



'''simple docstring'''




import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    AlbertForMaskedLM,
						    AlbertForMultipleChoice,
						    AlbertForPreTraining,
						    AlbertForQuestionAnswering,
						    AlbertForSequenceClassification,
						    AlbertForTokenClassification,
						    AlbertModel,
						)
						from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class       _lowerCAmelCase				:


	def __init__(self			, lowercase			, lowercase=13			, lowercase=7			, lowercase=True			, lowercase=True			, lowercase=True			, lowercase=True			, lowercase=99			, lowercase=16			, lowercase=36			, lowercase=6			, lowercase=6			, lowercase=6			, lowercase=37			, lowercase="gelu"			, lowercase=0.1			, lowercase=0.1			, lowercase=512			, lowercase=16			, lowercase=2			, lowercase=0.02			, lowercase=3			, lowercase=4			, lowercase=None			, ):
				A_      :		str      =	parent
				A_      :		Union[str, Any]      =	batch_size
				A_      :		str      =	seq_length
				A_      :		Optional[int]      =	is_training
				A_      :		Union[str, Any]      =	use_input_mask
				A_      :		Dict      =	use_token_type_ids
				A_      :		Tuple      =	use_labels
				A_      :		Any      =	vocab_size
				A_      :		Any      =	embedding_size
				A_      :		List[Any]      =	hidden_size
				A_      :		int      =	num_hidden_layers
				A_      :		List[str]      =	num_hidden_groups
				A_      :		List[str]      =	num_attention_heads
				A_      :		Tuple      =	intermediate_size
				A_      :		List[str]      =	hidden_act
				A_      :		Optional[Any]      =	hidden_dropout_prob
				A_      :		Any      =	attention_probs_dropout_prob
				A_      :		Dict      =	max_position_embeddings
				A_      :		Optional[int]      =	type_vocab_size
				A_      :		Optional[Any]      =	type_sequence_label_size
				A_      :		List[str]      =	initializer_range
				A_      :		Union[str, Any]      =	num_labels
				A_      :		Tuple      =	num_choices
				A_      :		Tuple      =	scope


	def        _a      (self				):
				A_      :		Dict      =	ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size				)

				A_      :		int      =	None
				if self.use_input_mask:
							A_      :		Dict      =	random_attention_mask([self.batch_size, self.seq_length]				)

				A_      :		str      =	None
				if self.use_token_type_ids:
							A_      :		Dict      =	ids_tensor([self.batch_size, self.seq_length]			, self.type_vocab_size				)

				A_      :		List[Any]      =	None
				A_      :		Tuple      =	None
				A_      :		List[Any]      =	None
				if self.use_labels:
							A_      :		str      =	ids_tensor([self.batch_size]			, self.type_sequence_label_size				)
							A_      :		Optional[int]      =	ids_tensor([self.batch_size, self.seq_length]			, self.num_labels				)
							A_      :		Any      =	ids_tensor([self.batch_size]			, self.num_choices				)

				A_      :		List[Any]      =	self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


	def        _a      (self				):
				return AlbertConfig(
				    vocab_size=self.vocab_size			, 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			, max_position_embeddings=self.max_position_embeddings			, type_vocab_size=self.type_vocab_size			, initializer_range=self.initializer_range			, num_hidden_groups=self.num_hidden_groups			, )


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):
				A_      :		Tuple      =	AlbertModel(config=lowerCAmelCase__				)
				model.to(lowerCAmelCase__				)
				model.eval()
				A_      :		List[str]      =	model(lowerCAmelCase__			, attention_mask=lowerCAmelCase__			, token_type_ids=lowerCAmelCase__				)
				A_      :		int      =	model(lowerCAmelCase__			, token_type_ids=lowerCAmelCase__				)
				A_      :		Optional[int]      =	model(lowerCAmelCase__				)
				self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)				)
				self.parent.assertEqual(result.pooler_output.shape			, (self.batch_size, self.hidden_size)				)


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):
				A_      :		Dict      =	AlbertForPreTraining(config=lowerCAmelCase__				)
				model.to(lowerCAmelCase__				)
				model.eval()
				A_      :		List[str]      =	model(
				    lowerCAmelCase__			, attention_mask=lowerCAmelCase__			, token_type_ids=lowerCAmelCase__			, labels=lowerCAmelCase__			, sentence_order_label=lowerCAmelCase__			, )
				self.parent.assertEqual(result.prediction_logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)				)
				self.parent.assertEqual(result.sop_logits.shape			, (self.batch_size, config.num_labels)				)


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):
				A_      :		List[Any]      =	AlbertForMaskedLM(config=lowerCAmelCase__				)
				model.to(lowerCAmelCase__				)
				model.eval()
				A_      :		Tuple      =	model(lowerCAmelCase__			, attention_mask=lowerCAmelCase__			, token_type_ids=lowerCAmelCase__			, labels=lowerCAmelCase__				)
				self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)				)


