

import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCAmelCase__								=		pytest.mark.integration






@pytest.mark.parametrize('path'							,       ['paws', 'csv']	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     int	)						->				Tuple:




	'''simple docstring'''




	inspect_dataset(__snake_case							,       __snake_case	)
	_UpperCamelCase   :		List[str]			=						path + '.py'
	assert script_name in os.listdir(__snake_case	)
	assert "__pycache__" not in os.listdir(__snake_case	)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'	)
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'	)
@pytest.mark.parametrize('path'							,       ['accuracy']	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Any	)						->				int:




	'''simple docstring'''




	inspect_metric(__snake_case							,       __snake_case	)
	_UpperCamelCase   :		Optional[Any]			=						path + '.py'
	assert script_name in os.listdir(__snake_case	)
	assert "__pycache__" not in os.listdir(__snake_case	)






@pytest.mark.parametrize(
    'path, config_name, expected_splits'							,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     int	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						get_dataset_config_info(__snake_case							,       config_name=__snake_case	)
	assert info.config_name == config_name
	assert list(info.splits.keys()	) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'							,       [
        ('paws', None, ValueError),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	with pytest.raises(__snake_case	):
		get_dataset_config_info(__snake_case							,       config_name=__snake_case	)






@pytest.mark.parametrize(
    'path, expected'							,       [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Any	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						get_dataset_config_names(__snake_case	)
	assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'							,       [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Dict	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						get_dataset_infos(__snake_case	)
	assert list(infos.keys()	) == expected_configs
	_UpperCamelCase   :		int			=						expected_configs[0]
	assert expected_config in infos
	_UpperCamelCase   :		Optional[int]			=						infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()	) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits'							,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     List[str]	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						get_dataset_infos(__snake_case	)
	assert expected_config in infos
	_UpperCamelCase   :		int			=						infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()	) == expected_splits







@pytest.mark.parametrize(
    'path, config_name, expected_exception'							,       [
        ('paws', None, ValueError),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Union[str, Any]	)						->				List[Any]:




	'''simple docstring'''




	with pytest.raises(__snake_case	):
		get_dataset_split_names(__snake_case							,       config_name=__snake_case	)







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 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




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

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

	if k.startswith('encoder'	):
		_UpperCamelCase   :		Optional[Any]			=						k.replace('.attn'							,       '.self_attn'	)
		_UpperCamelCase   :		Optional[int]			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'final_layer_norm'	)
	elif k.startswith('decoder'	):
		_UpperCamelCase   :		Any			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'encoder_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm3'							,       'final_layer_norm'	)
	return k






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_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   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		int			=						model['model']
	_UpperCamelCase   :		List[Any]			=						BlenderbotConfig.from_json_file(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						BlenderbotForConditionalGeneration(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						m.model.state_dict().keys()
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		int			=						{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		_UpperCamelCase   :		Optional[int]			=						rename_state_dict_key(UpperCAmelCase_	)
		if new_k not in valid_keys:
			failures.append([k, new_k]	)
		else:
			_UpperCamelCase   :		int			=						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)




