






"""simple docstring"""



import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
    from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 	a					:







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=1_3							,		_lowerCamelCase=3_2							,		_lowerCamelCase=2							,		_lowerCamelCase=3							,		_lowerCamelCase=1_6							,		_lowerCamelCase=[1, 2, 1]							,		_lowerCamelCase=[2, 2, 4]							,		_lowerCamelCase=2							,		_lowerCamelCase=2.0							,		_lowerCamelCase=True							,		_lowerCamelCase=0.0							,		_lowerCamelCase=0.0							,		_lowerCamelCase=0.1							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=False							,		_lowerCamelCase=True							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=1e-5							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=1_0							,		_lowerCamelCase=8							,		_lowerCamelCase=["stage1", "stage2"]							,		_lowerCamelCase=[1, 2]							,		):
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	image_size
      lowercase							   =	patch_size
      lowercase							   =	num_channels
      lowercase							   =	embed_dim
      lowercase							   =	depths
      lowercase							   =	num_heads
      lowercase							   =	window_size
      lowercase							   =	mlp_ratio
      lowercase							   =	qkv_bias
      lowercase							   =	hidden_dropout_prob
      lowercase							   =	attention_probs_dropout_prob
      lowercase							   =	drop_path_rate
      lowercase							   =	hidden_act
      lowercase							   =	use_absolute_embeddings
      lowercase							   =	patch_norm
      lowercase							   =	layer_norm_eps
      lowercase							   =	initializer_range
      lowercase							   =	is_training
      lowercase							   =	scope
      lowercase							   =	use_labels
      lowercase							   =	type_sequence_label_size
      lowercase							   =	encoder_stride
      lowercase							   =	out_features
      lowercase							   =	out_indices







    def       UpperCamelCase_				(      self	):
      lowercase							   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      lowercase							   =	None
      if self.use_labels:
        lowercase							   =	ids_tensor([self.batch_size]							,		self.type_sequence_label_size	)

      lowercase							   =	self.get_config()

      return config, pixel_values, labels







    def       UpperCamelCase_				(      self	):
      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							,		out_features=self.out_features							,		out_indices=self.out_indices							,		)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	SwinModel(config=lowercase__	)
      model.to(lowercase__	)
      model.eval()
      lowercase							   =	model(lowercase__	)

      lowercase							   =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
      lowercase							   =	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       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	SwinBackbone(config=lowercase__	)
      model.to(lowercase__	)
      model.eval()
      lowercase							   =	model(lowercase__	)

      # verify hidden states
      self.parent.assertEqual(len(result.feature_maps	)							,		len(config.out_features	)	)
      self.parent.assertListEqual(list(result.feature_maps[0].shape	)							,		[self.batch_size, model.channels[0], 1_6, 1_6]	)

      # verify channels
      self.parent.assertEqual(len(model.channels	)							,		len(config.out_features	)	)

      # verify backbone works with out_features=None
      lowercase							   =	None
      lowercase							   =	SwinBackbone(config=lowercase__	)
      model.to(lowercase__	)
      model.eval()
      lowercase							   =	model(lowercase__	)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps	)							,		1	)
      self.parent.assertListEqual(list(result.feature_maps[0].shape	)							,		[self.batch_size, model.channels[-1], 4, 4]	)

      # verify channels
      self.parent.assertEqual(len(model.channels	)							,		1	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	SwinForMaskedImageModeling(config=lowercase__	)
      model.to(lowercase__	)
      model.eval()
      lowercase							   =	model(lowercase__	)
      self.parent.assertEqual(
          result.logits.shape							,		(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

      # test greyscale images
      lowercase							   =	1
      lowercase							   =	SwinForMaskedImageModeling(lowercase__	)
      model.to(lowercase__	)
      model.eval()

      lowercase							   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
      lowercase							   =	model(lowercase__	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, 1, self.image_size, self.image_size)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	self.type_sequence_label_size
      lowercase							   =	SwinForImageClassification(lowercase__	)
      model.to(lowercase__	)
      model.eval()
      lowercase							   =	model(lowercase__							,		labels=lowercase__	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.type_sequence_label_size)	)

      # test greyscale images
      lowercase							   =	1
      lowercase							   =	SwinForImageClassification(lowercase__	)
      model.to(lowercase__	)
      model.eval()

      lowercase							   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
      lowercase							   =	model(lowercase__	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.type_sequence_label_size)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.prepare_config_and_inputs()
      (
          lowercase						
      )							   =	config_and_inputs
      lowercase							   =	{"pixel_values": pixel_values}
      return config, inputs_dict


