
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor



class 		UpperCamelCase__  :
			def __init__(self			: Tuple ,    snake_case_			: Tuple ,    snake_case_			: Any=1_3 ,    snake_case_			: Optional[Any]=3_0 ,    snake_case_			: Optional[int]=2 ,    snake_case_			: List[str]=3 ,    snake_case_			: str=True ,    snake_case_			: Any=True ,    snake_case_			: List[str]=3_2 ,    snake_case_			: str=2 ,    snake_case_			: Tuple=4 ,    snake_case_			: str=3_7 ,    snake_case_			: List[Any]="gelu" ,    snake_case_			: Optional[Any]=0.1 ,    snake_case_			: Union[str, Any]=0.1 ,    snake_case_			: Tuple=1_0 ,    snake_case_			: Tuple=0.02 ,    snake_case_			: Union[str, Any]=3 ,    snake_case_			: List[Any]=0.6 ,    snake_case_			: List[Any]=None ,    ):
				__a			:  List[str]		    =  parent
				__a			:  Tuple		    =  batch_size
				__a			:  Union[str, Any]		    =  image_size
				__a			:  Optional[int]		    =  patch_size
				__a			:  List[str]		    =  num_channels
				__a			:  Dict		    =  is_training
				__a			:  List[str]		    =  use_labels
				__a			:  int		    =  hidden_size
				__a			:  int		    =  num_hidden_layers
				__a			:  Optional[Any]		    =  num_attention_heads
				__a			:  Tuple		    =  intermediate_size
				__a			:  Dict		    =  hidden_act
				__a			:  Dict		    =  hidden_dropout_prob
				__a			:  str		    =  attention_probs_dropout_prob
				__a			:  Union[str, Any]		    =  type_sequence_label_size
				__a			:  Any		    =  initializer_range
				__a			:  str		    =  mask_ratio
				__a			:  str		    =  scope

				# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
				# (we add 1 for the [CLS] token)
				__a			:  Any		    =  (image_size // patch_size) ** 2
				__a			:  Tuple		    =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )
			def 				lowerCAmelCase      (self			: Optional[int]       ):
				__a			:  Union[str, Any]		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

				__a			:  Union[str, Any]		    =  None
				if self.use_labels:
					__a			:  Tuple		    =  ids_tensor([self.batch_size] ,    self.type_sequence_label_size       )

				__a			:  Optional[int]		    =  self.get_config()

				return config, pixel_values, labels
			def 				lowerCAmelCase      (self			: Tuple       ):
				return ViTMAEConfig(
				    image_size=self.image_size ,    patch_size=self.patch_size ,    num_channels=self.num_channels ,    hidden_size=self.hidden_size ,    num_hidden_layers=self.num_hidden_layers ,    num_attention_heads=self.num_attention_heads ,    intermediate_size=self.intermediate_size ,    decoder_hidden_size=self.hidden_size ,    decoder_num_hidden_layers=self.num_hidden_layers ,    decoder_num_attention_heads=self.num_attention_heads ,    decoder_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 ,    is_decoder=_lowercase ,    initializer_range=self.initializer_range ,    mask_ratio=self.mask_ratio ,    )
			def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: Any ,    snake_case_			: List[str] ,    snake_case_			: Union[str, Any]       ):
				__a			:  Union[str, Any]		    =  TFViTMAEModel(config=_lowercase       )
				__a			:  Union[str, Any]		    =  model(_lowercase ,    training=_lowercase       )
				self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.seq_length, self.hidden_size)       )
			def 				lowerCAmelCase      (self			: str ,    snake_case_			: Any ,    snake_case_			: List[str] ,    snake_case_			: int       ):
				__a			:  Dict		    =  TFViTMAEForPreTraining(_lowercase       )
				__a			:  Optional[int]		    =  model(_lowercase ,    training=_lowercase       )
				# expected sequence length = num_patches
				__a			:  List[str]		    =  (self.image_size // self.patch_size) ** 2
				__a			:  List[str]		    =  self.patch_size**2 * self.num_channels
				self.parent.assertEqual(result.logits.shape ,    (self.batch_size, num_patches, expected_num_channels)       )

				# test greyscale images
				__a			:  Union[str, Any]		    =  1
				__a			:  str		    =  TFViTMAEForPreTraining(_lowercase       )

				__a			:  Optional[Any]		    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
				__a			:  Union[str, Any]		    =  model(_lowercase ,    training=_lowercase       )
				__a			:  Any		    =  self.patch_size**2
				self.parent.assertEqual(result.logits.shape ,    (self.batch_size, num_patches, expected_num_channels)       )







			def 				lowerCAmelCase      (self			: List[Any]       ):
				__a			:  Tuple		    =  self.prepare_config_and_inputs()
				((__a)    ,	(__a)    ,	(__a))			:  Any		    =  config_and_inputs
				__a			:  str		    =  {'''pixel_values''': pixel_values}
				return config, inputs_dict





@require_tf
class 		UpperCamelCase__  (	_lowerCAmelCase   ,_lowerCAmelCase   ,unittest.TestCase   ):
			_SCREAMING_SNAKE_CASE				:							int     	=				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
			_SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

			_SCREAMING_SNAKE_CASE				:							Any     	=				False
			_SCREAMING_SNAKE_CASE				:							Dict     	=				False
			_SCREAMING_SNAKE_CASE				:							Dict     	=				False
			_SCREAMING_SNAKE_CASE				:							int     	=				False
			def 				lowerCAmelCase      (self			: Union[str, Any]       ):
				__a			:  Union[str, Any]		    =  TFViTMAEModelTester(self       )
				__a			:  Any		    =  ConfigTester(self ,    config_class=_lowercase ,    has_text_modality=_lowercase ,    hidden_size=3_7       )
			def 				lowerCAmelCase      (self			: Any       ):
				self.config_tester.run_common_tests()
			@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''       )
			def 				lowerCAmelCase      (self			: Dict       ):
				pass
			def 				lowerCAmelCase      (self			: str       ):
				__a    ,	__a			:  Optional[Any]		    =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					__a			:  Any		    =  model_class(_lowercase       )
					self.assertIsInstance(model.get_input_embeddings() ,    (tf.keras.layers.Layer)       )
					__a			:  Optional[Any]		    =  model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(_lowercase ,    tf.keras.layers.Layer       )       )
			def 				lowerCAmelCase      (self			: List[Any]       ):
				__a    ,	__a			:  List[str]		    =  self.model_tester.prepare_config_and_inputs_for_common()

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

					__a			:  Optional[int]		    =  ['''pixel_values''']
					self.assertListEqual(arg_names[:1] ,    _lowercase       )
			def 				lowerCAmelCase      (self			: int       ):
				__a			:  List[str]		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_lowercase       )
			def 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  Any		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*_lowercase       )
			def 				lowerCAmelCase      (self			: List[str]       ):
				# make the mask reproducible
				np.random.seed(2       )

				__a    ,	__a			:  Dict		    =  self.model_tester.prepare_config_and_inputs_for_common()

				__a			:  List[Any]		    =  int((config.image_size // config.patch_size) ** 2       )
				__a			:  int		    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

				for model_class in self.all_model_classes:
					__a			:  int		    =  model_class(_lowercase       )
					__a			:  Optional[int]		    =  self._prepare_for_class(_lowercase ,    _lowercase       )

					__a			:  List[str]		    =  model(_lowercase ,    noise=_lowercase       )

					__a			:  List[str]		    =  copy.deepcopy(self._prepare_for_class(_lowercase ,    _lowercase       )       )
					__a			:  str		    =  model(**_lowercase ,    noise=_lowercase       )
					__a			:  Any		    =  outputs_dict[0].numpy()
					__a			:  str		    =  outputs_keywords[0].numpy()

					self.assertLess(np.sum(np.abs(output_dict - output_keywords       )       ) ,    1E-6       )
			def 				lowerCAmelCase      (self			: str       ):
				# make the mask reproducible
				np.random.seed(2       )

