"""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    __snake_case       (     unittest.TestCase				):
      a__        =		ViTImageProcessor if is_vision_available() else None



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Optional[Any]										=	(3, 32, 1_28)
        a__:     Dict										=	tempfile.mkdtemp()

        # fmt: off
        a__:     Tuple										=	['[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
        a__:     Dict										=	dict(zip(lowercase    ,			range(len(lowercase))))

        a__:     Any										=	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(lowercase) + '\n')

        a__:     Dict										=	{
            'do_normalize': False,
            'do_resize': True,
            'image_processor_type': 'ViTImageProcessor',
            'resample': 3,
            'size': {'height': 32, 'width': 1_28},
        }
        a__:     str										=	os.path.join(self.tmpdirname    ,			lowercase)
        with open(self.image_processor_file    ,			'w'    ,			encoding='utf-8') as fp:
          json.dump(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			Dict:





        '''simple docstring'''





        return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,			**lowercase)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





        return ViTImageProcessor.from_pretrained(self.tmpdirname    ,			**lowercase)



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     str										=	np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)

        a__:     Tuple										=	Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1))

        return image_input



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.get_tokenizer()
        a__:     str										=	self.get_image_processor()

        a__:     List[Any]										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)
        processor.save_pretrained(self.tmpdirname)
        a__:     str										=	MgpstrProcessor.from_pretrained(self.tmpdirname    ,			use_fast=lowercase)

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

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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.get_tokenizer()
        a__:     List[str]										=	self.get_image_processor()

        a__:     List[str]										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)
        processor.save_pretrained(self.tmpdirname)

        a__:     List[str]										=	self.get_tokenizer(bos_token='(BOS)'    ,			eos_token='(EOS)')
        a__:     List[str]										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     List[Any]										=	MgpstrProcessor.from_pretrained(
            self.tmpdirname    ,			bos_token='(BOS)'    ,			eos_token='(EOS)'    ,			do_normalize=lowercase    ,			padding_value=1.0)

        self.assertEqual(processor.char_tokenizer.get_vocab()    ,			tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.char_tokenizer    ,			lowercase)

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



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	self.get_image_processor()
        a__:     Dict										=	self.get_tokenizer()

        a__:     List[Any]										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)

        a__:     str										=	self.prepare_image_inputs()

        a__:     Any										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     List[Any]										=	processor(images=lowercase    ,			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   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     List[Any]										=	self.get_image_processor()
        a__:     Optional[Any]										=	self.get_tokenizer()

        a__:     Tuple										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)

        a__:     List[str]										=	'test'

        a__:     Tuple										=	processor(text=lowercase)

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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.get_image_processor()
        a__:     List[str]										=	self.get_tokenizer()

        a__:     List[str]										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)

        a__:     Any										=	'test'
        a__:     Union[str, Any]										=	self.prepare_image_inputs()

        a__:     Any										=	processor(text=lowercase    ,			images=lowercase)

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

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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     int										=	self.get_image_processor()
        a__:     Dict										=	self.get_tokenizer()

        a__:     Dict										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)

        a__:     List[Any]										=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

        a__:     Tuple										=	processor.char_decode(lowercase)
        a__:     Tuple										=	tokenizer.batch_decode(lowercase)
        a__:     int										=	[seq.replace(' '    ,			'') for seq in decoded_tok]

        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	self.get_image_processor()
        a__:     List[str]										=	self.get_tokenizer()

        a__:     Optional[Any]										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)

        a__:     Union[str, Any]										=	None
        a__:     Any										=	self.prepare_image_inputs()

        a__:     Union[str, Any]										=	processor(text=lowercase    ,			images=lowercase)

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



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     int										=	self.get_image_processor()
        a__:     Dict										=	self.get_tokenizer()

        a__:     Optional[int]										=	MgpstrProcessor(tokenizer=lowercase    ,			image_processor=lowercase)

        a__:     int										=	torch.randn(1    ,			27    ,			38)
        a__:     Union[str, Any]										=	torch.randn(1    ,			27    ,			5_02_57)
        a__:     List[str]										=	torch.randn(1    ,			27    ,			3_05_22)

        a__:     Union[str, Any]										=	processor.batch_decode([char_input, bpe_input, wp_input])

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



"""simple docstring"""
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__					       =	'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		CamembertTokenizer
      a__        =		CamembertTokenizerFast
      a__        =		True
      a__        =		True



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Tuple										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'<pad>'
        a__:     List[Any]										=	1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)    ,			lowercase)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     str										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>NOTUSED')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_04)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_05)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        a__:     List[Any]										=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        a__:     Dict										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[int]										=	tokenizer.encode(lowercase)
        a__:     Any										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Optional[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        a__:     Tuple										=	tokenizer.convert_ids_to_tokens(lowercase)
        a__:     Tuple										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     Dict										=	self.get_tokenizer()
        a__:     str										=	self.get_rust_tokenizer()

        a__:     int										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     List[Any]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     str										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Union[str, Any]										=	tokenizer.encode(lowercase)
        a__:     List[Any]										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        a__:     int										=	[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='camembert-base'    ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,			sequences=lowercase    ,			)


