






"""simple docstring"""





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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
							__lowerCamelCase							=  'pt'
elif is_tf_available():
							__lowerCamelCase							=  'tf'
else:
							__lowerCamelCase							=  'jax'



class       __A		(   SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				ByTaTokenizer
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[Any]:
											super().setUp()
											__magic_name__:	Union[str, Any]							=       ByTaTokenizer()
											tokenizer.save_pretrained(self.tmpdirname					)

					@cached_property
					def   lowerCamelCase__   (			self  :      str					)			->							str:
											return ByTaTokenizer.from_pretrained("""google/byt5-small"""					)

					def   lowerCamelCase__   (			self  :      str     ,		**__snake_case  :      List[str]					)			->							ByTaTokenizer:
											return self.tokenizer_class.from_pretrained(self.tmpdirname     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[str]=False     ,		__snake_case  :      str=2_0     ,		__snake_case  :      Dict=5					)			->							Tuple[str, list]:
											# XXX The default common tokenizer tests assume that every ID is decodable on its own.
											# This assumption is invalid for ByT5 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.

											__magic_name__:	List[Any]							=       []
											for i in range(len(__snake_case					)					):
																	try:
																							__magic_name__:	Any							=       tokenizer.decode([i]     ,		clean_up_tokenization_spaces=__snake_case					)
																	except UnicodeDecodeError:
																							pass
																	toks.append((i, tok)					)

											__magic_name__:	List[Any]							=       list(filter(lambda __snake_case					: re.match(R"""^[ a-zA-Z]+$"""     ,		t[1]					)     ,		__snake_case					)					)
											__magic_name__:	Union[str, Any]							=       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:
																	__magic_name__:	Dict							=       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:
																							__magic_name__:	int							=       toks + toks
        # toks_str = [t[1] for t in toks]
											__magic_name__:	str							=       [t[0] for t in toks]

											# Ensure consistency
											__magic_name__:	int							=       tokenizer.decode(__snake_case     ,		clean_up_tokenization_spaces=__snake_case					)
											if " " not in output_txt and len(__snake_case					) > 1:
																	__magic_name__:	List[Any]							=       (
																	    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:
																	__magic_name__:	Any							=       """ """ + output_txt
											__magic_name__:	List[Any]							=       tokenizer.encode(__snake_case     ,		add_special_tokens=__snake_case					)
											return output_txt, output_ids

					def   lowerCamelCase__   (			self  :      Any					)			->							Optional[Any]:
											__magic_name__:	int							=       self.ta_base_tokenizer
											__magic_name__:	List[Any]							=       tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]					)
											__magic_name__:	List[Any]							=       tokenizer(["""hi""", """I went to the gym""", """"""]					)
											self.assertListEqual(batch_with_eos_added["""input_ids"""]     ,		batch_without_eos_added["""input_ids"""]					)

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											__magic_name__:	Any							=       self.ta_base_tokenizer
											__magic_name__:	str							=       """Unicode €."""
											__magic_name__:	Any							=       tokenizer(__snake_case					)
											__magic_name__:	Any							=       [8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
											self.assertEqual(encoded["""input_ids"""]     ,		__snake_case					)

											# decoding
											__magic_name__:	Optional[Any]							=       tokenizer.decode(__snake_case					)
											self.assertEqual(__snake_case     ,		"""Unicode €.</s>"""					)

											__magic_name__:	str							=       tokenizer("""e è é ê ë"""					)
											__magic_name__:	List[str]							=       [1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
											self.assertEqual(encoded["""input_ids"""]     ,		__snake_case					)
											# decoding
											__magic_name__:	Optional[int]							=       tokenizer.decode(__snake_case					)
											self.assertEqual(__snake_case     ,		"""e è é ê ë</s>"""					)

