


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     __a     (       unittest.TestCase ):
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=13							,					lowerCAmelCase__=3							,					lowerCAmelCase__=18							,					lowerCAmelCase__=30							,					lowerCAmelCase__=400							,					lowerCAmelCase__=True							,					lowerCAmelCase__=None							,					lowerCAmelCase__=True							,					lowerCAmelCase__=[0.5, 0.5, 0.5]							,					lowerCAmelCase__=[0.5, 0.5, 0.5]							,					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  size if size is not None else {'height': 18, 'width': 18}
        lowercase__:  Tuple         =  parent
        lowercase__:  str         =  batch_size
        lowercase__:  Optional[int]         =  num_channels
        lowercase__:  Union[str, Any]         =  image_size
        lowercase__:  int         =  min_resolution
        lowercase__:  int         =  max_resolution
        lowercase__:  Any         =  do_resize
        lowercase__:  int         =  size
        lowercase__:  List[str]         =  do_normalize
        lowercase__:  Any         =  image_mean
        lowercase__:  Any         =  image_std
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''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     __a     (       __UpperCamelCase			,			unittest.TestCase ):
 __lowercase					:				Tuple  	=					EfficientNetImageProcessor if is_vision_available() else None
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Dict         =  EfficientNetImageProcessorTester(self					)
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        lowercase__:  str         =  self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(__lowercase							,					'image_mean'					)					)
        self.assertTrue(hasattr(__lowercase							,					'image_std'					)					)
        self.assertTrue(hasattr(__lowercase							,					'do_normalize'					)					)
        self.assertTrue(hasattr(__lowercase							,					'do_resize'					)					)
        self.assertTrue(hasattr(__lowercase							,					'size'					)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size							,					{'height': 18, 'width': 18}					)

        lowercase__:  Optional[Any]         =  self.image_processing_class.from_dict(self.image_processor_dict							,					size=42					)
        self.assertEqual(image_processor.size							,					{'height': 42, 'width': 42}					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:  List[str]         =  self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        lowercase__:  List[str]         =  prepare_image_inputs(self.image_processor_tester							,					equal_resolution=__lowercase					)
        for image in image_inputs:
               self.assertIsInstance(__lowercase							,					Image.Image					)

        # Test not batched input
        lowercase__:  Optional[Any]         =  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
        lowercase__:  Optional[Any]         =  image_processing(__lowercase							,					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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:  List[str]         =  self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        lowercase__:  int         =  prepare_image_inputs(self.image_processor_tester							,					equal_resolution=__lowercase							,					numpify=__lowercase					)
        for image in image_inputs:
               self.assertIsInstance(__lowercase							,					np.ndarray					)

        # Test not batched input
        lowercase__:  List[Any]         =  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
        lowercase__:  Tuple         =  image_processing(__lowercase							,					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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:  Optional[Any]         =  self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        lowercase__:  Tuple         =  prepare_image_inputs(self.image_processor_tester							,					equal_resolution=__lowercase							,					torchify=__lowercase					)
        for image in image_inputs:
               self.assertIsInstance(__lowercase							,					torch.Tensor					)

        # Test not batched input
        lowercase__:  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.size['height'],
                self.image_processor_tester.size['width'],
            )							,					)

        # Test batched
        lowercase__:  Dict         =  image_processing(__lowercase							,					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'],
            )							,					)










"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


a  :						List[Any]					       =							logging.get_logger(__name__)

a  :						List[Any]					       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a  :						Any					       =							{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

a  :						str					       =							{
    '''microsoft/mpnet-base''': 512,
}

a  :						Union[str, Any]					       =							{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						VOCAB_FILES_NAMES
    __lowercase    :   Optional[int]											=						PRETRAINED_VOCAB_FILES_MAP
    __lowercase    :   Dict											=						PRETRAINED_INIT_CONFIGURATION
    __lowercase    :   Optional[Any]											=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase    :   Dict											=						MPNetTokenizer
    __lowercase    :   Dict											=						['input_ids', 'attention_mask']


    def __init__(      self   ,			__lowercase=None   ,			__lowercase=None   ,			__lowercase=True   ,			__lowercase="<s>"   ,			__lowercase="</s>"   ,			__lowercase="</s>"   ,			__lowercase="<s>"   ,			__lowercase="[UNK]"   ,			__lowercase="<pad>"   ,			__lowercase="<mask>"   ,			__lowercase=True   ,			__lowercase=None   ,			**__lowercase   ,			):
     super().__init__(
         __lowercase   ,			tokenizer_file=__lowercase   ,			do_lower_case=__lowercase   ,			bos_token=__lowercase   ,			eos_token=__lowercase   ,			sep_token=__lowercase   ,			cls_token=__lowercase   ,			unk_token=__lowercase   ,			pad_token=__lowercase   ,			mask_token=__lowercase   ,			tokenize_chinese_chars=__lowercase   ,			strip_accents=__lowercase   ,			**__lowercase   ,			)

     UpperCAmelCase__ 						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("""lowercase"""   ,			__lowercase			) != do_lower_case
         or pre_tok_state.get("""strip_accents"""   ,			__lowercase			) != strip_accents
     ):
      UpperCAmelCase__ 						=				getattr(__lowercase   ,			pre_tok_state.pop("""type"""			)			)
      UpperCAmelCase__ 						=				do_lower_case
      UpperCAmelCase__ 						=				strip_accents
      UpperCAmelCase__ 						=				pre_tok_class(**__lowercase			)

     UpperCAmelCase__ 						=				do_lower_case


    @property
    def 		A__      (      self			):
     if self._mask_token is None:
      if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""			)
      return None
     return str(self._mask_token			)


    @mask_token.setter
    def 		A__      (      self   ,			__lowercase			):
     UpperCAmelCase__ 						=				AddedToken(__lowercase   ,			lstrip=__lowercase   ,			rstrip=__lowercase			) if isinstance(__lowercase   ,			__lowercase			) else value
     UpperCAmelCase__ 						=				value


    def 		A__      (      self   ,			__lowercase   ,			__lowercase=None			):
     UpperCAmelCase__ 						=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				[self.sep_token_id]
     UpperCAmelCase__ 						=				[self.cls_token_id]

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






    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				self._tokenizer.model.save(__lowercase   ,			name=__lowercase			)
     return tuple(__lowercase			)



