





'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

class    lowerCAmelCase_		(		UpperCamelCase_		):
 __lowerCamelCase						:							Any       					=					["pixel_values"]




 def __init__(	self			,     _lowerCAmelCase = True			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = PILImageResampling.BILINEAR			,     _lowerCAmelCase = True			,     _lowerCAmelCase = 1 / 255			,     _lowerCAmelCase = True			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							None:
     super().__init__(**__a		)
     _lowerCAmelCase				=      size if size is not None else {'shortest_edge': 384}
     _lowerCAmelCase				=      get_size_dict(__a			,     default_to_square=__a		)

     _lowerCAmelCase				=      do_resize
     _lowerCAmelCase				=      size
     # Default value set here for backwards compatibility where the value in config is None
     _lowerCAmelCase				=      crop_pct if crop_pct is not None else 224 / 256
     _lowerCAmelCase				=      resample
     _lowerCAmelCase				=      do_rescale
     _lowerCAmelCase				=      rescale_factor
     _lowerCAmelCase				=      do_normalize
     _lowerCAmelCase				=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _lowerCAmelCase				=      image_std if image_std is not None else IMAGENET_STANDARD_STD




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = PILImageResampling.BICUBIC			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							np.ndarray:
     _lowerCAmelCase				=      get_size_dict(__a			,     default_to_square=__a		)
     if "shortest_edge" not in size:
         raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''		)
     _lowerCAmelCase				=      size['shortest_edge']

     if shortest_edge < 384:
         # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
         _lowerCAmelCase				=      int(shortest_edge / crop_pct		)
         _lowerCAmelCase				=      get_resize_output_image_size(__a			,     size=__a			,     default_to_square=__a		)
         _lowerCAmelCase				=      resize(image=__a			,     size=__a			,     resample=__a			,     data_format=__a			,     **__a		)
         # then crop to (shortest_edge, shortest_edge)
         return center_crop(image=__a			,     size=(shortest_edge, shortest_edge)			,     data_format=__a			,     **__a		)
     else:
         # warping (no cropping) when evaluated at 384 or larger
         return resize(
             __a			,     size=(shortest_edge, shortest_edge)			,     resample=__a			,     data_format=__a			,     **__a		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							Any:
     return rescale(__a			,     scale=__a			,     data_format=__a			,     **__a		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							np.ndarray:
     return normalize(__a			,     mean=__a			,     std=__a			,     data_format=__a			,     **__a		)





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = ChannelDimension.FIRST			,     **_lowerCAmelCase			,     )    ->							PIL.Image.Image:
     _lowerCAmelCase				=      do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase				=      crop_pct if crop_pct is not None else self.crop_pct
     _lowerCAmelCase				=      resample if resample is not None else self.resample
     _lowerCAmelCase				=      do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase				=      rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase				=      do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase				=      image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase				=      image_std if image_std is not None else self.image_std

     _lowerCAmelCase				=      size if size is not None else self.size
     _lowerCAmelCase				=      get_size_dict(__a			,     default_to_square=__a		)

     _lowerCAmelCase				=      make_list_of_images(__a		)

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

     if do_resize and size is None or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."		)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
         raise ValueError("crop_pct must be specified if size < 384."		)

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

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."		)

     # All transformations expect numpy arrays.
     _lowerCAmelCase				=      [to_numpy_array(__a		) for image in images]

     if do_resize:
         _lowerCAmelCase				=      [self.resize(image=__a			,     size=__a			,     crop_pct=__a			,     resample=__a		) for image in images]

     if do_rescale:
         _lowerCAmelCase				=      [self.rescale(image=__a			,     scale=__a		) for image in images]

     if do_normalize:
         _lowerCAmelCase				=      [self.normalize(image=__a			,     mean=__a			,     std=__a		) for image in images]

     _lowerCAmelCase				=      [to_channel_dimension_format(__a			,     __a		) for image in images]

