



'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors



@require_tokenizers
class    lowerCamelCase__				(			__lowerCamelCase	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     MvpTokenizer
      UpperCamelCase__			 =     MvpTokenizerFast
      UpperCamelCase__			 =     True
      UpperCamelCase__			 =     filter_roberta_detectors




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             super().setUp()
             a__								= [
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             a__								= dict(zip(a__			,range(len(a__   )   )   )   )
             a__								= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             a__								= {'unk_token': '<unk>'}

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




      def lowerCAmelCase_					(       self							:     List[str]			,**a__							:     List[Any]   ):
             kwargs.update(self.special_tokens_map   )
             return self.tokenizer_class.from_pretrained(self.tmpdirname			,**a__   )




      def lowerCAmelCase_					(       self							:     Optional[int]			,**a__							:     int   ):
             kwargs.update(self.special_tokens_map   )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,**a__   )




      def lowerCAmelCase_					(       self							:     List[Any]			,a__							:     Union[str, Any]   ):
             return "lower newer", "lower newer"




      @cached_property
      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             return MvpTokenizer.from_pretrained("RUCAIBox/mvp"   )




      @cached_property
      def lowerCAmelCase_					(       self							:     List[Any]   ):
             return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"   )




      @require_torch
      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
             a__								= [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

                    self.assertEqual((2, 9)			,batch.input_ids.shape   )
                    self.assertEqual((2, 9)			,batch.attention_mask.shape   )
                    a__								= batch.input_ids.tolist()[0]
                    self.assertListEqual(a__			,a__   )
                    # Test that special tokens are reset




      @require_torch
      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a__								= tokenizer(a__			,padding=a__			,return_tensors="pt"   )
                    # check if input_ids are returned and no labels
                    self.assertIn("input_ids"			,a__   )
                    self.assertIn("attention_mask"			,a__   )
                    self.assertNotIn("labels"			,a__   )
                    self.assertNotIn("decoder_attention_mask"			,a__   )




      @require_torch
      def lowerCAmelCase_					(       self							:     List[str]   ):
             a__								= [
                 'Summary of the text.',
                 'Another summary.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a__								= tokenizer(text_target=a__			,max_length=32			,padding="max_length"			,return_tensors="pt"   )
                    self.assertEqual(32			,targets["input_ids"].shape[1]   )




      @require_torch
      def lowerCAmelCase_					(       self							:     Dict   ):
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a__								= tokenizer(
                        ["I am a small frog" * 10_24, "I am a small frog"]			,padding=a__			,truncation=a__			,return_tensors="pt"   )
                    self.assertIsInstance(a__			,a__   )
                    self.assertEqual(batch.input_ids.shape			,(2, 10_24)   )




      @require_torch
      def lowerCAmelCase_					(       self							:     str   ):
             a__								= ['A long paragraph for summarization.']
             a__								= [
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a__								= tokenizer(a__			,text_target=a__			,return_tensors="pt"   )
                    a__								= inputs['input_ids']
                    a__								= inputs['labels']
                    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()   )




      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             pass




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                           a__								= self.rust_tokenizer_class.from_pretrained(a__			,**a__   )
                           a__								= self.tokenizer_class.from_pretrained(a__			,**a__   )
                           a__								= 'A, <mask> AllenNLP sentence.'
                           a__								= tokenizer_r.encode_plus(a__			,add_special_tokens=a__			,return_token_type_ids=a__   )
                           a__								= tokenizer_p.encode_plus(a__			,add_special_tokens=a__			,return_token_type_ids=a__   )

                           # token_type_ids should put 0 everywhere
                           self.assertEqual(sum(tokens_r["token_type_ids"]   )			,sum(tokens_p["token_type_ids"]   )   )

                           # attention_mask should put 1 everywhere, so sum over length should be 1
                           self.assertEqual(
                               sum(tokens_r["attention_mask"]   ) / len(tokens_r["attention_mask"]   )			,sum(tokens_p["attention_mask"]   ) / len(tokens_p["attention_mask"]   )			,)

                           a__								= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
                           a__								= tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]   )

                           # Rust correctly handles the space before the mask while python doesnt
                           self.assertSequenceEqual(tokens_p["input_ids"]			,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
                           self.assertSequenceEqual(tokens_r["input_ids"]			,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

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






"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """layoutlmv3"""
			def __init__(    self    , lowercase=50265    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-5    , lowercase=1    , lowercase=0    , lowercase=2    , lowercase=1024    , lowercase=128    , lowercase=128    , lowercase=True    , lowercase=32    , lowercase=128    , lowercase=64    , lowercase=256    , lowercase=True    , lowercase=True    , lowercase=True    , lowercase=224    , lowercase=3    , lowercase=16    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    vocab_size=lowercase    , hidden_size=lowercase    , num_hidden_layers=lowercase    , num_attention_heads=lowercase    , intermediate_size=lowercase    , hidden_act=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    , pad_token_id=lowercase    , bos_token_id=lowercase    , eos_token_id=lowercase    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       max_ad_position_embeddings
							_lowerCamelCase	:     Optional[Any]       =       coordinate_size
							_lowerCamelCase	:     int       =       shape_size
							_lowerCamelCase	:     Optional[Any]       =       has_relative_attention_bias
							_lowerCamelCase	:     Dict       =       rel_pos_bins
							_lowerCamelCase	:     Tuple       =       max_rel_pos
							_lowerCamelCase	:     int       =       has_spatial_attention_bias
							_lowerCamelCase	:     Optional[int]       =       rel_ad_pos_bins
							_lowerCamelCase	:     List[Any]       =       max_rel_ad_pos
							_lowerCamelCase	:     Any       =       text_embed
							_lowerCamelCase	:     List[Any]       =       visual_embed
							_lowerCamelCase	:     str       =       input_size
							_lowerCamelCase	:     Optional[Any]       =       num_channels
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Optional[int]       =       classifier_dropout






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     version.parse("""1.12"""		)
			@property
			def    A_       (    self    ):
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]    )
							else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]    )
			@property
			def    A_       (    self    ):
							return 1E-5
			@property
			def    A_       (    self    ):
							return 12






			def    A_       (    self    , lowercase    , lowercase = -1    , lowercase = -1    , lowercase = False    , lowercase = None    , lowercase = 3    , lowercase = 40    , lowercase = 40    , ):
							setattr(processor.image_processor    , 'apply_ocr'    , lowercase    )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_batch    , num_token_to_add=0    )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							_lowerCamelCase	:     Tuple       =       processor.tokenizer.num_special_tokens_to_add(lowercase    )
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_sequence    , num_token_to_add=lowercase    )
							# Generate dummy inputs according to compute batch and sequence
							_lowerCamelCase	:     List[Any]       =       [[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							_lowerCamelCase	:     List[str]       =       [[[48, 84, 73, 128]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							_lowerCamelCase	:     Tuple       =       self._generate_dummy_images(lowercase    , lowercase    , lowercase    , lowercase    )

							_lowerCamelCase	:     str       =       dict(
							    processor(
							        lowercase    , text=lowercase    , boxes=lowercase    , return_tensors=lowercase    , )    )

							return inputs