






'''simple docstring'''

import inspect
import unittest

from transformers import ResNetConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
						from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 				__magic_name__  :







	"""simple docstring"""



	def __init__(				self	,   _lowercase	,   _lowercase=3	,   _lowercase=32	,   _lowercase=3	,   _lowercase=10	,   _lowercase=[10, 20, 30, 40]	,   _lowercase=[1, 1, 2, 1]	,   _lowercase=True	,   _lowercase=True	,   _lowercase="relu"	,   _lowercase=3	,   _lowercase=None	,   _lowercase=["stage2", "stage3", "stage4"]	,   _lowercase=[2, 3, 4]	,   )    ->	int:
							lowercase_   : Union[str, Any]   	=      parent
							lowercase_   : Dict   	=      batch_size
							lowercase_   : Tuple   	=      image_size
							lowercase_   : List[Any]   	=      num_channels
							lowercase_   : Tuple   	=      embeddings_size
							lowercase_   : List[str]   	=      hidden_sizes
							lowercase_   : Tuple   	=      depths
							lowercase_   : List[Any]   	=      is_training
							lowercase_   : Dict   	=      use_labels
							lowercase_   : List[str]   	=      hidden_act
							lowercase_   : Union[str, Any]   	=      num_labels
							lowercase_   : List[str]   	=      scope
							lowercase_   : Optional[Any]   	=      len(_lowercase							)
							lowercase_   : List[Any]   	=      out_features
							lowercase_   : Optional[int]   	=      out_indices
	def 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							lowercase_   : List[Any]   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

							lowercase_   : List[str]   	=      None
							if self.use_labels:
													lowercase_   : Optional[int]   	=      ids_tensor([self.batch_size]	,   self.num_labels							)

							lowercase_   : int   	=      self.get_config()

							return config, pixel_values, labels
	def 							lowerCamelCase__		(				self							)    ->	int:
							return ResNetConfig(
							    num_channels=self.num_channels	,   embeddings_size=self.embeddings_size	,   hidden_sizes=self.hidden_sizes	,   depths=self.depths	,   hidden_act=self.hidden_act	,   num_labels=self.num_labels	,   out_features=self.out_features	,   out_indices=self.out_indices	,   )
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase							)    ->	str:
							lowercase_   : Optional[Any]   	=      ResNetModel(config=_lowercase							)
							model.to(_lowercase							)
							model.eval()
							lowercase_   : List[Any]   	=      model(_lowercase							)
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape	,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,   )
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase							)    ->	int:
							lowercase_   : List[str]   	=      self.num_labels
							lowercase_   : List[str]   	=      ResNetForImageClassification(_lowercase							)
							model.to(_lowercase							)
							model.eval()
							lowercase_   : int   	=      model(_lowercase	,   labels=_lowercase							)
							self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase							)    ->	List[str]:
							lowercase_   : Union[str, Any]   	=      ResNetBackbone(config=_lowercase							)
							model.to(_lowercase							)
							model.eval()
							lowercase_   : Any   	=      model(_lowercase							)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps							)	,   len(config.out_features							)							)
							self.parent.assertListEqual(list(result.feature_maps[0].shape							)	,   [self.batch_size, self.hidden_sizes[1], 4, 4]							)

							# verify channels
							self.parent.assertEqual(len(model.channels							)	,   len(config.out_features							)							)
							self.parent.assertListEqual(model.channels	,   config.hidden_sizes[1:]							)

							# verify backbone works with out_features=None
							lowercase_   : int   	=      None
							lowercase_   : int   	=      ResNetBackbone(config=_lowercase							)
							model.to(_lowercase							)
							model.eval()
							lowercase_   : Union[str, Any]   	=      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, self.hidden_sizes[-1], 1, 1]							)

							# verify channels
							self.parent.assertEqual(len(model.channels							)	,   1							)
							self.parent.assertListEqual(model.channels	,   [config.hidden_sizes[-1]]							)







