





import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      TransfoXLTokenizer
 __UpperCamelCase       =      False
 __UpperCamelCase       =      False

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    super().setUp()

    UpperCAmelCase__						=[
        "<unk>",
        "[CLS]",
        "[SEP]",
        "want",
        "unwanted",
        "wa",
        "un",
        "running",
        ",",
        "low",
        "l",
    ]
    UpperCAmelCase__						=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  __UpperCAmelCase						(  self,		**A_		)	->       Tuple:
    UpperCAmelCase__						=True
    return TransfoXLTokenizer.from_pretrained(self.tmpdirname,		**A_		)

 def  __UpperCAmelCase						(  self,		A_		)	->       str:
    UpperCAmelCase__						="<unk> UNwanted , running"
    UpperCAmelCase__						="<unk> unwanted, running"
    return input_text, output_text

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=TransfoXLTokenizer(vocab_file=self.vocab_file,		lower_case=A_		)

    UpperCAmelCase__						=tokenizer.tokenize("<unk> UNwanted , running"		)
    self.assertListEqual(A_,		["<unk>", "unwanted", ",", "running"]		)

    self.assertListEqual(tokenizer.convert_tokens_to_ids(A_		),		[0, 4, 8, 7]		)

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=TransfoXLTokenizer(lower_case=A_		)

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

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						=TransfoXLTokenizer(lower_case=A_		)

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

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=TransfoXLTokenizer(lower_case=A_		)
    UpperCAmelCase__						="Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
    UpperCAmelCase__						=[
        "Hello",
        "(",
        "bracket",
        ")",
        "and",
        "side",
        "@-@",
        "scrolled",
        "[",
        "and",
        "]",
        "Henry",
        "'s",
        "$",
        "5",
        "@,@",
        "000",
        "with",
        "3",
        "@.@",
        "34",
        "m",
        ".",
        "What",
        "'s",
        "up",
        "!",
        "?",
    ]

    self.assertListEqual(tokenizer.tokenize(A_		),		A_		)

    self.assertEqual(tokenizer.convert_tokens_to_string(A_		),		A_		)






 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						=self.get_tokenizer()
    UpperCAmelCase__						=len(A_		)

    tokenizer.add_tokens(["new1", "new2"]		)
    tokenizer.move_added_token("new1",		1		)

    # Check that moved token is not copied (duplicate)
    self.assertEqual(len(A_		),		original_len + 2		)
    # Check that token is moved to specified id
    self.assertEqual(tokenizer.encode("new1"		),		[1]		)
    self.assertEqual(tokenizer.decode([1]		),		"new1"		)










import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



