





'''simple docstring'''







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def  _lowerCamelCase				( lowercase						:      Any    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[Any]						)	->	Optional[Any]:
				# Initialise PyTorch model
				_a								= TaConfig.from_json_file(lowercase						)
				print(F'Building PyTorch model from configuration: {config}'						)
				_a								= TaForConditionalGeneration(lowercase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowercase    ,							lowercase    ,							lowercase						)

				# Save pytorch-model
				print(F'Save PyTorch model to {pytorch_dump_path}'						)
				model.save_pretrained(lowercase						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Dict      	=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
					    ),
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_					:    str      	=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








"""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

lowerCamelCase_					=      logging.get_logger(__name__)







@add_end_docstrings(__A     )
class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: int	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: List[str]						)						->			Optional[Any]:
				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      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Optional[int]=None						)						->			List[Any]:
				UpperCAmelCase_     : str			     =					{}
				if top_k is not None:
							UpperCAmelCase_     : List[str]			     =					top_k
				return {}, {}, postprocess_params





	def __call__(      self					: str	,	lowerCAmelCase_					: Union[str, List[str], "Image.Image", List["Image.Image"]]	,	**lowerCAmelCase_					: Any						)						->			Tuple:
				return super().__call__(lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					load_image(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
				return model_inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Dict						)						->			str:
				UpperCAmelCase_     : Any			     =					self.model(**lowerCAmelCase_						)
				return model_outputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]=5						)						->			Any:
				if top_k > self.model.config.num_labels:
							UpperCAmelCase_     : int			     =					self.model.config.num_labels

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

				UpperCAmelCase_     : int			     =					scores.tolist()
				UpperCAmelCase_     : Optional[Any]			     =					ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_	,	lowerCAmelCase_						)]