				__a    ,	__a			:  List[str]		    =  self.model_tester.prepare_config_and_inputs_for_common()

				__a			:  str		    =  int((config.image_size // config.patch_size) ** 2       )
				__a			:  Any		    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

				def prepare_numpy_arrays(snake_case_			: Optional[Any]       ):
					__a			:  Tuple		    =  {}
					for k, v in inputs_dict.items():
						if tf.is_tensor(_lowercase       ):
							__a			:  str		    =  v.numpy()
						else:
							__a			:  int		    =  np.array(_lowercase       )

					return inputs_np_dict

				for model_class in self.all_model_classes:
					__a			:  Dict		    =  model_class(_lowercase       )

					__a			:  Optional[Any]		    =  self._prepare_for_class(_lowercase ,    _lowercase       )
					__a			:  Optional[Any]		    =  prepare_numpy_arrays(_lowercase       )

					__a			:  Optional[Any]		    =  model(_lowercase ,    noise=_lowercase       )
					__a			:  Tuple		    =  model(**_lowercase ,    noise=_lowercase       )
					self.assert_outputs_same(_lowercase ,    _lowercase       )
			def 				lowerCAmelCase      (self			: Any ,    snake_case_			: List[Any] ,    snake_case_			: Optional[int] ,    snake_case_			: Union[str, Any]       ):
				# make masks reproducible
				np.random.seed(2       )

				__a			:  Union[str, Any]		    =  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2       )
				__a			:  List[str]		    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
				__a			:  List[str]		    =  tf.constant(_lowercase       )

				# Add `noise` argument.
				# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
				__a			:  Any		    =  tf_noise

				super().check_pt_tf_models(_lowercase ,    _lowercase ,    _lowercase       )
			def 				lowerCAmelCase      (self			: Union[str, Any]       ):
				# make mask reproducible
				np.random.seed(2       )

				__a    ,	__a			:  Optional[int]		    =  self.model_tester.prepare_config_and_inputs_for_common()

				__a			:  int		    =  {
				    module_member
				    for model_class in self.all_model_classes
				    for module in (import_module(model_class.__module__       ),)
				    for module_member_name in dir(_lowercase       )
				    if module_member_name.endswith('''MainLayer'''       )
				    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
				    and module_member_name[: -len('''MainLayer'''       )] == model_class.__name__[: -len('''Model'''       )]
				    for module_member in (getattr(_lowercase ,    _lowercase       ),)
				    if isinstance(_lowercase ,    _lowercase       )
				    and tf.keras.layers.Layer in module_member.__bases__
				    and getattr(_lowercase ,    '''_keras_serializable''' ,    _lowercase       )
				}

				__a			:  Any		    =  int((config.image_size // config.patch_size) ** 2       )
				__a			:  Union[str, Any]		    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
				__a			:  List[Any]		    =  tf.convert_to_tensor(_lowercase       )
				inputs_dict.update({'''noise''': noise}       )

				for main_layer_class in tf_main_layer_classes:
					__a			:  str		    =  main_layer_class(_lowercase       )

					__a			:  List[str]		    =  {
					    name: tf.keras.Input(tensor.shape[1:] ,    dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
					}

					__a			:  Dict		    =  tf.keras.Model(_lowercase ,    outputs=main_layer(_lowercase       )       )
					__a			:  Any		    =  model(_lowercase       )

					with tempfile.TemporaryDirectory() as tmpdirname:
						__a			:  int		    =  os.path.join(_lowercase ,    '''keras_model.h5'''       )
						model.save(_lowercase       )
						__a			:  List[str]		    =  tf.keras.models.load_model(
						    _lowercase ,    custom_objects={main_layer_class.__name__: main_layer_class}       )
						assert isinstance(_lowercase ,    tf.keras.Model       )
						__a			:  Tuple		    =  model(_lowercase       )
						self.assert_outputs_same(_lowercase ,    _lowercase       )
			@slow
			def 				lowerCAmelCase      (self			: Optional[int]       ):
				# make mask reproducible
				np.random.seed(2       )

