





'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
      from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor


class 					a_     :

    '''simple docstring'''


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


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

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

        _SCREAMING_SNAKE_CASE									=			self.get_config()

        return config, pixel_values, labels


    def 			snake_case_(		self       )		->   str:
        return SwinvaConfig(
            image_size=self.image_size ,		patch_size=self.patch_size ,		num_channels=self.num_channels ,		embed_dim=self.embed_dim ,		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 ,		)


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			SwinvaModel(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A       )

        _SCREAMING_SNAKE_CASE									=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A ,		A ,		A       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			SwinvaForMaskedImageModeling(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(
            result.logits.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

        # test greyscale images
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			SwinvaForMaskedImageModeling(A       )
        model.to(A       )
        model.eval()

        _SCREAMING_SNAKE_CASE									=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, 1, self.image_size, self.image_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.type_sequence_label_size
        _SCREAMING_SNAKE_CASE									=			SwinvaForImageClassification(A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)       )





    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			config_and_inputs
        _SCREAMING_SNAKE_CASE									=			{"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
class 					a_     (				snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (
        (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
    )
    UpperCamelCase     = (
        {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			SwinvaModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		embed_dim=37       )


    def 			snake_case_(		self       )		->   Optional[int]:
        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 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A       )


    @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""       )
    def 			snake_case_(		self       )		->   Optional[Any]:
        pass


    @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""       )
    def 			snake_case_(		self       )		->   int:
        pass


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

        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            self.assertIsInstance(model.get_input_embeddings() ,		(nn.Module)       )
            _SCREAMING_SNAKE_CASE									=			model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(A ,		nn.Linear       )       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

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

            _SCREAMING_SNAKE_CASE									=			["""pixel_values"""]
            self.assertListEqual(arg_names[:1] ,		A       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()
        _SCREAMING_SNAKE_CASE									=			True

        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			True
            _SCREAMING_SNAKE_CASE									=			False
            _SCREAMING_SNAKE_CASE									=			True
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            model.to(A       )
            model.eval()
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE									=			model(**self._prepare_for_class(A ,		A       )       )
            _SCREAMING_SNAKE_CASE									=			outputs.attentions
            _SCREAMING_SNAKE_CASE									=			len(self.model_tester.depths       )
            self.assertEqual(len(A       ) ,		A       )

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            _SCREAMING_SNAKE_CASE									=			True
            _SCREAMING_SNAKE_CASE									=			config.window_size**2
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            model.to(A       )
            model.eval()
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE									=			model(**self._prepare_for_class(A ,		A       )       )
            _SCREAMING_SNAKE_CASE									=			outputs.attentions
            self.assertEqual(len(A       ) ,		A       )

            self.assertListEqual(
                list(attentions[0].shape[-3:]       ) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)
            _SCREAMING_SNAKE_CASE									=			len(A       )

            # Check attention is always last and order is fine
            _SCREAMING_SNAKE_CASE									=			True
            _SCREAMING_SNAKE_CASE									=			True
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            model.to(A       )
            model.eval()
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE									=			model(**self._prepare_for_class(A ,		A       )       )

            if hasattr(self.model_tester ,		"""num_hidden_states_types"""       ):
                _SCREAMING_SNAKE_CASE									=			self.model_tester.num_hidden_states_types
            else:
                # also another +1 for reshaped_hidden_states
                _SCREAMING_SNAKE_CASE									=			2
            self.assertEqual(out_len + added_hidden_states ,		len(A       )       )

            _SCREAMING_SNAKE_CASE									=			outputs.attentions

            self.assertEqual(len(A       ) ,		A       )

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]       ) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)


    def 			snake_case_(		self ,		A ,		A ,		A ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			model_class(A       )
        model.to(A       )
        model.eval()

        with torch.no_grad():
            _SCREAMING_SNAKE_CASE									=			model(**self._prepare_for_class(A ,		A       )       )

        _SCREAMING_SNAKE_CASE									=			outputs.hidden_states

        _SCREAMING_SNAKE_CASE									=			getattr(
            self.model_tester ,		"""expected_num_hidden_layers""" ,		len(self.model_tester.depths       ) + 1       )
        self.assertEqual(len(A       ) ,		A       )

        # Swinv2 has a different seq_length
        _SCREAMING_SNAKE_CASE									=			(
            config.patch_size
            if isinstance(config.patch_size ,		collections.abc.Iterable       )
            else (config.patch_size, config.patch_size)
        )

        _SCREAMING_SNAKE_CASE									=			(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] ,		)

        _SCREAMING_SNAKE_CASE									=			outputs.reshaped_hidden_states
        self.assertEqual(len(A       ) ,		A       )

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


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

        _SCREAMING_SNAKE_CASE									=			(
            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:
            _SCREAMING_SNAKE_CASE									=			True
            self.check_hidden_states_output(A ,		A ,		A ,		A       )

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

            self.check_hidden_states_output(A ,		A ,		A ,		A       )


