import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.generation import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )


@require_flax
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :Optional[int]			  =		jnp.ones((batch_size, length)) / length
        return scores







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Union[str, Any]			  =		None
        __UpperCamelCase     :Any			  =		20

        __UpperCamelCase     :Any			  =		self._get_uniform_logits(batch_size=2 , length=_SCREAMING_SNAKE_CASE)

        # tweak scores to not be uniform anymore
        __UpperCamelCase     :Dict			  =		scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
        __UpperCamelCase     :Tuple			  =		scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

        # compute softmax
        __UpperCamelCase     :List[str]			  =		jax.nn.softmax(_SCREAMING_SNAKE_CASE , axis=-1)

        __UpperCamelCase     :Any			  =		FlaxTemperatureLogitsWarper(temperature=0.5)
        __UpperCamelCase     :Optional[int]			  =		FlaxTemperatureLogitsWarper(temperature=1.3)

        __UpperCamelCase     :Optional[Any]			  =		jax.nn.softmax(temp_dist_warper_sharper(_SCREAMING_SNAKE_CASE , scores.copy() , cur_len=_SCREAMING_SNAKE_CASE) , axis=-1)
        __UpperCamelCase     :List[Any]			  =		jax.nn.softmax(temp_dist_warper_smoother(_SCREAMING_SNAKE_CASE , scores.copy() , cur_len=_SCREAMING_SNAKE_CASE) , axis=-1)

        # uniform distribution stays uniform
        self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1E-3))
        self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1E-3))

        # sharp peaks get higher, valleys get lower
        self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max())
        self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min())

        # smooth peaks get lower, valleys get higher
        self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max())
        self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min())







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Any			  =		None
        __UpperCamelCase     :List[Any]			  =		10
        __UpperCamelCase     :str			  =		2

        # create ramp distribution
        __UpperCamelCase     :Any			  =		np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE)[None, :] , (batch_size, vocab_size)).copy()
        __UpperCamelCase     :List[Any]			  =		ramp_logits[1:, : vocab_size // 2] + vocab_size

        __UpperCamelCase     :Any			  =		FlaxTopKLogitsWarper(3)

        __UpperCamelCase     :Tuple			  =		top_k_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)

        # check that correct tokens are filtered
        self.assertListEqual(jnp.isinf(scores[0]).tolist() , 7 * [True] + 3 * [False])
        self.assertListEqual(jnp.isinf(scores[1]).tolist() , 2 * [True] + 3 * [False] + 5 * [True])

        # check special case
        __UpperCamelCase     :Optional[int]			  =		5
        __UpperCamelCase     :List[Any]			  =		FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3)

        __UpperCamelCase     :Optional[Any]			  =		np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE)[None, :] , (batch_size, length)).copy()
        __UpperCamelCase     :Dict			  =		top_k_warp_safety_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)

        # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
        self.assertListEqual((scores == 0.0).sum(axis=-1).tolist() , [2, 2])







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :str			  =		None
        __UpperCamelCase     :Optional[Any]			  =		10
        __UpperCamelCase     :Any			  =		2

        # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
        __UpperCamelCase     :Optional[int]			  =		np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

        __UpperCamelCase     :str			  =		FlaxTopPLogitsWarper(0.8)
        __UpperCamelCase     :Optional[int]			  =		np.exp(top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE))

        # dist should be filtered to keep min num values so that sum is >= top_p
        # exp (-inf) => 0
        __UpperCamelCase     :Tuple			  =		np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3))

        # check edge cases with negative and extreme logits
        __UpperCamelCase     :Union[str, Any]			  =		np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE)[None, :] , (batch_size, vocab_size)).copy() - (
            vocab_size // 2
        )

        # make ramp_logits more extreme
        __UpperCamelCase     :str			  =		ramp_logits[1] * 100.0

        # make sure at least 2 tokens are kept
        __UpperCamelCase     :str			  =		FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0)
        __UpperCamelCase     :str			  =		top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)

        # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
        self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist() , [3, 2])







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :str			  =		20
        __UpperCamelCase     :Union[str, Any]			  =		4
        __UpperCamelCase     :Optional[Any]			  =		0

        __UpperCamelCase     :Union[str, Any]			  =		FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_SCREAMING_SNAKE_CASE)

        # check that min length is applied at length 5
        __UpperCamelCase     :int			  =		ids_tensor((batch_size, 20) , vocab_size=20)
        __UpperCamelCase     :List[Any]			  =		5
        __UpperCamelCase     :Optional[int]			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Optional[int]			  =		min_dist_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('''inf''')])

        # check that min length is not applied anymore at length 15
        __UpperCamelCase     :Tuple			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Any			  =		15
        __UpperCamelCase     :int			  =		min_dist_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE).any())







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[int]			  =		20
        __UpperCamelCase     :Optional[int]			  =		4
        __UpperCamelCase     :Optional[int]			  =		0

        __UpperCamelCase     :Optional[Any]			  =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE)

        # check that all scores are -inf except the bos_token_id score
        __UpperCamelCase     :Optional[Any]			  =		ids_tensor((batch_size, 1) , vocab_size=20)
        __UpperCamelCase     :str			  =		1
        __UpperCamelCase     :List[str]			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :List[str]			  =		logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
        self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0])  # score for bos_token_id shold be zero

