






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     :

					'''simple docstring'''

					def __init__(  self				:	List[str]		,     __a				:	Optional[int]		,     __a				:	Any=2		,     __a				:	Any=3		,     __a				:	List[str]=4		,     __a				:	Optional[Any]=2		,     __a				:	Optional[Any]=7		,     __a				:	int=True		,     __a				:	Dict=True		,     __a				:	Dict=True		,     __a				:	List[Any]=True		,     __a				:	int=99		,     __a				:	Optional[int]=36		,     __a				:	Dict=3		,     __a				:	Union[str, Any]=4		,     __a				:	Dict=37		,     __a				:	List[str]="gelu"		,     __a				:	List[str]=0.1		,     __a				:	int=0.1		,     __a				:	Union[str, Any]=512		,     __a				:	Optional[Any]=16		,     __a				:	Any=2		,     __a				:	Optional[Any]=0.02		,     __a				:	List[Any]=6		,     __a				:	Optional[Any]=6		,     __a				:	List[Any]=3		,     __a				:	List[str]=4		,     __a				:	Dict=None		,     __a				:	Dict=1000		,     )			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     parent
												__lowercase      :							Tuple				=     batch_size
												__lowercase      :							Tuple				=     num_channels
												__lowercase      :							Union[str, Any]				=     image_size
												__lowercase      :							Optional[Any]				=     patch_size
												__lowercase      :							Optional[Any]				=     text_seq_length
												__lowercase      :							Tuple				=     is_training
												__lowercase      :							Any				=     use_input_mask
												__lowercase      :							str				=     use_token_type_ids
												__lowercase      :							Union[str, Any]				=     use_labels
												__lowercase      :							str				=     vocab_size
												__lowercase      :							Tuple				=     hidden_size
												__lowercase      :							Union[str, Any]				=     num_hidden_layers
												__lowercase      :							List[Any]				=     num_attention_heads
												__lowercase      :							int				=     intermediate_size
												__lowercase      :							Union[str, Any]				=     hidden_act
												__lowercase      :							List[Any]				=     hidden_dropout_prob
												__lowercase      :							Any				=     attention_probs_dropout_prob
												__lowercase      :							Optional[Any]				=     max_position_embeddings
												__lowercase      :							Any				=     type_vocab_size
												__lowercase      :							Any				=     type_sequence_label_size
												__lowercase      :							Tuple				=     initializer_range
												__lowercase      :							Tuple				=     coordinate_size
												__lowercase      :							List[str]				=     shape_size
												__lowercase      :							Tuple				=     num_labels
												__lowercase      :							int				=     num_choices
												__lowercase      :							Dict				=     scope
												__lowercase      :							Optional[Any]				=     range_bbox

												# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
												__lowercase      :							int				=     text_seq_length
												__lowercase      :							List[str]				=     (image_size // patch_size) ** 2 + 1
												__lowercase      :							Any				=     self.text_seq_length + self.image_seq_length



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							int				=     ids_tensor([self.batch_size, self.text_seq_length]		,     self.vocab_size							)

												__lowercase      :							str				=     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]:
																																	__lowercase      :							List[Any]				=     bbox[i, j, 3]
																																	__lowercase      :							str				=     bbox[i, j, 1]
																																	__lowercase      :							Union[str, Any]				=     t
																										if bbox[i, j, 2] < bbox[i, j, 0]:
																																	__lowercase      :							Union[str, Any]				=     bbox[i, j, 2]
																																	__lowercase      :							List[str]				=     bbox[i, j, 0]
																																	__lowercase      :							int				=     t

												__lowercase      :							Dict				=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

												__lowercase      :							List[str]				=     None
												if self.use_input_mask:
																			__lowercase      :							Optional[Any]				=     random_attention_mask([self.batch_size, self.text_seq_length]							)

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

												__lowercase      :							Optional[int]				=     None
												__lowercase      :							Any				=     None
												if self.use_labels:
																			__lowercase      :							List[Any]				=     ids_tensor([self.batch_size]		,     self.type_sequence_label_size							)
																			__lowercase      :							Optional[Any]				=     ids_tensor([self.batch_size, self.text_seq_length]		,     self.num_labels							)

												__lowercase      :							Optional[Any]				=     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       lowerCAmelCase				(  self				:	Optional[Any]		,     __a				:	List[Any]		,     __a				:	str		,     __a				:	Optional[Any]		,     __a				:	str		,     __a				:	List[str]		,     __a				:	Any		,     __a				:	Dict		,     __a				:	Union[str, Any]							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							List[str]				=     LayoutLMvaModel(config=__a							)
												model.to(__a							)
												model.eval()

