
"""simple docstring"""






import warnings
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 				a__					(						A__       ):
     UpperCAmelCase__         =      ['''image_processor''', '''tokenizer''']
     UpperCAmelCase__         =      '''ViltImageProcessor'''
     UpperCAmelCase__         =      ('''BertTokenizer''', '''BertTokenizerFast''')



     def __init__(							self :int      ,  _lowerCamelCase :int=None      ,  _lowerCamelCase :List[str]=None      ,  **_lowerCamelCase :Dict   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=None
      if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'      ,  _lowercase      ,  )
       UpperCamelCase_     :					Any   	=kwargs.pop('feature_extractor'   )

      UpperCamelCase_     :					str   	=image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('You need to specify an `image_processor`.'   )
      if tokenizer is None:
       raise ValueError('You need to specify a `tokenizer`.'   )

      super().__init__(_lowercase      ,  _lowercase   )
      UpperCamelCase_     :					Tuple   	=self.image_processor



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







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=self.tokenizer(
          text=_lowercase      ,  add_special_tokens=_lowercase      ,  padding=_lowercase      ,  truncation=_lowercase      ,  max_length=_lowercase      ,  stride=_lowercase      ,  pad_to_multiple_of=_lowercase      ,  return_token_type_ids=_lowercase      ,  return_attention_mask=_lowercase      ,  return_overflowing_tokens=_lowercase      ,  return_special_tokens_mask=_lowercase      ,  return_offsets_mapping=_lowercase      ,  return_length=_lowercase      ,  verbose=_lowercase      ,  return_tensors=_lowercase      ,  **_lowercase      ,  )
      # add pixel_values + pixel_mask
      UpperCamelCase_     :					str   	=self.image_processor(_lowercase      ,  return_tensors=_lowercase   )
      encoding.update(_lowercase   )

      return encoding



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  *_lowerCamelCase :Dict      ,  **_lowerCamelCase :str   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  *_lowerCamelCase :Any      ,  **_lowerCamelCase :List[Any]   ):







      '''simple docstring'''



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



     @property
     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=self.tokenizer.model_input_names
      UpperCamelCase_     :					Any   	=self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )



     @property
     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,  _lowercase      ,  )
      return self.image_processor_class




     @property
     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,  _lowercase      ,  )
      return self.image_processor






'''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				):



					'''simple docstring'''





					_UpperCamelCase							=		ViTImageProcessor if is_vision_available() else None



					@property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								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(_lowercase      ,       range(len(_lowercase))))

								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(_lowercase) + '\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      ,       _lowercase)
								with open(self.image_processor_file      ,       'w'      ,       encoding='utf-8') as fp:
											json.dump(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Optional[int])    ->							int:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								shutil.rmtree(self.tmpdirname)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								A_									= np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta)

								A_									= Image.fromarray(np.moveaxis(_lowercase      ,       0      ,       -1))

								return image_input



					def     __snake_case						(	self     :						Optional[Any])    ->							List[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)
								A_									= 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     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)

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

								A_									= 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     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= self.prepare_image_inputs()

								A_									= image_processor(_lowercase      ,       return_tensors='np')
								A_									= 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     __snake_case						(	self     :						Any)    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'

								A_									= processor(text=_lowercase)

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



					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

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

								A_									= 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     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								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(_lowercase)
								A_									= tokenizer.batch_decode(_lowercase)
								A_									= [seq.replace(' '      ,       '') for seq in decoded_tok]

								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= None
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

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



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

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= torch.randn(1      ,       27      ,       38)
								A_									= torch.randn(1      ,       27      ,       50_257)
								A_									= torch.randn(1      ,       27      ,       30_522)

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

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




