


"""simple docstring"""






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   assert isinstance(_lowerCamelCase			,		_lowerCamelCase       )
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"			,		[False, True]       )
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     tmp_path / "cache"
   _lowerCamelCase   :				Optional[int]							=     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _lowerCamelCase   :				Dict							=     ParquetDatasetReader(_lowerCamelCase			,		cache_dir=_lowerCamelCase			,		keep_in_memory=_lowerCamelCase       ).read()
   _check_parquet_dataset(_lowerCamelCase			,		_lowerCamelCase       )







@pytest.mark.parametrize(
    "features"			,		[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,		)
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     tmp_path / "cache"
   _lowerCamelCase   :				List[str]							=     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowerCamelCase   :				Optional[Any]							=     features.copy() if features else default_expected_features
   _lowerCamelCase   :				Optional[Any]							=     (
       Features({feature: Value(_lowerCamelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
   )
   _lowerCamelCase   :				Any							=     ParquetDatasetReader(_lowerCamelCase			,		features=_lowerCamelCase			,		cache_dir=_lowerCamelCase       ).read()
   _check_parquet_dataset(_lowerCamelCase			,		_lowerCamelCase       )







@pytest.mark.parametrize("split"			,		[None, NamedSplit("train"       ), "train", "test"]       )
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     str:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     tmp_path / "cache"
   _lowerCamelCase   :				Dict							=     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowerCamelCase   :				List[str]							=     ParquetDatasetReader(_lowerCamelCase			,		cache_dir=_lowerCamelCase			,		split=_lowerCamelCase       ).read()
   _check_parquet_dataset(_lowerCamelCase			,		_lowerCamelCase       )
   assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"			,		[str, list]       )
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





   if issubclass(_lowerCamelCase			,		_lowerCamelCase       ):
      _lowerCamelCase   :				Tuple							=     parquet_path
   elif issubclass(_lowerCamelCase			,		_lowerCamelCase       ):
      _lowerCamelCase   :				Any							=     [parquet_path]
   _lowerCamelCase   :				Any							=     tmp_path / "cache"
   _lowerCamelCase   :				Optional[int]							=     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowerCamelCase   :				Union[str, Any]							=     ParquetDatasetReader(_lowerCamelCase			,		cache_dir=_lowerCamelCase       ).read()
   _check_parquet_dataset(_lowerCamelCase			,		_lowerCamelCase       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=("train",)       )    ->     Dict:
   '''simple docstring'''





   assert isinstance(_lowerCamelCase			,		_lowerCamelCase       )
   for split in splits:
      _lowerCamelCase   :				List[str]							=     dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"			,		[False, True]       )
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     tmp_path / "cache"
   _lowerCamelCase   :				Tuple							=     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _lowerCamelCase   :				Any							=     ParquetDatasetReader(
          {"train": parquet_path}			,		cache_dir=_lowerCamelCase			,		keep_in_memory=_lowerCamelCase       ).read()
   _check_parquet_datasetdict(_lowerCamelCase			,		_lowerCamelCase       )







@pytest.mark.parametrize(
    "features"			,		[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,		)
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     tmp_path / "cache"
   _lowerCamelCase   :				List[Any]							=     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowerCamelCase   :				Union[str, Any]							=     features.copy() if features else default_expected_features
   _lowerCamelCase   :				str							=     (
       Features({feature: Value(_lowerCamelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
   )
   _lowerCamelCase   :				str							=     ParquetDatasetReader({"train": parquet_path}			,		features=_lowerCamelCase			,		cache_dir=_lowerCamelCase       ).read()
   _check_parquet_datasetdict(_lowerCamelCase			,		_lowerCamelCase       )







@pytest.mark.parametrize("split"			,		[None, NamedSplit("train"       ), "train", "test"]       )
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   if split:
      _lowerCamelCase   :				Union[str, Any]							=     {split: parquet_path}
   else:
      _lowerCamelCase   :				Optional[Any]							=     "train"
      _lowerCamelCase   :				Optional[Any]							=     {"train": parquet_path, "test": parquet_path}
   _lowerCamelCase   :				Optional[int]							=     tmp_path / "cache"
   _lowerCamelCase   :				List[str]							=     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowerCamelCase   :				Tuple							=     ParquetDatasetReader(_lowerCamelCase			,		cache_dir=_lowerCamelCase       ).read()
   _check_parquet_datasetdict(_lowerCamelCase			,		_lowerCamelCase			,		splits=list(path.keys()       )       )
   assert all(dataset[split].split == split for split in path.keys()       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     ParquetDatasetWriter(_lowerCamelCase			,		tmp_path / "foo.parquet"       )
   assert writer.write() > 0
   _lowerCamelCase   :				Tuple							=     pq.ParquetFile(tmp_path / "foo.parquet"       )
   _lowerCamelCase   :				List[Any]							=     pf.read()
   assert dataset.data.table == output_table







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     str(shared_datadir / "test_image_rgb.jpg"       )
   _lowerCamelCase   :				Optional[Any]							=     {"image": [image_path]}
   _lowerCamelCase   :				List[str]							=     Features({"image": Image()}       )
   _lowerCamelCase   :				List[str]							=     Dataset.from_dict(_lowerCamelCase			,		features=_lowerCamelCase       )
   _lowerCamelCase   :				List[Any]							=     ParquetDatasetWriter(_lowerCamelCase			,		tmp_path / "foo.parquet"       )
   assert writer.write() > 0

   _lowerCamelCase   :				List[Any]							=     Dataset.from_parquet(str(tmp_path / "foo.parquet"       )       )
   assert dataset.features == reloaded_dataset.features

   _lowerCamelCase   :				List[str]							=     ParquetDatasetReader(str(tmp_path / "foo.parquet"       )			,		streaming=_lowerCamelCase       ).read()
   assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    "feature, expected"			,		[
        (Features({"foo": Value("int32"       )}       ), None),
        (Features({"image": Image(), "foo": Value("int32"       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]			,		)
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   assert get_writer_batch_size(_lowerCamelCase       ) == expected

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


A		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: Any								=     {'''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()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: List[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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												_lowercase					       =					checkpoint_path.replace("""."""		,   """-"""       )
						_lowercase					       =					BartConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					bart.encode(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       )
						_lowercase					       =					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":
												_lowercase					       =					bart.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					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_       )
												_lowercase					       =					BartForSequenceClassification(SCREAMING_SNAKE_CASE_       ).eval()
												model.load_state_dict(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					bart.predict("""mnli"""		,   SCREAMING_SNAKE_CASE_		,   return_logits=SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )[0]  # logits
						else:  # no classification heads to worry about
												_lowercase					       =					bart.model.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""decoder.embed_tokens.weight"""]
												_lowercase					       =					bart.extract_features(SCREAMING_SNAKE_CASE_       )
												if hf_checkpoint_name == "facebook/bart-large":
																		_lowercase					       =					BartModel(SCREAMING_SNAKE_CASE_       ).eval()
																		model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					model(SCREAMING_SNAKE_CASE_       ).model[0]
												else:
																		_lowercase					       =					BartForConditionalGeneration(SCREAMING_SNAKE_CASE_       ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		if hasattr(SCREAMING_SNAKE_CASE_		,   """lm_head"""       ):
																								_lowercase					       =					make_linear_from_emb(model.model.shared       )
																		_lowercase					       =					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__":
		A		: List[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=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)