
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 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   Union[str, Any]							=      [
           "encoder.version",
           "decoder.version",
           "model.encoder.version",
           "model.decoder.version",
           "_float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(_lowerCamelCase				,					_lowerCamelCase    )


def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : List[Any]				,					__lowerCamelCase     : int    ):
       snake_case       :   str							=      dct.pop(_lowerCamelCase    )
       snake_case       :   Dict							=      val


def 			UpperCamelCase					(    __lowerCamelCase     : Dict    ):
       snake_case       :   Tuple							=      torch.load(_lowerCamelCase				,					map_location="cpu"    )
       snake_case       :   List[Any]							=      torch.hub.load("pytorch/fairseq"				,					"bart.large.cnn"    ).eval()
       hub_interface.model.load_state_dict(sd["model"]    )
       return hub_interface


def 			UpperCamelCase					(    __lowerCamelCase     : Tuple    ):
       snake_case       :   Tuple							=      emb.weight.shape
       snake_case       :   str							=      nn.Linear(_lowerCamelCase				,					_lowerCamelCase				,					bias=_lowerCamelCase    )
       snake_case       :   List[Any]							=      emb.weight.data
       return lin_layer


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[Any]=None    ):





       if not os.path.exists(_lowerCamelCase    ):
              snake_case       :   str							=      torch.hub.load("pytorch/fairseq"				,					_lowerCamelCase    ).eval()
       else:
              snake_case       :   Dict							=      load_xsum_checkpoint(_lowerCamelCase    )

       bart.model.upgrade_state_dict(bart.model.state_dict()    )
       if hf_checkpoint_name is None:
              snake_case       :   Dict							=      checkpoint_path.replace("."				,					"-"    )
       snake_case       :   Optional[int]							=      BartConfig.from_pretrained(_lowerCamelCase    )
       snake_case       :   int							=      bart.encode(_lowerCamelCase    ).unsqueeze(0    )
       snake_case       :   Union[str, Any]							=      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":
              snake_case       :   List[str]							=      bart.state_dict()
              remove_ignore_keys_(_lowerCamelCase    )
              snake_case       :   Union[str, Any]							=      state_dict["model.decoder.embed_tokens.weight"]
              for src, dest in mnli_rename_keys:
                     rename_key(_lowerCamelCase				,					_lowerCamelCase				,					_lowerCamelCase    )
              snake_case       :   Optional[Any]							=      BartForSequenceClassification(_lowerCamelCase    ).eval()
              model.load_state_dict(_lowerCamelCase    )
              snake_case       :   Any							=      bart.predict("mnli"				,					_lowerCamelCase				,					return_logits=_lowerCamelCase    )
              snake_case       :   Any							=      model(_lowerCamelCase    )[0]  # logits
       else:  # no classification heads to worry about
              snake_case       :   Any							=      bart.model.state_dict()
              remove_ignore_keys_(_lowerCamelCase    )
              snake_case       :   Any							=      state_dict["decoder.embed_tokens.weight"]
              snake_case       :   List[Any]							=      bart.extract_features(_lowerCamelCase    )
              if hf_checkpoint_name == "facebook/bart-large":
                     snake_case       :   Optional[Any]							=      BartModel(_lowerCamelCase    ).eval()
                     model.load_state_dict(_lowerCamelCase    )
                     snake_case       :   Optional[int]							=      model(_lowerCamelCase    ).model[0]
              else:
                     snake_case       :   Dict							=      BartForConditionalGeneration(_lowerCamelCase    ).eval()  # an existing summarization ckpt
                     model.model.load_state_dict(_lowerCamelCase    )
                     if hasattr(_lowerCamelCase				,					"lm_head"    ):
                            snake_case       :   Any							=      make_linear_from_emb(model.model.shared    )
                     snake_case       :   Union[str, Any]							=      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							=       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)







# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase  :					Optional[int]													=       Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase  :					Optional[Any]													=       {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowercase  :					str													=       'zero2'
lowercase  :					Optional[int]													=       'zero3'
lowercase  :					Optional[Any]													=       [ZEROa, ZEROa]




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			List[str])					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					parameterized.to_safe_name("_".join(str(_lowerCamelCase) for x in param.args))
							return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase  :					List[str]													=       list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[Any]     ,   a		:str						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str						)		-> List[Any]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]     ,   a		:int						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[Any]     ,   a		:Dict						)		-> int:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[str]						)		-> Optional[Any]:
								# XXX: run_asr is premature and doesn't save any results
								# so all we check for now is that the process didn't fail
								pass







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:bool = True     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Any:
								__UpperCamelCase	:   Optional[Any]			   =					models[model]

								__UpperCamelCase	:   List[Any]			   =					self.run_trainer(
								    stage=a     ,   model_name=a     ,   eval_steps=a     ,   num_train_epochs=1     ,   distributed=a     ,   fpaa=a     ,   )

								self.do_checks(a						)

								return output_dir







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:int = 1     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Dict:
								__UpperCamelCase	:   int			   =					self.get_auto_remove_tmp_dir("./xxx"     ,   after=a						)
								__UpperCamelCase	:   int			   =					f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(a						)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

								if fpaa:
															args.extend(["--fp16"]						)

								# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
								# hence the separate config files
								__UpperCamelCase	:   Dict			   =					f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
								__UpperCamelCase	:   int			   =					[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
								__UpperCamelCase	:   Optional[Any]			   =					self.get_launcher(a						)

								__UpperCamelCase	:   Optional[int]			   =					launcher + script + args + ds_args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(a     ,   env=self.get_env()						)

								return output_dir







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]=False						)		-> List[Any]:
								# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
								# - it won't be able to handle that
								# 2. for now testing with just 2 gpus max (since some quality tests may give different
								# results with mode gpus because we use very little data)
								__UpperCamelCase	:   List[Any]			   =					min(2     ,   get_gpu_count()						) if distributed else 1
								return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()