

'''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


A_	= None
A_	= '<' 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
A_	= [
    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  UpperCAmelCase :

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					True
		SCREAMING_SNAKE_CASE_           =					None
		# Automatically constructed
		SCREAMING_SNAKE_CASE_           =					"PIL.Image.Image"
		SCREAMING_SNAKE_CASE_           =					pa.struct({'bytes': pa.binary(), 'path': pa.string()}    )
		SCREAMING_SNAKE_CASE_           =					field(default='Image'  , init=snake_case_  , repr=snake_case_    )
		def __call__(					self		)  ->     List[str]:






							'''simple docstring'''



							return self.pa_type
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



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

							if isinstance(lowercase__   ,			lowercase__		):
												lowerCamelCase_  				=      np.array(lowercase__		)

							if isinstance(lowercase__   ,			lowercase__		):
												return {"path": value, "bytes": None}
							elif isinstance(lowercase__   ,			lowercase__		):
												return {"path": None, "bytes": value}
							elif isinstance(lowercase__   ,			np.ndarray		):
												# convert the image array to PNG/TIFF bytes
												return encode_np_array(lowercase__		)
							elif isinstance(lowercase__   ,			PIL.Image.Image		):
												# convert the PIL image to bytes (default format is PNG/TIFF)
												return encode_pil_image(lowercase__		)
							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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=None		)  ->     Union[str, Any]:






							'''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:
												lowerCamelCase_  				=      {}

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






							'''simple docstring'''



							from .features import Value

							return (
							    self
							    if self.decode
							    else {
							        "bytes": Value('binary'		),
							        "path": Value('string'		),
							    }
							)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



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






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Tuple:






							'''simple docstring'''



							@no_op_if_value_is_null
							def path_to_bytes(SCREAMING_SNAKE_CASE_		):
												with xopen(lowercase__   ,			'rb'		) as f:
																	lowerCamelCase_  				=      f.read()
												return bytes_

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







def   _UpperCamelCase	(		)       ->			Tuple:


					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()
										lowerCamelCase_  				=      list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
					return _IMAGE_COMPRESSION_FORMATS







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Dict:
					lowerCamelCase_  				=      BytesIO()
					if image.format in list_image_compression_formats():
										lowerCamelCase_  				=      image.format
					else:
										lowerCamelCase_  				=      """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
					image.save(UpperCamelCase__    ,format=UpperCamelCase__	)
					return buffer.getvalue()







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Dict:


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







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[Any]:


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

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

					lowerCamelCase_  				=      None

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







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[str]:


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

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









'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