@require_torch
class 	a					(       _UpperCAmelCase,    _UpperCAmelCase,    unittest.TestCase     ):
    UpperCAmelCase_			:					Any     					=(
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase_			:					Dict     					=(
        {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    UpperCAmelCase_			:					int     					=True

    UpperCAmelCase_			:					int     					=False
    UpperCAmelCase_			:					int     					=False
    UpperCAmelCase_			:					Optional[int]     					=False







    def       UpperCamelCase_				(      self	):
      lowercase							   =	SwinModelTester(self	)
      lowercase							   =	ConfigTester(self							,		config_class=lowercase__							,		embed_dim=3_7	)







    def       UpperCamelCase_				(      self	):
      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       UpperCamelCase_				(      self	):
      return







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase__	)







    @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'	)
    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      super().test_training_gradient_checkpointing()







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*lowercase__	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowercase__	)







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







    @unittest.skip(reason='Swin Transformer does not use feedforward chunking'	)
    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase							   =	model_class(lowercase__	)
        self.assertIsInstance(model.get_input_embeddings()							,		(nn.Module)	)
        lowercase							   =	model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowercase__							,		nn.Linear	)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase							   =	model_class(lowercase__	)
        lowercase							   =	inspect.signature(model.forward	)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase							   =	[*signature.parameters.keys()]

        lowercase							   =	["pixel_values"]
        self.assertListEqual(arg_names[:1]							,		lowercase__	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()
      lowercase							   =	True

      for model_class in self.all_model_classes:
        lowercase							   =	True
        lowercase							   =	False
        lowercase							   =	True
        lowercase							   =	model_class(lowercase__	)
        model.to(lowercase__	)
        model.eval()
        with torch.no_grad():
          lowercase							   =	model(**self._prepare_for_class(lowercase__							,		lowercase__	)	)
        lowercase							   =	outputs.attentions
        lowercase							   =	len(self.model_tester.depths	)
        self.assertEqual(len(lowercase__	)							,		lowercase__	)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        lowercase							   =	True
        lowercase							   =	config.window_size**2
        lowercase							   =	model_class(lowercase__	)
        model.to(lowercase__	)
        model.eval()
        with torch.no_grad():
          lowercase							   =	model(**self._prepare_for_class(lowercase__							,		lowercase__	)	)
        lowercase							   =	outputs.attentions
        self.assertEqual(len(lowercase__	)							,		lowercase__	)

        self.assertListEqual(
            list(attentions[0].shape[-3:]	)							,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,		)
        lowercase							   =	len(lowercase__	)

        # Check attention is always last and order is fine
        lowercase							   =	True
        lowercase							   =	True
        lowercase							   =	model_class(lowercase__	)
        model.to(lowercase__	)
        model.eval()
        with torch.no_grad():
          lowercase							   =	model(**self._prepare_for_class(lowercase__							,		lowercase__	)	)

        # also another +1 for reshaped_hidden_states
        lowercase							   =	1 if model_class.__name__ == "SwinBackbone" else 2
        self.assertEqual(out_len + added_hidden_states							,		len(lowercase__	)	)

        lowercase							   =	outputs.attentions

        self.assertEqual(len(lowercase__	)							,		lowercase__	)

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	model_class(lowercase__	)
      model.to(lowercase__	)
      model.eval()

      with torch.no_grad():
        lowercase							   =	model(**self._prepare_for_class(lowercase__							,		lowercase__	)	)

      lowercase							   =	outputs.hidden_states

      lowercase							   =	getattr(
          self.model_tester							,		'expected_num_hidden_layers'							,		len(self.model_tester.depths	) + 1	)
      self.assertEqual(len(lowercase__	)							,		lowercase__	)

      # Swin has a different seq_length
      lowercase							   =	(
          config.patch_size
          if isinstance(config.patch_size							,		collections.abc.Iterable	)
          else (config.patch_size, config.patch_size)
      )

      lowercase							   =	(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]							,		)

      if not model_class.__name__ == "SwinBackbone":
        lowercase							   =	outputs.reshaped_hidden_states
        self.assertEqual(len(lowercase__	)							,		lowercase__	)

        lowercase							   =	reshaped_hidden_states[0].shape
        lowercase							   =	(
            reshaped_hidden_states[0].view(lowercase__							,		lowercase__							,		height * width	).permute(0							,		2							,		1	)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]	)							,		[num_patches, self.model_tester.embed_dim]							,		)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()

      lowercase							   =	(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size							,		collections.abc.Iterable	)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
        lowercase							   =	True
        self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		lowercase__	)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase							   =	True

        self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		lowercase__	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()
      lowercase							   =	3

      lowercase							   =	(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size							,		collections.abc.Iterable	)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      lowercase							   =	(
          config.patch_size
          if isinstance(config.patch_size							,		collections.abc.Iterable	)
          else (config.patch_size, config.patch_size)
      )

      lowercase							   =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      lowercase							   =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
        lowercase							   =	True
        self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		(padded_height, padded_width)	)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase							   =	True
        self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		(padded_height, padded_width)	)







