





'''simple docstring'''


import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     EfficientFormerForImageClassification,
     EfficientFormerForImageClassificationWithTeacher,
     EfficientFormerModel,
 )
 from transformers.models.efficientformer.modeling_efficientformer import (
     EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )


if is_vision_available():
 from PIL import Image

 from transformers import EfficientFormerImageProcessor
class    a_     :





     def __init__(				self	:      Optional[int]			,   lowercase	:      Tuple			,   lowercase	:      int = 13			,   lowercase	:      int = 64			,   lowercase	:      int = 2			,   lowercase	:      int = 3			,   lowercase	:      int = 3			,   lowercase	:      bool = True			,   lowercase	:      bool = True			,   lowercase	:      int = 128			,   lowercase	:      Dict=[16, 32, 64, 128]			,   lowercase	:      int = 7			,   lowercase	:      int = 4			,   lowercase	:      int = 37			,   lowercase	:      str = "gelu"			,   lowercase	:      float = 0.1			,   lowercase	:      float = 0.1			,   lowercase	:      int = 10			,   lowercase	:      float = 0.02			,   lowercase	:      int = 2			,   lowercase	:      int = 1			,   lowercase	:      int = 128			,   lowercase	:      List[int] = [2, 2, 2, 2]			,   lowercase	:      int = 2			,   lowercase	:      int = 2			,   ):





        """simple docstring"""




        lowercase_    :Tuple					    =				parent
        lowercase_    :Union[str, Any]					    =				batch_size
        lowercase_    :List[str]					    =				image_size
        lowercase_    :List[Any]					    =				patch_size
        lowercase_    :Any					    =				num_channels
        lowercase_    :Any					    =				is_training
        lowercase_    :str					    =				use_labels
        lowercase_    :List[str]					    =				hidden_size
        lowercase_    :int					    =				num_hidden_layers
        lowercase_    :Dict					    =				num_attention_heads
        lowercase_    :Optional[int]					    =				intermediate_size
        lowercase_    :Optional[int]					    =				hidden_act
        lowercase_    :Optional[int]					    =				hidden_dropout_prob
        lowercase_    :Union[str, Any]					    =				attention_probs_dropout_prob
        lowercase_    :List[str]					    =				type_sequence_label_size
        lowercase_    :Optional[int]					    =				initializer_range
        lowercase_    :List[Any]					    =				encoder_stride
        lowercase_    :str					    =				num_attention_outputs
        lowercase_    :int					    =				embed_dim
        lowercase_    :Optional[int]					    =				embed_dim + 1
        lowercase_    :Optional[Any]					    =				resolution
        lowercase_    :Any					    =				depths
        lowercase_    :List[Any]					    =				hidden_sizes
        lowercase_    :Tuple					    =				dim
        lowercase_    :Tuple					    =				mlp_expansion_ratio





     def 							lowercase__   (				self	:      List[Any]		):





        """simple docstring"""




        lowercase_    :List[str]					    =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        lowercase_    :Dict					    =				None
        if self.use_labels:
           lowercase_    :str					    =				ids_tensor([self.batch_size]			,   self.type_sequence_label_size		)

        lowercase_    :List[str]					    =				self.get_config()
        return config, pixel_values, labels





     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        return EfficientFormerConfig(
            image_size=self.image_size			,   patch_size=self.patch_size			,   num_channels=self.num_channels			,   hidden_size=self.hidden_size			,   num_hidden_layers=self.num_hidden_layers			,   num_attention_heads=self.num_attention_heads			,   intermediate_size=self.intermediate_size			,   hidden_act=self.hidden_act			,   hidden_dropout_prob=self.hidden_dropout_prob			,   attention_probs_dropout_prob=self.attention_probs_dropout_prob			,   is_decoder=lowercase			,   initializer_range=self.initializer_range			,   encoder_stride=self.encoder_stride			,   resolution=self.resolution			,   depths=self.depths			,   hidden_sizes=self.hidden_sizes			,   dim=self.dim			,   mlp_expansion_ratio=self.mlp_expansion_ratio			,   )





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      List[str]			,   lowercase	:      Tuple			,   lowercase	:      str		):





