





'''simple docstring'''

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   UpperCAmelCase      (			UpperCamelCase__     ,			unittest.TestCase							):
 __lowercase  						=							XLMTokenizer
 __lowercase  						=							False
 def 	UpperCAmelCase_				(							self :Any				)->				List[Any]:
     super().setUp()

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

     A__							=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["vocab_file"]				)
     A__							=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["merges_file"]				)
     with open(self.vocab_file		,			"w"				) as fp:
         fp.write(json.dumps(lowercase_				)				)
     with open(self.merges_file		,			"w"				) as fp:
         fp.write("\n".join(lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :int				)->				Optional[int]:
     A__							=     "lower newer"
     A__							=     "lower newer"
     return input_text, output_text
 def 	UpperCAmelCase_				(							self :List[str]				)->				Any:
     A__							=     XLMTokenizer(self.vocab_file		,			self.merges_file				)

     A__							=     "lower"
     A__							=     ["low", "er</w>"]
     A__							=     tokenizer.tokenize(lowercase_				)
     self.assertListEqual(lowercase_		,			lowercase_				)

     A__							=     tokens + ["<unk>"]
     A__							=     [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_				)		,			lowercase_				)



 @slow
 def 	UpperCAmelCase_				(							self :Any				)->				int:
     A__							=     XLMTokenizer.from_pretrained("xlm-mlm-en-2048"				)

     A__							=     tokenizer.encode("sequence builders"		,			add_special_tokens=lowercase_				)
     A__							=     tokenizer.encode("multi-sequence build"		,			add_special_tokens=lowercase_				)

     A__							=     tokenizer.build_inputs_with_special_tokens(lowercase_				)
     A__							=     tokenizer.build_inputs_with_special_tokens(lowercase_		,			lowercase_				)

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










'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



