

"""simple docstring"""




import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         FocalNetBackbone,
         FocalNetForImageClassification,
         FocalNetForMaskedImageModeling,
         FocalNetModel,
     )
     from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class 		_A       :





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=13    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=16    ,						SCREAMING_SNAKE_CASE__=[32, 64, 128]    ,						SCREAMING_SNAKE_CASE__=[1, 2, 1]    ,						SCREAMING_SNAKE_CASE__=[2, 2, 4]    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=2.0    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=1e-5    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=10    ,						SCREAMING_SNAKE_CASE__=8    ,						SCREAMING_SNAKE_CASE__=["stage1", "stage2"]    ,						SCREAMING_SNAKE_CASE__=[1, 2]    ,						)   ->					Tuple:
         lowerCamelCase__						=			parent
         lowerCamelCase__						=			batch_size
         lowerCamelCase__						=			image_size
         lowerCamelCase__						=			patch_size
         lowerCamelCase__						=			num_channels
         lowerCamelCase__						=			embed_dim
         lowerCamelCase__						=			hidden_sizes
         lowerCamelCase__						=			depths
         lowerCamelCase__						=			num_heads
         lowerCamelCase__						=			window_size
         lowerCamelCase__						=			mlp_ratio
         lowerCamelCase__						=			qkv_bias
         lowerCamelCase__						=			hidden_dropout_prob
         lowerCamelCase__						=			attention_probs_dropout_prob
         lowerCamelCase__						=			drop_path_rate
         lowerCamelCase__						=			hidden_act
         lowerCamelCase__						=			use_absolute_embeddings
         lowerCamelCase__						=			patch_norm
         lowerCamelCase__						=			layer_norm_eps
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			is_training
         lowerCamelCase__						=			scope
         lowerCamelCase__						=			use_labels
         lowerCamelCase__						=			type_sequence_label_size
         lowerCamelCase__						=			encoder_stride
         lowerCamelCase__						=			out_features
         lowerCamelCase__						=			out_indices





      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		)   ->					Any:
         return FocalNetConfig(
             image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						embed_dim=self.embed_dim    ,						hidden_sizes=self.hidden_sizes    ,						depths=self.depths    ,						num_heads=self.num_heads    ,						window_size=self.window_size    ,						mlp_ratio=self.mlp_ratio    ,						qkv_bias=self.qkv_bias    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						drop_path_rate=self.drop_path_rate    ,						hidden_act=self.hidden_act    ,						use_absolute_embeddings=self.use_absolute_embeddings    ,						path_norm=self.patch_norm    ,						layer_norm_eps=self.layer_norm_eps    ,						initializer_range=self.initializer_range    ,						encoder_stride=self.encoder_stride    ,						out_features=self.out_features    ,						out_indices=self.out_indices    ,						)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			FocalNetModel(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
         lowerCamelCase__						=			int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, expected_seq_len, expected_dim)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			FocalNetBackbone(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps		)    ,						len(config.out_features		)		)
         self.parent.assertListEqual(list(result.feature_maps[0].shape		)    ,						[self.batch_size, self.image_size, 8, 8]		)

         # verify channels
         self.parent.assertEqual(len(model.channels		)    ,						len(config.out_features		)		)
         self.parent.assertListEqual(model.channels    ,						config.hidden_sizes[:-1]		)

         # verify backbone works with out_features=None
         lowerCamelCase__						=			None
         lowerCamelCase__						=			FocalNetBackbone(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps		)    ,						1		)
         self.parent.assertListEqual(list(result.feature_maps[0].shape		)    ,						[self.batch_size, self.image_size * 2, 4, 4]		)

         # verify channels
         self.parent.assertEqual(len(model.channels		)    ,						1		)
         self.parent.assertListEqual(model.channels    ,						[config.hidden_sizes[-1]]		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			FocalNetForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(
             result.reconstruction.shape    ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

         # test greyscale images
         lowerCamelCase__						=			1
         lowerCamelCase__						=			FocalNetForMaskedImageModeling(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

         lowerCamelCase__						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.reconstruction.shape    ,						(self.batch_size, 1, self.image_size, self.image_size)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         lowerCamelCase__						=			self.type_sequence_label_size
         lowerCamelCase__						=			FocalNetForImageClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)		)

