

'''simple docstring'''


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      List[Any]=   MgpstrTokenizer
							_lowerCamelCase		:      Dict=   False
							_lowerCamelCase		:      Union[str, Any]=   {}
							_lowerCamelCase		:      Any=   False


							def 						_snake_case							(			self)							->				List[str]:
								super().setUp()

								# fmt: off
								UpperCAmelCase_		:      Any       	=					['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
								# fmt: on
								UpperCAmelCase_		:      List[str]       	=					dict(zip(_snake_case    ,			range(len(_snake_case))))

								UpperCAmelCase_		:      List[Any]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file'])
								with open(self.vocab_file    ,			'w'    ,			encoding='utf-8') as fp:
									fp.write(json.dumps(_snake_case) + '\n')


							def 						_snake_case							(			self    ,			**_snake_case)							->				Dict:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,			**_snake_case)


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      List[str]       	=					'tester'
								UpperCAmelCase_		:      Optional[Any]       	=					'tester'
								return input_text, output_text


							@unittest.skip('MGP-STR always lower cases letters.')
							def 						_snake_case							(			self)							->				Dict:
								pass


							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.get_tokenizers(do_lower_case=_snake_case)
								for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										UpperCAmelCase_		:      Union[str, Any]       	=					'[SPECIAL_TOKEN]'

										tokenizer.add_special_tokens({'cls_token': special_token})
										UpperCAmelCase_		:      Dict       	=					tokenizer.encode([special_token]    ,			add_special_tokens=_snake_case)
										self.assertEqual(len(_snake_case)    ,			1)

										UpperCAmelCase_		:      Dict       	=					tokenizer.decode(_snake_case    ,			skip_special_tokens=_snake_case)
										self.assertTrue(special_token not in decoded)


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      str       	=					self.get_tokenizers()
								for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.get_input_output_texts(_snake_case)

										UpperCAmelCase_		:      str       	=					tokenizer.tokenize(_snake_case)
										UpperCAmelCase_		:      str       	=					tokenizer.convert_tokens_to_ids(_snake_case)
										UpperCAmelCase_		:      List[str]       	=					tokenizer.encode(_snake_case    ,			add_special_tokens=_snake_case)
										self.assertListEqual(_snake_case    ,			_snake_case)

										UpperCAmelCase_		:      Dict       	=					tokenizer.convert_ids_to_tokens(_snake_case)
										self.assertNotEqual(len(_snake_case)    ,			0)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.decode(_snake_case)
										self.assertIsInstance(_snake_case    ,			_snake_case)

										self.assertEqual(text_a.replace(' '    ,			'')    ,			_snake_case)


							@unittest.skip('MGP-STR tokenizer only handles one sequence.')
							def 						_snake_case							(			self)							->				List[str]:
								pass





							@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer')
							def 						_snake_case							(			self)							->				Optional[int]:
								pass









'''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)






