





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=		{
    """Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
    """Salesforce/blip-vqa-capfit-large""": (
        """https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-base""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-large""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
    ),
    """Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
    """Salesforce/blip-itm-large-flikr""": (
        """https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Dict       =       "blip_text_model"






						def __init__(      self      :      Optional[int]		,   _A      :      Dict=30524		,   _A      :      Union[str, Any]=768		,   _A      :      Optional[int]=768		,   _A      :      Tuple=3072		,   _A      :      List[Any]=768		,   _A      :      Tuple=12		,   _A      :      Tuple=8		,   _A      :      List[str]=512		,   _A      :      Optional[Any]="gelu"		,   _A      :      Optional[Any]=1E-12		,   _A      :      Optional[Any]=0.0		,   _A      :      str=0.0		,   _A      :      List[Any]=0.0_2		,   _A      :      int=30522		,   _A      :      Union[str, Any]=2		,   _A      :      List[Any]=0		,   _A      :      str=102		,   _A      :      int=True		,   _A      :      Tuple=True		,   **_A      :      List[str]		,   )							->    Optional[Any]:







							"""simple docstring"""


							super().__init__(
							    pad_token_id=_UpperCAmelCase		,   bos_token_id=_UpperCAmelCase		,   eos_token_id=_UpperCAmelCase		,   sep_token_id=_UpperCAmelCase		,   **_UpperCAmelCase		,   )

							snake_case_		:			Optional[Any]											=	vocab_size
							snake_case_		:			Tuple											=	hidden_size
							snake_case_		:			List[Any]											=	encoder_hidden_size
							snake_case_		:			Union[str, Any]											=	intermediate_size
							snake_case_		:			List[str]											=	projection_dim
							snake_case_		:			int											=	hidden_dropout_prob
							snake_case_		:			List[str]											=	num_hidden_layers
							snake_case_		:			Tuple											=	num_attention_heads
							snake_case_		:			Any											=	max_position_embeddings
							snake_case_		:			str											=	layer_norm_eps
							snake_case_		:			Any											=	hidden_act
							snake_case_		:			Union[str, Any]											=	initializer_range
							snake_case_		:			Any											=	attention_probs_dropout_prob
							snake_case_		:			Union[str, Any]											=	is_decoder
							snake_case_		:			Tuple											=	use_cache






						@classmethod
						def UpperCAmelCase_				(      cls      :      Any		,   _A      :      Union[str, os.PathLike]		,   **_A      :      Optional[Any]	)							->    "PretrainedConfig":







							"""simple docstring"""


							cls._set_token_in_kwargs(_UpperCAmelCase	)

							snake_case_						,snake_case_		:			Any											=	cls.get_config_dict(_UpperCAmelCase		,   **_UpperCAmelCase	)

							# get the text config dict if we are loading from BlipConfig
							if config_dict.get('model_type'	) == "blip":
								snake_case_		:			Dict											=	config_dict['text_config']

							if "model_type" in config_dict and hasattr(cls		,   'model_type'	) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
								    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""	)

							return cls.from_dict(_UpperCAmelCase		,   **_UpperCAmelCase	)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			List[str]       =       "blip_vision_model"






						def __init__(      self      :      Optional[int]		,   _A      :      List[Any]=768		,   _A      :      Dict=3072		,   _A      :      Any=512		,   _A      :      Optional[Any]=12		,   _A      :      List[Any]=12		,   _A      :      List[str]=384		,   _A      :      Optional[Any]=16		,   _A      :      Tuple="gelu"		,   _A      :      Optional[Any]=1E-5		,   _A      :      int=0.0		,   _A      :      Union[str, Any]=1E-10		,   **_A      :      Optional[Any]		,   )							->    List[str]:







							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)

							snake_case_		:			List[str]											=	hidden_size
							snake_case_		:			int											=	intermediate_size
							snake_case_		:			Optional[int]											=	projection_dim
							snake_case_		:			Any											=	num_hidden_layers
							snake_case_		:			Any											=	num_attention_heads
							snake_case_		:			Optional[Any]											=	patch_size
							snake_case_		:			Tuple											=	image_size
							snake_case_		:			Tuple											=	initializer_range
							snake_case_		:			List[Any]											=	attention_dropout
							snake_case_		:			Tuple											=	layer_norm_eps
							snake_case_		:			Tuple											=	hidden_act






						@classmethod
						def UpperCAmelCase_				(      cls      :      str		,   _A      :      Union[str, os.PathLike]		,   **_A      :      List[str]	)							->    "PretrainedConfig":







							"""simple docstring"""


							cls._set_token_in_kwargs(_UpperCAmelCase	)

							snake_case_						,snake_case_		:			Optional[int]											=	cls.get_config_dict(_UpperCAmelCase		,   **_UpperCAmelCase	)

							# get the vision config dict if we are loading from BlipConfig
							if config_dict.get('model_type'	) == "blip":
								snake_case_		:			Optional[int]											=	config_dict['vision_config']

							if "model_type" in config_dict and hasattr(cls		,   'model_type'	) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
								    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""	)

							return cls.from_dict(_UpperCAmelCase		,   **_UpperCAmelCase	)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			List[Any]       =       "blip"
						__magic_name__:			int       =       True






						def __init__(      self      :      Tuple		,   _A      :      Any=None		,   _A      :      List[str]=None		,   _A      :      int=512		,   _A      :      Dict=2.6_5_9_2		,   _A      :      int=256		,   **_A      :      str		,   )							->    Optional[Any]:







							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)

							if text_config is None:
								snake_case_		:			List[str]											=	{}
								logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'	)

							if vision_config is None:
								snake_case_		:			Dict											=	{}
								logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'	)

							snake_case_		:			List[Any]											=	BlipTextConfig(**_UpperCAmelCase	)
							snake_case_		:			Any											=	BlipVisionConfig(**_UpperCAmelCase	)

							snake_case_		:			Optional[int]											=	self.vision_config.hidden_size

							snake_case_		:			Union[str, Any]											=	projection_dim
							snake_case_		:			List[Any]											=	logit_scale_init_value
							snake_case_		:			str											=	1.0
							snake_case_		:			int											=	0.0_2
							snake_case_		:			str											=	image_text_hidden_size






						@classmethod
						def UpperCAmelCase_				(      cls      :      List[Any]		,   _A      :      BlipTextConfig		,   _A      :      BlipVisionConfig		,   **_A      :      Any	)							->    Tuple:







							"""simple docstring"""


							return cls(text_config=text_config.to_dict()		,   vision_config=vision_config.to_dict()		,   **_UpperCAmelCase	)






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







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	copy.deepcopy(self.__dict__	)
							snake_case_		:			str											=	self.text_config.to_dict()
							snake_case_		:			Dict											=	self.vision_config.to_dict()
							snake_case_		:			List[Any]											=	self.__class__.model_type
							return output








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






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







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






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







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A	)		,   _A	)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A	)		,   _A	)






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






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







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






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







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
							    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
							    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
							    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
							    'telles que la traduction et la synthèse de texte.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

