



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      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Optional[int]			       =    ViTImageProcessor if is_vision_available() else None



					@property
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								return self.image_processor_tester.prepare_image_processor_dict()



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    (3, 32, 1_28)
								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(_snake_case				,					range(len(_snake_case					)					)					)					)

								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(_snake_case					) + '\n'					)

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



					def     _a							(     self					:       str				,					**_snake_case					:       Tuple					):






								"""simple docstring"""


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



					def     _a							(     self					:       Union[str, Any]				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       str					):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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

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

								return image_input



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()
								A__       =    self.get_image_processor()

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

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

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



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()
								A__       =    self.get_image_processor()

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

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

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

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

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



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

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

								A__       =    self.prepare_image_inputs()

								A__       =    image_processor(_snake_case				,					return_tensors='np'					)
								A__       =    processor(images=_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     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

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

								A__       =    'test'

								A__       =    processor(text=_snake_case					)

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



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

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

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

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

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

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



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

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

								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(_snake_case					)
								A__       =    tokenizer.batch_decode(_snake_case					)
								A__       =    [seq.replace(' '				,					''					) for seq in decoded_tok]

								self.assertListEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

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

								A__       =    None
								A__       =    self.prepare_image_inputs()

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

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



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

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

								A__       =    torch.randn(1				,					27				,					38					)
								A__       =    torch.randn(1				,					27				,					5_02_57					)
								A__       =    torch.randn(1				,					27				,					3_05_22					)

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

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









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								return self.tokenizer.batch_decode(*_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return self.tokenizer.decode(*_snake_case				,					**_snake_case					)



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.tokenizer.model_input_names
								A__       =    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




