'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_:					Any  =get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
    from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE_:					int  =25_00_04
SCREAMING_SNAKE_CASE_:					Optional[int]  =25_00_20




@require_sentencepiece
@require_tokenizers
class 	__A				(			a__		,     unittest.TestCase   ):
    a__	:						Dict	       =  MBartaaTokenizer
    a__	:						List[str]	       =  MBartaaTokenizerFast
    a__	:						Any	       =  True
    a__	:						Any	       =  True



    def  _lowercase						(self   :   Optional[Any]			):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_	   =       MBartaaTokenizer(__a       ,      src_lang="en_XX"       ,      tgt_lang="ro_RO"       ,      keep_accents=__a			)
         tokenizer.save_pretrained(self.tmpdirname			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       "<s>"
         UpperCAmelCase_	   =       0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a			)       ,      __a			)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a			)       ,      __a			)



    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       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(__a			)       ,      1054			)



    def  _lowercase						(self   :   Union[str, Any]			):
         self.assertEqual(self.get_tokenizer().vocab_size       ,      1054			)



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       MBartaaTokenizer(__a       ,      src_lang="en_XX"       ,      tgt_lang="ro_RO"       ,      keep_accents=__a			)

         UpperCAmelCase_	   =       tokenizer.tokenize("This is a test"			)
         self.assertListEqual(__a       ,      ["▁This", "▁is", "▁a", "▁t", "est"]			)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__a			)       ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,      )

         UpperCAmelCase_	   =       tokenizer.tokenize("I was born in 92000, and this is falsé."			)
         self.assertListEqual(
             __a       ,      [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", "."]       ,      )
         UpperCAmelCase_	   =       tokenizer.convert_tokens_to_ids(__a			)
         self.assertListEqual(
             __a       ,      [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]       ,      )

         UpperCAmelCase_	   =       tokenizer.convert_ids_to_tokens(__a			)
         self.assertListEqual(
             __a       ,      [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>", "."]       ,      )



    @slow
    def  _lowercase						(self   :   Any			):
         # fmt: off
         UpperCAmelCase_	   =       {"input_ids": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=__a       ,      model_name="facebook/mbart-large-50"       ,      revision="d3913889c59cd5c9e456b269c376325eabad57e2"       ,      )






    def  _lowercase						(self   :   Tuple			):
         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

         UpperCAmelCase_	   =       (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
                   UpperCAmelCase_	   =       self.rust_tokenizer_class.from_pretrained(__a       ,      **__a			)
                   UpperCAmelCase_	   =       self.tokenizer_class.from_pretrained(__a       ,      **__a			)

                   UpperCAmelCase_	   =       tempfile.mkdtemp()

                   UpperCAmelCase_	   =       tokenizer_r.save_pretrained(__a			)
                   UpperCAmelCase_	   =       tokenizer_p.save_pretrained(__a			)

                   # 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			)			)
                   UpperCAmelCase_	   =       tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f			)
                   self.assertSequenceEqual(__a       ,      __a			)

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_	   =       tokenizer_r.from_pretrained(__a			)
                   UpperCAmelCase_	   =       tokenizer_p.from_pretrained(__a			)

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

                   shutil.rmtree(__a			)

                   # Save tokenizer rust, legacy_format=True
                   UpperCAmelCase_	   =       tempfile.mkdtemp()

                   UpperCAmelCase_	   =       tokenizer_r.save_pretrained(__a       ,      legacy_format=__a			)
                   UpperCAmelCase_	   =       tokenizer_p.save_pretrained(__a			)

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

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_	   =       tokenizer_r.from_pretrained(__a			)
                   UpperCAmelCase_	   =       tokenizer_p.from_pretrained(__a			)

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

                   shutil.rmtree(__a			)

                   # Save tokenizer rust, legacy_format=False
                   UpperCAmelCase_	   =       tempfile.mkdtemp()

                   UpperCAmelCase_	   =       tokenizer_r.save_pretrained(__a       ,      legacy_format=__a			)
                   UpperCAmelCase_	   =       tokenizer_p.save_pretrained(__a			)

