



"""simple docstring"""







import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

UpperCAmelCase:  str			     =				logging.get_logger(__name__)

UpperCAmelCase:  Tuple			     =				{"""vocab_file""": """tokenizer.model"""}

UpperCAmelCase:  int			     =				{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
UpperCAmelCase:  Union[str, Any]			     =				{
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCAmelCase:  List[Any]			     =				"""▁"""

UpperCAmelCase     , UpperCAmelCase:  int			     =				"""[INST]""", """[/INST]"""
UpperCAmelCase     , UpperCAmelCase:  Union[str, Any]			     =				"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCAmelCase:  Optional[int]			     =				"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_							:       int						       =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_							:       Dict						       =  ["input_ids", "attention_mask"]






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_="<unk>"				,UpperCAmelCase_="<s>"				,UpperCAmelCase_="</s>"				,UpperCAmelCase_=None				,UpperCAmelCase_ = None				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=True				,**UpperCAmelCase_				,):
			_lowercase				: List[str]     =   {} if sp_model_kwargs is None else sp_model_kwargs
			_lowercase				: List[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else bos_token
			_lowercase				: Union[str, Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else eos_token
			_lowercase				: Optional[int]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else unk_token
			_lowercase				: Optional[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else pad_token
			super().__init__(
			    bos_token=UpperCAmelCase_				,eos_token=UpperCAmelCase_				,unk_token=UpperCAmelCase_				,pad_token=UpperCAmelCase_				,add_bos_token=UpperCAmelCase_				,add_eos_token=UpperCAmelCase_				,sp_model_kwargs=self.sp_model_kwargs				,clean_up_tokenization_spaces=UpperCAmelCase_				,legacy=UpperCAmelCase_				,**UpperCAmelCase_				,)
			if legacy:
					logger.warning_once(
					    f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
					    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""   )
			_lowercase				: Tuple     =   legacy
			_lowercase				: Optional[int]     =   vocab_file
			_lowercase				: Any     =   add_bos_token
			_lowercase				: Union[str, Any]     =   add_eos_token
			_lowercase				: Union[str, Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(UpperCAmelCase_   )






	def __getstate__(     self   ):
			_lowercase				: Union[str, Any]     =   self.__dict__.copy()
			_lowercase				: Optional[int]     =   None
			_lowercase				: Optional[Any]     =   self.sp_model.serialized_model_proto()
			return state






	def __setstate__(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   d
			_lowercase				: List[Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.sp_model.get_piece_size()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   {self.convert_ids_to_tokens(UpperCAmelCase_   ): i for i in range(self.vocab_size   )}
			vocab.update(self.added_tokens_encoder   )
			return vocab






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,**UpperCAmelCase_   ):
			# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
			# the beginning of the text
			if not self.legacy:
					_lowercase				: str     =   SPIECE_UNDERLINE + text.replace(UpperCAmelCase_				,""" """   )
			return super().tokenize(UpperCAmelCase_				,**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if not self.legacy:
					_lowercase				: Any     =   text.startswith(UpperCAmelCase_   )
					if is_first:
							_lowercase				: str     =   text[1:]

			_lowercase				: Dict     =   self.sp_model.encode(UpperCAmelCase_				,out_type=UpperCAmelCase_   )

			if not self.legacy and not is_first and not text.startswith(""" """   ) and tokens[0].startswith(UpperCAmelCase_   ):
					_lowercase				: List[str]     =   ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
			return tokens






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return self.sp_model.piece_to_id(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   self.sp_model.IdToPiece(UpperCAmelCase_   )
			return token






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   []
			_lowercase				: Dict     =   """"""
			_lowercase				: List[Any]     =   False
			for i, token in enumerate(UpperCAmelCase_   ):
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special and i != 0:
									out_string += " "
							out_string += self.sp_model.decode(UpperCAmelCase_   ) + token
							_lowercase				: Optional[Any]     =   True
							_lowercase				: str     =   []
					else:
							current_sub_tokens.append(UpperCAmelCase_   )
							_lowercase				: Optional[Any]     =   False
			out_string += self.sp_model.decode(UpperCAmelCase_   )
			return out_string






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			if not os.path.isdir(UpperCAmelCase_   ):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
					return
			_lowercase				: str     =   os.path.join(
			    UpperCAmelCase_				,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

			if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase_   ) and os.path.isfile(self.vocab_file   ):
					copyfile(self.vocab_file				,UpperCAmelCase_   )
			elif not os.path.isfile(self.vocab_file   ):
					with open(UpperCAmelCase_				,"""wb"""   ) as fi:
							_lowercase				: List[Any]     =   self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_   )

			return (out_vocab_file,)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			_lowercase				: Any     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: List[str]     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Union[str, Any]     =   bos_token_id + token_ids_a + eos_token_id

			if token_ids_a is not None:
					_lowercase				: int     =   output + bos_token_id + token_ids_a + eos_token_id

			return output






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

			_lowercase				: Tuple     =   [1] if self.add_bos_token else []
			_lowercase				: Dict     =   [1] if self.add_eos_token else []

			if token_ids_a is None:
					return bos_token_id + ([0] * len(UpperCAmelCase_   )) + eos_token_id
			return (
			    bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			    + bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Union[str, Any]     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: str     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Any     =   [0] * len(bos_token_id + token_ids_a + eos_token_id   )

			if token_ids_a is not None:
					output += [1] * len(bos_token_id + token_ids_a + eos_token_id   )

			return output






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   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...)"""   )

