



'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, TensorType

snake_case_			:     Optional[int]  	=  logging.get_logger(__name__)

snake_case_			:     Union[str, Any]  	=  {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """imagegpt"""
	lowercase__      =      ["""past_key_values"""]
	lowercase__      =      {
	    """hidden_size""": """n_embd""",
	    """max_position_embeddings""": """n_positions""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(			self :    int			,lowerCamelCase__ :    Union[str, Any]=512 + 1			,lowerCamelCase__ :    str=32 * 32			,lowerCamelCase__ :    Dict=512			,lowerCamelCase__ :    str=24			,lowerCamelCase__ :    Optional[int]=8			,lowerCamelCase__ :    Tuple=None			,lowerCamelCase__ :    str="quick_gelu"			,lowerCamelCase__ :    int=0.1			,lowerCamelCase__ :    List[Any]=0.1			,lowerCamelCase__ :    Dict=0.1			,lowerCamelCase__ :    str=1E-5			,lowerCamelCase__ :    Optional[Any]=0.0_2			,lowerCamelCase__ :    Optional[int]=True			,lowerCamelCase__ :    Optional[int]=True			,lowerCamelCase__ :    List[str]=False			,lowerCamelCase__ :    Dict=False			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    Union[str, Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       vocab_size
							_UpperCamelCase :		Union[str, Any]			    =       n_positions
							_UpperCamelCase :		List[Any]			    =       n_embd
							_UpperCamelCase :		Optional[Any]			    =       n_layer
							_UpperCamelCase :		Dict			    =       n_head
							_UpperCamelCase :		Dict			    =       n_inner
							_UpperCamelCase :		str			    =       activation_function
							_UpperCamelCase :		List[str]			    =       resid_pdrop
							_UpperCamelCase :		Optional[int]			    =       embd_pdrop
							_UpperCamelCase :		Optional[Any]			    =       attn_pdrop
							_UpperCamelCase :		List[Any]			    =       layer_norm_epsilon
							_UpperCamelCase :		Optional[int]			    =       initializer_range
							_UpperCamelCase :		Any			    =       scale_attn_weights
							_UpperCamelCase :		List[str]			    =       use_cache
							_UpperCamelCase :		int			    =       scale_attn_by_inverse_layer_idx
							_UpperCamelCase :		Any			    =       reorder_and_upcast_attn
							_UpperCamelCase :		int			    =       tie_word_embeddings

							super().__init__(tie_word_embeddings=lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):





	@property
	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							return OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'sequence'}),
							    ]			)





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    "FeatureExtractionMixin"			,lowerCamelCase__ :    int = 1			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional["TensorType"] = None			,lowerCamelCase__ :    int = 3			,lowerCamelCase__ :    int = 32			,lowerCamelCase__ :    int = 32			,):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       self._generate_dummy_images(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       dict(preprocessor(images=lowerCamelCase__			,return_tensors=lowerCamelCase__			)			)

							return inputs







'''simple docstring'''
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Initialise PyTorch model
						_UpperCamelCase :		Any			    =       LxmertConfig.from_json_file(UpperCAmelCase_							)
						print(f'Building PyTorch model from configuration: {config}'							)
						_UpperCamelCase :		int			    =       LxmertForPreTraining(UpperCAmelCase_							)

						# Load weights from tf checkpoint
						load_tf_weights_in_lxmert(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)

						# Save pytorch-model
						print(f'Save PyTorch model to {pytorch_dump_path}'							)
						torch.save(model.state_dict()  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					snake_case_			:     int  	=  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 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.'
					)
					snake_case_			:     List[str]  	=  parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


