





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


A_												=     logging.get_logger(__name__)

A_												=     {
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "falcon"
					A__				 =    ["past_key_values"]





					def __init__( self	,  _lowerCAmelCase=6_5024	,  _lowerCAmelCase=4544	,  _lowerCAmelCase=32	,  _lowerCAmelCase=71	,  _lowerCAmelCase=1E-5	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=True	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=None	,  _lowerCAmelCase=False	,  _lowerCAmelCase=False	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=False	,  _lowerCAmelCase=11	,  _lowerCAmelCase=11	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       vocab_size
										# Backward compatibility with n_embed kwarg
										lowerCamelCase__       =       kwargs.pop("n_embed"	,  _lowerCAmelCase	)
										lowerCamelCase__       =       hidden_size if n_embed is None else n_embed
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       layer_norm_epsilon
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       use_cache
										lowerCamelCase__       =       hidden_dropout
										lowerCamelCase__       =       attention_dropout

										lowerCamelCase__       =       bos_token_id
										lowerCamelCase__       =       eos_token_id
										lowerCamelCase__       =       num_attention_heads if num_kv_heads is None else num_kv_heads
										lowerCamelCase__       =       alibi
										lowerCamelCase__       =       new_decoder_architecture
										lowerCamelCase__       =       multi_query  # Ignored when new_decoder_architecture is True
										lowerCamelCase__       =       parallel_attn
										lowerCamelCase__       =       bias

										super().__init__(bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)





					@property
					def 					__magic_name__ ( self	):
										return self.hidden_size // self.num_attention_heads






					@property
					def 					__magic_name__ ( self	):
										return not self.alibi








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

