
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


__A				=['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
__A				={'''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()
__A				=logging.get_logger(__name__)

__A				=''' Hello world! cécé herlolip'''

__A				=[
    ('''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 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			dct.pop(lowerCamelCase__    )
				lowerCamelCase_						=			val





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





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_	,    lowerCamelCase_						=			emb.weight.shape
				lowerCamelCase_						=			nn.Linear(lowerCamelCase__	, lowerCamelCase__	, bias=lowerCamelCase__    )
				lowerCamelCase_						=			emb.weight.data
				return lin_layer





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None    ):
				if not os.path.exists(lowerCamelCase__    ):
								lowerCamelCase_						=			torch.hub.load("pytorch/fairseq"	, lowerCamelCase__    ).eval()
				else:
								lowerCamelCase_						=			load_xsum_checkpoint(lowerCamelCase__    )

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

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


if __name__ == "__main__":
			__A				=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'''
			)
			__A				=parser.parse_args()
			convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)

__a          =		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__a          =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      for attribute in key.split("""."""		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

      if weight_type is not None:
            snake_case__  :    List[Any]    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		).shape
      else:
            snake_case__  :    Union[str, Any]    						=      hf_pointer.shape

      assert hf_shape == value.shape, (
          f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
          f" {value.shape} for {full_name}"
      )

      if weight_type == "weight":
            snake_case__  :    int    						=      value
      elif weight_type == "weight_g":
            snake_case__  :    List[str]    						=      value
      elif weight_type == "weight_v":
            snake_case__  :    List[str]    						=      value
      elif weight_type == "bias":
            snake_case__  :    Optional[Any]    						=      value
      else:
            snake_case__  :    str    						=      value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    Dict    						=      fairseq_model.state_dict()

      snake_case__  :    List[Any]    						=      hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      snake_case__  :    Optional[int]    						=      None

      for name, value in fairseq_dict.items():
            snake_case__  :    List[Any]    						=      False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
                  snake_case__  :    Union[str, Any]    						=      True
            elif name.split("""."""		)[0] == "proj":
                  snake_case__  :    Tuple    						=      fairseq_model.proj
                  snake_case__  :    int    						=      True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                              snake_case__  :    Optional[Any]    						=      True
                              if "*" in mapped_key:
                                    snake_case__  :    Optional[int]    						=      name.split(_lowerCAmelCase		)[0].split("""."""		)[-2]
                                    snake_case__  :    Tuple    						=      mapped_key.replace("""*"""     ,   _lowerCAmelCase		)
                              if "weight_g" in name:
                                    snake_case__  :    str    						=      """weight_g"""
                              elif "weight_v" in name:
                                    snake_case__  :    int    						=      """weight_v"""
                              elif "bias" in name:
                                    snake_case__  :    Dict    						=      """bias"""
                              elif "weight" in name:
                                    snake_case__  :    Union[str, Any]    						=      """weight"""
                              else:
                                    snake_case__  :    Union[str, Any]    						=      None
                              set_recursively(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(_lowerCAmelCase		)

      logger.warning(f"Unused weights: {unused_weights}"		)

      return proj_weight





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    int    						=      full_name.split("""conv_layers."""		)[-1]
      snake_case__  :    Dict    						=      name.split("""."""		)
      snake_case__  :    Any    						=      int(items[0]		)
      snake_case__  :    Optional[Any]    						=      int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  snake_case__  :    str    						=      value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  snake_case__  :    Union[str, Any]    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__						,				snake_case__  :    str    						=      emb.weight.shape
      snake_case__  :    List[str]    						=      nn.Linear(_lowerCAmelCase     ,   _lowerCAmelCase     ,   bias=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      emb.weight.data
      return lin_layer





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      with open(_lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8"""		) as f:
            snake_case__  :    int    						=      f.readlines()
            snake_case__  :    List[Any]    						=      [line.split(""" """		)[0] for line in lines]

      snake_case__  :    Union[str, Any]    						=      len(_lowerCAmelCase		)

      snake_case__  :    Any    						=      {
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

      vocab_dict.update(dict(zip(_lowerCAmelCase     ,   range(4     ,   num_words + 4		)		)		)		)
      return vocab_dict





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   )		-> int:
      snake_case__  :    Optional[Any]    						=      WavaVecaConfig.from_pretrained(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SpeechaTextaConfig.from_pretrained(
          _lowerCAmelCase     ,   vocab_size=_lowerCAmelCase     ,   decoder_layers=_lowerCAmelCase     ,   do_stable_layer_norm=_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      WavaVecaFeatureExtractor(
          feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=_lowerCAmelCase     ,   return_attention_mask=_lowerCAmelCase     ,   )

      snake_case__						,				snake_case__						,				snake_case__  :    Union[str, Any]    						=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]     ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
      snake_case__  :    Tuple    						=      model[0].eval()

      # set weights for wav2vec2 encoder
      snake_case__  :    Optional[Any]    						=      WavaVecaModel(_lowerCAmelCase		)
      snake_case__  :    Dict    						=      recursively_load_weights_wavaveca(model.encoder     ,   _lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      SpeechaTextaForCausalLM(_lowerCAmelCase		)
      snake_case__						,				snake_case__  :    Tuple    						=      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,   strict=_lowerCAmelCase		)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""		)
      snake_case__  :    Tuple    						=      nn.Parameter(model.decoder.embed_out.detach()		)

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"		)
      logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"		)

      snake_case__  :    List[Any]    						=      SpeechEncoderDecoderModel(encoder=_lowerCAmelCase     ,   decoder=_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      False

      # add projection layer
      snake_case__  :    Union[str, Any]    						=      nn.Parameter(projection_layer.weight		)
      snake_case__  :    int    						=      nn.Parameter(projection_layer.bias		)

      snake_case__  :    Tuple    						=      create_vocab_dict(_lowerCAmelCase		)

      with open(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)     ,   """w"""		) as fp:
            json.dump(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Tuple    						=      SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)		)
      tokenizer.save_pretrained(_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      hf_wavavec.config.to_dict()
      snake_case__  :    Tuple    						=      tokenizer.pad_token_id
      snake_case__  :    Optional[Any]    						=      tokenizer.bos_token_id
      snake_case__  :    int    						=      tokenizer.eos_token_id
      snake_case__  :    str    						=      """speech_to_text_2"""
      snake_case__  :    List[Any]    						=      """wav2vec2"""

      snake_case__  :    List[str]    						=      SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase		)

      hf_wavavec.save_pretrained(_lowerCAmelCase		)
      feature_extractor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
       parser.add_argument(
           "--encoder_config_path",
           default="facebook/wav2vec2-large-lv60",
           type=str,
           help="Path to hf encoder wav2vec2 checkpoint config",
       )
       parser.add_argument(
           "--decoder_config_path",
           default="facebook/s2t-small-mustc-en-fr-st",
           type=str,
           help="Path to hf decoder s2t checkpoint config",
       )
       parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       __a          =		parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           vocab_size=args.vocab_size,
           num_decoder_layers=args.num_decoder_layers,
       )
