
'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]						)      ->  int:
  _lowerCAmelCase    :					int							=   [
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """_float_tensor""",
      """decoder.output_projection.weight""",
  ]
  for k in ignore_keys:
    state_dict.pop(_lowerCamelCase					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]						)      ->  Any:
  _lowerCAmelCase			,   _lowerCAmelCase    :					Optional[int]							=   emb.weight.shape
  _lowerCAmelCase    :					Dict							=   nn.Linear(_lowerCamelCase					, _lowerCamelCase					, bias=_lowerCamelCase						)
  _lowerCAmelCase    :					str							=   emb.weight.data
  return lin_layer


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    str="uclanlp/plbart-base"					, _lowerCamelCase  :    Tuple=False					, _lowerCamelCase  :    Union[str, Any]=False						)      ->  Union[str, Any]:
  _lowerCAmelCase    :					Optional[Any]							=   torch.load(_lowerCamelCase					, map_location="""cpu"""						)["""model"""]
  remove_ignore_keys_(_lowerCamelCase						)
  _lowerCAmelCase    :					str							=   state_dict["""encoder.embed_tokens.weight"""].shape[0]

  _lowerCAmelCase    :					List[str]							=   PLBartConfig.from_pretrained(_lowerCamelCase					, vocab_size=_lowerCamelCase						)

  _lowerCAmelCase    :					List[str]							=   state_dict["""decoder.embed_tokens.weight"""]
  if not classification:
    _lowerCAmelCase    :					int							=   PLBartForConditionalGeneration(_lowerCamelCase						)
    model.model.load_state_dict(_lowerCamelCase						)
    if finetuned:
      _lowerCAmelCase    :					Any							=   make_linear_from_emb(model.model.shared						)

  else:
    _lowerCAmelCase    :					List[Any]							=   {}
    for key, value in state_dict.copy().items():
      if key.startswith("""classification_heads.sentence_classification_head"""						):
        _lowerCAmelCase    :					Union[str, Any]							=   value
        state_dict.pop(_lowerCamelCase						)
    _lowerCAmelCase    :					Union[str, Any]							=   PLBartForSequenceClassification(_lowerCamelCase						)
    model.model.load_state_dict(_lowerCamelCase						)
    model.classification_head.load_state_dict(_lowerCamelCase						)

  return model


if __name__ == "__main__":
     UpperCamelCase_            =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("""fairseq_path""", type=str, help="""model.pt on local filesystem.""")
     parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument(
         """--hf_config""",
         default="""uclanlp/plbart-base""",
         type=str,
         help="""Which huggingface architecture to use: plbart-base""",
     )
     parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
     parser.add_argument(
         """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
     )
     UpperCamelCase_            =    parser.parse_args()
     UpperCamelCase_            =    convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_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


UpperCamelCase_            =    logging.get_logger(__name__)

UpperCamelCase_            =    {
    """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 					a_					(_a      ):
       __lowerCAmelCase      :			List[str]      = """mobilevit"""




       def __init__(       self				,				snake_case_=3				,				snake_case_=2_5_6				,				snake_case_=2				,				snake_case_=[1_4_4, 1_9_2, 2_4_0]				,				snake_case_=[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]				,				snake_case_=4				,				snake_case_=2.0				,				snake_case_=4.0				,				snake_case_="silu"				,				snake_case_=3				,				snake_case_=3_2				,				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_=2_5_6				,				snake_case_=[6, 1_2, 1_8]				,				snake_case_=0.1				,				snake_case_=2_5_5				,				**snake_case_				,				):
         super().__init__(**snake_case_					)

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

         # decode head attributes for semantic segmentation
         _lowerCAmelCase    :					str							=   aspp_out_channels
         _lowerCAmelCase    :					Optional[Any]							=   atrous_rates
         _lowerCAmelCase    :					Any							=   aspp_dropout_prob
         _lowerCAmelCase    :					Union[str, Any]							=   semantic_loss_ignore_index



class 					a_					(_a      ):
       __lowerCAmelCase      :			Optional[int]      = version.parse("""1.11"""      )




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




       @property
       def 							__UpperCamelCase    (       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 							__UpperCamelCase    (       self					):
         return 1E-4