												# text + image
												__lowercase      :							Dict				=     model(__a		,     pixel_values=__a							)
												__lowercase      :							int				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__a							)
												__lowercase      :							List[Any]				=     model(__a		,     bbox=__a		,     pixel_values=__a		,     token_type_ids=__a							)
												__lowercase      :							Union[str, Any]				=     model(__a		,     bbox=__a		,     pixel_values=__a							)

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

												# text only
												__lowercase      :							Optional[Any]				=     model(__a							)
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,     (self.batch_size, self.text_seq_length, self.hidden_size)							)

												# image only
												__lowercase      :							Tuple				=     model(pixel_values=__a							)
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,     (self.batch_size, self.image_seq_length, self.hidden_size)							)



					def       lowerCAmelCase				(  self				:	Optional[Any]		,     __a				:	List[Any]		,     __a				:	List[Any]		,     __a				:	Union[str, Any]		,     __a				:	List[str]		,     __a				:	List[str]		,     __a				:	Optional[int]		,     __a				:	List[str]		,     __a				:	Optional[Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     self.num_labels
												__lowercase      :							str				=     LayoutLMvaForSequenceClassification(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							str				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)							)



					def       lowerCAmelCase				(  self				:	Dict		,     __a				:	Optional[Any]		,     __a				:	str		,     __a				:	List[str]		,     __a				:	str		,     __a				:	Optional[Any]		,     __a				:	Optional[Any]		,     __a				:	Optional[int]		,     __a				:	List[Any]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.num_labels
												__lowercase      :							Optional[int]				=     LayoutLMvaForTokenClassification(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							int				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.text_seq_length, self.num_labels)							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[str]		,     __a				:	Dict		,     __a				:	int		,     __a				:	Tuple		,     __a				:	List[str]		,     __a				:	Union[str, Any]		,     __a				:	Union[str, Any]		,     __a				:	List[Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							int				=     LayoutLMvaForQuestionAnswering(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Optional[Any]				=     model(
												    __a		,     bbox=__a		,     pixel_values=__a		,     attention_mask=__a		,     token_type_ids=__a		,     start_positions=__a		,     end_positions=__a		,     )
												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       lowerCAmelCase				(  self				:	Any							)			->  int:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.prepare_config_and_inputs()
												(
												    __lowercase			
												)      :							Any				=     config_and_inputs
												__lowercase      :							Optional[int]				=     {
												    """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			):

					'''simple docstring'''

					_A						:			Optional[int]									=      False
					_A						:			List[Any]									=      False
					_A						:			Tuple									=      False

					_A						:			Union[str, Any]									=      (
					    (
					        LayoutLMvaModel,
					        LayoutLMvaForSequenceClassification,
					        LayoutLMvaForTokenClassification,
					        LayoutLMvaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					_A						:			Union[str, Any]									=      (
					    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
					    if is_torch_available()
					    else {}
					)



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Optional[Any]		,     __a				:	List[Any]		,     __a				:	List[Any]		,     __a				:	str							)			->  List[Any]:


												"""simple docstring"""

												return True



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  str:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     LayoutLMvaModelTester(self							)
												__lowercase      :							int				=     ConfigTester(self		,     config_class=__a		,     hidden_size=37							)



					def       lowerCAmelCase				(  self				:	int		,     __a				:	List[str]		,     __a				:	int		,     __a				:	Optional[Any]=False							)			->  str:


												"""simple docstring"""

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

												return inputs_dict



					def       lowerCAmelCase				(  self				:	Any							)			->  Union[str, Any]:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__a							)



					def       lowerCAmelCase				(  self				:	int							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																			__lowercase      :							Optional[Any]				=     type
																			self.model_tester.create_and_check_model(*__a							)



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__a							)



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							int				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__a							)



					def       lowerCAmelCase				(  self				:	str							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__a							)



					@slow
					def       lowerCAmelCase				(  self				:	Dict							)			->  int:


												"""simple docstring"""

												for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__lowercase      :							Optional[Any]				=     LayoutLMvaModel.from_pretrained(__a							)
																			self.assertIsNotNone(__a							)







def       snake_case_		(     ):
							__lowercase      :							Any				=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_torch
class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					@cached_property
					def       lowerCAmelCase				(  self				:	Dict							)			->  Tuple:


