
"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   [
        """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  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase    ,_UpperCAmelCase          =   emb.weight.shape
    _UpperCAmelCase          =   nn.Linear(_A					,      _A					,      bias=_A      )
    _UpperCAmelCase          =   emb.weight.data
    return lin_layer


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   torch.load(_A					,      map_location="""cpu"""      )
    _UpperCAmelCase          =   mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
    _UpperCAmelCase          =   mam_aaa["""model"""]
    remove_ignore_keys_(_A      )
    _UpperCAmelCase          =   state_dict["""encoder.embed_tokens.weight"""].shape[0]

    _UpperCAmelCase          =   MaMaaaConfig(
        vocab_size=_A					,      max_position_embeddings=1_0_2_4					,      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					,      encoder_layerdrop=args.encoder_layerdrop					,      decoder_layerdrop=args.decoder_layerdrop					,      dropout=args.dropout					,      attention_dropout=args.attention_dropout					,      activation_dropout=args.activation_dropout					,      activation_function="""relu"""					,      )

    _UpperCAmelCase          =   state_dict["""decoder.embed_tokens.weight"""]
    _UpperCAmelCase          =   MaMaaaForConditionalGeneration(_A      )
    model.model.load_state_dict(_A					,      strict=_A      )
    _UpperCAmelCase          =   make_linear_from_emb(model.model.shared      )

    return model


if __name__ == "__main__":
 a					:	int							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
 parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 a					:	Optional[Any]							=		parser.parse_args()
 a					:	List[Any]							=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)

"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int = 16     ,					__UpperCamelCase			:       int = 88     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       float = 0.0     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       bool = False     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       str = "geglu"     ,					__UpperCamelCase			:       Optional[int] = None     ,					)		->Dict:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                for _ in range(2     )
            ]     )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   0
        # attention_mask is not used yet
        for i in range(2     ):
            # for each of the two transformers, pass the corresponding condition tokens
            _UpperCAmelCase          =   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _UpperCAmelCase          =   self.transformer_index_for_condition[i]
            _UpperCAmelCase          =   self.transformers[transformer_index](
                __UpperCamelCase     ,					encoder_hidden_states=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					cross_attention_kwargs=__UpperCamelCase     ,					return_dict=__UpperCamelCase     ,					)[0]
            encoded_states.append(encoded_state - input_states     )
            tokens_start += self.condition_lengths[i]

        _UpperCAmelCase          =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _UpperCAmelCase          =   output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )