"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a							=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Tuple  							=	['''pixel_values''']

				def __init__(				self     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = PILImageResampling.BICUBIC     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			_lowerCamelCase = 1 / 255     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			**_lowerCamelCase     ,			):
										super().__init__(**_lowerCamelCase				)
										lowerCamelCase__ =size if size is not None else {"shortest_edge": 224}
										lowerCamelCase__ =get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase				)
										lowerCamelCase__ =crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__ =get_size_dict(_lowerCamelCase				)

										lowerCamelCase__ =do_resize
										lowerCamelCase__ =size
										lowerCamelCase__ =resample
										lowerCamelCase__ =do_center_crop
										lowerCamelCase__ =crop_size
										lowerCamelCase__ =do_rescale
										lowerCamelCase__ =rescale_factor
										lowerCamelCase__ =do_normalize
										lowerCamelCase__ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowerCamelCase__ =image_std if image_std is not None else OPENAI_CLIP_STD
										lowerCamelCase__ =do_convert_rgb

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = PILImageResampling.BICUBIC     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
										lowerCamelCase__ =get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase				)
										lowerCamelCase__ =get_resize_output_image_size(
										    _lowerCamelCase     ,			size=(size["height"], size["width"])     ,			default_to_square=_lowerCamelCase				)
										return resize(_lowerCamelCase     ,			size=_lowerCamelCase     ,			resample=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
										lowerCamelCase__ =get_size_dict(_lowerCamelCase				)
										return center_crop(_lowerCamelCase     ,			size=(size["height"], size["width"])     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
										return rescale(_lowerCamelCase     ,			scale=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
										return normalize(_lowerCamelCase     ,			mean=_lowerCamelCase     ,			std=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase				)






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = ChannelDimension.FIRST     ,			**_lowerCamelCase     ,			):
										lowerCamelCase__ =do_resize if do_resize is not None else self.do_resize
										lowerCamelCase__ =size if size is not None else self.size
										lowerCamelCase__ =get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase				)
										lowerCamelCase__ =resample if resample is not None else self.resample
										lowerCamelCase__ =do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase__ =crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__ =get_size_dict(_lowerCamelCase				)
										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__ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCamelCase__ =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_resize and size is None:
																raise ValueError("Size must be specified if do_resize is True."				)

										if do_center_crop and crop_size is None:
																raise ValueError("Crop size must be specified if do_center_crop is True."				)

										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."				)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																lowerCamelCase__ =[convert_to_rgb(_lowerCamelCase				) for image in images]

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

										if do_resize:
																lowerCamelCase__ =[self.resize(image=_lowerCamelCase     ,			size=_lowerCamelCase     ,			resample=_lowerCamelCase				) for image in images]

										if do_center_crop:
																lowerCamelCase__ =[self.center_crop(image=_lowerCamelCase     ,			size=_lowerCamelCase				) for image in images]

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

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

										lowerCamelCase__ =[to_channel_dimension_format(_lowerCamelCase     ,			_lowerCamelCase				) for image in images]

										lowerCamelCase__ ={"pixel_values": images}
										return BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase				)



"""simple docstring"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a							={
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

a							=datasets.load_dataset('xnli', split='test+validation')

a							=0
a							=0
a							=0
a							=0





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						if spm_diff == list(reversed(__lowerCAmelCase    )    ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) and fast.decode(__lowerCAmelCase    ) == fast.decode(__lowerCAmelCase    ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						lowerCamelCase__ =slow.encode(slow.decode(__lowerCAmelCase    )    )
						lowerCamelCase__ =fast.encode(fast.decode(__lowerCAmelCase    )    )
						if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
												# Type 3 error.
												# Snehagatha ->
												#       Sne, h, aga, th, a
												#       Sne, ha, gat, ha
												# Encoding the wrong with sp does not even recover what spm gave us
												# It fits tokenizer however...
												return True
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =fast.encode_plus(__lowerCAmelCase    )[0]
						lowerCamelCase__ =enc.offsets
						lowerCamelCase__ ,				lowerCamelCase__ =offsets[idx], offsets[idx - 1]
						if curr is not None and line[curr[0] : curr[1]] == "\u200f":
												return True
						if prev is not None and line[prev[0] : prev[1]] == "\u200f":
												return True





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Tuple:




						'''simple docstring'''



						for i, (spm_id, tok_id) in enumerate(zip(__lowerCAmelCase       ,   __lowerCAmelCase    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowerCAmelCase    )       ,   reversed(__lowerCAmelCase    )    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =len(__lowerCAmelCase    ) - i

						lowerCamelCase__ =spm_ids[first:last]
						lowerCamelCase__ =tok_ids[first:last]

						if check_diff(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if check_LTR_mark(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if last - first > 5:
												# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
												lowerCamelCase__ =Counter(spm_ids[first:last]    )
												lowerCamelCase__ =Counter(tok_ids[first:last]    )

												lowerCamelCase__ ={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0    ) == si}
												lowerCamelCase__ =3
												for i in range(last - first - min_width    ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowerCAmelCase    )    ):
																								lowerCamelCase__ =[
																								    k
																								    for k in range(last - first - min_width    )
																								    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																								]
																								for j in possible_matches:
																														if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ) and check_details(
																														    __lowerCAmelCase       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   ):
																																				return True

						print(F'''Spm: {[fast.decode([spm_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						try:
												print(F'''Tok: {[fast.decode([tok_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						except Exception:
												pass

						lowerCamelCase__ =fast.decode(spm_ids[:first]    )
						lowerCamelCase__ =fast.decode(spm_ids[last:]    )
						lowerCamelCase__ =fast.decode(spm_ids[first:last]    )
						print()
						print(__lowerCAmelCase    )
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						global perfect
						global imperfect
						global wrong
						global total

						lowerCamelCase__ =slow.encode(__lowerCAmelCase    )
						lowerCamelCase__ =fast.encode(__lowerCAmelCase    )

						lowerCamelCase__ =False
						total += 1

						if slow_ids != fast_ids:
												if check_details(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
																		lowerCamelCase__ =True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 1_0000 == 0:
												print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''    )

						if skip_assert:
												return

						assert (
						    slow_ids == fast_ids
						), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowerCAmelCase    )}\n{fast.tokenize(__lowerCAmelCase    )}'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						global batch_total
						for i in range(len(__lowerCAmelCase    )    ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )

												# hypothesis, all languages
												for text in dataset[i]["hypothesis"]["translation"]:
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		a							=list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a							=0
			a							=0
			a							=0
			a							=0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			a							=slow_class.from_pretrained(checkpoint, force_download=True)
			a							=fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')


