'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[int]     				=					[
         '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(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[Any]     				=					list(s_dict.keys()							)
     for key in keys:
          if "transformer_layers" in key:
               lowercase_					:       List[str]     				=					s_dict.pop(SCREAMING_SNAKE_CASE_							)
          elif "subsample" in key:
               lowercase_					:       List[Any]     				=					s_dict.pop(SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Any     				=					emb.weight.shape
     lowercase_					:       Dict     				=					nn.Linear(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       bias=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					emb.weight.data
     return lin_layer

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Any     				=					torch.load(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)
     lowercase_					:       List[Any]     				=					mam_aaa['args']
     lowercase_					:       List[Any]     				=					mam_aaa['model']
     lowercase_					:       Optional[int]     				=					state_dict['decoder.output_projection.weight']

     remove_ignore_keys_(SCREAMING_SNAKE_CASE_							)
     rename_keys(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Any     				=					state_dict['decoder.embed_tokens.weight'].shape[0]

     lowercase_					:       str     				=					args.share_decoder_input_output_embed

     lowercase_					:       Tuple     				=					[int(SCREAMING_SNAKE_CASE_							) for i in args.conv_kernel_sizes.split(','							)]
     lowercase_					:       Union[str, Any]     				=					SpeechaTextConfig(
         vocab_size=SCREAMING_SNAKE_CASE_  ,       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(SCREAMING_SNAKE_CASE_							)  ,       conv_channels=args.conv_channels  ,       conv_kernel_sizes=SCREAMING_SNAKE_CASE_  ,       input_feat_per_channel=args.input_feat_per_channel  ,       input_channels=args.input_channels  ,       tie_word_embeddings=SCREAMING_SNAKE_CASE_  ,       num_beams=5  ,       max_length=200  ,       use_cache=SCREAMING_SNAKE_CASE_  ,       decoder_start_token_id=2  ,       early_stopping=SCREAMING_SNAKE_CASE_  ,       )

     lowercase_					:       List[Any]     				=					SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[Any]     				=					model.model.load_state_dict(SCREAMING_SNAKE_CASE_  ,       strict=SCREAMING_SNAKE_CASE_							)
     if len(SCREAMING_SNAKE_CASE_							) > 0 and not set(SCREAMING_SNAKE_CASE_							) <= {
         "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:
          lowercase_					:       int     				=					make_linear_from_emb(model.model.decoder.embed_tokens							)
     else:
          lowercase_					:       List[str]     				=					lm_head_weights

     model.save_pretrained(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 _A								=   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.')
 _A								=   parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A								=   logging.get_logger(__name__)

_A								=   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Any     =    '''mra'''
   def __init__(self       ,				_a=50_265       ,				_a=768       ,				_a=12       ,				_a=12       ,				_a=3_072       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=1       ,				_a=0.02       ,				_a=1e-5       ,				_a="absolute"       ,				_a=4       ,				_a="full"       ,				_a=0       ,				_a=0       ,				_a=1       ,				_a=0       ,				_a=2       ,				**_a       ,				)    ->     int:
        super().__init__(pad_token_id=_a       ,				bos_token_id=_a       ,				eos_token_id=_a       ,				**_a				)

        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       List[str]     				=					max_position_embeddings
        lowercase_					:       Optional[Any]     				=					hidden_size
        lowercase_					:       Optional[int]     				=					num_hidden_layers
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       Optional[int]     				=					intermediate_size
        lowercase_					:       Dict     				=					hidden_act
        lowercase_					:       str     				=					hidden_dropout_prob
        lowercase_					:       int     				=					attention_probs_dropout_prob
        lowercase_					:       str     				=					initializer_range
        lowercase_					:       Union[str, Any]     				=					type_vocab_size
        lowercase_					:       Any     				=					layer_norm_eps
        lowercase_					:       Union[str, Any]     				=					position_embedding_type
        lowercase_					:       Any     				=					block_per_row
        lowercase_					:       Optional[int]     				=					approx_mode
        lowercase_					:       int     				=					initial_prior_first_n_blocks
        lowercase_					:       str     				=					initial_prior_diagonal_n_blocks




