





'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.swin.modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		)


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor







class       lowerCamelCase       :


					def __init__(       self						:	Optional[int]							,  __snake_case						:	Any							,  __snake_case						:	Union[str, Any]=13							,  __snake_case						:	Any=32							,  __snake_case						:	str=2							,  __snake_case						:	Tuple=3							,  __snake_case						:	List[str]=16							,  __snake_case						:	Tuple=[1, 2, 1]							,  __snake_case						:	Any=[2, 2, 4]							,  __snake_case						:	Union[str, Any]=2							,  __snake_case						:	Any=2.0							,  __snake_case						:	Union[str, Any]=True							,  __snake_case						:	int=0.0							,  __snake_case						:	int=0.0							,  __snake_case						:	int=0.1							,  __snake_case						:	List[Any]="gelu"							,  __snake_case						:	List[Any]=False							,  __snake_case						:	List[str]=True							,  __snake_case						:	Any=0.02							,  __snake_case						:	Optional[Any]=1e-5							,  __snake_case						:	Any=True							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	List[Any]=True							,  __snake_case						:	Union[str, Any]=10							,  __snake_case						:	Union[str, Any]=8							,  ):





											'''simple docstring'''





											_snake_case: List[Any]	       =		parent
											_snake_case: int	       =		batch_size
											_snake_case: str	       =		image_size
											_snake_case: List[Any]	       =		patch_size
											_snake_case: Any	       =		num_channels
											_snake_case: Optional[int]	       =		embed_dim
											_snake_case: int	       =		depths
											_snake_case: List[str]	       =		num_heads
											_snake_case: Tuple	       =		window_size
											_snake_case: Union[str, Any]	       =		mlp_ratio
											_snake_case: List[str]	       =		qkv_bias
											_snake_case: Optional[Any]	       =		hidden_dropout_prob
											_snake_case: Optional[int]	       =		attention_probs_dropout_prob
											_snake_case: str	       =		drop_path_rate
											_snake_case: Optional[Any]	       =		hidden_act
											_snake_case: Any	       =		use_absolute_embeddings
											_snake_case: Optional[Any]	       =		patch_norm
											_snake_case: int	       =		layer_norm_eps
											_snake_case: str	       =		initializer_range
											_snake_case: str	       =		is_training
											_snake_case: Optional[int]	       =		scope
											_snake_case: Dict	       =		use_labels
											_snake_case: List[str]	       =		type_sequence_label_size
											_snake_case: int	       =		encoder_stride


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: Tuple	       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

											_snake_case: str	       =		None
											if self.use_labels:
																	_snake_case: Dict	       =		ids_tensor([self.batch_size]							,  self.type_sequence_label_size							)

											_snake_case: Tuple	       =		self.get_config()

											return config, pixel_values, labels


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											return SwinConfig(
											    image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  embed_dim=self.embed_dim							,  depths=self.depths							,  num_heads=self.num_heads							,  window_size=self.window_size							,  mlp_ratio=self.mlp_ratio							,  qkv_bias=self.qkv_bias							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  drop_path_rate=self.drop_path_rate							,  hidden_act=self.hidden_act							,  use_absolute_embeddings=self.use_absolute_embeddings							,  path_norm=self.patch_norm							,  layer_norm_eps=self.layer_norm_eps							,  initializer_range=self.initializer_range							,  encoder_stride=self.encoder_stride							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Any							,  __snake_case						:	Any							):





											'''simple docstring'''





											_snake_case: List[str]	       =		TFSwinModel(config=__snake_case							)
											_snake_case: int	       =		model(__snake_case							)

											_snake_case: Tuple	       =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
											_snake_case: Any	       =		int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, expected_seq_len, expected_dim)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Optional[int]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: List[str]	       =		TFSwinForMaskedImageModeling(config=__snake_case							)
											_snake_case: Optional[int]	       =		model(__snake_case							)
											self.parent.assertEqual(
											    result.logits.shape							,  (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

