





from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TFLEDForConditionalGeneration, TFLEDModel


@require_tf
class     __magic_name__     :





     """simple docstring"""



     __UpperCamelCase    			=   LEDConfig
     __UpperCamelCase    			=   {}
     __UpperCamelCase    			=   "gelu"



     def __init__(							self				:str	,					snake_case				:int	,					snake_case				:Union[str, Any]=13	,					snake_case				:Dict=7	,					snake_case				:List[str]=True	,					snake_case				:Dict=False	,					snake_case				:List[Any]=99	,					snake_case				:List[Any]=32	,					snake_case				:Optional[int]=2	,					snake_case				:Tuple=4	,					snake_case				:str=37	,					snake_case				:Union[str, Any]=0.1	,					snake_case				:Any=0.1	,					snake_case				:Tuple=20	,					snake_case				:Tuple=2	,					snake_case				:Optional[int]=1	,					snake_case				:str=0	,					snake_case				:Tuple=4	,					):





        '''simple docstring'''




        A_    : Optional[Any]								=	parent
        A_    : Union[str, Any]								=	batch_size
        A_    : Optional[Any]								=	seq_length
        A_    : List[Any]								=	is_training
        A_    : Union[str, Any]								=	use_labels
        A_    : Optional[int]								=	vocab_size
        A_    : str								=	hidden_size
        A_    : List[str]								=	num_hidden_layers
        A_    : Any								=	num_attention_heads
        A_    : List[str]								=	intermediate_size

        A_    : List[str]								=	hidden_dropout_prob
        A_    : List[Any]								=	attention_probs_dropout_prob
        A_    : List[Any]								=	max_position_embeddings
        A_    : Any								=	eos_token_id
        A_    : Tuple								=	pad_token_id
        A_    : Union[str, Any]								=	bos_token_id
        A_    : Dict								=	attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        A_    : int								=	self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        A_    : Optional[int]								=	(
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]     ):





        '''simple docstring'''




        A_    : str								=	ids_tensor([self.batch_size, self.seq_length - 1]	,					self.vocab_size     )
        A_    : Optional[Any]								=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )	,					1     )
        A_    : Any								=	tf.concat([input_ids, eos_tensor]	,					axis=1     )

        A_    : List[Any]								=	ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size     )

        A_    : Dict								=	self.config_cls(
            vocab_size=self.vocab_size	,					d_model=self.hidden_size	,					encoder_layers=self.num_hidden_layers	,					decoder_layers=self.num_hidden_layers	,					encoder_attention_heads=self.num_attention_heads	,					decoder_attention_heads=self.num_attention_heads	,					encoder_ffn_dim=self.intermediate_size	,					decoder_ffn_dim=self.intermediate_size	,					dropout=self.hidden_dropout_prob	,					attention_dropout=self.attention_probs_dropout_prob	,					max_position_embeddings=self.max_position_embeddings	,					eos_token_ids=[2]	,					bos_token_id=self.bos_token_id	,					pad_token_id=self.pad_token_id	,					decoder_start_token_id=self.pad_token_id	,					attention_window=self.attention_window	,					**self.config_updates	,					)
        A_    : Any								=	prepare_led_inputs_dict(SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_     )
        A_    : Union[str, Any]								=	tf.concat(
            [tf.zeros_like(SCREAMING_SNAKE_CASE_     )[:, :-1], tf.ones_like(SCREAMING_SNAKE_CASE_     )[:, -1:]]	,					axis=-1	,					)
        A_    : Optional[Any]								=	global_attention_mask
        return config, inputs_dict





     def   SCREAMING_SNAKE_CASE						(							self				:int	,					snake_case				:List[str]	,					snake_case				:Optional[int]     ):





        '''simple docstring'''




        A_    : str								=	TFLEDModel(config=SCREAMING_SNAKE_CASE_     ).get_decoder()
        A_    : Any								=	inputs_dict['input_ids']

        A_    : List[Any]								=	input_ids[:1, :]
        A_    : Any								=	inputs_dict['attention_mask'][:1, :]
        A_    : str								=	1

        # first forward pass
        A_    : Union[str, Any]								=	model(SCREAMING_SNAKE_CASE_	,					attention_mask=SCREAMING_SNAKE_CASE_	,					use_cache=SCREAMING_SNAKE_CASE_     )

