




"""simple docstring"""







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

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


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  SCREAMING_SNAKE_CASE_	     =       None


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_	     =       '▁'

SCREAMING_SNAKE_CASE_	     =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_	     =       {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

SCREAMING_SNAKE_CASE_	     =       {
    'google/pegasus-xsum': 512,
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       PRETRAINED_VOCAB_FILES_MAP
   A_				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       PegasusTokenizer
   A_				      =       ['''input_ids''', '''attention_mask''']







   def __init__(		self       ,					lowerCamelCase_=None       ,					lowerCamelCase_=None       ,					lowerCamelCase_="<pad>"       ,					lowerCamelCase_="</s>"       ,					lowerCamelCase_="<unk>"       ,					lowerCamelCase_="<mask_2>"       ,					lowerCamelCase_="<mask_1>"       ,					lowerCamelCase_=None       ,					lowerCamelCase_=1_0_3       ,					**lowerCamelCase_       ,					)				->   Optional[int]:
       UpperCamelCase              =	offset

       if additional_special_tokens is not None:
           if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
               raise TypeError(
                   F'additional_special_tokens should be of type {type(lowerCamelCase_)}, but is'
                   F' {type(lowerCamelCase_)}')

           UpperCamelCase              =	(
               ([mask_token_sent] + additional_special_tokens)
               if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
               else additional_special_tokens
           )
           # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
           additional_special_tokens_extended += [
               F'<unk_{i}>' for i in range(len(lowerCamelCase_)       ,					self.offset - 1)
           ]

           if len(set(lowerCamelCase_)) != len(lowerCamelCase_):
               raise ValueError(
                   '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                   F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
           UpperCamelCase              =	additional_special_tokens_extended
       else:
           UpperCamelCase              =	[mask_token_sent] if mask_token_sent is not None else []
           additional_special_tokens += [F'<unk_{i}>' for i in range(2       ,					self.offset)]

       super().__init__(
           lowerCamelCase_       ,					tokenizer_file=lowerCamelCase_       ,					pad_token=lowerCamelCase_       ,					eos_token=lowerCamelCase_       ,					unk_token=lowerCamelCase_       ,					mask_token=lowerCamelCase_       ,					mask_token_sent=lowerCamelCase_       ,					offset=lowerCamelCase_       ,					additional_special_tokens=lowerCamelCase_       ,					**lowerCamelCase_       ,					)
       UpperCamelCase              =	vocab_file
       UpperCamelCase              =	False if not self.vocab_file else True







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   List[Any]:
       UpperCamelCase              =	set(self.all_special_ids)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
           raise ValueError(
               '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
               F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

       return [1 if x in all_special_ids else 0 for x in seq]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = False)				->   List[int]:
       if already_has_special_tokens:
           return self._special_token_mask(lowerCamelCase_)
       elif token_ids_a is None:
           return self._special_token_mask(lowerCamelCase_) + [1]
       else:
           return self._special_token_mask(token_ids_a + token_ids_a) + [1]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=None)				->   List[int]:
       if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = 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(lowerCamelCase_):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory')
           return
       UpperCamelCase              =	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_):
           copyfile(self.vocab_file       ,					lowerCamelCase_)

       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 ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       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    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	np.ndarray)

   # Test not batched input
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)






