


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def      UpperCAmelCase__ (lowerCAmelCase_     ):




	'''simple docstring'''


	__SCREAMING_SNAKE_CASE		     =       [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(lowerCAmelCase_						,		lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




	'''simple docstring'''


	__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       emb.weight.shape
	__SCREAMING_SNAKE_CASE		     =       nn.Linear(lowerCAmelCase_						,		lowerCAmelCase_						,		bias=lowerCAmelCase_     )
	__SCREAMING_SNAKE_CASE		     =       emb.weight.data
	return lin_layer




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_="facebook/mbart-large-en-ro"						,		lowerCAmelCase_=False						,		lowerCAmelCase_=False     ):




	'''simple docstring'''


	__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_						,		map_location="cpu"     )["model"]
	remove_ignore_keys_(lowerCAmelCase_     )
	__SCREAMING_SNAKE_CASE		     =       state_dict["encoder.embed_tokens.weight"].shape[0]

	__SCREAMING_SNAKE_CASE		     =       MBartConfig.from_pretrained(lowerCAmelCase_						,		vocab_size=lowerCAmelCase_     )
	if mbart_aa and finetuned:
		__SCREAMING_SNAKE_CASE		     =       "relu"

	__SCREAMING_SNAKE_CASE		     =       state_dict["decoder.embed_tokens.weight"]
	__SCREAMING_SNAKE_CASE		     =       MBartForConditionalGeneration(lowerCAmelCase_     )
	model.model.load_state_dict(lowerCAmelCase_     )

	if finetuned:
		__SCREAMING_SNAKE_CASE		     =       make_linear_from_emb(model.model.shared     )

	return model


if __name__ == "__main__":
		a__       :							Any        =  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='''facebook/mbart-large-cc25''',
		    type=str,
		    help='''Which huggingface architecture to use: mbart-large''',
		)
		parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
		parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
		a__       :							Optional[Any]        =  parser.parse_args()
		a__       :							Optional[int]        =  convert_fairseq_mbart_checkpoint_from_disk(
		    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
		)
		model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return EnvironmentCommand()




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       parser.add_parser("env"     )
						download_parser.set_defaults(func=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       huggingface_hub.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						__SCREAMING_SNAKE_CASE		     =       "NA"
						if is_torch_available():
											import torch

											__SCREAMING_SNAKE_CASE		     =       torch.__version__
											__SCREAMING_SNAKE_CASE		     =       torch.cuda.is_available()

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_transformers_available():
											import transformers

											__SCREAMING_SNAKE_CASE		     =       transformers.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_accelerate_available():
											import accelerate

											__SCREAMING_SNAKE_CASE		     =       accelerate.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_xformers_available():
											import xformers

											__SCREAMING_SNAKE_CASE		     =       xformers.__version__

						__SCREAMING_SNAKE_CASE		     =       {
						    "`diffusers` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
						    "Huggingface_hub version": hub_version,
						    "Transformers version": transformers_version,
						    "Accelerate version": accelerate_version,
						    "xFormers version": xformers_version,
						    "Using GPU in script?": "<fill in>",
						    "Using distributed or parallel set-up in script?": "<fill in>",
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"     )
						print(self.format_dict(UpperCAmelCase__     )     )

						return info




	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"
