




"""simple docstring"""


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor






class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def __init__(       self:	List[Any],   a_:	Union[str, Any],   a_:	Tuple=7,   a_:	List[Any]=3,   a_:	int=30,   a_:	Dict=400,   a_:	str=True,   a_:	Optional[int]=None,   a_:	int=True,   a_:	Optional[Any]=[0.5, 0.5, 0.5],   a_:	Any=[0.5, 0.5, 0.5],   a_:	List[Any]=True,   a_:	Union[str, Any]=1 / 255,   a_:	List[Any]=True,   ):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
   _snake_case							:	Dict			=						parent
   _snake_case							:	Any			=						batch_size
   _snake_case							:	str			=						num_channels
   _snake_case							:	List[Any]			=						min_resolution
   _snake_case							:	Tuple			=						max_resolution
   _snake_case							:	Union[str, Any]			=						do_resize
   _snake_case							:	List[str]			=						size
   _snake_case							:	Union[str, Any]			=						do_normalize
   _snake_case							:	List[Any]			=						image_mean
   _snake_case							:	List[str]			=						image_std
   _snake_case							:	Optional[int]			=						do_rescale
   _snake_case							:	List[str]			=						rescale_factor
   _snake_case							:	List[Any]			=						do_pad


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Any,   a_:	Any,   a_:	Tuple=False	):




   '''simple docstring'''



   if not batched:
     _snake_case							:	int			=						image_inputs[0]
     if isinstance(a_,   Image.Image	):
       _snake_case	,      _snake_case							:	Optional[int]			=						image.size
     else:
       _snake_case	,      _snake_case							:	Dict			=						image.shape[1], image.shape[2]
     if w < h:
       _snake_case							:	List[Any]			=						int(self.size["""shortest_edge"""] * h / w	)
       _snake_case							:	str			=						self.size["""shortest_edge"""]
     elif w > h:
       _snake_case							:	Any			=						self.size["""shortest_edge"""]
       _snake_case							:	Dict			=						int(self.size["""shortest_edge"""] * w / h	)
     else:
       _snake_case							:	Dict			=						self.size["""shortest_edge"""]
       _snake_case							:	Dict			=						self.size["""shortest_edge"""]

   else:
     _snake_case							:	Union[str, Any]			=						[]
     for image in image_inputs:
       _snake_case	,      _snake_case							:	int			=						self.get_expected_values([image]	)
       expected_values.append((expected_height, expected_width)	)
     _snake_case							:	List[Any]			=						max(a_,   key=lambda a_	: item[0]	)[0]
     _snake_case							:	Union[str, Any]			=						max(a_,   key=lambda a_	: item[1]	)[1]

   return expected_height, expected_width







@require_torch
@require_vision
class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      YolosImageProcessor if is_vision_available() else None


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						YolosImageProcessingTester(self	)


 @property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.image_processing_class(**self.image_processor_dict	)
   self.assertTrue(hasattr(a_,   """image_mean"""	)	)
   self.assertTrue(hasattr(a_,   """image_std"""	)	)
   self.assertTrue(hasattr(a_,   """do_normalize"""	)	)
   self.assertTrue(hasattr(a_,   """do_resize"""	)	)
   self.assertTrue(hasattr(a_,   """size"""	)	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	str			=						self.image_processing_class.from_dict(self.image_processor_dict	)
   self.assertEqual(image_processor.size,   {"""shortest_edge""": 18, """longest_edge""": 1_333}	)
   self.assertEqual(image_processor.do_pad,   a_	)

   _snake_case							:	Dict			=						self.image_processing_class.from_dict(
       self.image_processor_dict,   size=42,   max_size=84,   pad_and_return_pixel_mask=a_	)
   self.assertEqual(image_processor.size,   {"""shortest_edge""": 42, """longest_edge""": 84}	)
   self.assertEqual(image_processor.do_pad,   a_	)


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	List[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=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   Image.Image	)

   # Test not batched input
   _snake_case							:	Optional[Any]			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values

   _snake_case	,      _snake_case							:	Dict			=						self.image_processor_tester.get_expected_values(a_	)

   self.assertEqual(
       encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

   # Test batched
   _snake_case	,      _snake_case							:	Dict			=						self.image_processor_tester.get_expected_values(a_,   batched=a_	)

   _snake_case							:	Any			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ),   )