											# encode/decode, but with `encode` instead of `__call__`
											self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""					)					)     ,		"""e è é ê ë</s>"""					)

					def   lowerCamelCase__   (			self  :      str					)			->							List[Any]:
											__magic_name__:	Optional[int]							=       self.ta_base_tokenizer
											__magic_name__:	Any							=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											# fmt: off
											__magic_name__:	Any							=       [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
											# fmt: on
											__magic_name__:	Dict							=       tokenizer(__snake_case     ,		padding=__snake_case     ,		return_tensors=__snake_case					)
											self.assertIsInstance(__snake_case     ,		__snake_case					)

											if FRAMEWORK != "jax":
																	__magic_name__:	Optional[Any]							=       list(batch.input_ids.numpy()[0]					)
											else:
																	__magic_name__:	List[str]							=       list(batch.input_ids.tolist()[0]					)

											self.assertListEqual(__snake_case     ,		__snake_case					)

											self.assertEqual((2, 3_7)     ,		batch.input_ids.shape					)
											self.assertEqual((2, 3_7)     ,		batch.attention_mask.shape					)

					def   lowerCamelCase__   (			self  :      List[str]					)			->							int:
											__magic_name__:	Tuple							=       self.ta_base_tokenizer
											__magic_name__:	Union[str, Any]							=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											__magic_name__:	Tuple							=       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   lowerCamelCase__   (			self  :      List[str]					)			->							List[Any]:
											__magic_name__:	Optional[int]							=       self.ta_base_tokenizer
											__magic_name__:	Dict							=       [
											    """Summary of the text.""",
											    """Another summary.""",
											]
											__magic_name__:	List[str]							=       tokenizer(
											    text_target=__snake_case     ,		max_length=3_2     ,		padding="""max_length"""     ,		truncation=__snake_case     ,		return_tensors=__snake_case					)
											self.assertEqual(3_2     ,		targets["""input_ids"""].shape[1]					)

					def   lowerCamelCase__   (			self  :      List[str]					)			->							int:
											__magic_name__:	str							=       self.ta_base_tokenizer
											__magic_name__:	Union[str, Any]							=       ["""A long paragraph for summarization. </s>"""]
											__magic_name__:	Any							=       ["""Summary of the text. </s>"""]
											# fmt: off
											__magic_name__:	int							=       [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
											__magic_name__:	List[str]							=       [8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
											# fmt: on

											__magic_name__:	Optional[int]							=       tokenizer(__snake_case     ,		text_target=__snake_case					)

											self.assertEqual(__snake_case     ,		batch["""input_ids"""][0]					)
											self.assertEqual(__snake_case     ,		batch["""labels"""][0]					)

					def   lowerCamelCase__   (			self  :      Dict					)			->							str:
											# safety check on max_len default value so we are sure the test works
											__magic_name__:	int							=       self.get_tokenizers()
											for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'					):
																							self.assertNotEqual(tokenizer.model_max_length     ,		4_2					)

        # Now let's start the test
											__magic_name__:	Any							=       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
																							__magic_name__:	Optional[Any]							=       tempfile.mkdtemp()

																							__magic_name__:	str							=       """ He is very happy, UNwant\u00E9d,running"""
																							__magic_name__:	Dict							=       tokenizer.encode(__snake_case     ,		add_special_tokens=__snake_case					)
																							tokenizer.save_pretrained(__snake_case					)

																							__magic_name__:	int							=       tokenizer.__class__.from_pretrained(__snake_case					)
																							__magic_name__:	Any							=       after_tokenizer.encode(__snake_case     ,		add_special_tokens=__snake_case					)
																							self.assertListEqual(__snake_case     ,		__snake_case					)

																							shutil.rmtree(__snake_case					)

											__magic_name__:	Optional[int]							=       self.get_tokenizers(model_max_length=4_2					)
											for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'					):
																							# Isolate this from the other tests because we save additional tokens/etc
																							__magic_name__:	Optional[int]							=       tempfile.mkdtemp()

																							__magic_name__:	str							=       """ He is very happy, UNwant\u00E9d,running"""
																							tokenizer.add_tokens(["""bim""", """bambam"""]					)
																							__magic_name__:	List[str]							=       tokenizer.additional_special_tokens
																							additional_special_tokens.append("""new_additional_special_token"""					)
																							tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}					)
																							__magic_name__:	List[Any]							=       tokenizer.encode(__snake_case     ,		add_special_tokens=__snake_case					)
																							tokenizer.save_pretrained(__snake_case					)

																							__magic_name__:	List[str]							=       tokenizer.__class__.from_pretrained(__snake_case					)
																							__magic_name__:	Any							=       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     ,		4_2					)

																							__magic_name__:	Optional[int]							=       tokenizer.__class__.from_pretrained(__snake_case     ,		model_max_length=4_3					)
																							self.assertEqual(tokenizer.model_max_length     ,		4_3					)

																							shutil.rmtree(__snake_case					)

					def   lowerCamelCase__   (			self  :      str					)			->							int:
											__magic_name__:	Optional[int]							=       []
											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:
																													__magic_name__:	Any							=       json.load(__snake_case					)

																							with open(os.path.join(__snake_case     ,		"""tokenizer_config.json"""					)     ,		encoding="""utf-8"""					) as json_file:
																													__magic_name__:	Any							=       json.load(__snake_case					)

																							__magic_name__:	Union[str, Any]							=       [F'<extra_id_{i}>' for i in range(1_2_5					)]

																							__magic_name__:	List[str]							=       added_tokens_extra_ids + [
																							    """an_additional_special_token"""
																							]
																							__magic_name__:	List[Any]							=       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
																							__magic_name__:	Dict							=       tokenizer_class.from_pretrained(
																							    __snake_case     ,		)
																							self.assertIn(
																							    """an_additional_special_token"""     ,		tokenizer_without_change_in_init.additional_special_tokens					)
																							# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																							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
																							__magic_name__:	Optional[int]							=       added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""     ,		lstrip=__snake_case					)]
																							__magic_name__:	List[Any]							=       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   lowerCamelCase__   (			self  :      Dict					)			->							Union[str, Any]:
											__magic_name__:	List[str]							=       []
											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					)

																							__magic_name__:	Dict							=       tokenizer_class.from_pretrained(__snake_case					)

																							self.assertTrue(tokenizer.decode([2_5_5]					) == """"""					)

					def   lowerCamelCase__   (			self  :      str					)			->							str:
											pass

					def   lowerCamelCase__   (			self  :      str					)			->							Tuple:
											pass

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[Any]:
											pass

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Optional[Any]:
											pass

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[str]:
											# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
											# and special added tokens as tokens
											__magic_name__:	Tuple							=       self.get_tokenizers(fast=__snake_case     ,		do_lower_case=__snake_case					)
											for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'					):
																							__magic_name__:	Any							=       ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
																							__magic_name__:	Optional[Any]							=       tokenizer.convert_tokens_to_string(__snake_case					)

																							self.assertIsInstance(__snake_case     ,		__snake_case					)






					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Any:
											__magic_name__:	Union[str, Any]							=       self.get_tokenizers()
											for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'					):
																							__magic_name__:	str							=       [
																							    """bos_token""",
																							    """eos_token""",
																							    """unk_token""",
																							    """sep_token""",
																							    """pad_token""",
																							    """cls_token""",
																							    """mask_token""",
																							]

																							__magic_name__:	List[Any]							=       0
																							__magic_name__:	Optional[int]							=       tokenizer.convert_ids_to_tokens(
																							    __snake_case     ,		skip_special_tokens=__snake_case					)

																							for attr in attributes_list:
																													setattr(__snake_case     ,		attr + """_id"""     ,		__snake_case					)
																													self.assertEqual(getattr(__snake_case     ,		__snake_case					)     ,		__snake_case					)
																													self.assertEqual(getattr(__snake_case     ,		attr + """_id"""					)     ,		__snake_case					)

