


'''simple docstring'''





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase												=						get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			_snake_case						, unittest.TestCase						):
 UpperCAmelCase				=					XGLMTokenizer
 UpperCAmelCase				=					XGLMTokenizerFast
 UpperCAmelCase				=					True
 UpperCAmelCase				=					True


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    super().setUp()

    # We have a SentencePiece fixture for testing
    UpperCAmelCase__	:Optional[int]      =      XGLMTokenizer(__lowerCamelCase			,       keep_accents=__lowerCamelCase )
    tokenizer.save_pretrained(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    UpperCAmelCase__	:str      =      '''<pad>'''
    UpperCAmelCase__	:Union[str, Any]      =      1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase )			,       __lowerCamelCase )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase )			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Any      =      list(self.get_tokenizer().get_vocab().keys() )

    self.assertEqual(vocab_keys[0]			,       '''<s>''' )
    self.assertEqual(vocab_keys[1]			,       '''<pad>''' )
    self.assertEqual(len(__lowerCamelCase )			,       1_0_0_8 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    self.assertEqual(self.get_tokenizer().vocab_size			,       1_0_0_8 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Union[str, Any]      =      XGLMTokenizer(__lowerCamelCase			,       keep_accents=__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      tokenizer.tokenize('''This is a test''' )
    self.assertListEqual(__lowerCamelCase			,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(__lowerCamelCase )			,       [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,       )

    UpperCAmelCase__	:Union[str, Any]      =      tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
    self.assertListEqual(
        __lowerCamelCase			,       [
            SPIECE_UNDERLINE + '''I''',
            SPIECE_UNDERLINE + '''was''',
            SPIECE_UNDERLINE + '''b''',
            '''or''',
            '''n''',
            SPIECE_UNDERLINE + '''in''',
            SPIECE_UNDERLINE + '''''',
            '''9''',
            '''2''',
            '''0''',
            '''0''',
            '''0''',
            ''',''',
            SPIECE_UNDERLINE + '''and''',
            SPIECE_UNDERLINE + '''this''',
            SPIECE_UNDERLINE + '''is''',
            SPIECE_UNDERLINE + '''f''',
            '''al''',
            '''s''',
            '''é''',
            '''.''',
        ]			,       )
    UpperCAmelCase__	:Tuple      =      tokenizer.convert_tokens_to_ids(__lowerCamelCase )
    self.assertListEqual(
        __lowerCamelCase			,       [
            value + tokenizer.fairseq_offset
            for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
        ]			,       )

    UpperCAmelCase__	:Any      =      tokenizer.convert_ids_to_tokens(__lowerCamelCase )
    self.assertListEqual(
        __lowerCamelCase			,       [
            SPIECE_UNDERLINE + '''I''',
            SPIECE_UNDERLINE + '''was''',
            SPIECE_UNDERLINE + '''b''',
            '''or''',
            '''n''',
            SPIECE_UNDERLINE + '''in''',
            SPIECE_UNDERLINE + '''''',
            '''<unk>''',
            '''2''',
            '''0''',
            '''0''',
            '''0''',
            ''',''',
            SPIECE_UNDERLINE + '''and''',
            SPIECE_UNDERLINE + '''this''',
            SPIECE_UNDERLINE + '''is''',
            SPIECE_UNDERLINE + '''f''',
            '''al''',
            '''s''',
            '''<unk>''',
            '''.''',
        ]			,       )


 @cached_property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    return XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    with tempfile.NamedTemporaryFile() as f:
       shutil.copyfile(__lowerCamelCase			,       f.name )
       UpperCAmelCase__	:str      =      XGLMTokenizer(f.name			,       keep_accents=__lowerCamelCase )
       UpperCAmelCase__	:List[Any]      =      pickle.dumps(__lowerCamelCase )
    pickle.loads(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    if not self.test_rust_tokenizer:
       return

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

    UpperCAmelCase__	:Any      =      '''I was born in 92000, and this is falsé.'''

    UpperCAmelCase__	:str      =      tokenizer.tokenize(__lowerCamelCase )
    UpperCAmelCase__	:Optional[int]      =      rust_tokenizer.tokenize(__lowerCamelCase )
    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer.encode(__lowerCamelCase			,       add_special_tokens=__lowerCamelCase )
    UpperCAmelCase__	:Dict      =      rust_tokenizer.encode(__lowerCamelCase			,       add_special_tokens=__lowerCamelCase )
    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )

    UpperCAmelCase__	:Optional[int]      =      self.get_rust_tokenizer()
    UpperCAmelCase__	:List[str]      =      tokenizer.encode(__lowerCamelCase )
    UpperCAmelCase__	:str      =      rust_tokenizer.encode(__lowerCamelCase )
    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:int      =      '''Hello World!'''
    UpperCAmelCase__	:List[Any]      =      [2, 3_1_2_2_7, 4_4_4_7, 3_5]

    self.assertListEqual(__lowerCamelCase			,       self.big_tokenizer.encode(__lowerCamelCase ) )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:int      =      (
        '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
        ''' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'''
    )
    # fmt: off
    UpperCAmelCase__	:Dict      =      [2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
    # fmt: on

    self.assertListEqual(__lowerCamelCase			,       self.big_tokenizer.encode(__lowerCamelCase ) )





 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    # fmt: off
    UpperCAmelCase__	:Tuple      =      {
        '''input_ids''': [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
        '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
    }  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=__lowerCamelCase			,       model_name='''facebook/xglm-564M'''			,       padding=__lowerCamelCase			,       )







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



