






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


SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		logging.get_logger(__name__)
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''pixel_values''']







				def __init__(    self							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       __lowerCAmelCase = PILImageResampling.BILINEAR							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       __lowerCAmelCase = True							,       __lowerCAmelCase = 1 / 2_5_5							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   size if size is not None else {'''shortest_edge''': 2_5_6}
								__magic_name__		:List[Any]   =   get_size_dict(__lowerCAmelCase							,       default_to_square=__lowerCAmelCase  )
								__magic_name__		:List[str]   =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								__magic_name__		:int   =   get_size_dict(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   do_resize
								__magic_name__		:List[str]   =   size
								__magic_name__		:List[str]   =   resample
								__magic_name__		:str   =   do_center_crop
								__magic_name__		:Union[str, Any]   =   crop_size
								__magic_name__		:Tuple   =   do_rescale
								__magic_name__		:Optional[Any]   =   rescale_factor
								__magic_name__		:Dict   =   do_normalize
								__magic_name__		:List[Any]   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__magic_name__		:str   =   image_std if image_std is not None else IMAGENET_STANDARD_STD







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = PILImageResampling.BICUBIC							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		: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()}'''  )
								__magic_name__		:Dict   =   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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:Any   =   get_size_dict(__lowerCAmelCase  )
								return center_crop(__lowerCAmelCase							,       size=(size['''height'''], size['''width'''])							,       data_format=__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return rescale(__lowerCAmelCase							,       scale=__lowerCAmelCase							,       data_format=__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								return normalize(__lowerCAmelCase							,       mean=__lowerCAmelCase							,       std=__lowerCAmelCase							,       data_format=__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = ChannelDimension.FIRST							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   do_resize if do_resize is not None else self.do_resize
								__magic_name__		:Union[str, Any]   =   size if size is not None else self.size
								__magic_name__		:Dict   =   get_size_dict(__lowerCAmelCase							,       default_to_square=__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   resample if resample is not None else self.resample
								__magic_name__		:Dict   =   do_center_crop if do_center_crop is not None else self.do_center_crop
								__magic_name__		:List[str]   =   crop_size if crop_size is not None else self.crop_size
								__magic_name__		:str   =   get_size_dict(__lowerCAmelCase  )
								__magic_name__		:Tuple   =   do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__		:Optional[Any]   =   rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__		:Union[str, Any]   =   do_normalize if do_normalize is not None else self.do_normalize
								__magic_name__		:Union[str, Any]   =   image_mean if image_mean is not None else self.image_mean
								__magic_name__		:Union[str, Any]   =   image_std if image_std is not None else self.image_std

								__magic_name__		:List[Any]   =   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.'''  )

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

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

								if do_center_crop:
												__magic_name__		:str   =   [self.center_crop(image=__lowerCAmelCase							,       size=__lowerCAmelCase  ) for image in images]

								if do_rescale:
												__magic_name__		:List[str]   =   [self.rescale(image=__lowerCAmelCase							,       scale=__lowerCAmelCase  ) for image in images]

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

								__magic_name__		:Dict   =   [to_channel_dimension_format(__lowerCAmelCase							,       __lowerCAmelCase  ) for image in images]

								__magic_name__		:Any   =   {'''pixel_values''': images}
								return BatchFeature(data=__lowerCAmelCase							,       tensor_type=__lowerCAmelCase  )











'''simple docstring'''





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:str     =       MgpstrTokenizer
       lowerCAmelCase__	:Tuple     =       False
       lowerCAmelCase__	:Any     =       {}
       lowerCAmelCase__	:List[Any]     =       False



       def 				_a   (					self   )	->				Union[str, Any]:
           super().setUp()

           # fmt: off
           lowercase__									=    ["[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
           lowercase__									=    dict(zip(UpperCAmelCase_  ,range(len(UpperCAmelCase_   )   )   )   )

           lowercase__									=    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(UpperCAmelCase_   ) + "\n"   )



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				Any:
           return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    "tester"
           lowercase__									=    "tester"
           return input_text, output_text



       @unittest.skip("MGP-STR always lower cases letters."   )
       def 				_a   (					self   )	->				Dict:
           pass



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    self.get_tokenizers(do_lower_case=UpperCAmelCase_   )
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__									=    "[SPECIAL_TOKEN]"

                   tokenizer.add_special_tokens({"cls_token": special_token}   )
                   lowercase__									=    tokenizer.encode([special_token]  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertEqual(len(UpperCAmelCase_   )  ,1   )

                   lowercase__									=    tokenizer.decode(UpperCAmelCase_  ,skip_special_tokens=UpperCAmelCase_   )
                   self.assertTrue(special_token not in decoded   )



       def 				_a   (					self   )	->				str:
           lowercase__									=    self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__							,							lowercase__									=    self.get_input_output_texts(UpperCAmelCase_   )

                   lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

                   lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
                   self.assertNotEqual(len(UpperCAmelCase_   )  ,0   )
                   lowercase__									=    tokenizer.decode(UpperCAmelCase_   )
                   self.assertIsInstance(UpperCAmelCase_  ,UpperCAmelCase_   )

                   self.assertEqual(text_a.replace(" "  ,""   )  ,UpperCAmelCase_   )



       @unittest.skip("MGP-STR tokenizer only handles one sequence."   )
       def 				_a   (					self   )	->				Optional[int]:
           pass







       @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"   )
       def 				_a   (					self   )	->				Any:
           pass




