





import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase						=      logging.get_logger(__name__)

__UpperCAmelCase						=      {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__UpperCAmelCase						=      {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__UpperCAmelCase						=      {
    'microsoft/biogpt': 10_24,
}
def 			__UpperCamelCase				(			lowercase__	:       str		)	->      str:
    '''simple docstring'''





    lowerCAmelCase_					:	Tuple						=   set()
    lowerCAmelCase_					:	str						=   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        lowerCAmelCase_					:	Any						=   char
    return pairs

class 		__a							(	UpperCAmelCase_      ):
       __snake_case						:   Optional[int]      =		VOCAB_FILES_NAMES
       __snake_case						:   Union[str, Any]      =		PRETRAINED_VOCAB_FILES_MAP
       __snake_case						:   List[Any]      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __snake_case						:   int      =		["""input_ids""", """attention_mask"""]



       def __init__(     self	:   Any					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Dict="<unk>"					,			UpperCAmelCase	:   Optional[Any]="<s>"					,			UpperCAmelCase	:   str="</s>"					,			UpperCAmelCase	:   Optional[Any]="</s>"					,			UpperCAmelCase	:   List[str]="<pad>"					,			**UpperCAmelCase	:   int					,			):
           super().__init__(
               bos_token=__UpperCamelCase					,			eos_token=__UpperCamelCase					,			sep_token=__UpperCamelCase					,			unk_token=__UpperCamelCase					,			pad_token=__UpperCamelCase					,			**__UpperCamelCase					,			)

           try:
               import sacremoses
           except ImportError:
               raise ImportError(
                   """You need to install sacremoses to use BioGptTokenizer. """
                   """See https://pypi.org/project/sacremoses/ for installation."""			)

           lowerCAmelCase_					:	List[Any]						=   """en"""
           lowerCAmelCase_					:	Optional[Any]						=   sacremoses
           # cache of sm.MosesTokenizer instance
           lowerCAmelCase_					:	List[Any]						=   {}
           lowerCAmelCase_					:	List[str]						=   {}
           with open(__UpperCamelCase					,			encoding="""utf-8"""			) as vocab_handle:
               lowerCAmelCase_					:	str						=   json.load(__UpperCamelCase			)
           lowerCAmelCase_					:	Union[str, Any]						=   {v: k for k, v in self.encoder.items()}
           with open(__UpperCamelCase					,			encoding="""utf-8"""			) as merges_handle:
               lowerCAmelCase_					:	Any						=   merges_handle.read().split("""\n"""			)[:-1]
           lowerCAmelCase_					:	Any						=   [tuple(merge.split()[:2]			) for merge in merges]
           lowerCAmelCase_					:	Optional[int]						=   dict(zip(__UpperCamelCase					,			range(len(__UpperCamelCase			)			)			)			)
           lowerCAmelCase_					:	List[Any]						=   {}



       @property
       def  A							(     self	:   Union[str, Any]			):
           return len(self.encoder			)



       def  A							(     self	:   List[str]			):
           return dict(self.encoder					,			**self.added_tokens_encoder			)



       def  A							(     self	:   List[Any]					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   str			):
           if lang not in self.cache_moses_tokenizer:
               lowerCAmelCase_					:	str						=   self.sm.MosesTokenizer(lang=__UpperCamelCase			)
               lowerCAmelCase_					:	Tuple						=   moses_tokenizer
           return self.cache_moses_tokenizer[lang].tokenize(
               __UpperCamelCase					,			aggressive_dash_splits=__UpperCamelCase					,			return_str=__UpperCamelCase					,			escape=__UpperCamelCase			)



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   Union[str, Any]			):
           if lang not in self.cache_moses_detokenizer:
               lowerCAmelCase_					:	Any						=   self.sm.MosesDetokenizer(lang=__UpperCamelCase			)
               lowerCAmelCase_					:	Any						=   moses_detokenizer
           return self.cache_moses_detokenizer[lang].detokenize(__UpperCamelCase			)



       def  A							(     self	:   int					,			UpperCAmelCase	:   Union[str, Any]			):
           lowerCAmelCase_					:	Dict						=   tuple(token[:-1]			) + (token[-1] + """</w>""",)
           if token in self.cache:
               return self.cache[token]
           lowerCAmelCase_					:	List[Any]						=   get_pairs(__UpperCamelCase			)

