

import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam

class 			a					(		datasets.BeamBasedBuilder			):






						"""simple docstring"""




						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return datasets.DatasetInfo(
										    features=datasets.Features({"content": datasets.Value("string"		)}		)  ,   supervised_keys=A_  ,   )


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,   gen_kwargs={"examples": get_test_dummy_examples()}		)]





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										import apache_beam as beam

										return pipeline | "Load Examples" >> beam.Create(A_		)







class 			a					(		datasets.BeamBasedBuilder			):






						"""simple docstring"""




						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return datasets.DatasetInfo(
										    features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"		)}		)}		)  ,   supervised_keys=A_  ,   )


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										return [
										    datasets.SplitGenerator(name=datasets.Split.TRAIN  ,   gen_kwargs={"examples": get_test_nested_examples()}		)
										]





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										import apache_beam as beam

										return pipeline | "Load Examples" >> beam.Create(A_		)







def 							__SCREAMING_SNAKE_CASE							(			)				->    List[str]:
				return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"]	)]

def 							__SCREAMING_SNAKE_CASE							(			)				->    List[str]:
				return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"]	)]





class 			a					(		UpperCAmelCase			):






						"""simple docstring"""




						@require_beam
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      len(get_test_dummy_examples()		)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
														_UpperCAmelCase    :						Tuple							=      DummyBeamDataset(cache_dir=A_  ,   beam_runner="DirectRunner"		)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(A_  ,   builder.name  ,   "default"  ,   "0.0.0"  ,   f'{builder.name}-train.arrow'		)		)		)
														self.assertDictEqual(builder.info.features  ,   datasets.Features({"content": datasets.Value("string"		)}		)		)
														_UpperCAmelCase    :						int							=      builder.as_dataset()
														self.assertEqual(dset["train"].num_rows  ,   A_		)
														self.assertEqual(dset["train"].info.splits["train"].num_examples  ,   A_		)
														self.assertDictEqual(dset["train"][0]  ,   get_test_dummy_examples()[0][1]		)
														self.assertDictEqual(
														    dset["train"][expected_num_examples - 1]  ,   get_test_dummy_examples()[expected_num_examples - 1][1]		)
														self.assertTrue(
														    os.path.exists(os.path.join(A_  ,   builder.name  ,   "default"  ,   "0.0.0"  ,   "dataset_info.json"		)		)		)
														del dset


						@require_beam
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										import apache_beam as beam

										_UpperCAmelCase    :						Optional[int]							=      beam.io.parquetio.WriteToParquet

										_UpperCAmelCase    :						Optional[int]							=      len(get_test_dummy_examples()		)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
														_UpperCAmelCase    :						List[str]							=      DummyBeamDataset(cache_dir=A_  ,   beam_runner="DirectRunner"		)
														with patch("apache_beam.io.parquetio.WriteToParquet"		) as write_parquet_mock:
																		_UpperCAmelCase    :						Tuple							=      partial(A_  ,   num_shards=2		)
																		builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            A_  ,   builder.name  ,   "default"  ,   "0.0.0"  ,   f'{builder.name}-train-00000-of-00002.arrow'		)		)		)
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            A_  ,   builder.name  ,   "default"  ,   "0.0.0"  ,   f'{builder.name}-train-00000-of-00002.arrow'		)		)		)
														self.assertDictEqual(builder.info.features  ,   datasets.Features({"content": datasets.Value("string"		)}		)		)
														_UpperCAmelCase    :						Dict							=      builder.as_dataset()
														self.assertEqual(dset["train"].num_rows  ,   A_		)
														self.assertEqual(dset["train"].info.splits["train"].num_examples  ,   A_		)
														# Order is not preserved when sharding, so we just check that all the elements are there
														self.assertListEqual(sorted(dset["train"]["content"]		)  ,   sorted(["foo", "bar", "foobar"]		)		)
														self.assertTrue(
														    os.path.exists(os.path.join(A_  ,   builder.name  ,   "default"  ,   "0.0.0"  ,   "dataset_info.json"		)		)		)
														del dset


						@require_beam
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmp_cache_dir:
														_UpperCAmelCase    :						int							=      DummyBeamDataset(cache_dir=A_		)
														self.assertRaises(datasets.builder.MissingBeamOptions  ,   builder.download_and_prepare		)





						@require_beam
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      len(get_test_nested_examples()		)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
														_UpperCAmelCase    :						Optional[int]							=      NestedBeamDataset(cache_dir=A_  ,   beam_runner="DirectRunner"		)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(A_  ,   builder.name  ,   "default"  ,   "0.0.0"  ,   f'{builder.name}-train.arrow'		)		)		)
														self.assertDictEqual(
														    builder.info.features  ,   datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"		)}		)}		)		)
														_UpperCAmelCase    :						List[str]							=      builder.as_dataset()
														self.assertEqual(dset["train"].num_rows  ,   A_		)
														self.assertEqual(dset["train"].info.splits["train"].num_examples  ,   A_		)
														self.assertDictEqual(dset["train"][0]  ,   get_test_nested_examples()[0][1]		)
														self.assertDictEqual(
														    dset["train"][expected_num_examples - 1]  ,   get_test_nested_examples()[expected_num_examples - 1][1]		)
														self.assertTrue(
														    os.path.exists(os.path.join(A_  ,   builder.name  ,   "default"  ,   "0.0.0"  ,   "dataset_info.json"		)		)		)
														del dset





import argparse

import torch

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


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

SCREAMING_SNAKE_CASE_            = [
    ['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							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

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

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "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    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = 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'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


