


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





      def __call__(		self						:					Optional[int]       )    ->   Optional[Any]:






         """simple docstring"""







         return self.pa_type





      def        _lowercase				(		self						:					int					,       __lowerCamelCase						:					Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]       )    ->   dict:






         """simple docstring"""







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

         if isinstance(__lowerCamelCase					,       __lowerCamelCase       ):
            UpperCAmelCase							=     np.array(__lowerCamelCase       )

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

         UpperCAmelCase				,  UpperCAmelCase							=     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(__lowerCamelCase       ):
                  UpperCAmelCase							=     PIL.Image.open(__lowerCamelCase       )
               else:
                  UpperCAmelCase							=     path.split("""::"""       )[-1]
                  try:
                     UpperCAmelCase							=     string_to_dict(__lowerCamelCase					,       config.HUB_DATASETS_URL       )["""repo_id"""]
                     UpperCAmelCase							=     token_per_repo_id.get(__lowerCamelCase       )
                  except ValueError:
                     UpperCAmelCase							=     None
                  with xopen(__lowerCamelCase					,       """rb"""					,       use_auth_token=__lowerCamelCase       ) as f:
                     UpperCAmelCase							=     BytesIO(f.read()       )
                  UpperCAmelCase							=     PIL.Image.open(bytes_       )
         else:
            UpperCAmelCase							=     PIL.Image.open(BytesIO(bytes_       )       )
         image.load()  # to avoid "Too many open files" errors
         return image





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Union["FeatureType", Dict[str, "FeatureType"]]:






         """simple docstring"""







         from .features import Value

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





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					Union[pa.StringArray, pa.StructArray, pa.ListArray]       )    ->   pa.StructArray:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					pa.StructArray       )    ->   pa.StructArray:






         """simple docstring"""







         @no_op_if_value_is_null
         def path_to_bytes(__lowerCamelCase						:					Tuple       ):
            with xopen(__lowerCamelCase					,       """rb"""       ) as f:
               UpperCAmelCase							=     f.read()
            return bytes_

         UpperCAmelCase							=     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							=     pa.array(
             [os.path.basename(__lowerCamelCase       ) if path is not None else None for path in storage.field("""path"""       ).to_pylist()]					,       type=pa.string()					,       )
         UpperCAmelCase							=     pa.StructArray.from_arrays([bytes_array, path_array]					,       ["""bytes""", """path"""]					,       mask=bytes_array.is_null()       )
         return array_cast(__lowerCamelCase					,       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							=     list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
   return _IMAGE_COMPRESSION_FORMATS
def   _UpperCamelCase				(						lowerCAmelCase_   )						->bytes:
   UpperCAmelCase							=     BytesIO()
   if image.format in list_image_compression_formats():
      UpperCAmelCase							=     image.format
   else:
      UpperCAmelCase							=     """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
   image.save(lowerCAmelCase_  ,						format=lowerCAmelCase_   )
   return buffer.getvalue()
def   _UpperCamelCase				(						lowerCAmelCase_   )						->dict:
   if hasattr(lowerCAmelCase_  ,						"""filename"""   ) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_   )}
def   _UpperCamelCase				(						lowerCAmelCase_   )						->dict:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("""To support encoding images, please install 'Pillow'."""   )

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

   UpperCAmelCase							=     None

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







def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[dict]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("""To support encoding images, please install 'Pillow'."""   )

   if objs:
      UpperCAmelCase				,  UpperCAmelCase							=     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   ):
         UpperCAmelCase							=     no_op_if_value_is_null(lowerCAmelCase_   )
         return [obj_to_image_dict_func(lowerCAmelCase_   ) for obj in objs]
      elif isinstance(lowerCAmelCase_  ,						PIL.Image.Image   ):
         UpperCAmelCase							=     no_op_if_value_is_null(lowerCAmelCase_   )
         return [obj_to_image_dict_func(lowerCAmelCase_   ) for obj in objs]
      else:
         return objs
   else:
      return objs







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class   __lowercase				(  unittest.TestCase  ):
      UpperCamelCase								=       ViTImageProcessor if is_vision_available() else None





      @property
      def        _lowercase				(		self						:					Tuple       )    ->   int:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def        _lowercase				(		self						:					str       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     (3, 3_2, 1_2_8)
         UpperCAmelCase							=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase							=     ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
         # fmt: on
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )

         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""vocab_file"""]       )
         with open(self.vocab_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write(json.dumps(__lowerCamelCase       ) + """\n"""       )

         UpperCAmelCase							=     {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







         return MgpstrTokenizer.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					int					,       **__lowerCamelCase						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Dict:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     np.random.randint(2_5_5					,       size=(3, 3_0, 4_0_0)					,       dtype=np.uinta       )

         UpperCAmelCase							=     Image.fromarray(np.moveaxis(__lowerCamelCase					,       0					,       -1       )       )

         return image_input





      def        _lowercase				(		self						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     MgpstrProcessor.from_pretrained(self.tmpdirname					,       use_fast=__lowerCamelCase       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

         self.assertEqual(processor.image_processor.to_json_string()					,       image_processor.to_json_string()       )
         self.assertIsInstance(processor.image_processor					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )

         UpperCAmelCase							=     self.get_tokenizer(bos_token="""(BOS)"""					,       eos_token="""(EOS)"""       )
         UpperCAmelCase							=     self.get_image_processor(do_normalize=__lowerCamelCase					,       padding_value=1.0       )

         UpperCAmelCase							=     MgpstrProcessor.from_pretrained(
             self.tmpdirname					,       bos_token="""(BOS)"""					,       eos_token="""(EOS)"""					,       do_normalize=__lowerCamelCase					,       padding_value=1.0       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

         self.assertEqual(processor.image_processor.to_json_string()					,       image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase							=     processor(images=__lowerCamelCase					,       return_tensors="""np"""       )

         for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum()					,       input_processor[key].sum()					,       delta=1e-2       )





      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""

         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     tokenizer(__lowerCamelCase       )
         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]					,       encoded_processor[key]       )





      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

         self.assertListEqual(list(inputs.keys()       )					,       ["""pixel_values""", """labels"""]       )

         # test if it raises when no input is passed
         with pytest.raises(__lowerCamelCase       ):
            processor()





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase							=     processor.char_decode(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.batch_decode(__lowerCamelCase       )
         UpperCAmelCase							=     [seq.replace(""" """					,       """"""       ) for seq in decoded_tok]

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     None
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

         self.assertListEqual(list(inputs.keys()       )					,       processor.model_input_names       )





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_8       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       5_0_2_5_7       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_0_5_2_2       )

         UpperCAmelCase							=     processor.batch_decode([char_input, bpe_input, wp_input]       )

         self.assertListEqual(list(results.keys()       )					,       ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]       )



