




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  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def __init__(							self:   Tuple		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Tuple=7		,					_lowerCamelCase:   Tuple=3		,					_lowerCamelCase:   Optional[Any]=30		,					_lowerCamelCase:   Any=4_00		,					_lowerCamelCase:   Optional[int]=True		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   Dict=0.9		,					_lowerCamelCase:   Dict=None		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   List[str]=[0.5, 0.5, 0.5]		,					_lowerCamelCase:   str=[0.5, 0.5, 0.5]		,					):
											SCREAMING_SNAKE_CASE_   	=    size if size is not None else {'''shortest_edge''': 30}
											SCREAMING_SNAKE_CASE_   	=    crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    batch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    min_resolution
											SCREAMING_SNAKE_CASE_   	=    max_resolution
											SCREAMING_SNAKE_CASE_   	=    do_resize_and_center_crop
											SCREAMING_SNAKE_CASE_   	=    size
											SCREAMING_SNAKE_CASE_   	=    crop_pct
											SCREAMING_SNAKE_CASE_   	=    crop_size
											SCREAMING_SNAKE_CASE_   	=    do_normalize
											SCREAMING_SNAKE_CASE_   	=    image_mean
											SCREAMING_SNAKE_CASE_   	=    image_std





				def     _A							(							self:   Any		):
											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  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							int	      =					PoolFormerImageProcessor if is_vision_available() else None


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    PoolFormerImageProcessingTester(self		)


				@property
				def     _A							(							self:   List[Any]		):
											return self.image_processor_tester.prepare_image_processor_dict()


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_resize_and_center_crop'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''size'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''crop_pct'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_normalize'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''image_mean'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''image_std'''		)		)


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size		,					{'''shortest_edge''': 30}		)
											self.assertEqual(image_processor.crop_size		,					{'''height''': 30, '''width''': 30}		)

											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		,					crop_size=84		)
											self.assertEqual(image_processor.size		,					{'''shortest_edge''': 42}		)
											self.assertEqual(image_processor.crop_size		,					{'''height''': 84, '''width''': 84}		)


				def     _A							(							self:   Dict		):
											pass


				def     _A							(							self:   Union[str, Any]		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											SCREAMING_SNAKE_CASE_   	=    prepare_image_inputs(self.image_processor_tester		,					equal_resolution=_lowerCamelCase		)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCamelCase		,					Image.Image		)

											# Test not batched input
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    image_processing(_lowerCamelCase		,					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     _A							(							self:   Optional[Any]		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE_   	=    prepare_image_inputs(self.image_processor_tester		,					equal_resolution=_lowerCamelCase		,					numpify=_lowerCamelCase		)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCamelCase		,					np.ndarray		)

											# Test not batched input
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    image_processing(_lowerCamelCase		,					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     _A							(							self:   Optional[Any]		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE_   	=    prepare_image_inputs(self.image_processor_tester		,					equal_resolution=_lowerCamelCase		,					torchify=_lowerCamelCase		)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCamelCase		,					torch.Tensor		)

											# Test not batched input
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    image_processing(_lowerCamelCase		,					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'''],
											    )		,					)








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""": """spiece.model"""}

__SCREAMING_SNAKE_CASE 						={
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

__SCREAMING_SNAKE_CASE 						={
    """google/reformer-crime-and-punishment""": 524_288,
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					["input_ids", "attention_mask"]


				def __init__(							self:   List[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[Any]="</s>"		,					_lowerCamelCase:   str="<unk>"		,					_lowerCamelCase:   Union[str, Any]=[]		,					_lowerCamelCase:   Optional[Dict[str, Any]] = None		,					**_lowerCamelCase:   List[str]		,					):
											SCREAMING_SNAKE_CASE_   	=    {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    eos_token=_lowerCamelCase		,					unk_token=_lowerCamelCase		,					additional_special_tokens=_lowerCamelCase		,					sp_model_kwargs=self.sp_model_kwargs		,					**_lowerCamelCase		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(_lowerCamelCase		)


				@property
				def     _A							(							self:   Optional[int]		):
											return self.sp_model.get_piece_size()


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    {self.convert_ids_to_tokens(_lowerCamelCase		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


				def __getstate__(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.__dict__.copy()
											SCREAMING_SNAKE_CASE_   	=    None
											return state


				def __setstate__(							self:   Dict		,					_lowerCamelCase:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    d

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

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


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   str		):
											return self.sp_model.encode(_lowerCamelCase		,					out_type=_lowerCamelCase		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Optional[int]		):
											return self.sp_model.piece_to_id(_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Tuple		):
											if index < self.sp_model.get_piece_size():
																		SCREAMING_SNAKE_CASE_   	=    self.sp_model.IdToPiece(_lowerCamelCase		)
											return token


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    []
											SCREAMING_SNAKE_CASE_   	=    ''''''
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(_lowerCamelCase		) + token
																									SCREAMING_SNAKE_CASE_   	=    []
																		else:
																									current_sub_tokens.append(_lowerCamelCase		)
											out_string += self.sp_model.decode(_lowerCamelCase		)
											return out_string.strip()





				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[str] = None		):
											if not os.path.isdir(_lowerCamelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    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:
																									SCREAMING_SNAKE_CASE_   	=    self.sp_model.serialized_model_proto()
																									fi.write(_lowerCamelCase		)

											return (out_vocab_file,)