         # test greyscale images
         lowerCamelCase__						=			1
         lowerCamelCase__						=			FocalNetForImageClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

         lowerCamelCase__						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			self.prepare_config_and_inputs()

         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			config_and_inputs
         lowerCamelCase__						=			{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 		_A       (			__a				,       __a				,       unittest.TestCase					):
      __a								=	(
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      __a								=	(
          {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      __a								=	False

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





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			FocalNetModelTester(self		)
         lowerCamelCase__						=			ConfigTester(self    ,						config_class=SCREAMING_SNAKE_CASE__    ,						embed_dim=37    ,						has_text_modality=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         return





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





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__		)





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





      @unittest.skip(reason="FocalNet does not use feedforward chunking"		)
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         pass





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

         for model_class in self.all_model_classes[:-1]:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)		)
            lowerCamelCase__						=			model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__    ,						nn.Linear		)		)





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

         for model_class in self.all_model_classes[:-1]:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			inspect.signature(model.forward		)
            # 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    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					str:
         lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

         with torch.no_grad():
            lowerCamelCase__						=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)		)

         lowerCamelCase__						=			outputs.hidden_states

         lowerCamelCase__						=			getattr(
             self.model_tester    ,						"expected_num_hidden_layers"    ,						len(self.model_tester.depths		) + 1		)
         self.assertEqual(len(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)

         # FocalNet has a different seq_length
         lowerCamelCase__						=			(
             config.patch_size
             if isinstance(config.patch_size    ,						collections.abc.Iterable		)
             else (config.patch_size, config.patch_size)
         )

         lowerCamelCase__						=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]		)    ,						[num_patches, self.model_tester.embed_dim]    ,						)

         lowerCamelCase__						=			outputs.reshaped_hidden_states
         self.assertEqual(len(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			reshaped_hidden_states[0].shape
         lowerCamelCase__						=			(
             reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						height * width		).permute(0    ,						2    ,						1		)
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]		)    ,						[num_patches, self.model_tester.embed_dim]    ,						)





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

         lowerCamelCase__						=			(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size    ,						collections.abc.Iterable		)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes[:-1]:
            lowerCamelCase__						=			True
            self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowerCamelCase__						=			True

            self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()
         lowerCamelCase__						=			3

         lowerCamelCase__						=			(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size    ,						collections.abc.Iterable		)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         lowerCamelCase__						=			(
             config.patch_size
             if isinstance(config.patch_size    ,						collections.abc.Iterable		)
             else (config.patch_size, config.patch_size)
         )

         lowerCamelCase__						=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         lowerCamelCase__						=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes[:-1]:
            lowerCamelCase__						=			True
            self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						(padded_height, padded_width)		)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowerCamelCase__						=			True
            self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						(padded_height, padded_width)		)





      @slow
      def 					_lowerCamelCase  (  self		)   ->					int:
         for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCamelCase__						=			FocalNetModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)





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

         lowerCamelCase__						=			_config_zero_init(SCREAMING_SNAKE_CASE__		)
         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(config=SCREAMING_SNAKE_CASE__		)
            for name, param in model.named_parameters():
               if "embeddings" not in name and param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item()    ,						[0.0, 1.0]    ,						msg=f'Parameter {name} of model {model_class} seems not properly initialized'    ,						)






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





      @cached_property
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # TODO update organization
         return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"		) if is_vision_available() else None





      @slow
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.default_image_processor

         lowerCamelCase__						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
         lowerCamelCase__						=			image_processor(images=SCREAMING_SNAKE_CASE__    ,						return_tensors="pt"		).to(SCREAMING_SNAKE_CASE__		)

         # forward pass
         with torch.no_grad():
            lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__		)

         # verify the logits
         lowerCamelCase__						=			torch.Size((1, 1000)		)
         self.assertEqual(outputs.logits.shape    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			torch.tensor([0.21_66, -0.43_68, 0.21_91]		).to(SCREAMING_SNAKE_CASE__		)
         self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-4		)		)
         self.assertTrue(outputs.logits.argmax(dim=-1		).item()    ,						281		)






@require_torch
class 		_A       (			__a				,       unittest.TestCase					):
      __a								=	(FocalNetBackbone,) if is_torch_available() else ()
      __a								=	FocalNetConfig

      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			FocalNetModelTester(self		)







"""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