			_lowercase				: List[int]     =   []
			if len(conversation.past_user_inputs   ) > 0:
					if not conversation.past_user_inputs[0].startswith(UpperCAmelCase_   ) or E_SYS not in conversation.past_user_inputs[0]:
							_lowercase				: Optional[int]     =   (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(UpperCAmelCase_   ) or E_SYS not in dialogue[0][1]:
					_lowercase				: List[str]     =   (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=UpperCAmelCase_   )
			        + [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=UpperCAmelCase_   )
			return dialog_tokens








"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class   UpperCAmelCase__		(   unittest.TestCase  ):



		def __init__(			self	:     Dict   ,				snake_case	:     Dict   ,				snake_case	:     Optional[Any]=3   ,				snake_case	:     Union[str, Any]=32   ,				snake_case	:     int=3   ,				snake_case	:     Any=10   ,				snake_case	:     int=[10, 20, 30, 40]   ,				snake_case	:     List[Any]=[1, 1, 2, 1]   ,				snake_case	:     List[str]=True   ,				snake_case	:     Any=True   ,				snake_case	:     str="relu"   ,				snake_case	:     List[str]=3   ,				snake_case	:     List[str]=None   ,				)      ->    int:






									'''simple docstring'''

									A    =       parent
									A    =       batch_size
									A    =       image_size
									A    =       num_channels
									A    =       embeddings_size
									A    =       hidden_sizes
									A    =       depths
									A    =       is_training
									A    =       use_labels
									A    =       hidden_act
									A    =       num_labels
									A    =       scope
									A    =       len(snake_case					)



		def        A_		(			self	:     Optional[int]					)      ->    Any:






									'''simple docstring'''

									A    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									A    =       self.get_config()

									return config, pixel_values



		def        A_		(			self	:     int					)      ->    List[Any]:






									'''simple docstring'''

									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   ,				image_size=self.image_size   ,				)



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     str   ,				snake_case	:     Any					)      ->    int:






									'''simple docstring'''

									A    =       FlaxResNetModel(config=snake_case					)
									A    =       model(snake_case					)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,				)



		def        A_		(			self	:     Dict   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[Any]					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       FlaxResNetForImageClassification(config=snake_case					)
									A    =       model(snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)



		def        A_		(			self	:     List[str]					)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									A   ,	A    =       config_and_inputs
									A    =       {'pixel_values': pixel_values}
									return config, inputs_dict






@require_flax
class   UpperCAmelCase__		(   UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						List[str]							=  (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		lowerCAmelCase_							:						Tuple							=  False
		lowerCAmelCase_							:						List[str]							=  False
		lowerCAmelCase_							:						List[str]							=  False



		def        A_		(			self	:     Optional[Any]					)      ->    None:






									'''simple docstring'''

									A    =       FlaxResNetModelTester(self					)
									A    =       ConfigTester(self   ,				config_class=snake_case   ,				has_text_modality=snake_case					)



		def        A_		(			self	:     List[str]					)      ->    Dict:






									'''simple docstring'''

									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        A_		(			self	:     Dict					)      ->    List[Any]:






									'''simple docstring'''

									return



		def        A_		(			self	:     str					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case					)



		def        A_		(			self	:     Optional[int]					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case					)



		@unittest.skip(reason='ResNet does not use inputs_embeds'					)
		def        A_		(			self	:     Tuple					)      ->    str:






									'''simple docstring'''

									pass



		@unittest.skip(reason='ResNet does not support input and output embeddings'					)
		def        A_		(			self	:     Tuple					)      ->    Any:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Optional[Any]					)      ->    int:






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

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

																A    =       ['pixel_values']
																self.assertListEqual(arg_names[:1]   ,				snake_case					)



		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									def check_hidden_states_output(snake_case	:     Tuple   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Optional[int]					):
																A    =       model_class(snake_case					)

																A    =       model(**self._prepare_for_class(snake_case   ,				snake_case					)					)

																A    =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																A    =       self.model_tester.num_stages
																self.assertEqual(len(snake_case					)   ,				expected_num_stages + 1					)



		@unittest.skip(reason='ResNet does not use feedforward chunking'					)
		def        A_		(			self	:     Optional[int]					)      ->    List[Any]:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Union[str, Any]					)      ->    Union[str, Any]:






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																							A    =       self._prepare_for_class(snake_case   ,				snake_case					)
																							A    =       model_class(snake_case					)

																							@jax.jit
																							def model_jitted(snake_case	:     Any   ,				**snake_case	:     int					):
																														return model(pixel_values=snake_case   ,				**snake_case					)

																							with self.subTest('JIT Enabled'					):
																														A    =       model_jitted(**snake_case					).to_tuple()

																							with self.subTest('JIT Disabled'					):
																														with jax.disable_jit():
																																					A    =       model_jitted(**snake_case					).to_tuple()

																							self.assertEqual(len(snake_case					)   ,				len(snake_case					)					)
																							for jitted_output, output in zip(snake_case   ,				snake_case					):
																														self.assertEqual(jitted_output.shape   ,				output.shape					)



def 	lowerCAmelCase__      (  )			->				int:
							A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							return image






@require_flax
class   UpperCAmelCase__		(   unittest.TestCase  ):



		@cached_property
		def        A_		(			self	:     Union[str, Any]					)      ->    int:






									'''simple docstring'''

									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'					) if is_vision_available() else None



		@slow
		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									A    =       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'					)

									A    =       self.default_image_processor
									A    =       prepare_img()
									A    =       image_processor(images=snake_case   ,				return_tensors='np'					)

									A    =       model(**snake_case					)

									# verify the logits
									A    =       (1, 1_000)
									self.assertEqual(outputs.logits.shape   ,				snake_case					)

									A    =       jnp.array([-11.1069, -9.7877, -8.3777]					)

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



