





"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase						,     _UpperCamelCase      ):







  '''simple docstring'''


  @register_to_config
  def __init__(						self      :				Dict    ,      __a      :				int    ,      __a      :				int    ,      __a      :				int    ,      __a      :				float    ,      __a      :				int    ,      __a      :				int    ,      __a      :				int    ,      __a      :				int    ,      __a      :				str    ,      __a      :				bool = False    ,      )						-> Optional[Any]:
        super().__init__()

        _UpperCamelCase					:		Dict  =				nn.Linear(__a    ,      __a    ,      bias=__a		)

        _UpperCamelCase					:		Any  =				nn.Embedding(__a    ,      __a		)
        _UpperCamelCase					:		Union[str, Any]  =				False

        _UpperCamelCase					:		Dict  =				nn.Dropout(p=__a		)

        _UpperCamelCase					:		str  =				TaConfig(
            d_model=__a    ,      num_heads=__a    ,      d_kv=__a    ,      d_ff=__a    ,      feed_forward_proj=__a    ,      dropout_rate=__a    ,      is_decoder=__a    ,      is_encoder_decoder=__a    ,      )
        _UpperCamelCase					:		str  =				nn.ModuleList()
        for lyr_num in range(__a		):
              _UpperCamelCase					:		int  =				TaBlock(__a		)
              self.encoders.append(__a		)

        _UpperCamelCase					:		Union[str, Any]  =				TaLayerNorm(__a		)
        _UpperCamelCase					:		Any  =				nn.Dropout(p=__a		)


  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				Optional[int]    ,      __a      :				Dict		)						-> Dict:
        _UpperCamelCase					:		Dict  =				self.input_proj(__a		)

        # terminal relative positional encodings
        _UpperCamelCase					:		Tuple  =				encoder_inputs.shape[1]
        _UpperCamelCase					:		Any  =				torch.arange(__a    ,      device=encoder_inputs.device		)

        _UpperCamelCase					:		int  =				encoder_inputs_mask.sum(-1		)
        _UpperCamelCase					:		int  =				torch.roll(input_positions.unsqueeze(0		)    ,      tuple(seq_lens.tolist()		)    ,      dims=0		)
        x += self.position_encoding(__a		)

        _UpperCamelCase					:		Any  =				self.dropout_pre(__a		)

        # inverted the attention mask
        _UpperCamelCase					:		Any  =				encoder_inputs.size()
        _UpperCamelCase					:		int  =				self.get_extended_attention_mask(__a    ,      __a		)

        for lyr in self.encoders:
              _UpperCamelCase					:		List[str]  =				lyr(__a    ,      __a		)[0]
        _UpperCamelCase					:		List[Any]  =				self.layer_norm(__a		)

        return self.dropout_post(__a		), encoder_inputs_mask







"""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
  )
