
"""simple docstring"""






import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
					from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=13  ,    _lowerCamelCase=64  ,    _lowerCamelCase=2  ,    _lowerCamelCase=3  ,    _lowerCamelCase=True  ,    _lowerCamelCase=True  ,    _lowerCamelCase=32  ,    _lowerCamelCase=5  ,    _lowerCamelCase=4  ,    _lowerCamelCase=37  ,    _lowerCamelCase="gelu"  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=10  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=[1, 16, 4, 4]  ,    _lowerCamelCase=None  ,    )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       parent
										__lowercase       =       batch_size
										__lowercase       =       image_size
										__lowercase       =       patch_size
										__lowercase       =       num_channels
										__lowercase       =       is_training
										__lowercase       =       use_labels
										__lowercase       =       hidden_size
										__lowercase       =       num_hidden_layers
										__lowercase       =       num_attention_heads
										__lowercase       =       intermediate_size
										__lowercase       =       hidden_act
										__lowercase       =       hidden_dropout_prob
										__lowercase       =       attention_probs_dropout_prob
										__lowercase       =       type_sequence_label_size
										__lowercase       =       initializer_range
										__lowercase       =       scope
										__lowercase       =       backbone_featmap_shape

										# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										# the number of patches is based on the feature map of the backbone, which by default uses an output stride
										# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
										__lowercase       =       (self.image_size // 32) ** 2
										__lowercase       =       num_patches + 1
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										__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   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       {
										    "global_padding": "same",
										    "layer_type": "bottleneck",
										    "depths": [3, 4, 9],
										    "out_features": ["stage1", "stage2", "stage3"],
										    "embedding_dynamic_padding": True,
										    "hidden_sizes": [4, 8, 16, 32],
										    "num_groups": 2,
										}

										return ViTHybridConfig(
										    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  ,    hidden_act=self.hidden_act  ,    hidden_dropout_prob=self.hidden_dropout_prob  ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,    is_decoder=_lowerCamelCase  ,    initializer_range=self.initializer_range  ,    backbone_featmap_shape=self.backbone_featmap_shape  ,    backbone_config=_lowerCamelCase  ,    )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       ViTHybridModel(config=_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()
										__lowercase       =       model(_lowerCamelCase       )
										self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       self.type_sequence_label_size
										__lowercase       =       ViTHybridForImageClassification(_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()
										__lowercase       =       model(_lowerCamelCase  ,    labels=_lowerCamelCase       )
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)       )





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




										'''simple docstring'''
										__lowercase       =       self.prepare_config_and_inputs()
										__lowercase   ,					__lowercase   ,					__lowercase       =       config_and_inputs
										__lowercase       =       {"pixel_values": pixel_values}
										return config, inputs_dict





@require_torch
class 					__a     ( __a					,   __a					,   unittest.TestCase       ):



							'''simple docstring'''







							_lowerCamelCase					:			Any          =   (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
							_lowerCamelCase					:			Dict          =   (
							    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
							    if is_torch_available()
							    else {}
							)
							_lowerCamelCase					:			Tuple          =   False
							_lowerCamelCase					:			str          =   False
							_lowerCamelCase					:			int          =   False
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       ViTHybridModelTester(self       )
										__lowercase       =       ConfigTester(self  ,    config_class=_lowerCamelCase  ,    has_text_modality=_lowerCamelCase  ,    hidden_size=37       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										self.config_tester.run_common_tests()
							@unittest.skip(reason="ViT does not use inputs_embeds"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										pass
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase   ,					__lowercase       =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowercase       =       model_class(_lowerCamelCase       )
													self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)       )
													__lowercase       =       model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_lowerCamelCase  ,    nn.Linear       )       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										__lowercase   ,					__lowercase       =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowercase       =       model_class(_lowerCamelCase       )
													__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]  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										__lowercase   ,					__lowercase       =       self.model_tester.prepare_config_and_inputs_for_common()

										__lowercase       =       _config_zero_init(_lowerCamelCase       )
										for model_class in self.all_model_classes:
													__lowercase       =       model_class(config=_lowerCamelCase       )
													# Skip the check for the backbone
													for name, module in model.named_modules():
																if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																			__lowercase       =       [f'''{name}.{key}''' for key in module.state_dict().keys()]
																			break

													for name, param in model.named_parameters():
																if param.requires_grad:
																			if name in backbone_params:
																						continue
																			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'''  ,    )





							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowercase       =       ViTHybridModel.from_pretrained(_lowerCamelCase       )
													self.assertIsNotNone(_lowerCamelCase       )





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







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



							'''simple docstring'''







							@cached_property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										return (
										    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
										    if is_vision_available()
										    else None
										)
							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
										    _lowerCamelCase       )

										__lowercase       =       self.default_image_processor
										__lowercase       =       prepare_img()
										__lowercase       =       image_processor(images=_lowerCamelCase  ,    return_tensors="pt"       ).to(_lowerCamelCase       )

										# forward pass
										with torch.no_grad():
													__lowercase       =       model(**_lowerCamelCase       )

										# verify the logits
										__lowercase       =       torch.Size((1, 1_000)       )
										self.assertEqual(outputs.logits.shape  ,    _lowerCamelCase       )

										__lowercase       =       torch.tensor([-1.9090, -0.4993, -0.2389]       ).to(_lowerCamelCase       )

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





							@slow
							@require_accelerate
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										__lowercase       =       ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"       )
										__lowercase       =       ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"  ,    device_map="auto"       )

										__lowercase       =       prepare_img()

										__lowercase       =       image_processor(images=_lowerCamelCase  ,    return_tensors="pt"       )
										__lowercase       =       model(**_lowerCamelCase       )
										__lowercase       =       outputs.logits
										# model predicts one of the 1000 ImageNet classes
										__lowercase       =       logits.argmax(-1       ).item()

										self.assertTrue(model.config.idalabel[predicted_class_idx]  ,    "tabby, tabby cat"       )







"""simple docstring"""






import json
import os
from functools import lru_cache
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

_lowercase							=				logging.get_logger(__name__)


_lowercase							=				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowercase							=				{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

_lowercase							=				{'''facebook/blenderbot-3B''': 128}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowerCAmelCase__			(		)						->Any:
			__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(__magic_name__      )
									cs.append(2**8 + n      )
									n += 1
			__lowercase       =       [chr(__magic_name__      ) for n in cs]
			return dict(zip(__magic_name__			,   __magic_name__      )      )



def      lowerCAmelCase__			(		__magic_name__      )						->Tuple:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[str]          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			Tuple          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			int          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase					:			Dict          =   ["""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  ,    )			->							int:




										'''simple docstring'''
										__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
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       "".join(_lowerCamelCase       )
										__lowercase       =       bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"  ,    errors=self.errors       )
										return text
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''
										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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=False  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__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)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple:




										'''simple docstring'''
										return token_ids_a + [self.eos_token_id]





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       []
										for is_user, text in conversation.iter_texts():
													if is_user:
																# We need to space prefix as it's being done within blenderbot
																inputs.append(" " + text       )
													else:
																# Generated responses should contain them already.
																inputs.append(_lowerCamelCase       )

										__lowercase       =       "  ".join(_lowerCamelCase       )
										__lowercase       =       self.encode(_lowerCamelCase       )
										if len(_lowerCamelCase       ) > self.model_max_length:
													__lowercase       =       input_ids[-self.model_max_length :]
													logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''       )
										return input_ids