           if not pairs:
               return token + "</w>"

           while True:
               lowerCAmelCase_					:	List[Any]						=   min(__UpperCamelCase					,			key=lambda UpperCAmelCase			: self.bpe_ranks.get(__UpperCamelCase					,			float("""inf"""			)			)			)
               if bigram not in self.bpe_ranks:
                   break
               lowerCAmelCase_		,		lowerCAmelCase_					:	Union[str, Any]						=   bigram
               lowerCAmelCase_					:	Optional[int]						=   []
               lowerCAmelCase_					:	Dict						=   0
               while i < len(__UpperCamelCase			):
                   try:
                       lowerCAmelCase_					:	Optional[Any]						=   word.index(__UpperCamelCase					,			__UpperCamelCase			)
                   except ValueError:
                       new_word.extend(word[i:]			)
                       break
                   else:
                       new_word.extend(word[i:j]			)
                       lowerCAmelCase_					:	List[Any]						=   j

                   if word[i] == first and i < len(__UpperCamelCase			) - 1 and word[i + 1] == second:
                       new_word.append(first + second			)
                       i += 2
                   else:
                       new_word.append(word[i]			)
                       i += 1
               lowerCAmelCase_					:	Dict						=   tuple(__UpperCamelCase			)
               lowerCAmelCase_					:	Optional[Any]						=   new_word
               if len(__UpperCamelCase			) == 1:
                   break
               else:
                   lowerCAmelCase_					:	Union[str, Any]						=   get_pairs(__UpperCamelCase			)
           lowerCAmelCase_					:	Union[str, Any]						=   """ """.join(__UpperCamelCase			)
           if word == "\n  </w>":
               lowerCAmelCase_					:	Tuple						=   """\n</w>"""
           lowerCAmelCase_					:	Tuple						=   word
           return word



       def  A							(     self	:   str					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   Tuple=False			):
           if bypass_tokenizer:
               lowerCAmelCase_					:	Dict						=   text.split()
           else:
               lowerCAmelCase_					:	Optional[int]						=   self.moses_tokenize(__UpperCamelCase					,			self.lang			)

           lowerCAmelCase_					:	List[str]						=   []
           for token in text:
               if token:
                   split_tokens.extend(list(self.bpe(__UpperCamelCase			).split(""" """			)			)			)

           return split_tokens



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   Any			):
           return self.encoder.get(__UpperCamelCase					,			self.encoder.get(self.unk_token			)			)



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   Optional[Any]			):
           return self.decoder.get(__UpperCamelCase					,			self.unk_token			)



       def  A							(     self	:   str					,			UpperCAmelCase	:   Optional[int]			):
           lowerCAmelCase_					:	List[Any]						=   [t.replace(""" """					,			""""""			).replace("""</w>"""					,			""" """			) for t in tokens]
           lowerCAmelCase_					:	Optional[int]						=   """""".join(__UpperCamelCase			).split()
           # detokenize
           lowerCAmelCase_					:	Optional[int]						=   self.moses_detokenize(__UpperCamelCase					,			self.lang			)
           return text



       def  A							(     self	:   Tuple					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   str = None			):
           if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
           lowerCAmelCase_					:	List[Any]						=   [self.sep_token_id]
           return sep + token_ids_a + sep + token_ids_a



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   List[str] = None					,			UpperCAmelCase	:   Optional[Any] = False			):
           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCamelCase					,			token_ids_a=__UpperCamelCase					,			already_has_special_tokens=__UpperCamelCase			)
           # no bos used in fairseq
           if token_ids_a is not None:
               return [1] + ([0] * len(__UpperCamelCase			)) + [1] + ([0] * len(__UpperCamelCase			))
           return [1] + ([0] * len(__UpperCamelCase			))



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Dict = None			):
           lowerCAmelCase_					:	List[Any]						=   [self.sep_token_id]

