





from typing import Dict, List, Optional

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


_lowerCamelCase 					=      logging.get_logger(__name__)


_lowerCamelCase 					=      {
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase 					=      1114112

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

_lowerCamelCase 					=      0XE0_00
_lowerCamelCase 					=      0XE0_01
_lowerCamelCase 					=      0XE0_02
_lowerCamelCase 					=      0XE0_03
_lowerCamelCase 					=      0XE0_04

# Maps special codepoints to human-readable names.
_lowerCamelCase 					=      {
    # 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.
_lowerCamelCase 					=      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


							def __init__(				self     :str				,					__A     :str=chr(__A		)				,					__A     :str=chr(__A		)				,					__A     :Dict=chr(__A		)				,					__A     :str=chr(__A		)				,					__A     :Union[str, Any]=chr(__A		)				,					__A     :str=chr(__A		)				,					__A     :int=False				,					__A     :int=2048				,					**__A     :Dict				,					) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else bos_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else eos_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else sep_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else cls_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else pad_token

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

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

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

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

									SCREAMING_SNAKE_CASE__								=   UNICODE_VOCAB_SIZE
									SCREAMING_SNAKE_CASE__								=   len(self._special_codepoints		)


							@property
							def   _snake_case	(				self     :Optional[Any]		) -> int:



									"""simple docstring"""



									return self._unicode_vocab_size


							def   _snake_case	(				self     :Tuple				,					__A     :str		) -> List[str]:



									"""simple docstring"""



									return list(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :str		) -> int:



									"""simple docstring"""



									try:
											return ord(__A		)
									except TypeError:
											raise ValueError(f'''invalid token: \'{token}\''''		)


							def   _snake_case	(				self     :str				,					__A     :int		) -> str:



									"""simple docstring"""



									try:
											if index in SPECIAL_CODEPOINTS:
													return SPECIAL_CODEPOINTS[index]
											return chr(__A		)
									except TypeError:
											raise ValueError(f'''invalid id: {index}'''		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Optional[int]		) -> Any:



									"""simple docstring"""



									return "".join(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [self.cls_token_id]

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


							def   _snake_case	(				self     :List[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None				,					__A     :bool = False		) -> List[int]:



									"""simple docstring"""



									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__A				,					token_ids_a=__A				,					already_has_special_tokens=__A		)

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


							def   _snake_case	(				self     :List[str]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [self.cls_token_id]

									SCREAMING_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   _snake_case	(				self     :int				,					__A     :str				,					__A     :Optional[str] = None		) -> Any:



									"""simple docstring"""



									return ()




"""simple docstring"""







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       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				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": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
				    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
				}
				A__											=					os.path.join(self.tmpdirname						,	SCREAMING_SNAKE_CASE__		)
				with open(self.image_processor_file						,	"w"						,	encoding="utf-8"		) as fp:
					json.dump(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				return BertTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				return BertTokenizerFast.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[str]:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]
				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]
				return image_inputs


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.get_tokenizer()
				A__											=					self.get_rust_tokenizer()
				A__											=					self.get_image_processor()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_slow.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.from_pretrained(self.tmpdirname						,	use_fast=SCREAMING_SNAKE_CASE__		)

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				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						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.tokenizer						,	SCREAMING_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						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				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=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					AlignProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

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

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	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		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	padding="max_length"						,	max_length=64		)

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


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_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(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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




