


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_      : Tuple  					=       get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     Optional[Any]        =						XLMRobertaTokenizer
      snake_case__				:     str        =						XLMRobertaTokenizerFast
      snake_case__				:     Optional[int]        =						True
      snake_case__				:     Union[str, Any]        =						True
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[Any]:
             super().setUp()

             # We have a SentencePiece fixture for testing
             a_					:		Optional[Any]							=  XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__     ,							keep_accents=SCREAMING_SNAKE_CASE__     )
             tokenizer.save_pretrained(self.tmpdirname     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			List[Any]:
             a_					:		int							=  '<pad>'
             a_					:		Optional[int]							=  1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__     )     ,							SCREAMING_SNAKE_CASE__     )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__     )     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             a_					:		List[str]							=  list(self.get_tokenizer().get_vocab().keys()     )

             self.assertEqual(vocab_keys[0]     ,							'<s>'     )
             self.assertEqual(vocab_keys[1]     ,							'<pad>'     )
             self.assertEqual(vocab_keys[-1]     ,							'<mask>'     )
             self.assertEqual(len(SCREAMING_SNAKE_CASE__     )     ,							1_0_0_2     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Union[str, Any]:
             self.assertEqual(self.get_tokenizer().vocab_size     ,							1_0_0_2     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			str:
             a_					:		List[Any]							=  XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__     ,							keep_accents=SCREAMING_SNAKE_CASE__     )

             a_					:		Dict							=  tokenizer.tokenize('This is a test'     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							['▁This', '▁is', '▁a', '▁t', 'est']     )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )     ,							[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,							)

             a_					:		List[Any]							=  tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__     ,							[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]     ,							)
             a_					:		int							=  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__     ,							[
                     value + tokenizer.fairseq_offset
                     for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                     #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                 ]     ,							)

             a_					:		List[Any]							=  tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__     ,							[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[Any]:
             if not self.test_slow_tokenizer:
                    # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                    return

             a_					:		Union[str, Any]							=  (self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                           a_					:		str							=  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
                           a_					:		Union[str, Any]							=  self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

                           a_					:		Tuple							=  tempfile.mkdtemp()

                           a_					:		Tuple							=  tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__     )
                           a_					:		int							=  tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                           # Checks it save with the same files + the tokenizer.json file for the fast one
                           self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files     )     )
                           a_					:		Optional[Any]							=  tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f     )
                           self.assertSequenceEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

                           # Checks everything loads correctly in the same way
                           a_					:		List[Any]							=  tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                           a_					:		Optional[Any]							=  tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )     )
                                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                           shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                           # Save tokenizer rust, legacy_format=True
                           a_					:		Union[str, Any]							=  tempfile.mkdtemp()

                           a_					:		Optional[int]							=  tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__     ,							legacy_format=SCREAMING_SNAKE_CASE__     )
                           a_					:		List[str]							=  tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                           # Checks it save with the same files
                           self.assertSequenceEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

                           # Checks everything loads correctly in the same way
                           a_					:		Optional[int]							=  tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                           a_					:		str							=  tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )     )

                           shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                           # Save tokenizer rust, legacy_format=False
                           a_					:		Dict							=  tempfile.mkdtemp()

                           a_					:		Any							=  tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__     ,							legacy_format=SCREAMING_SNAKE_CASE__     )
                           a_					:		Tuple							=  tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                           # Checks it saved the tokenizer.json file
                           self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files     )     )

                           # Checks everything loads correctly in the same way
                           a_					:		List[str]							=  tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                           a_					:		Any							=  tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )     )

                           shutil.rmtree(SCREAMING_SNAKE_CASE__     )
      @cached_property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Optional[int]:
             return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             with tempfile.NamedTemporaryFile() as f:
                    shutil.copyfile(SCREAMING_SNAKE_CASE__     ,							f.name     )
                    a_					:		Union[str, Any]							=  XLMRobertaTokenizer(f.name     ,							keep_accents=SCREAMING_SNAKE_CASE__     )
                    a_					:		int							=  pickle.dumps(SCREAMING_SNAKE_CASE__     )
             pickle.loads(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Union[str, Any]:
             if not self.test_rust_tokenizer:
                    return

             a_					:		int							=  self.get_tokenizer()
             a_					:		Union[str, Any]							=  self.get_rust_tokenizer()

             a_					:		List[str]							=  'I was born in 92000, and this is falsé.'