	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : int   	=      self.prepare_config_and_inputs()
							lowercase_	,   lowercase_	,   lowercase_   : Tuple   	=      config_and_inputs
							lowercase_   : List[Any]   	=      {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 				__magic_name__  (						UpperCAmelCase_,    UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	(
	    (
	        ResNetModel,
	        ResNetForImageClassification,
	        ResNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_       :   str   =	(
	    {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE_       :   Optional[int]   =	True
	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Tuple   =	False
	SCREAMING_SNAKE_CASE_       :   List[str]   =	False
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : Optional[Any]   	=      ResNetModelTester(self							)
							lowercase_   : str   	=      ConfigTester(self	,   config_class=_lowercase	,   has_text_modality=_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	int:
							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 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							return
	@unittest.skip(reason='ResNet does not use inputs_embeds'							)
	def 							lowerCamelCase__		(				self							)    ->	str:
							pass
	@unittest.skip(reason='ResNet does not support input and output embeddings'							)
	def 							lowerCamelCase__		(				self							)    ->	Any:
							pass
	def 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							lowercase_	,   lowercase_   : Optional[int]   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase_   : str   	=      ['pixel_values']
													self.assertListEqual(arg_names[:1]	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Optional[Any]   	=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							lowercase_   : Union[str, Any]   	=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_	,   lowercase_   : Dict   	=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_   : Optional[int]   	=      model_class(config=_lowercase							)
													for name, module in model.named_modules():
																			if isinstance(_lowercase	,   (nn.BatchNormad, nn.GroupNorm)							):
																									self.assertTrue(
																									    torch.all(module.weight == 1							)	,   msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,   )
																									self.assertTrue(
																									    torch.all(module.bias == 0							)	,   msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,   )
	def 							lowerCamelCase__		(				self							)    ->	int:
							def check_hidden_states_output(_lowercase	,   _lowercase	,   _lowercase							):
													lowercase_   : Optional[int]   	=      model_class(_lowercase							)
													model.to(_lowercase							)
													model.eval()

													with torch.no_grad():
																			lowercase_   : str   	=      model(**self._prepare_for_class(_lowercase	,   _lowercase							)							)

													lowercase_   : Optional[Any]   	=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowercase_   : Dict   	=      self.model_tester.num_stages
													self.assertEqual(len(_lowercase							)	,   expected_num_stages + 1							)

													# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]							)	,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,   )

							lowercase_	,   lowercase_   : List[str]   	=      self.model_tester.prepare_config_and_inputs_for_common()
							lowercase_   : Dict   	=      ['basic', 'bottleneck']
							for model_class in self.all_model_classes:
													for layer_type in layers_type:
																			lowercase_   : Any   	=      layer_type
																			lowercase_   : str   	=      True
																			check_hidden_states_output(_lowercase	,   _lowercase	,   _lowercase							)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			lowercase_   : Union[str, Any]   	=      True

																			check_hidden_states_output(_lowercase	,   _lowercase	,   _lowercase							)
	@unittest.skip(reason='ResNet does not use feedforward chunking'							)
	def 							lowerCamelCase__		(				self							)    ->	Any:
							pass
	def 							lowerCamelCase__		(				self							)    ->	int:
							lowercase_   : List[Any]   	=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowercase							)







	@slow
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase_   : Any   	=      ResNetModel.from_pretrained(_lowercase							)
													self.assertIsNotNone(_lowercase							)






def 						_UpperCAmelCase     (	)      ->      Dict:





						"""simple docstring"""
						lowercase_   : Union[str, Any]   	=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
						return image






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







	"""simple docstring"""



	@cached_property
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							return (
							    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
							    if is_vision_available()
							    else None
							)







	@slow
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : int   	=      ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(_lowercase							)

							lowercase_   : Any   	=      self.default_image_processor
							lowercase_   : Tuple   	=      prepare_img()
							lowercase_   : str   	=      image_processor(images=_lowercase	,   return_tensors='pt'							).to(_lowercase							)

							# forward pass
							with torch.no_grad():
													lowercase_   : Optional[int]   	=      model(**_lowercase							)

							# verify the logits
							lowercase_   : Optional[int]   	=      torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape	,   _lowercase							)

							lowercase_   : str   	=      torch.tensor([-11.10_69, -9.78_77, -8.37_77]							).to(_lowercase							)

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






@require_torch
class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   List[Any]   =	(ResNetBackbone,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE_       :   str   =	False
	SCREAMING_SNAKE_CASE_       :   Optional[int]   =	ResNetConfig
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : List[Any]   	=      ResNetModelTester(self							)








'''simple docstring'''

import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A:   List[str]						=					logging.get_logger(__name__)

A:   List[Any]						=					{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

A:   Tuple						=					{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

A:   Optional[int]						=					{
    "microsoft/deberta-base": 5_1_2,
    "microsoft/deberta-large": 5_1_2,
    "microsoft/deberta-xlarge": 5_1_2,
    "microsoft/deberta-base-mnli": 5_1_2,
    "microsoft/deberta-large-mnli": 5_1_2,
    "microsoft/deberta-xlarge-mnli": 5_1_2,
}

A:   Any						=					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : Any   	=      (
						    list(range(ord('!'				)    ,      ord('~'				) + 1				)				) + list(range(ord('¡'				)    ,      ord('¬'				) + 1				)				) + list(range(ord('®'				)    ,      ord('ÿ'				) + 1				)				)
						)
						lowercase_   : Union[str, Any]   	=      bs[:]
						lowercase_   : Dict   	=      0
						for b in range(2**8				):
												if b not in bs:
																		bs.append(a				)
																		cs.append(2**8 + n				)
																		n += 1
						lowercase_   : List[str]   	=      [chr(a				) for n in cs]
						return dict(zip(a    ,      a				)				)






def 						_UpperCAmelCase     (	a		:						Optional[int]				)      ->      Union[str, Any]:





