





'''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


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

snake_case_     =	""" Hello world! cécé herlolip"""

snake_case_     =	[
    ("""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(  UpperCamelCase__    : List[Any]		)		->  Union[str, Any]:
    A       :       Dict							=						[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''_float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase__    ,       UpperCamelCase__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Dict		)		->  Any:
    A       :       List[str]							=						dct.pop(UpperCamelCase__		)
    A       :       Optional[int]							=						val
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]		)		->  Union[str, Any]:
    A       :       List[Any]							=						torch.load(UpperCamelCase__    ,       map_location='''cpu'''		)
    A       :       Dict							=						torch.hub.load('''pytorch/fairseq'''    ,       '''bart.large.cnn'''		).eval()
    hub_interface.model.load_state_dict(sd['''model''']		)
    return hub_interface
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  Any:
    A,	A       :       Dict							=						emb.weight.shape
    A       :       Dict							=						nn.Linear(UpperCamelCase__    ,       UpperCamelCase__    ,       bias=UpperCamelCase__		)
    A       :       str							=						emb.weight.data
    return lin_layer
@torch.no_grad()
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : List[Any]=None		)		->  Any:
    if not os.path.exists(UpperCamelCase__		):
        A       :       Tuple							=						torch.hub.load('''pytorch/fairseq'''    ,       UpperCamelCase__		).eval()
    else:
        A       :       Optional[Any]							=						load_xsum_checkpoint(UpperCamelCase__		)

    bart.model.upgrade_state_dict(bart.model.state_dict()		)
    if hf_checkpoint_name is None:
        A       :       Union[str, Any]							=						checkpoint_path.replace('''.'''    ,       '''-'''		)
    A       :       Optional[Any]							=						BartConfig.from_pretrained(UpperCamelCase__		)
    A       :       Any							=						bart.encode(UpperCamelCase__		).unsqueeze(0		)
    A       :       Optional[Any]							=						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":
        A       :       List[str]							=						bart.state_dict()
        remove_ignore_keys_(UpperCamelCase__		)
        A       :       Tuple							=						state_dict['''model.decoder.embed_tokens.weight''']
        for src, dest in mnli_rename_keys:
            rename_key(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        A       :       Dict							=						BartForSequenceClassification(UpperCamelCase__		).eval()
        model.load_state_dict(UpperCamelCase__		)
        A       :       str							=						bart.predict('''mnli'''    ,       UpperCamelCase__    ,       return_logits=UpperCamelCase__		)
        A       :       Tuple							=						model(UpperCamelCase__		)[0]  # logits
    else:  # no classification heads to worry about
        A       :       Tuple							=						bart.model.state_dict()
        remove_ignore_keys_(UpperCamelCase__		)
        A       :       int							=						state_dict['''decoder.embed_tokens.weight''']
        A       :       Optional[int]							=						bart.extract_features(UpperCamelCase__		)
        if hf_checkpoint_name == "facebook/bart-large":
            A       :       List[Any]							=						BartModel(UpperCamelCase__		).eval()
            model.load_state_dict(UpperCamelCase__		)
            A       :       str							=						model(UpperCamelCase__		).model[0]
        else:
            A       :       Dict							=						BartForConditionalGeneration(UpperCamelCase__		).eval()  # an existing summarization ckpt
            model.model.load_state_dict(UpperCamelCase__		)
            if hasattr(UpperCamelCase__    ,       '''lm_head'''		):
                A       :       List[Any]							=						make_linear_from_emb(model.model.shared		)
            A       :       Tuple							=						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__":
     snake_case_     =	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"""
     )
     snake_case_     =	parser.parse_args()
     convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


snake_case_     =	{
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	["""SpeechT5Tokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SpeechT5ForSpeechToText""",
         """SpeechT5ForSpeechToSpeech""",
         """SpeechT5ForTextToSpeech""",
         """SpeechT5Model""",
         """SpeechT5PreTrainedModel""",
         """SpeechT5HifiGan""",
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
