

'''simple docstring'''


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

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


lowerCAmelCase__										= logging.getLogger(__name__)



class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case=-1)							->				str:
								# in NER datasets, the last column is usually reserved for NER label
								UpperCAmelCase_		:      Dict       	=					label_idx


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				List[InputExample]:
								if isinstance(_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      Optional[Any]       	=					mode.value
								UpperCAmelCase_		:      Optional[int]       	=					os.path.join(_snake_case    ,			F"""{mode}.txt""")
								UpperCAmelCase_		:      Union[str, Any]       	=					1
								UpperCAmelCase_		:      Union[str, Any]       	=					[]
								with open(_snake_case    ,			encoding='utf-8') as f:
									UpperCAmelCase_		:      Union[str, Any]       	=					[]
									UpperCAmelCase_		:      Tuple       	=					[]
									for line in f:
										if line.startswith('-DOCSTART-') or line == "" or line == "\n":
											if words:
												examples.append(InputExample(guid=F"""{mode}-{guid_index}"""    ,			words=_snake_case    ,			labels=_snake_case))
												guid_index += 1
												UpperCAmelCase_		:      Tuple       	=					[]
												UpperCAmelCase_		:      Tuple       	=					[]
										else:
											UpperCAmelCase_		:      Optional[int]       	=					line.split(' ')
											words.append(splits[0])
											if len(_snake_case) > 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=_snake_case    ,			labels=_snake_case))
								return examples


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      Dict       	=					0
								for line in test_input_reader:
									if line.startswith('-DOCSTART-') or line == "" or line == "\n":
										writer.write(_snake_case)
										if not preds_list[example_id]:
											example_id += 1
									elif preds_list[example_id]:
										UpperCAmelCase_		:      Tuple       	=					line.split()[0] + ' ' + preds_list[example_id].pop(0) + '\n'
										writer.write(_snake_case)
									else:
										logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'    ,			line.split()[0])





							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								if path:
									with open(_snake_case    ,			'r') as f:
										UpperCAmelCase_		:      List[str]       	=					f.read().splitlines()
									if "O" not in labels:
										UpperCAmelCase_		:      List[Any]       	=					['O'] + labels
									return labels
								else:
									return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 					lowercase						(  a_ ):


							def __init__(			self)							->				List[Any]:
								# in CONLL2003 dataset chunk column is second-to-last
								super().__init__(label_idx=-2)





							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								if path:
									with open(_snake_case    ,			'r') as f:
										UpperCAmelCase_		:      str       	=					f.read().splitlines()
									if "O" not in labels:
										UpperCAmelCase_		:      Tuple       	=					['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 					lowercase						(  a_ ):


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				List[InputExample]:
								if isinstance(_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      Any       	=					mode.value
								UpperCAmelCase_		:      str       	=					os.path.join(_snake_case    ,			F"""{mode}.txt""")
								UpperCAmelCase_		:      Optional[int]       	=					1
								UpperCAmelCase_		:      Union[str, Any]       	=					[]

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


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Optional[Any]:
								UpperCAmelCase_		:      Dict       	=					0
								for sentence in parse_incr(_snake_case):
									UpperCAmelCase_		:      List[Any]       	=					preds_list[example_id]
									UpperCAmelCase_		:      Union[str, Any]       	=					''
									for token in sentence:
										out += F"""{token["form"]} ({token["upos"]}|{s_p.pop(0)}) """
									out += "\n"
									writer.write(_snake_case)
									example_id += 1





							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								if path:
									with open(_snake_case    ,			'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",
									]









'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Union[str, Any]       	=					[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		UpperCAmelCase_		:      Optional[int]       	=					sd.pop(UpperCamelCase  )
		UpperCAmelCase_		:      List[Any]       	=					k.replace('layernorm_embedding'   ,'layer_norm'  )
		assert new_k not in sd
		UpperCAmelCase_		:      Any       	=					v


lowerCAmelCase__										= ["START"]







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      str       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
	UpperCAmelCase_		:      Tuple       	=					model['model']
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotConfig.from_json_file(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotForConditionalGeneration(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					m.model.state_dict().keys()
	UpperCAmelCase_		:      Optional[Any]       	=					[]
	UpperCAmelCase_		:      Dict       	=					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		UpperCAmelCase_		:      Union[str, Any]       	=					rename_state_dict_key(UpperCamelCase  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			UpperCAmelCase_		:      List[str]       	=					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase  )
	m.model.load_state_dict(UpperCamelCase   ,strict=UpperCamelCase  )
	m.half()
	m.save_pretrained(UpperCamelCase  )


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
						parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
						parser.add_argument(
						    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
						)
						lowerCAmelCase__										= parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