             a_					:		List[Any]							=  tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  self.get_rust_tokenizer()
             a_					:		int							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[Any]:
             a_					:		Optional[Any]							=  'Hello World!'
             a_					:		Any							=  [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
             # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
             # xlmr.eval()
             # xlmr.encode(symbols)

             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__     )     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			str:
             a_					:		str							=  (
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             a_					:		Tuple							=  [
                 0,
                 3_2_9_3,
                 8_3,
                 1_0,
                 4_5_5_2,
                 4_9_8_9,
                 7_9_8_6,
                 6_7_8,
                 1_0,
                 5_9_1_5,
                 1_1_1,
                 1_7_9_4_5_9,
                 1_2_4_8_5_0,
                 4,
                 6_0_4_4,
                 2_3_7,
                 1_2,
                 6,
                 5,
                 6,
                 4,
                 6_7_8_0,
                 7_0_5,
                 1_5,
                 1_3_8_8,
                 4_4,
                 3_7_8,
                 1_0_1_1_4,
                 7_1_1,
                 1_5_2,
                 2_0,
                 6,
                 5,
                 2_2_3_7_6,
                 6_4_2,
                 1_2_2_1,
                 1_5_1_9_0,
                 3_4_1_5_3,
                 4_5_0,
                 5_6_0_8,
                 9_5_9,
                 1_1_1_9,
                 5_7_7_0_2,
                 1_3_6,
                 1_8_6,
                 4_7,
                 1_0_9_8,
                 2_9_3_6_7,
                 4_7,
                 # 4426, # What fairseq tokenizes from "<unk>": "_<"
                 # 3678, # What fairseq tokenizes from "<unk>": "unk"
                 # 2740, # What fairseq tokenizes from "<unk>": ">"
                 3,  # What we tokenize from "<unk>": "<unk>"
                 6,  # Residue from the tokenization: an extra sentencepiece underline
                 4,
                 6_0_4_4,
                 2_3_7,
                 6_2_8_4,
                 5_0_9_0_1,
                 5_2_8,
                 3_1,
                 9_0,
                 3_4,
                 9_2_7,
                 2,
             ]
             # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
             # xlmr.eval()
             # xlmr.encode(symbols)

             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__     )     )

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[str]:
             # fmt: off
             a_					:		int							=  {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=SCREAMING_SNAKE_CASE__     ,							model_name='xlm-roberta-base'     ,							revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'     ,							)






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


UpperCAmelCase_      : Optional[int]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						['''pixel_values''']
      def __init__(       self				:       List[str]     ,							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 / 2_5_5     ,							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__				:       Union[str, Any]     ,							)		->			None:
             super().__init__(**SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  size if size is not None else {'shortest_edge': 2_5_6}
             a_					:		Any							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  do_resize
             a_					:		Dict							=  size
             a_					:		Optional[Any]							=  resample
             a_					:		Optional[int]							=  do_center_crop
             a_					:		Dict							=  crop_size
             a_					:		int							=  do_rescale
             a_					:		int							=  rescale_factor
             a_					:		Tuple							=  do_normalize
             a_					:		int							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             a_					:		Tuple							=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							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__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		List[Any]							=  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_					:		Tuple							=  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				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		str							=  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				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       float     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[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__				:       List[str]     ,							)		->			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				:       Dict     ,							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__				:       Union[str, Any]     ,							)		->			Union[str, Any]:
             a_					:		List[str]							=  do_resize if do_resize is not None else self.do_resize
             a_					:		Dict							=  size if size is not None else self.size
             a_					:		Dict							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  resample if resample is not None else self.resample
             a_					:		List[Any]							=  do_center_crop if do_center_crop is not None else self.do_center_crop
             a_					:		int							=  crop_size if crop_size is not None else self.crop_size
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  do_rescale if do_rescale is not None else self.do_rescale
             a_					:		Dict							=  rescale_factor if rescale_factor is not None else self.rescale_factor
             a_					:		Any							=  do_normalize if do_normalize is not None else self.do_normalize
             a_					:		str							=  image_mean if image_mean is not None else self.image_mean
             a_					:		Dict							=  image_std if image_std is not None else self.image_std

             a_					:		Optional[int]							=  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_					:		Any							=  [to_numpy_array(SCREAMING_SNAKE_CASE__     ) for image in images]

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

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

             if do_rescale:
                    a_					:		Optional[Any]							=  [self.rescale(image=SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ) for image in images]

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

             a_					:		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Tuple							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,							tensor_type=SCREAMING_SNAKE_CASE__     )


