

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

lowerCAmelCase   :      int     =     logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__)
class        _A						(     UpperCAmelCase__):

  def __init__( self       ,    *_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        super().__init__(*snake_case_       ,    **snake_case_    )
        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        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE=None    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[str]							=   {}
        if top_k is not None:
              SCREAMING_SNAKE_CASE_ :     Any							=   top_k
        return {}, {}, postprocess_params

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





        """simple docstring"""







        return super().__call__(snake_case_       ,    **snake_case_    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[Any]							=   load_image(snake_case_    )
        SCREAMING_SNAKE_CASE_ :     Any							=   self.image_processor(images=snake_case_       ,    return_tensors=self.framework    )
        return model_inputs

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   self.model(**snake_case_    )
        return model_outputs



  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=5    ):





        """simple docstring"""







        if top_k > self.model.config.num_labels:
              SCREAMING_SNAKE_CASE_ :     Optional[int]							=   self.model.config.num_labels

        if self.framework == "pt":
              SCREAMING_SNAKE_CASE_ :     Dict							=   model_outputs.logits.softmax(-1    )[0]
              SCREAMING_SNAKE_CASE_ :     Dict							=   probs.topk(snake_case_    )
        elif self.framework == "tf":
              SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   stable_softmax(model_outputs.logits       ,    axis=-1    )[0]
              SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   tf.math.top_k(snake_case_       ,    k=snake_case_    )
              SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   topk.values.numpy(), topk.indices.numpy()
        else:
              raise ValueError(f"Unsupported framework: {self.framework}"    )

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













"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)