											# test greyscale images
											_snake_case: Any	       =		1
											_snake_case: Optional[int]	       =		TFSwinForMaskedImageModeling(__snake_case							)

											_snake_case: List[str]	       =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
											_snake_case: Optional[Any]	       =		model(__snake_case							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, 1, self.image_size, self.image_size)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	List[str]							):





											'''simple docstring'''





											_snake_case: Any	       =		self.type_sequence_label_size
											_snake_case: Tuple	       =		TFSwinForImageClassification(__snake_case							)
											_snake_case: Any	       =		model(__snake_case							,  labels=__snake_case							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)							)

											# test greyscale images
											_snake_case: Any	       =		1
											_snake_case: Optional[Any]	       =		TFSwinForImageClassification(__snake_case							)
											_snake_case: Optional[int]	       =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
											_snake_case: Tuple	       =		model(__snake_case							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: Dict	       =		self.prepare_config_and_inputs()
											_snake_case       ,    _snake_case       ,    _snake_case: Dict	       =		config_and_inputs
											_snake_case: int	       =		{'pixel_values': pixel_values}
											return config, inputs_dict







@require_tf
class       lowerCamelCase       (    __UpperCAmelCase  ,				__UpperCAmelCase  ,				unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     (
					    (
					        TFSwinModel,
					        TFSwinForImageClassification,
					        TFSwinForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE				 =     (
					    {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
					    if is_tf_available()
					    else {}
					)

					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		TFSwinModelTester(self							)
											_snake_case: str	       =		ConfigTester(self							,  config_class=__snake_case							,  embed_dim=37							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											return


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case: Tuple	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case							)


					@unittest.skip(reason='Swin does not use inputs_embeds'							)
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											pass


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: int	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	_snake_case: Optional[int]	       =		model_class(__snake_case							)
																	self.assertIsInstance(model.get_input_embeddings()							,  tf.keras.layers.Layer							)
																	_snake_case: Any	       =		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__snake_case							,  tf.keras.layers.Dense							)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Union[str, Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	_snake_case: int	       =		model_class(__snake_case							)
																	_snake_case: Optional[int]	       =		inspect.signature(model.call							)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	_snake_case: str	       =		[*signature.parameters.keys()]

																	_snake_case: List[Any]	       =		['pixel_values']
																	self.assertListEqual(arg_names[:1]							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Any	       =		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case: Tuple	       =		True

											for model_class in self.all_model_classes:
																	_snake_case: List[Any]	       =		True
																	_snake_case: Union[str, Any]	       =		False
																	_snake_case: Optional[Any]	       =		True
																	_snake_case: Tuple	       =		model_class(__snake_case							)
																	_snake_case: int	       =		model(**self._prepare_for_class(__snake_case							,  __snake_case							)							)
																	_snake_case: Optional[int]	       =		outputs.attentions
																	_snake_case: List[str]	       =		len(self.model_tester.depths							)
																	self.assertEqual(len(__snake_case							)							,  __snake_case							)

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	_snake_case: Dict	       =		True
																	_snake_case: Dict	       =		config.window_size**2
																	_snake_case: str	       =		model_class(__snake_case							)
																	_snake_case: Optional[int]	       =		model(**self._prepare_for_class(__snake_case							,  __snake_case							)							)
																	_snake_case: Union[str, Any]	       =		outputs.attentions
																	self.assertEqual(len(__snake_case							)							,  __snake_case							)

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]							)							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )
																	_snake_case: List[Any]	       =		len(__snake_case							)

																	# Check attention is always last and order is fine
																	_snake_case: Any	       =		True
																	_snake_case: List[str]	       =		True
																	_snake_case: Dict	       =		model_class(__snake_case							)
																	_snake_case: Tuple	       =		model(**self._prepare_for_class(__snake_case							,  __snake_case							)							)

																	if hasattr(self.model_tester							,  'num_hidden_states_types'							):
																							_snake_case: Tuple	       =		self.model_tester.num_hidden_states_types
																	else:
																							# also another +1 for reshaped_hidden_states
																							_snake_case: Union[str, Any]	       =		2
																	self.assertEqual(out_len + added_hidden_states							,  len(__snake_case							)							)

