
import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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_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 transformers import (
		    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    MODEL_MAPPING,
		    EfficientFormerForImageClassification,
		    EfficientFormerForImageClassificationWithTeacher,
		    EfficientFormerModel,
		)
		from transformers.models.efficientformer.modeling_efficientformer import (
		    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


if is_vision_available():
		from PIL import Image

		from transformers import EfficientFormerImageProcessor


class 		__magic_name__   :





		def __init__(							self    :	Optional[int]						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	int = 1_3						,  lowerCamelCase__    :	int = 6_4						,  lowerCamelCase__    :	int = 2						,  lowerCamelCase__    :	int = 3						,  lowerCamelCase__    :	int = 3						,  lowerCamelCase__    :	bool = True						,  lowerCamelCase__    :	bool = True						,  lowerCamelCase__    :	int = 1_2_8						,  lowerCamelCase__    :	int=[1_6, 3_2, 6_4, 1_2_8]						,  lowerCamelCase__    :	int = 7						,  lowerCamelCase__    :	int = 4						,  lowerCamelCase__    :	int = 3_7						,  lowerCamelCase__    :	str = "gelu"						,  lowerCamelCase__    :	float = 0.1						,  lowerCamelCase__    :	float = 0.1						,  lowerCamelCase__    :	int = 1_0						,  lowerCamelCase__    :	float = 0.0_2						,  lowerCamelCase__    :	int = 2						,  lowerCamelCase__    :	int = 1						,  lowerCamelCase__    :	int = 1_2_8						,  lowerCamelCase__    :	List[int] = [2, 2, 2, 2]						,  lowerCamelCase__    :	int = 2						,  lowerCamelCase__    :	int = 2						,  ):
							lowerCAmelCase		:					List[str]  	=     parent
							lowerCAmelCase		:					Union[str, Any]  	=     batch_size
							lowerCAmelCase		:					Union[str, Any]  	=     image_size
							lowerCAmelCase		:					Union[str, Any]  	=     patch_size
							lowerCAmelCase		:					List[Any]  	=     num_channels
							lowerCAmelCase		:					Optional[int]  	=     is_training
							lowerCAmelCase		:					List[str]  	=     use_labels
							lowerCAmelCase		:					Union[str, Any]  	=     hidden_size
							lowerCAmelCase		:					Dict  	=     num_hidden_layers
							lowerCAmelCase		:					Any  	=     num_attention_heads
							lowerCAmelCase		:					Dict  	=     intermediate_size
							lowerCAmelCase		:					List[Any]  	=     hidden_act
							lowerCAmelCase		:					List[str]  	=     hidden_dropout_prob
							lowerCAmelCase		:					Any  	=     attention_probs_dropout_prob
							lowerCAmelCase		:					int  	=     type_sequence_label_size
							lowerCAmelCase		:					Dict  	=     initializer_range
							lowerCAmelCase		:					List[Any]  	=     encoder_stride
							lowerCAmelCase		:					str  	=     num_attention_outputs
							lowerCAmelCase		:					Dict  	=     embed_dim
							lowerCAmelCase		:					Tuple  	=     embed_dim + 1
							lowerCAmelCase		:					Union[str, Any]  	=     resolution
							lowerCAmelCase		:					Optional[Any]  	=     depths
							lowerCAmelCase		:					int  	=     hidden_sizes
							lowerCAmelCase		:					List[Any]  	=     dim
							lowerCAmelCase		:					Optional[int]  	=     mlp_expansion_ratio





		def       _A							(							self    :	Any				):
							lowerCAmelCase		:					Any  	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							lowerCAmelCase		:					Tuple  	=     None
							if self.use_labels:
												lowerCAmelCase		:					Optional[int]  	=     ids_tensor([self.batch_size]						,  self.type_sequence_label_size				)

							lowerCAmelCase		:					Optional[int]  	=     self.get_config()
							return config, pixel_values, labels





		def       _A							(							self    :	Optional[Any]				):
							return EfficientFormerConfig(
							    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						,  encoder_stride=self.encoder_stride						,  resolution=self.resolution						,  depths=self.depths						,  hidden_sizes=self.hidden_sizes						,  dim=self.dim						,  mlp_expansion_ratio=self.mlp_expansion_ratio						,  )





