



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xlnet import XLNetTokenizer
else:
 a_   :str        =      None


a_   :int        =      logging.get_logger(__name__)

a_   :List[str]        =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_   :str        =      {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

a_   :Any        =      {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a_   :Optional[int]        =      "▁"

# Segments (not really needed)
a_   :Optional[int]        =      0
a_   :Union[str, Any]        =      1
a_   :Optional[Any]        =      2
a_   :Optional[int]        =      3
a_   :List[Any]        =      4




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						=	"""left"""
     _SCREAMING_SNAKE_CASE						=	XLNetTokenizer






     def __init__(       self  :  Optional[Any], _snake_case  :  Any=None, _snake_case  :  Optional[Any]=None, _snake_case  :  Any=False, _snake_case  :  List[Any]=True, _snake_case  :  List[str]=False, _snake_case  :  Union[str, Any]="<s>", _snake_case  :  Any="</s>", _snake_case  :  str="<unk>", _snake_case  :  Tuple="<sep>", _snake_case  :  str="<pad>", _snake_case  :  Optional[int]="<cls>", _snake_case  :  Any="<mask>", _snake_case  :  Optional[int]=["<eop>", "<eod>"], **_snake_case  :  Tuple, )   ->Optional[Any]:
            # Mask token behave like a normal word, i.e. include the space before it
            snake_case__							:	Dict   	=	AddedToken(_snake_case, lstrip=_snake_case, rstrip=_snake_case					) if isinstance(_snake_case, _snake_case					) else mask_token

            super().__init__(
                vocab_file=_snake_case, tokenizer_file=_snake_case, do_lower_case=_snake_case, remove_space=_snake_case, keep_accents=_snake_case, bos_token=_snake_case, eos_token=_snake_case, unk_token=_snake_case, sep_token=_snake_case, pad_token=_snake_case, cls_token=_snake_case, mask_token=_snake_case, additional_special_tokens=_snake_case, **_snake_case, )

            snake_case__							:	Dict   	=	3
            snake_case__							:	List[str]   	=	do_lower_case
            snake_case__							:	List[str]   	=	remove_space
            snake_case__							:	Tuple   	=	keep_accents
            snake_case__							:	Any   	=	vocab_file
            snake_case__							:	Tuple   	=	False if not self.vocab_file else True






     def    lowercase_   (       self  :  Any, _snake_case  :  List[int], _snake_case  :  Optional[List[int]] = None					)   ->List[int]:
            snake_case__							:	Dict   	=	[self.sep_token_id]
            snake_case__							:	Dict   	=	[self.cls_token_id]
            if token_ids_a is None:
                   return token_ids_a + sep + cls
            return token_ids_a + sep + token_ids_a + sep + cls






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  List[int], _snake_case  :  Optional[List[int]] = None					)   ->List[int]:
            snake_case__							:	List[str]   	=	[self.sep_token_id]
            snake_case__							:	Dict   	=	[2]

            if token_ids_a is None:
                   return len(token_ids_a + sep					) * [0] + cls_segment_id
            return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id






     def    lowercase_   (       self  :  Any, _snake_case  :  str, _snake_case  :  Optional[str] = None					)   ->Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                       'tokenizer.'					)

            if not os.path.isdir(_snake_case					):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                   return
            snake_case__							:	Union[str, Any]   	=	os.path.join(
                _snake_case, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

            if os.path.abspath(self.vocab_file					) != os.path.abspath(_snake_case					):
                   copyfile(self.vocab_file, _snake_case					)

            return (out_vocab_file,)











import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[int], _snake_case  :  List[Any], _snake_case  :  str=7, _snake_case  :  Tuple=3, _snake_case  :  List[str]=3_0, _snake_case  :  Tuple=4_0_0, _snake_case  :  Any=True, _snake_case  :  List[Any]=None, _snake_case  :  int=0.9, _snake_case  :  Optional[Any]=None, _snake_case  :  str=True, _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], )   ->List[Any]:
            snake_case__							:	int   	=	size if size is not None else {'shortest_edge': 3_0}
            snake_case__							:	Tuple   	=	crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
            snake_case__							:	Union[str, Any]   	=	parent
            snake_case__							:	Dict   	=	batch_size
            snake_case__							:	int   	=	num_channels
            snake_case__							:	Tuple   	=	min_resolution
            snake_case__							:	Any   	=	max_resolution
            snake_case__							:	List[Any]   	=	do_resize_and_center_crop
            snake_case__							:	str   	=	size
            snake_case__							:	str   	=	crop_pct
            snake_case__							:	List[str]   	=	crop_size
            snake_case__							:	Optional[int]   	=	do_normalize
            snake_case__							:	Tuple   	=	image_mean
            snake_case__							:	Tuple   	=	image_std






     def    lowercase_   (       self  :  Optional[int]					)   ->int:
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	PoolFormerImageProcessor if is_vision_available() else None






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Union[str, Any]   	=	PoolFormerImageProcessingTester(self					)






     @property
     def    lowercase_   (       self  :  int					)   ->Dict:
            return self.image_processor_tester.prepare_image_processor_dict()






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[int]:
            snake_case__							:	List[str]   	=	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(_snake_case, 'do_resize_and_center_crop'					)					)
            self.assertTrue(hasattr(_snake_case, 'size'					)					)
            self.assertTrue(hasattr(_snake_case, 'crop_pct'					)					)
            self.assertTrue(hasattr(_snake_case, 'do_normalize'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_mean'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_std'					)					)






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size, {'shortest_edge': 3_0}					)
            self.assertEqual(image_processor.crop_size, {'height': 3_0, 'width': 3_0}					)

            snake_case__							:	int   	=	self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4					)
            self.assertEqual(image_processor.size, {'shortest_edge': 4_2}					)
            self.assertEqual(image_processor.crop_size, {'height': 8_4, 'width': 8_4}					)






     def    lowercase_   (       self  :  List[Any]					)   ->List[Any]:
            pass






     def    lowercase_   (       self  :  List[str]					)   ->str:
            # Initialize image_processing
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, Image.Image					)

            # Test not batched input
            snake_case__							:	Optional[int]   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values

            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	str   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  int					)   ->List[Any]:
            # Initialize image_processing
            snake_case__							:	Optional[int]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            snake_case__							:	Dict   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, numpify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, np.ndarray					)

            # Test not batched input
            snake_case__							:	Dict   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	List[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            # Initialize image_processing
            snake_case__							:	Tuple   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, torchify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, torch.Tensor					)

            # Test not batched input
            snake_case__							:	Tuple   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	Optional[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






