


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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''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  _a							(    _lowercase):
	_a  :  List[str]				   =							'''gptj'''
	_a  :  Any				   =							{
	    '''max_position_embeddings''': '''n_positions''',
	    '''hidden_size''': '''n_embd''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




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

			lowerCAmelCase__       :      List[Any]   					= bos_token_id
			lowerCAmelCase__       :      Dict   					= eos_token_id

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





class  _a							(    _lowercase):




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




	@property
	def 							UpperCAmelCase__( self						:		Dict		)->	Mapping[str, Mapping[int, str]]:
			lowerCAmelCase__       :      Optional[int]   					= OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
			if self.use_past:
					self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE		, direction='''inputs'''		)
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''past_sequence + sequence'''}
			else:
					lowerCAmelCase__       :      Optional[int]   					= {0: '''batch''', 1: '''sequence'''}

			return common_inputs




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




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




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		PreTrainedTokenizer		, _SCREAMING_SNAKE_CASE						:		int = -1		, _SCREAMING_SNAKE_CASE						:		int = -1		, _SCREAMING_SNAKE_CASE						:		bool = False		, _SCREAMING_SNAKE_CASE						:		Optional[TensorType] = None		, )->	Mapping[str, Any]:
			lowerCAmelCase__       :      Tuple   					= super(_SCREAMING_SNAKE_CASE		, self		).generate_dummy_inputs(
			    _SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE		, seq_length=_SCREAMING_SNAKE_CASE		, is_pair=_SCREAMING_SNAKE_CASE		, framework=_SCREAMING_SNAKE_CASE		)

			# We need to order the input in the way they appears in the forward()
			lowerCAmelCase__       :      List[str]   					= 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

							lowerCAmelCase__       :      Optional[int]   					= common_inputs['''input_ids'''].shape
							# Not using the same length for past_key_values
							lowerCAmelCase__       :      List[Any]   					= seqlen + 2
							lowerCAmelCase__       :      str   					= (
							    batch,
							    self.num_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // self.num_attention_heads,
							)
							lowerCAmelCase__       :      List[Any]   					= [
							    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(self.num_layers		)
							]

			lowerCAmelCase__       :      List[str]   					= common_inputs['''attention_mask''']
			if self.use_past:
					lowerCAmelCase__       :      Union[str, Any]   					= ordered_inputs['''attention_mask'''].dtype
					lowerCAmelCase__       :      Optional[int]   					= torch.cat(
					    [ordered_inputs['''attention_mask'''], torch.ones(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, dtype=_SCREAMING_SNAKE_CASE		)]		, dim=1		)

			return ordered_inputs




	@property
	def 							UpperCAmelCase__( self						:		List[Any]		)->	int:
			return 13








import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		def wrapper(*_a     ,       **_a	):
				lowerCAmelCase__       :      List[str]   					= timeit.default_timer()
				lowerCAmelCase__       :      List[Any]   					= func(*_a     ,       **_a	)
				lowerCAmelCase__       :      Any   					= timeit.default_timer() - starttime
				return delta

		lowerCAmelCase__       :      Any   					= func.__name__

		return wrapper






def 					lowerCamelCase_   (	_a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__       :      str   					= seq_shapes or {}
		for i in range(_a	):
				lowerCAmelCase__       :      List[str]   					= {}
				for col_id, (k, v) in enumerate(features.items()	):
						if isinstance(_a     ,       _ArrayXD	):
								lowerCAmelCase__       :      List[str]   					= np.random.rand(*v.shape	).astype(v.dtype	)
						elif isinstance(_a     ,       datasets.Value	):
								if v.dtype == "string":
										lowerCAmelCase__       :      Dict   					= '''The small grey turtle was surprisingly fast when challenged.'''
								else:
										lowerCAmelCase__       :      Any   					= np.random.randint(10     ,       size=1	).astype(v.dtype	).item()
						elif isinstance(_a     ,       datasets.Sequence	):
								while isinstance(_a     ,       datasets.Sequence	):
										lowerCAmelCase__       :      Optional[int]   					= v.feature
								lowerCAmelCase__       :      str   					= seq_shapes[k]
								lowerCAmelCase__       :      Any   					= np.random.rand(*_a	).astype(v.dtype	)
						lowerCAmelCase__       :      int   					= data

				dummy_data.append((i, example)	)

		return dummy_data






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= generate_examples(_a     ,       num_examples=_a     ,       seq_shapes=_a	)

		with ArrowWriter(features=_a     ,       path=_a	) as writer:
				for key, record in dummy_data:
						lowerCAmelCase__       :      Optional[int]   					= features.encode_example(_a	)
						writer.write(_a	)

				lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= writer.finalize()

		if not num_final_examples == num_examples:
				raise ValueError(
				    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'	)

		lowerCAmelCase__       :      List[Any]   					= datasets.Dataset.from_file(filename=_a     ,       info=datasets.DatasetInfo(features=_a	)	)

		return dataset




