



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

UpperCamelCase     =							""" Hello world! cécé herlolip"""

UpperCamelCase     =							[
    ("""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_ :       Union[str, Any]     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					[
	    "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_ :       str,	UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       Union[str, Any]     )	-> Optional[Any]:






	"""simple docstring"""





	A__											=					dct.pop(UpperCAmelCase_     )
	A__											=					val

def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[int]     )	-> List[Any]:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[int]     )	-> Dict:






	"""simple docstring"""





	A__							,							A__											=					emb.weight.shape
	A__											=					nn.Linear(UpperCAmelCase_,	UpperCAmelCase_,	bias=UpperCAmelCase_     )
	A__											=					emb.weight.data
	return lin_layer

@torch.no_grad()
def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Optional[int]=None     )	-> Any:






	"""simple docstring"""





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

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









"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


UpperCamelCase     =							{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase     =							{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase     =							{
    """google/electra-small-generator""": 512,
    """google/electra-base-generator""": 512,
    """google/electra-large-generator""": 512,
    """google/electra-small-discriminator""": 512,
    """google/electra-base-discriminator""": 512,
    """google/electra-large-discriminator""": 512,
}

UpperCamelCase     =							{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Dict							  =     VOCAB_FILES_NAMES
			A__	:					str							  =     PRETRAINED_VOCAB_FILES_MAP
			A__	:					List[Any]							  =     PRETRAINED_INIT_CONFIGURATION
			A__	:					Any							  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__	:					List[str]							  =     ElectraTokenizer


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__="[UNK]"						,	SCREAMING_SNAKE_CASE__="[SEP]"						,	SCREAMING_SNAKE_CASE__="[PAD]"						,	SCREAMING_SNAKE_CASE__="[CLS]"						,	SCREAMING_SNAKE_CASE__="[MASK]"						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	str:
				super().__init__(
				    SCREAMING_SNAKE_CASE__						,	tokenizer_file=SCREAMING_SNAKE_CASE__						,	do_lower_case=SCREAMING_SNAKE_CASE__						,	unk_token=SCREAMING_SNAKE_CASE__						,	sep_token=SCREAMING_SNAKE_CASE__						,	pad_token=SCREAMING_SNAKE_CASE__						,	cls_token=SCREAMING_SNAKE_CASE__						,	mask_token=SCREAMING_SNAKE_CASE__						,	tokenize_chinese_chars=SCREAMING_SNAKE_CASE__						,	strip_accents=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__						,	)

				A__											=					json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get("lowercase"						,	SCREAMING_SNAKE_CASE__		) != do_lower_case
				    or normalizer_state.get("strip_accents"						,	SCREAMING_SNAKE_CASE__		) != strip_accents
				    or normalizer_state.get("handle_chinese_chars"						,	SCREAMING_SNAKE_CASE__		) != tokenize_chinese_chars
				):
					A__											=					getattr(SCREAMING_SNAKE_CASE__						,	normalizer_state.pop("type"		)		)
					A__											=					do_lower_case
					A__											=					strip_accents
					A__											=					tokenize_chinese_chars
					A__											=					normalizer_class(**SCREAMING_SNAKE_CASE__		)

				A__											=					do_lower_case


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None		)      ->	List[str]:
				A__											=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
					output += token_ids_a + [self.sep_token_id]

				return output


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None		)      ->	List[int]:
				A__											=					[self.sep_token_id]
				A__											=					[self.cls_token_id]
				if token_ids_a is None:
					return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None		)      ->	Tuple[str]:
				A__											=					self._tokenizer.model.save(SCREAMING_SNAKE_CASE__						,	name=SCREAMING_SNAKE_CASE__		)
				return tuple(SCREAMING_SNAKE_CASE__		)