																	_snake_case: Optional[int]	       =		outputs.attentions

																	self.assertEqual(len(__snake_case							)							,  __snake_case							)

																	self.assertListEqual(
																	    list(self_attentions[0].shape[-3:]							)							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	str							,  __snake_case						:	int							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Tuple	       =		model_class(__snake_case							)
											_snake_case: Tuple	       =		model(**self._prepare_for_class(__snake_case							,  __snake_case							)							)
											_snake_case: List[str]	       =		outputs.hidden_states

											_snake_case: Tuple	       =		getattr(
											    self.model_tester							,  'expected_num_hidden_layers'							,  len(self.model_tester.depths							) + 1							)
											self.assertEqual(len(__snake_case							)							,  __snake_case							)

											# Swin has a different seq_length
											_snake_case: Optional[Any]	       =		to_atuple(config.patch_size							)

											_snake_case: Dict	       =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]							)							,  [num_patches, self.model_tester.embed_dim]							,  )

											_snake_case: Dict	       =		outputs.reshaped_hidden_states
											self.assertEqual(len(__snake_case							)							,  __snake_case							)

											_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: int	       =		reshaped_hidden_states[0].shape

											_snake_case: Dict	       =		tf.reshape(reshaped_hidden_states[0]							,  (batch_size, num_channels, height * width)							)
											_snake_case: List[str]	       =		tf.transpose(__snake_case							,  (0, 2, 1)							)

											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]							)							,  [num_patches, self.model_tester.embed_dim]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: str	       =		self.model_tester.prepare_config_and_inputs_for_common()

											_snake_case: List[str]	       =		to_atuple(self.model_tester.image_size							)

											for model_class in self.all_model_classes:
																	_snake_case: Optional[int]	       =		True
																	self.check_hidden_states_output(__snake_case							,  __snake_case							,  __snake_case							,  __snake_case							)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	_snake_case: Any	       =		True

																	self.check_hidden_states_output(__snake_case							,  __snake_case							,  __snake_case							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: str	       =		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case: Tuple	       =		3

											_snake_case: List[str]	       =		to_atuple(self.model_tester.image_size							)
											_snake_case: int	       =		to_atuple(config.patch_size							)

											_snake_case: List[str]	       =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											_snake_case: Union[str, Any]	       =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
																	_snake_case: Optional[int]	       =		True
																	self.check_hidden_states_output(__snake_case							,  __snake_case							,  __snake_case							,  (padded_height, padded_width)							)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	_snake_case: str	       =		True
																	self.check_hidden_states_output(__snake_case							,  __snake_case							,  __snake_case							,  (padded_height, padded_width)							)




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_snake_case: str	       =		TFSwinModel.from_pretrained(__snake_case							)
																	self.assertIsNotNone(__snake_case							)







@require_vision
@require_tf
class       lowerCamelCase       (    unittest.TestCase	):


					@cached_property
					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											return (
											    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
											    if is_vision_available()
											    else None
											)




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
											_snake_case: int	       =		self.default_image_processor

											_snake_case: Optional[Any]	       =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
											_snake_case: Dict	       =		image_processor(images=__snake_case							,  return_tensors='tf'							)

											# forward pass
											_snake_case: List[str]	       =		model(__snake_case							)

											# verify the logits
											_snake_case: Tuple	       =		tf.TensorShape((1, 10_00)							)
											self.assertEqual(outputs.logits.shape							,  __snake_case							)
											_snake_case: Tuple	       =		tf.constant([-0.0_948, -0.6_454, -0.0_921]							)
											self.assertTrue(np.allclose(outputs.logits[0, :3]							,  __snake_case							,  atol=1e-4							)							)












'''simple docstring'''






import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A :			Optional[Any]							   =      logging.get_logger(__name__)

A :			Optional[Any]							   =      {
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A :			Tuple							   =      {
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

