"""simple docstring"""


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      LEDTokenizer
   snake_case_							=      LEDTokenizerFast
   snake_case_							=      True


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().setUp()
        lowercase__				 =							[
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        lowercase__				 =							dict(zip(_UpperCAmelCase,				range(len(_UpperCAmelCase  )  )  )  )
        lowercase__				 =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        lowercase__				 =							{"unk_token": "<unk>"}

        lowercase__				 =							os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES["vocab_file"]  )
        lowercase__				 =							os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES["merges_file"]  )
        with open(self.vocab_file,				"w",				encoding="utf-8"  ) as fp:
             fp.write(json.dumps(_UpperCAmelCase  ) + "\n"  )
        with open(self.merges_file,				"w",				encoding="utf-8"  ) as fp:
             fp.write("\n".join(_UpperCAmelCase  )  )


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return "lower newer", "lower newer"


   @cached_property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return LEDTokenizer.from_pretrained("allenai/led-base-16384"  )


   @cached_property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							["A long paragraph for summarization.", "Another paragraph for summarization."]
        lowercase__				 =							[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowercase__				 =							tokenizer(_UpperCAmelCase,				max_length=len(_UpperCAmelCase  ),				padding=_UpperCAmelCase,				return_tensors="pt"  )
             self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )

             self.assertEqual((2, 9),				batch.input_ids.shape  )
             self.assertEqual((2, 9),				batch.attention_mask.shape  )
             lowercase__				 =							batch.input_ids.tolist()[0]
             self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							["A long paragraph for summarization.", "Another paragraph for summarization."]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowercase__				 =							tokenizer(_UpperCAmelCase,				padding=_UpperCAmelCase,				return_tensors="pt"  )
             self.assertIn("input_ids",				_UpperCAmelCase  )
             self.assertIn("attention_mask",				_UpperCAmelCase  )
             self.assertNotIn("labels",				_UpperCAmelCase  )
             self.assertNotIn("decoder_attention_mask",				_UpperCAmelCase  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							[
            "Summary of the text.",
            "Another summary.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowercase__				 =							tokenizer(text_target=_UpperCAmelCase,				max_length=32,				padding="max_length",				return_tensors="pt"  )
             self.assertEqual(32,				targets["input_ids"].shape[1]  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowercase__				 =							tokenizer(
                 ["I am a small frog" * 1024, "I am a small frog"],				padding=_UpperCAmelCase,				truncation=_UpperCAmelCase,				return_tensors="pt"  )
             self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )
             self.assertEqual(batch.input_ids.shape,				(2, 5122)  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							["A long paragraph for summarization."]
        lowercase__				 =							[
            "Summary of the text.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowercase__				 =							tokenizer(_UpperCAmelCase,				return_tensors="pt"  )
             lowercase__				 =							tokenizer(text_target=_UpperCAmelCase,				return_tensors="pt"  )
             lowercase__				 =							inputs["input_ids"]
             lowercase__				 =							targets["input_ids"]
             self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
             self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
             self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
             self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowercase__				 =							["Summary of the text.", "Another summary."]
             lowercase__				 =							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

             lowercase__				 =							tokenizer(_UpperCAmelCase,				padding=_UpperCAmelCase  )
             lowercase__				 =							[[0] * len(_UpperCAmelCase  ) for x in encoded_output["input_ids"]]
             lowercase__				 =							tokenizer.pad(_UpperCAmelCase  )
             self.assertSequenceEqual(outputs["global_attention_mask"],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                  lowercase__				 =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase,				**_UpperCAmelCase  )
                  lowercase__				 =							self.tokenizer_class.from_pretrained(_UpperCAmelCase,				**_UpperCAmelCase  )
                  lowercase__				 =							"A, <mask> AllenNLP sentence."
                  lowercase__				 =							tokenizer_r.encode_plus(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase,				return_token_type_ids=_UpperCAmelCase  )
                  lowercase__				 =							tokenizer_p.encode_plus(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase,				return_token_type_ids=_UpperCAmelCase  )
                  self.assertEqual(sum(tokens_r["token_type_ids"]  ),				sum(tokens_p["token_type_ids"]  )  )
                  self.assertEqual(
                      sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  ),				sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  ),				)

                  lowercase__				 =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
                  lowercase__				 =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )
                  self.assertSequenceEqual(tokens_p["input_ids"],				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                  self.assertSequenceEqual(tokens_r["input_ids"],				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                  self.assertSequenceEqual(
                      _UpperCAmelCase,				["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
                  self.assertSequenceEqual(
                      _UpperCAmelCase,				["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_:					List[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					int     =					{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}






class    a__    (   _a	):
   snake_case_							=      "markuplm"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=2,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=0,				_UpperCAmelCase=2,				_UpperCAmelCase=256,				_UpperCAmelCase=1024,				_UpperCAmelCase=216,				_UpperCAmelCase=1001,				_UpperCAmelCase=32,				_UpperCAmelCase=50,				_UpperCAmelCase="absolute",				_UpperCAmelCase=True,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            pad_token_id=_UpperCAmelCase,				bos_token_id=_UpperCAmelCase,				eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							position_embedding_type
        lowercase__				 =							use_cache
        lowercase__				 =							classifier_dropout
        # additional properties
        lowercase__				 =							max_depth
        lowercase__				 =							max_xpath_tag_unit_embeddings
        lowercase__				 =							max_xpath_subs_unit_embeddings
        lowercase__				 =							tag_pad_id
        lowercase__				 =							subs_pad_id
        lowercase__				 =							xpath_unit_hidden_size