				__a    ,	__a			:  List[str]		    =  self.model_tester.prepare_config_and_inputs_for_common()

				__a			:  int		    =  int((config.image_size // config.patch_size) ** 2       )
				__a			:  List[str]		    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

				for model_class in self.all_model_classes:
					__a			:  Union[str, Any]		    =  model_class(_lowercase       )
					__a			:  str		    =  self._prepare_for_class(_lowercase ,    _lowercase       )
					__a			:  Dict		    =  model(_lowercase ,    noise=_lowercase       )

					if model_class.__name__ == "TFViTMAEModel":
						__a			:  List[str]		    =  outputs.last_hidden_state.numpy()
						__a			:  str		    =  0
					else:
						__a			:  str		    =  outputs.logits.numpy()
						__a			:  Dict		    =  0

					with tempfile.TemporaryDirectory() as tmpdirname:
						model.save_pretrained(_lowercase ,    saved_model=_lowercase       )
						__a			:  Optional[int]		    =  model_class.from_pretrained(_lowercase       )
						__a			:  List[str]		    =  model(_lowercase ,    noise=_lowercase       )

						if model_class.__name__ == "TFViTMAEModel":
							__a			:  Optional[Any]		    =  after_outputs['''last_hidden_state'''].numpy()
							__a			:  List[str]		    =  0
						else:
							__a			:  Any		    =  after_outputs['''logits'''].numpy()
							__a			:  Dict		    =  0

						__a			:  Union[str, Any]		    =  np.amax(np.abs(out_a - out_a       )       )
						self.assertLessEqual(_lowercase ,    1E-5       )
			def 				lowerCAmelCase      (self			: List[str]       ):
				# make mask reproducible
				np.random.seed(2       )

				__a    ,	__a			:  List[str]		    =  self.model_tester.prepare_config_and_inputs_for_common()

				__a			:  Optional[Any]		    =  int((config.image_size // config.patch_size) ** 2       )
				__a			:  List[Any]		    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

				for model_class in self.all_model_classes:
					__a			:  Tuple		    =  model_class(_lowercase       )
					__a			:  Any		    =  self._prepare_for_class(_lowercase ,    _lowercase       )

					__a			:  Dict		    =  model(_lowercase ,    noise=_lowercase       )
					__a			:  List[str]		    =  model.get_config()
					# make sure that returned config is jsonifiable, which is required by keras
					json.dumps(_lowercase       )
					__a			:  Union[str, Any]		    =  model_class.from_config(model.get_config()       )
					# make sure it also accepts a normal config
					__a			:  Tuple		    =  model_class.from_config(model.config       )
					__a			:  int		    =  new_model(_lowercase       )  # Build model
					new_model.set_weights(model.get_weights()       )
					__a			:  List[Any]		    =  new_model(_lowercase ,    noise=_lowercase       )

					self.assert_outputs_same(_lowercase ,    _lowercase       )
			@unittest.skip(
			    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''       )
			def 				lowerCAmelCase      (self			: Optional[int]       ):
				pass
			@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''       )
			def 				lowerCAmelCase      (self			: Tuple       ):
				pass







			@slow
			def 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  Any		    =  TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''       )
				self.assertIsNotNone(_lowercase       )





def        __UpperCamelCase					(    ):
	__a			:  List[Any]		    =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
	return image



@require_tf
@require_vision
class 		UpperCamelCase__  (	unittest.TestCase   ):
			@cached_property
			def 				lowerCAmelCase      (self			: Tuple       ):
				return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''       ) if is_vision_available() else None







			@slow
			def 				lowerCAmelCase      (self			: Tuple       ):
				# make random mask reproducible across the PT and TF model
				np.random.seed(2       )

				__a			:  Union[str, Any]		    =  TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''       )

				__a			:  Dict		    =  self.default_image_processor
				__a			:  str		    =  prepare_img()
				__a			:  List[str]		    =  image_processor(images=_lowercase ,    return_tensors='''tf'''       )

				# prepare a noise vector that will be also used for testing the TF model
				# (this way we can ensure that the PT and TF models operate on the same inputs)
				__a			:  Any		    =  ViTMAEConfig()
				__a			:  List[str]		    =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
				__a			:  List[str]		    =  np.random.uniform(size=(1, num_patches)       )

				# forward pass
				__a			:  int		    =  model(**_lowercase ,    noise=_lowercase       )

				# verify the logits
				__a			:  Optional[Any]		    =  tf.convert_to_tensor([1, 1_9_6, 7_6_8]       )
				self.assertEqual(outputs.logits.shape ,    _lowercase       )

