




"""simple docstring"""







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   :  Any					=      logging.get_logger(__name__)

__A   :  Optional[int]					=      {
    '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       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     str       =      "gptj"
				__UpperCAmelCase :     List[Any]       =      {
				    "max_position_embeddings": "n_positions",
				    "hidden_size": "n_embd",
				    "num_attention_heads": "n_head",
				    "num_hidden_layers": "n_layer",
				}



				def __init__(     self					:	Dict    ,   lowercase__					:	Dict=5_0_4_0_0    ,   lowercase__					:	str=2_0_4_8    ,   lowercase__					:	Any=4_0_9_6    ,   lowercase__					:	Optional[Any]=2_8    ,   lowercase__					:	Union[str, Any]=1_6    ,   lowercase__					:	Tuple=6_4    ,   lowercase__					:	Any=None    ,   lowercase__					:	Union[str, Any]="gelu_new"    ,   lowercase__					:	Dict=0.0    ,   lowercase__					:	Dict=0.0    ,   lowercase__					:	str=0.0    ,   lowercase__					:	str=1e-5    ,   lowercase__					:	Optional[Any]=0.0_2    ,   lowercase__					:	List[str]=True    ,   lowercase__					:	int=5_0_2_5_6    ,   lowercase__					:	int=5_0_2_5_6    ,   lowercase__					:	Optional[int]=False    ,   **lowercase__					:	List[Any]    ,   ):
						__lowercase				:		Optional[int]												=				vocab_size
						__lowercase				:		Optional[int]												=				n_positions
						__lowercase				:		Optional[int]												=				n_embd
						__lowercase				:		Optional[int]												=				n_layer
						__lowercase				:		Tuple												=				n_head
						__lowercase				:		List[Any]												=				n_inner
						__lowercase				:		Union[str, Any]												=				rotary_dim
						__lowercase				:		int												=				activation_function
						__lowercase				:		Dict												=				resid_pdrop
						__lowercase				:		List[str]												=				embd_pdrop
						__lowercase				:		int												=				attn_pdrop
						__lowercase				:		Tuple												=				layer_norm_epsilon
						__lowercase				:		Any												=				initializer_range
						__lowercase				:		Tuple												=				use_cache

						__lowercase				:		Union[str, Any]												=				bos_token_id
						__lowercase				:		Dict												=				eos_token_id

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





class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





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



				@property
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		List[str]												=				OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
						if self.use_past:
								self.fill_with_past_key_values_(lowercase__    ,   direction="inputs"						)
								__lowercase				:		Union[str, Any]												=				{0: "batch", 1: "past_sequence + sequence"}
						else:
								__lowercase				:		Dict												=				{0: "batch", 1: "sequence"}

						return common_inputs



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return self._config.n_layer



				@property
				def       snake_case    (     self					:	str						):
						return self._config.n_head



				def       snake_case    (     self					:	Any    ,   lowercase__					:	PreTrainedTokenizer    ,   lowercase__					:	int = -1    ,   lowercase__					:	int = -1    ,   lowercase__					:	bool = False    ,   lowercase__					:	Optional[TensorType] = None    ,   ):
						__lowercase				:		str												=				super(lowercase__    ,   self						).generate_dummy_inputs(
						    lowercase__    ,   batch_size=lowercase__    ,   seq_length=lowercase__    ,   is_pair=lowercase__    ,   framework=lowercase__						)

						# We need to order the input in the way they appears in the forward()
						__lowercase				:		Optional[Any]												=				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

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

						__lowercase				:		Any												=				common_inputs["attention_mask"]
						if self.use_past:
								__lowercase				:		Union[str, Any]												=				ordered_inputs["attention_mask"].dtype
								__lowercase				:		Optional[Any]												=				torch.cat(
								    [ordered_inputs["attention_mask"], torch.ones(lowercase__    ,   lowercase__    ,   dtype=lowercase__						)]    ,   dim=1						)

						return ordered_inputs



				@property
				def       snake_case    (     self					:	Dict						):
						return 1_3






"""simple docstring"""







import argparse
from collections import defaultdict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				F'{file}_{class_name}_{test_name}'
		done_test[_id] += 1

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		Union[str, Any]												=				f.readlines()

		__lowercase				:		str												=				F'class {class_name}('
		__lowercase				:		Union[str, Any]												=				F'{4 * " "}def {test_name}('
		__lowercase				:		List[Any]												=				F'{8 * " "}{correct_line.split()[0]}'
		__lowercase				:		Optional[int]												=				F'{16 * " "}{correct_line.split()[0]}'
		__lowercase				:		List[Any]												=				False
		__lowercase				:		int												=				False
		__lowercase				:		int												=				False
		__lowercase				:		List[Any]												=				False
		__lowercase				:		str												=				0
		__lowercase				:		Dict												=				0

		__lowercase				:		Optional[int]												=				[]
		for line in lines:
				if line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and in_func and (line.startswith(_lowerCamelCase				) or line.startswith(_lowerCamelCase				)):
						__lowercase				:		int												=				len(line.split(correct_line.split()[0]				)[0]				)
						count += 1

						if count == done_test[_id]:
								__lowercase				:		Any												=				True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								__lowercase				:		int												=				True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F'{spaces * " "}{correct_line}'				)
						__lowercase				:		List[Any]												=				False
				else:
						new_lines.append(_lowerCamelCase				)

		with open(_lowerCamelCase,  "w"				) as f:
				for line in new_lines:
						f.write(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->List[Any]:






		"""simple docstring"""


		if fail is not None:
				with open(_lowerCamelCase,  "r"				) as f:
						__lowercase				:		str												=				{l.strip() for l in f.readlines()}
		else:
				__lowercase				:		List[Any]												=				None

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		str												=				f.readlines()

		__lowercase				:		List[str]												=				defaultdict(_lowerCamelCase				)
		for line in correct_lines:
				__lowercase						,__lowercase						,__lowercase						,__lowercase				:		str												=				line.split(";"				)
				if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
						overwrite_file(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)


if __name__ == "__main__":
						__A   :  str					=      argparse.ArgumentParser()
						parser.add_argument('--correct_filename', help='filename of tests with expected result')
						parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
						__A   :  Union[str, Any]					=      parser.parse_args()

						main(args.correct_filename, args.fail_filename)