                   # 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
                   UpperCAmelCase_	   =       tokenizer_r.from_pretrained(__a			)
                   UpperCAmelCase_	   =       tokenizer_p.from_pretrained(__a			)

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

                   shutil.rmtree(__a			)




@require_torch
@require_sentencepiece
@require_tokenizers
class 	__A				(			unittest.TestCase   ):
    a__	:						int	       =  """facebook/mbart-large-50-one-to-many-mmt"""
    a__	:						str	       =  [
        """ UN Chief Says There Is No Military Solution in Syria""",
        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
    ]
    a__	:						Dict	       =  [
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
        """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
        """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
        """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
    ]
    a__	:						Union[str, Any]	       =  [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]



    @classmethod
    def  _lowercase						(cls   :   str			):
         UpperCAmelCase_	   =       MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name       ,      src_lang="en_XX"       ,      tgt_lang="ro_RO"			)
         UpperCAmelCase_	   =       1
         return cls



    def  _lowercase						(self   :   Any			):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]       ,      250001			)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]       ,      250004			)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]       ,      250020			)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]       ,      250038			)



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
         self.assertListEqual(self.expected_src_tokens       ,      __a			)



    def  _lowercase						(self   :   Tuple			):
         self.assertIn(__a       ,      self.tokenizer.all_special_ids			)
         UpperCAmelCase_	   =       [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
         UpperCAmelCase_	   =       self.tokenizer.decode(__a       ,      skip_special_tokens=__a			)
         UpperCAmelCase_	   =       self.tokenizer.decode(generated_ids[1:]       ,      skip_special_tokens=__a			)
         self.assertEqual(__a       ,      __a			)
         self.assertNotIn(self.tokenizer.eos_token       ,      __a			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       ["this is gunna be a long sentence " * 20]
         assert isinstance(src_text[0]       ,      __a			)
         UpperCAmelCase_	   =       10
         UpperCAmelCase_	   =       self.tokenizer(__a       ,      max_length=__a       ,      truncation=__a			).input_ids[0]
         self.assertEqual(ids[0]       ,      __a			)
         self.assertEqual(ids[-1]       ,      2			)
         self.assertEqual(len(__a			)       ,      __a			)



    def  _lowercase						(self   :   str			):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]			)       ,      [250053, 250001]			)



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       tempfile.mkdtemp()
         UpperCAmelCase_	   =       self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__a			)
         UpperCAmelCase_	   =       MBartaaTokenizer.from_pretrained(__a			)
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,      __a			)



    @require_torch
    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       self.tokenizer(self.src_text       ,      text_target=self.tgt_text       ,      padding=__a       ,      return_tensors="pt"			)
         UpperCAmelCase_	   =       shift_tokens_right(batch["labels"]       ,      self.tokenizer.pad_token_id			)

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



    @require_torch
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       self.tokenizer(
             self.src_text       ,      text_target=self.tgt_text       ,      padding=__a       ,      truncation=__a       ,      max_length=len(self.expected_src_tokens			)       ,      return_tensors="pt"       ,      )
         UpperCAmelCase_	   =       shift_tokens_right(batch["labels"]       ,      self.tokenizer.pad_token_id			)

         self.assertIsInstance(__a       ,      __a			)

         self.assertEqual((2, 14)       ,      batch.input_ids.shape			)
         self.assertEqual((2, 14)       ,      batch.attention_mask.shape			)
         UpperCAmelCase_	   =       batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens       ,      __a			)
         self.assertEqual(2       ,      batch.decoder_input_ids[0, 0]			)  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens       ,      [EN_CODE]			)
         self.assertEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       self.tokenizer(self.src_text       ,      padding=__a       ,      truncation=__a       ,      max_length=3       ,      return_tensors="pt"			)
         UpperCAmelCase_	   =       self.tokenizer(
             text_target=self.tgt_text       ,      padding=__a       ,      truncation=__a       ,      max_length=10       ,      return_tensors="pt"			)
         UpperCAmelCase_	   =       targets["input_ids"]
         UpperCAmelCase_	   =       shift_tokens_right(__a       ,      self.tokenizer.pad_token_id			)

         self.assertEqual(batch.input_ids.shape[1]       ,      3			)
         self.assertEqual(batch.decoder_input_ids.shape[1]       ,      10			)