				__a			:  int		    =  tf.convert_to_tensor(
				    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

				tf.debugging.assert_near(outputs.logits[0, :3, :3] ,    _lowercase ,    atol=1E-4       )


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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase__          =logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
lowercase__          ={
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowercase__          ={
    'allenai/led-base-16384': 16384,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def        __UpperCamelCase					(    ):
 __a			:  Tuple		    =  (
     list(range(ord('''!'''  )  ,       ord('''~'''  ) + 1  )  ) + list(range(ord('''¡'''  )  ,       ord('''¬'''  ) + 1  )  ) + list(range(ord('''®'''  )  ,       ord('''ÿ'''  ) + 1  )  )
 )
 __a			:  List[str]		    =  bs[:]
 __a			:  Tuple		    =  0
 for b in range(2**8  ):
  if b not in bs:
   bs.append(lowerCAmelCase__  )
   cs.append(2**8 + n  )
   n += 1
 __a			:  Union[str, Any]		    =  [chr(lowerCAmelCase__  ) for n in cs]
 return dict(zip(lowerCAmelCase__  ,       lowerCAmelCase__  )  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ):
 __a			:  Any		    =  set()
 __a			:  List[str]		    =  word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)  )
  __a			:  Tuple		    =  char
 return pairs



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				["input_ids", "attention_mask"]
   def __init__(self			: Any ,    snake_case_			: int ,    snake_case_			: Dict ,    snake_case_			: Union[str, Any]="replace" ,    snake_case_			: Any="<s>" ,    snake_case_			: Any="</s>" ,    snake_case_			: Tuple="</s>" ,    snake_case_			: Dict="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: List[Any]="<pad>" ,    snake_case_			: Dict="<mask>" ,    snake_case_			: Dict=False ,    **snake_case_			: Tuple ,    ):
    __a			:  List[str]		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else bos_token
    __a			:  Tuple		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else eos_token
    __a			:  Union[str, Any]		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else sep_token
    __a			:  Dict		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else cls_token
    __a			:  List[str]		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else unk_token
    __a			:  str		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else pad_token

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

    super().__init__(
        errors=snake_case_ ,    bos_token=snake_case_ ,    eos_token=snake_case_ ,    unk_token=snake_case_ ,    sep_token=snake_case_ ,    cls_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    add_prefix_space=snake_case_ ,    **snake_case_ ,    )

    with open(snake_case_ ,    encoding='''utf-8'''       ) as vocab_handle:
     __a			:  Union[str, Any]		    =  json.load(snake_case_       )
    __a			:  Union[str, Any]		    =  {v: k for k, v in self.encoder.items()}
    __a			:  List[Any]		    =  errors  # how to handle errors in decoding
    __a			:  Union[str, Any]		    =  bytes_to_unicode()
    __a			:  Tuple		    =  {v: k for k, v in self.byte_encoder.items()}
    with open(snake_case_ ,    encoding='''utf-8'''       ) as merges_handle:
     __a			:  List[Any]		    =  merges_handle.read().split('''\n'''       )[1:-1]
    __a			:  int		    =  [tuple(merge.split()       ) for merge in bpe_merges]
    __a			:  Tuple		    =  dict(zip(snake_case_ ,    range(len(snake_case_       )       )       )       )
    __a			:  Optional[int]		    =  {}
    __a			:  Any		    =  add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    __a			:  str		    =  re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )
   @property
   # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
   def 				lowerCAmelCase      (self			: int       ):
    return len(self.encoder       )
   def 				lowerCAmelCase      (self			: str       ):
    return dict(self.encoder ,    **self.added_tokens_encoder       )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: Optional[Any]       ):
    if token in self.cache:
     return self.cache[token]
    __a			:  str		    =  tuple(snake_case_       )
    __a			:  Optional[Any]		    =  get_pairs(snake_case_       )

    if not pairs:
     return token

    while True:
     __a			:  Optional[int]		    =  min(snake_case_ ,    key=lambda snake_case_       : self.bpe_ranks.get(snake_case_ ,    float('''inf'''       )       )       )
     if bigram not in self.bpe_ranks:
      break
     __a    ,	__a			:  Any		    =  bigram
     __a			:  Union[str, Any]		    =  []
     __a			:  List[Any]		    =  0
     while i < len(snake_case_       ):
      try:
       __a			:  int		    =  word.index(snake_case_ ,    snake_case_       )
      except ValueError:
       new_word.extend(word[i:]       )
       break
      else:
       new_word.extend(word[i:j]       )
       __a			:  Optional[int]		    =  j

