




'''simple docstring'''





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCamelCase_						:      List[Any]       =						['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
UpperCamelCase_						:      Tuple       =						{'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
   raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
UpperCamelCase_						:      List[Any]       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[Any]       =						''' Hello world! cécé herlolip'''

UpperCamelCase_						:      Dict       =						[
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]





def      __a     (       _UpperCamelCase:   Union[str, Any]   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[
         "encoder.version",
         "decoder.version",
         "model.encoder.version",
         "model.decoder.version",
         "_float_tensor",
     ]
     for k in ignore_keys:
          state_dict.pop(_UpperCamelCase ,						_UpperCamelCase   )





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Any ,						_UpperCamelCase:   Optional[int]   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				dct.pop(_UpperCamelCase   )
     _snake_case							=				val





def      __a     (       _UpperCamelCase:   Dict   )						->			int:

     """simple docstring"""




     _snake_case							=				torch.load(_UpperCamelCase ,						map_location="cpu"   )
     _snake_case							=				torch.hub.load("pytorch/fairseq" ,						"bart.large.cnn"   ).eval()
     hub_interface.model.load_state_dict(sd["model"]   )
     return hub_interface





def      __a     (       _UpperCamelCase:   Optional[int]   )						->			Tuple:

     """simple docstring"""




     _snake_case							, _snake_case							=				emb.weight.shape
     _snake_case							=				nn.Linear(_UpperCamelCase ,						_UpperCamelCase ,						bias=_UpperCamelCase   )
     _snake_case							=				emb.weight.data
     return lin_layer





@torch.no_grad()
def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   List[Any]=None   )						->			int:

     """simple docstring"""




     if not os.path.exists(_UpperCamelCase   ):
          _snake_case							=				torch.hub.load("pytorch/fairseq" ,						_UpperCamelCase   ).eval()
     else:
          _snake_case							=				load_xsum_checkpoint(_UpperCamelCase   )

     bart.model.upgrade_state_dict(bart.model.state_dict()   )
     if hf_checkpoint_name is None:
          _snake_case							=				checkpoint_path.replace("." ,						"-"   )
     _snake_case							=				BartConfig.from_pretrained(_UpperCamelCase   )
     _snake_case							=				bart.encode(_UpperCamelCase   ).unsqueeze(0   )
     _snake_case							=				BartTokenizer.from_pretrained(_UpperCamelCase   ).encode(_UpperCamelCase ,						return_tensors="pt"   ).unsqueeze(0   )
     if not torch.eq(_UpperCamelCase ,						_UpperCamelCase   ).all():
          raise ValueError(
              F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""   )

     if checkpoint_path == "bart.large.mnli":
          _snake_case							=				bart.state_dict()
          remove_ignore_keys_(_UpperCamelCase   )
          _snake_case							=				state_dict["model.decoder.embed_tokens.weight"]
          for src, dest in mnli_rename_keys:
               rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
          _snake_case							=				BartForSequenceClassification(_UpperCamelCase   ).eval()
          model.load_state_dict(_UpperCamelCase   )
          _snake_case							=				bart.predict("mnli" ,						_UpperCamelCase ,						return_logits=_UpperCamelCase   )
          _snake_case							=				model(_UpperCamelCase   )[0]  # logits
     else:  # no classification heads to worry about
          _snake_case							=				bart.model.state_dict()
          remove_ignore_keys_(_UpperCamelCase   )
          _snake_case							=				state_dict["decoder.embed_tokens.weight"]
          _snake_case							=				bart.extract_features(_UpperCamelCase   )
          if hf_checkpoint_name == "facebook/bart-large":
               _snake_case							=				BartModel(_UpperCamelCase   ).eval()
               model.load_state_dict(_UpperCamelCase   )
               _snake_case							=				model(_UpperCamelCase   ).model[0]
          else:
               _snake_case							=				BartForConditionalGeneration(_UpperCamelCase   ).eval()  # an existing summarization ckpt
               model.model.load_state_dict(_UpperCamelCase   )
               if hasattr(_UpperCamelCase ,						"lm_head"   ):
                    _snake_case							=				make_linear_from_emb(model.model.shared   )
               _snake_case							=				model.model(_UpperCamelCase   )[0]

    # Check results
     if fairseq_output.shape != new_model_outputs.shape:
          raise ValueError(
              F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""   )
     if (fairseq_output != new_model_outputs).any().item():
          raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"   )
     Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
     model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCamelCase_						:      List[Any]       =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
   parser.add_argument(
       '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
   )
   UpperCamelCase_						:      List[str]       =						parser.parse_args()
   convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)








'''simple docstring'''





import functools
import operator

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


UpperCamelCase_						:      str       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[Any]       =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						"""sew"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-5 ,_SCREAMING_SNAKE_CASE="group" ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=0.0_5 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE="mean" ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=256 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=2 ,**_SCREAMING_SNAKE_CASE ,)							->      str:
          super().__init__(**_SCREAMING_SNAKE_CASE ,pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				hidden_size
          _snake_case							=				feat_extract_norm
          _snake_case							=				feat_extract_activation
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				conv_bias
          _snake_case							=				num_conv_pos_embeddings
          _snake_case							=				num_conv_pos_embedding_groups
          _snake_case							=				len(self.conv_dim   )
          _snake_case							=				num_hidden_layers
          _snake_case							=				intermediate_size
          _snake_case							=				squeeze_factor
          _snake_case							=				hidden_act
          _snake_case							=				num_attention_heads
          _snake_case							=				hidden_dropout
          _snake_case							=				attention_dropout
          _snake_case							=				activation_dropout
          _snake_case							=				feat_proj_dropout
          _snake_case							=				final_dropout
          _snake_case							=				layerdrop
          _snake_case							=				layer_norm_eps
          _snake_case							=				initializer_range
          _snake_case							=				vocab_size

          if (
              (len(self.conv_stride   ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
              or (len(self.conv_dim   ) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   f"""but is `len(config.conv_dim) = {len(self.conv_dim   )}`, `len(config.conv_stride)"""
                   f"""= {len(self.conv_stride   )}`, `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          _snake_case							=				apply_spec_augment
          _snake_case							=				mask_time_prob
          _snake_case							=				mask_time_length
          _snake_case							=				mask_time_min_masks
          _snake_case							=				mask_feature_prob
          _snake_case							=				mask_feature_length
          _snake_case							=				mask_feature_min_masks

          # ctc loss
          _snake_case							=				ctc_loss_reduction
          _snake_case							=				ctc_zero_infinity

          # sequence classification
          _snake_case							=				use_weighted_layer_sum
          _snake_case							=				classifier_proj_size






     @property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return functools.reduce(operator.mul ,self.conv_stride ,1   )


