

import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 	snake_case_   (		__A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Dict									=				XLMTokenizer
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				False







			def 				snake_case__(     self :				List[str]							)      ->Tuple:
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									snake_case_   =	[
									    '''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>''',
									]
									snake_case_   =	dict(zip(_UpperCamelCase							,   range(len(_UpperCamelCase							)							)							)							)
									snake_case_   =	['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

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







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				int							)      ->Any:
									snake_case_   =	'''lower newer'''
									snake_case_   =	'''lower newer'''
									return input_text, output_text







			def 				snake_case__(     self :				Dict							)      ->List[Any]:
									snake_case_   =	XLMTokenizer(self.vocab_file							,   self.merges_file							)

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

									snake_case_   =	tokens + ['''<unk>''']
									snake_case_   =	[1_4, 1_5, 2_0]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)							,   _UpperCamelCase							)







			@slow
			def 				snake_case__(     self :				Any							)      ->Dict:
									snake_case_   =	XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''							)

									snake_case_   =	tokenizer.encode('''sequence builders'''							,   add_special_tokens=_UpperCamelCase							)
									snake_case_   =	tokenizer.encode('''multi-sequence build'''							,   add_special_tokens=_UpperCamelCase							)

									snake_case_   =	tokenizer.build_inputs_with_special_tokens(_UpperCamelCase							)
									snake_case_   =	tokenizer.build_inputs_with_special_tokens(_UpperCamelCase							,   _UpperCamelCase							)

									assert encoded_sentence == [0] + text + [1]
									assert encoded_pair == [0] + text + [1] + text_a + [1]




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


__UpperCAmelCase          =       None
__UpperCAmelCase          =       '''<''' 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
__UpperCAmelCase          =       [
    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_		:
       UpperCAmelCase__				:					bool        =  True
       UpperCAmelCase__				:					Optional[str]        =  None
       # Automatically constructed
       UpperCAmelCase__				:					ClassVar[str]        =  "PIL.Image.Image"
       UpperCAmelCase__				:					ClassVar[Any]        =  pa.struct({"bytes": pa.binary(), "path": pa.string()}							)
       UpperCAmelCase__				:					str        =  field(default="Image"       ,    init=a__       ,    repr=a__							)
       def __call__(			self       )     ->		Any:
          return self.pa_type
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		dict:
          if config.PIL_AVAILABLE:
             import PIL.Image
          else:
             raise ImportError('To support encoding images, please install \'Pillow\'.'       )

          if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Optional[int]							=	np.array(SCREAMING_SNAKE_CASE_       )

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

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

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






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		pa.StructArray:

          @no_op_if_value_is_null
          def path_to_bytes(SCREAMING_SNAKE_CASE_       ):
             with xopen(SCREAMING_SNAKE_CASE_,			'rb'       ) as f:
                UpperCamelCase    :  Optional[int]							=	f.read()
             return bytes_

          UpperCamelCase    :  Union[str, 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(),			)
          UpperCamelCase    :  Any							=	pa.array(
              [os.path.basename(SCREAMING_SNAKE_CASE_       ) if path is not None else None for path in storage.field('path'       ).to_pylist()],			type=pa.string(),			)
          UpperCamelCase    :  int							=	pa.StructArray.from_arrays([bytes_array, path_array],			['bytes', 'path'],			mask=bytes_array.is_null()       )
          return array_cast(SCREAMING_SNAKE_CASE_,			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    :  Dict							=	list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
   return _IMAGE_COMPRESSION_FORMATS
def 		UpperCamelCase	(   snake_case__       :    "PIL.Image.Image"	)	->     bytes:
   UpperCamelCase    :  Any							=	BytesIO()
   if image.format in list_image_compression_formats():
      UpperCamelCase    :  Tuple							=	image.format
   else:
      UpperCamelCase    :  List[str]							=	'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
   image.save(snake_case__       ,     format=snake_case__	)
   return buffer.getvalue()
def 		UpperCamelCase	(   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 		UpperCamelCase	(   snake_case__       :    np.ndarray	)	->     dict:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

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

   UpperCamelCase    :  int							=	None

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


def 		UpperCamelCase	(   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:
      UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	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	):
         UpperCamelCase    :  List[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	):
         UpperCamelCase    :  Optional[int]							=	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



