

"""simple docstring"""




import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    LayoutLMvaConfig,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaModel,
						)
						from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import LayoutLMvaImageProcessor





class    lowerCamelCase						:

			def __init__(					self				:						str    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]=2    ,				__UpperCAmelCase				:						Dict=3    ,				__UpperCAmelCase				:						Any=4    ,				__UpperCAmelCase				:						Tuple=2    ,				__UpperCAmelCase				:						str=7    ,				__UpperCAmelCase				:						Optional[int]=True    ,				__UpperCAmelCase				:						List[Any]=True    ,				__UpperCAmelCase				:						Dict=True    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						Optional[int]=9_9    ,				__UpperCAmelCase				:						List[str]=3_6    ,				__UpperCAmelCase				:						Optional[int]=3    ,				__UpperCAmelCase				:						Optional[Any]=4    ,				__UpperCAmelCase				:						Union[str, Any]=3_7    ,				__UpperCAmelCase				:						List[str]="gelu"    ,				__UpperCAmelCase				:						Tuple=0.1    ,				__UpperCAmelCase				:						List[Any]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=5_1_2    ,				__UpperCAmelCase				:						List[Any]=1_6    ,				__UpperCAmelCase				:						str=2    ,				__UpperCAmelCase				:						List[str]=0.02    ,				__UpperCAmelCase				:						int=6    ,				__UpperCAmelCase				:						Any=6    ,				__UpperCAmelCase				:						Dict=3    ,				__UpperCAmelCase				:						Tuple=4    ,				__UpperCAmelCase				:						Tuple=None    ,				__UpperCAmelCase				:						Optional[Any]=1_0_0_0    ,				)   ->    str:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      text_seq_length
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_input_mask
						SCREAMING_SNAKE_CASE__								=      use_token_type_ids
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      max_position_embeddings
						SCREAMING_SNAKE_CASE__								=      type_vocab_size
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      coordinate_size
						SCREAMING_SNAKE_CASE__								=      shape_size
						SCREAMING_SNAKE_CASE__								=      num_labels
						SCREAMING_SNAKE_CASE__								=      num_choices
						SCREAMING_SNAKE_CASE__								=      scope
						SCREAMING_SNAKE_CASE__								=      range_bbox

						# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
						SCREAMING_SNAKE_CASE__								=      text_seq_length
						SCREAMING_SNAKE_CASE__								=      (image_size // patch_size) ** 2 + 1
						SCREAMING_SNAKE_CASE__								=      self.text_seq_length + self.image_seq_length

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size, self.text_seq_length]    ,				self.vocab_size       )

						SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size, self.text_seq_length, 4]    ,				self.range_bbox       )
						# Ensure that bbox is legal
						for i in range(bbox.shape[0]       ):
									for j in range(bbox.shape[1]       ):
												if bbox[i, j, 3] < bbox[i, j, 1]:
															SCREAMING_SNAKE_CASE__								=      bbox[i, j, 3]
															SCREAMING_SNAKE_CASE__								=      bbox[i, j, 1]
															SCREAMING_SNAKE_CASE__								=      t
												if bbox[i, j, 2] < bbox[i, j, 0]:
															SCREAMING_SNAKE_CASE__								=      bbox[i, j, 2]
															SCREAMING_SNAKE_CASE__								=      bbox[i, j, 0]
															SCREAMING_SNAKE_CASE__								=      t

						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						SCREAMING_SNAKE_CASE__								=      None
						if self.use_input_mask:
									SCREAMING_SNAKE_CASE__								=      random_attention_mask([self.batch_size, self.text_seq_length]       )

						SCREAMING_SNAKE_CASE__								=      None
						if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size, self.text_seq_length]    ,				self.type_vocab_size       )

						SCREAMING_SNAKE_CASE__								=      None
						SCREAMING_SNAKE_CASE__								=      None
						if self.use_labels:
									SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size       )
									SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size, self.text_seq_length]    ,				self.num_labels       )

						SCREAMING_SNAKE_CASE__								=      LayoutLMvaConfig(
						    vocab_size=self.vocab_size    ,				hidden_size=self.hidden_size    ,				num_hidden_layers=self.num_hidden_layers    ,				num_attention_heads=self.num_attention_heads    ,				intermediate_size=self.intermediate_size    ,				hidden_act=self.hidden_act    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				max_position_embeddings=self.max_position_embeddings    ,				type_vocab_size=self.type_vocab_size    ,				initializer_range=self.initializer_range    ,				coordinate_size=self.coordinate_size    ,				shape_size=self.shape_size    ,				input_size=self.image_size    ,				patch_size=self.patch_size    ,				)

						return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      LayoutLMvaModel(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

