"""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       ):
 _snake_case       :					List[Any]							  =						ViTImageProcessor if is_vision_available() else None


 @property
 def 						a__   ( self						):
        return self.image_processor_tester.prepare_image_processor_dict()


 def 						a__   ( self						):
        __a       =    (3, 32, 128)
        __a       =    tempfile.mkdtemp()

        # fmt: off
        __a       =    ['''[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(zip(a__    ,		range(len(a__						)						)						)						)

        __a       =    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"						)

        __a       =    {
            '''do_normalize''': False,
            '''do_resize''': True,
            '''image_processor_type''': '''ViTImageProcessor''',
            '''resample''': 3,
            '''size''': {'''height''': 32, '''width''': 128},
        }
        __a       =    os.path.join(self.tmpdirname    ,		a__						)
        with open(self.image_processor_file    ,		"w"    ,		encoding="utf-8"						) as fp:
               json.dump(a__    ,		a__						)


 def 						a__   ( self    ,		**lowerCamelCase						):
        return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,		**a__						)


 def 						a__   ( self    ,		**lowerCamelCase						):
        return ViTImageProcessor.from_pretrained(self.tmpdirname    ,		**a__						)


 def 						a__   ( self						):
        shutil.rmtree(self.tmpdirname						)


 def 						a__   ( self						):
        __a       =    np.random.randint(255    ,		size=(3, 30, 400)    ,		dtype=np.uinta						)

        __a       =    Image.fromarray(np.moveaxis(a__    ,		0    ,		-1						)						)

        return image_input


 def 						a__   ( self						):
        __a       =    self.get_tokenizer()
        __a       =    self.get_image_processor()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)
        processor.save_pretrained(self.tmpdirname						)
        __a       =    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 						a__   ( self						):
        __a       =    self.get_tokenizer()
        __a       =    self.get_image_processor()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)
        processor.save_pretrained(self.tmpdirname						)

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

        __a       =    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 						a__   ( self						):
        __a       =    self.get_image_processor()
        __a       =    self.get_tokenizer()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)

        __a       =    self.prepare_image_inputs()

        __a       =    image_processor(a__    ,		return_tensors="np"						)
        __a       =    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 						a__   ( self						):
        __a       =    self.get_image_processor()
        __a       =    self.get_tokenizer()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)

        __a       =    '''test'''

        __a       =    processor(text=a__						)

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


 def 						a__   ( self						):
        __a       =    self.get_image_processor()
        __a       =    self.get_tokenizer()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)

        __a       =    '''test'''
        __a       =    self.prepare_image_inputs()

        __a       =    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 						a__   ( self						):
        __a       =    self.get_image_processor()
        __a       =    self.get_tokenizer()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)

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

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

        self.assertListEqual(a__    ,		a__						)


 def 						a__   ( self						):
        __a       =    self.get_image_processor()
        __a       =    self.get_tokenizer()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)

        __a       =    None
        __a       =    self.prepare_image_inputs()

        __a       =    processor(text=a__    ,		images=a__						)

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






 def 						a__   ( self						):
        __a       =    self.get_image_processor()
        __a       =    self.get_tokenizer()

        __a       =    MgpstrProcessor(tokenizer=a__    ,		image_processor=a__						)

        __a       =    torch.randn(1    ,		27    ,		38						)
        __a       =    torch.randn(1    ,		27    ,		50257						)
        __a       =    torch.randn(1    ,		27    ,		30522						)

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

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







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DeiTImageProcessor instead.'''   ,				a__   ,				)
             super().__init__(*a__   ,				**a__)