						"""simple docstring"""
						lowercase_   : str   	=      set()
						lowercase_   : Optional[int]   	=      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)				)
												lowercase_   : str   	=      char
						return pairs






class 				__magic_name__  (						UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   List[str]   =	VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_       :   Tuple   =	PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_       :   Dict   =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_       :   Dict   =	['input_ids', 'attention_mask', 'token_type_ids']
	def __init__(				self	,   _lowercase	,   _lowercase	,   _lowercase="replace"	,   _lowercase="[CLS]"	,   _lowercase="[SEP]"	,   _lowercase="[SEP]"	,   _lowercase="[CLS]"	,   _lowercase="[UNK]"	,   _lowercase="[PAD]"	,   _lowercase="[MASK]"	,   _lowercase=False	,   _lowercase=False	,   **_lowercase	,   )    ->	Union[str, Any]:
							lowercase_   : List[str]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else bos_token
							lowercase_   : str   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else eos_token
							lowercase_   : Tuple   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else sep_token
							lowercase_   : List[Any]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else cls_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else unk_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_   : Union[str, Any]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else mask_token

							super().__init__(
							    errors=_lowercase	,   bos_token=_lowercase	,   eos_token=_lowercase	,   unk_token=_lowercase	,   sep_token=_lowercase	,   cls_token=_lowercase	,   pad_token=_lowercase	,   mask_token=_lowercase	,   add_prefix_space=_lowercase	,   add_bos_token=_lowercase	,   **_lowercase	,   )
							lowercase_   : str   	=      add_bos_token

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

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							lowercase_   : Tuple   	=      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.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							return len(self.encoder							)
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							return dict(self.encoder	,   **self.added_tokens_encoder							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							if token in self.cache:
													return self.cache[token]
							lowercase_   : Any   	=      tuple(_lowercase							)
							lowercase_   : Union[str, Any]   	=      get_pairs(_lowercase							)

							if not pairs:
													return token

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

																			if word[i] == first and i < len(_lowercase							) - 1 and word[i + 1] == second:
																									new_word.append(first + second							)
																									i += 2
																			else:
																									new_word.append(word[i]							)
																									i += 1
													lowercase_   : Optional[int]   	=      tuple(_lowercase							)
													lowercase_   : Tuple   	=      new_word
													if len(_lowercase							) == 1:
																			break
													else:
																			lowercase_   : Optional[Any]   	=      get_pairs(_lowercase							)
							lowercase_   : Tuple   	=      ' '.join(_lowercase							)
							lowercase_   : int   	=      word
							return word
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_   : Union[str, Any]   	=      [self.cls_token_id]
							lowercase_   : Optional[Any]   	=      [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None	,   _lowercase = False							)    ->	List[int]:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowercase	,   token_ids_a=_lowercase	,   already_has_special_tokens=_lowercase							)

							if token_ids_a is None:
													return [1] + ([0] * len(_lowercase							)) + [1]
							return [1] + ([0] * len(_lowercase							)) + [1] + ([0] * len(_lowercase							)) + [1]
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							lowercase_   : Optional[Any]   	=      [self.sep_token_id]
							lowercase_   : 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							) * [0] + len(token_ids_a + sep							) * [1]
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Optional[int]   	=      []
							for token in re.findall(self.pat	,   _lowercase							):
													lowercase_   : List[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(_lowercase							).split(' '							)							)
							return bpe_tokens
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[str]:
							return self.encoder.get(_lowercase	,   self.encoder.get(self.unk_token							)							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							return self.decoder.get(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	int:
							lowercase_   : List[str]   	=      ''.join(_lowercase							)
							lowercase_   : List[str]   	=      bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'	,   errors=self.errors							)
							return text
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	Tuple[str]:
							if not os.path.isdir(_lowercase							):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
													return
							lowercase_   : Any   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
							lowercase_   : Optional[Any]   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']							)

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

							lowercase_   : List[str]   	=      0
							with open(_lowercase	,   '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 _lowercase							: 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_   : Any   	=      token_index
																			writer.write(' '.join(_lowercase							) + '\n'							)
																			index += 1

							return vocab_file, merge_file
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase=False	,   **_lowercase							)    ->	Union[str, Any]:
							lowercase_   : List[Any]   	=      kwargs.pop('add_prefix_space'	,   self.add_prefix_space							)
							if (is_split_into_words or add_prefix_space) and (len(_lowercase							) > 0 and not text[0].isspace()):
													lowercase_   : Dict   	=      ' ' + text
							return (text, kwargs)







	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[int]:
							lowercase_   : Optional[int]   	=      []
							for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(_lowercase	,   add_special_tokens=_lowercase							) + [self.eos_token_id]							)
							if len(_lowercase							) > self.model_max_length:
													lowercase_   : Optional[int]   	=      input_ids[-self.model_max_length :]
							return input_ids