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):
				A_      :		Dict      =	AlbertForQuestionAnswering(config=lowerCAmelCase__				)
				model.to(lowerCAmelCase__				)
				model.eval()
				A_      :		List[str]      =	model(
				    lowerCAmelCase__			, attention_mask=lowerCAmelCase__			, token_type_ids=lowerCAmelCase__			, start_positions=lowerCAmelCase__			, end_positions=lowerCAmelCase__			, )
				self.parent.assertEqual(result.start_logits.shape			, (self.batch_size, self.seq_length)				)
				self.parent.assertEqual(result.end_logits.shape			, (self.batch_size, self.seq_length)				)


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):
				A_      :		Union[str, Any]      =	self.num_labels
				A_      :		Optional[int]      =	AlbertForSequenceClassification(lowerCAmelCase__				)
				model.to(lowerCAmelCase__				)
				model.eval()
				A_      :		int      =	model(lowerCAmelCase__			, attention_mask=lowerCAmelCase__			, token_type_ids=lowerCAmelCase__			, labels=lowerCAmelCase__				)
				self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)				)


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):
				A_      :		Optional[int]      =	self.num_labels
				A_      :		Tuple      =	AlbertForTokenClassification(config=lowerCAmelCase__				)
				model.to(lowerCAmelCase__				)
				model.eval()
				A_      :		Optional[Any]      =	model(lowerCAmelCase__			, attention_mask=lowerCAmelCase__			, token_type_ids=lowerCAmelCase__			, labels=lowerCAmelCase__				)
				self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.num_labels)				)


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):
				A_      :		Any      =	self.num_choices
				A_      :		Any      =	AlbertForMultipleChoice(config=lowerCAmelCase__				)
				model.to(lowerCAmelCase__				)
				model.eval()
				A_      :		Tuple      =	input_ids.unsqueeze(1				).expand(-1			, self.num_choices			, -1				).contiguous()
				A_      :		List[str]      =	token_type_ids.unsqueeze(1				).expand(-1			, self.num_choices			, -1				).contiguous()
				A_      :		List[str]      =	input_mask.unsqueeze(1				).expand(-1			, self.num_choices			, -1				).contiguous()
				A_      :		Any      =	model(
				    lowerCAmelCase__			, attention_mask=lowerCAmelCase__			, token_type_ids=lowerCAmelCase__			, labels=lowerCAmelCase__			, )
				self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_choices)				)


	def        _a      (self				):
				A_      :		List[Any]      =	self.prepare_config_and_inputs()
				(
				    (
				    A_							
				),       (
				    A_							
				),       (
				    A_							
				),       (
				    A_							
				),       (
				    A_							
				),       (
				    A_							
				),       (
				    A_							
				),       							
				)      :		Tuple      =	config_and_inputs
				A_      :		str      =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
				return config, inputs_dict







@require_torch
class       _lowerCAmelCase				(     __UpperCAmelCase ,     __UpperCAmelCase ,     unittest.TestCase					):
	__SCREAMING_SNAKE_CASE  :						Union[str, Any]						     =   (
	    (
	        AlbertModel,
	        AlbertForPreTraining,
	        AlbertForMaskedLM,
	        AlbertForMultipleChoice,
	        AlbertForSequenceClassification,
	        AlbertForTokenClassification,
	        AlbertForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE  :						List[Any]						     =   (
	    {
	        'feature-extraction': AlbertModel,
	        'fill-mask': AlbertForMaskedLM,
	        'question-answering': AlbertForQuestionAnswering,
	        'text-classification': AlbertForSequenceClassification,
	        'token-classification': AlbertForTokenClassification,
	        'zero-shot': AlbertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE  :						List[Any]						     =   True


	def        _a      (self			, lowercase			, lowercase			, lowercase=False				):
				A_      :		Any      =	super()._prepare_for_class(lowerCAmelCase__			, lowerCAmelCase__			, return_labels=lowerCAmelCase__				)

				if return_labels:
							if model_class in get_values(lowerCAmelCase__				):
										A_      :		List[str]      =	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.seq_length)			, dtype=torch.long			, device=lowerCAmelCase__				)
										A_      :		List[Any]      =	torch.zeros(
										    self.model_tester.batch_size			, dtype=torch.long			, device=lowerCAmelCase__				)
				return inputs_dict


	def        _a      (self				):
				A_      :		Tuple      =	AlbertModelTester(self				)
				A_      :		str      =	ConfigTester(self			, config_class=lowerCAmelCase__			, hidden_size=37				)


	def        _a      (self				):
				self.config_tester.run_common_tests()


	def        _a      (self				):
				A_      :		List[str]      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase__				)


	def        _a      (self				):
				A_      :		Union[str, Any]      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__				)


	def        _a      (self				):
				A_      :		Union[str, Any]      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__				)


	def        _a      (self				):
				A_      :		List[Any]      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__				)


	def        _a      (self				):
				A_      :		Any      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__				)


	def        _a      (self				):
				A_      :		Tuple      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__				)


	def        _a      (self				):
				A_      :		Dict      =	self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							A_      :		Any      =	type
							self.model_tester.create_and_check_model(*lowerCAmelCase__				)