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[str]						,  lowerCamelCase__    :	List[str]						,  lowerCamelCase__    :	int				):
							lowerCAmelCase		:					Union[str, Any]  	=     EfficientFormerModel(config=lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()
							lowerCAmelCase		:					List[str]  	=     model(lowerCamelCase__				)
							self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)				)





		def       _A							(							self    :	List[str]						,  lowerCamelCase__    :	Tuple						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	Optional[int]				):
							lowerCAmelCase		:					Dict  	=     self.type_sequence_label_size
							lowerCAmelCase		:					int  	=     EfficientFormerForImageClassification(lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()
							lowerCAmelCase		:					Tuple  	=     model(lowerCamelCase__						,  labels=lowerCamelCase__				)
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)				)

							# test greyscale images
							lowerCAmelCase		:					List[Any]  	=     1
							lowerCAmelCase		:					int  	=     EfficientFormerForImageClassification(lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()

							lowerCAmelCase		:					int  	=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
							lowerCAmelCase		:					Optional[Any]  	=     model(lowerCamelCase__				)
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)				)





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



@require_torch
class 		__magic_name__   (						snake_case,				snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	(
		    (
		        EfficientFormerModel,
		        EfficientFormerForImageClassificationWithTeacher,
		        EfficientFormerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_lowerCAmelCase		=	(
		    {
		        "feature-extraction": EfficientFormerModel,
		        "image-classification": (
		            EfficientFormerForImageClassification,
		            EfficientFormerForImageClassificationWithTeacher,
		        ),
		    }
		    if is_torch_available()
		    else {}
		)
		_lowerCAmelCase		=	False

		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					List[Any]  	=     EfficientFormerModelTester(self				)
							lowerCAmelCase		:					str  	=     ConfigTester(
							    self						,  config_class=lowerCamelCase__						,  has_text_modality=lowerCamelCase__						,  hidden_size=3_7				)





		def       _A							(							self    :	Tuple				):
							self.config_tester.run_common_tests()





		@unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''				)
		def       _A							(							self    :	Tuple				):
							pass





		@unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''				)
		def       _A							(							self    :	Tuple				):
							pass





		def       _A							(							self    :	Dict				):
							lowerCAmelCase	,							lowerCAmelCase		:					Tuple  	=     self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCAmelCase		:					Optional[Any]  	=     ['''pixel_values''']
												self.assertListEqual(arg_names[:1]						,  lowerCamelCase__				)





		def       _A							(							self    :	Any				):
							def check_hidden_states_output(lowerCamelCase__    :	int						,  lowerCamelCase__    :	Optional[int]						,  lowerCamelCase__    :	List[str]				):
												lowerCAmelCase		:					Tuple  	=     model_class(lowerCamelCase__				)
												model.to(lowerCamelCase__				)
												model.eval()

												with torch.no_grad():
																	lowerCAmelCase		:					Any  	=     model(**self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)				)

												lowerCAmelCase		:					str  	=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowerCAmelCase		:					int  	=     getattr(
												    self.model_tester						,  '''expected_num_hidden_layers'''						,  self.model_tester.num_hidden_layers + 1				)
												self.assertEqual(len(lowerCamelCase__				)						,  lowerCamelCase__				)

												if hasattr(self.model_tester						,  '''encoder_seq_length'''				):
																	lowerCAmelCase		:					str  	=     self.model_tester.encoder_seq_length
																	if hasattr(self.model_tester						,  '''chunk_length'''				) and self.model_tester.chunk_length > 1:
																						lowerCAmelCase		:					List[Any]  	=     seq_length * self.model_tester.chunk_length
												else:
																	lowerCAmelCase		:					List[Any]  	=     self.model_tester.seq_length

												self.assertListEqual(
												    list(hidden_states[-1].shape[-2:]				)						,  [seq_length, self.model_tester.hidden_size]						,  )

												if config.is_encoder_decoder:
																	lowerCAmelCase		:					Union[str, Any]  	=     outputs.decoder_hidden_states

																	self.assertIsInstance(lowerCamelCase__						,  (list, tuple)				)
																	self.assertEqual(len(lowerCamelCase__				)						,  lowerCamelCase__				)
																	lowerCAmelCase		:					Optional[int]  	=     getattr(self.model_tester						,  '''seq_length'''						,  lowerCamelCase__				)
																	lowerCAmelCase		:					Optional[int]  	=     getattr(self.model_tester						,  '''decoder_seq_length'''						,  lowerCamelCase__				)

																	self.assertListEqual(
																	    list(hidden_states[-1].shape[-2:]				)						,  [decoder_seq_length, self.model_tester.hidden_size]						,  )

