

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_       	=	['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase_       	=	{'''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_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	''' Hello world! cécé herlolip'''

lowerCAmelCase_       	=	[
    ('''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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''encoder.version''',
						    '''decoder.version''',
						    '''model.encoder.version''',
						    '''model.decoder.version''',
						    '''_float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	dct.pop(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	val


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	torch.load(SCREAMING_SNAKE_CASE__  ,		map_location='''cpu'''       )
						snake_case_   =	torch.hub.load('''pytorch/fairseq'''  ,		'''bart.large.cnn'''       ).eval()
						hub_interface.model.load_state_dict(sd['''model''']       )
						return hub_interface


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_,    snake_case_   =	emb.weight.shape
						snake_case_   =	nn.Linear(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		bias=SCREAMING_SNAKE_CASE__       )
						snake_case_   =	emb.weight.data
						return lin_layer


@torch.no_grad()
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None       ):
						if not os.path.exists(SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	torch.hub.load('''pytorch/fairseq'''  ,		SCREAMING_SNAKE_CASE__       ).eval()
						else:
												snake_case_   =	load_xsum_checkpoint(SCREAMING_SNAKE_CASE__       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												snake_case_   =	checkpoint_path.replace('''.'''  ,		'''-'''       )
						snake_case_   =	BartConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	bart.encode(SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )
						snake_case_   =	BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       ).encode(SCREAMING_SNAKE_CASE__  ,		return_tensors='''pt'''       ).unsqueeze(0       )
						if not torch.eq(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ).all():
												raise ValueError(
												    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''       )

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


if __name__ == "__main__":
					lowerCAmelCase_       	=	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_       	=	parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case				:    int       				=     '''Create a default config file for Accelerate with only a few flags set.'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any]="no" ,  UpperCAmelCase_						:		str = default_json_config_file ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[str] 							=       Path(UpperCAmelCase_						)
      path.parent.mkdir(parents=UpperCAmelCase_ ,  exist_ok=UpperCAmelCase_						)
      if path.exists():
            print(
                F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''						)
            return False
      a						:Optional[Any] 							=       mixed_precision.lower()
      if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
            raise ValueError(
                F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''						)
      a						:List[Any] 							=       {
          '''compute_environment''': '''LOCAL_MACHINE''',
          '''mixed_precision''': mixed_precision,
      }
      if torch.cuda.is_available():
            a						:Dict 							=       torch.cuda.device_count()
            a						:Tuple 							=       num_gpus
            a						:int 							=       False
            if num_gpus > 1:
                  a						:str 							=       '''MULTI_GPU'''
            else:
                  a						:List[Any] 							=       '''NO'''
      elif is_xpu_available() and use_xpu:
            a						:List[Any] 							=       torch.xpu.device_count()
            a						:Optional[int] 							=       num_xpus
            a						:List[Any] 							=       False
            if num_xpus > 1:
                  a						:int 							=       '''MULTI_XPU'''
            else:
                  a						:str 							=       '''NO'''
      elif is_npu_available():
            a						:List[str] 							=       torch.npu.device_count()
            a						:Any 							=       num_npus
            a						:Optional[int] 							=       False
            if num_npus > 1:
                  a						:List[str] 							=       '''MULTI_NPU'''
            else:
                  a						:Dict 							=       '''NO'''
      else:
            a						:str 							=       0
            a						:Optional[Any] 							=       True
            a						:Optional[Any] 							=       1
            a						:str 							=       '''NO'''
      a						:List[str] 							=       ClusterConfig(**UpperCAmelCase_						)
      config.to_json_file(UpperCAmelCase_						)
      return path
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:List[Any] 							=       parser.add_parser('''default''' ,  parents=UpperCAmelCase_ ,  help=UpperCAmelCase_ ,  formatter_class=UpperCAmelCase_						)
      parser.add_argument(
          '''--config_file''' ,  default=UpperCAmelCase_ ,  help=(
              '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
              '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
              '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
              '''with \'huggingface\'.'''
          ) ,  dest='''save_location''' ,  )

      parser.add_argument(
          '''--mixed_precision''' ,  choices=['''no''', '''fp16''', '''bf16'''] ,  type=UpperCAmelCase_ ,  help='''Whether or not to use mixed precision training. '''
          '''Choose between FP16 and BF16 (bfloat16) training. '''
          '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' ,  default='''no''' ,  )
      parser.set_defaults(func=UpperCAmelCase_						)
      return parser






def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:Optional[Any] 							=       write_basic_config(args.mixed_precision ,  args.save_location						)
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''						)





