

"""simple docstring"""




from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor


class 		_A       :





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=13    ,						SCREAMING_SNAKE_CASE__=30    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=37    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=10    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=0.6    ,						SCREAMING_SNAKE_CASE__=None    ,						)   ->					List[str]:
         lowerCamelCase__						=			parent
         lowerCamelCase__						=			batch_size
         lowerCamelCase__						=			image_size
         lowerCamelCase__						=			patch_size
         lowerCamelCase__						=			num_channels
         lowerCamelCase__						=			is_training
         lowerCamelCase__						=			use_labels
         lowerCamelCase__						=			hidden_size
         lowerCamelCase__						=			num_hidden_layers
         lowerCamelCase__						=			num_attention_heads
         lowerCamelCase__						=			intermediate_size
         lowerCamelCase__						=			hidden_act
         lowerCamelCase__						=			hidden_dropout_prob
         lowerCamelCase__						=			attention_probs_dropout_prob
         lowerCamelCase__						=			type_sequence_label_size
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			mask_ratio
         lowerCamelCase__						=			scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         lowerCamelCase__						=			(image_size // patch_size) ** 2
         lowerCamelCase__						=			int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         lowerCamelCase__						=			None
         if self.use_labels:
            lowerCamelCase__						=			ids_tensor([self.batch_size]    ,						self.type_sequence_label_size		)

         lowerCamelCase__						=			self.get_config()

         return config, pixel_values, labels





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return ViTMAEConfig(
             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    ,						decoder_hidden_size=self.hidden_size    ,						decoder_num_hidden_layers=self.num_hidden_layers    ,						decoder_num_attention_heads=self.num_attention_heads    ,						decoder_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=SCREAMING_SNAKE_CASE__    ,						initializer_range=self.initializer_range    ,						mask_ratio=self.mask_ratio    ,						)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			TFViTMAEModel(config=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         lowerCamelCase__						=			TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__		)
         # expected sequence length = num_patches
         lowerCamelCase__						=			(self.image_size // self.patch_size) ** 2
         lowerCamelCase__						=			self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)		)

         # test greyscale images
         lowerCamelCase__						=			1
         lowerCamelCase__						=			TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.patch_size**2
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			self.prepare_config_and_inputs()
         ((lowerCamelCase__)					,       (lowerCamelCase__)					,       (lowerCamelCase__))						=			config_and_inputs
         lowerCamelCase__						=			{"pixel_values": pixel_values}
         return config, inputs_dict






@require_tf
class 		_A       (			__a				,       __a				,       unittest.TestCase					):
      __a								=	(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
      __a								=	{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

      __a								=	False
      __a								=	False
      __a								=	False
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			TFViTMAEModelTester(self		)
         lowerCamelCase__						=			ConfigTester(self    ,						config_class=SCREAMING_SNAKE_CASE__    ,						has_text_modality=SCREAMING_SNAKE_CASE__    ,						hidden_size=37		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         self.config_tester.run_common_tests()





      @unittest.skip(reason="ViTMAE does not use inputs_embeds"		)
      def 					_lowerCamelCase  (  self		)   ->					Any:
         pass





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            self.assertIsInstance(model.get_input_embeddings()    ,						(tf.keras.layers.Layer)		)
            lowerCamelCase__						=			model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__    ,						tf.keras.layers.Layer		)		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			inspect.signature(model.call		)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            lowerCamelCase__						=			[*signature.parameters.keys()]

            lowerCamelCase__						=			["pixel_values"]
            self.assertListEqual(arg_names[:1]    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # make the mask reproducible
         np.random.seed(2		)

         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         lowerCamelCase__						=			int((config.image_size // config.patch_size) ** 2		)
         lowerCamelCase__						=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)		)
            lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			outputs_dict[0].numpy()
            lowerCamelCase__						=			outputs_keywords[0].numpy()

            self.assertLess(np.sum(np.abs(output_dict - output_keywords		)		)    ,						1e-6		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         # make the mask reproducible
         np.random.seed(2		)