     _lowerCAmelCase				=      {'pixel_values': images}
     return BatchFeature(data=__a			,     tensor_type=__a		)









'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"▁"

_SCREAMING_SNAKE_CASE	 =							{"vocab_file": "sentencepiece.bpe.model"}

_SCREAMING_SNAKE_CASE	 =							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/xglm-564M": 20_48,
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							int         =					VOCAB_FILES_NAMES
 __lowerCamelCase						:							Optional[int]         =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase						:							Tuple         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase						:							str         =					["input_ids", "attention_mask"]




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase="<s>"			,     _lowerCAmelCase="</s>"			,     _lowerCAmelCase="</s>"			,     _lowerCAmelCase="<s>"			,     _lowerCAmelCase="<unk>"			,     _lowerCAmelCase="<pad>"			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							None:
     _lowerCAmelCase				=      {} if sp_model_kwargs is None else sp_model_kwargs

     # Compatibility with the original tokenizer
     _lowerCAmelCase				=      7
     _lowerCAmelCase				=      [f'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

     _lowerCAmelCase				=      kwargs.get("additional_special_tokens"			,     []		)
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         bos_token=_lowerCAmelCase			,     eos_token=_lowerCAmelCase			,     unk_token=_lowerCAmelCase			,     sep_token=_lowerCAmelCase			,     cls_token=_lowerCAmelCase			,     pad_token=_lowerCAmelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **_lowerCAmelCase			,     )

     _lowerCAmelCase				=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(str(_lowerCAmelCase		)		)
     _lowerCAmelCase				=      vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     _lowerCAmelCase				=      1

     # Mimic fairseq token-to-id alignment for the first 4 token
     _lowerCAmelCase				=      {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

     _lowerCAmelCase				=      len(self.sp_model		)
     _lowerCAmelCase				=      {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
     self.fairseq_tokens_to_ids.update(_lowerCAmelCase		)

     _lowerCAmelCase				=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}




 def __getstate__(	self		)    ->							List[str]:
     _lowerCAmelCase				=      self.__dict__.copy()
     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      self.sp_model.serialized_model_proto()
     return state




 def __setstate__(	self			,     _lowerCAmelCase		)    ->							Optional[int]:
     _lowerCAmelCase				=      d

     # for backward compatibility
     if not hasattr(self			,     "sp_model_kwargs"		):
         _lowerCAmelCase				=      {}

     _lowerCAmelCase				=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							List[int]:

     if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
     _lowerCAmelCase				=      [self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = False		)    ->							List[int]:

     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_lowerCAmelCase			,     token_ids_a=_lowerCAmelCase			,     already_has_special_tokens=_lowerCAmelCase		)

     if token_ids_a is None:
         return [1] + ([0] * len(_lowerCAmelCase		))
     return [1] + ([0] * len(_lowerCAmelCase		)) + [1, 1] + ([0] * len(_lowerCAmelCase		))




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							List[int]:
     _lowerCAmelCase				=      [self.sep_token_id]

     if token_ids_a is None:
         return len(sep + token_ids_a		) * [0]
     return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]




 @property
 def 							_snake_case   (	self		)    ->							str:
     return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[str]:
     return self.sp_model.encode(_lowerCAmelCase			,     out_type=_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[str]:
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     _lowerCAmelCase				=      self.sp_model.PieceToId(_lowerCAmelCase		)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[Any]:
     if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      "".join(_lowerCAmelCase		).replace(_lowerCAmelCase			,     " "		).strip()
     return out_string





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase				=      os.path.join(
         _lowerCAmelCase			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

     if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		) and os.path.isfile(self.vocab_file		):
         copyfile(self.vocab_file			,     _lowerCAmelCase		)
     elif not os.path.isfile(self.vocab_file		):
         with open(_lowerCAmelCase			,     "wb"		) as fi:
             _lowerCAmelCase				=      self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase		)

     return (out_vocab_file,)