						# text + image
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				pixel_values=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(
						    __UpperCAmelCase    ,				bbox=__UpperCAmelCase    ,				pixel_values=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				token_type_ids=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				bbox=__UpperCAmelCase    ,				pixel_values=__UpperCAmelCase    ,				token_type_ids=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				bbox=__UpperCAmelCase    ,				pixel_values=__UpperCAmelCase       )

						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)       )

						# text only
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.last_hidden_state.shape    ,				(self.batch_size, self.text_seq_length, self.hidden_size)       )

						# image only
						SCREAMING_SNAKE_CASE__								=      model(pixel_values=__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.last_hidden_state.shape    ,				(self.batch_size, self.image_seq_length, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.num_labels
						SCREAMING_SNAKE_CASE__								=      LayoutLMvaForSequenceClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(
						    __UpperCAmelCase    ,				bbox=__UpperCAmelCase    ,				pixel_values=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				token_type_ids=__UpperCAmelCase    ,				labels=__UpperCAmelCase    ,				)
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Dict       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.num_labels
						SCREAMING_SNAKE_CASE__								=      LayoutLMvaForTokenClassification(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(
						    __UpperCAmelCase    ,				bbox=__UpperCAmelCase    ,				pixel_values=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				token_type_ids=__UpperCAmelCase    ,				labels=__UpperCAmelCase    ,				)
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.text_seq_length, self.num_labels)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Dict       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      LayoutLMvaForQuestionAnswering(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(
						    __UpperCAmelCase    ,				bbox=__UpperCAmelCase    ,				pixel_values=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				token_type_ids=__UpperCAmelCase    ,				start_positions=__UpperCAmelCase    ,				end_positions=__UpperCAmelCase    ,				)
						self.parent.assertEqual(result.start_logits.shape    ,				(self.batch_size, self.seq_length)       )
						self.parent.assertEqual(result.end_logits.shape    ,				(self.batch_size, self.seq_length)       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		
						)								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {
						    """input_ids""": input_ids,
						    """bbox""": bbox,
						    """pixel_values""": pixel_values,
						    """token_type_ids""": token_type_ids,
						    """attention_mask""": input_mask,
						}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Tuple							=							False
			lowerCamelCase__   :		str							=							False
			lowerCamelCase__   :		str							=							False

			lowerCamelCase__   :		Any							=							(
			    (
			        LayoutLMvaModel,
			        LayoutLMvaForSequenceClassification,
			        LayoutLMvaForTokenClassification,
			        LayoutLMvaForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__   :		int							=							(
			    {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
			    if is_torch_available()
			    else {}
			)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						int       )   ->    Tuple:
						# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
						# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
						# the sequence dimension of the text embedding only.
						# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
						return True

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      LayoutLMvaModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[int]=False       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      copy.deepcopy(__UpperCAmelCase       )
						if model_class in get_values(__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      {
									    k: v.unsqueeze(1       ).expand(-1    ,				self.model_tester.num_choices    ,				-1       ).contiguous()
									    if isinstance(__UpperCAmelCase    ,				torch.Tensor       ) and v.ndim > 1
									    else v
									    for k, v in inputs_dict.items()
									}
						if return_labels:
									if model_class in get_values(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__								=      torch.ones(self.model_tester.batch_size    ,				dtype=torch.long    ,				device=__UpperCAmelCase       )
									elif model_class in get_values(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__								=      torch.zeros(
												    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      torch.zeros(
												    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=__UpperCAmelCase       )
									elif model_class in [
									    *get_values(__UpperCAmelCase       ),
									]:
												SCREAMING_SNAKE_CASE__								=      torch.zeros(
												    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=__UpperCAmelCase       )
									elif model_class in [
									    *get_values(__UpperCAmelCase       ),
									]:
												SCREAMING_SNAKE_CASE__								=      torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,				dtype=torch.long    ,				device=__UpperCAmelCase    ,				)

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						self.config_tester.run_common_tests()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									SCREAMING_SNAKE_CASE__								=      type
									self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      LayoutLMvaModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image





@require_torch
class    lowerCamelCase						(unittest.TestCase			):

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
						return LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase       ) if is_vision_available() else None





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""       ).to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).pixel_values.to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([[1, 2]]       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]       ).unsqueeze(0       )

