




"""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 GLPNImageProcessor

class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=7       ,					lowerCamelCase_=3       ,					lowerCamelCase_=1_8       ,					lowerCamelCase_=3_0       ,					lowerCamelCase_=4_0_0       ,					lowerCamelCase_=True       ,					lowerCamelCase_=3_2       ,					lowerCamelCase_=True       ,					)				->   Union[str, Any]:
       UpperCamelCase              =	parent
       UpperCamelCase              =	batch_size
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	image_size
       UpperCamelCase              =	min_resolution
       UpperCamelCase              =	max_resolution
       UpperCamelCase              =	do_resize
       UpperCamelCase              =	size_divisor
       UpperCamelCase              =	do_rescale







   def   UpperCAmelCase__			(		self)				->   Any:
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }

@require_torch
@require_vision
class 				snake_case_			(					lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       GLPNImageProcessor if is_vision_available() else None







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	GLPNImageProcessingTester(self)







   @property
   def   UpperCAmelCase__			(		self)				->   str:
       return self.image_processor_tester.prepare_image_processor_dict()







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	self.image_processing_class(**self.image_processor_dict)
       self.assertTrue(hasattr(lowerCamelCase_       ,					'''do_resize'''))
       self.assertTrue(hasattr(lowerCamelCase_       ,					'''size_divisor'''))
       self.assertTrue(hasattr(lowerCamelCase_       ,					'''resample'''))
       self.assertTrue(hasattr(lowerCamelCase_       ,					'''do_rescale'''))







   def   UpperCAmelCase__			(		self)				->   str:
       pass







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       # Initialize image_processing
       UpperCamelCase              =	self.image_processing_class(**self.image_processor_dict)
       # create random PIL images
       UpperCamelCase              =	prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=lowerCamelCase_)
       for image in image_inputs:
           self.assertIsInstance(lowerCamelCase_       ,					Image.Image)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase              =	image_processing(image_inputs[0]       ,					return_tensors='''pt''').pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       # Initialize image_processing
       UpperCamelCase              =	self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase              =	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 (GLPNImageProcessor doesn't support batching)
       UpperCamelCase              =	image_processing(image_inputs[0]       ,					return_tensors='''pt''').pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       # Initialize image_processing
       UpperCamelCase              =	self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase              =	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 (GLPNImageProcessor doesn't support batching)
       UpperCamelCase              =	image_processing(image_inputs[0]       ,					return_tensors='''pt''').pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)







'''simple docstring'''



import warnings
from typing import Dict, List, Optional, Tuple

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


__UpperCamelCase   :							int         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["input_ids", "attention_mask"]




      def __init__(	self			, lowerCamelCase="</s>"			, lowerCamelCase="<unk>"			, lowerCamelCase="<pad>"			, lowerCamelCase=125			, lowerCamelCase=None			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
            __a					  =     [F"""<extra_id_{i}>""" for i in range(lowerCamelCase   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            __a					  =     len(set(filter(lambda lowerCamelCase   : bool('extra_id' in str(lowerCamelCase   )   )			, lowerCamelCase   )   )   )
            if extra_tokens != extra_ids:
               raise ValueError(
                   F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                   ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
                   ' extra_ids tokens'   )

         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else pad_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else eos_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else unk_token

         super().__init__(
             eos_token=lowerCamelCase			, unk_token=lowerCamelCase			, pad_token=lowerCamelCase			, extra_ids=lowerCamelCase			, additional_special_tokens=lowerCamelCase			, **lowerCamelCase			, )

         __a					  =     extra_ids

         __a					  =     2**8  # utf is 8 bits

         # define special tokens dict
         __a					  =     {
             self.pad_token: 0,
             self.eos_token: 1,
             self.unk_token: 2,
         }
         __a					  =     len(self.special_tokens_encoder   )
         __a					  =     len(lowerCamelCase   )
         for i, token in enumerate(lowerCamelCase   ):
            __a					  =     self.vocab_size + i - n
         __a					  =     {v: k for k, v in self.special_tokens_encoder.items()}




      @property
      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = False   )				->List[int]:



         '''simple docstring'''
         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCamelCase			, token_ids_a=lowerCamelCase			, already_has_special_tokens=lowerCamelCase   )

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




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[int]:



         '''simple docstring'''
         if len(lowerCamelCase   ) > 0 and token_ids[-1] == self.eos_token_id:
            warnings.warn(
                F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                ' eos tokens being added.'   )
            return token_ids
         else:
            return token_ids + [self.eos_token_id]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     [self.eos_token_id]

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




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
         if token_ids_a is None:
            return token_ids_a
         else:
            __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
            return token_ids_a + token_ids_a




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     [chr(lowerCamelCase   ) for i in text.encode('utf-8'   )]
         return tokens




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         if token in self.special_tokens_encoder:
            __a					  =     self.special_tokens_encoder[token]
         elif token in self.added_tokens_encoder:
            __a					  =     self.added_tokens_encoder[token]
         elif len(lowerCamelCase   ) != 1:
            __a					  =     self.unk_token_id
         else:
            __a					  =     ord(lowerCamelCase   ) + self._num_special_tokens
         return token_id




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         if index in self.special_tokens_decoder:
            __a					  =     self.special_tokens_decoder[index]
         else:
            __a					  =     chr(index - self._num_special_tokens   )
         return token




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     B''
         for token in tokens:
            if token in self.special_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.added_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.special_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            elif token in self.added_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            else:
               __a					  =     bytes([ord(lowerCamelCase   )]   )
            bstring += tok_string
         __a					  =     bstring.decode('utf-8'			, errors='ignore'   )
         return string






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->Tuple[str]:



         '''simple docstring'''
         return ()