        """simple docstring"""




        lowercase_    :Tuple					    =				EfficientFormerModel(config=lowercase		)
        model.to(lowercase		)
        model.eval()
        lowercase_    :Dict					    =				model(lowercase		)
        self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size)		)





     def 							lowercase__   (				self	:      Union[str, Any]			,   lowercase	:      int			,   lowercase	:      Tuple			,   lowercase	:      Optional[int]		):





        """simple docstring"""




        lowercase_    :List[str]					    =				self.type_sequence_label_size
        lowercase_    :str					    =				EfficientFormerForImageClassification(lowercase		)
        model.to(lowercase		)
        model.eval()
        lowercase_    :Tuple					    =				model(lowercase			,   labels=lowercase		)
        self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        lowercase_    :List[str]					    =				1
        lowercase_    :int					    =				EfficientFormerForImageClassification(lowercase		)
        model.to(lowercase		)
        model.eval()

        lowercase_    :Dict					    =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        lowercase_    :List[str]					    =				model(lowercase		)
        self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)		)







     def 							lowercase__   (				self	:      int		):





        """simple docstring"""




        lowercase_    :List[Any]					    =				self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )    ,							(
            lowercase_
        )    ,							(
            lowercase_
        )    ,							
        )    :int					    =				config_and_inputs
        lowercase_    :str					    =				{"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class    a_     (       _lowerCAmelCase			,		_lowerCAmelCase			,		unittest.TestCase							):
     __A  				=    (
         (
             EfficientFormerModel,
             EfficientFormerForImageClassificationWithTeacher,
             EfficientFormerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     __A  				=    (
         {
             "feature-extraction": EfficientFormerModel,
             "image-classification": (
                 EfficientFormerForImageClassification,
                 EfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_torch_available()
         else {}
     )
     __A  				=    False

     __A  				=    False
     __A  				=    False
     __A  				=    False





     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        lowercase_    :List[Any]					    =				EfficientFormerModelTester(self		)
        lowercase_    :str					    =				ConfigTester(
            self			,   config_class=lowercase			,   has_text_modality=lowercase			,   hidden_size=37		)





     def 							lowercase__   (				self	:      List[Any]		):





        """simple docstring"""




        self.config_tester.run_common_tests()





     @unittest.skip(reason="EfficientFormer does not use inputs_embeds"		)
     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        pass





     @unittest.skip(reason="EfficientFormer does not support input and output embeddings"		)
     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        pass





     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        lowercase_    ,							lowercase_    :List[str]					    =				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowercase_    :Optional[Any]					    =				model_class(lowercase		)
           lowercase_    :int					    =				inspect.signature(model.forward		)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowercase_    :List[Any]					    =				[*signature.parameters.keys()]

           lowercase_    :str					    =				["pixel_values"]
           self.assertListEqual(arg_names[:1]			,   lowercase		)





     def 							lowercase__   (				self	:      int		):





        """simple docstring"""




        def check_hidden_states_output(lowercase	:      Dict			,   lowercase	:      Tuple			,   lowercase	:      List[str]		):
           lowercase_    :int					    =				model_class(lowercase		)
           model.to(lowercase		)
           model.eval()

           with torch.no_grad():
              lowercase_    :Optional[int]					    =				model(**self._prepare_for_class(lowercase			,   lowercase		)		)

           lowercase_    :Dict					    =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           lowercase_    :Tuple					    =				getattr(
               self.model_tester			,   "expected_num_hidden_layers"			,   self.model_tester.num_hidden_layers + 1		)
           self.assertEqual(len(lowercase		)			,   lowercase		)

           if hasattr(self.model_tester			,   "encoder_seq_length"		):
              lowercase_    :Tuple					    =				self.model_tester.encoder_seq_length
              if hasattr(self.model_tester			,   "chunk_length"		) and self.model_tester.chunk_length > 1:
                 lowercase_    :Optional[Any]					    =				seq_length * self.model_tester.chunk_length
           else:
              lowercase_    :List[Any]					    =				self.model_tester.seq_length

           self.assertListEqual(
               list(hidden_states[-1].shape[-2:]		)			,   [seq_length, self.model_tester.hidden_size]			,   )

           if config.is_encoder_decoder:
              lowercase_    :str					    =				outputs.decoder_hidden_states

