

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




  def __call__(       self      )						->				Any:
        '''simple docstring'''






        return self.pa_type




  def 						A__  (       self							,     lowerCAmelCase      )						->				dict:
        '''simple docstring'''






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

        if isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =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 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase=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:
              _lowercase			 ={}

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




  def 						A__  (       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 						A__  (       self							,     lowerCAmelCase      )						->				pa.StructArray:
        '''simple docstring'''






        if pa.types.is_string(storage.type      ):
              _lowercase			 =pa.array([None] * len(lowerCAmelCase      )							,     type=pa.binary()      )
              _lowercase			 =pa.StructArray.from_arrays([bytes_array, storage]							,     ['bytes', 'path']							,     mask=storage.is_null()      )
        elif pa.types.is_binary(storage.type      ):
              _lowercase			 =pa.array([None] * len(lowerCAmelCase      )							,     type=pa.string()      )
              _lowercase			 =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:
                    _lowercase			 =storage.field('bytes'      )
              else:
                    _lowercase			 =pa.array([None] * len(lowerCAmelCase      )							,     type=pa.binary()      )
              if storage.type.get_field_index('path'      ) >= 0:
                    _lowercase			 =storage.field('path'      )
              else:
                    _lowercase			 =pa.array([None] * len(lowerCAmelCase      )							,     type=pa.string()      )
              _lowercase			 =pa.StructArray.from_arrays([bytes_array, path_array]							,     ['bytes', 'path']							,     mask=storage.is_null()      )
        elif pa.types.is_list(storage.type      ):
              _lowercase			 =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()							,     )
              _lowercase			 =pa.array([None] * len(lowerCAmelCase      )							,     type=pa.string()      )
              _lowercase			 =pa.StructArray.from_arrays(
                  [bytes_array, path_array]							,     ['bytes', 'path']							,     mask=bytes_array.is_null()      )
        return array_cast(lowerCAmelCase							,     self.pa_type      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				pa.StructArray:
        '''simple docstring'''






        @no_op_if_value_is_null
        def path_to_bytes(lowerCAmelCase      ):
              with xopen(lowerCAmelCase							,     'rb'      ) as f:
                    _lowercase			 =f.read()
              return bytes_

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





      """simple docstring"""



      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()
            _lowercase			 =list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
      return _IMAGE_COMPRESSION_FORMATS
def   a   (    A__			:     "PIL.Image.Image"  )					->   bytes:





      """simple docstring"""



      _lowercase			 =BytesIO()
      if image.format in list_image_compression_formats():
            _lowercase			 =image.format
      else:
            _lowercase			 ='PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
      image.save(A__   ,     format=A__  )
      return buffer.getvalue()
def   a   (    A__			:     "PIL.Image.Image"  )					->   dict:





      """simple docstring"""



      if hasattr(A__   ,     'filename'  ) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(A__  )}
def   a   (    A__			:     np.ndarray  )					->   dict:





      """simple docstring"""



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

      _lowercase			 =array.dtype
      _lowercase			 =dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
      _lowercase			 =dtype.kind
      _lowercase			 =dtype.itemsize

      _lowercase			 =None

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







def   a   (    A__			:     Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]  )					->   List[dict]:





      """simple docstring"""



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

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





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						OpenAIGPTTokenizer
  _a		    =						OpenAIGPTTokenizerFast
  _a		    =						True
  _a		    =						False




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _lowercase			 =[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            'w</w>',
            'r</w>',
            't</w>',
            'lo',
            'low',
            'er</w>',
            'low</w>',
            'lowest</w>',
            'newer</w>',
            'wider</w>',
            '<unk>',
        ]
        _lowercase			 =dict(zip(lowerCAmelCase							,     range(len(lowerCAmelCase      )      )      )      )
        _lowercase			 =['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

        _lowercase			 =os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES['vocab_file']      )
        _lowercase			 =os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES['merges_file']      )
        with open(self.vocab_file							,     'w'      ) as fp:
              fp.write(json.dumps(lowerCAmelCase      )      )
        with open(self.merges_file							,     'w'      ) as fp:
              fp.write('\n'.join(lowerCAmelCase      )      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        return "lower newer", "lower newer"




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =OpenAIGPTTokenizer(self.vocab_file							,     self.merges_file      )

        _lowercase			 ='lower'
        _lowercase			 =['low', 'er</w>']
        _lowercase			 =tokenizer.tokenize(lowerCAmelCase      )
        self.assertListEqual(lowerCAmelCase							,     lowerCAmelCase      )

        _lowercase			 =tokens + ['<unk>']
        _lowercase			 =[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase      )							,     lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase=15      )						->				Union[str, Any]:
        '''simple docstring'''






        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                    _lowercase			 =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase							,     **lowerCAmelCase      )

                    # Simple input
                    _lowercase			 ='This is a simple input'
                    _lowercase			 =['This is a simple input 1', 'This is a simple input 2']
                    _lowercase			 =('This is a simple input', 'This is a pair')
                    _lowercase			 =[
                        ('This is a simple input 1', 'This is a simple input 2'),
                        ('This is a simple pair 1', 'This is a simple pair 2'),
                    ]

                    # Simple input tests
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Simple input
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Simple input
                    self.assertRaises(
                        lowerCAmelCase							,     tokenizer_r.batch_encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'							,     )

                    # Pair input
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Pair input
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Pair input
                    self.assertRaises(
                        lowerCAmelCase							,     tokenizer_r.batch_encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'							,     )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        pass





@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  pass