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

							for model_class in self.all_model_classes:
												lowerCAmelCase		:					Any  	=     True
												check_hidden_states_output(lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				)

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

												check_hidden_states_output(lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				)





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[Any]						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	Dict=False				):
							lowerCAmelCase		:					Tuple  	=     super()._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__						,  return_labels=lowerCamelCase__				)

							if return_labels:
												if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
																	del inputs_dict["labels"]

							return inputs_dict





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Any  	=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__				)





		@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''				)
		def       _A							(							self    :	int				):
							lowerCAmelCase		:					str  	=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__				)





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Tuple  	=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__				)





		def       _A							(							self    :	Optional[int]				):
							if not self.model_tester.is_training:
												return

							lowerCAmelCase	,							lowerCAmelCase		:					int  	=     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase		:					Optional[Any]  	=     True

							for model_class in self.all_model_classes:
												# EfficientFormerForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(lowerCamelCase__				)
												    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
												):
																	continue
												lowerCAmelCase		:					Optional[int]  	=     model_class(lowerCamelCase__				)
												model.to(lowerCamelCase__				)
												model.train()
												lowerCAmelCase		:					Tuple  	=     self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__						,  return_labels=lowerCamelCase__				)
												lowerCAmelCase		:					Any  	=     model(**lowerCamelCase__				).loss
												loss.backward()





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase		:					List[str]  	=     [
							    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
							    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
							    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
							]

							for model_class in self.all_model_classes:
												if (
												    model_class
												    not in [
												        *get_values(lowerCamelCase__				),
												    ]
												    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
												):
																	continue

												for problem_type in problem_types:
																	with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''				):
																						lowerCAmelCase		:					int  	=     problem_type['''title''']
																						lowerCAmelCase		:					int  	=     problem_type['''num_labels''']

																						lowerCAmelCase		:					str  	=     model_class(lowerCamelCase__				)
																						model.to(lowerCamelCase__				)
																						model.train()

																						lowerCAmelCase		:					Tuple  	=     self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__						,  return_labels=lowerCamelCase__				)

																						if problem_type["num_labels"] > 1:
																											lowerCAmelCase		:					int  	=     inputs['''labels'''].unsqueeze(1				).repeat(1						,  problem_type['''num_labels''']				)

																						lowerCAmelCase		:					Union[str, Any]  	=     inputs['''labels'''].to(problem_type['''dtype''']				)

																						# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																						# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																						# they have the same size." which is a symptom something in wrong for the regression problem.
																						# See https://github.com/huggingface/transformers/issues/11780
																						with warnings.catch_warnings(record=lowerCamelCase__				) as warning_list:
																											lowerCAmelCase		:					int  	=     model(**lowerCamelCase__				).loss
																						for w in warning_list:
																											if "Using a target size that is different to the input size" in str(w.message				):
																																raise ValueError(
																																    f'''Something is going wrong in the regression problem: intercepted {w.message}'''				)

																						loss.backward()





		@slow
		def       _A							(							self    :	Union[str, Any]				):
							for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCAmelCase		:					Any  	=     EfficientFormerModel.from_pretrained(lowerCamelCase__				)
												self.assertIsNotNone(lowerCamelCase__				)





		def       _A							(							self    :	Dict				):
							lowerCAmelCase	,							lowerCAmelCase		:					str  	=     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase		:					str  	=     True

							lowerCAmelCase		:					Optional[Any]  	=     getattr(self.model_tester						,  '''seq_length'''						,  lowerCamelCase__				)
							lowerCAmelCase		:					int  	=     getattr(self.model_tester						,  '''encoder_seq_length'''						,  lowerCamelCase__				)
							lowerCAmelCase		:					str  	=     getattr(self.model_tester						,  '''key_length'''						,  lowerCamelCase__				)
							lowerCAmelCase		:					List[str]  	=     getattr(self.model_tester						,  '''chunk_length'''						,  lowerCamelCase__				)

