





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    ="""mobilevit"""







  def __init__(					self					:    Dict					, UpperCamelCase					:    int=3					, UpperCamelCase					:    Optional[int]=2_56					, UpperCamelCase					:    int=2					, UpperCamelCase					:    Tuple=[1_44, 1_92, 2_40]					, UpperCamelCase					:    int=[16, 32, 64, 96, 1_28, 1_60, 6_40]					, UpperCamelCase					:    Optional[Any]=4					, UpperCamelCase					:    Dict=2.0					, UpperCamelCase					:    Union[str, Any]=4.0					, UpperCamelCase					:    Optional[Any]="silu"					, UpperCamelCase					:    str=3					, UpperCamelCase					:    Optional[int]=32					, UpperCamelCase					:    List[str]=0.1					, UpperCamelCase					:    List[Any]=0.0					, UpperCamelCase					:    Tuple=0.1					, UpperCamelCase					:    str=0.02					, UpperCamelCase					:    Dict=1e-5					, UpperCamelCase					:    Dict=True					, UpperCamelCase					:    Union[str, Any]=2_56					, UpperCamelCase					:    Union[str, Any]=[6, 12, 18]					, UpperCamelCase					:    Dict=0.1					, UpperCamelCase					:    Any=2_55					, **UpperCamelCase					:    List[Any]					, ):


    '''simple docstring'''





    super().__init__(**UpperCamelCase							)

    _snake_case      : Tuple														=		num_channels
    _snake_case      : Optional[int]														=		image_size
    _snake_case      : List[Any]														=		patch_size
    _snake_case      : Optional[Any]														=		hidden_sizes
    _snake_case      : List[str]														=		neck_hidden_sizes
    _snake_case      : str														=		num_attention_heads
    _snake_case      : int														=		mlp_ratio
    _snake_case      : Optional[Any]														=		expand_ratio
    _snake_case      : str														=		hidden_act
    _snake_case      : Tuple														=		conv_kernel_size
    _snake_case      : List[Any]														=		output_stride
    _snake_case      : Optional[Any]														=		hidden_dropout_prob
    _snake_case      : Optional[int]														=		attention_probs_dropout_prob
    _snake_case      : int														=		classifier_dropout_prob
    _snake_case      : List[Any]														=		initializer_range
    _snake_case      : Optional[int]														=		layer_norm_eps
    _snake_case      : Optional[Any]														=		qkv_bias

    # decode head attributes for semantic segmentation
    _snake_case      : str														=		aspp_out_channels
    _snake_case      : Tuple														=		atrous_rates
    _snake_case      : List[Any]														=		aspp_dropout_prob
    _snake_case      : List[Any]														=		semantic_loss_ignore_index




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				Dict					    =version.parse("""1.11"""							)







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]							)







  @property
  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    if self.task == "image-classification":
      return OrderedDict([('logits', {0: 'batch'})]							)
    else:
      return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]							)







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    return 1e-4











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 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

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

  # Save pytorch-model
  _snake_case      : List[Any]														=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _snake_case      : Optional[Any]														=		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
  torch.save(model.state_dict()			,							lowerCAmelCase )
  print(F"""Save configuration file to {pytorch_config_dump_path}""" )
  with open(lowerCAmelCase			,							'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
      )




