

'''simple docstring'''

import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class 							_snake_case (       _a	,  unittest.TestCase						):
   _A       :     List[Any]       =					RoCBertTokenizer
   _A       :     Dict       =					None
   _A       :     Any       =					False
   _A       :     List[str]       =					True
   _A       :     Union[str, Any]       =					filter_non_english
   def     __UpperCamelCase						( self			:      int							):
     super().setUp()

     SCREAMING_SNAKE_CASE:List[str]						      =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
     SCREAMING_SNAKE_CASE:int						      =					{}
     SCREAMING_SNAKE_CASE:Tuple						      =					{}
     for i, value in enumerate(SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any						      =					i
       SCREAMING_SNAKE_CASE:Tuple						      =					i
     SCREAMING_SNAKE_CASE:List[Any]						      =					os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"]							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["word_shape_file"]							)
     SCREAMING_SNAKE_CASE:int						      =					os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["word_pronunciation_file"]							)
     with open(self.vocab_file     ,"w"     ,encoding="utf-8"							) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)
     with open(self.word_shape_file     ,"w"     ,encoding="utf-8"							) as word_shape_writer:
       json.dump(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,ensure_ascii=SCREAMING_SNAKE_CASE__							)
     with open(self.word_pronunciation_file     ,"w"     ,encoding="utf-8"							) as word_pronunciation_writer:
       json.dump(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,ensure_ascii=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.tokenizer_class(self.vocab_file     ,self.word_shape_file     ,self.word_pronunciation_file							)

     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer.tokenize("你好[SEP]你是谁"							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,["你", "好", "[SEP]", "你", "是", "谁"]							)
     self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)     ,[5, 6, 2, 5, 7, 8]							)
     self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__							)     ,[5, 6, 2, 5, 7, 8]							)
     self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__							)     ,[5, 6, 2, 5, 7, 8]							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Any						      =					RoCBertBasicTokenizer()

     self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"							)     ,["ah", "\u535A", "\u63A8", "zz"]							)
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:Dict						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)     ,["hello", "!", "how", "are", "you", "?"]							)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)     ,["hello"]							)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Any						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__     ,strip_accents=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)     ,["hällo", "!", "how", "are", "you", "?"]							)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)     ,["h\u00E9llo"]							)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__     ,strip_accents=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)     ,["hallo", "!", "how", "are", "you", "?"]							)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)     ,["hello"]							)
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)     ,["hallo", "!", "how", "are", "you", "?"]							)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)     ,["hello"]							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Dict						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)     ,["HeLLo", "!", "how", "Are", "yoU", "?"]							)
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:Tuple						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__     ,strip_accents=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)     ,["HäLLo", "!", "how", "Are", "yoU", "?"]							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__     ,strip_accents=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)     ,["HaLLo", "!", "how", "Are", "yoU", "?"]							)
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__     ,never_split=["[UNK]"]							)

     self.assertListEqual(
         tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"							)     ,["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

     SCREAMING_SNAKE_CASE:List[str]						      =					{}
     for i, token in enumerate(SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any						      =					i
     SCREAMING_SNAKE_CASE:List[Any]						      =					RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__     ,unk_token="[UNK]"							)

     self.assertListEqual(tokenizer.tokenize(""							)     ,[]							)

     self.assertListEqual(tokenizer.tokenize("unwanted running"							)     ,["un", "##want", "##ed", "runn", "##ing"]							)

     self.assertListEqual(tokenizer.tokenize("unwantedX running"							)     ,["[UNK]", "runn", "##ing"]							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     self.assertTrue(_is_whitespace(" "							)							)
     self.assertTrue(_is_whitespace("\t"							)							)
     self.assertTrue(_is_whitespace("\r"							)							)
     self.assertTrue(_is_whitespace("\n"							)							)
     self.assertTrue(_is_whitespace("\u00A0"							)							)

     self.assertFalse(_is_whitespace("A"							)							)
     self.assertFalse(_is_whitespace("-"							)							)
   def     __UpperCamelCase						( self			:      str							):
     self.assertTrue(_is_control("\u0005"							)							)

     self.assertFalse(_is_control("A"							)							)
     self.assertFalse(_is_control(" "							)							)
     self.assertFalse(_is_control("\t"							)							)
     self.assertFalse(_is_control("\r"							)							)
   def     __UpperCamelCase						( self			:      Dict							):
     self.assertTrue(_is_punctuation("-"							)							)
     self.assertTrue(_is_punctuation("$"							)							)
     self.assertTrue(_is_punctuation("`"							)							)
     self.assertTrue(_is_punctuation("."							)							)

     self.assertFalse(_is_punctuation("A"							)							)
     self.assertFalse(_is_punctuation(" "							)							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.get_tokenizer()

     # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
     self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE__							) for t in ["Test", "\xad", "test"]]     ,[["[UNK]"], [], ["[UNK]"]]							)

     if self.test_rust_tokenizer:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					self.get_rust_tokenizer()
       self.assertListEqual(
           [rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__							) for t in ["Test", "\xad", "test"]]     ,[["[UNK]"], [], ["[UNK]"]]							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

         SCREAMING_SNAKE_CASE:Tuple						      =					F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenizer_r.encode_plus(
             SCREAMING_SNAKE_CASE__     ,return_attention_mask=SCREAMING_SNAKE_CASE__     ,return_token_type_ids=SCREAMING_SNAKE_CASE__     ,return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__     ,)

         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE__     ,"do_lower_case"							) else False
         SCREAMING_SNAKE_CASE:Any						      =					(
             [
                 ((0, 0), tokenizer_r.cls_token),
                 ((0, 1), "A"),
                 ((1, 2), ","),
                 ((3, 5), "na"),
                 ((5, 6), "##ï"),
                 ((6, 8), "##ve"),
                 ((9, 15), tokenizer_r.mask_token),
                 ((16, 21), "Allen"),
                 ((21, 23), "##NL"),
                 ((23, 24), "##P"),
                 ((25, 33), "sentence"),
                 ((33, 34), "."),
                 ((0, 0), tokenizer_r.sep_token),
             ]
             if not do_lower_case
             else [
                 ((0, 0), tokenizer_r.cls_token),
                 ((0, 1), "a"),
                 ((1, 2), ","),
                 ((3, 8), "naive"),
                 ((9, 15), tokenizer_r.mask_token),
                 ((16, 21), "allen"),
                 ((21, 23), "##nl"),
                 ((23, 24), "##p"),
                 ((25, 33), "sentence"),
                 ((33, 34), "."),
                 ((0, 0), tokenizer_r.sep_token),
             ]
         )

         self.assertEqual(
             [e[1] for e in expected_results]     ,tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]							)							)
         self.assertEqual([e[0] for e in expected_results]     ,tokens["offset_mapping"]							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:str						      =					["的", "人", "有"]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"".join(SCREAMING_SNAKE_CASE__							)
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
         SCREAMING_SNAKE_CASE:str						      =					True
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Tuple						      =					self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

         SCREAMING_SNAKE_CASE:int						      =					tokenizer_p.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Dict						      =					tokenizer_r.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)

         SCREAMING_SNAKE_CASE:int						      =					tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Any						      =					tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)

         # it is expected that each Chinese character is not preceded by "##"
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					False
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

         SCREAMING_SNAKE_CASE:List[Any]						      =					tokenizer_r.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Tuple						      =					tokenizer_p.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)

         SCREAMING_SNAKE_CASE:List[Any]						      =					tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:str						      =					tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)

         # it is expected that only the first Chinese character is not preceded by "##".
         SCREAMING_SNAKE_CASE:List[str]						      =					[
             F'''##{token}''' if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE__							)
         ]
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   @slow
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:str						      =					self.tokenizer_class(self.vocab_file     ,self.word_shape_file     ,self.word_pronunciation_file							)

     SCREAMING_SNAKE_CASE:str						      =					tokenizer.encode("你好"     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					tokenizer.encode("你是谁"     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     assert encoded_sentence == [1] + text + [2]
     assert encoded_pair == [1] + text + [2] + text_a + [2]







   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__							)
     for tokenizer in tokenizers:
       with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
         SCREAMING_SNAKE_CASE:List[str]						      =					"你好，你是谁"
         SCREAMING_SNAKE_CASE:str						      =					tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Dict						      =					tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:str						      =					tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Any						      =					tokenizer.prepare_for_model(
             SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)

         SCREAMING_SNAKE_CASE:Any						      =					tokenizer.encode_plus(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)

         self.assertEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)









'''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, is_vision_available, logging


if is_vision_available():
    import PIL


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Union[str, Any]       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BILINEAR     ,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__			:      List[str]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 384}
     SCREAMING_SNAKE_CASE:List[str]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					do_resize
     SCREAMING_SNAKE_CASE:List[Any]						      =					size
     # Default value set here for backwards compatibility where the value in config is None
     SCREAMING_SNAKE_CASE:Dict						      =					crop_pct if crop_pct is not None else 224 / 256
     SCREAMING_SNAKE_CASE:Any						      =					resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[int]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_normalize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Tuple						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      float     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Any						      =					size["shortest_edge"]

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       SCREAMING_SNAKE_CASE:Any						      =					int(shortest_edge / crop_pct							)
       SCREAMING_SNAKE_CASE:Any						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,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__			:      Union[str, Any]     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      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__			:      ChannelDimension = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:List[str]						      =					crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:List[str]						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:str						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:str						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Any						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					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 or resample is None:
       raise ValueError("Size and resample must be specified if do_resize is True."							)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError("crop_pct must be specified if size < 384."							)

     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.
     SCREAMING_SNAKE_CASE:int						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,crop_pct=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Any						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






