





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_												=     logging.get_logger(__name__)

A_												=     {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "mvp"
					A__				 =    ["past_key_values"]
					A__				 =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





					def __init__( self	,  _lowerCAmelCase=5_0267	,  _lowerCAmelCase=1024	,  _lowerCAmelCase=12	,  _lowerCAmelCase=4096	,  _lowerCAmelCase=16	,  _lowerCAmelCase=12	,  _lowerCAmelCase=4096	,  _lowerCAmelCase=16	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase="gelu"	,  _lowerCAmelCase=1024	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=False	,  _lowerCAmelCase=True	,  _lowerCAmelCase=1	,  _lowerCAmelCase=0	,  _lowerCAmelCase=2	,  _lowerCAmelCase=True	,  _lowerCAmelCase=2	,  _lowerCAmelCase=2	,  _lowerCAmelCase=False	,  _lowerCAmelCase=100	,  _lowerCAmelCase=800	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       d_model
										lowerCamelCase__       =       encoder_ffn_dim
										lowerCamelCase__       =       encoder_layers
										lowerCamelCase__       =       encoder_attention_heads
										lowerCamelCase__       =       decoder_ffn_dim
										lowerCamelCase__       =       decoder_layers
										lowerCamelCase__       =       decoder_attention_heads
										lowerCamelCase__       =       dropout
										lowerCamelCase__       =       attention_dropout
										lowerCamelCase__       =       activation_dropout
										lowerCamelCase__       =       activation_function
										lowerCamelCase__       =       init_std
										lowerCamelCase__       =       encoder_layerdrop
										lowerCamelCase__       =       decoder_layerdrop
										lowerCamelCase__       =       classifier_dropout
										lowerCamelCase__       =       use_cache
										lowerCamelCase__       =       encoder_layers
										lowerCamelCase__       =       scale_embedding  # scale factor will be sqrt(d_model) if True
										lowerCamelCase__       =       use_prompt
										lowerCamelCase__       =       prompt_length
										lowerCamelCase__       =       prompt_mid_dim

										super().__init__(
										    pad_token_id=_lowerCAmelCase	,  bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  is_encoder_decoder=_lowerCAmelCase	,  decoder_start_token_id=_lowerCAmelCase	,  forced_eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	,  )

										if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"	,  _lowerCAmelCase	):
															lowerCamelCase__       =       self.bos_token_id
															warnings.warn(
															    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
															    "The config can simply be saved and uploaded again to be fixed."	)








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 SCREAMING_SNAKE_CASE_ (					datasets.BeamBasedBuilder   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										return datasets.DatasetInfo(
										    features=datasets.Features({"content": datasets.Value("string"	)}	)	,  supervised_keys=_lowerCAmelCase	,  )





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






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

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

class SCREAMING_SNAKE_CASE_ (					datasets.BeamBasedBuilder   ):





					"""simple docstring"""


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





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






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

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





def    __UpperCamelCase      (    ) ->Optional[Any]:
					return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"])]





def    __UpperCamelCase      (    ) ->Optional[Any]:
					return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"])]







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train.arrow"	)	)	)
															self.assertDictEqual(builder.info.features	,  datasets.Features({"content": datasets.Value("string"	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															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(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset





					@require_beam
					def 					__magic_name__ ( self	):
										import apache_beam as beam

										lowerCamelCase__       =       beam.io.parquetio.WriteToParquet

										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															with patch("apache_beam.io.parquetio.WriteToParquet"	) as write_parquet_mock:
																				lowerCamelCase__       =       partial(_lowerCAmelCase	,  num_shards=2	)
																				builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            _lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train-00000-of-00002.arrow"	)	)	)
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            _lowerCAmelCase	,  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"	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															# 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(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset





					@require_beam
					def 					__magic_name__ ( self	):
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	)
															self.assertRaises(datasets.builder.MissingBeamOptions	,  builder.download_and_prepare	)






					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_nested_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       NestedBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(_lowerCAmelCase	,  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"	)}	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															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(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset

