
'''simple docstring'''
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 MobileViTImageProcessor






class    UpperCAmelCase_				(	unittest.TestCase					):





 def __init__(    self ,       lowercase_ ,       lowercase_=7 ,       lowercase_=3 ,       lowercase_=18 ,       lowercase_=30 ,       lowercase_=4_00 ,       lowercase_=True ,       lowercase_=None ,       lowercase_=True ,       lowercase_=None ,       lowercase_=True ,       ):
     snake_case_ :    Any     			=						size if size is not None else {"shortest_edge": 20}
     snake_case_ :    List[Any]     			=						crop_size if crop_size is not None else {"height": 18, "width": 18}
     snake_case_ :    List[Any]     			=						parent
     snake_case_ :    Tuple     			=						batch_size
     snake_case_ :    List[str]     			=						num_channels
     snake_case_ :    Dict     			=						image_size
     snake_case_ :    List[str]     			=						min_resolution
     snake_case_ :    Tuple     			=						max_resolution
     snake_case_ :    List[str]     			=						do_resize
     snake_case_ :    Any     			=						size
     snake_case_ :    Tuple     			=						do_center_crop
     snake_case_ :    int     			=						crop_size
     snake_case_ :    Union[str, Any]     			=						do_flip_channel_order





 def        snake_case__		(    self):
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_flip_channel_order": self.do_flip_channel_order,
     }






@require_torch
@require_vision
class    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						MobileViTImageProcessor if is_vision_available() else None





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						MobileViTImageProcessingTester(self)





 @property
 def        snake_case__		(    self):
     return self.image_processor_tester.prepare_image_processor_dict()





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(lowercase_ ,       "do_resize"))
     self.assertTrue(hasattr(lowercase_ ,       "size"))
     self.assertTrue(hasattr(lowercase_ ,       "do_center_crop"))
     self.assertTrue(hasattr(lowercase_ ,       "center_crop"))
     self.assertTrue(hasattr(lowercase_ ,       "do_flip_channel_order"))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size ,       {"shortest_edge": 20})
     self.assertEqual(image_processor.crop_size ,       {"height": 18, "width": 18})

     snake_case_ :    int     			=						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        snake_case__		(    self):
     pass





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    str     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     snake_case_ :    Optional[Any]     			=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=lowercase_)
     for image in image_inputs:
         self.assertIsInstance(lowercase_ ,       Image.Image)

     # Test not batched input
     snake_case_ :    str     			=						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
     snake_case_ :    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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         ) ,       )





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    Optional[Any]     			=						self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     snake_case_ :    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
     snake_case_ :    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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         ) ,       )

     # Test batched
     snake_case_ :    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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         ) ,       )





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    Tuple     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     snake_case_ :    Dict     			=						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
     snake_case_ :    int     			=						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
     snake_case_ :    Optional[int]     			=						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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         ) ,       )






'''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:
  a_											=						None


a_											=						logging.get_logger(__name__)


a_											=						"▁"

a_											=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "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"
    },
}

a_											=						{
    "google/pegasus-xsum": 512,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						PegasusTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<pad>" ,       lowercase_="</s>" ,       lowercase_="<unk>" ,       lowercase_="<mask_2>" ,       lowercase_="<mask_1>" ,       lowercase_=None ,       lowercase_=1_03 ,       **lowercase_ ,       ):
     snake_case_ :    List[Any]     			=						offset

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

         snake_case_ :    Tuple     			=						(
             ([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(lowercase_) ,       self.offset - 1)
         ]

         if len(set(lowercase_)) != len(lowercase_):
             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}.')
         snake_case_ :    Optional[Any]     			=						additional_special_tokens_extended
     else:
         snake_case_ :    Tuple     			=						[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__(
         lowercase_ ,       tokenizer_file=lowercase_ ,       pad_token=lowercase_ ,       eos_token=lowercase_ ,       unk_token=lowercase_ ,       mask_token=lowercase_ ,       mask_token_sent=lowercase_ ,       offset=lowercase_ ,       additional_special_tokens=lowercase_ ,       **lowercase_ ,       )
     snake_case_ :    int     			=						vocab_file
     snake_case_ :    str     			=						False if not self.vocab_file else True





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						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        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = False):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a) + [1]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     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(lowercase_):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
     snake_case_ :    Dict     			=						os.path.join(
         lowercase_ ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

     if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
         copyfile(self.vocab_file ,       lowercase_)

     return (out_vocab_file,)