												"""simple docstring"""

												return LayoutLMvaImageProcessor(apply_ocr=__a							) if is_vision_available() else None



					@slow
					def       lowerCAmelCase				(  self				:	Dict							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Dict				=     LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""							).to(__a							)

												__lowercase      :							Tuple				=     self.default_image_processor
												__lowercase      :							Tuple				=     prepare_img()
												__lowercase      :							Optional[Any]				=     image_processor(images=__a		,     return_tensors="""pt"""							).pixel_values.to(__a							)

												__lowercase      :							Optional[Any]				=     torch.tensor([[1, 2]]							)
												__lowercase      :							int				=     torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]							).unsqueeze(0							)

												# forward pass
												__lowercase      :							Tuple				=     model(
												    input_ids=input_ids.to(__a							)		,     bbox=bbox.to(__a							)		,     pixel_values=pixel_values.to(__a							)		,     )

												# verify the logits
												__lowercase      :							Union[str, Any]				=     torch.Size((1, 199, 768)							)
												self.assertEqual(outputs.last_hidden_state.shape		,     __a							)

												__lowercase      :							Optional[Any]				=     torch.tensor(
												    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							).to(__a							)

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







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		lowerCAmelCase     ( __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			str									=      LongformerTokenizer
					_A						:			int									=      True
					_A						:			Optional[int]									=      LongformerTokenizerFast
					_A						:			int									=      True



					def       lowerCAmelCase				(  self				:	Any							)			->  List[str]:


												"""simple docstring"""

												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												__lowercase      :							Union[str, Any]				=     [
												    """l""",
												    """o""",
												    """w""",
												    """e""",
												    """r""",
												    """s""",
												    """t""",
												    """i""",
												    """d""",
												    """n""",
												    """\u0120""",
												    """\u0120l""",
												    """\u0120n""",
												    """\u0120lo""",
												    """\u0120low""",
												    """er""",
												    """\u0120lowest""",
												    """\u0120newer""",
												    """\u0120wider""",
												    """<unk>""",
												]
												__lowercase      :							Union[str, Any]				=     dict(zip(__a		,     range(len(__a							)							)							)							)
												__lowercase      :							Any				=     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
												__lowercase      :							Optional[int]				=     {"""unk_token""": """<unk>"""}

												__lowercase      :							Dict				=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]							)
												__lowercase      :							str				=     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				:	Optional[int]		,     **__a				:	Optional[Any]							)			->  str:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Tuple		,     **__a				:	Tuple							)			->  str:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	str		,     __a				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     """lower newer"""
												__lowercase      :							int				=     """lower newer"""
												return input_text, output_text



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.tokenizer_class(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map							)
												__lowercase      :							Dict				=     """lower newer"""
												__lowercase      :							Optional[Any]				=     ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
												__lowercase      :							str				=     tokenizer.tokenize(__a							)  # , add_prefix_space=True)
												self.assertListEqual(__a		,     __a							)

												__lowercase      :							int				=     tokens + [tokenizer.unk_token]
												__lowercase      :							str				=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__a							)		,     __a							)



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.get_tokenizer()

												self.assertListEqual(tokenizer.encode("""Hello world!"""		,     add_special_tokens=__a							)		,     [0, 31414, 232, 328, 2]							)
												self.assertListEqual(
												    tokenizer.encode("""Hello world! cécé herlolip 418"""		,     add_special_tokens=__a							)		,     [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,     )



					@slow
					def       lowerCAmelCase				(  self				:	Tuple							)			->  str:


												"""simple docstring"""

												__lowercase      :							Any				=     self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""							)

												__lowercase      :							Optional[Any]				=     tokenizer.encode("""sequence builders"""		,     add_special_tokens=__a							)
												__lowercase      :							List[str]				=     tokenizer.encode("""multi-sequence build"""		,     add_special_tokens=__a							)

												__lowercase      :							Optional[Any]				=     tokenizer.encode(
												    """sequence builders"""		,     add_special_tokens=__a		,     add_prefix_space=__a							)
												__lowercase      :							Union[str, Any]				=     tokenizer.encode(
												    """sequence builders"""		,     """multi-sequence build"""		,     add_special_tokens=__a		,     add_prefix_space=__a							)

												__lowercase      :							List[Any]				=     tokenizer.build_inputs_with_special_tokens(__a							)
												__lowercase      :							Any				=     tokenizer.build_inputs_with_special_tokens(__a		,     __a							)

												assert encoded_sentence == encoded_text_from_decode
												assert encoded_pair == encoded_pair_from_decode



					def       lowerCAmelCase				(  self				:	Dict							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.get_tokenizer()