              self.assertIsInstance(lowercase			,   (list, tuple)		)
              self.assertEqual(len(lowercase		)			,   lowercase		)
              lowercase_    :List[Any]					    =				getattr(self.model_tester			,   "seq_length"			,   lowercase		)
              lowercase_    :Dict					    =				getattr(self.model_tester			,   "decoder_seq_length"			,   lowercase		)

              self.assertListEqual(
                  list(hidden_states[-1].shape[-2:]		)			,   [decoder_seq_length, self.model_tester.hidden_size]			,   )

        lowercase_    ,							lowercase_    :str					    =				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowercase_    :Optional[Any]					    =				True
           check_hidden_states_output(lowercase			,   lowercase			,   lowercase		)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           lowercase_    :List[str]					    =				True

           check_hidden_states_output(lowercase			,   lowercase			,   lowercase		)





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      Any			,   lowercase	:      Tuple			,   lowercase	:      Any=False		):





        """simple docstring"""




        lowercase_    :List[Any]					    =				super()._prepare_for_class(lowercase			,   lowercase			,   return_labels=lowercase		)

        if return_labels:
           if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
              del inputs_dict["labels"]

        return inputs_dict





     def 							lowercase__   (				self	:      int		):





        """simple docstring"""




        lowercase_    :Tuple					    =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase		)





     @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"		)
     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        lowercase_    :Tuple					    =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowercase		)





     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        lowercase_    :Optional[Any]					    =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase		)





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        if not self.model_tester.is_training:
           return

        lowercase_    ,							lowercase_    :int					    =				self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_    :List[Any]					    =				True

        for model_class in self.all_model_classes:
           # EfficientFormerForImageClassificationWithTeacher supports inference-only
           if (
               model_class in get_values(lowercase		)
               or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
           ):
              continue
           lowercase_    :int					    =				model_class(lowercase		)
           model.to(lowercase		)
           model.train()
           lowercase_    :List[str]					    =				self._prepare_for_class(lowercase			,   lowercase			,   return_labels=lowercase		)
           lowercase_    :Union[str, Any]					    =				model(**lowercase		).loss
           loss.backward()





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    ,							lowercase_    :Dict					    =				self.model_tester.prepare_config_and_inputs_for_common()

        lowercase_    :Tuple					    =				[
            {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
            {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
            {"title": "regression", "num_labels": 1, "dtype": torch.float},
        ]

        for model_class in self.all_model_classes:
           if (
               model_class
               not in [
                   *get_values(lowercase		),
               ]
               or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
           ):
              continue

           for problem_type in problem_types:
              with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'		):
                 lowercase_    :List[Any]					    =				problem_type["title"]
                 lowercase_    :Optional[int]					    =				problem_type["num_labels"]

                 lowercase_    :Any					    =				model_class(lowercase		)
                 model.to(lowercase		)
                 model.train()

                 lowercase_    :List[Any]					    =				self._prepare_for_class(lowercase			,   lowercase			,   return_labels=lowercase		)

                 if problem_type["num_labels"] > 1:
                    lowercase_    :Dict					    =				inputs["labels"].unsqueeze(1		).repeat(1			,   problem_type["num_labels"]		)

                 lowercase_    :Optional[Any]					    =				inputs["labels"].to(problem_type["dtype"]		)

                 # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                 # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                 # they have the same size." which is a symptom something in wrong for the regression problem.
                 # See https://github.com/huggingface/transformers/issues/11780
                 with warnings.catch_warnings(record=lowercase		) as warning_list:
                    lowercase_    :Dict					    =				model(**lowercase		).loss
                 for w in warning_list:
                    if "Using a target size that is different to the input size" in str(w.message		):
                       raise ValueError(
                           F'Something is going wrong in the regression problem: intercepted {w.message}'		)

                 loss.backward()





     @slow
     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase_    :List[Any]					    =				EfficientFormerModel.from_pretrained(lowercase		)
           self.assertIsNotNone(lowercase		)







