





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(__SCREAMING_SNAKE_CASE  )
class 		SCREAMING_SNAKE_CASE__    (	__SCREAMING_SNAKE_CASE  ):


      def __init__(self	:				Tuple    ,      *a__	:				Dict    ,      **a__	:				Dict				):





         """simple docstring"""



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


      def __call__(self	:				str    ,      a__	:				Optional[int]    ,      **a__	:				Any				):





         """simple docstring"""



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


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





         """simple docstring"""



         return {}, {}, {}


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





         """simple docstring"""



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


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





         """simple docstring"""



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


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





         """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=_snake_case				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(_snake_case				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(_snake_case				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict








import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
snake_case_           =      data_utils.TransfoXLTokenizer
snake_case_           =      data_utils.TransfoXLCorpus
snake_case_           =      data_utils
snake_case_           =      data_utils






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  int	,				snake_case_     :  List[Any]	,				snake_case_     :  int	)				->    Dict:
   if transfo_xl_dataset_file:
      # Convert a pre-processed corpus (see original TensorFlow repo)
      with open(snake_case_	,				'''rb'''	) as fp:
         __snake_case								=       pickle.load(snake_case_	,				encoding='''latin1'''	)
      # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
      __snake_case								=       pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
      print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""	)
      __snake_case								=       corpus.vocab.__dict__
      torch.save(snake_case_	,				snake_case_	)

      __snake_case								=       corpus.__dict__
      corpus_dict_no_vocab.pop('''vocab'''	,				snake_case_	)
      __snake_case								=       pytorch_dump_folder_path + '''/''' + CORPUS_NAME
      print(f"""Save dataset to {pytorch_dataset_dump_path}"""	)
      torch.save(snake_case_	,				snake_case_	)

   if tf_checkpoint_path:
      # Convert a pre-trained TensorFlow model
      __snake_case								=       os.path.abspath(snake_case_	)
      __snake_case								=       os.path.abspath(snake_case_	)

      print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""	)
      # Initialise PyTorch model
      if transfo_xl_config_file == "":
         __snake_case								=       TransfoXLConfig()
      else:
         __snake_case								=       TransfoXLConfig.from_json_file(snake_case_	)
      print(f"""Building PyTorch model from configuration: {config}"""	)
      __snake_case								=       TransfoXLLMHeadModel(snake_case_	)

      __snake_case								=       load_tf_weights_in_transfo_xl(snake_case_	,				snake_case_	,				snake_case_	)
      # Save pytorch-model
      __snake_case								=       os.path.join(snake_case_	,				snake_case_	)
      __snake_case								=       os.path.join(snake_case_	,				snake_case_	)
      print(f"""Save PyTorch model to {os.path.abspath(snake_case_	)}"""	)
      torch.save(model.state_dict()	,				snake_case_	)
      print(f"""Save configuration file to {os.path.abspath(snake_case_	)}"""	)
      with open(snake_case_	,				'''w'''	,				encoding='''utf-8'''	) as f:
         f.write(config.to_json_string()	)


if __name__ == "__main__":
    snake_case_           =      argparse.ArgumentParser()
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the folder to store the PyTorch model or dataset/vocab.',
    )
    parser.add_argument(
        '--tf_checkpoint_path',
        default='',
        type=str,
        help='An optional path to a TensorFlow checkpoint path to be converted.',
    )
    parser.add_argument(
        '--transfo_xl_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained BERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--transfo_xl_dataset_file',
        default='',
        type=str,
        help='An optional dataset file to be converted in a vocabulary.',
    )
    snake_case_           =      parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )

