
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class      _lowerCAmelCase (     __a ,				__a ,				__a	):




				@register_to_config
				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = False    ,    )    ->				List[str]:
							super().__init__()

							lowerCAmelCase_      =     nn.Linear(_UpperCamelCase    ,    _UpperCamelCase    ,    bias=_UpperCamelCase					)

							lowerCAmelCase_      =     nn.Embedding(_UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     False

							lowerCAmelCase_      =     nn.Dropout(p=_UpperCamelCase					)

							lowerCAmelCase_      =     TaConfig(
							    d_model=_UpperCamelCase    ,    num_heads=_UpperCamelCase    ,    d_kv=_UpperCamelCase    ,    d_ff=_UpperCamelCase    ,    feed_forward_proj=_UpperCamelCase    ,    dropout_rate=_UpperCamelCase    ,    is_decoder=_UpperCamelCase    ,    is_encoder_decoder=_UpperCamelCase    ,    )
							lowerCAmelCase_      =     nn.ModuleList()
							for lyr_num in range(_UpperCamelCase					):
										lowerCAmelCase_      =     TaBlock(_UpperCamelCase					)
										self.encoders.append(_UpperCamelCase					)

							lowerCAmelCase_      =     TaLayerNorm(_UpperCamelCase					)
							lowerCAmelCase_      =     nn.Dropout(p=_UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:
							lowerCAmelCase_      =     self.input_proj(_UpperCamelCase					)

							# terminal relative positional encodings
							lowerCAmelCase_      =     encoder_inputs.shape[1]
							lowerCAmelCase_      =     torch.arange(_UpperCamelCase    ,    device=encoder_inputs.device					)

							lowerCAmelCase_      =     encoder_inputs_mask.sum(-1					)
							lowerCAmelCase_      =     torch.roll(input_positions.unsqueeze(0					)    ,    tuple(seq_lens.tolist()					)    ,    dims=0					)
							x += self.position_encoding(_UpperCamelCase					)

							lowerCAmelCase_      =     self.dropout_pre(_UpperCamelCase					)

							# inverted the attention mask
							lowerCAmelCase_      =     encoder_inputs.size()
							lowerCAmelCase_      =     self.get_extended_attention_mask(_UpperCamelCase    ,    _UpperCamelCase					)

							for lyr in self.encoders:
										lowerCAmelCase_      =     lyr(_UpperCamelCase    ,    _UpperCamelCase					)[0]
							lowerCAmelCase_      =     self.layer_norm(_UpperCamelCase					)

							return self.dropout_post(_UpperCamelCase					), encoder_inputs_mask





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_A       =					10_00_00_00_00_00
_A       =					1_00_00

_A,				_A       =					os.path.split(__file__)
_A       =					os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     pa.Table.from_pydict({"col": [0] * chunk_size}    )
			lowerCAmelCase_      =     pa.concat_tables([table] * (num_examples // chunk_size)    )
			return datasets.Dataset(__lowerCAmelCase   ,							fingerprint="table_100B"    )





@dataclass
class      _lowerCAmelCase :
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     np.random.default_rng(self.seed					)
							lowerCAmelCase_      =     rng.integers(low=self.low    ,    high=self.high    ,    size=self.size					).tolist()




				def __iter__( self					)    ->				List[str]:
							return iter(self._sampled_values					)




				def __len__( self					)    ->				Optional[int]:
							return self.size






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[0]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[-1]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[range(len(__lowerCAmelCase    ) // 2   ,							len(__lowerCAmelCase    ) // 2 + 1024    )]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[RandIter(0   ,							len(__lowerCAmelCase    )   ,							1024   ,							seed=42    )]






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     {"num examples": SPEED_TEST_N_EXAMPLES}
			lowerCAmelCase_      =     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
			print("generating dataset"    )
			lowerCAmelCase_      =     generate_100B_dataset(num_examples=__lowerCAmelCase   ,							chunk_size=__lowerCAmelCase    )
			print("Functions"    )
			for func in functions:
						print(func.__name__    )
						lowerCAmelCase_      =     func(__lowerCAmelCase    )

			with open(__lowerCAmelCase   ,							"wb"    ) as f:
						f.write(json.dumps(__lowerCAmelCase    ).encode("utf-8"    )    )


if __name__ == "__main__":  # useful to run the profiler
			benchmark_table_100B()



