"""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


SCREAMING_SNAKE_CASE__:Any		   =	logging.get_logger(__name__)






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  question_encoder
        __a     				=  generator
        __a     				=  self.question_encoder


 def 		a__ (				self  ,						lowerCamelCase		):
        if os.path.isfile(__A		):
               raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"		)
        os.makedirs(__A  ,						exist_ok=__A		)
        __a     				=  os.path.join(__A  ,						"question_encoder_tokenizer"		)
        __a     				=  os.path.join(__A  ,						"generator_tokenizer"		)
        self.question_encoder.save_pretrained(__A		)
        self.generator.save_pretrained(__A		)


 @classmethod
 def 		a__ (				cls  ,						lowerCamelCase  ,						**lowerCamelCase		):
        # dynamically import AutoTokenizer
        from ..auto.tokenization_auto import AutoTokenizer

        __a     				=  kwargs.pop("config"  ,						__A		)

        if config is None:
               __a     				=  RagConfig.from_pretrained(__A		)

        __a     				=  AutoTokenizer.from_pretrained(
            __A  ,						config=config.question_encoder  ,						subfolder="question_encoder_tokenizer"		)
        __a     				=  AutoTokenizer.from_pretrained(
            __A  ,						config=config.generator  ,						subfolder="generator_tokenizer"		)
        return cls(question_encoder=__A  ,						generator=__A		)


 def __call__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.current_tokenizer(*__A  ,						**__A		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.generator.batch_decode(*__A  ,						**__A		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.generator.decode(*__A  ,						**__A		)


 def 		a__ (				self		):
        __a     				=  self.question_encoder


 def 		a__ (				self		):
        __a     				=  self.generator






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = "longest"  ,						lowerCamelCase = None  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        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"  ,						__A  ,						)
        if max_length is None:
               __a     				=  self.current_tokenizer.model_max_length
        __a     				=  self(
            __A  ,						add_special_tokens=__A  ,						return_tensors=__A  ,						max_length=__A  ,						padding=__A  ,						truncation=__A  ,						**__A  ,						)
        if tgt_texts is None:
               return model_inputs
        # Process tgt_texts
        if max_target_length is None:
               __a     				=  self.current_tokenizer.model_max_length
        __a     				=  self(
            text_target=__A  ,						add_special_tokens=__A  ,						return_tensors=__A  ,						padding=__A  ,						max_length=__A  ,						truncation=__A  ,						**__A  ,						)
        __a     				=  labels["input_ids"]
        return model_inputs

"""simple docstring"""



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   import tensorflow as tf

   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   from ..tf_utils import stable_softmax

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        requires_backends(self  ,						"vision"		)
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)


 def 		a__ (				self  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

        if self.framework == "pt":
               __a     				=  model_outputs.logits.softmax(-1		)[0]
               __a				, __a     				=  probs.topk(lowerCamelCase		)
        elif self.framework == "tf":
               __a     				=  stable_softmax(model_outputs.logits  ,						axis=-1		)[0]
               __a     				=  tf.math.top_k(lowerCamelCase  ,						k=lowerCamelCase		)
               __a				, __a     				=  topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(F"Unsupported framework: {self.framework}"		)

        __a     				=  scores.tolist()
        __a     				=  ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase  ,						lowerCamelCase		)]