        A_    : List[str]								=	outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        A_    : List[str]								=	ids_tensor((self.batch_size, 3)	,					config.vocab_size     )
        A_    : Union[str, Any]								=	tf.cast(ids_tensor((self.batch_size, 3)	,					2     )	,					tf.inta     )

        # append to next input_ids and
        A_    : Tuple								=	tf.concat([input_ids, next_tokens]	,					axis=-1     )
        A_    : str								=	tf.concat([attention_mask, next_attn_mask]	,					axis=-1     )

        A_    : str								=	model(SCREAMING_SNAKE_CASE_	,					attention_mask=SCREAMING_SNAKE_CASE_     )[0]
        A_    : List[str]								=	model(SCREAMING_SNAKE_CASE_	,					attention_mask=SCREAMING_SNAKE_CASE_	,					past_key_values=SCREAMING_SNAKE_CASE_     )[0]

        self.parent.assertEqual(next_tokens.shape[1]	,					output_from_past.shape[1]     )

        # select random slice
        A_    : Dict								=	int(ids_tensor((1,)	,					output_from_past.shape[-1]     )     )
        A_    : Optional[int]								=	output_from_no_past[:, -3:, random_slice_idx]
        A_    : Union[str, Any]								=	output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					rtol=1e-3     )




def    __snake_case  (    _lowerCAmelCase       :       int , _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       int , _lowerCAmelCase       :       Union[str, Any]=None , _lowerCAmelCase       :       Optional[Any]=None , _lowerCAmelCase       :       Union[str, Any]=None , _lowerCAmelCase       :       Tuple=None , )       ->  Dict:
   if attention_mask is None:
      A_    : Union[str, Any]								=	tf.cast(tf.math.not_equal(snake_case__ , config.pad_token_id		) , tf.inta		)
   if decoder_attention_mask is None:
      A_    : str								=	tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta		),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id		) , tf.inta		),
          ] , axis=-1 , )
   if head_mask is None:
      A_    : int								=	tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
   if decoder_head_mask is None:
      A_    : Dict								=	tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
       "decoder_input_ids": decoder_input_ids,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
   }


@require_tf
class     __magic_name__     (							a__			, a__			, unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase    			=   (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
     __UpperCamelCase    			=   (TFLEDForConditionalGeneration,) if is_tf_available() else ()
     __UpperCamelCase    			=   (
         {
             "conversational": TFLEDForConditionalGeneration,
             "feature-extraction": TFLEDModel,
             "summarization": TFLEDForConditionalGeneration,
             "text2text-generation": TFLEDForConditionalGeneration,
             "translation": TFLEDForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __UpperCamelCase    			=   True
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   False



     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        A_    : str								=	TFLEDModelTester(self     )
        A_    : Dict								=	ConfigTester(self	,					config_class=SCREAMING_SNAKE_CASE_     )



     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        self.config_tester.run_common_tests()



     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        A_    : str								=	self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_     )



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




        A_    : Any								=	self.model_tester.prepare_config_and_inputs_for_common()
        A_    : List[str]								=	tf.zeros_like(inputs_dict["attention_mask"]     )
        A_    : List[str]								=	2
        A_    : List[Any]								=	tf.where(
            tf.range(self.model_tester.seq_length     )[None, :] < num_global_attn_indices	,					1	,					inputs_dict["global_attention_mask"]	,					)

        A_    : List[str]								=	True
        A_    : List[str]								=	self.model_tester.seq_length
        A_    : int								=	self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(snake_case				:Union[str, Any]     ):
           A_    : Optional[Any]								=	outputs.decoder_attentions
           self.assertEqual(len(SCREAMING_SNAKE_CASE_     )	,					self.model_tester.num_hidden_layers     )
           self.assertListEqual(
               list(decoder_attentions[0].shape[-3:]     )	,					[self.model_tester.num_attention_heads, seq_length, seq_length]	,					)

