
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = """M-CLIP"""

			def __init__(       self :       Optional[Any]						,	snake_case_ :       Union[str, Any]=1024						,	snake_case_ :       Any=768						,	**snake_case_ :       str      ):
							UpperCamelCase_:							Optional[Any]	=						transformerDimSize
							UpperCamelCase_:							List[Any]	=						imageDimSize
							super().__init__(**snake_case_      )




class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = MCLIPConfig

			def __init__(       self :       List[Any]						,	snake_case_ :       Tuple						,	*snake_case_ :       List[Any]						,	**snake_case_ :       List[str]      ):
							super().__init__(snake_case_						,	*snake_case_						,	**snake_case_      )
							UpperCamelCase_:							Optional[int]	=						XLMRobertaModel(snake_case_      )
							UpperCamelCase_:							Tuple	=						torch.nn.Linear(
							    in_features=config.transformerDimensions						,	out_features=config.numDims      )




			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       Optional[int]						,	snake_case_ :       List[Any]      ):
							UpperCamelCase_:							int	=						self.transformer(input_ids=snake_case_						,	attention_mask=snake_case_      )[0]
							UpperCamelCase_:							List[Any]	=						(embs * attention_mask.unsqueeze(2      )).sum(dim=1      ) / attention_mask.sum(dim=1      )[:, None]
							return self.LinearTransformation(snake_case_      ), embs








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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 T5 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."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