         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         lowerCamelCase__						=			int((config.image_size // config.patch_size) ** 2		)
         lowerCamelCase__						=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

         def prepare_numpy_arrays(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			{}
            for k, v in inputs_dict.items():
               if tf.is_tensor(SCREAMING_SNAKE_CASE__		):
                  lowerCamelCase__						=			v.numpy()
               else:
                  lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		)

            return inputs_np_dict

         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			prepare_numpy_arrays(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)
            self.assert_outputs_same(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         # make masks reproducible
         np.random.seed(2		)

         lowerCamelCase__						=			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2		)
         lowerCamelCase__						=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
         lowerCamelCase__						=			tf.constant(SCREAMING_SNAKE_CASE__		)

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         lowerCamelCase__						=			tf_noise

         super().check_pt_tf_models(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # make mask reproducible
         np.random.seed(2		)

         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         lowerCamelCase__						=			{
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__		),)
             for module_member_name in dir(SCREAMING_SNAKE_CASE__		)
             if module_member_name.endswith("MainLayer"		)
             # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
             and module_member_name[: -len("MainLayer"		)] == model_class.__name__[: -len("Model"		)]
             for module_member in (getattr(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		),)
             if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(SCREAMING_SNAKE_CASE__    ,						"_keras_serializable"    ,						SCREAMING_SNAKE_CASE__		)
         }

         lowerCamelCase__						=			int((config.image_size // config.patch_size) ** 2		)
         lowerCamelCase__						=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
         lowerCamelCase__						=			tf.convert_to_tensor(SCREAMING_SNAKE_CASE__		)
         inputs_dict.update({"noise": noise}		)

         for main_layer_class in tf_main_layer_classes:
            lowerCamelCase__						=			main_layer_class(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			{
                name: tf.keras.Input(tensor.shape[1:]    ,						dtype=tensor.dtype		) for name, tensor in inputs_dict.items()
            }

            lowerCamelCase__						=			tf.keras.Model(SCREAMING_SNAKE_CASE__    ,						outputs=main_layer(SCREAMING_SNAKE_CASE__		)		)
            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)

            with tempfile.TemporaryDirectory() as tmpdirname:
               lowerCamelCase__						=			os.path.join(SCREAMING_SNAKE_CASE__    ,						"keras_model.h5"		)
               model.save(SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			tf.keras.models.load_model(
                   SCREAMING_SNAKE_CASE__    ,						custom_objects={main_layer_class.__name__: main_layer_class}		)
               assert isinstance(SCREAMING_SNAKE_CASE__    ,						tf.keras.Model		)
               lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
               self.assert_outputs_same(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      @slow
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         # make mask reproducible
         np.random.seed(2		)

         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         lowerCamelCase__						=			int((config.image_size // config.patch_size) ** 2		)
         lowerCamelCase__						=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)

            if model_class.__name__ == "TFViTMAEModel":
               lowerCamelCase__						=			outputs.last_hidden_state.numpy()
               lowerCamelCase__						=			0
            else:
               lowerCamelCase__						=			outputs.logits.numpy()
               lowerCamelCase__						=			0

            with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(SCREAMING_SNAKE_CASE__    ,						saved_model=SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			model_class.from_pretrained(SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)

               if model_class.__name__ == "TFViTMAEModel":
                  lowerCamelCase__						=			after_outputs["last_hidden_state"].numpy()
                  lowerCamelCase__						=			0
               else:
                  lowerCamelCase__						=			after_outputs["logits"].numpy()
                  lowerCamelCase__						=			0

               lowerCamelCase__						=			np.amax(np.abs(out_a - out_a		)		)
               self.assertLessEqual(SCREAMING_SNAKE_CASE__    ,						1e-5		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         # make mask reproducible
         np.random.seed(2		)

