
'''simple docstring'''







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowercase    :							Union[str, Any]      =					logging.get_logger(__name__)


class 						__UpperCAmelCase      :


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		question_encoder
								_snake_case   						=		generator
								_snake_case   						=		self.question_encoder


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if os.path.isfile(lowerCAmelCase_  ):
									raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'  )
								os.makedirs(lowerCAmelCase_    ,   exist_ok=lowerCAmelCase_  )
								_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'question_encoder_tokenizer'  )
								_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'generator_tokenizer'  )
								self.question_encoder.save_pretrained(lowerCAmelCase_  )
								self.generator.save_pretrained(lowerCAmelCase_  )


							@classmethod
							def  lowerCamelCase				(       cls    ,   lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								from ..auto.tokenization_auto import AutoTokenizer

								_snake_case   						=		kwargs.pop('config'    ,   lowerCAmelCase_  )

								if config is None:
									_snake_case   						=		RagConfig.from_pretrained(lowerCAmelCase_  )

								_snake_case   						=		AutoTokenizer.from_pretrained(
								    lowerCAmelCase_    ,   config=config.question_encoder    ,   subfolder='question_encoder_tokenizer'  )
								_snake_case   						=		AutoTokenizer.from_pretrained(
								    lowerCAmelCase_    ,   config=config.generator    ,   subfolder='generator_tokenizer'  )
								return cls(question_encoder=lowerCAmelCase_    ,   generator=lowerCAmelCase_  )


							def __call__(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								return self.current_tokenizer(*lowerCAmelCase_    ,   **lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								return self.generator.batch_decode(*lowerCAmelCase_    ,   **lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								return self.generator.decode(*lowerCAmelCase_    ,   **lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.question_encoder


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.generator




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = "longest"    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = True    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								warnings.warn(
								    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
								    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
								    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
								    'details'    ,   lowerCAmelCase_    ,   )
								if max_length is None:
									_snake_case   						=		self.current_tokenizer.model_max_length
								_snake_case   						=		self(
								    lowerCAmelCase_    ,   add_special_tokens=lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   padding=lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   **lowerCAmelCase_    ,   )
								if tgt_texts is None:
									return model_inputs
								# Process tgt_texts
								if max_target_length is None:
									_snake_case   						=		self.current_tokenizer.model_max_length
								_snake_case   						=		self(
								    text_target=lowerCAmelCase_    ,   add_special_tokens=lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   padding=lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   **lowerCAmelCase_    ,   )
								_snake_case   						=		labels['input_ids']
								return model_inputs



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :							Any      =					{
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[Any]      =					["ChineseCLIPFeatureExtractor"]
					lowercase    :							List[Any]      =					["ChineseCLIPImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Any      =					[
					    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ChineseCLIPModel",
					    "ChineseCLIPPreTrainedModel",
					    "ChineseCLIPTextModel",
					    "ChineseCLIPVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_chinese_clip import (
					    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ChineseCLIPConfig,
					    ChineseCLIPOnnxConfig,
					    ChineseCLIPTextConfig,
					    ChineseCLIPVisionConfig,
					)
					from .processing_chinese_clip import ChineseCLIPProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_chinese_clip import (
										    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ChineseCLIPModel,
										    ChineseCLIPPreTrainedModel,
										    ChineseCLIPTextModel,
										    ChineseCLIPVisionModel,
										)

else:
					import sys

					lowercase    :							List[Any]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

