





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor

@require_vision
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						tempfile.mkdtemp()

            # fmt: off
            _UpperCamelCase              =						['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
            # fmt: on
            _UpperCamelCase              =						dict(zip(A,       range(len(A      )      )      )      )
            _UpperCamelCase              =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
            _UpperCamelCase              =						{'''unk_token''': '''<unk>'''}

            _UpperCamelCase              =						os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES['''vocab_file''']      )
            _UpperCamelCase              =						os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES['''merges_file''']      )
            with open(self.vocab_file,       '''w''',       encoding='''utf-8'''      ) as fp:
                   fp.write(json.dumps(A      ) + '''\n'''      )
            with open(self.merges_file,       '''w''',       encoding='''utf-8'''      ) as fp:
                   fp.write('''\n'''.join(A      )      )

            _UpperCamelCase              =						{
                '''do_resize''': True,
                '''size''': 20,
                '''do_center_crop''': True,
                '''crop_size''': 18,
                '''do_normalize''': True,
                '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
                '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
            }
            _UpperCamelCase              =						os.path.join(self.tmpdirname,       A      )
            with open(self.image_processor_file,       '''w''',       encoding='''utf-8'''      ) as fp:
                   json.dump(A,       A      )

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return CLIPTokenizer.from_pretrained(self.tmpdirname,       **A      )

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return CLIPTokenizerFast.from_pretrained(self.tmpdirname,       **A      )

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return ViTImageProcessor.from_pretrained(self.tmpdirname,       **A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						[np.random.randint(255,       size=(3, 30, 400),       dtype=np.uinta      )]

            _UpperCamelCase              =						[Image.fromarray(np.moveaxis(A,       0,       -1      )      ) for x in image_inputs]

            return image_inputs

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_rust_tokenizer()
            _UpperCamelCase              =						self.get_image_processor()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )
            processor_slow.save_pretrained(self.tmpdirname      )
            _UpperCamelCase              =						CLIPSegProcessor.from_pretrained(self.tmpdirname,       use_fast=A      )

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )
            processor_fast.save_pretrained(self.tmpdirname      )
            _UpperCamelCase              =						CLIPSegProcessor.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,       A      )
            self.assertIsInstance(processor_fast.tokenizer,       A      )

            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,       A      )
            self.assertIsInstance(processor_fast.image_processor,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=self.get_tokenizer(),       image_processor=self.get_image_processor()      )
            processor.save_pretrained(self.tmpdirname      )

            _UpperCamelCase              =						self.get_tokenizer(bos_token='''(BOS)''',       eos_token='''(EOS)'''      )
            _UpperCamelCase              =						self.get_image_processor(do_normalize=A,       padding_value=1.0      )

            _UpperCamelCase              =						CLIPSegProcessor.from_pretrained(
                self.tmpdirname,       bos_token='''(BOS)''',       eos_token='''(EOS)''',       do_normalize=A,       padding_value=1.0      )

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

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						image_processor(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						processor(images=A,       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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''

            _UpperCamelCase              =						processor(text=A      )

            _UpperCamelCase              =						tokenizer(A      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''
            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						processor(text=A,       images=A      )

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

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						self.prepare_image_inputs()
            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						processor(images=A,       visual_prompt=A      )

            self.assertListEqual(list(inputs.keys()      ),       ['''pixel_values''', '''conditional_pixel_values''']      )

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



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _UpperCamelCase              =						processor.batch_decode(A      )
            _UpperCamelCase              =						tokenizer.batch_decode(A      )

            self.assertListEqual(A,       A      )













import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_big_bird import BigBirdTokenizer
else:
       lowercase    : Union[str, Any]    	=  None

lowercase    : Union[str, Any]    	=  logging.get_logger(__name__)
lowercase    : Tuple    	=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase    : Any    	=  {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase    : Tuple    	=  {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


lowercase    : List[str]    	=  """▁"""

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	  =       BigBirdTokenizer
     __A	  =       ["input_ids", "attention_mask"]
     __A	  =       []

     def __init__(      self,       A=None,       A=None,       A="<unk>",       A="<s>",       A="</s>",       A="<pad>",       A="[SEP]",       A="[MASK]",       A="[CLS]",       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else bos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else eos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else unk_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else pad_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else cls_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else sep_token

            # Mask token behave like a normal word, i.e. include the space before it
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else mask_token

            super().__init__(
                A,       tokenizer_file=A,       bos_token=A,       eos_token=A,       unk_token=A,       sep_token=A,       pad_token=A,       cls_token=A,       mask_token=A,       **A,       )

            _UpperCamelCase              =						vocab_file
            _UpperCamelCase              =						False if not self.vocab_file else True

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]
            if token_ids_a is None:
                   return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep

     def 	_UpperCamelCase     (      self,       A,       A = None,       A = False      ):





            """simple docstring"""

            if already_has_special_tokens:
                   if token_ids_a is not None:
                          raise ValueError(
                              '''You should not supply a second sequence if the provided sequence of '''
                              '''ids is already formatted with special tokens for the model.'''      )
                   return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is None:
                   return [1] + ([0] * len(A      )) + [1]
            return [1] + ([0] * len(A      )) + [1] + ([0] * len(A      )) + [1]

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]

            if token_ids_a is None:
                   return len(cls + token_ids_a + sep      ) * [0]
            return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                       '''tokenizer.'''      )

            if not os.path.isdir(A      ):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
                   return
            _UpperCamelCase              =						os.path.join(
                A,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

            if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
                   copyfile(self.vocab_file,       A      )

            return (out_vocab_file,)