           # no bos used in fairseq
           if token_ids_a is None:
               return len(token_ids_a + sep			) * [0]
           return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   List[str] = None			):
           if not os.path.isdir(__UpperCamelCase			):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
               return
           lowerCAmelCase_					:	int						=   os.path.join(
               __UpperCamelCase					,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)
           lowerCAmelCase_					:	List[str]						=   os.path.join(
               __UpperCamelCase					,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]			)

           with open(__UpperCamelCase					,			"""w"""					,			encoding="""utf-8"""			) as f:
               f.write(json.dumps(self.encoder					,			indent=2					,			sort_keys=__UpperCamelCase					,			ensure_ascii=__UpperCamelCase			) + """\n"""			)

           lowerCAmelCase_					:	Any						=   0
           with open(__UpperCamelCase					,			"""w"""					,			encoding="""utf-8"""			) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,			key=lambda UpperCAmelCase			: kv[1]			):
                   if index != token_index:
                       logger.warning(
                           F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                           """ Please check that the tokenizer is not corrupted!"""			)
                       lowerCAmelCase_					:	List[Any]						=   token_index
                   writer.write(""" """.join(__UpperCamelCase			) + """\n"""			)
                   index += 1

           return vocab_file, merge_file



       def __getstate__(     self	:   Optional[int]			):
           lowerCAmelCase_					:	int						=   self.__dict__.copy()
           lowerCAmelCase_					:	Any						=   None
           return state







       def __setstate__(     self	:   Optional[int]					,			UpperCAmelCase	:   Any			):
           lowerCAmelCase_					:	List[str]						=   d

           try:
               import sacremoses
           except ImportError:
               raise ImportError(
                   """You need to install sacremoses to use XLMTokenizer. """
                   """See https://pypi.org/project/sacremoses/ for installation."""			)

           lowerCAmelCase_					:	List[str]						=   sacremoses









import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )

@require_vision
class 		__a							(	unittest.TestCase      ):



       def  A							(     self	:   List[str]			):
           lowerCAmelCase_					:	Dict						=   tempfile.mkdtemp()

           # fmt: off
           lowerCAmelCase_					:	Dict						=   ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
           # fmt: on
           lowerCAmelCase_					:	Any						=   os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES["""vocab_file"""]			)

           with open(self.vocab_file					,			"""w"""					,			encoding="""utf-8"""			) as fp:
               fp.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)

           lowerCAmelCase_					:	int						=   {
               """image_mean""": FLAVA_IMAGE_MEAN,
               """image_std""": FLAVA_IMAGE_STD,
               """do_normalize""": True,
               """do_resize""": True,
               """size""": 2_24,
               """do_center_crop""": True,
               """crop_size""": 2_24,
               """input_size_patches""": 14,
               """total_mask_patches""": 75,
               """mask_group_max_patches""": None,
               """mask_group_min_patches""": 16,
               """mask_group_min_aspect_ratio""": 0.3,
               """mask_group_max_aspect_ratio""": None,
               """codebook_do_resize""": True,
               """codebook_size""": 1_12,
               """codebook_do_center_crop""": True,
               """codebook_crop_size""": 1_12,
               """codebook_do_map_pixels""": True,
               """codebook_do_normalize""": True,
               """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
               """codebook_image_std""": FLAVA_CODEBOOK_STD,
           }

           lowerCAmelCase_					:	Union[str, Any]						=   os.path.join(self.tmpdirname					,			UpperCAmelCase			)
           with open(self.image_processor_file					,			"""w"""					,			encoding="""utf-8"""			) as fp:
               json.dump(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   Any					,			**UpperCAmelCase	:   Union[str, Any]			):
           return BertTokenizer.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   List[str]					,			**UpperCAmelCase	:   str			):
           return BertTokenizerFast.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Any					,			**UpperCAmelCase	:   int			):
           return FlavaImageProcessor.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Optional[int]			):
           shutil.rmtree(self.tmpdirname			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Union[str, Any]						=   [np.random.randint(2_55					,			size=(3, 30, 4_00)					,			dtype=np.uinta			)]

           lowerCAmelCase_					:	int						=   [Image.fromarray(np.moveaxis(UpperCAmelCase					,			0					,			-1			)			) for x in image_inputs]

           return image_inputs



       def  A							(     self	:   Tuple			):
           lowerCAmelCase_					:	str						=   self.get_tokenizer()
           lowerCAmelCase_					:	Dict						=   self.get_rust_tokenizer()
           lowerCAmelCase_					:	Optional[int]						=   self.get_image_processor()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)
           processor_slow.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	List[str]						=   FlavaProcessor.from_pretrained(self.tmpdirname					,			use_fast=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)
           processor_fast.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	List[str]						=   FlavaProcessor.from_pretrained(self.tmpdirname			)