																													setattr(__snake_case     ,		attr + """_id"""     ,		__snake_case					)
																													self.assertEqual(getattr(__snake_case     ,		__snake_case					)     ,		__snake_case					)
																													self.assertEqual(getattr(__snake_case     ,		attr + """_id"""					)     ,		__snake_case					)

																							setattr(__snake_case     ,		"""additional_special_tokens_ids"""     ,		[]					)
																							self.assertListEqual(getattr(__snake_case     ,		"""additional_special_tokens"""					)     ,		[]					)
																							self.assertListEqual(getattr(__snake_case     ,		"""additional_special_tokens_ids"""					)     ,		[]					)

																							setattr(__snake_case     ,		"""additional_special_tokens_ids"""     ,		[token_id_to_test_setters]					)
																							self.assertListEqual(getattr(__snake_case     ,		"""additional_special_tokens"""					)     ,		[token_to_test_setters]					)
																							self.assertListEqual(getattr(__snake_case     ,		"""additional_special_tokens_ids"""					)     ,		[token_id_to_test_setters]					)








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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    _A      =       tempfile.mkdtemp()

    _A      =       [
        '''[UNK]''',
        '''[CLS]''',
        '''[SEP]''',
        '''[PAD]''',
        '''[MASK]''',
        '''want''',
        '''##want''',
        '''##ed''',
        '''wa''',
        '''un''',
        '''runn''',
        '''##ing''',
        ''',''',
        '''low''',
        '''lowest''',
    ]
    _A      =       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]      )      )

    _A      =       {
        '''do_resize''': True,
        '''size''': 20,
        '''do_center_crop''': True,
        '''crop_size''': 18,
        '''do_normalize''': True,
        '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
        '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    _A      =       os.path.join(self.tmpdirname			,						__lowerCAmelCase      )
    with open(self.image_processor_file			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
       json.dump(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict			,						**__lowerCAmelCase					:					int      )   ->	Optional[int]:
    return BertTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	Tuple:
    return BertTokenizerFast.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple			,						**__lowerCAmelCase					:					str      )   ->	Union[str, Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    shutil.rmtree(self.tmpdirname      )

 def     snake_case_      (    self					:					int      )   ->	Optional[Any]:
    _A      =       [np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta      )]
    _A      =       [Image.fromarray(np.moveaxis(__lowerCAmelCase			,						0			,						-1      )      ) for x in image_inputs]
    return image_inputs

 def     snake_case_      (    self					:					Dict      )   ->	List[str]:
    _A      =       self.get_tokenizer()
    _A      =       self.get_rust_tokenizer()
    _A      =       self.get_image_processor()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_slow.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.from_pretrained(self.tmpdirname			,						use_fast=__lowerCAmelCase      )

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_fast.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.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			,						__lowerCAmelCase      )
    self.assertIsInstance(processor_fast.tokenizer			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					List[Any]      )   ->	List[str]:
    _A      =       AlignProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

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

    _A      =       AlignProcessor.from_pretrained(
        self.tmpdirname			,						bos_token='''(BOS)'''			,						eos_token='''(EOS)'''			,						do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

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

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

 def     snake_case_      (    self					:					str      )   ->	List[Any]:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       self.prepare_image_inputs()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''np'''      )
    _A      =       processor(images=__lowerCAmelCase			,						return_tensors='''np'''      )

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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''

    _A      =       processor(text=__lowerCAmelCase      )

    _A      =       tokenizer(__lowerCAmelCase			,						padding='''max_length'''			,						max_length=64      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Any:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

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

    _A      =       processor(text=__lowerCAmelCase			,						images=__lowerCAmelCase      )

    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(__lowerCAmelCase      ):
       processor()

 def     snake_case_      (    self					:					Optional[Any]      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

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

    _A      =       processor.batch_decode(__lowerCAmelCase      )
    _A      =       tokenizer.batch_decode(__lowerCAmelCase      )

    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

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

    _A      =       processor(text=__lowerCAmelCase			,						images=__lowerCAmelCase      )

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