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	str			=						self.image_processing_class(**self.image_processor_dict	)
   # create random numpy tensors
   _snake_case							:	List[Any]			=						prepare_image_inputs(self.image_processor_tester,   equal_resolution=a_,   numpify=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   np.ndarray	)

   # Test not batched input
   _snake_case							:	Any			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values

   _snake_case	,      _snake_case							:	List[Any]			=						self.image_processor_tester.get_expected_values(a_	)

   self.assertEqual(
       encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

   # Test batched
   _snake_case							:	Tuple			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values

   _snake_case	,      _snake_case							:	Optional[Any]			=						self.image_processor_tester.get_expected_values(a_,   batched=a_	)

   self.assertEqual(
       encoded_images.shape,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ),   )


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						self.image_processing_class(**self.image_processor_dict	)
   # create random PyTorch tensors
   _snake_case							:	List[Any]			=						prepare_image_inputs(self.image_processor_tester,   equal_resolution=a_,   torchify=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   torch.Tensor	)

   # Test not batched input
   _snake_case							:	Dict			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values

   _snake_case	,      _snake_case							:	str			=						self.image_processor_tester.get_expected_values(a_	)

   self.assertEqual(
       encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

   # Test batched
   _snake_case							:	List[str]			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values

   _snake_case	,      _snake_case							:	List[str]			=						self.image_processor_tester.get_expected_values(a_,   batched=a_	)

   self.assertEqual(
       encoded_images.shape,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ),   )


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.image_processing_class(**self.image_processor_dict	)
   _snake_case							:	Dict			=						self.image_processing_class(do_resize=a_,   do_normalize=a_,   do_rescale=a_	)
   # create random PyTorch tensors
   _snake_case							:	Union[str, Any]			=						prepare_image_inputs(self.image_processor_tester,   equal_resolution=a_,   torchify=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   torch.Tensor	)

   # Test whether the method "pad" and calling the image processor return the same tensors
   _snake_case							:	int			=						image_processing_a.pad(a_,   return_tensors="""pt"""	)
   _snake_case							:	int			=						image_processing_a(a_,   return_tensors="""pt"""	)

   self.assertTrue(
       torch.allclose(encoded_images_with_method["""pixel_values"""],   encoded_images["""pixel_values"""],   atol=1E-4	)	)


 @slow
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
   with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""",   """r"""	) as f:
     _snake_case							:	int			=						json.loads(f.read()	)

   _snake_case							:	int			=						{"""image_id""": 39_769, """annotations""": target}

   # encode them
   _snake_case							:	Optional[Any]			=						YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""	)
   _snake_case							:	int			=						image_processing(images=a_,   annotations=a_,   return_tensors="""pt"""	)

   # verify pixel values
   _snake_case							:	Any			=						torch.Size([1, 3, 800, 1_066]	)
   self.assertEqual(encoding["""pixel_values"""].shape,   a_	)

   _snake_case							:	int			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
   self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3],   a_,   atol=1E-4	)	)

   # verify area
   _snake_case							:	List[str]			=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],   a_	)	)
   # verify boxes
   _snake_case							:	Union[str, Any]			=						torch.Size([6, 4]	)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,   a_	)
   _snake_case							:	Union[str, Any]			=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],   a_,   atol=1E-3	)	)
   # verify image_id
   _snake_case							:	Dict			=						torch.tensor([39_769]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],   a_	)	)
   # verify is_crowd
   _snake_case							:	Optional[int]			=						torch.tensor([0, 0, 0, 0, 0, 0]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],   a_	)	)
   # verify class_labels
   _snake_case							:	Union[str, Any]			=						torch.tensor([75, 75, 63, 65, 17, 17]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],   a_	)	)
   # verify orig_size
   _snake_case							:	Union[str, Any]			=						torch.tensor([480, 640]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],   a_	)	)
   # verify size
   _snake_case							:	Tuple			=						torch.tensor([800, 1_066]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],   a_	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
   with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""",   """r"""	) as f:
     _snake_case							:	Any			=						json.loads(f.read()	)

   _snake_case							:	Optional[Any]			=						{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

   _snake_case							:	List[Any]			=						pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""	)

   # encode them
   _snake_case							:	List[Any]			=						YolosImageProcessor(format="""coco_panoptic"""	)
   _snake_case							:	str			=						image_processing(images=a_,   annotations=a_,   masks_path=a_,   return_tensors="""pt"""	)

   # verify pixel values
   _snake_case							:	Union[str, Any]			=						torch.Size([1, 3, 800, 1_066]	)
   self.assertEqual(encoding["""pixel_values"""].shape,   a_	)

   _snake_case							:	Union[str, Any]			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
   self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3],   a_,   atol=1E-4	)	)

   # verify area
   _snake_case							:	Any			=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],   a_	)	)
   # verify boxes
   _snake_case							:	int			=						torch.Size([6, 4]	)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,   a_	)
   _snake_case							:	str			=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],   a_,   atol=1E-3	)	)
   # verify image_id
   _snake_case							:	int			=						torch.tensor([39_769]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],   a_	)	)
   # verify is_crowd
   _snake_case							:	Optional[int]			=						torch.tensor([0, 0, 0, 0, 0, 0]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],   a_	)	)
   # verify class_labels
   _snake_case							:	Optional[int]			=						torch.tensor([17, 17, 63, 75, 75, 93]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],   a_	)	)
   # verify masks
   _snake_case							:	Tuple			=						822_873
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item(),   a_	)
   # verify orig_size
   _snake_case							:	Optional[int]			=						torch.tensor([480, 640]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],   a_	)	)
   # verify size
   _snake_case							:	List[Any]			=						torch.tensor([800, 1_066]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],   a_	)	)








"""simple docstring"""


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     A_      			=       '''pt'''
elif is_tf_available():
     A_      			=       '''tf'''
else:
     A_      			=       '''jax'''






class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ByTaTokenizer
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   super().setUp()
   _snake_case							:	List[str]			=						ByTaTokenizer()
   tokenizer.save_pretrained(self.tmpdirname	)


 @cached_property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   return ByTaTokenizer.from_pretrained("""google/byt5-small"""	)


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	int	):




   '''simple docstring'''



   return self.tokenizer_class.from_pretrained(self.tmpdirname,   **a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Optional[Any],   a_:	List[Any]=False,   a_:	int=20,   a_:	Union[str, Any]=5	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						[]
   for i in range(len(a_	)	):
     try:
       _snake_case							:	Optional[Any]			=						tokenizer.decode([i],   clean_up_tokenization_spaces=a_	)
     except UnicodeDecodeError:
       pass
     toks.append((i, tok)	)

   _snake_case							:	str			=						list(filter(lambda a_	: re.match(r"""^[ a-zA-Z]+$""",   t[1]	),   a_	)	)
   _snake_case							:	List[Any]			=						list(filter(lambda a_	: [t[0]] == tokenizer.encode(t[1],   add_special_tokens=a_	),   a_	)	)
   if max_length is not None and len(a_	) > max_length:
     _snake_case							:	Tuple			=						toks[:max_length]
   if min_length is not None and len(a_	) < min_length and len(a_	) > 0:
     while len(a_	) < min_length:
       _snake_case							:	List[str]			=						toks + toks
        # toks_str = [t[1] for t in toks]
   _snake_case							:	Tuple			=						[t[0] for t in toks]

   # Ensure consistency
   _snake_case							:	Optional[Any]			=						tokenizer.decode(a_,   clean_up_tokenization_spaces=a_	)
   if " " not in output_txt and len(a_	) > 1:
     _snake_case							:	Dict			=						(
         tokenizer.decode([toks_ids[0]],   clean_up_tokenization_spaces=a_	)
         + """ """
         + tokenizer.decode(toks_ids[1:],   clean_up_tokenization_spaces=a_	)
     )
   if with_prefix_space:
     _snake_case							:	Union[str, Any]			=						""" """ + output_txt
   _snake_case							:	Any			=						tokenizer.encode(a_,   add_special_tokens=a_	)
   return output_txt, output_ids


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.ta_base_tokenizer
   _snake_case							:	Optional[Any]			=						tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]	)
   _snake_case							:	int			=						tokenizer(["""hi""", """I went to the gym""", """"""]	)
   self.assertListEqual(batch_with_eos_added["""input_ids"""],   batch_without_eos_added["""input_ids"""]	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.ta_base_tokenizer
   _snake_case							:	Tuple			=						"""Unicode €."""
   _snake_case							:	List[Any]			=						tokenizer(a_	)
   _snake_case							:	Tuple			=						[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
   self.assertEqual(encoded["""input_ids"""],   a_	)

   # decoding
   _snake_case							:	Tuple			=						tokenizer.decode(a_	)
   self.assertEqual(a_,   """Unicode €.</s>"""	)

   _snake_case							:	Tuple			=						tokenizer("""e è é ê ë"""	)
   _snake_case							:	List[Any]			=						[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
   self.assertEqual(encoded["""input_ids"""],   a_	)
   # decoding
   _snake_case							:	int			=						tokenizer.decode(a_	)
   self.assertEqual(a_,   """e è é ê ë</s>"""	)