      if word[i] == first and i < len(snake_case_       ) - 1 and word[i + 1] == second:
       new_word.append(first + second       )
       i += 2
      else:
       new_word.append(word[i]       )
       i += 1
     __a			:  Tuple		    =  tuple(snake_case_       )
     __a			:  List[Any]		    =  new_word
     if len(snake_case_       ) == 1:
      break
     else:
      __a			:  Tuple		    =  get_pairs(snake_case_       )
    __a			:  Optional[int]		    =  ''' '''.join(snake_case_       )
    __a			:  Union[str, Any]		    =  word
    return word
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: str       ):
    __a			:  Optional[Any]		    =  []
    for token in re.findall(self.pat ,    snake_case_       ):
     __a			:  str		    =  ''''''.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(snake_case_       ).split(''' '''       )       )
    return bpe_tokens
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: int       ):
    return self.encoder.get(snake_case_ ,    self.encoder.get(self.unk_token       )       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[str]       ):
    return self.decoder.get(snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: str       ):
    __a			:  Any		    =  ''''''.join(snake_case_       )
    __a			:  Union[str, Any]		    =  bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8''' ,    errors=self.errors       )
    return text
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    if not os.path.isdir(snake_case_       ):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
     return
    __a			:  Any		    =  os.path.join(
        snake_case_ ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
    __a			:  str		    =  os.path.join(
        snake_case_ ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

    __a			:  Optional[int]		    =  0
    with open(snake_case_ ,    '''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 snake_case_       : 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!'''       )
       __a			:  Union[str, Any]		    =  token_index
      writer.write(''' '''.join(snake_case_       ) + '''\n'''       )
      index += 1

    return vocab_file, merge_file
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __a			:  Union[str, Any]		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1, 1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    __a			:  Optional[int]		    =  [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 				lowerCAmelCase      (self			: str ,    snake_case_			: int ,    snake_case_			: Union[str, Any]=False ,    **snake_case_			: str       ):
    __a			:  List[str]		    =  kwargs.pop('''add_prefix_space''' ,    self.add_prefix_space       )
    if (is_split_into_words or add_prefix_space) and (len(snake_case_       ) > 0 and not text[0].isspace()):
     __a			:  str		    =  ''' ''' + text
    return (text, kwargs)







   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: Union[Dict[str, EncodedInput], BatchEncoding] ,    snake_case_			: Optional[int] = None ,    snake_case_			: PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,    snake_case_			: Optional[int] = None ,    snake_case_			: Optional[bool] = None ,    ):
    __a			:  int		    =  super()._pad(
        encoded_inputs=snake_case_ ,    max_length=snake_case_ ,    padding_strategy=snake_case_ ,    pad_to_multiple_of=snake_case_ ,    return_attention_mask=snake_case_ ,    )

    # Load from model defaults
    if return_attention_mask is None:
     __a			:  Optional[Any]		    =  '''attention_mask''' in self.model_input_names

    if return_attention_mask and "global_attention_mask" in encoded_inputs:
     __a			:  List[Any]		    =  encoded_inputs[self.model_input_names[0]]
     # `global_attention_mask` need to have the same length as other (sequential) inputs.
     __a			:  List[Any]		    =  len(encoded_inputs['''global_attention_mask''']       ) != len(snake_case_       )

     if needs_to_be_padded:
      __a			:  int		    =  len(snake_case_       ) - len(encoded_inputs['''global_attention_mask''']       )

      if self.padding_side == "right":
       # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
       __a			:  List[str]		    =  (
           encoded_inputs['''global_attention_mask'''] + [-1] * difference
       )
      elif self.padding_side == "left":
       __a			:  List[str]		    =  [-1] * difference + encoded_inputs[
           '''global_attention_mask'''
       ]
      else:
       raise ValueError('''Invalid padding strategy:''' + str(self.padding_side       )       )

    return encoded_inputs
