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


lowerCAmelCase__     						=			None
lowerCAmelCase__     						=			'<' 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
lowerCAmelCase__     						=			[
    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     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= None
 # Automatically constructed
 UpperCAmelCase_     			= "PIL.Image.Image"
 UpperCAmelCase_     			= pa.struct({'bytes': pa.binary(), 'path': pa.string()}						)
 UpperCAmelCase_     			= field(default='Image'	,					init=SCREAMING_SNAKE_CASE	,					repr=SCREAMING_SNAKE_CASE						)




 def __call__(      self :  Dict):







        '''simple docstring'''



        return self.pa_type




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]):







        '''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 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  dict      ,   lowercase__ :  Optional[int]=None):







        '''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__     , 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 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        from .features import Value

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




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Union[pa.StringArray, pa.StructArray, pa.ListArray]):







        '''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 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  pa.StructArray):







        '''simple docstring'''



        @no_op_if_value_is_null
        def path_to_bytes(lowercase__ :  Union[str, Any]):
               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 				__lowerCamelCase		(						):
       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 				__lowerCamelCase		(						lowerCAmelCase__     ):
       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(lowerCAmelCase__     ,  format=lowerCAmelCase__     )
       return buffer.getvalue()






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






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       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(lowerCAmelCase__     )
                     lowerCAmelCase__					     = np.dtype(lowerCAmelCase__     )
                     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(lowerCAmelCase__     )     )
       return {"path": None, "bytes": image_to_bytes(lowerCAmelCase__     )}






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

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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XCLIPModel',
        'XCLIPPreTrainedModel',
        'XCLIPTextModel',
        'XCLIPVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_x_clip import (
        XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XCLIPConfig,
        XCLIPTextConfig,
        XCLIPVisionConfig,
    )
    from .processing_x_clip import XCLIPProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_x_clip import (
            XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            XCLIPModel,
            XCLIPPreTrainedModel,
            XCLIPTextModel,
            XCLIPVisionModel,
        )

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
