'''simple docstring'''

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any])						->		List[str]:



      """simple docstring"""

      if "model" in orig_key:
            snake_case   :					Tuple							=						orig_key.replace("model.",			"")
      if "norm1" in orig_key:
            snake_case   :					Optional[Any]							=						orig_key.replace("norm1",			"attention.output.LayerNorm")
      if "norm2" in orig_key:
            snake_case   :					str							=						orig_key.replace("norm2",			"output.LayerNorm")
      if "norm" in orig_key:
            snake_case   :					List[Any]							=						orig_key.replace("norm",			"LayerNorm")
      if "transformer" in orig_key:
            snake_case   :					str							=						orig_key.split(".")[0].split("_")[-1]
            snake_case   :					List[str]							=						orig_key.replace(f"""transformer_{layer_num}""",			f"""encoder.layer.{layer_num}""")
      if "mha.attn" in orig_key:
            snake_case   :					Optional[int]							=						orig_key.replace("mha.attn",			"attention.self")
      if "mha" in orig_key:
            snake_case   :					int							=						orig_key.replace("mha",			"attention")
      if "W_q" in orig_key:
            snake_case   :					Any							=						orig_key.replace("W_q",			"self.query")
      if "W_k" in orig_key:
            snake_case   :					List[str]							=						orig_key.replace("W_k",			"self.key")
      if "W_v" in orig_key:
            snake_case   :					str							=						orig_key.replace("W_v",			"self.value")
      if "ff1" in orig_key:
            snake_case   :					Dict							=						orig_key.replace("ff1",			"intermediate.dense")
      if "ff2" in orig_key:
            snake_case   :					List[str]							=						orig_key.replace("ff2",			"output.dense")
      if "ff" in orig_key:
            snake_case   :					List[Any]							=						orig_key.replace("ff",			"output.dense")
      if "mlm_class" in orig_key:
            snake_case   :					Any							=						orig_key.replace("mlm.mlm_class",			"cls.predictions.decoder")
      if "mlm" in orig_key:
            snake_case   :					Dict							=						orig_key.replace("mlm",			"cls.predictions.transform")
      if "cls" not in orig_key:
            snake_case   :					Tuple							=						"nystromformer." + orig_key

      return orig_key


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Any,			_lowerCAmelCase						: Optional[int])						->		Optional[int]:



      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            snake_case   :					str							=						orig_state_dict.pop(_lowerCAmelCase)

            if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
                  continue
            else:
                  snake_case   :					int							=						val

      snake_case   :					str							=						orig_state_dict["cls.predictions.decoder.bias"]
      snake_case   :					Any							=						(
          torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
      )

      return orig_state_dict


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[Any],			_lowerCAmelCase						: str,			_lowerCAmelCase						: Union[str, Any])						->		Tuple:



      """simple docstring"""

      snake_case   :					List[str]							=						torch.load(_lowerCAmelCase,			map_location="cpu")["model_state_dict"]
      snake_case   :					List[str]							=						NystromformerConfig.from_json_file(_lowerCAmelCase)
      snake_case   :					List[str]							=						NystromformerForMaskedLM(_lowerCAmelCase)

      snake_case   :					Tuple							=						convert_checkpoint_helper(_lowerCAmelCase,			_lowerCAmelCase)

      model.load_state_dict(_lowerCAmelCase)
      model.eval()
      model.save_pretrained(_lowerCAmelCase)

      print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""")


if __name__ == "__main__":
      A_        =   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The json file for Nystromformer model config.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      A_        =   parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''

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


A_        =   logging.get_logger(__name__)

A_        =   {
    "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 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					"""mobilevit"""



			def __init__(       self							, snake_case_=3							, snake_case_=256							, snake_case_=2							, snake_case_=[144, 192, 240]							, snake_case_=[16, 32, 64, 96, 128, 160, 640]							, snake_case_=4							, snake_case_=2.0							, snake_case_=4.0							, snake_case_="silu"							, snake_case_=3							, snake_case_=32							, snake_case_=0.1							, snake_case_=0.0							, snake_case_=0.1							, snake_case_=0.02							, snake_case_=1e-5							, snake_case_=True							, snake_case_=256							, snake_case_=[6, 12, 18]							, snake_case_=0.1							, snake_case_=255							, **snake_case_							, ):
									super().__init__(**snake_case_				)

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

									# decode head attributes for semantic segmentation
									snake_case   :					Optional[int]							=						aspp_out_channels
									snake_case   :					Dict							=						atrous_rates
									snake_case   :					Any							=						aspp_dropout_prob
									snake_case   :					Any							=						semantic_loss_ignore_index







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					version.parse("""1.11"""			)



			@property
			def      lowerCamelCase   (       self				):
									return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]				)



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






			@property
			def      lowerCamelCase   (       self				):
									return 1e-4