     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    ,							lowercase_    :Optional[int]					    =				self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_    :Any					    =				True

        lowercase_    :List[str]					    =				getattr(self.model_tester			,   "seq_length"			,   lowercase		)
        lowercase_    :Optional[Any]					    =				getattr(self.model_tester			,   "encoder_seq_length"			,   lowercase		)
        lowercase_    :Optional[Any]					    =				getattr(self.model_tester			,   "key_length"			,   lowercase		)
        lowercase_    :List[Any]					    =				getattr(self.model_tester			,   "chunk_length"			,   lowercase		)

        if chunk_length is not None and hasattr(self.model_tester			,   "num_hashes"		):
           lowercase_    :Dict					    =				encoder_seq_length * self.model_tester.num_hashes

        for model_class in self.all_model_classes:
           lowercase_    :List[str]					    =				True
           lowercase_    :List[str]					    =				False
           lowercase_    :Optional[Any]					    =				True
           lowercase_    :Optional[int]					    =				model_class(lowercase		)
           model.to(lowercase		)
           model.eval()
           with torch.no_grad():
              lowercase_    :List[str]					    =				model(**self._prepare_for_class(lowercase			,   lowercase		)		)
           lowercase_    :Tuple					    =				outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(lowercase		)			,   self.model_tester.num_attention_outputs		)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           lowercase_    :Union[str, Any]					    =				True
           lowercase_    :Optional[int]					    =				model_class(lowercase		)
           model.to(lowercase		)
           model.eval()
           with torch.no_grad():
              lowercase_    :List[Any]					    =				model(**self._prepare_for_class(lowercase			,   lowercase		)		)
           lowercase_    :Optional[Any]					    =				outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(lowercase		)			,   self.model_tester.num_attention_outputs		)

           if chunk_length is not None:
              self.assertListEqual(
                  list(attentions[0].shape[-4:]		)			,   [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]			,   )
           else:
              self.assertListEqual(
                  list(attentions[0].shape[-3:]		)			,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,   )







def 							UpperCAmelCase_						(		):
   lowercase_    :List[Any]					    =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
   return image


@require_torch
@require_vision
class    a_     (       unittest.TestCase							):





     @cached_property
     def 							lowercase__   (				self	:      int		):





        """simple docstring"""




        return (
            EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"		)
            if is_vision_available()
            else None
        )





     @slow
     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :List[str]					    =				EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"		).to(
            lowercase		)

        lowercase_    :Any					    =				self.default_image_processor
        lowercase_    :List[Any]					    =				prepare_img()
        lowercase_    :List[Any]					    =				image_processor(images=lowercase			,   return_tensors="pt"		).to(lowercase		)

        # forward pass
        with torch.no_grad():
           lowercase_    :Optional[int]					    =				model(**lowercase		)

        # verify the logits
        lowercase_    :Any					    =				(1, 1_000)
        self.assertEqual(outputs.logits.shape			,   lowercase		)

        lowercase_    :Any					    =				torch.tensor([-0.05_55, 0.48_25, -0.08_52]		).to(lowercase		)
        self.assertTrue(torch.allclose(outputs.logits[0][:3]			,   lowercase			,   atol=1e-4		)		)







     @slow
     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        lowercase_    :int					    =				EfficientFormerForImageClassificationWithTeacher.from_pretrained(
            "snap-research/efficientformer-l1-300"		).to(lowercase		)

        lowercase_    :str					    =				self.default_image_processor
        lowercase_    :Tuple					    =				prepare_img()
        lowercase_    :Union[str, Any]					    =				image_processor(images=lowercase			,   return_tensors="pt"		).to(lowercase		)

        # forward pass
        with torch.no_grad():
           lowercase_    :Union[str, Any]					    =				model(**lowercase		)

        # verify the logits
        lowercase_    :Optional[Any]					    =				(1, 1_000)
        self.assertEqual(outputs.logits.shape			,   lowercase		)

        lowercase_    :Optional[int]					    =				torch.tensor([-0.13_12, 0.43_53, -1.04_99]		).to(lowercase		)
        self.assertTrue(torch.allclose(outputs.logits[0][:3]			,   lowercase			,   atol=1e-4		)		)









