
'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      _lowerCamelCase					: str		    = [
          "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(_lowerCAmelCase					,						_lowerCAmelCase				)







def 		A_							(						_lowerCAmelCase :     Tuple				):



      """simple docstring"""




      _lowerCamelCase					: List[str]		    = list(s_dict.keys()				)
      for key in keys:
            if "transformer_layers" in key:
                  _lowerCamelCase					: Tuple		    = s_dict.pop(_lowerCAmelCase				)
            elif "subsample" in key:
                  _lowerCamelCase					: Tuple		    = s_dict.pop(_lowerCAmelCase				)







def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      _lowerCamelCase    ,		_lowerCamelCase					: Optional[int]		    = emb.weight.shape
      _lowerCamelCase					: Optional[Any]		    = nn.Linear(_lowerCAmelCase					,						_lowerCAmelCase					,						bias=_lowerCAmelCase				)
      _lowerCamelCase					: Optional[Any]		    = emb.weight.data
      return lin_layer







def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Optional[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = torch.load(_lowerCAmelCase					,						map_location="cpu"				)
      _lowerCamelCase					: Tuple		    = mam_aaa["args"]
      _lowerCamelCase					: str		    = mam_aaa["model"]
      _lowerCamelCase					: List[str]		    = state_dict["decoder.output_projection.weight"]

      remove_ignore_keys_(_lowerCAmelCase				)
      rename_keys(_lowerCAmelCase				)

      _lowerCamelCase					: int		    = state_dict["decoder.embed_tokens.weight"].shape[0]

      _lowerCamelCase					: List[Any]		    = args.share_decoder_input_output_embed

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

      _lowerCamelCase					: str		    = SpeechaTextForConditionalGeneration(_lowerCAmelCase				)
      _lowerCamelCase    ,		_lowerCamelCase					: Dict		    = model.model.load_state_dict(_lowerCAmelCase					,						strict=_lowerCAmelCase				)
      if len(_lowerCAmelCase				) > 0 and not set(_lowerCAmelCase				) <= {
          "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:
            _lowerCamelCase					: Optional[Any]		    = make_linear_from_emb(model.model.decoder.embed_tokens				)
      else:
            _lowerCamelCase					: Tuple		    = lm_head_weights

      model.save_pretrained(_lowerCAmelCase				)


if __name__ == "__main__":
 UpperCAmelCase_							: Optional[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_							: int =							parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







"""simple docstring"""



import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