        # check that bos_token_id is not forced if current length is greater than 1
        __UpperCamelCase     :Optional[int]			  =		3
        __UpperCamelCase     :List[Any]			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Tuple			  =		logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE).any())







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Union[str, Any]			  =		20
        __UpperCamelCase     :str			  =		4
        __UpperCamelCase     :Dict			  =		0
        __UpperCamelCase     :Optional[int]			  =		5

        __UpperCamelCase     :Tuple			  =		FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE)

        # check that all scores are -inf except the eos_token_id when max_length is reached
        __UpperCamelCase     :List[Any]			  =		ids_tensor((batch_size, 4) , vocab_size=20)
        __UpperCamelCase     :Any			  =		4
        __UpperCamelCase     :Optional[Any]			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :List[Any]			  =		logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
        self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0])  # score for eos_token_id should be zero

        # check that eos_token_id is not forced if max_length is not reached
        __UpperCamelCase     :int			  =		3
        __UpperCamelCase     :Union[str, Any]			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Dict			  =		logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE).any())







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :str			  =		4
        __UpperCamelCase     :Dict			  =		10
        __UpperCamelCase     :Union[str, Any]			  =		15
        __UpperCamelCase     :str			  =		2
        __UpperCamelCase     :int			  =		1
        __UpperCamelCase     :List[str]			  =		15

        # dummy input_ids and scores
        __UpperCamelCase     :Tuple			  =		ids_tensor((batch_size, sequence_length) , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :int			  =		input_ids.copy()

        __UpperCamelCase     :List[Any]			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Union[str, Any]			  =		scores.copy()

        # instantiate all dist processors
        __UpperCamelCase     :Dict			  =		FlaxTemperatureLogitsWarper(temperature=0.5)
        __UpperCamelCase     :Any			  =		FlaxTopKLogitsWarper(3)
        __UpperCamelCase     :List[Any]			  =		FlaxTopPLogitsWarper(0.8)

        # instantiate all logits processors
        __UpperCamelCase     :List[Any]			  =		FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Optional[int]			  =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Dict			  =		FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE)

        __UpperCamelCase     :Union[str, Any]			  =		10

        # no processor list
        __UpperCamelCase     :int			  =		temp_dist_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :List[str]			  =		top_k_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Any			  =		top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Dict			  =		min_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Optional[int]			  =		bos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Optional[Any]			  =		eos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)

        # with processor list
        __UpperCamelCase     :Any			  =		FlaxLogitsProcessorList(
            [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
        __UpperCamelCase     :List[str]			  =		processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)

        # scores should be equal
        self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3))

        # input_ids should never be changed
        self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :str			  =		4
        __UpperCamelCase     :Dict			  =		10
        __UpperCamelCase     :Tuple			  =		15
        __UpperCamelCase     :List[str]			  =		2
        __UpperCamelCase     :List[str]			  =		1
        __UpperCamelCase     :Union[str, Any]			  =		15

        # dummy input_ids and scores
        __UpperCamelCase     :Any			  =		ids_tensor((batch_size, sequence_length) , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Union[str, Any]			  =		input_ids.copy()

        __UpperCamelCase     :Optional[Any]			  =		self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Tuple			  =		scores.copy()

        # instantiate all dist processors
        __UpperCamelCase     :List[str]			  =		FlaxTemperatureLogitsWarper(temperature=0.5)
        __UpperCamelCase     :Optional[Any]			  =		FlaxTopKLogitsWarper(3)
        __UpperCamelCase     :int			  =		FlaxTopPLogitsWarper(0.8)

        # instantiate all logits processors
        __UpperCamelCase     :List[Any]			  =		FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Dict			  =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Optional[Any]			  =		FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE)

        __UpperCamelCase     :str			  =		10

        # no processor list
        def run_no_processor_list(__lowercase , __lowercase , __lowercase):
            __UpperCamelCase     :int			  =		temp_dist_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
            __UpperCamelCase     :Optional[Any]			  =		top_k_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
            __UpperCamelCase     :List[Any]			  =		top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
            __UpperCamelCase     :Dict			  =		min_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
            __UpperCamelCase     :Any			  =		bos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
            __UpperCamelCase     :Optional[Any]			  =		eos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
            return scores

        # with processor list
        def run_processor_list(__lowercase , __lowercase , __lowercase):
            __UpperCamelCase     :Optional[int]			  =		FlaxLogitsProcessorList(
                [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
            __UpperCamelCase     :Optional[int]			  =		processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE)
            return scores

        __UpperCamelCase     :Optional[int]			  =		jax.jit(_SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :Union[str, Any]			  =		jax.jit(_SCREAMING_SNAKE_CASE)

        __UpperCamelCase     :Dict			  =		jitted_run_no_processor_list(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
        __UpperCamelCase     :List[Any]			  =		jitted_run_processor_list(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)

        # scores should be equal
        self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3))

        # input_ids should never be changed
        self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())









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