'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase      :  Any      							=logging.get_logger(__name__)
class    a_     (       _lowerCAmelCase							):
     __A  				=    ["pixel_values"]





     def __init__(				self	:      Tuple			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      PILImageResampling = PILImageResampling.BICUBIC			,   lowercase	:      bool = True			,   lowercase	:      Union[int, float] = 1 / 255			,   lowercase	:      bool = True			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   **lowercase	:      int			,   ):





        """simple docstring"""




        super().__init__(**lowercase		)
        lowercase_    :Dict					    =				crop_size if crop_size is not None else {"height": 256, "width": 256}
        lowercase_    :str					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Optional[Any]					    =				size if size is not None else {"height": 224, "width": 224}
        lowercase_    :Union[str, Any]					    =				get_size_dict(lowercase		)

        lowercase_    :Union[str, Any]					    =				do_center_crop
        lowercase_    :str					    =				crop_size
        lowercase_    :List[str]					    =				do_resize
        lowercase_    :str					    =				size
        lowercase_    :Tuple					    =				resample
        lowercase_    :int					    =				do_rescale
        lowercase_    :Union[str, Any]					    =				rescale_factor
        lowercase_    :Tuple					    =				do_normalize
        lowercase_    :List[Any]					    =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_    :Optional[int]					    =				image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 							lowercase__   (				self	:      Any			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      Optional[int] = None			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[Any]			,   ):





        """simple docstring"""




        lowercase_    :Dict					    =				self.size if size is None else size
        lowercase_    :Dict					    =				get_size_dict(lowercase		)
        lowercase_    :List[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)

        lowercase_    ,							lowercase_    :List[str]					    =				get_image_size(lowercase		)
        lowercase_    :List[str]					    =				min(lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				(size["height"] / crop_size["height"]) * min_dim
        lowercase_    :int					    =				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowercase			,   size=(cropped_height, cropped_width)			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      Union[str, Any]			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      PILImageResampling = PIL.Image.BICUBIC			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[str]			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				get_size_dict(lowercase		)
        if "height" not in size or "width" not in size:
           raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'		)
        return resize(
            lowercase			,   size=(size["height"], size["width"])			,   resample=lowercase			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[int, float]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      Union[str, Any]			,   ):





        """simple docstring"""




        return rescale(lowercase			,   scale=lowercase			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      str			,   ):





        """simple docstring"""




        return normalize(lowercase			,   mean=lowercase			,   std=lowercase			,   data_format=lowercase			,   **lowercase		)







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      ImageInput			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      PILImageResampling = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[float] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[str, TensorType]] = None			,   lowercase	:      ChannelDimension = ChannelDimension.FIRST			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_    :Union[str, Any]					    =				crop_size if crop_size is not None else self.crop_size
        lowercase_    :Optional[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Union[str, Any]					    =				do_resize if do_resize is not None else self.do_resize
        lowercase_    :Any					    =				size if size is not None else self.size
        lowercase_    :List[Any]					    =				get_size_dict(lowercase		)
        lowercase_    :Optional[Any]					    =				resample if resample is not None else self.resample
        lowercase_    :Dict					    =				do_rescale if do_rescale is not None else self.do_rescale
        lowercase_    :List[Any]					    =				rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_    :str					    =				do_normalize if do_normalize is not None else self.do_normalize
        lowercase_    :Dict					    =				image_mean if image_mean is not None else self.image_mean
        lowercase_    :List[Any]					    =				image_std if image_std is not None else self.image_std

        lowercase_    :Any					    =				make_list_of_images(lowercase		)

        if not valid_images(lowercase		):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."		)

        if do_center_crop and crop_size is None:
           raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."		)

        if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        lowercase_    :Optional[int]					    =				[to_numpy_array(lowercase		) for image in images]

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

        if do_resize:
           lowercase_    :List[str]					    =				[self.resize(image=lowercase			,   size=lowercase			,   resample=lowercase		) for image in images]

        if do_rescale:
           lowercase_    :List[Any]					    =				[self.rescale(image=lowercase			,   scale=lowercase		) for image in images]

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

        lowercase_    :Optional[Any]					    =				[to_channel_dimension_format(lowercase			,   lowercase		) for image in images]

        lowercase_    :Union[str, Any]					    =				{"pixel_values": images}
        return BatchFeature(data=lowercase			,   tensor_type=lowercase		)