    @require_torch
    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.tokenizer._build_translation_inputs(
             "A test"       ,      return_tensors="pt"       ,      src_lang="en_XX"       ,      tgt_lang="ar_AR"			)

         self.assertEqual(
             nested_simplify(__a			)       ,      {
                 # en_XX, A, test, EOS
                 "input_ids": [[250004, 62, 3034, 2]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # ar_AR
                 "forced_bos_token_id": 250001,
             }       ,      )





'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  ["""pixel_values"""]



    def __init__(self   :   Any       ,      __a   :   bool = True       ,      __a   :   Optional[Dict[str, int]] = None       ,      __a   :   PILImageResampling = PILImageResampling.BICUBIC       ,      __a   :   bool = True       ,      __a   :   bool = True       ,      __a   :   Union[int, float] = 1 / 255       ,      __a   :   Dict[str, int] = None       ,      __a   :   bool = True       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         UpperCAmelCase_	   =       size if size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a			)
         UpperCAmelCase_	   =       crop_size if crop_size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a       ,      default_to_square=__a       ,      param_name="crop_size"			)

         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       do_center_crop
         UpperCAmelCase_	   =       crop_size
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       resample
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_	   =       image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def  _lowercase						(self   :   Tuple       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   PILImageResampling = PILImageResampling.BILINEAR       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if "shortest_edge" in size:
              UpperCAmelCase_	   =       get_resize_output_image_size(__a       ,      size=size["shortest_edge"]       ,      default_to_square=__a			)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
              UpperCAmelCase_	   =       (size["height"], size["width"])
         else:
              raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""			)
         return resize(__a       ,      size=__a       ,      resample=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   int       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)
         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(__a       ,      size=(size["height"], size["width"])       ,      data_format=__a       ,      **__a			)



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



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






    def  _lowercase						(self   :   Dict       ,      __a   :   ImageInput       ,      __a   :   Optional[bool] = None       ,      __a   :   Dict[str, int] = None       ,      __a   :   PILImageResampling = None       ,      __a   :   bool = None       ,      __a   :   int = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[float] = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[str, TensorType]] = None       ,      __a   :   Union[str, ChannelDimension] = ChannelDimension.FIRST       ,      **__a   :   Dict       ,      ):
         UpperCAmelCase_	   =       do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_	   =       do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_	   =       do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_	   =       do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_	   =       crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_	   =       get_size_dict(__a       ,      param_name="crop_size"       ,      default_to_square=__a			)
         UpperCAmelCase_	   =       resample if resample is not None else self.resample
         UpperCAmelCase_	   =       rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_	   =       image_std if image_std is not None else self.image_std

         UpperCAmelCase_	   =       size if size is not None else self.size
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if not is_batched(__a			):
              UpperCAmelCase_	   =       [images]

         if not valid_images(__a			):
              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."			)

         # All transformations expect numpy arrays.
         UpperCAmelCase_	   =       [to_numpy_array(__a			) for image in images]

         if do_resize:
              UpperCAmelCase_	   =       [self.resize(image=__a       ,      size=__a       ,      resample=__a			) for image in images]

         if do_center_crop:
              UpperCAmelCase_	   =       [self.center_crop(image=__a       ,      size=__a			) for image in images]

         if do_rescale:
              UpperCAmelCase_	   =       [self.rescale(image=__a       ,      scale=__a			) for image in images]

         if do_normalize:
              UpperCAmelCase_	   =       [self.normalize(image=__a       ,      mean=__a       ,      std=__a			) for image in images]

         UpperCAmelCase_	   =       [to_channel_dimension_format(__a       ,      __a			) for image in images]

         UpperCAmelCase_	   =       {"pixel_values": images}
         return BatchFeature(data=__a       ,      tensor_type=__a			)




