






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a_		:    List[str]   							=     logging.get_logger(__name__)

a_		:    Dict   							=     {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class 		_snake_case	(	A__       ):
			_lowercase		:     int        =				'''gptj'''
			_lowercase		:     int        =				{
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}






			def __init__(							self		,       a=5_0400		,       a=2048		,       a=4096		,       a=28		,       a=16		,       a=64		,       a=None		,       a="gelu_new"		,       a=0.0		,       a=0.0		,       a=0.0		,       a=1E-5		,       a=0.02		,       a=True		,       a=5_0256		,       a=5_0256		,       a=False		,       **a		,       )   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			vocab_size
						SCREAMING_SNAKE_CASE							=			n_positions
						SCREAMING_SNAKE_CASE							=			n_embd
						SCREAMING_SNAKE_CASE							=			n_layer
						SCREAMING_SNAKE_CASE							=			n_head
						SCREAMING_SNAKE_CASE							=			n_inner
						SCREAMING_SNAKE_CASE							=			rotary_dim
						SCREAMING_SNAKE_CASE							=			activation_function
						SCREAMING_SNAKE_CASE							=			resid_pdrop
						SCREAMING_SNAKE_CASE							=			embd_pdrop
						SCREAMING_SNAKE_CASE							=			attn_pdrop
						SCREAMING_SNAKE_CASE							=			layer_norm_epsilon
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			use_cache

						SCREAMING_SNAKE_CASE							=			bos_token_id
						SCREAMING_SNAKE_CASE							=			eos_token_id

						super().__init__(
						    bos_token_id=a		,       eos_token_id=a		,       tie_word_embeddings=a		,       **a)



class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a = "default"		,       a = None		,       a = False		,       )   ->							Union[str, Any]:
						super().__init__(a		,       task=a		,       patching_specs=a		,       use_past=a)
						if not getattr(self._config		,       'pad_token_id'		,       a):
									# TODO: how to do that better?
									SCREAMING_SNAKE_CASE							=			0






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Mapping[str, Mapping[int, str]]:
						SCREAMING_SNAKE_CASE							=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
						if self.use_past:
									self.fill_with_past_key_values_(a		,       direction='inputs')
									SCREAMING_SNAKE_CASE							=			{0: 'batch', 1: 'past_sequence + sequence'}
						else:
									SCREAMING_SNAKE_CASE							=			{0: 'batch', 1: 'sequence'}

						return common_inputs






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return self._config.n_layer






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return self._config.n_head






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = -1		,       a = -1		,       a = False		,       a = None		,       )   ->							Mapping[str, Any]:
						SCREAMING_SNAKE_CASE							=			super(a		,       self).generate_dummy_inputs(
						    a		,       batch_size=a		,       seq_length=a		,       is_pair=a		,       framework=a)

						# We need to order the input in the way they appears in the forward()
						SCREAMING_SNAKE_CASE							=			OrderedDict({'input_ids': common_inputs['input_ids']})

						# Need to add the past_keys
						if self.use_past:
									if not is_torch_available():
												raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
									else:
												import torch

												SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			common_inputs['input_ids'].shape
												# Not using the same length for past_key_values
												SCREAMING_SNAKE_CASE							=			seqlen + 2
												SCREAMING_SNAKE_CASE							=			(
												    batch,
												    self.num_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // self.num_attention_heads,
												)
												SCREAMING_SNAKE_CASE							=			[
												    (torch.zeros(a), torch.zeros(a)) for _ in range(self.num_layers)
												]

						SCREAMING_SNAKE_CASE							=			common_inputs['attention_mask']
						if self.use_past:
									SCREAMING_SNAKE_CASE							=			ordered_inputs['attention_mask'].dtype
									SCREAMING_SNAKE_CASE							=			torch.cat(
									    [ordered_inputs['attention_mask'], torch.ones(a		,       a		,       dtype=a)]		,       dim=1)

						return ordered_inputs







			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return 13








# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

a			:			Dict												=					'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
	raise ImportWarning(
	    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
	)

if version.parse(pyarrow.__version__).major < 8:
	raise ImportWarning(
	    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
	    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
	)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

a			:			Optional[int]												=					concatenate_datasets
a			:			List[Any]												=					DownloadConfig
a			:			List[Any]												=					DownloadManager
a			:			str												=					DownloadMode
a			:			int												=					DownloadConfig
a			:			List[str]												=					DownloadMode
a			:			Optional[int]												=					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


