





"""simple docstring"""


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard



class    __SCREAMING_SNAKE_CASE			(  unittest.TestCase      ):







  '''simple docstring'''


  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Union[str, Any]:
        _UpperCamelCase					:		Any  =				{
            "model_details": {
                "Organization": "testing",
                "Model date": "today",
                "Model version": "v2.1, Developed by Test Corp in 2019.",
                "Architecture": "Convolutional Neural Network.",
            },
            "metrics": "BLEU and ROUGE-1",
            "evaluation_data": {
                "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
                "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
            },
            "training_data": {
                "Dataset": "English Wikipedia dump dated 2018-12-01",
                "Preprocessing": (
                    "Using SentencePiece vocabulary of size 52k tokens. See details on"
                    " https://arxiv.org/pdf/1810.03993.pdf"
                ),
            },
            "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
        }

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> Any:
        _UpperCamelCase					:		Tuple  =				ModelCard.from_dict(self.inputs_dict		)
        self.assertTrue(hasattr(__a    ,      "model_details"		)		)
        self.assertTrue(hasattr(__a    ,      "intended_use"		)		)
        self.assertTrue(hasattr(__a    ,      "factors"		)		)
        self.assertTrue(hasattr(__a    ,      "metrics"		)		)
        self.assertTrue(hasattr(__a    ,      "evaluation_data"		)		)
        self.assertTrue(hasattr(__a    ,      "training_data"		)		)
        self.assertTrue(hasattr(__a    ,      "quantitative_analyses"		)		)
        self.assertTrue(hasattr(__a    ,      "ethical_considerations"		)		)
        self.assertTrue(hasattr(__a    ,      "caveats_and_recommendations"		)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> List[str]:
        _UpperCamelCase					:		str  =				ModelCard.from_dict(self.inputs_dict		)
        _UpperCamelCase					:		Optional[Any]  =				json.loads(modelcard.to_json_string()		)
        for key, value in self.inputs_dict.items():
              self.assertEqual(obj[key]    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Optional[int]:
        _UpperCamelCase					:		int  =				ModelCard.from_dict(self.inputs_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
              _UpperCamelCase					:		Tuple  =				os.path.join(__a    ,      "modelcard.json"		)
              model_card_first.to_json_file(__a		)
              _UpperCamelCase					:		List[str]  =				ModelCard.from_json_file(__a		)

        self.assertEqual(model_card_second.to_dict()    ,      model_card_first.to_dict()		)


  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Tuple:
        _UpperCamelCase					:		Dict  =				ModelCard.from_dict(self.inputs_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
              model_card_first.save_pretrained(__a		)
              _UpperCamelCase					:		Any  =				ModelCard.from_pretrained(__a		)

        self.assertEqual(model_card_second.to_dict()    ,      model_card_first.to_dict()		)







"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

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


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