        def check_encoder_attentions_output(snake_case				:Optional[int]     ):
           A_    : str								=	[t.numpy() for t in outputs.encoder_attentions]
           A_    : Dict								=	[t.numpy() for t in outputs.encoder_global_attentions]
           self.assertEqual(len(SCREAMING_SNAKE_CASE_     )	,					self.model_tester.num_hidden_layers     )
           self.assertEqual(len(SCREAMING_SNAKE_CASE_     )	,					self.model_tester.num_hidden_layers     )
           self.assertListEqual(
               list(attentions[0].shape[-3:]     )	,					[self.model_tester.num_attention_heads, seq_length, seq_length]	,					)
           self.assertListEqual(
               list(global_attentions[0].shape[-3:]     )	,					[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]	,					)

        for model_class in self.all_model_classes:
           A_    : List[str]								=	True
           A_    : Dict								=	False
           A_    : Optional[int]								=	False
           A_    : str								=	model_class(SCREAMING_SNAKE_CASE_     )
           A_    : List[str]								=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_     )     )
           A_    : Any								=	len(SCREAMING_SNAKE_CASE_     )
           self.assertEqual(config.output_hidden_states	,					SCREAMING_SNAKE_CASE_     )
           check_encoder_attentions_output(SCREAMING_SNAKE_CASE_     )

           if self.is_encoder_decoder:
              A_    : Optional[int]								=	model_class(SCREAMING_SNAKE_CASE_     )
              A_    : Any								=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_     )     )
              self.assertEqual(config.output_hidden_states	,					SCREAMING_SNAKE_CASE_     )
              check_decoder_attentions_output(SCREAMING_SNAKE_CASE_     )

           # Check that output attentions can also be changed via the config
           del inputs_dict["output_attentions"]
           A_    : Tuple								=	True
           A_    : Optional[Any]								=	model_class(SCREAMING_SNAKE_CASE_     )
           A_    : Optional[Any]								=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_     )     )
           self.assertEqual(config.output_hidden_states	,					SCREAMING_SNAKE_CASE_     )
           check_encoder_attentions_output(SCREAMING_SNAKE_CASE_     )

           # Check attention is always last and order is fine
           A_    : List[Any]								=	True
           A_    : List[Any]								=	True
           A_    : Tuple								=	model_class(SCREAMING_SNAKE_CASE_     )
           A_    : int								=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_     )     )

           self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)	,					len(SCREAMING_SNAKE_CASE_     )     )
           self.assertEqual(model.config.output_hidden_states	,					SCREAMING_SNAKE_CASE_     )
           check_encoder_attentions_output(SCREAMING_SNAKE_CASE_     )



     @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."     )
     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




        pass





     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        pass




def    __snake_case  (    _lowerCAmelCase       :       Optional[Any]		)       ->  Optional[int]:
   return tf.constant(snake_case__ , dtype=tf.intaa		)


_lowerCAmelCase							:					Optional[Any]		    =		1e-4





@slow
@require_tf
class     __magic_name__     (							unittest.TestCase							):





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : Optional[int]								=	TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"     ).led

        # change to intended input here
        A_    : List[Any]								=	_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]     )
        A_    : Optional[Any]								=	_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]     )
        A_    : str								=	prepare_led_inputs_dict(model.config	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_     )
        A_    : Any								=	model(**SCREAMING_SNAKE_CASE_     )[0]
        A_    : int								=	(1, 1_024, 768)
        self.assertEqual(output.shape	,					SCREAMING_SNAKE_CASE_     )
        # change to expected output here
        A_    : Tuple								=	tf.convert_to_tensor(
            [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]	,					)
        tf.debugging.assert_near(output[:, :3, :3]	,					SCREAMING_SNAKE_CASE_	,					atol=1e-3     )





     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : Tuple								=	TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"     )

        # change to intended input here
        A_    : int								=	_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]     )
        A_    : Dict								=	_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]     )
        A_    : Optional[int]								=	prepare_led_inputs_dict(model.config	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_     )
        A_    : Optional[Any]								=	model(**SCREAMING_SNAKE_CASE_     )[0]
        A_    : Any								=	(1, 1_024, model.config.vocab_size)
        self.assertEqual(output.shape	,					SCREAMING_SNAKE_CASE_     )
        # change to expected output here
        A_    : Optional[Any]								=	tf.convert_to_tensor(
            [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]	,					)
        tf.debugging.assert_near(output[:, :3, :3]	,					SCREAMING_SNAKE_CASE_	,					atol=1e-3	,					rtol=1e-3     )





