


'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCamelCase												=						logging.get_logger(__name__)


if is_vision_available():
    import PIL




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["pixel_values"]


 def __init__(      self							:	Union[str, Any]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Dict[str, int] = None			,       __lowerCamelCase							:	PILImageResampling = PILImageResampling.BICUBIC			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Dict[str, int] = None			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Union[int, float] = 1 / 2_5_5			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	bool = True			,       **__lowerCamelCase							:	Union[str, Any]			,       ):
    super().__init__(**__lowerCamelCase )
    UpperCAmelCase__	:Optional[int]      =      size if size is not None else {'''shortest_edge''': 2_2_4}
    UpperCAmelCase__	:Optional[Any]      =      get_size_dict(__lowerCamelCase			,       default_to_square=__lowerCamelCase )
    UpperCAmelCase__	:List[str]      =      crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
    UpperCAmelCase__	:List[str]      =      get_size_dict(__lowerCamelCase			,       default_to_square=__lowerCamelCase			,       param_name='''crop_size''' )

    UpperCAmelCase__	:Dict      =      do_resize
    UpperCAmelCase__	:Tuple      =      size
    UpperCAmelCase__	:List[Any]      =      resample
    UpperCAmelCase__	:Tuple      =      do_center_crop
    UpperCAmelCase__	:List[str]      =      crop_size
    UpperCAmelCase__	:Any      =      do_rescale
    UpperCAmelCase__	:List[Any]      =      rescale_factor
    UpperCAmelCase__	:Any      =      do_normalize
    UpperCAmelCase__	:Dict      =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    UpperCAmelCase__	:str      =      image_std if image_std is not None else OPENAI_CLIP_STD
    UpperCAmelCase__	:Optional[int]      =      do_convert_rgb


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Dict[str, int]			,       __lowerCamelCase							:	PILImageResampling = PILImageResampling.BICUBIC			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	Tuple			,       ):
    UpperCAmelCase__	:Optional[int]      =      get_size_dict(__lowerCamelCase			,       default_to_square=__lowerCamelCase )
    if "shortest_edge" not in size:
       raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
    UpperCAmelCase__	:Optional[int]      =      get_resize_output_image_size(__lowerCamelCase			,       size=size['''shortest_edge''']			,       default_to_square=__lowerCamelCase )
    return resize(__lowerCamelCase			,       size=__lowerCamelCase			,       resample=__lowerCamelCase			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Dict[str, int]			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	Optional[Any]			,       ):
    UpperCAmelCase__	:Optional[Any]      =      get_size_dict(__lowerCamelCase )
    if "height" not in size or "width" not in size:
       raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
    return center_crop(__lowerCamelCase			,       size=(size['''height'''], size['''width'''])			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Union[int, float]			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	Union[str, Any]			,       ):
    return rescale(__lowerCamelCase			,       scale=__lowerCamelCase			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Union[float, List[float]]			,       __lowerCamelCase							:	Union[float, List[float]]			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	str			,       ):
    return normalize(__lowerCamelCase			,       mean=__lowerCamelCase			,       std=__lowerCamelCase			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	ImageInput			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	Dict[str, int] = None			,       __lowerCamelCase							:	PILImageResampling = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	int = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	float = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	Optional[Union[str, TensorType]] = None			,       __lowerCamelCase							:	Optional[ChannelDimension] = ChannelDimension.FIRST			,       **__lowerCamelCase							:	int			,       ):
    UpperCAmelCase__	:List[Any]      =      do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase__	:Optional[Any]      =      size if size is not None else self.size
    UpperCAmelCase__	:List[str]      =      get_size_dict(__lowerCamelCase			,       param_name='''size'''			,       default_to_square=__lowerCamelCase )
    UpperCAmelCase__	:Union[str, Any]      =      resample if resample is not None else self.resample
    UpperCAmelCase__	:str      =      do_center_crop if do_center_crop is not None else self.do_center_crop
    UpperCAmelCase__	:Optional[Any]      =      crop_size if crop_size is not None else self.crop_size
    UpperCAmelCase__	:Optional[int]      =      get_size_dict(__lowerCamelCase			,       param_name='''crop_size'''			,       default_to_square=__lowerCamelCase )
    UpperCAmelCase__	:List[str]      =      do_rescale if do_rescale is not None else self.do_rescale
    UpperCAmelCase__	:Union[str, Any]      =      rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCAmelCase__	:Tuple      =      do_normalize if do_normalize is not None else self.do_normalize
    UpperCAmelCase__	:Any      =      image_mean if image_mean is not None else self.image_mean
    UpperCAmelCase__	:List[Any]      =      image_std if image_std is not None else self.image_std
    UpperCAmelCase__	:Tuple      =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    UpperCAmelCase__	:List[str]      =      make_list_of_images(__lowerCamelCase )

    if not valid_images(__lowerCamelCase ):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

    if do_resize and size is None:
       raise ValueError('''Size must be specified if do_resize is True.''' )

    if do_center_crop and crop_size is None:
       raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

    if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

    # PIL RGBA images are converted to RGB
    if do_convert_rgb:
       UpperCAmelCase__	:List[str]      =      [convert_to_rgb(__lowerCamelCase ) for image in images]

    # All transformations expect numpy arrays.
    UpperCAmelCase__	:List[Any]      =      [to_numpy_array(__lowerCamelCase ) for image in images]

    if do_resize:
       UpperCAmelCase__	:List[str]      =      [self.resize(image=__lowerCamelCase			,       size=__lowerCamelCase			,       resample=__lowerCamelCase ) for image in images]

    if do_center_crop:
       UpperCAmelCase__	:int      =      [self.center_crop(image=__lowerCamelCase			,       size=__lowerCamelCase ) for image in images]

    if do_rescale:
       UpperCAmelCase__	:int      =      [self.rescale(image=__lowerCamelCase			,       scale=__lowerCamelCase ) for image in images]

    if do_normalize:
       UpperCAmelCase__	:List[str]      =      [self.normalize(image=__lowerCamelCase			,       mean=__lowerCamelCase			,       std=__lowerCamelCase ) for image in images]

    UpperCAmelCase__	:Any      =      [to_channel_dimension_format(__lowerCamelCase			,       __lowerCamelCase ) for image in images]

    UpperCAmelCase__	:Optional[Any]      =      {'''pixel_values''': images}
    return BatchFeature(data=__lowerCamelCase			,       tensor_type=__lowerCamelCase )







'''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				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	:Union[str, Any]      =      ['''[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
    UpperCAmelCase__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      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(__lowerCamelCase ) + '''\n''' )

    UpperCAmelCase__	:Dict      =      {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    UpperCAmelCase__	:Optional[Any]      =      os.path.join(self.tmpdirname			,       __lowerCamelCase )
    with open(self.image_processor_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       json.dump(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[int]      =      MgpstrProcessor.from_pretrained(self.tmpdirname			,       use_fast=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:List[Any]      =      self.get_image_processor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:Any      =      MgpstrProcessor.from_pretrained(
        self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__lowerCamelCase			,       padding_value=1.0 )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:Tuple      =      processor(images=__lowerCamelCase			,       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        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer(__lowerCamelCase )
    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,       encoded_processor[key] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Any      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

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

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

    UpperCAmelCase__	:Optional[Any]      =      processor.batch_decode([char_input, bpe_input, wp_input] )

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



