



'''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


lowerCAmelCase	:		Any				=   ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase	:		Dict				=   {'''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()
lowerCAmelCase	:		Union[str, Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Dict				=   ''' Hello world! cécé herlolip'''

lowerCAmelCase	:		Optional[Any]				=   [
    ('''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   :						List[str]					):






	'''simple docstring'''




	__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   :						int				,		lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  dct.pop(lowerCamelCase					)
	__lowerCAmelCase												=  val



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	__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   :						List[Any]					):






	'''simple docstring'''




	__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   :						Optional[Any]				,		lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Optional[Any]=None					):






	'''simple docstring'''




	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__":
	lowerCAmelCase	:		Optional[int]				=   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'''
	)
	lowerCAmelCase	:		List[str]				=   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_speech_available,
    is_torch_available,
)


lowerCAmelCase	:		Optional[Any]				=   {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TrOCRForCausalLM''',
	    '''TrOCRPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
	from .processing_trocr import TrOCRProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
	import sys

	lowerCAmelCase	:		Optional[Any]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)