






'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase									=   logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					["pixel_values"]






			def __init__(			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Optional[Dict[str, int]] = None	,    __SCREAMING_SNAKE_CASE   :      PILImageResampling = PILImageResampling.BILINEAR	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Union[int, float] = 1 / 255	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    **__SCREAMING_SNAKE_CASE   :      Tuple	,    )      ->      None:
							super().__init__(**__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   size if size is not None else {'''shortest_edge''': 256}
							a_		:	Union[str, Any]									=   get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
							a_		:	int									=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							a_		:	Any									=   get_size_dict(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   do_resize
							a_		:	str									=   size
							a_		:	Dict									=   resample
							a_		:	List[str]									=   do_center_crop
							a_		:	List[str]									=   crop_size
							a_		:	Dict									=   do_rescale
							a_		:	Tuple									=   rescale_factor
							a_		:	List[str]									=   do_normalize
							a_		:	Tuple									=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							a_		:	Optional[int]									=   image_std if image_std is not None else IMAGENET_STANDARD_STD






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






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int]	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      Any	,    )      ->      np.ndarray:
							a_		:	Dict									=   get_size_dict(__SCREAMING_SNAKE_CASE       )
							return center_crop(__SCREAMING_SNAKE_CASE	,    size=(size['''height'''], size['''width'''])	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )






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






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







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

							a_		:	List[str]									=   make_list_of_images(__SCREAMING_SNAKE_CASE       )

							if not valid_images(__SCREAMING_SNAKE_CASE       ):
											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.'''       )

							# All transformations expect numpy arrays.
							a_		:	Union[str, Any]									=   [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

							if do_resize:
											a_		:	Tuple									=   [self.resize(image=__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE	,    resample=__SCREAMING_SNAKE_CASE       ) for image in images]

							if do_center_crop:
											a_		:	Any									=   [self.center_crop(image=__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE       ) for image in images]

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

							if do_normalize:
											a_		:	Optional[int]									=   [self.normalize(image=__SCREAMING_SNAKE_CASE	,    mean=__SCREAMING_SNAKE_CASE	,    std=__SCREAMING_SNAKE_CASE       ) for image in images]

							a_		:	List[Any]									=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) for image in images]

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












'''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 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):
			snake_case__	=					ViTImageProcessor if is_vision_available() else None






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






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   (3, 32, 128)
							a_		:	Optional[Any]									=   tempfile.mkdtemp()

							# fmt: off
							a_		:	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
							a_		:	List[Any]									=   dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

							a_		:	Union[str, 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(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )

							a_		:	Union[str, Any]									=   {
							    '''do_normalize''': False,
							    '''do_resize''': True,
							    '''image_processor_type''': '''ViTImageProcessor''',
							    '''resample''': 3,
							    '''size''': {'''height''': 32, '''width''': 128},
							}
							a_		:	Any									=   os.path.join(self.tmpdirname	,    __SCREAMING_SNAKE_CASE       )
							with open(self.image_processor_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											json.dump(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Union[str, Any]:
							shutil.rmtree(self.tmpdirname       )






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

							a_		:	Optional[int]									=   Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,    0	,    -1       )       )

							return image_input






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	str									=   self.get_tokenizer()
							a_		:	List[Any]									=   self.get_image_processor()

							a_		:	Union[str, Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )
							a_		:	Tuple									=   MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Any									=   self.get_tokenizer()
							a_		:	Dict									=   self.get_image_processor()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )

							a_		:	str									=   self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''       )
							a_		:	str									=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							a_		:	Optional[int]									=   MgpstrProcessor.from_pretrained(
							    self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

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

							a_		:	List[Any]									=   image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )
							a_		:	int									=   processor(images=__SCREAMING_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       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	int									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   '''test'''

							a_		:	Any									=   processor(text=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   tokenizer(__SCREAMING_SNAKE_CASE       )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

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

							a_		:	Tuple									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_		:	int									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	Dict									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:	List[Any]									=   processor.char_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   [seq.replace(''' '''	,    ''''''       ) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	int									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   None
							a_		:	Tuple									=   self.prepare_image_inputs()

							a_		:	Optional[int]									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

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







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.get_image_processor()
							a_		:	str									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   torch.randn(1	,    27	,    38       )
							a_		:	int									=   torch.randn(1	,    27	,    5_0257       )
							a_		:	Union[str, Any]									=   torch.randn(1	,    27	,    3_0522       )

							a_		:	Tuple									=   processor.batch_decode([char_input, bpe_input, wp_input]       )

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




