

import json
import os
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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    tempfile.mkdtemp()

											snake_case_							=    [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    '的',
											    '价',
											    '格',
											    '是',
											    '15',
											    '便',
											    'alex',
											    '##andra',
											    '，',
											    '。',
											    '-',
											    't',
											    'shirt',
											]
											snake_case_							=    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]       )       )

											snake_case_							=    {
											    'do_resize': True,
											    'size': {'height': 224, 'width': 224},
											    'do_center_crop': True,
											    'crop_size': {'height': 18, 'width': 18},
											    'do_normalize': True,
											    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
											    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
											    'do_convert_rgb': True,
											}
											snake_case_							=    os.path.join(self.tmpdirname      ,   snake_case       )
											with open(self.image_processor_file      ,   'w'      ,   encoding='utf-8'       ) as fp:
																		json.dump(snake_case      ,   snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizer.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											shutil.rmtree(self.tmpdirname       )




				def     a							(					self       ):
											snake_case_							=    [np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta       )]

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

											return image_inputs




				def     a							(					self       ):
											snake_case_							=    self.get_tokenizer()
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    self.get_image_processor()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=snake_case       )

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.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      ,   snake_case       )
											self.assertIsInstance(processor_fast.tokenizer      ,   snake_case       )

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




				def     a							(					self       ):
											snake_case_							=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											snake_case_							=    self.get_tokenizer(cls_token='(CLS)'      ,   sep_token='(SEP)'       )
											snake_case_							=    self.get_image_processor(do_normalize=snake_case       )

											snake_case_							=    ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname      ,   cls_token='(CLS)'      ,   sep_token='(SEP)'      ,   do_normalize=snake_case       )

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

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




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    image_processor(snake_case      ,   return_tensors='np'       )
											snake_case_							=    processor(images=snake_case      ,   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       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'

											snake_case_							=    processor(text=snake_case       )

											snake_case_							=    tokenizer(snake_case       )

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




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

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

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




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

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

											snake_case_							=    processor.batch_decode(snake_case       )
											snake_case_							=    tokenizer.batch_decode(snake_case       )

											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

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







from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase		:   Any										=       logging.get_logger(__name__)


_UpperCAmelCase		:   Dict										=       {
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_UpperCAmelCase		:   Tuple										=       111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_UpperCAmelCase		:   List[str]										=       0

_UpperCAmelCase		:   Any										=       0xE000
_UpperCAmelCase		:   Dict										=       0xE001
_UpperCAmelCase		:   Optional[int]										=       0xE002
_UpperCAmelCase		:   Tuple										=       0xE003
_UpperCAmelCase		:   Tuple										=       0xE004

# Maps special codepoints to human-readable names.
_UpperCAmelCase		:   Dict[int, str]										=       {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_UpperCAmelCase		:   Dict[str, int]										=       {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: str       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




				def __init__(					self      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=False      ,   snake_case=2048      ,   **snake_case      ,   ):
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else bos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else eos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else sep_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else cls_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else pad_token

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

											super().__init__(
											    bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   model_max_length=snake_case      ,   **snake_case      ,   )

											# Creates a mapping for looking up the IDs of special symbols.
											snake_case_							=    {}
											for codepoint, name in SPECIAL_CODEPOINTS.items():
																		snake_case_							=    codepoint

											# Creates a mapping for looking up the string forms of special symbol IDs.
											snake_case_							=    {
											    codepoint: name for name, codepoint in self._special_codepoints.items()
											}

											snake_case_							=    UNICODE_VOCAB_SIZE
											snake_case_							=    len(self._special_codepoints       )




				@property
				def     a							(					self       ):
											return self._unicode_vocab_size




				def     a							(					self      ,   snake_case       ):
											return list(snake_case       )




				def     a							(					self      ,   snake_case       ):
											try:
																		return ord(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid token: \'{token}\''''       )




				def     a							(					self      ,   snake_case       ):
											try:
																		if index in SPECIAL_CODEPOINTS:
																									return SPECIAL_CODEPOINTS[index]
																		return chr(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid id: {index}'''       )




				def     a							(					self      ,   snake_case       ):
											return "".join(snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    cls + token_ids_a + sep
											if token_ids_a is not None:
																		result += token_ids_a + sep
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = False       ):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case      ,   token_ids_a=snake_case      ,   already_has_special_tokens=snake_case       )

											snake_case_							=    [1] + ([0] * len(snake_case       )) + [1]
											if token_ids_a is not None:
																		result += ([0] * len(snake_case       )) + [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    len(cls + token_ids_a + sep       ) * [0]
											if token_ids_a is not None:
																		result += len(token_ids_a + sep       ) * [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											return ()




