"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def   UpperCAmelCase					(	UpperCamelCase__				):






       """simple docstring"""





       A__	     =							[
           '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					(	UpperCamelCase__				):






       """simple docstring"""





       A__	     =							list(s_dict.keys()				)
       for key in keys:
              if "transformer_layers" in key:
                     A__	     =							s_dict.pop(_SCREAMING_SNAKE_CASE				)
              elif "subsample" in key:
                     A__	     =							s_dict.pop(_SCREAMING_SNAKE_CASE				)






def   UpperCAmelCase					(	UpperCamelCase__				):






       """simple docstring"""





       A__     ,  A__	     =							emb.weight.shape
       A__	     =							nn.Linear(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    bias=_SCREAMING_SNAKE_CASE				)
       A__	     =							emb.weight.data
       return lin_layer






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






       """simple docstring"""





       A__	     =							torch.load(_SCREAMING_SNAKE_CASE       ,    map_location='cpu'				)
       A__	     =							mam_aaa['args']
       A__	     =							mam_aaa['model']
       A__	     =							state_dict['decoder.output_projection.weight']

       remove_ignore_keys_(_SCREAMING_SNAKE_CASE				)
       rename_keys(_SCREAMING_SNAKE_CASE				)

       A__	     =							state_dict['decoder.embed_tokens.weight'].shape[0]

       A__	     =							args.share_decoder_input_output_embed

       A__	     =							[int(_SCREAMING_SNAKE_CASE				) for i in args.conv_kernel_sizes.split(','				)]
       A__	     =							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       ,    )

       A__	     =							SpeechaTextForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
       A__     ,  A__	     =							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:
              A__	     =							make_linear_from_emb(model.model.decoder.embed_tokens				)
       else:
              A__	     =							lm_head_weights

       model.save_pretrained(_SCREAMING_SNAKE_CASE				)


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






"""simple docstring"""




import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				str:
													A__	     =							get_activation('swish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							get_activation('silu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('mish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.Mish     )

													self.assertEqual(act(torch.tensor(-2_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('gelu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.GELU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





