






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE__				     =			logging.getLogger()

def        UpperCAmelCase__		(						lowerCamelCase_       :       Path   ,  lowerCamelCase_       :       list				):
			__a						:  Tuple									=     '\n'.join(lowerCamelCase_				)
			Path(lowerCamelCase_				).open('w'				).writelines(lowerCamelCase_				)


SCREAMING_SNAKE_CASE__				     =			'''patrickvonplaten/t5-tiny-random'''
SCREAMING_SNAKE_CASE__				     =			'''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE__				     =			'''sshleifer/tiny-mbart'''

SCREAMING_SNAKE_CASE__				     =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			_UpperCamelCase(       __lowerCamelCase		):

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Dict									=     Path(self.get_auto_remove_tmp_dir()    ) / 'utest_input.source'
					__a						:  str									=     input_file_name.parent / 'utest_output.txt'
					assert not output_file_name.exists()
					__a						:  Optional[Any]									=     [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
					_dump_articles(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

					__a						:  List[str]									=     str(Path(self.get_auto_remove_tmp_dir()    ) / 'scores.json'    )
					__a						:  Any									=     'translation_en_to_de' if model == T5_TINY else 'summarization'
					__a						:  Union[str, Any]									=     f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

					with patch.object(SCREAMING_SNAKE_CASE__    ,       'argv'    ,       SCREAMING_SNAKE_CASE__    ):
								run_generate()
								assert Path(SCREAMING_SNAKE_CASE__    ).exists()
								# os.remove(Path(output_file_name))

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					self.run_eval_tester(SCREAMING_SNAKE_CASE__    )

		@parameterized.expand([BART_TINY, MBART_TINY]    )
		@slow
		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ):



					'''simple docstring'''



					self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



		@parameterized.expand([T5_TINY, MBART_TINY]    )
		@slow
		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  str									=     Path(self.get_auto_remove_tmp_dir()    ) / 'utest_input.source'
					__a						:  Any									=     input_file_name.parent / 'utest_output.txt'
					assert not output_file_name.exists()

					__a						:  Dict									=     {
					    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
					    'de': [
					        'Maschinelles Lernen ist großartig, oder?',
					        'Ich esse gerne Bananen',
					        'Morgen ist wieder ein toller Tag!',
					    ],
					}

					__a						:  Dict									=     Path(self.get_auto_remove_tmp_dir()    )
					__a						:  Tuple									=     str(tmp_dir / 'scores.json'    )
					__a						:  List[str]									=     str(tmp_dir / 'val.target'    )
					_dump_articles(SCREAMING_SNAKE_CASE__    ,       text['en']    )
					_dump_articles(SCREAMING_SNAKE_CASE__    ,       text['de']    )
					__a						:  Dict									=     'translation_en_to_de' if model == T5_TINY else 'summarization'
					__a						:  Any									=     f'''
            run_eval_search.py
            {model}
            {str(SCREAMING_SNAKE_CASE__    )}
            {str(SCREAMING_SNAKE_CASE__    )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
					testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']    )

					with patch.object(SCREAMING_SNAKE_CASE__    ,       'argv'    ,       SCREAMING_SNAKE_CASE__    ):
								with CaptureStdout() as cs:
											run_search()
								__a						:  List[str]									=     [' num_beams | length_penalty', model, 'Best score args']
								__a						:  Optional[Any]									=     ['Info']
								if "translation" in task:
											expected_strings.append('bleu'    )
								else:
											expected_strings.extend(SCREAMING_SNAKE_CASE__    )
								for w in expected_strings:
											assert w in cs.out
								for w in un_expected_strings:
											assert w not in cs.out
								assert Path(SCREAMING_SNAKE_CASE__    ).exists()
								os.remove(Path(SCREAMING_SNAKE_CASE__    )    )









import argparse

import torch

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


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["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 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	if k.startswith("""encoder"""    ):
		A             =					k.replace(""".attn"""				,       """.self_attn"""    )
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """final_layer_norm"""    )
	elif k.startswith("""decoder"""    ):
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """encoder_attn_layer_norm"""    )
		A             =					k.replace("""norm3"""				,       """final_layer_norm"""    )
	return k





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		A             =					rename_state_dict_key(UpperCamelCase__    )
		if new_k not in valid_keys:
			failures.append([k, new_k]    )
		else:
			A             =					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__":
				_lowercase							:   str						=						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"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



