


import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
a					:				Dict       =      'stas/tiny-wmt19-en-ru'

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   FSMTTokenizer
       _UpperCamelCase     		=   False

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        snake_case :			Any							=   [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        snake_case :			List[Any]							=   dict(zip(a__						,    range(len(a__       )       )       )       )
        snake_case :			Any							=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        snake_case :			str							=   ['''en''', '''ru''']
        snake_case :			Tuple							=   {
            '''langs''': self.langs,
            '''src_vocab_size''': 10,
            '''tgt_vocab_size''': 20,
        }

        snake_case :			List[str]							=   os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['''src_vocab_file''']       )
        snake_case :			List[Any]							=   os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['''tgt_vocab_file''']       )
        snake_case :			Tuple							=   os.path.join(self.tmpdirname						,    '''tokenizer_config.json'''       )
        snake_case :			Union[str, Any]							=   os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['''merges_file''']       )
        with open(self.src_vocab_file						,    '''w'''       ) as fp:
         fp.write(json.dumps(a__       )       )
        with open(self.tgt_vocab_file						,    '''w'''       ) as fp:
         fp.write(json.dumps(a__       )       )
        with open(self.merges_file						,    '''w'''       ) as fp:
         fp.write('''\n'''.join(a__       )       )
        with open(a__						,    '''w'''       ) as fp:
         fp.write(json.dumps(a__       )       )

       @cached_property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''       )

       @cached_property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   FSMTTokenizer.from_pretrained(a__       )
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]						,    ['''en''', '''ru''']       )
        self.assertEqual(tokenizer.src_vocab_size						,    21       )
        self.assertEqual(tokenizer.tgt_vocab_size						,    21       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   FSMTTokenizer(self.langs						,    self.src_vocab_file						,    self.tgt_vocab_file						,    self.merges_file       )

        snake_case :			Union[str, Any]							=   '''lower'''
        snake_case :			str							=   ['''low''', '''er</w>''']
        snake_case :			List[Any]							=   tokenizer.tokenize(a__       )
        self.assertListEqual(a__						,    a__       )

        snake_case :			Union[str, Any]							=   tokens + ['''<unk>''']
        snake_case :			Union[str, Any]							=   [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(a__       )						,    a__       )

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[Any]							=   self.tokenizer_ru_en

        snake_case :			Optional[Any]							=   tokenizer.encode('''sequence builders'''						,    add_special_tokens=a__       )
        snake_case :			List[str]							=   tokenizer.encode('''multi-sequence build'''						,    add_special_tokens=a__       )

        snake_case :			str							=   tokenizer.build_inputs_with_special_tokens(a__       )
        snake_case :			Union[str, Any]							=   tokenizer.build_inputs_with_special_tokens(a__						,    a__       )

        assert encoded_sentence == text + [2]
        assert encoded_pair == text + [2] + text_a + [2]

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			int							=   self.tokenizer_en_ru
        snake_case :			Optional[Any]							=   self.tokenizer_ru_en

        snake_case :			List[Any]							=   [
            [
                '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
                [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
            ],
            ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
        ]

        # if data needs to be recreated or added, run:
        # import torch
        # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
        # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

        for src_text, tgt_input_ids in targets:
         snake_case :			List[str]							=   tokenizer_enc.encode(a__						,    return_tensors=a__       )
         self.assertListEqual(a__						,    a__       )

         # and decode backward, using the reversed languages model
         snake_case :			str							=   tokenizer_dec.decode(a__						,    skip_special_tokens=a__       )
         self.assertEqual(a__						,    a__       )

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''						,    do_lower_case=a__       )
        snake_case :			Tuple							=   tokenizer.tokenize('''USA is United States of America'''       )
        snake_case :			int							=   ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
        self.assertListEqual(a__						,    a__       )

       @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''       )
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        pass



       @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''       )
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        pass










import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a					:				Dict       =      logging.get_logger(__name__)

a					:				str       =      {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hubert"""

       def __init__(      self						,    a__=32						,    a__=7_68						,    a__=12						,    a__=12						,    a__=30_72						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=0.1						,    a__=True						,    a__=0.0						,    a__=0.1						,    a__=0.1						,    a__=0.02						,    a__=1e-5						,    a__="group"						,    a__="gelu"						,    a__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,    a__=(5, 2, 2, 2, 2, 2, 2)						,    a__=(10, 3, 3, 3, 3, 2, 2)						,    a__=False						,    a__=1_28						,    a__=16						,    a__=False						,    a__=True						,    a__=0.05						,    a__=10						,    a__=2						,    a__=0.0						,    a__=10						,    a__=0						,    a__="sum"						,    a__=False						,    a__=False						,    a__=2_56						,    a__=0						,    a__=1						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__						,    pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__       )
        snake_case :			List[str]							=   hidden_size
        snake_case :			List[str]							=   feat_extract_norm
        snake_case :			List[str]							=   feat_extract_activation
        snake_case :			Dict							=   list(a__       )
        snake_case :			Tuple							=   list(a__       )
        snake_case :			Dict							=   list(a__       )
        snake_case :			str							=   conv_bias
        snake_case :			Dict							=   num_conv_pos_embeddings
        snake_case :			Union[str, Any]							=   num_conv_pos_embedding_groups
        snake_case :			Optional[int]							=   len(self.conv_dim       )
        snake_case :			Tuple							=   num_hidden_layers
        snake_case :			Any							=   intermediate_size
        snake_case :			Optional[Any]							=   hidden_act
        snake_case :			str							=   num_attention_heads
        snake_case :			List[Any]							=   hidden_dropout
        snake_case :			Union[str, Any]							=   attention_dropout
        snake_case :			Dict							=   activation_dropout
        snake_case :			List[str]							=   feat_proj_layer_norm
        snake_case :			List[str]							=   feat_proj_dropout
        snake_case :			Union[str, Any]							=   final_dropout
        snake_case :			List[str]							=   layerdrop
        snake_case :			str							=   layer_norm_eps
        snake_case :			Tuple							=   initializer_range
        snake_case :			str							=   vocab_size
        snake_case :			Tuple							=   do_stable_layer_norm
        snake_case :			Dict							=   use_weighted_layer_sum
        snake_case :			Optional[Any]							=   classifier_proj_size

        if (
            (len(self.conv_stride       ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
            or (len(self.conv_dim       ) != self.num_feat_extract_layers)
        ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        snake_case :			Optional[int]							=   apply_spec_augment
        snake_case :			int							=   mask_time_prob
        snake_case :			Dict							=   mask_time_length
        snake_case :			Any							=   mask_time_min_masks
        snake_case :			Any							=   mask_feature_prob
        snake_case :			Union[str, Any]							=   mask_feature_length
        snake_case :			List[Any]							=   mask_feature_min_masks

        # ctc loss
        snake_case :			List[str]							=   ctc_loss_reduction
        snake_case :			Dict							=   ctc_zero_infinity



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return functools.reduce(operator.mul						,    self.conv_stride						,    1       )