A :			Optional[int]							   =      {'Helsinki-NLP/opus-mt-en-de': 512}
A :			Any							   =      {}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_INIT_CONFIGURATION
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE				 =     ["input_ids", "attention_mask"]
					_SCREAMING_SNAKE_CASE				 =     re.compile(">>.+<<"	)  # type: re.Pattern


					def __init__(       self						:	Optional[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Optional[int]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[Any]=None							,  __snake_case						:	int=None							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	Optional[int]="<unk>"							,  __snake_case						:	str="</s>"							,  __snake_case						:	Tuple="<pad>"							,  __snake_case						:	List[str]=5_12							,  __snake_case						:	Optional[Dict[str, Any]] = None							,  __snake_case						:	Union[str, Any]=False							,  **__snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: List[str]	       =		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
											    source_lang=__snake_case							,  target_lang=__snake_case							,  unk_token=__snake_case							,  eos_token=__snake_case							,  pad_token=__snake_case							,  model_max_length=__snake_case							,  sp_model_kwargs=self.sp_model_kwargs							,  target_vocab_file=__snake_case							,  separate_vocabs=__snake_case							,  **__snake_case							,  )
											assert Path(__snake_case							).exists(), f'''cannot find spm source {source_spm}'''

											_snake_case: List[Any]	       =		separate_vocabs
											_snake_case: List[Any]	       =		load_json(__snake_case							)
											if self.unk_token not in self.encoder:
																	raise KeyError('<unk> token must be in vocab'							)
											assert self.pad_token in self.encoder

											if separate_vocabs:
																	_snake_case: List[str]	       =		load_json(__snake_case							)
																	_snake_case: Optional[int]	       =		{v: k for k, v in self.target_encoder.items()}
																	_snake_case: Tuple	       =		[]
											else:
																	_snake_case: Any	       =		{v: k for k, v in self.encoder.items()}
																	_snake_case: list	       =		[k for k in self.encoder if k.startswith('>>'							) and k.endswith('<<'							)]

											_snake_case: List[Any]	       =		source_lang
											_snake_case: List[str]	       =		target_lang
											_snake_case: List[str]	       =		[source_spm, target_spm]

											# load SentencePiece model for pre-processing
											_snake_case: Optional[Any]	       =		load_spm(__snake_case							,  self.sp_model_kwargs							)
											_snake_case: List[str]	       =		load_spm(__snake_case							,  self.sp_model_kwargs							)
											_snake_case: str	       =		self.spm_source
											_snake_case: int	       =		self.encoder

											# Multilingual target side: default to using first supported language code.

											self._setup_normalizer()


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											try:
																	from sacremoses import MosesPunctNormalizer

																	_snake_case: List[Any]	       =		MosesPunctNormalizer(self.source_lang							).normalize
											except (ImportError, FileNotFoundError):
																	warnings.warn('Recommended: pip install sacremoses.'							)
																	_snake_case: List[Any]	       =		lambda __snake_case							: x


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	str							):





											'''simple docstring'''





											return self.punc_normalizer(__snake_case							) if x else ""


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											return self.current_encoder.get(__snake_case							,  self.current_encoder[self.unk_token]							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.language_code_re.match(__snake_case							)
											_snake_case: list	       =		[match.group(0							)] if match else []
											return code, self.language_code_re.sub(''							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[Any]	       =		self.remove_language_code(__snake_case							)
											_snake_case: Union[str, Any]	       =		self.current_spm.encode(__snake_case							,  out_type=__snake_case							)
											return code + pieces


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	int							):





											'''simple docstring'''





											return self.decoder.get(__snake_case							,  self.unk_token							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Tuple							,  **__snake_case						:	int							):





											'''simple docstring'''





											return super().batch_decode(__snake_case							,  **__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Any							,  **__snake_case						:	List[str]							):





											'''simple docstring'''





											return super().decode(__snake_case							,  **__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	List[str]							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
											_snake_case: List[Any]	       =		[]
											_snake_case: Any	       =		''
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							out_string += sp_model.decode_pieces(__snake_case							) + token + " "
																							_snake_case: Any	       =		[]
																	else:
																							current_sub_tokens.append(__snake_case							)
											out_string += sp_model.decode_pieces(__snake_case							)
											return out_string.strip()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]=None							):





