



from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A__       =			logging.get_logger(__name__)





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							['''input_ids''', '''attention_mask''']




				def __init__(				self		,							_snake_case="[PAD]"		,							_snake_case="[BOS]"		,							_snake_case="[EOS]"		,							_snake_case="[MASK]"		,							_snake_case="[CLS]"		,							_snake_case="[SEP]"		,							_snake_case=2048		,							**_snake_case		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case						) if isinstance(_snake_case		,							_snake_case						) else pad_token
									_lowerCAmelCase         =       AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case						) if isinstance(_snake_case		,							_snake_case						) else bos_token
									_lowerCAmelCase         =       AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case						) if isinstance(_snake_case		,							_snake_case						) else eos_token
									_lowerCAmelCase         =       AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case						) if isinstance(_snake_case		,							_snake_case						) else mask_token
									_lowerCAmelCase         =       AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case						) if isinstance(_snake_case		,							_snake_case						) else cls_token
									_lowerCAmelCase         =       AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case						) if isinstance(_snake_case		,							_snake_case						) else sep_token

									super().__init__(
									    pad_token=_snake_case		,							bos_token=_snake_case		,							eos_token=_snake_case		,							mask_token=_snake_case		,							cls_token=_snake_case		,							sep_token=_snake_case		,							model_max_length=_snake_case		,							**_snake_case		,							)

									_lowerCAmelCase         =       2**8  # utf is 8 bits

									# define special tokens dict
									_lowerCAmelCase         =       {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									_lowerCAmelCase         =       len(self.special_tokens_encoder						)
									_lowerCAmelCase         =       {v: k for k, v in self.special_tokens_encoder.items()}




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder						)
									for i in range(self._utf_vocab_size						):
														_lowerCAmelCase         =       chr(_snake_case						)
														_lowerCAmelCase         =       i + len(self.special_tokens_encoder						)
									return vocab




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return self._utf_vocab_size + self._num_special_tokens




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None		,							_snake_case = False						):



									"""simple docstring"""





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

									# normal case: some special tokens
									if token_ids_a is None:
														return [1] + [0] * len(_snake_case						) + [1]
									return [1] + ([0] * len(_snake_case						)) + [1] + ([0] * len(_snake_case						)) + [1]




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None						):



									"""simple docstring"""





									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       [chr(_snake_case						) for i in text.encode("""utf-8"""						)]
									return tokens




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									if token in self.special_tokens_encoder:
														_lowerCAmelCase         =       self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
														_lowerCAmelCase         =       self.added_tokens_encoder[token]
									elif len(_snake_case						) != 1:
														_lowerCAmelCase         =       self.unk_token_id
									else:
														_lowerCAmelCase         =       ord(_snake_case						) + self._num_special_tokens
									return token_id




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									if index in self.special_tokens_decoder:
														_lowerCAmelCase         =       self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
														_lowerCAmelCase         =       self.added_tokens_decoder[index]
									else:
														_lowerCAmelCase         =       chr(index - self._num_special_tokens						)
									return token




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       b""""""
									for token in tokens:
														if token in self.special_tokens_decoder:
																			_lowerCAmelCase         =       self.special_tokens_decoder[token].encode("""utf-8"""						)
														elif token in self.added_tokens_decoder:
																			_lowerCAmelCase         =       self.special_tokens_decoder[token].encode("""utf-8"""						)
														elif token in self.special_tokens_encoder:
																			_lowerCAmelCase         =       token.encode("""utf-8"""						)
														elif token in self.added_tokens_encoder:
																			_lowerCAmelCase         =       token.encode("""utf-8"""						)
														else:
																			_lowerCAmelCase         =       bytes([ord(_snake_case						)]						)
														bstring += tok_string
									_lowerCAmelCase         =       bstring.decode("""utf-8"""		,							errors="""replace"""						)
									return string




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None						):



									"""simple docstring"""





									return ()








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 EfficientNetImageProcessor





class    __lowerCAmelCase					(	unittest.TestCase			):




				def __init__(				self		,							_snake_case		,							_snake_case=13		,							_snake_case=3		,							_snake_case=18		,							_snake_case=30		,							_snake_case=400		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=[0.5, 0.5, 0.5]		,							_snake_case=[0.5, 0.5, 0.5]		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       size if size is not None else {"""height""": 18, """width""": 18}
									_lowerCAmelCase         =       parent
									_lowerCAmelCase         =       batch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       min_resolution
									_lowerCAmelCase         =       max_resolution
									_lowerCAmelCase         =       do_resize
									_lowerCAmelCase         =       size
									_lowerCAmelCase         =       do_normalize
									_lowerCAmelCase         =       image_mean
									_lowerCAmelCase         =       image_std




				def 				snake_case  (				self						):



									"""simple docstring"""





									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}





@require_torch
@require_vision
class    __lowerCAmelCase					(	lowerCamelCase__			,		unittest.TestCase			):
				__lowerCamelCase					=							EfficientNetImageProcessor if is_vision_available() else None




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       EfficientNetImageProcessorTester(self						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_snake_case		,							"""image_mean"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""image_std"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_normalize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_resize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""size"""						)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size		,							{"""height""": 18, """width""": 18}						)

									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict		,							size=42						)
									self.assertEqual(image_processor.size		,							{"""height""": 42, """width""": 42}						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)



