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 SCREAMING_SNAKE_CASE					(  lowercase_:						Tuple		,    lowercase_:						Optional[Any]		,    lowercase_:						int					):
       # Construct configuration depending on size
       _UpperCamelCase  :							Tuple	    =	{'small': (512, 8, 24), 'medium': (1024, 8, 36), 'large': (1536, 16, 48)}
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	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  :							Optional[int]	    =	pytorch_dump_folder_path + '/' + WEIGHTS_NAME
       _UpperCamelCase  :							Optional[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__":
 UpperCAmelCase      		=					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.'''
 )
 UpperCAmelCase      		=					parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )





'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_           =    {
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SamModel",
      "SamPreTrainedModel",
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSamModel",
      "TFSamPreTrainedModel",
  ]
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    ["SamImageProcessor"]


if TYPE_CHECKING:
  from .configuration_sam import (
      SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SamConfig,
      SamMaskDecoderConfig,
      SamPromptEncoderConfig,
      SamVisionConfig,
  )
  from .processing_sam import SamProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_sam import SamImageProcessor

else:
  import sys

  a_           =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