   # encode/decode, but with `encode` instead of `__call__`
   self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""	)	),   """e è é ê ë</s>"""	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.ta_base_tokenizer
   _snake_case							:	List[Any]			=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   # fmt: off
   _snake_case							:	Union[str, Any]			=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
   # fmt: on
   _snake_case							:	int			=						tokenizer(a_,   padding=a_,   return_tensors=a_	)
   self.assertIsInstance(a_,   a_	)

   if FRAMEWORK != "jax":
     _snake_case							:	List[str]			=						list(batch.input_ids.numpy()[0]	)
   else:
     _snake_case							:	Optional[int]			=						list(batch.input_ids.tolist()[0]	)

   self.assertListEqual(a_,   a_	)

   self.assertEqual((2, 37),   batch.input_ids.shape	)
   self.assertEqual((2, 37),   batch.attention_mask.shape	)


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.ta_base_tokenizer
   _snake_case							:	Optional[int]			=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   _snake_case							:	Tuple			=						tokenizer(a_,   padding=a_,   return_tensors=a_	)
   # check if input_ids are returned and no decoder_input_ids
   self.assertIn("""input_ids""",   a_	)
   self.assertIn("""attention_mask""",   a_	)
   self.assertNotIn("""decoder_input_ids""",   a_	)
   self.assertNotIn("""decoder_attention_mask""",   a_	)


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.ta_base_tokenizer
   _snake_case							:	Dict			=						[
       """Summary of the text.""",
       """Another summary.""",
   ]
   _snake_case							:	Optional[int]			=						tokenizer(
       text_target=a_,   max_length=32,   padding="""max_length""",   truncation=a_,   return_tensors=a_	)
   self.assertEqual(32,   targets["""input_ids"""].shape[1]	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						self.ta_base_tokenizer
   _snake_case							:	Optional[int]			=						["""A long paragraph for summarization. </s>"""]
   _snake_case							:	Dict			=						["""Summary of the text. </s>"""]
   # fmt: off
   _snake_case							:	Optional[int]			=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
   _snake_case							:	Optional[Any]			=						[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
   # fmt: on

   _snake_case							:	Optional[Any]			=						tokenizer(a_,   text_target=a_	)

   self.assertEqual(a_,   batch["""input_ids"""][0]	)
   self.assertEqual(a_,   batch["""labels"""][0]	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       self.assertNotEqual(tokenizer.model_max_length,   42	)

        # Now let's start the test
   _snake_case							:	str			=						self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       # Isolate this from the other tests because we save additional tokens/etc
       _snake_case							:	List[str]			=						tempfile.mkdtemp()

       _snake_case							:	List[str]			=						""" He is very happy, UNwant\u00E9d,running"""
       _snake_case							:	Any			=						tokenizer.encode(a_,   add_special_tokens=a_	)
       tokenizer.save_pretrained(a_	)

       _snake_case							:	List[Any]			=						tokenizer.__class__.from_pretrained(a_	)
       _snake_case							:	Dict			=						after_tokenizer.encode(a_,   add_special_tokens=a_	)
       self.assertListEqual(a_,   a_	)

       shutil.rmtree(a_	)

   _snake_case							:	Tuple			=						self.get_tokenizers(model_max_length=42	)
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       # Isolate this from the other tests because we save additional tokens/etc
       _snake_case							:	Union[str, Any]			=						tempfile.mkdtemp()

       _snake_case							:	List[Any]			=						""" He is very happy, UNwant\u00E9d,running"""
       tokenizer.add_tokens(["""bim""", """bambam"""]	)
       _snake_case							:	Optional[Any]			=						tokenizer.additional_special_tokens
       additional_special_tokens.append("""new_additional_special_token"""	)
       tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}	)
       _snake_case							:	Any			=						tokenizer.encode(a_,   add_special_tokens=a_	)
       tokenizer.save_pretrained(a_	)

       _snake_case							:	Optional[Any]			=						tokenizer.__class__.from_pretrained(a_	)
       _snake_case							:	str			=						after_tokenizer.encode(a_,   add_special_tokens=a_	)
       self.assertListEqual(a_,   a_	)
       self.assertIn("""new_additional_special_token""",   after_tokenizer.additional_special_tokens	)
       self.assertEqual(after_tokenizer.model_max_length,   42	)

