A8', 'zz']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    BasicTokenizer(do_lower_case=__UpperCamelCase    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '    )   , ['hello', '!', 'how', 'are', 'you', '?']    )
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )   , ['hello']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BasicTokenizer(do_lower_case=__UpperCamelCase   , strip_accents=__UpperCamelCase    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )   , ['hällo', '!', 'how', 'are', 'you', '?']    )
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )   , ['h\u00E9llo']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    BasicTokenizer(do_lower_case=__UpperCamelCase   , strip_accents=__UpperCamelCase    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )   , ['hallo', '!', 'how', 'are', 'you', '?']    )
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )   , ['hello']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    BasicTokenizer(do_lower_case=__UpperCamelCase    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )   , ['hallo', '!', 'how', 'are', 'you', '?']    )
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )   , ['hello']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    BasicTokenizer(do_lower_case=__UpperCamelCase    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '    )   , ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    BasicTokenizer(do_lower_case=__UpperCamelCase   , strip_accents=__UpperCamelCase    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )   , ['HäLLo', '!', 'how', 'Are', 'yoU', '?']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    BasicTokenizer(do_lower_case=__UpperCamelCase   , strip_accents=__UpperCamelCase    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )   , ['HaLLo', '!', 'how', 'Are', 'yoU', '?']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    BasicTokenizer(do_lower_case=__UpperCamelCase   , never_split=['[UNK]']    )

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'    )   , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

        A_    :			Optional[Any]									=    {}
        for i, token in enumerate(__UpperCamelCase    ):
          A_    :			Union[str, Any]									=    i
        A_    :			Tuple									=    WordpieceTokenizer(vocab=__UpperCamelCase   , unk_token='[UNK]'    )

        self.assertListEqual(tokenizer.tokenize(''    )   , []    )

        self.assertListEqual(tokenizer.tokenize('unwanted running'    )   , ['un', '##want', '##ed', 'runn', '##ing']    )

        self.assertListEqual(tokenizer.tokenize('unwantedX running'    )   , ['[UNK]', 'runn', '##ing']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.assertTrue(_is_whitespace(' '    )    )
        self.assertTrue(_is_whitespace('\t'    )    )
        self.assertTrue(_is_whitespace('\r'    )    )
        self.assertTrue(_is_whitespace('\n'    )    )
        self.assertTrue(_is_whitespace('\u00A0'    )    )

        self.assertFalse(_is_whitespace('A'    )    )
        self.assertFalse(_is_whitespace('-'    )    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.assertTrue(_is_control('\u0005'    )    )

        self.assertFalse(_is_control('A'    )    )
        self.assertFalse(_is_control(' '    )    )
        self.assertFalse(_is_control('\t'    )    )
        self.assertFalse(_is_control('\r'    )    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.assertTrue(_is_punctuation('-'    )    )
        self.assertTrue(_is_punctuation('$'    )    )
        self.assertTrue(_is_punctuation('`'    )    )
        self.assertTrue(_is_punctuation('.'    )    )

        self.assertFalse(_is_punctuation('A'    )    )
        self.assertFalse(_is_punctuation(' '    )    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    self.get_tokenizer()

        # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
        self.assertListEqual([tokenizer.tokenize(__UpperCamelCase    ) for t in ['Test', '\xad', 'test']]   , [['[UNK]'], [], ['[UNK]']]    )

        if self.test_rust_tokenizer:
          A_    :			int									=    self.get_rust_tokenizer()
          self.assertListEqual(
              [rust_tokenizer.tokenize(__UpperCamelCase    ) for t in ['Test', '\xad', 'test']]   , [['[UNK]'], [], ['[UNK]']]    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    self.tokenizer_class.from_pretrained('bert-base-uncased'    )

        A_    :			int									=    tokenizer.encode('sequence builders'   , add_special_tokens=__UpperCamelCase    )
        A_    :			List[str]									=    tokenizer.encode('multi-sequence build'   , add_special_tokens=__UpperCamelCase    )

