





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


A    :					Tuple =      logging.getLogger(__name__)


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			def __init__(			self   :    int							,	__lowerCAmelCase   :    List[Any]=-1   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   label_idx






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    Union[Split, str]   )	->	List[InputExample]:







					"""simple docstring"""






					if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   ):
							A__        =   mode.value
					A__        =   os.path.join(__lowerCAmelCase							,	f'{mode}.txt'   )
					A__        =   1
					A__        =   []
					with open(__lowerCAmelCase							,	encoding="""utf-8"""   ) as f:
							A__        =   []
							A__        =   []
							for line in f:
									if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
											if words:
													examples.append(InputExample(guid=f'{mode}-{guid_index}'							,	words=__lowerCAmelCase							,	labels=__lowerCAmelCase   )   )
													guid_index += 1
													A__        =   []
													A__        =   []
									else:
											A__        =   line.split(""" """   )
											words.append(splits[0]   )
											if len(__lowerCAmelCase   ) > 1:
													labels.append(splits[self.label_idx].replace("""\n"""							,	""""""   )   )
											else:
													# Examples could have no label for mode = "test"
													labels.append("""O"""   )
							if words:
									examples.append(InputExample(guid=f'{mode}-{guid_index}'							,	words=__lowerCAmelCase							,	labels=__lowerCAmelCase   )   )
					return examples






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    TextIO							,	__lowerCAmelCase   :    TextIO							,	__lowerCAmelCase   :    List   )	->	List[Any]:







					"""simple docstring"""






					A__        =   0
					for line in test_input_reader:
							if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
									writer.write(__lowerCAmelCase   )
									if not preds_list[example_id]:
											example_id += 1
							elif preds_list[example_id]:
									A__        =   line.split()[0] + """ """ + preds_list[example_id].pop(0   ) + """\n"""
									writer.write(__lowerCAmelCase   )
							else:
									logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""							,	line.split()[0]   )







			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    str   )	->	List[str]:







					"""simple docstring"""






					if path:
							with open(__lowerCAmelCase							,	"""r"""   ) as f:
									A__        =   f.read().splitlines()
							if "O" not in labels:
									A__        =   ["""O"""] + labels
							return labels
					else:
							return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			def __init__(			self   :    Optional[int]   )	->	List[Any]:







					"""simple docstring"""






					super().__init__(label_idx=-2   )







			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    str   )	->	List[str]:







					"""simple docstring"""






					if path:
							with open(__lowerCAmelCase							,	"""r"""   ) as f:
									A__        =   f.read().splitlines()
							if "O" not in labels:
									A__        =   ["""O"""] + labels
							return labels
					else:
							return [
							    "O",
							    "B-ADVP",
							    "B-INTJ",
							    "B-LST",
							    "B-PRT",
							    "B-NP",
							    "B-SBAR",
							    "B-VP",
							    "B-ADJP",
							    "B-CONJP",
							    "B-PP",
							    "I-ADVP",
							    "I-INTJ",
							    "I-LST",
							    "I-PRT",
							    "I-NP",
							    "I-SBAR",
							    "I-VP",
							    "I-ADJP",
							    "I-CONJP",
							    "I-PP",
							]


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Union[Split, str]   )	->	List[InputExample]:







					"""simple docstring"""






					if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   ):
							A__        =   mode.value
					A__        =   os.path.join(__lowerCAmelCase							,	f'{mode}.txt'   )
					A__        =   1
					A__        =   []

					with open(__lowerCAmelCase							,	encoding="""utf-8"""   ) as f:
							for sentence in parse_incr(__lowerCAmelCase   ):
									A__        =   []
									A__        =   []
									for token in sentence:
											words.append(token["""form"""]   )
											labels.append(token["""upos"""]   )
									assert len(__lowerCAmelCase   ) == len(__lowerCAmelCase   )
									if words:
											examples.append(InputExample(guid=f'{mode}-{guid_index}'							,	words=__lowerCAmelCase							,	labels=__lowerCAmelCase   )   )
											guid_index += 1
					return examples






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    TextIO							,	__lowerCAmelCase   :    TextIO							,	__lowerCAmelCase   :    List   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   0
					for sentence in parse_incr(__lowerCAmelCase   ):
							A__        =   preds_list[example_id]
							A__        =   """"""
							for token in sentence:
									out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0   )}) '
							out += "\n"
							writer.write(__lowerCAmelCase   )
							example_id += 1







			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    str   )	->	List[str]:







					"""simple docstring"""






					if path:
							with open(__lowerCAmelCase							,	"""r"""   ) as f:
									return f.read().splitlines()
					else:
							return [
							    "ADJ",
							    "ADP",
							    "ADV",
							    "AUX",
							    "CCONJ",
							    "DET",
							    "INTJ",
							    "NOUN",
							    "NUM",
							    "PART",
							    "PRON",
							    "PROPN",
							    "PUNCT",
							    "SCONJ",
							    "SYM",
							    "VERB",
							    "X",
							]










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''fairseq_path''', type=str, help='''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.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