         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         lowerCamelCase__						=			int((config.image_size // config.patch_size) ** 2		)
         lowerCamelCase__						=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			model_class.from_config(model.get_config()		)
            # make sure it also accepts a normal config
            lowerCamelCase__						=			model_class.from_config(model.config		)
            lowerCamelCase__						=			new_model(SCREAMING_SNAKE_CASE__		)  # Build model
            new_model.set_weights(model.get_weights()		)
            lowerCamelCase__						=			new_model(SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)

            self.assert_outputs_same(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      @unittest.skip(
          reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."		)
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         pass





      @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"		)
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         pass





      @slow
      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"		)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)






def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
   return image


@require_tf
@require_vision
class 		_A       (			unittest.TestCase					):





      @cached_property
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"		) if is_vision_available() else None





      @slow
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         # make random mask reproducible across the PT and TF model
         np.random.seed(2		)

         lowerCamelCase__						=			TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"		)

         lowerCamelCase__						=			self.default_image_processor
         lowerCamelCase__						=			prepare_img()
         lowerCamelCase__						=			image_processor(images=SCREAMING_SNAKE_CASE__    ,						return_tensors="tf"		)

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         lowerCamelCase__						=			ViTMAEConfig()
         lowerCamelCase__						=			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
         lowerCamelCase__						=			np.random.uniform(size=(1, num_patches)		)

         # forward pass
         lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__    ,						noise=SCREAMING_SNAKE_CASE__		)

         # verify the logits
         lowerCamelCase__						=			tf.convert_to_tensor([1, 196, 768]		)
         self.assertEqual(outputs.logits.shape    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			tf.convert_to_tensor(
             [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]		)

         tf.debugging.assert_near(outputs.logits[0, :3, :3]    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-4		)







"""simple docstring"""




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


SCREAMING_SNAKE_CASE_     :			Optional[List[str]]	  =			None
SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE_     :			int	  =			[
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]


@dataclass
class 		_A       :
      __a								=	True
      __a								=	None
      # Automatically constructed
      __a								=	"PIL.Image.Image"
      __a								=	pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
      __a								=	field(default='Image'				,       init=__a				,       repr=__a					)





      def __call__(  self		)   ->					Dict:
         return self.pa_type





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					dict:
         if config.PIL_AVAILABLE:
            import PIL.Image
         else:
            raise ImportError("To support encoding images, please install 'Pillow'."		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": value, "bytes": None}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": None, "bytes": value}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						np.ndarray		):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(SCREAMING_SNAKE_CASE__		)
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						PIL.Image.Image		):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(SCREAMING_SNAKE_CASE__		)
         elif value.get("path"		) is not None and os.path.isfile(value["path"]		):
            # we set "bytes": None to not duplicate the data if they're already available locally
            return {"bytes": None, "path": value.get("path"		)}
         elif value.get("bytes"		) is not None or value.get("path"		) is not None:
            # store the image bytes, and path is used to infer the image format using the file extension
            return {"bytes": value.get("bytes"		), "path": value.get("path"		)}
         else:
            raise ValueError(
                f'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					"PIL.Image.Image":
         if not self.decode:
            raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."		)

         if config.PIL_AVAILABLE:
            import PIL.Image
         else:
            raise ImportError("To support decoding images, please install 'Pillow'."		)