           self.assertEqual(processor_slow.tokenizer.get_vocab()					,			tokenizer_slow.get_vocab()			)
           self.assertEqual(processor_fast.tokenizer.get_vocab()					,			tokenizer_fast.get_vocab()			)
           self.assertEqual(tokenizer_slow.get_vocab()					,			tokenizer_fast.get_vocab()			)
           self.assertIsInstance(processor_slow.tokenizer					,			UpperCAmelCase			)
           self.assertIsInstance(processor_fast.tokenizer					,			UpperCAmelCase			)

           self.assertEqual(processor_slow.image_processor.to_json_string()					,			image_processor.to_json_string()			)
           self.assertEqual(processor_fast.image_processor.to_json_string()					,			image_processor.to_json_string()			)
           self.assertIsInstance(processor_slow.image_processor					,			UpperCAmelCase			)
           self.assertIsInstance(processor_fast.image_processor					,			UpperCAmelCase			)



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	Any						=   FlavaProcessor(tokenizer=self.get_tokenizer()					,			image_processor=self.get_image_processor()			)
           processor.save_pretrained(self.tmpdirname			)

           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer(bos_token="""(BOS)"""					,			eos_token="""(EOS)"""			)
           lowerCAmelCase_					:	Optional[Any]						=   self.get_image_processor(do_normalize=UpperCAmelCase					,			padding_value=1.0			)

           lowerCAmelCase_					:	Any						=   FlavaProcessor.from_pretrained(
               self.tmpdirname					,			bos_token="""(BOS)"""					,			eos_token="""(EOS)"""					,			do_normalize=UpperCAmelCase					,			padding_value=1.0			)

           self.assertEqual(processor.tokenizer.get_vocab()					,			tokenizer_add_kwargs.get_vocab()			)
           self.assertIsInstance(processor.tokenizer					,			UpperCAmelCase			)

           self.assertEqual(processor.image_processor.to_json_string()					,			image_processor_add_kwargs.to_json_string()			)
           self.assertIsInstance(processor.image_processor					,			UpperCAmelCase			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	Tuple						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	List[str]						=   image_processor(UpperCAmelCase					,			return_tensors="""np"""			)
           lowerCAmelCase_					:	Tuple						=   processor(images=UpperCAmelCase					,			return_tensors="""np"""			)

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()					,			input_processor[key].sum()					,			delta=1e-2			)

           # With rest of the args
           random.seed(12_34			)
           lowerCAmelCase_					:	Optional[Any]						=   image_processor(
               UpperCAmelCase					,			return_image_mask=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_tensors="""np"""			)
           random.seed(12_34			)
           lowerCAmelCase_					:	Dict						=   processor(
               images=UpperCAmelCase					,			return_image_mask=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_tensors="""np"""			)

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()					,			input_processor[key].sum()					,			delta=1e-2			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	List[Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	str						=   """lower newer"""

           lowerCAmelCase_					:	Dict						=   processor(text=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   tokenizer(UpperCAmelCase			)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]					,			encoded_processor[key]			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	Optional[int]						=   self.get_image_processor()
           lowerCAmelCase_					:	List[Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	str						=   """lower newer"""
           lowerCAmelCase_					:	Any						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	Optional[int]						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase			)

           self.assertListEqual(list(inputs.keys()			)					,			["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]			)

           # add extra args
           lowerCAmelCase_					:	str						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_image_mask=UpperCAmelCase			)

           self.assertListEqual(
               list(inputs.keys()			)					,			[
                   """input_ids""",
                   """token_type_ids""",
                   """attention_mask""",
                   """pixel_values""",
                   """codebook_pixel_values""",
                   """bool_masked_pos""",
               ]					,			)

           # test if it raises when no input is passed
           with pytest.raises(UpperCAmelCase			):
               processor()



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	str						=   self.get_image_processor()
           lowerCAmelCase_					:	int						=   self.get_tokenizer()

           lowerCAmelCase_					:	Union[str, Any]						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[Any]						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowerCAmelCase_					:	List[str]						=   processor.batch_decode(UpperCAmelCase			)
           lowerCAmelCase_					:	Tuple						=   tokenizer.batch_decode(UpperCAmelCase			)

           self.assertListEqual(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	int						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	List[str]						=   """lower newer"""
           lowerCAmelCase_					:	Dict						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	Optional[int]						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase			)

           self.assertListEqual(list(inputs.keys()			)					,			processor.model_input_names			)