						# forward pass
						SCREAMING_SNAKE_CASE__								=      model(
						    input_ids=input_ids.to(__UpperCAmelCase       )    ,				bbox=bbox.to(__UpperCAmelCase       )    ,				pixel_values=pixel_values.to(__UpperCAmelCase       )    ,				)

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_9_9, 7_6_8)       )
						self.assertEqual(outputs.last_hidden_state.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]       ).to(__UpperCAmelCase       )

						self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,				__UpperCAmelCase    ,				atol=1e-4       )       )





"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


A_							:			Optional[int]											= logging.get_logger(__name__)

A_							:			Union[str, Any]											= {"vocab_file": "sentencepiece.bpe.model"}

A_							:			str											= {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

A_							:			Tuple											= {
    "camembert-base": 512,
}

A_							:			int											= "▁"





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		List[str]							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		Optional[int]							=							['input_ids', 'attention_mask']

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						int="<s>"    ,				__UpperCAmelCase				:						Union[str, Any]="</s>"    ,				__UpperCAmelCase				:						List[str]="</s>"    ,				__UpperCAmelCase				:						List[Any]="<s>"    ,				__UpperCAmelCase				:						int="<unk>"    ,				__UpperCAmelCase				:						Dict="<pad>"    ,				__UpperCAmelCase				:						int="<mask>"    ,				__UpperCAmelCase				:						List[Any]=["<s>NOTUSED", "</s>NOTUSED"]    ,				__UpperCAmelCase				:						Optional[Dict[str, Any]] = None    ,				**__UpperCAmelCase				:						Optional[Any]    ,				)   ->    None:
						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else mask_token

						SCREAMING_SNAKE_CASE__								=      {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				mask_token=__UpperCAmelCase    ,				additional_special_tokens=__UpperCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCAmelCase    ,				)
						SCREAMING_SNAKE_CASE__								=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(str(__UpperCAmelCase       )       )
						SCREAMING_SNAKE_CASE__								=      vocab_file
						# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
						# sentencepiece vocabulary (this is the case for <s> and </s>
						SCREAMING_SNAKE_CASE__								=      {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
						SCREAMING_SNAKE_CASE__								=      len(self.fairseq_tokens_to_ids       )
						SCREAMING_SNAKE_CASE__								=      len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:

						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.cls_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None    ,				__UpperCAmelCase				:						bool = False       )   ->    List[int]:
						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase       )

						if token_ids_a is None:
									return [1] + ([0] * len(__UpperCAmelCase       )) + [1]
						return [1] + ([0] * len(__UpperCAmelCase       )) + [1, 1] + ([0] * len(__UpperCAmelCase       )) + [1]

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.cls_token_id]

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

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Any:
						return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      {self.convert_ids_to_tokens(__UpperCAmelCase       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						str       )   ->    List[str]:
						return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Tuple       )   ->    Optional[Any]:
						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						elif self.sp_model.PieceToId(__UpperCAmelCase       ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
						return self.fairseq_offset + self.sp_model.PieceToId(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						int       )   ->    Dict:
						if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      """"""
						SCREAMING_SNAKE_CASE__								=      False
						for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
												if not prev_is_special:
															out_string += " "
												out_string += self.sp_model.decode(__UpperCAmelCase       ) + token
												SCREAMING_SNAKE_CASE__								=      True
												SCREAMING_SNAKE_CASE__								=      []
									else:
												current_sub_tokens.append(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      False
						out_string += self.sp_model.decode(__UpperCAmelCase       )
						return out_string.strip()

			def __getstate__(					self				:						Optional[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.__dict__.copy()
						SCREAMING_SNAKE_CASE__								=      None
						return state

			def __setstate__(					self				:						int    ,				__UpperCAmelCase				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      d

						# for backward compatibility
						if not hasattr(self    ,				"""sp_model_kwargs"""       ):
									SCREAMING_SNAKE_CASE__								=      {}

						SCREAMING_SNAKE_CASE__								=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(self.vocab_file       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						if not os.path.isdir(__UpperCAmelCase       ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
									return
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

						if os.path.abspath(self.vocab_file       ) != os.path.abspath(__UpperCAmelCase       ) and os.path.isfile(self.vocab_file       ):
									copyfile(self.vocab_file    ,				__UpperCAmelCase       )
						elif not os.path.isfile(self.vocab_file       ):
									with open(__UpperCAmelCase    ,				"""wb"""       ) as fi:
												SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
												fi.write(__UpperCAmelCase       )

						return (out_vocab_file,)


