




"""simple docstring"""


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					__snake_case						):
   _lowerCamelCase    :List[Any]     				=     ["pixel_values"]






   def __init__(		self       :		Tuple   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Union[int, float] = 1 / 2_55   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		int = 8   ,     **UpperCamelCase       :		List[str]   ,     )			->						None:

         """simple docstring"""
         super().__init__(**lowerCamelCase_   )

         lowerCAmelCase__      :			Any   =	do_rescale
         lowerCAmelCase__      :			str   =	rescale_factor
         lowerCAmelCase__      :			Any   =	do_pad
         lowerCAmelCase__      :			Tuple   =	pad_size






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		float   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		List[Any]   )			->						np.ndarray:

         """simple docstring"""
         return rescale(lowerCamelCase_   ,     scale=lowerCamelCase_   ,     data_format=lowerCamelCase_   ,     **lowerCamelCase_   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	get_image_size(lowerCamelCase_   )
         lowerCAmelCase__      :			Tuple   =	(old_height // size + 1) * size - old_height
         lowerCAmelCase__      :			List[Any]   =	(old_width // size + 1) * size - old_width

         return pad(lowerCamelCase_   ,     ((0, pad_height), (0, pad_width))   ,     mode="""symmetric"""   ,     data_format=lowerCamelCase_   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		ImageInput   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		Optional[float] = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     UpperCamelCase       :		Union[str, ChannelDimension] = ChannelDimension.FIRST   ,     **UpperCamelCase       :		str   ,     )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__      :			Optional[int]   =	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__      :			Dict   =	do_pad if do_pad is not None else self.do_pad
         lowerCAmelCase__      :			Dict   =	pad_size if pad_size is not None else self.pad_size

         lowerCAmelCase__      :			Any   =	make_list_of_images(lowerCamelCase_   )

         if not valid_images(lowerCamelCase_   ):
               raise ValueError(
                   """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                   """torch.Tensor, tf.Tensor or jax.ndarray."""   )

         if do_rescale and rescale_factor is None:
               raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

         # All transformations expect numpy arrays.
         lowerCAmelCase__      :			Tuple   =	[to_numpy_array(lowerCamelCase_   ) for image in images]

         if do_rescale:
               lowerCAmelCase__      :			Union[str, Any]   =	[self.rescale(image=lowerCamelCase_   ,     scale=lowerCamelCase_   ) for image in images]

         if do_pad:
               lowerCAmelCase__      :			List[str]   =	[self.pad(lowerCamelCase_   ,     size=lowerCamelCase_   ) for image in images]

         lowerCAmelCase__      :			Dict   =	[to_channel_dimension_format(lowerCamelCase_   ,     lowerCamelCase_   ) for image in images]

         lowerCAmelCase__      :			Optional[int]   =	{"""pixel_values""": images}
         return BatchFeature(data=lowerCamelCase_   ,     tensor_type=lowerCamelCase_   )











"""simple docstring"""


import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A													=      logging.get_logger(__name__)

_A													=      {"""vocab_file""": """vocab.json"""}

_A													=      {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

_A													=      {"""mgp-str""": 2_7}


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :Union[str, Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Optional[int]     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






   def __init__(		self       :		Optional[int]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Union[str, Any]="[GO]"   ,     UpperCamelCase       :		Any="[GO]"   ,     UpperCamelCase       :		Tuple="[s]"   ,     UpperCamelCase       :		List[Any]="[GO]"   ,     **UpperCamelCase       :		Dict   )			->						Union[str, Any]:

         """simple docstring"""
         super().__init__(
             unk_token=UpperCamelCase   ,     bos_token=UpperCamelCase   ,     eos_token=UpperCamelCase   ,     pad_token=UpperCamelCase   ,     **UpperCamelCase   ,     )

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as vocab_handle:
               lowerCAmelCase__      :			Optional[Any]   =	json.load(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{v: k for k, v in self.vocab.items()}






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         return len(self.vocab   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Any:

         """simple docstring"""
         return dict(self.vocab   ,     **self.added_tokens_encoder   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	[]
         for s in text:
               char_tokens.extend(UpperCamelCase   )
         return char_tokens






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		str   )			->						Tuple:

         """simple docstring"""
         return self.vocab.get(UpperCamelCase   ,     self.vocab.get(self.unk_token   )   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		int   )			->						Any:

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase   )   )
               return
         lowerCAmelCase__      :			Union[str, Any]   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as f:
               f.write(json.dumps(self.vocab   ,     indent=2   ,     sort_keys=UpperCamelCase   ,     ensure_ascii=UpperCamelCase   ) + """\n"""   )

         return (vocab_file,)





