





'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : int											=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase : Optional[int]											=			{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

__lowerCamelCase : Union[str, Any]											=			{
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

__lowerCamelCase : Optional[int]											=			{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Optional[Any] 			=		VOCAB_FILES_NAMES
		UpperCAmelCase					:	Tuple 			=		PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase					:	Tuple 			=		PRETRAINED_INIT_CONFIGURATION
		UpperCAmelCase					:	Union[str, Any] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase					:	int 			=		BertTokenizer




		def __init__(self      :	Tuple							,			A__      :	Any=None							,			A__      :	str=None							,			A__      :	List[str]=True							,			A__      :	int="[UNK]"							,			A__      :	Dict="[SEP]"							,			A__      :	Any="[PAD]"							,			A__      :	List[str]="[CLS]"							,			A__      :	Union[str, Any]="[MASK]"							,			A__      :	int=True							,			A__      :	List[Any]=None							,			**A__      :	str							,			)					-> Dict:
						super().__init__(
						    A__							,			tokenizer_file=A__							,			do_lower_case=A__							,			unk_token=A__							,			sep_token=A__							,			pad_token=A__							,			cls_token=A__							,			mask_token=A__							,			tokenize_chinese_chars=A__							,			strip_accents=A__							,			**A__							,			)

						lowercase         = json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
						if (
						    normalizer_state.get("lowercase"							,			A__				) != do_lower_case
						    or normalizer_state.get("strip_accents"							,			A__				) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"							,			A__				) != tokenize_chinese_chars
						):
										lowercase         = getattr(A__							,			normalizer_state.pop("type"				)				)
										lowercase         = do_lower_case
										lowercase         = strip_accents
										lowercase         = tokenize_chinese_chars
										lowercase         = normalizer_class(**A__				)

						lowercase         = do_lower_case




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Union[str, Any]							,			A__      :	Optional[Any]=None				)					-> str:
						lowercase         = [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
										output += token_ids_a + [self.sep_token_id]

						return output




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None				)					-> List[int]:
						lowercase         = [self.sep_token_id]
						lowercase         = [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      :	Tuple							,			A__      :	str							,			A__      :	Optional[str] = None				)					-> Tuple[str]:
						lowercase         = self._tokenizer.model.save(A__							,			name=A__				)
						return tuple(A__				)











'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
			import torch


if is_vision_available():
			from PIL import Image

			from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class       UpperCAmelCase (						unittest.TestCase  ):
		UpperCAmelCase					:	Optional[int] 			=		ViTImageProcessor if is_vision_available() else None




		@property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = (3, 3_2, 1_2_8)
						lowercase         = tempfile.mkdtemp()

						# fmt: off
						lowercase         = ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
						# fmt: on
						lowercase         = dict(zip(A__							,			range(len(A__				)				)				)				)

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

						lowercase         = {
						    "do_normalize": False,
						    "do_resize": True,
						    "image_processor_type": "ViTImageProcessor",
						    "resample": 3,
						    "size": {"height": 3_2, "width": 1_2_8},
						}
						lowercase         = 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      :	Dict							,			**A__      :	List[Any]				)					-> Union[str, Any]:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			**A__      :	Tuple				)					-> Tuple:
						return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**A__				)




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




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta				)

						lowercase         = Image.fromarray(np.moveaxis(A__							,			0							,			-1				)				)

						return image_input




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

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)
						lowercase         = MgpstrProcessor.from_pretrained(self.tmpdirname							,			use_fast=A__				)

						self.assertEqual(processor.char_tokenizer.get_vocab()							,			tokenizer.get_vocab()				)
						self.assertIsInstance(processor.char_tokenizer							,			A__				)

						self.assertEqual(processor.image_processor.to_json_string()							,			image_processor.to_json_string()				)
						self.assertIsInstance(processor.image_processor							,			A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)

						lowercase         = self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"				)
						lowercase         = self.get_image_processor(do_normalize=A__							,			padding_value=1.0				)

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

						self.assertEqual(processor.char_tokenizer.get_vocab()							,			tokenizer_add_kwargs.get_vocab()				)
						self.assertIsInstance(processor.char_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      :	List[Any]				)					-> List[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = self.prepare_image_inputs()

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

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"

						lowercase         = processor(text=A__				)

						lowercase         = tokenizer(A__				)
						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"
						lowercase         = self.prepare_image_inputs()

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

						self.assertListEqual(list(inputs.keys()				)							,			["pixel_values", "labels"]				)

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




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

						lowercase         = processor.char_decode(A__				)
						lowercase         = tokenizer.batch_decode(A__				)
						lowercase         = [seq.replace(" "							,			""				) for seq in decoded_tok]

						self.assertListEqual(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = None
						lowercase         = self.prepare_image_inputs()

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

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





		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = torch.randn(1							,			2_7							,			3_8				)
						lowercase         = torch.randn(1							,			2_7							,			5_0_2_5_7				)
						lowercase         = torch.randn(1							,			2_7							,			3_0_5_2_2				)

						lowercase         = processor.batch_decode([char_input, bpe_input, wp_input]				)

						self.assertListEqual(list(results.keys()				)							,			["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]				)




