



from typing import Dict

from .base import GenericTensor, Pipeline



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					**__UpperCAmelCase					):



    '''simple docstring'''
    if tokenize_kwargs is None:
       __lowerCamelCase    				=     {}

    if truncation is not None:
       if "truncation" in tokenize_kwargs:
          raise ValueError(
              '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''					)
       __lowerCamelCase    				=     truncation

    __lowerCamelCase    				=     tokenize_kwargs

    __lowerCamelCase    				=     {}
    if return_tensors is not None:
       __lowerCamelCase    				=     return_tensors

    return preprocess_params, {}, postprocess_params




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.framework
    __lowerCamelCase    				=     self.tokenizer(__UpperCAmelCase						,					return_tensors=__UpperCAmelCase						,					**__UpperCAmelCase					)
    return model_inputs




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model(**__UpperCAmelCase					)
    return model_outputs




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=False					):



    '''simple docstring'''
    # [0] is the first available tensor, logits or last_hidden_state.
    if return_tensors:
       return model_outputs[0]
    if self.framework == "pt":
       return model_outputs[0].tolist()
    elif self.framework == "tf":
       return model_outputs[0].numpy().tolist()




 def __call__(    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    return super().__call__(*__UpperCAmelCase						,					**__UpperCAmelCase					)











import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''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>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