    @slow
    def       UpperCamelCase_				(      self	):






      for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase							   =	SwinModel.from_pretrained(lowercase__	)
        self.assertIsNotNone(lowercase__	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()

      lowercase							   =	_config_zero_init(lowercase__	)
      for model_class in self.all_model_classes:
        lowercase							   =	model_class(config=lowercase__	)
        for name, param in model.named_parameters():
          if "embeddings" not in name and param.requires_grad:
            self.assertIn(
                ((param.data.mean() * 1e9).round() / 1e9).item()							,		[0.0, 1.0]							,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'							,		)


@require_vision
@require_torch
class 	a					(       unittest.TestCase     ):







    @cached_property
    def       UpperCamelCase_				(      self	):
      return (
          AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'	)
          if is_vision_available()
          else None
      )







    @slow
    def       UpperCamelCase_				(      self	):
      lowercase							   =	SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'	).to(lowercase__	)
      lowercase							   =	self.default_image_processor

      lowercase							   =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
      lowercase							   =	image_processor(images=lowercase__							,		return_tensors='pt'	).to(lowercase__	)

      # forward pass
      with torch.no_grad():
        lowercase							   =	model(**lowercase__	)

      # verify the logits
      lowercase							   =	torch.Size((1, 1_0_0_0)	)
      self.assertEqual(outputs.logits.shape							,		lowercase__	)
      lowercase							   =	torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]	).to(lowercase__	)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]							,		lowercase__							,		atol=1e-4	)	)


@require_torch
class 	a					(       unittest.TestCase,    _UpperCAmelCase     ):
    UpperCAmelCase_			:					Tuple     					=(SwinBackbone,) if is_torch_available() else ()
    UpperCAmelCase_			:					Optional[Any]     					=SwinConfig







    def       UpperCamelCase_				(      self	):
      lowercase							   =	SwinModelTester(self	)









"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


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


_UpperCamelCase		:  Any						=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCamelCase		:  Optional[int]						=		{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

_UpperCamelCase		:  Union[str, Any]						=		{
    'RUCAIBox/mvp': 1_0_2_4,
}
@lru_cache()
def 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





  lowercase							   =	(
      list(range(ord('!'   )		, ord('~'   ) + 1   )   ) + list(range(ord('¡'   )		, ord('¬'   ) + 1   )   ) + list(range(ord('®'   )		, ord('ÿ'   ) + 1   )   )
  )
  lowercase							   =	bs[:]
  lowercase							   =	0
  for b in range(2**8   ):
    if b not in bs:
      bs.append(__snake_case   )
      cs.append(2**8 + n   )
      n += 1
  lowercase							   =	[chr(__snake_case   ) for n in cs]
  return dict(zip(__snake_case		, __snake_case   )   )
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int   ):





  '''simple docstring'''





  lowercase							   =	set()
  lowercase							   =	word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)   )
    lowercase							   =	char
  return pairs



class 	a					(       a_     ):
    UpperCAmelCase_			:					Tuple     					=VOCAB_FILES_NAMES
    UpperCAmelCase_			:					int     					=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_			:					Optional[int]     					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_			:					Any     					=["input_ids", "attention_mask"]







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase="replace"							,		_lowerCamelCase="<s>"							,		_lowerCamelCase="</s>"							,		_lowerCamelCase="</s>"							,		_lowerCamelCase="<s>"							,		_lowerCamelCase="<unk>"							,		_lowerCamelCase="<pad>"							,		_lowerCamelCase="<mask>"							,		_lowerCamelCase=False							,		**_lowerCamelCase							,		):
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else bos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else eos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else sep_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else cls_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else unk_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else mask_token

