

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)






import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__A							:			List[Any]         =			"http://www.mocksite.com/file1.txt"
__A							:			List[Any]         =			"\"text\": [\"foo\", \"foo\"]"
__A							:			Dict         =			"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"







class      lowerCamelCase:
     '''simple docstring'''


     __magic_name__   =			200
     __magic_name__   =			{'Content-Length': '100'}
     __magic_name__   =			{}




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         return [bytes(snake_case_				,     'utf-8'							)]





def 				__lowerCAmelCase(       *_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    return MockResponse()





@pytest.mark.parametrize('urls_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    import requests

    monkeypatch.setattr(_SCREAMING_SNAKE_CASE							,     'request'							,     _SCREAMING_SNAKE_CASE				)

    _A     =							URL
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							url
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[url]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': url}
    _A     =							'dummy'
    _A     =							'downloads'
    _A     =							tmp_path
    _A     =							DownloadConfig(
        cache_dir=os.path.join(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.download(_SCREAMING_SNAKE_CASE				)
    _A     =							urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[downloaded_paths]
            _A     =							[urls]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in downloaded_paths.keys()
            _A     =							downloaded_paths.values()
            _A     =							urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            _A     =							downloaded_path.read_text()
            assert content == CONTENT
            _A     =							downloaded_path.with_suffix('.json'				)
            assert metadata_downloaded_path.exists()
            _A     =							json.loads(metadata_downloaded_path.read_text()				)
            assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize('paths_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							str(_SCREAMING_SNAKE_CASE				)
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							filename
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[filename]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': filename}
    _A     =							'dummy'
    _A     =							xz_file.parent
    _A     =							'extracted'
    _A     =							DownloadConfig(
        cache_dir=_SCREAMING_SNAKE_CASE							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.extract(_SCREAMING_SNAKE_CASE				)
    _A     =							paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[extracted_paths]
            _A     =							[paths]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in extracted_paths.keys()
            _A     =							extracted_paths.values()
            _A     =							paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_SCREAMING_SNAKE_CASE							,     etag=_SCREAMING_SNAKE_CASE				)
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            _A     =							extracted_path.read_text()
            _A     =							text_file.read_text()
            assert extracted_file_content == expected_file_content





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    assert path.endswith('.jsonl'				)
    for num_items, line in enumerate(_SCREAMING_SNAKE_CASE							,     start=1				):
        _A     =							json.loads(line.decode('utf-8'				)				)
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4





@pytest.mark.parametrize('archive_jsonl'							,     ['tar_jsonl_path', 'zip_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_jsonl == 2





@pytest.mark.parametrize('archive_nested_jsonl'							,     ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
            _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_tar == 1
    assert num_jsonl == 2





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        assert os.path.basename(_SCREAMING_SNAKE_CASE				) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2