	@slow
	def        _a      (self				):




				for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_      :		Any      =	AlbertModel.from_pretrained(lowerCAmelCase__				)
							self.assertIsNotNone(lowerCAmelCase__				)







@require_torch
class       _lowerCAmelCase				(     unittest.TestCase					):


	@slow
	def        _a      (self				):
				A_      :		Union[str, Any]      =	AlbertModel.from_pretrained("""albert-base-v2"""				)
				A_      :		Optional[int]      =	torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
				A_      :		Union[str, Any]      =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
				with torch.no_grad():
							A_      :		int      =	model(lowerCAmelCase__			, attention_mask=lowerCAmelCase__				)[0]
				A_      :		Union[str, Any]      =	torch.Size((1, 11, 768)				)
				self.assertEqual(output.shape			, lowerCAmelCase__				)
				A_      :		List[str]      =	torch.tensor(
				    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]				)

				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			, lowerCAmelCase__			, 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__		:     Optional[List[str]]			=							None
lowercase__		:     List[Any]			=							'<' 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__		:     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 							__lowerCAmelCase	:






       """simple docstring"""
       _snake_case      :	bool									=       True
       _snake_case      :	Optional[str]									=       None
       # Automatically constructed
       _snake_case      :	ClassVar[str]									=       "PIL.Image.Image"
       _snake_case      :	ClassVar[Any]									=       pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
       _snake_case      :	str									=       field(default='Image'		,		init=__magic_name__		,		repr=__magic_name__					)





       def __call__(				self  :						Any	)       ->	str:

            '''simple docstring'''




            return self.pa_type





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]	)       ->	dict:

            '''simple docstring'''




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

            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			np.array(lowerCAmelCase__	)

            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 return {"path": value, "bytes": None}
            elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 return {"path": None, "bytes": value}
            elif isinstance(lowerCAmelCase__			,					np.ndarray	):
                 # convert the image array to PNG/TIFF bytes
                 return encode_np_array(lowerCAmelCase__	)
            elif isinstance(lowerCAmelCase__			,					PIL.Image.Image	):
                 # convert the PIL image to bytes (default format is PNG/TIFF)
                 return encode_pil_image(lowerCAmelCase__	)
            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  :						Tuple			,					lowerCAmelCase__  :						dict			,					lowerCAmelCase__  :						Dict=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:
                 _UpperCamelCase							       =			{}

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





       def snake_case__		(				self  :						List[str]	)       ->	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  :						int			,					lowerCAmelCase__  :						Union[pa.StringArray, pa.StructArray, pa.ListArray]	)       ->	pa.StructArray:

            '''simple docstring'''




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







       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						pa.StructArray	)       ->	pa.StructArray:

            '''simple docstring'''




            @no_op_if_value_is_null
            def path_to_bytes(lowerCAmelCase__  :						Optional[Any]	):
                 with xopen(lowerCAmelCase__			,					'''rb'''	) as f:
                      _UpperCamelCase							       =			f.read()
                 return bytes_

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







def    a__							(							)						->						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()
          _UpperCamelCase							       =			list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
     return _IMAGE_COMPRESSION_FORMATS







def    a__							(							lowercase   :	"PIL.Image.Image" )						->						bytes:





     """simple docstring"""

     _UpperCamelCase							       =			BytesIO()
     if image.format in list_image_compression_formats():
          _UpperCamelCase							       =			image.format
     else:
          _UpperCamelCase							       =			'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
     image.save(lowercase,   format=lowercase )
     return buffer.getvalue()







def    a__							(							lowercase   :	"PIL.Image.Image" )						->						dict:





     """simple docstring"""

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







def    a__							(							lowercase   :	np.ndarray )						->						dict:





     """simple docstring"""

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

     _UpperCamelCase							       =			array.dtype
     _UpperCamelCase							       =			dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
     _UpperCamelCase							       =			dtype.kind
     _UpperCamelCase							       =			dtype.itemsize

     _UpperCamelCase							       =			None

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







def    a__							(							lowercase   :	Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] )						->						List[dict]:





     """simple docstring"""

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

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