      super().__init__(
          errors=_lowerCamelCase							,		bos_token=_lowerCamelCase							,		eos_token=_lowerCamelCase							,		unk_token=_lowerCamelCase							,		sep_token=_lowerCamelCase							,		cls_token=_lowerCamelCase							,		pad_token=_lowerCamelCase							,		mask_token=_lowerCamelCase							,		add_prefix_space=_lowerCamelCase							,		**_lowerCamelCase							,		)
      with open(_lowerCamelCase							,		encoding='utf-8'	) as vocab_handle:
        lowercase							   =	json.load(_lowerCamelCase	)
      lowercase							   =	{v: k for k, v in self.encoder.items()}
      lowercase							   =	errors  # how to handle errors in decoding
      lowercase							   =	bytes_to_unicode()
      lowercase							   =	{v: k for k, v in self.byte_encoder.items()}
      with open(_lowerCamelCase							,		encoding='utf-8'	) as merges_handle:
        lowercase							   =	merges_handle.read().split('\n'	)[1:-1]
      lowercase							   =	[tuple(merge.split()	) for merge in bpe_merges]
      lowercase							   =	dict(zip(_lowerCamelCase							,		range(len(_lowerCamelCase	)	)	)	)
      lowercase							   =	{}
      lowercase							   =	add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      lowercase							   =	re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)







    @property
    def       UpperCamelCase_				(      self	):
      return len(self.encoder	)







    def       UpperCamelCase_				(      self	):
      return dict(self.encoder							,		**self.added_tokens_encoder	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      if token in self.cache:
        return self.cache[token]
      lowercase							   =	tuple(_lowerCamelCase	)
      lowercase							   =	get_pairs(_lowerCamelCase	)

      if not pairs:
        return token

      while True:
        lowercase							   =	min(_lowerCamelCase							,		key=lambda _lowerCamelCase	: self.bpe_ranks.get(_lowerCamelCase							,		float('inf'	)	)	)
        if bigram not in self.bpe_ranks:
          break
        lowercase   ,					lowercase							   =	bigram
        lowercase							   =	[]
        lowercase							   =	0
        while i < len(_lowerCamelCase	):
          try:
            lowercase							   =	word.index(_lowerCamelCase							,		_lowerCamelCase	)
          except ValueError:
            new_word.extend(word[i:]	)
            break
          else:
            new_word.extend(word[i:j]	)
            lowercase							   =	j

          if word[i] == first and i < len(_lowerCamelCase	) - 1 and word[i + 1] == second:
            new_word.append(first + second	)
            i += 2
          else:
            new_word.append(word[i]	)
            i += 1
        lowercase							   =	tuple(_lowerCamelCase	)
        lowercase							   =	new_word
        if len(_lowerCamelCase	) == 1:
          break
        else:
          lowercase							   =	get_pairs(_lowerCamelCase	)
      lowercase							   =	' '.join(_lowerCamelCase	)
      lowercase							   =	word
      return word







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	[]
      for token in re.findall(self.pat							,		_lowerCamelCase	):
        lowercase							   =	''.join(
            self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase	).split(' '	)	)
      return bpe_tokens







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.encoder.get(_lowerCamelCase							,		self.encoder.get(self.unk_token	)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.decoder.get(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	''.join(_lowerCamelCase	)
      lowercase							   =	bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'							,		errors=self.errors	)
      return text







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      if not os.path.isdir(_lowerCamelCase	):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
        return
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

      with open(_lowerCamelCase							,		'w'							,		encoding='utf-8'	) as f:
        f.write(json.dumps(self.encoder							,		indent=2							,		sort_keys=_lowerCamelCase							,		ensure_ascii=_lowerCamelCase	) + '\n'	)

      lowercase							   =	0
      with open(_lowerCamelCase							,		'w'							,		encoding='utf-8'	) as writer:
        writer.write('#version: 0.2\n'	)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,		key=lambda _lowerCamelCase	: kv[1]	):
          if index != token_index:
            logger.warning(
                F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                ' Please check that the tokenizer is not corrupted!'	)
            lowercase							   =	token_index
          writer.write(' '.join(_lowerCamelCase	) + '\n'	)
          index += 1

      return vocab_file, merge_file







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowercase							   =	[self.cls_token_id]
      lowercase							   =	[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = False	):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowerCamelCase							,		token_ids_a=_lowerCamelCase							,		already_has_special_tokens=_lowerCamelCase	)

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      lowercase							   =	[self.sep_token_id]
      lowercase							   =	[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]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		**_lowerCamelCase	):
      lowercase							   =	kwargs.pop('add_prefix_space'							,		self.add_prefix_space	)
      if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase	) > 0 and not text[0].isspace()):
        lowercase							   =	' ' + text
      return (text, kwargs)


