

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					_UpperCAmelCase		:   Any										=       """pt"""
elif is_tf_available():
					_UpperCAmelCase		:   Optional[Any]										=       """tf"""
else:
					_UpperCAmelCase		:   Union[str, Any]										=       """jax"""







class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			PerceiverTokenizer
				__SCREAMING_SNAKE_CASE					: str       			=			False




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    PerceiverTokenizer()
											tokenizer.save_pretrained(self.tmpdirname       )




				@cached_property
				def     a							(					self       ):
											return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'       )




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




				def     a							(					self      ,   snake_case      ,   snake_case=False      ,   snake_case=20      ,   snake_case=5       ):
											# XXX The default common tokenizer tests assume that every ID is decodable on its own.
											# This assumption is invalid for Perceiver because single bytes might not be
											# valid utf-8 (byte 128 for instance).
											# Here we're overriding the smallest possible method to provide
											# a clean sequence without making the same assumption.

											snake_case_							=    []
											for i in range(len(snake_case       )       ):
																		try:
																									snake_case_							=    tokenizer.decode([i]      ,   clean_up_tokenization_spaces=snake_case       )
																		except UnicodeDecodeError:
																									pass
																		toks.append((i, tok)       )

											snake_case_							=    list(filter(lambda snake_case       : re.match(R'^[ a-zA-Z]+$'      ,   t[1]       )      ,   snake_case       )       )
											snake_case_							=    list(filter(lambda snake_case       : [t[0]] == tokenizer.encode(t[1]      ,   add_special_tokens=snake_case       )      ,   snake_case       )       )
											if max_length is not None and len(snake_case       ) > max_length:
																		snake_case_							=    toks[:max_length]
											if min_length is not None and len(snake_case       ) < min_length and len(snake_case       ) > 0:
																		while len(snake_case       ) < min_length:
																									snake_case_							=    toks + toks
        # toks_str = [t[1] for t in toks]
											snake_case_							=    [t[0] for t in toks]

											# Ensure consistency
											snake_case_							=    tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       )
											if " " not in output_txt and len(snake_case       ) > 1:
																		snake_case_							=    (
																		    tokenizer.decode([toks_ids[0]]      ,   clean_up_tokenization_spaces=snake_case       )
																		    + ' '
																		    + tokenizer.decode(toks_ids[1:]      ,   clean_up_tokenization_spaces=snake_case       )
																		)
											if with_prefix_space:
																		snake_case_							=    ' ' + output_txt
											snake_case_							=    tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
											return output_txt, output_ids




				def     a							(					self       ):
											snake_case_							=    self.perceiver_tokenizer
											snake_case_							=    'Unicode €.'
											snake_case_							=    tokenizer(snake_case       )
											snake_case_							=    [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
											self.assertEqual(encoded['input_ids']      ,   snake_case       )

											# decoding
											snake_case_							=    tokenizer.decode(snake_case       )
											self.assertEqual(snake_case      ,   '[CLS]Unicode €.[SEP]'       )

											snake_case_							=    tokenizer('e è é ê ë'       )
											snake_case_							=    [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
											self.assertEqual(encoded['input_ids']      ,   snake_case       )
											# decoding
											snake_case_							=    tokenizer.decode(snake_case       )
											self.assertEqual(snake_case      ,   '[CLS]e è é ê ë[SEP]'       )

											# encode/decode, but with `encode` instead of `__call__`
											self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'       )       )      ,   '[CLS]e è é ê ë[SEP]'       )




				def     a							(					self       ):
											snake_case_							=    self.perceiver_tokenizer
											snake_case_							=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											# fmt: off
											snake_case_							=    [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
											# fmt: on
											snake_case_							=    tokenizer(snake_case      ,   padding=snake_case      ,   return_tensors=snake_case       )
											self.assertIsInstance(snake_case      ,   snake_case       )

											if FRAMEWORK != "jax":
																		snake_case_							=    list(batch.input_ids.numpy()[0]       )
											else:
																		snake_case_							=    list(batch.input_ids.tolist()[0]       )

											self.assertListEqual(snake_case      ,   snake_case       )

											self.assertEqual((2, 38)      ,   batch.input_ids.shape       )
											self.assertEqual((2, 38)      ,   batch.attention_mask.shape       )




				def     a							(					self       ):
											snake_case_							=    self.perceiver_tokenizer
											snake_case_							=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_							=    tokenizer(snake_case      ,   padding=snake_case      ,   return_tensors=snake_case       )
											# check if input_ids are returned and no decoder_input_ids
											self.assertIn('input_ids'      ,   snake_case       )
											self.assertIn('attention_mask'      ,   snake_case       )
											self.assertNotIn('decoder_input_ids'      ,   snake_case       )
											self.assertNotIn('decoder_attention_mask'      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.perceiver_tokenizer
											snake_case_							=    [
											    'Summary of the text.',
											    'Another summary.',
											]
											snake_case_							=    tokenizer(
											    text_target=snake_case      ,   max_length=32      ,   padding='max_length'      ,   truncation=snake_case      ,   return_tensors=snake_case       )
											self.assertEqual(32      ,   targets['input_ids'].shape[1]       )




				def     a							(					self       ):
											# safety check on max_len default value so we are sure the test works
											snake_case_							=    self.get_tokenizers()
											for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
																									self.assertNotEqual(tokenizer.model_max_length      ,   42       )

