



"""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


__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    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(_lowercase,					_lowercase	)
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			dct.pop(_lowercase	)
						snake_case_ :Optional[Any]						=			val
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Tuple						=			torch.load(_lowercase,					map_location="""cpu"""	)
						snake_case_ :Union[str, Any]						=			torch.hub.load("""pytorch/fairseq""",					"""bart.large.cnn"""	).eval()
						hub_interface.model.load_state_dict(sd["""model"""]	)
						return hub_interface
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_,       snake_case_ :Any						=			emb.weight.shape
						snake_case_ :Dict						=			nn.Linear(_lowercase,					_lowercase,					bias=_lowercase	)
						snake_case_ :int						=			emb.weight.data
						return lin_layer
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase=None	):







						'''simple docstring'''


						if not os.path.exists(_lowercase	):
												snake_case_ :str						=			torch.hub.load("""pytorch/fairseq""",					_lowercase	).eval()
						else:
												snake_case_ :List[Any]						=			load_xsum_checkpoint(_lowercase	)

						bart.model.upgrade_state_dict(bart.model.state_dict()	)
						if hf_checkpoint_name is None:
												snake_case_ :Union[str, Any]						=			checkpoint_path.replace(""".""",					"""-"""	)
						snake_case_ :List[Any]						=			BartConfig.from_pretrained(_lowercase	)
						snake_case_ :Tuple						=			bart.encode(_lowercase	).unsqueeze(0	)
						snake_case_ :Any						=			BartTokenizer.from_pretrained(_lowercase	).encode(_lowercase,					return_tensors="""pt"""	).unsqueeze(0	)
						if not torch.eq(_lowercase,					_lowercase	).all():
												raise ValueError(
												    f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""	)

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


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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a      =				{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["MobileBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MobileBertForMaskedLM",
						    "MobileBertForMultipleChoice",
						    "MobileBertForNextSentencePrediction",
						    "MobileBertForPreTraining",
						    "MobileBertForQuestionAnswering",
						    "MobileBertForSequenceClassification",
						    "MobileBertForTokenClassification",
						    "MobileBertLayer",
						    "MobileBertModel",
						    "MobileBertPreTrainedModel",
						    "load_tf_weights_in_mobilebert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMobileBertForMaskedLM",
						    "TFMobileBertForMultipleChoice",
						    "TFMobileBertForNextSentencePrediction",
						    "TFMobileBertForPreTraining",
						    "TFMobileBertForQuestionAnswering",
						    "TFMobileBertForSequenceClassification",
						    "TFMobileBertForTokenClassification",
						    "TFMobileBertMainLayer",
						    "TFMobileBertModel",
						    "TFMobileBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mobilebert import (
						    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileBertConfig,
						    MobileBertOnnxConfig,
						)
						from .tokenization_mobilebert import MobileBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mobilebert_fast import MobileBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilebert import (
												    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileBertForMaskedLM,
												    MobileBertForMultipleChoice,
												    MobileBertForNextSentencePrediction,
												    MobileBertForPreTraining,
												    MobileBertForQuestionAnswering,
												    MobileBertForSequenceClassification,
												    MobileBertForTokenClassification,
												    MobileBertLayer,
												    MobileBertModel,
												    MobileBertPreTrainedModel,
												    load_tf_weights_in_mobilebert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilebert import (
												    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileBertForMaskedLM,
												    TFMobileBertForMultipleChoice,
												    TFMobileBertForNextSentencePrediction,
												    TFMobileBertForPreTraining,
												    TFMobileBertForQuestionAnswering,
												    TFMobileBertForSequenceClassification,
												    TFMobileBertForTokenClassification,
												    TFMobileBertMainLayer,
												    TFMobileBertModel,
												    TFMobileBertPreTrainedModel,
												)

else:
						import sys

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