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_		:
       UpperCAmelCase__				:					bool        =  True
       UpperCAmelCase__				:					Optional[str]        =  None
       # Automatically constructed
       UpperCAmelCase__				:					ClassVar[str]        =  "PIL.Image.Image"
       UpperCAmelCase__				:					ClassVar[Any]        =  pa.struct({"bytes": pa.binary(), "path": pa.string()}							)
       UpperCAmelCase__				:					str        =  field(default="Image"       ,    init=a__       ,    repr=a__							)
       def __call__(			self       )     ->		Any:
          return self.pa_type
       def 					snake_case_       (			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_       ):
             UpperCamelCase    :  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":
          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    :  Any							=	{}

          UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	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_       ):
                   UpperCamelCase    :  int							=	PIL.Image.open(SCREAMING_SNAKE_CASE_       )
                else:
                   UpperCamelCase    :  int							=	path.split('::'       )[-1]
                   try:
                      UpperCamelCase    :  Optional[Any]							=	string_to_dict(SCREAMING_SNAKE_CASE_,			config.HUB_DATASETS_URL       )['repo_id']
                      UpperCamelCase    :  str							=	token_per_repo_id.get(SCREAMING_SNAKE_CASE_       )
                   except ValueError:
                      UpperCamelCase    :  Tuple							=	None
                   with xopen(SCREAMING_SNAKE_CASE_,			'rb',			use_auth_token=SCREAMING_SNAKE_CASE_       ) as f:
                      UpperCamelCase    :  Optional[int]							=	BytesIO(f.read()       )
                   UpperCamelCase    :  int							=	PIL.Image.open(bytes_       )
          else:
             UpperCamelCase    :  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"]]:
          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:
          if pa.types.is_string(storage.type       ):
             UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.binary()       )
             UpperCamelCase    :  Dict							=	pa.StructArray.from_arrays([bytes_array, storage],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_binary(storage.type       ):
             UpperCamelCase    :  Optional[int]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  Union[str, Any]							=	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    :  List[str]							=	storage.field('bytes'       )
             else:
                UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.binary()       )
             if storage.type.get_field_index('path'       ) >= 0:
                UpperCamelCase    :  List[str]							=	storage.field('path'       )
             else:
                UpperCamelCase    :  Optional[Any]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  Optional[int]							=	pa.StructArray.from_arrays([bytes_array, path_array],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_list(storage.type       ):
             UpperCamelCase    :  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(),			)
             UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  int							=	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:

          @no_op_if_value_is_null
          def path_to_bytes(SCREAMING_SNAKE_CASE_       ):
             with xopen(SCREAMING_SNAKE_CASE_,			'rb'       ) as f:
                UpperCamelCase    :  Optional[int]							=	f.read()
             return bytes_

          UpperCamelCase    :  Union[str, Any]							=	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    :  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(),			)
          UpperCamelCase    :  int							=	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]:
   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    :  Dict							=	list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
   return _IMAGE_COMPRESSION_FORMATS
def 		UpperCamelCase	(   snake_case__       :    "PIL.Image.Image"	)	->     bytes:
   UpperCamelCase    :  Any							=	BytesIO()
   if image.format in list_image_compression_formats():
      UpperCamelCase    :  Tuple							=	image.format
   else:
      UpperCamelCase    :  List[str]							=	'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
   image.save(snake_case__       ,     format=snake_case__	)
   return buffer.getvalue()
def 		UpperCamelCase	(   snake_case__       :    "PIL.Image.Image"	)	->     dict:
   if hasattr(snake_case__       ,     'filename'	) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(snake_case__	)}
def 		UpperCamelCase	(   snake_case__       :    np.ndarray	)	->     dict:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

   UpperCamelCase    :  Union[str, Any]							=	array.dtype
   UpperCamelCase    :  List[Any]							=	dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
   UpperCamelCase    :  Optional[Any]							=	dtype.kind
   UpperCamelCase    :  Any							=	dtype.itemsize

   UpperCamelCase    :  int							=	None

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


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