											'''simple docstring'''





											if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.spm_source
											_snake_case: List[str]	       =		self.encoder


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: int	       =		self.spm_target
											if self.separate_vocabs:
																	_snake_case: Union[str, Any]	       =		self.target_encoder


					@property
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											return len(self.encoder							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	str							,  __snake_case						:	Optional[str] = None							):





											'''simple docstring'''





											if not os.path.isdir(__snake_case							):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
																	return
											_snake_case: str	       =		[]

											if self.separate_vocabs:
																	_snake_case: Optional[int]	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']							,  )
																	_snake_case: Optional[int]	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']							,  )
																	save_json(self.encoder							,  __snake_case							)
																	save_json(self.target_encoder							,  __snake_case							)
																	saved_files.append(__snake_case							)
																	saved_files.append(__snake_case							)
											else:
																	_snake_case: int	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']							)
																	save_json(self.encoder							,  __snake_case							)
																	saved_files.append(__snake_case							)

											for spm_save_filename, spm_orig_path, spm_model in zip(
											    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]							,  self.spm_files							,  [self.spm_source, self.spm_target]							,  ):
																	_snake_case: Any	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + spm_save_filename							)
																	if os.path.abspath(__snake_case							) != os.path.abspath(__snake_case							) and os.path.isfile(__snake_case							):
																							copyfile(__snake_case							,  __snake_case							)
																							saved_files.append(__snake_case							)
																	elif not os.path.isfile(__snake_case							):
																							with open(__snake_case							,  'wb'							) as fi:
																													_snake_case: Any	       =		spm_model.serialized_model_proto()
																													fi.write(__snake_case							)
																							saved_files.append(__snake_case							)

											return tuple(__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											return self.get_src_vocab()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											return dict(self.encoder							,  **self.added_tokens_encoder							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											return dict(self.target_encoder							,  **self.added_tokens_decoder							)


					def __getstate__(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: str	       =		self.__dict__.copy()
											state.update(
											    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}							)
											return state


					def __setstate__(       self						:	str							,  __snake_case						:	Dict							):





											'''simple docstring'''





											_snake_case: str	       =		d

											# for backward compatibility
											if not hasattr(self							,  'sp_model_kwargs'							):
																	_snake_case: List[Any]	       =		{}

											_snake_case       ,    _snake_case: Dict	       =		(load_spm(__snake_case							,  self.sp_model_kwargs							) for f in self.spm_files)
											_snake_case: str	       =		self.spm_source
											self._setup_normalizer()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  *__snake_case						:	Tuple							,  **__snake_case						:	Tuple							):





											'''simple docstring'''





											return 1


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Dict	       =		set(self.all_special_ids							)  # call it once instead of inside list comp
											all_special_ids.remove(self.unk_token_id							)  # <unk> is only sometimes special
											return [1 if x in all_special_ids else 0 for x in seq]




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	List							,  __snake_case						:	Optional[List] = None							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											if already_has_special_tokens:
																	return self._special_token_mask(__snake_case							)
											elif token_ids_a is None:
																	return self._special_token_mask(__snake_case							) + [1]
											else:
																	return self._special_token_mask(token_ids_a + token_ids_a							) + [1]





def    lowercase_   (		lowercase__					,	lowercase__    ) ->sentencepiece.SentencePieceProcessor:
						_snake_case: Union[str, Any]	       =		sentencepiece.SentencePieceProcessor(**lowercase__    )
						spm.Load(lowercase__    )
						return spm





def    lowercase_   (		lowercase__					,	lowercase__    ) ->None:
						with open(lowercase__					,	'w'    ) as f:
												json.dump(lowercase__					,	lowercase__					,	indent=2    )





def    lowercase_   (		lowercase__    ) ->Union[Dict, List]:
						with open(lowercase__					,	'r'    ) as f:
												return json.load(lowercase__    )





