





import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def lowerCamelCase__   (							snake_case_     :  List[str]	,				snake_case_     :  Dict	,				snake_case_     :  List[str]	)				->    Optional[int]:
			# Construct model
			if gpta_config_file == "":
						__snake_case								=       GPTaConfig()
			else:
						__snake_case								=       GPTaConfig.from_json_file(snake_case_	)
			__snake_case								=       GPTaModel(snake_case_	)

			# Load weights from numpy
			load_tf_weights_in_gpta(snake_case_	,				snake_case_	,				snake_case_	)

			# Save pytorch-model
			__snake_case								=       pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			__snake_case								=       pytorch_dump_folder_path + '''/''' + CONFIG_NAME
			print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
			torch.save(model.state_dict()	,				snake_case_	)
			print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
			with open(snake_case_	,				'''w'''	,				encoding='''utf-8'''	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
				snake_case_           =      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--gpt2_config_file',
				    default='',
				    type=str,
				    help=(
				        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
				        'This specifies the model architecture.'
				    ),
				)
				snake_case_           =      parser.parse_args()
				convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, 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_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



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


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

