


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 (
       TFDeiTForImageClassification,
       TFDeiTForImageClassificationWithTeacher,
       TFDeiTForMaskedImageModeling,
       TFDeiTModel,
   )
   from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_3     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=3     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       Any=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=3_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=4     ,							SCREAMING_SNAKE_CASE__				:       Any=3_7     ,							SCREAMING_SNAKE_CASE__				:       Tuple="gelu"     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0.1     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0.1     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=1_0     ,							SCREAMING_SNAKE_CASE__				:       str=0.02     ,							SCREAMING_SNAKE_CASE__				:       str=3     ,							SCREAMING_SNAKE_CASE__				:       Any=None     ,							SCREAMING_SNAKE_CASE__				:       Tuple=2     ,							)		->			List[str]:
             a_					:		List[Any]							=  parent
             a_					:		int							=  batch_size
             a_					:		Optional[int]							=  image_size
             a_					:		Union[str, Any]							=  patch_size
             a_					:		Optional[int]							=  num_channels
             a_					:		Tuple							=  is_training
             a_					:		Dict							=  use_labels
             a_					:		List[str]							=  hidden_size
             a_					:		Dict							=  num_hidden_layers
             a_					:		List[Any]							=  num_attention_heads
             a_					:		int							=  intermediate_size
             a_					:		Optional[Any]							=  hidden_act
             a_					:		Tuple							=  hidden_dropout_prob
             a_					:		Optional[Any]							=  attention_probs_dropout_prob
             a_					:		Tuple							=  type_sequence_label_size
             a_					:		List[Any]							=  initializer_range
             a_					:		Dict							=  scope
             a_					:		Optional[Any]							=  encoder_stride

             # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
             a_					:		Optional[int]							=  (image_size // patch_size) ** 2
             a_					:		Dict							=  num_patches + 2
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             a_					:		Union[str, Any]							=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

             a_					:		Optional[Any]							=  None
             if self.use_labels:
                    a_					:		List[str]							=  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size     )

             a_					:		Any							=  self.get_config()

             return config, pixel_values, labels
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			List[Any]:
             return DeiTConfig(
                 image_size=self.image_size     ,							patch_size=self.patch_size     ,							num_channels=self.num_channels     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							is_decoder=SCREAMING_SNAKE_CASE__     ,							initializer_range=self.initializer_range     ,							encoder_stride=self.encoder_stride     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			List[Any]:
             a_					:		Optional[int]							=  TFDeiTModel(config=SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  model(SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			List[str]:
             a_					:		str							=  TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  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
             a_					:		Optional[Any]							=  1
             a_					:		str							=  TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
             a_					:		List[Any]							=  model(SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.reconstruction.shape     ,							(self.batch_size, 1, self.image_size, self.image_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Dict:
             a_					:		Dict							=  self.type_sequence_label_size
             a_					:		Optional[int]							=  TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  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
             a_					:		Any							=  1
             a_					:		List[Any]							=  TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__     )

             a_					:		List[str]							=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
             a_					:		Optional[int]							=  model(SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)     )

      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Union[str, Any]:
             a_					:		List[str]							=  self.prepare_config_and_inputs()
             a_							,		a_							,		a_					:		Tuple							=  config_and_inputs
             a_					:		Union[str, Any]							=  {'pixel_values': pixel_values}
             return config, inputs_dict
@require_tf
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__						,    unittest.TestCase			):
      snake_case__				:     Tuple        =						(
          (
              TFDeiTModel,
              TFDeiTForImageClassification,
              TFDeiTForImageClassificationWithTeacher,
              TFDeiTForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      snake_case__				:     str        =						(
          {
              '''feature-extraction''': TFDeiTModel,
              '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
          }
          if is_tf_available()
          else {}
      )

      snake_case__				:     List[Any]        =						False
      snake_case__				:     int        =						False
      snake_case__				:     Dict        =						False
      snake_case__				:     List[str]        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Union[str, Any]:
             a_					:		str							=  TFDeiTModelTester(self     )
             a_					:		List[str]							=  ConfigTester(self     ,							config_class=SCREAMING_SNAKE_CASE__     ,							has_text_modality=SCREAMING_SNAKE_CASE__     ,							hidden_size=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Any:
             self.config_tester.run_common_tests()
      @unittest.skip(reason='DeiT does not use inputs_embeds'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Any:
             pass
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			int:
             a_							,		a_					:		Optional[int]							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    a_					:		List[str]							=  model_class(SCREAMING_SNAKE_CASE__     )
                    self.assertIsInstance(model.get_input_embeddings()     ,							(tf.keras.layers.Layer)     )
                    a_					:		Optional[Any]							=  model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__     ,							tf.keras.layers.Dense     )     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[Any]:
             a_							,		a_					:		List[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    a_					:		Optional[int]							=  model_class(SCREAMING_SNAKE_CASE__     )
                    a_					:		Union[str, Any]							=  inspect.signature(model.call     )
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    a_					:		Any							=  [*signature.parameters.keys()]

                    a_					:		str							=  ['pixel_values']
                    self.assertListEqual(arg_names[:1]     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Any:
             a_					:		List[Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			List[Any]:
             a_					:		str							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             a_					:		Optional[int]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       int=False     )		->			List[str]:
             a_					:		Tuple							=  super()._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							return_labels=SCREAMING_SNAKE_CASE__     )

             if return_labels:
                    if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call     ).parameters:
                           del inputs_dict["labels"]

             return inputs_dict

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			int:
             for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_					:		int							=  TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )







def        SCREAMING_SNAKE_CASE_						(			)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image
@require_tf
@require_vision
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @cached_property
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			int:
             return (
                 DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'     )
                 if is_vision_available()
                 else None
             )