        A_    :			Tuple									=    tokenizer.build_inputs_with_special_tokens(__UpperCamelCase    )
        A_    :			Optional[int]									=    tokenizer.build_inputs_with_special_tokens(__UpperCamelCase   , __UpperCamelCase    )

        assert encoded_sentence == [1_0_1] + text + [1_0_2]
        assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
            A_    :			Optional[Any]									=    self.rust_tokenizer_class.from_pretrained(__UpperCamelCase   , **__UpperCamelCase    )

            A_    :			Optional[Any]									=    F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
            A_    :			Optional[int]									=    tokenizer_r.encode_plus(
                __UpperCamelCase   , return_attention_mask=__UpperCamelCase   , return_token_type_ids=__UpperCamelCase   , return_offsets_mapping=__UpperCamelCase   , add_special_tokens=__UpperCamelCase   , )

            A_    :			str									=    tokenizer_r.do_lower_case if hasattr(__UpperCamelCase   , 'do_lower_case'    ) else False
            A_    :			Any									=    (
                [
                    ((0, 0), tokenizer_r.cls_token),
                    ((0, 1), 'A'),
                    ((1, 2), ','),
                    ((3, 5), 'na'),
                    ((5, 6), '##ï'),
                    ((6, 8), '##ve'),
                    ((9, 1_5), tokenizer_r.mask_token),
                    ((1_6, 2_1), 'Allen'),
                    ((2_1, 2_3), '##NL'),
                    ((2_3, 2_4), '##P'),
                    ((2_5, 3_3), 'sentence'),
                    ((3_3, 3_4), '.'),
                    ((0, 0), tokenizer_r.sep_token),
                ]
                if not do_lower_case
                else [
                    ((0, 0), tokenizer_r.cls_token),
                    ((0, 1), 'a'),
                    ((1, 2), ','),
                    ((3, 8), 'naive'),
                    ((9, 1_5), tokenizer_r.mask_token),
                    ((1_6, 2_1), 'allen'),
                    ((2_1, 2_3), '##nl'),
                    ((2_3, 2_4), '##p'),
                    ((2_5, 3_3), 'sentence'),
                    ((3_3, 3_4), '.'),
                    ((0, 0), tokenizer_r.sep_token),
                ]
            )

            self.assertEqual(
                [e[1] for e in expected_results]   , tokenizer_r.convert_ids_to_tokens(tokens['input_ids']    )    )
            self.assertEqual([e[0] for e in expected_results]   , tokens['offset_mapping']    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
            A_    :			Tuple									=    self.rust_tokenizer_class.from_pretrained(__UpperCamelCase   , **__UpperCamelCase    )
            A_    :			List[Any]									=    self.tokenizer_class.from_pretrained(__UpperCamelCase   , **__UpperCamelCase    )
            A_    :			str									=    [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

            A_    :			str									=    tokenizer_r.batch_encode_candidates(__UpperCamelCase   , max_length=1_0   , return_tensors='np'    )
            A_    :			List[Any]									=    tokenizer_p.batch_encode_candidates(__UpperCamelCase   , max_length=1_0   , return_tensors='np'    )

            A_    :			Optional[int]									=    (2, 2, 1_0)

            self.assertEqual(encoded_sentence_r['input_ids'].shape   , __UpperCamelCase    )
            self.assertEqual(encoded_sentence_r['attention_mask'].shape   , __UpperCamelCase    )
            self.assertEqual(encoded_sentence_r['token_type_ids'].shape   , __UpperCamelCase    )

            self.assertEqual(encoded_sentence_p['input_ids'].shape   , __UpperCamelCase    )
            self.assertEqual(encoded_sentence_p['attention_mask'].shape   , __UpperCamelCase    )
            self.assertEqual(encoded_sentence_p['token_type_ids'].shape   , __UpperCamelCase    )

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