							if chunk_length is not None and hasattr(self.model_tester						,  '''num_hashes'''				):
												lowerCAmelCase		:					str  	=     encoder_seq_length * self.model_tester.num_hashes

							for model_class in self.all_model_classes:
												lowerCAmelCase		:					Tuple  	=     True
												lowerCAmelCase		:					List[str]  	=     False
												lowerCAmelCase		:					Optional[int]  	=     True
												lowerCAmelCase		:					List[Any]  	=     model_class(lowerCamelCase__				)
												model.to(lowerCamelCase__				)
												model.eval()
												with torch.no_grad():
																	lowerCAmelCase		:					Optional[Any]  	=     model(**self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)				)
												lowerCAmelCase		:					str  	=     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
												self.assertEqual(len(lowerCamelCase__				)						,  self.model_tester.num_attention_outputs				)

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												lowerCAmelCase		:					str  	=     True
												lowerCAmelCase		:					Dict  	=     model_class(lowerCamelCase__				)
												model.to(lowerCamelCase__				)
												model.eval()
												with torch.no_grad():
																	lowerCAmelCase		:					Union[str, Any]  	=     model(**self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)				)
												lowerCAmelCase		:					str  	=     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
												self.assertEqual(len(lowerCamelCase__				)						,  self.model_tester.num_attention_outputs				)

												if chunk_length is not None:
																	self.assertListEqual(
																	    list(attentions[0].shape[-4:]				)						,  [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]						,  )
												else:
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]				)						,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						,  )



def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
					return image


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





		@cached_property
		def       _A							(							self    :	List[Any]				):
							return (
							    EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''				)
							    if is_vision_available()
							    else None
							)





		@slow
		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					Any  	=     EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''				).to(
							    lowerCamelCase__				)

							lowerCAmelCase		:					List[Any]  	=     self.default_image_processor
							lowerCAmelCase		:					Optional[int]  	=     prepare_img()
							lowerCAmelCase		:					List[Any]  	=     image_processor(images=lowerCamelCase__						,  return_tensors='''pt'''				).to(lowerCamelCase__				)

							# forward pass
							with torch.no_grad():
												lowerCAmelCase		:					str  	=     model(**lowerCamelCase__				)

							# verify the logits
							lowerCAmelCase		:					List[str]  	=     (1, 1_0_0_0)
							self.assertEqual(outputs.logits.shape						,  lowerCamelCase__				)

							lowerCAmelCase		:					List[str]  	=     torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]				).to(lowerCamelCase__				)
							self.assertTrue(torch.allclose(outputs.logits[0][:3]						,  lowerCamelCase__						,  atol=1E-4				)				)





		@slow
		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase		:					Dict  	=     EfficientFormerForImageClassificationWithTeacher.from_pretrained(
							    '''snap-research/efficientformer-l1-300'''				).to(lowerCamelCase__				)

							lowerCAmelCase		:					Optional[Any]  	=     self.default_image_processor
							lowerCAmelCase		:					str  	=     prepare_img()
							lowerCAmelCase		:					List[str]  	=     image_processor(images=lowerCamelCase__						,  return_tensors='''pt'''				).to(lowerCamelCase__				)

							# forward pass
							with torch.no_grad():
												lowerCAmelCase		:					Optional[Any]  	=     model(**lowerCamelCase__				)

							# verify the logits
							lowerCAmelCase		:					Optional[int]  	=     (1, 1_0_0_0)
							self.assertEqual(outputs.logits.shape						,  lowerCamelCase__				)

							lowerCAmelCase		:					Dict  	=     torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]				).to(lowerCamelCase__				)
							self.assertTrue(torch.allclose(outputs.logits[0][:3]						,  lowerCamelCase__						,  atol=1E-4				)				)


import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

require_version('tokenizers>=0.13.3')

if is_sentencepiece_available():
		from .tokenization_llama import LlamaTokenizer
else:
		__SCREAMING_SNAKE_CASE			:   int         =     None