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

        _SCREAMING_SNAKE_CASE									=			(
            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)
        )
        _SCREAMING_SNAKE_CASE									=			(
            config.patch_size
            if isinstance(config.patch_size ,		collections.abc.Iterable       )
            else (config.patch_size, config.patch_size)
        )

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

        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			True
            self.check_hidden_states_output(A ,		A ,		A ,		(padded_height, padded_width)       )

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


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*A       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A       )


    @slow
    def 			snake_case_(		self       )		->   List[str]:
        for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE									=			SwinvaModel.from_pretrained(A       )
            self.assertIsNotNone(A       )





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

        _SCREAMING_SNAKE_CASE									=			_config_zero_init(A       )
        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(config=A       )
            for name, param in model.named_parameters():
                if "embeddings" not in name and "logit_scale" 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       ):

    '''simple docstring'''


    @cached_property
    def 			snake_case_(		self       )		->   Optional[Any]:
        return (
            AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       )
            if is_vision_available()
            else None
        )





    @slow
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       ).to(
            A       )
        _SCREAMING_SNAKE_CASE									=			self.default_image_processor

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

        # forward pass
        with torch.no_grad():
            _SCREAMING_SNAKE_CASE									=			model(**A       )

        # verify the logits
        _SCREAMING_SNAKE_CASE									=			torch.Size((1, 1000)       )
        self.assertEqual(outputs.logits.shape ,		A       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor([-0.3947, -0.4306, 0.0026]       ).to(A       )
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,		A ,		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


lowercase_					= None
lowercase_					= """<""" 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
lowercase_					= [
    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_     :

    '''simple docstring'''
    UpperCamelCase     = True
    UpperCamelCase     = None
    # Automatically constructed
    UpperCamelCase     = "PIL.Image.Image"
    UpperCamelCase     = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}       )
    UpperCamelCase     = field(default='''Image'''      ,    init=snake_case_      ,    repr=snake_case_       )


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


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

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			np.array(A       )

        if isinstance(A ,		A       ):
            return {"path": value, "bytes": None}
        elif isinstance(A ,		A       ):
            return {"path": None, "bytes": value}
        elif isinstance(A ,		np.ndarray       ):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(A       )
        elif isinstance(A ,		PIL.Image.Image       ):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(A       )
        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 			snake_case_(		self ,		A ,		A=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:
            _SCREAMING_SNAKE_CASE									=			{}

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			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(A       ):
                    _SCREAMING_SNAKE_CASE									=			PIL.Image.open(A       )
                else:
                    _SCREAMING_SNAKE_CASE									=			path.split("""::"""       )[-1]
                    try:
                        _SCREAMING_SNAKE_CASE									=			string_to_dict(A ,		config.HUB_DATASETS_URL       )["""repo_id"""]
                        _SCREAMING_SNAKE_CASE									=			token_per_repo_id.get(A       )
                    except ValueError:
                        _SCREAMING_SNAKE_CASE									=			None
                    with xopen(A ,		"""rb""" ,		use_auth_token=A       ) as f:
                        _SCREAMING_SNAKE_CASE									=			BytesIO(f.read()       )
                    _SCREAMING_SNAKE_CASE									=			PIL.Image.open(bytes_       )
        else:
            _SCREAMING_SNAKE_CASE									=			PIL.Image.open(BytesIO(bytes_       )       )
        image.load()  # to avoid "Too many open files" errors
        return image


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

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


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





    def 			snake_case_(		self ,		A       )		->   pa.StructArray:

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

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





def lowerCamelCase						(     )						->List[str]:
    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()
        _SCREAMING_SNAKE_CASE									=			list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
    return _IMAGE_COMPRESSION_FORMATS





def lowerCamelCase						(     __lowerCamelCase :    "PIL.Image.Image"    )						->bytes:
    _SCREAMING_SNAKE_CASE									=			BytesIO()
    if image.format in list_image_compression_formats():
        _SCREAMING_SNAKE_CASE									=			image.format
    else:
        _SCREAMING_SNAKE_CASE									=			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
    image.save(__lowerCamelCase					,       format=__lowerCamelCase    )
    return buffer.getvalue()





def lowerCamelCase						(     __lowerCamelCase :    "PIL.Image.Image"    )						->dict:
    if hasattr(__lowerCamelCase					,       """filename"""    ) and image.filename != "":
        return {"path": image.filename, "bytes": None}
    else:
        return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}





def lowerCamelCase						(     __lowerCamelCase :    np.ndarray    )						->dict:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("""To support encoding images, please install 'Pillow'."""    )

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

    _SCREAMING_SNAKE_CASE									=			None

    # Multi-channel array case (only np.dtype("|u1") is allowed)
    if array.shape[2:]:
        _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			dtype_byteorder + dtype_kind + str(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			np.dtype(__lowerCamelCase    )
            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}'    )
    _SCREAMING_SNAKE_CASE									=			PIL.Image.fromarray(array.astype(__lowerCamelCase    )    )
    return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}






def lowerCamelCase						(     __lowerCamelCase :    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    )						->List[dict]:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("""To support encoding images, please install 'Pillow'."""    )

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



