





'''simple docstring'''




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


lowercase__   :				Optional[List[str]]      =   None
lowercase__   :				List[Any]      =   '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowercase__   :				str      =   [
    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 			SCREAMING_SNAKE_CASE   :
     lowerCAmelCase		      =	True
     lowerCAmelCase		      =	None
     # Automatically constructed
     lowerCAmelCase		      =	"PIL.Image.Image"
     lowerCAmelCase		      =	pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}							)
     lowerCAmelCase		      =	field(default='''Image''' ,		init=a__ ,		repr=a__							)


     def __call__(						self):

          '''simple docstring'''






          return self.pa_type


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






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

          if isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
               __A						:			Dict								=	np.array(_UpperCAmelCase)

          if isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
               return {"path": value, "bytes": None}
          elif isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
               return {"path": None, "bytes": value}
          elif isinstance(_UpperCAmelCase						,     np.ndarray):
               # convert the image array to PNG/TIFF bytes
               return encode_np_array(_UpperCAmelCase)
          elif isinstance(_UpperCAmelCase						,     PIL.Image.Image):
               # convert the PIL image to bytes (default format is PNG/TIFF)
               return encode_pil_image(_UpperCAmelCase)
          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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=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:
               __A						:			List[str]								=	{}

          __A			,__A						:			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(_UpperCAmelCase):
                         __A						:			List[Any]								=	PIL.Image.open(_UpperCAmelCase)
                    else:
                         __A						:			Optional[int]								=	path.split('::')[-1]
                         try:
                              __A						:			int								=	string_to_dict(_UpperCAmelCase						,     config.HUB_DATASETS_URL)['repo_id']
                              __A						:			Dict								=	token_per_repo_id.get(_UpperCAmelCase)
                         except ValueError:
                              __A						:			Tuple								=	None
                         with xopen(_UpperCAmelCase						,     'rb'						,     use_auth_token=_UpperCAmelCase) as f:
                              __A						:			Optional[Any]								=	BytesIO(f.read())
                         __A						:			Optional[Any]								=	PIL.Image.open(bytes_)
          else:
               __A						:			Union[str, Any]								=	PIL.Image.open(BytesIO(bytes_))
          image.load()  # to avoid "Too many open files" errors
          return image


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          from .features import Value

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          @no_op_if_value_is_null
          def path_to_bytes(_UpperCAmelCase):
               with xopen(_UpperCAmelCase						,     'rb') as f:
                    __A						:			Dict								=	f.read()
               return bytes_

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






def 	_lowerCAmelCase      (  )					->					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()
          __A						:			Tuple								=	list(set(PIL.Image.OPEN.keys()      ) & set(PIL.Image.SAVE.keys()      )      )
     return _IMAGE_COMPRESSION_FORMATS






def 	_lowerCAmelCase      (  __snake_case  :			"PIL.Image.Image"      )					->					bytes:
     __A						:			Union[str, Any]								=	BytesIO()
     if image.format in list_image_compression_formats():
          __A						:			Union[str, Any]								=	image.format
     else:
          __A						:			Dict								=	'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
     image.save(__snake_case			,							format=__snake_case      )
     return buffer.getvalue()






def 	_lowerCAmelCase      (  __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 	_lowerCAmelCase      (  __snake_case  :			np.ndarray      )					->					dict:
     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'      )

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

     __A						:			Optional[Any]								=	None

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







def 	_lowerCAmelCase      (  __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:
          __A			,__A						:			Tuple								=	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      ):
               __A						:			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      ):
               __A						:			str								=	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






'''simple docstring'''




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class 			SCREAMING_SNAKE_CASE   (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	SMALL_MODEL_IDENTIFIER
          __A						:			Any								=	'pt'
          __A						:			str								=	'tf'


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	AutoModel.from_pretrained(self.test_model)
          model_pt.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	TFAutoModel.from_pretrained(self.test_model						,     from_pt=_UpperCAmelCase)
          model_tf.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	'mock_framework'

          # Framework provided - return whatever the user provides
          __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model						,     _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # Local checkpoint and framework provided - return provided framework
          # PyTorch checkpoint
          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			Optional[int]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Invalid local checkpoint
          with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase):
               __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # PyTorch not in environment -> use TensorFlow
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			List[Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Both in environment -> use PyTorch
          __A						:			Any								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Dict								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			int								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # Both not in environment -> raise error
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			int								=	FeaturesManager.determine_framework(self.test_model)