__SCREAMING_SNAKE_CASE			:   List[Any]         =     logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE			:   Optional[int]         =     {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Dict         =     '[INST]', '[/INST]'
__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   List[Any]         =     '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__SCREAMING_SNAKE_CASE			:   List[Any]         =     'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	VOCAB_FILES_NAMES
		_lowerCAmelCase		=	LlamaTokenizer
		_lowerCAmelCase		=	"left"
		_lowerCAmelCase		=	["input_ids", "attention_mask"]





		def __init__(							self    :	List[Any]						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	Union[str, Any]=None						,  lowerCamelCase__    :	List[str]=False						,  lowerCamelCase__    :	int="<unk>"						,  lowerCamelCase__    :	int="<s>"						,  lowerCamelCase__    :	str="</s>"						,  lowerCamelCase__    :	Optional[Any]=True						,  lowerCamelCase__    :	int=False						,  **lowerCamelCase__    :	List[str]						,  ):
							super().__init__(
							    vocab_file=lowerCamelCase__						,  tokenizer_file=lowerCamelCase__						,  clean_up_tokenization_spaces=lowerCamelCase__						,  unk_token=lowerCamelCase__						,  bos_token=lowerCamelCase__						,  eos_token=lowerCamelCase__						,  **lowerCamelCase__						,  )
							lowerCAmelCase		:					Optional[int]  	=     add_bos_token
							lowerCAmelCase		:					int  	=     add_eos_token
							self.update_post_processor()

							lowerCAmelCase		:					List[Any]  	=     vocab_file
							lowerCAmelCase		:					List[str]  	=     False if not self.vocab_file else True





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					List[str]  	=     self.bos_token
							lowerCAmelCase		:					Optional[Any]  	=     self.bos_token_id

							lowerCAmelCase		:					Optional[Any]  	=     self.eos_token
							lowerCAmelCase		:					List[str]  	=     self.eos_token_id

							lowerCAmelCase		:					Dict  	=     f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
							lowerCAmelCase		:					Optional[int]  	=     f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

							lowerCAmelCase		:					Any  	=     []
							if self.add_bos_token:
												special_tokens.append((bos, bos_token_id)				)
							if self.add_eos_token:
												special_tokens.append((eos, eos_token_id)				)
							lowerCAmelCase		:					Union[str, Any]  	=     processors.TemplateProcessing(
							    single=lowerCamelCase__						,  pair=lowerCamelCase__						,  special_tokens=lowerCamelCase__				)





		@property
		def       _A							(							self    :	str				):
							return self._add_eos_token





		@property
		def       _A							(							self    :	Dict				):
							return self._add_bos_token





		@add_eos_token.setter
		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	str				):
							lowerCAmelCase		:					Any  	=     value
							self.update_post_processor()





		@add_bos_token.setter
		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	Optional[Any]				):
							lowerCAmelCase		:					List[Any]  	=     value
							self.update_post_processor()





		def       _A							(							self    :	int						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Optional[str] = None				):
							if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''				)

							if not os.path.isdir(lowerCamelCase__				):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
												return
							lowerCAmelCase		:					str  	=     os.path.join(
							    lowerCamelCase__						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
												copyfile(self.vocab_file						,  lowerCamelCase__				)

							return (out_vocab_file,)





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	"Conversation"				):
							lowerCAmelCase		:					int  	=     list(conversation.iter_texts()				)
							if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
							    [not is_user for is_user, msg in dialogue[1::2]]				):
												raise ValueError(
												    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)

							lowerCAmelCase		:					List[str]  	=     []
							if len(conversation.past_user_inputs				) > 0:
												if not conversation.past_user_inputs[0].startswith(lowerCamelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																	lowerCAmelCase		:					Tuple  	=     (
																	    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																	)
							elif not dialogue[0][1].startswith(lowerCamelCase__				) or E_SYS not in dialogue[0][1]:
												lowerCAmelCase		:					Optional[Any]  	=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

							dialog_tokens += sum(
							    [
							        [self.bos_token_id]
							        + self.encode(
							            f'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''						,  add_special_tokens=lowerCamelCase__				)
							        + [self.eos_token_id]
							        for prompt, answer in zip(dialogue[::2]						,  dialogue[1::2]				)
							    ]						,  []						,  )
							if not (dialogue[-1][0]):
												raise ValueError(f'''Last message must be from user, got {dialogue[-1]["role"]}'''				)
							dialog_tokens += [self.bos_token_id] + self.encode(
							    f'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''						,  add_special_tokens=lowerCamelCase__				)
							return dialog_tokens