												__lowercase      :							Tuple				=     """Encode this sequence."""
												__lowercase      :							Optional[Any]				=     tokenizer.byte_encoder[""" """.encode("""utf-8"""							)[0]]

												# Testing encoder arguments
												__lowercase      :							Dict				=     tokenizer.encode(__a		,     add_special_tokens=__a		,     add_prefix_space=__a							)
												__lowercase      :							Tuple				=     tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
												self.assertNotEqual(__a		,     __a							)

												__lowercase      :							List[str]				=     tokenizer.encode(__a		,     add_special_tokens=__a		,     add_prefix_space=__a							)
												__lowercase      :							Any				=     tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
												self.assertEqual(__a		,     __a							)

												tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
												__lowercase      :							str				=     tokenizer.encode(__a		,     add_special_tokens=__a							)
												__lowercase      :							Dict				=     tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
												self.assertNotEqual(__a		,     __a							)

												# Testing spaces after special tokens
												__lowercase      :							List[Any]				=     """<mask>"""
												tokenizer.add_special_tokens(
												    {"""mask_token""": AddedToken(__a		,     lstrip=__a		,     rstrip=__a							)}							)  # mask token has a left space
												__lowercase      :							Dict				=     tokenizer.convert_tokens_to_ids(__a							)

												__lowercase      :							List[str]				=     """Encode <mask> sequence"""
												__lowercase      :							List[str]				=     """Encode <mask>sequence"""

												__lowercase      :							Union[str, Any]				=     tokenizer.encode(__a							)
												__lowercase      :							Dict				=     encoded.index(__a							)
												__lowercase      :							List[Any]				=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
												self.assertEqual(__a		,     __a							)

												__lowercase      :							int				=     tokenizer.encode(__a							)
												__lowercase      :							Union[str, Any]				=     encoded.index(__a							)
												__lowercase      :							List[Any]				=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
												self.assertNotEqual(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	int							)			->  str:


												"""simple docstring"""

												pass



					def       lowerCAmelCase				(  self				:	int							)			->  Dict:


												"""simple docstring"""

												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
																										__lowercase      :							List[str]				=     self.rust_tokenizer_class.from_pretrained(__a		,     **__a							)
																										__lowercase      :							List[Any]				=     self.tokenizer_class.from_pretrained(__a		,     **__a							)
																										__lowercase      :							Optional[Any]				=     """A, <mask> AllenNLP sentence."""
																										__lowercase      :							Union[str, Any]				=     tokenizer_r.encode_plus(__a		,     add_special_tokens=__a		,     return_token_type_ids=__a							)
																										__lowercase      :							Optional[Any]				=     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"""]							)		,     )

																										__lowercase      :							Dict				=     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
																										__lowercase      :							str				=     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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
																										self.assertSequenceEqual(tokens_r["""input_ids"""]		,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

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



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Dict:


												"""simple docstring"""

												for trim_offsets, add_prefix_space in itertools.product([True, False]		,     repeat=2							):
																			__lowercase      :							Dict				=     self.rust_tokenizer_class.from_pretrained(
																			    self.tmpdirname		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)

																			__lowercase      :							Optional[int]				=     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
																			__lowercase      :							Any				=     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

																			self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,     __a							)

																			self.assertEqual(post_processor_state["""add_prefix_space"""]		,     __a							)
																			self.assertEqual(post_processor_state["""trim_offsets"""]		,     __a							)



					def       lowerCAmelCase				(  self				:	int							)			->  Tuple:


												"""simple docstring"""

												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
																										__lowercase      :							List[str]				=     """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																										__lowercase      :							int				=     F"{text_of_1_token} {text_of_1_token}"

																										__lowercase      :							List[str]				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Any				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							) + 1, len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							str				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Tuple				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							) + 1, len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							Optional[int]				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							str				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							), len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							str				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							int				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							), len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							Any				=     F" {text}"

																										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																										# )
																										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																										# self.assertEqual(
																										#     encoding.offset_mapping[1],
																										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																										# )

																										__lowercase      :							Optional[Any]				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							str				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (1, 1 + len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (1 + len(__a							) + 1, 1 + len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							int				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Dict				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (1 + len(__a							), 1 + len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							int				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Tuple				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (1 + len(__a							), 1 + len(__a							) + 1 + len(__a							))		,     )