        # Now let's start the test
											snake_case_							=    self.get_tokenizers()
											for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
																									# Isolate this from the other tests because we save additional tokens/etc
																									snake_case_							=    tempfile.mkdtemp()

																									snake_case_							=    ' He is very happy, UNwant\u00E9d,running'
																									snake_case_							=    tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
																									tokenizer.save_pretrained(snake_case       )

																									snake_case_							=    tokenizer.__class__.from_pretrained(snake_case       )
																									snake_case_							=    after_tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
																									self.assertListEqual(snake_case      ,   snake_case       )

																									shutil.rmtree(snake_case       )

											snake_case_							=    self.get_tokenizers(model_max_length=42       )
											for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
																									# Isolate this from the other tests because we save additional tokens/etc
																									snake_case_							=    tempfile.mkdtemp()

																									snake_case_							=    ' He is very happy, UNwant\u00E9d,running'
																									tokenizer.add_tokens(['bim', 'bambam']       )
																									snake_case_							=    tokenizer.additional_special_tokens
																									additional_special_tokens.append('new_additional_special_token'       )
																									tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}       )
																									snake_case_							=    tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
																									tokenizer.save_pretrained(snake_case       )

																									snake_case_							=    tokenizer.__class__.from_pretrained(snake_case       )
																									snake_case_							=    after_tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
																									self.assertListEqual(snake_case      ,   snake_case       )
																									self.assertIn('new_additional_special_token'      ,   after_tokenizer.additional_special_tokens       )
																									self.assertEqual(after_tokenizer.model_max_length      ,   42       )

																									snake_case_							=    tokenizer.__class__.from_pretrained(snake_case      ,   model_max_length=43       )
																									self.assertEqual(tokenizer.model_max_length      ,   43       )

																									shutil.rmtree(snake_case       )




				def     a							(					self       ):
											snake_case_							=    []
											if self.test_slow_tokenizer:
																		tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

											if self.test_rust_tokenizer:
																		tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

											for tokenizer_class, tokenizer_utils in tokenizer_list:
																		with tempfile.TemporaryDirectory() as tmp_dir:
																									tokenizer_utils.save_pretrained(snake_case       )

																									with open(os.path.join(snake_case      ,   'special_tokens_map.json'       )      ,   encoding='utf-8'       ) as json_file:
																																snake_case_							=    json.load(snake_case       )

																									with open(os.path.join(snake_case      ,   'tokenizer_config.json'       )      ,   encoding='utf-8'       ) as json_file:
																																snake_case_							=    json.load(snake_case       )

																									snake_case_							=    [F'''<extra_id_{i}>''' for i in range(125       )]

																									snake_case_							=    added_tokens_extra_ids + [
																									    'an_additional_special_token'
																									]
																									snake_case_							=    added_tokens_extra_ids + [
																									    'an_additional_special_token'
																									]

																									with open(os.path.join(snake_case      ,   'special_tokens_map.json'       )      ,   'w'      ,   encoding='utf-8'       ) as outfile:
																																json.dump(snake_case      ,   snake_case       )
																									with open(os.path.join(snake_case      ,   'tokenizer_config.json'       )      ,   'w'      ,   encoding='utf-8'       ) as outfile:
																																json.dump(snake_case      ,   snake_case       )

																									# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																									# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																									# "special_tokens_map.json" files
																									snake_case_							=    tokenizer_class.from_pretrained(
																									    snake_case      ,   )
																									self.assertIn(
																									    'an_additional_special_token'      ,   tokenizer_without_change_in_init.additional_special_tokens       )
																									self.assertEqual(
																									    ['an_additional_special_token']      ,   tokenizer_without_change_in_init.convert_ids_to_tokens(
																									        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']       )       )      ,   )

																									# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																									snake_case_							=    added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'      ,   lstrip=snake_case       )]
																									snake_case_							=    tokenizer_class.from_pretrained(
																									    snake_case      ,   additional_special_tokens=snake_case      ,   )

																									self.assertIn('a_new_additional_special_token'      ,   tokenizer.additional_special_tokens       )
																									self.assertEqual(
																									    ['a_new_additional_special_token']      ,   tokenizer.convert_ids_to_tokens(
																									        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']       )       )      ,   )




				def     a							(					self       ):
											snake_case_							=    self.perceiver_tokenizer
											self.assertEqual(tokenizer.decode([178]       )      ,   '�'       )




				def     a							(					self       ):
											pass




				def     a							(					self       ):
											pass




				def     a							(					self       ):
											pass




				def     a							(					self       ):
											pass




				def     a							(					self       ):
											# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
											# strings and special added tokens as tokens
											snake_case_							=    self.get_tokenizers(fast=snake_case      ,   do_lower_case=snake_case       )
											for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
																									snake_case_							=    ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
																									snake_case_							=    tokenizer.convert_tokens_to_string(snake_case       )

																									self.assertIsInstance(snake_case      ,   snake_case       )







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       )