       _snake_case							:	Optional[int]			=						tokenizer.__class__.from_pretrained(a_,   model_max_length=43	)
       self.assertEqual(tokenizer.model_max_length,   43	)

       shutil.rmtree(a_	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						[]
   if self.test_slow_tokenizer:
     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

   if self.test_rust_tokenizer:
     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

   for tokenizer_class, tokenizer_utils in tokenizer_list:
     with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer_utils.save_pretrained(a_	)

       with open(os.path.join(a_,   """special_tokens_map.json"""	),   encoding="""utf-8"""	) as json_file:
         _snake_case							:	Union[str, Any]			=						json.load(a_	)

       with open(os.path.join(a_,   """tokenizer_config.json"""	),   encoding="""utf-8"""	) as json_file:
         _snake_case							:	List[Any]			=						json.load(a_	)

       _snake_case							:	int			=						[f"<extra_id_{i}>" for i in range(125	)]

       _snake_case							:	Optional[int]			=						added_tokens_extra_ids + [
           """an_additional_special_token"""
       ]
       _snake_case							:	Dict			=						added_tokens_extra_ids + [
           """an_additional_special_token"""
       ]

       with open(os.path.join(a_,   """special_tokens_map.json"""	),   """w""",   encoding="""utf-8"""	) as outfile:
         json.dump(a_,   a_	)
       with open(os.path.join(a_,   """tokenizer_config.json"""	),   """w""",   encoding="""utf-8"""	) as outfile:
         json.dump(a_,   a_	)

       # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
       # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
       # "special_tokens_map.json" files
       _snake_case							:	Optional[int]			=						tokenizer_class.from_pretrained(
           a_,   )
       self.assertIn(
           """an_additional_special_token""",   tokenizer_without_change_in_init.additional_special_tokens	)
       # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
       self.assertEqual(
           ["""an_additional_special_token"""],   tokenizer_without_change_in_init.convert_ids_to_tokens(
               tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]	)	),   )

       # Now we test that we can change the value of additional_special_tokens in the from_pretrained
       _snake_case							:	Union[str, Any]			=						added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""",   lstrip=a_	)]
       _snake_case							:	List[Any]			=						tokenizer_class.from_pretrained(
           a_,   additional_special_tokens=a_,   )

       self.assertIn("""a_new_additional_special_token""",   tokenizer.additional_special_tokens	)
       self.assertEqual(
           ["""a_new_additional_special_token"""],   tokenizer.convert_ids_to_tokens(
               tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]	)	),   )


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						[]
   if self.test_slow_tokenizer:
     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

   if self.test_rust_tokenizer:
     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

   for tokenizer_class, tokenizer_utils in tokenizer_list:
     with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer_utils.save_pretrained(a_	)

       _snake_case							:	Optional[Any]			=						tokenizer_class.from_pretrained(a_	)

       self.assertTrue(tokenizer.decode([255]	) == """"""	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.get_tokenizers(fast=a_,   do_lower_case=a_	)
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       _snake_case							:	Dict			=						["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
       _snake_case							:	List[Any]			=						tokenizer.convert_tokens_to_string(a_	)

       self.assertIsInstance(a_,   a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	str			=						self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       _snake_case							:	Optional[int]			=						[
           """bos_token""",
           """eos_token""",
           """unk_token""",
           """sep_token""",
           """pad_token""",
           """cls_token""",
           """mask_token""",
       ]

       _snake_case							:	Any			=						0
       _snake_case							:	Union[str, Any]			=						tokenizer.convert_ids_to_tokens(
           a_,   skip_special_tokens=a_	)

       for attr in attributes_list:
         setattr(a_,   attr + """_id""",   a_	)
         self.assertEqual(getattr(a_,   a_	),   a_	)
         self.assertEqual(getattr(a_,   attr + """_id"""	),   a_	)

         setattr(a_,   attr + """_id""",   a_	)
         self.assertEqual(getattr(a_,   a_	),   a_	)
         self.assertEqual(getattr(a_,   attr + """_id"""	),   a_	)

       setattr(a_,   """additional_special_tokens_ids""",   []	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens"""	),   []	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens_ids"""	),   []	)

       setattr(a_,   """additional_special_tokens_ids""",   [token_id_to_test_setters]	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens"""	),   [token_to_test_setters]	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens_ids"""	),   [token_id_to_test_setters]	)


