






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil







@pytest.fixture
def       lowercase__	(					A_:						str		)	->				Optional[int]:







       """simple docstring"""






       __UpperCAmelCase     				=tmp_path / """file.csv"""
       __UpperCAmelCase     				=textwrap.dedent(
           """\
        header1,header2
        1,2
        10,20
        """		)
       with open(A_  ,      """w"""		) as f:
              f.write(A_		)
       return str(A_		)







@pytest.fixture
def       lowercase__	(					A_:						Union[str, Any]		)	->				Dict:







       """simple docstring"""






       __UpperCAmelCase     				=tmp_path / """malformed_file.csv"""
       __UpperCAmelCase     				=textwrap.dedent(
           """\
        header1,header2
        1,2
        10,20,
        """		)
       with open(A_  ,      """w"""		) as f:
              f.write(A_		)
       return str(A_		)







@pytest.fixture
def       lowercase__	(					A_:						Union[str, Any]  ,      A_:						List[Any]		)	->				List[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=tmp_path / """csv_with_image.csv"""
       __UpperCAmelCase     				=textwrap.dedent(
           F'''\
        image
        {image_file}
        '''		)
       with open(A_  ,      """w"""		) as f:
              f.write(A_		)
       return str(A_		)







@pytest.fixture
def       lowercase__	(					A_:						Optional[int]		)	->				List[str]:







       """simple docstring"""






       __UpperCAmelCase     				=tmp_path / """csv_with_label.csv"""
       __UpperCAmelCase     				=textwrap.dedent(
           """\
        label
        good
        bad
        good
        """		)
       with open(A_  ,      """w"""		) as f:
              f.write(A_		)
       return str(A_		)







@pytest.fixture
def       lowercase__	(					A_:						List[str]		)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=tmp_path / """csv_with_int_list.csv"""
       __UpperCAmelCase     				=textwrap.dedent(
           """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """		)
       with open(A_  ,      """w"""		) as f:
              f.write(A_		)
       return str(A_		)







def       lowercase__	(					A_:						Dict  ,      A_:						Optional[Any]  ,      A_:						Union[str, Any]		)	->				Dict:







       """simple docstring"""






       __UpperCAmelCase     				=Csv()
       __UpperCAmelCase     				=csv._generate_tables([[csv_file, malformed_csv_file]]		)
       with pytest.raises(A_  ,      match="""Error tokenizing data"""		):
              for _ in generator:
                     pass
       assert any(
           record.levelname == """ERROR"""
           and """Failed to read file""" in record.message
           and os.path.basename(A_		) in record.message
           for record in caplog.records		)







@require_pil
def       lowercase__	(					A_:						Tuple		)	->				Union[str, Any]:







       """simple docstring"""






       with open(A_  ,      encoding="""utf-8"""		) as f:
              __UpperCAmelCase     				=f.read().splitlines()[1]
       __UpperCAmelCase     				=Csv(encoding="""utf-8"""  ,      features=Features({"""image""": Image()}		)		)
       __UpperCAmelCase     				=csv._generate_tables([[csv_file_with_image]]		)
       __UpperCAmelCase     				=pa.concat_tables([table for _, table in generator]		)
       assert pa_table.schema.field("""image"""		).type == Image()()
       __UpperCAmelCase     				=pa_table.to_pydict()["""image"""]
       assert generated_content == [{"path": image_file, "bytes": None}]







def       lowercase__	(					A_:						List[str]		)	->				Any:







       """simple docstring"""






       with open(A_  ,      encoding="""utf-8"""		) as f:
              __UpperCAmelCase     				=f.read().splitlines()[1:]
       __UpperCAmelCase     				=Csv(encoding="""utf-8"""  ,      features=Features({"""label""": ClassLabel(names=["""good""", """bad"""]		)}		)		)
       __UpperCAmelCase     				=csv._generate_tables([[csv_file_with_label]]		)
       __UpperCAmelCase     				=pa.concat_tables([table for _, table in generator]		)
       assert pa_table.schema.field("""label"""		).type == ClassLabel(names=["""good""", """bad"""]		)()
       __UpperCAmelCase     				=pa_table.to_pydict()["""label"""]
       assert generated_content == [ClassLabel(names=["""good""", """bad"""]		).straint(A_		) for label in labels]







def       lowercase__	(					A_:						List[str]		)	->				Optional[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=Csv(encoding="""utf-8"""  ,      sep=""","""  ,      converters={"""int_list""": lambda A_		: [int(A_		) for i in x.split()]}		)
       __UpperCAmelCase     				=csv._generate_tables([[csv_file_with_int_list]]		)
       __UpperCAmelCase     				=pa.concat_tables([table for _, table in generator]		)
       assert pa.types.is_list(pa_table.schema.field("""int_list"""		).type		)
       __UpperCAmelCase     				=pa_table.to_pydict()["""int_list"""]
       assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]






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)