






"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_      			=       logging.get_logger(__name__)

A_      			=       {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      '''table-transformer'''
 lowercase__				      =      ['''past_key_values''']
 lowercase__				      =      {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }


 def __init__(       self:	int,   a_:	str=True,   a_:	List[Any]=None,   a_:	List[str]=3,   a_:	Optional[Any]=100,   a_:	Optional[int]=6,   a_:	Optional[Any]=2_048,   a_:	str=8,   a_:	Any=6,   a_:	Dict=2_048,   a_:	Any=8,   a_:	Union[str, Any]=0.0,   a_:	Optional[Any]=0.0,   a_:	List[str]=True,   a_:	Union[str, Any]="relu",   a_:	Union[str, Any]=256,   a_:	Union[str, Any]=0.1,   a_:	str=0.0,   a_:	Optional[Any]=0.0,   a_:	Tuple=0.02,   a_:	str=1.0,   a_:	Dict=False,   a_:	Dict="sine",   a_:	Tuple="resnet50",   a_:	int=True,   a_:	Dict=False,   a_:	Tuple=1,   a_:	Union[str, Any]=5,   a_:	int=2,   a_:	Optional[int]=1,   a_:	Tuple=1,   a_:	str=5,   a_:	str=2,   a_:	Optional[Any]=0.1,   **a_:	Any,   ):




   '''simple docstring'''



   if backbone_config is not None and use_timm_backbone:
     raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""	)

   if not use_timm_backbone:
     if backbone_config is None:
       logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""	)
       _snake_case							:	Dict			=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]	)
     elif isinstance(A__,   A__	):
       _snake_case							:	Any			=						backbone_config.get("""model_type"""	)
       _snake_case							:	int			=						CONFIG_MAPPING[backbone_model_type]
       _snake_case							:	str			=						config_class.from_dict(A__	)
     # set timm attributes to None
     _snake_case							:	Any			=						None, None, None

   _snake_case							:	Optional[int]			=						use_timm_backbone
   _snake_case							:	List[str]			=						backbone_config
   _snake_case							:	Any			=						num_channels
   _snake_case							:	Union[str, Any]			=						num_queries
   _snake_case							:	Optional[int]			=						d_model
   _snake_case							:	Union[str, Any]			=						encoder_ffn_dim
   _snake_case							:	str			=						encoder_layers
   _snake_case							:	List[Any]			=						encoder_attention_heads
   _snake_case							:	str			=						decoder_ffn_dim
   _snake_case							:	Any			=						decoder_layers
   _snake_case							:	Tuple			=						decoder_attention_heads
   _snake_case							:	List[Any]			=						dropout
   _snake_case							:	int			=						attention_dropout
   _snake_case							:	Any			=						activation_dropout
   _snake_case							:	Tuple			=						activation_function
   _snake_case							:	Tuple			=						init_std
   _snake_case							:	Union[str, Any]			=						init_xavier_std
   _snake_case							:	int			=						encoder_layerdrop
   _snake_case							:	List[str]			=						decoder_layerdrop
   _snake_case							:	Any			=						encoder_layers
   _snake_case							:	List[str]			=						auxiliary_loss
   _snake_case							:	List[str]			=						position_embedding_type
   _snake_case							:	Tuple			=						backbone
   _snake_case							:	List[Any]			=						use_pretrained_backbone
   _snake_case							:	List[Any]			=						dilation
   # Hungarian matcher
   _snake_case							:	Tuple			=						class_cost
   _snake_case							:	Union[str, Any]			=						bbox_cost
   _snake_case							:	List[Any]			=						giou_cost
   # Loss coefficients
   _snake_case							:	Any			=						mask_loss_coefficient
   _snake_case							:	int			=						dice_loss_coefficient
   _snake_case							:	Union[str, Any]			=						bbox_loss_coefficient
   _snake_case							:	str			=						giou_loss_coefficient
   _snake_case							:	int			=						eos_coefficient
   super().__init__(is_encoder_decoder=A__,   **A__	)


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




   '''simple docstring'''



   return self.encoder_attention_heads


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




   '''simple docstring'''



   return self.d_model







class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      version.parse("1.11"				)


 @property
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ("""pixel_mask""", {0: """batch"""}),
       ]	)


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




   '''simple docstring'''



   return 1E-5


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




   '''simple docstring'''



   return 12








"""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]	)