         if token_per_repo_id is None:
            lowerCamelCase__						=			{}

         lowerCamelCase__					,       lowerCamelCase__						=			value["path"], value["bytes"]
         if bytes_ is None:
            if path is None:
               raise ValueError(f'An image should have one of \'path\' or \'bytes\' but both are None in {value}.'		)
            else:
               if is_local_path(SCREAMING_SNAKE_CASE__		):
                  lowerCamelCase__						=			PIL.Image.open(SCREAMING_SNAKE_CASE__		)
               else:
                  lowerCamelCase__						=			path.split("::"		)[-1]
                  try:
                     lowerCamelCase__						=			string_to_dict(SCREAMING_SNAKE_CASE__    ,						config.HUB_DATASETS_URL		)["repo_id"]
                     lowerCamelCase__						=			token_per_repo_id.get(SCREAMING_SNAKE_CASE__		)
                  except ValueError:
                     lowerCamelCase__						=			None
                  with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"    ,						use_auth_token=SCREAMING_SNAKE_CASE__		) as f:
                     lowerCamelCase__						=			BytesIO(f.read()		)
                  lowerCamelCase__						=			PIL.Image.open(bytes_		)
         else:
            lowerCamelCase__						=			PIL.Image.open(BytesIO(bytes_		)		)
         image.load()  # to avoid "Too many open files" errors
         return image





      def 					_lowerCamelCase  (  self		)   ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value("binary"		),
                 "path": Value("string"		),
             }
         )





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:
         if pa.types.is_string(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, storage]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([storage, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_struct(storage.type		):
            if storage.type.get_field_index("bytes"		) >= 0:
               lowerCamelCase__						=			storage.field("bytes"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            if storage.type.get_field_index("path"		) >= 0:
               lowerCamelCase__						=			storage.field("path"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
            lowerCamelCase__						=			pa.array(
                [encode_np_array(np.array(SCREAMING_SNAKE_CASE__		)		)["bytes"] if arr is not None else None for arr in storage.to_pylist()]    ,						type=pa.binary()    ,						)
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays(
                [bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null()		)
         return array_cast(SCREAMING_SNAKE_CASE__    ,						self.pa_type		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(SCREAMING_SNAKE_CASE__		):
            with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"		) as f:
               lowerCamelCase__						=			f.read()
            return bytes_

         lowerCamelCase__						=			pa.array(
             [
                 (path_to_bytes(x["path"]		) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]    ,						type=pa.binary()    ,						)
         lowerCamelCase__						=			pa.array(
             [os.path.basename(SCREAMING_SNAKE_CASE__		) if path is not None else None for path in storage.field("path"		).to_pylist()]    ,						type=pa.string()    ,						)
         lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null()		)
         return array_cast(SCREAMING_SNAKE_CASE__    ,						self.pa_type		)






def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      lowerCamelCase__						=			list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
   return _IMAGE_COMPRESSION_FORMATS



def 	UpperCAmelCase__   (				A__   )      ->      bytes:




   """simple docstring"""
   lowerCamelCase__						=			BytesIO()
   if image.format in list_image_compression_formats():
      lowerCamelCase__						=			image.format
   else:
      lowerCamelCase__						=			"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
   image.save(A__	,    format=A__   )
   return buffer.getvalue()



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if hasattr(A__	,    "filename"   ) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   lowerCamelCase__						=			array.dtype
   lowerCamelCase__						=			dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
   lowerCamelCase__						=			dtype.kind
   lowerCamelCase__						=			dtype.itemsize

   lowerCamelCase__						=			None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      lowerCamelCase__						=			np.dtype("|u1"   )
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'   )
      if dtype is not dest_dtype:
         warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''   )
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      lowerCamelCase__						=			dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         lowerCamelCase__						=			dtype_byteorder + dtype_kind + str(A__   )
         lowerCamelCase__						=			np.dtype(A__   )
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''   )
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'   )
   lowerCamelCase__						=			PIL.Image.fromarray(array.astype(A__   )   )
   return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      List[dict]:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   if objs:
      lowerCamelCase__					,       lowerCamelCase__						=			first_non_null_value(A__   )
      if isinstance(A__	,    A__   ):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(A__	,    np.ndarray   ):
         lowerCamelCase__						=			no_op_if_value_is_null(A__   )
         return [obj_to_image_dict_func(A__   ) for obj in objs]
      elif isinstance(A__	,    PIL.Image.Image   ):
         lowerCamelCase__						=			no_op_if_value_is_null(A__   )
         return [obj_to_image_dict_func(A__   ) for obj in objs]
      else:
         return objs
   else:
      return objs