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Any:
             a_					:		Dict							=  TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'     )

             a_					:		List[Any]							=  self.default_image_processor
             a_					:		str							=  prepare_img()
             a_					:		Tuple							=  image_processor(images=SCREAMING_SNAKE_CASE__     ,							return_tensors='tf'     )

             # forward pass
             a_					:		List[Any]							=  model(**SCREAMING_SNAKE_CASE__     )

             # verify the logits
             a_					:		Tuple							=  tf.TensorShape((1, 1_0_0_0)     )
             self.assertEqual(outputs.logits.shape     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  tf.constant([-1.0266, 0.1912, -1.2861]     )

             self.assertTrue(np.allclose(outputs.logits[0, :3]     ,							SCREAMING_SNAKE_CASE__     ,							atol=1E-4     )     )






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


UpperCamelCase								=	None
UpperCamelCase								=	"""<""" 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
UpperCamelCase								=	[
    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   _lowerCamelCase :




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



       def __call__(      self		)->int:




         '''simple docstring'''





         return self.pa_type



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->dict:




         '''simple docstring'''





         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		):
           A_      :				Optional[int]		     =					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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None		)->"PIL.Image.Image":




         '''simple docstring'''





         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:
           A_      :				List[str]		     =					{}

         A_     ,       A_      :				str		     =					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		):
               A_      :				List[str]		     =					PIL.Image.open(_SCREAMING_SNAKE_CASE		)
             else:
               A_      :				List[str]		     =					path.split('''::'''		)[-1]
               try:
                 A_      :				int		     =					string_to_dict(_SCREAMING_SNAKE_CASE				,       config.HUB_DATASETS_URL		)['''repo_id''']
                 A_      :				Optional[int]		     =					token_per_repo_id.get(_SCREAMING_SNAKE_CASE		)
               except ValueError:
                 A_      :				Any		     =					None
               with xopen(_SCREAMING_SNAKE_CASE				,       '''rb'''				,       use_auth_token=_SCREAMING_SNAKE_CASE		) as f:
                 A_      :				Optional[Any]		     =					BytesIO(f.read()		)
               A_      :				Dict		     =					PIL.Image.open(bytes_		)
         else:
           A_      :				Optional[int]		     =					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"]]:




         '''simple docstring'''





         from .features import Value

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->pa.StructArray:




         '''simple docstring'''





         if pa.types.is_string(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           A_      :				List[Any]		     =					pa.StructArray.from_arrays([bytes_array, storage]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[str]		     =					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:
             A_      :				Tuple		     =					storage.field('''bytes'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           if storage.type.get_field_index('''path'''		) >= 0:
             A_      :				Optional[Any]		     =					storage.field('''path'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				Union[str, Any]		     =					pa.StructArray.from_arrays([bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
           A_      :				Optional[Any]		     =					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()				,       )
           A_      :				str		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[Any]		     =					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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->pa.StructArray:




         '''simple docstring'''





         @no_op_if_value_is_null
         def path_to_bytes(_SCREAMING_SNAKE_CASE		):
           with xopen(_SCREAMING_SNAKE_CASE				,       '''rb'''		) as f:
             A_      :				Any		     =					f.read()
           return bytes_

         A_      :				Dict		     =					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()				,       )
         A_      :				List[Any]		     =					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()				,       )
         A_      :				str		     =					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   _SCREAMING_SNAKE_CASE	( ):
  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()
    A_      :				Dict		     =					list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
  return _IMAGE_COMPRESSION_FORMATS


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Dict		     =					BytesIO()
  if image.format in list_image_compression_formats():
    A_      :				Tuple		     =					image.format
  else:
    A_      :				List[str]		     =					'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
  image.save(SCREAMING_SNAKE_CASE ,			format=SCREAMING_SNAKE_CASE       )
  return buffer.getvalue()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if hasattr(SCREAMING_SNAKE_CASE ,			'''filename'''       ) and image.filename != "":
    return {"path": image.filename, "bytes": None}
  else:
    return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE       )}


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  A_      :				Union[str, Any]		     =					array.dtype
  A_      :				Dict		     =					dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
  A_      :				Any		     =					dtype.kind
  A_      :				Any		     =					dtype.itemsize

  A_      :				Dict		     =					None

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







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  if objs:
    A_     ,       A_      :				Union[str, Any]		     =					first_non_null_value(SCREAMING_SNAKE_CASE       )
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
    if isinstance(SCREAMING_SNAKE_CASE ,			np.ndarray       ):
      A_      :				Tuple		     =					no_op_if_value_is_null(SCREAMING_SNAKE_CASE       )
      return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE       ) for obj in objs]
    elif isinstance(SCREAMING_SNAKE_CASE ,			PIL.Image.Image       ):
      A_      :				List[str]		     =					no_op_if_value_is_null(SCREAMING_SNAKE_CASE       )
      return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE       ) for obj in objs]
    else:
      return objs
  else:
    return objs




