


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]				)	->		Dict:

       """simple docstring"""

       a_					:		Dict							=  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(__A  ,    __A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			str				)	->		Optional[int]:

       """simple docstring"""

       a_					:		Optional[int]							=  list(s_dict.keys()				)
       for key in keys:
              if "transformer_layers" in key:
                     a_					:		Tuple							=  s_dict.pop(__A				)
              elif "subsample" in key:
                     a_					:		int							=  s_dict.pop(__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]				)	->		Dict:

       """simple docstring"""

       a_							,		a_					:		List[Any]							=  emb.weight.shape
       a_					:		Optional[Any]							=  nn.Linear(__A  ,    __A  ,    bias=__A				)
       a_					:		List[Any]							=  emb.weight.data
       return lin_layer







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]  ,    __A :			List[str]				)	->		List[Any]:

       """simple docstring"""

       a_					:		Optional[int]							=  torch.load(__A  ,    map_location='cpu'				)
       a_					:		List[str]							=  mam_aaa['args']
       a_					:		Union[str, Any]							=  mam_aaa['model']
       a_					:		Optional[Any]							=  state_dict['decoder.output_projection.weight']

       remove_ignore_keys_(__A				)
       rename_keys(__A				)

       a_					:		int							=  state_dict['decoder.embed_tokens.weight'].shape[0]

       a_					:		Dict							=  args.share_decoder_input_output_embed

       a_					:		Tuple							=  [int(__A				) for i in args.conv_kernel_sizes.split(','				)]
       a_					:		Union[str, Any]							=  SpeechaTextConfig(
           vocab_size=__A  ,    max_source_positions=args.max_source_positions  ,    max_target_positions=args.max_target_positions  ,    encoder_layers=args.encoder_layers  ,    decoder_layers=args.decoder_layers  ,    encoder_attention_heads=args.encoder_attention_heads  ,    decoder_attention_heads=args.decoder_attention_heads  ,    encoder_ffn_dim=args.encoder_ffn_embed_dim  ,    decoder_ffn_dim=args.decoder_ffn_embed_dim  ,    d_model=args.encoder_embed_dim  ,    dropout=args.dropout  ,    attention_dropout=args.attention_dropout  ,    activation_dropout=args.activation_dropout  ,    activation_function='relu'  ,    num_conv_layers=len(__A				)  ,    conv_channels=args.conv_channels  ,    conv_kernel_sizes=__A  ,    input_feat_per_channel=args.input_feat_per_channel  ,    input_channels=args.input_channels  ,    tie_word_embeddings=__A  ,    num_beams=5  ,    max_length=2_00  ,    use_cache=__A  ,    decoder_start_token_id=2  ,    early_stopping=__A  ,    )

       a_					:		Optional[int]							=  SpeechaTextForConditionalGeneration(__A				)
       a_							,		a_					:		Optional[int]							=  model.model.load_state_dict(__A  ,    strict=__A				)
       if len(__A				) > 0 and not set(__A				) <= {
           "encoder.embed_positions.weights",
           "decoder.embed_positions.weights",
       }:
              raise ValueError(
                  'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
                  F""" but all the following weights are missing {missing}"""				)

       if tie_embeds:
              a_					:		Dict							=  make_linear_from_emb(model.model.decoder.embed_tokens				)
       else:
              a_					:		Dict							=  lm_head_weights

       model.save_pretrained(__A				)


if __name__ == "__main__":
   UpperCAmelCase_      : int  					=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   UpperCAmelCase_      : str  					=       parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_      : Optional[int]  					=       {
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase_      : List[str]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mask2former'''
      snake_case__				:     Any        =						['''swin''']
      snake_case__				:     str        =						{'''hidden_size''': '''hidden_dim'''}
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict] = None     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     ,							SCREAMING_SNAKE_CASE__				:       int = 6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0     ,							SCREAMING_SNAKE_CASE__				:       int = 8     ,							SCREAMING_SNAKE_CASE__				:       float = 0.0     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       int = 4     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.1     ,							SCREAMING_SNAKE_CASE__				:       float = 2.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2_5_4_4     ,							SCREAMING_SNAKE_CASE__				:       float = 3.0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.75     ,							SCREAMING_SNAKE_CASE__				:       float = 0.02     ,							SCREAMING_SNAKE_CASE__				:       float = 1.0     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       List[int] = [4, 8, 1_6, 3_2]     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							**SCREAMING_SNAKE_CASE__				:       int     ,							)		->			List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'     )
                    a_					:		Dict							=  CONFIG_MAPPING['swin'](
                        image_size=2_2_4     ,							in_channels=3     ,							patch_size=4     ,							embed_dim=9_6     ,							depths=[2, 2, 1_8, 2]     ,							num_heads=[3, 6, 1_2, 2_4]     ,							window_size=7     ,							drop_path_rate=0.3     ,							use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,							)

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  backbone_config.pop('model_type'     )
                    a_					:		Optional[Any]							=  CONFIG_MAPPING[backbone_model_type]
                    a_					:		List[str]							=  config_class.from_dict(SCREAMING_SNAKE_CASE__     )

             # verify that the backbone is supported
             if backbone_config.model_type not in self.backbones_supported:
                    logger.warning_once(
                        F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
                        F"""Supported model types: {",".join(self.backbones_supported     )}"""     )

             a_					:		Dict							=  backbone_config
             a_					:		List[str]							=  feature_size
             a_					:		List[str]							=  mask_feature_size
             a_					:		int							=  hidden_dim
             a_					:		Dict							=  encoder_feedforward_dim
             a_					:		str							=  activation_function
             a_					:		List[str]							=  encoder_layers
             a_					:		List[str]							=  decoder_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		str							=  dropout
             a_					:		Tuple							=  dim_feedforward
             a_					:		List[str]							=  pre_norm
             a_					:		Optional[int]							=  enforce_input_projection
             a_					:		Any							=  common_stride
             a_					:		Optional[int]							=  ignore_value
             a_					:		int							=  num_queries
             a_					:		Tuple							=  no_object_weight
             a_					:		Dict							=  class_weight
             a_					:		Optional[int]							=  mask_weight
             a_					:		Optional[int]							=  dice_weight
             a_					:		str							=  train_num_points
             a_					:		List[str]							=  oversample_ratio
             a_					:		List[Any]							=  importance_sample_ratio
             a_					:		Any							=  init_std
             a_					:		Union[str, Any]							=  init_xavier_std
             a_					:		Union[str, Any]							=  use_auxiliary_loss
             a_					:		Dict							=  feature_strides
             a_					:		List[str]							=  output_auxiliary_logits
             a_					:		Dict							=  decoder_layers

             super().__init__(**SCREAMING_SNAKE_CASE__     )
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       str     ,							SCREAMING_SNAKE_CASE__				:       PretrainedConfig     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			List[Any]:
             return cls(
                 backbone_config=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict[str, any]:
             a_					:		Optional[int]							=  copy.deepcopy(self.__dict__     )
             a_					:		List[Any]							=  self.backbone_config.to_dict()
             a_					:		Optional[Any]							=  self.__class__.model_type
             return output


