





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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = {
    '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 	UpperCamelCase__				(   UpperCamelCase_     ):



					'''simple docstring'''

					__snake_case					:			int				=    "gptj"
					__snake_case					:			Any				=    {
					    "max_position_embeddings": "n_positions",
					    "hidden_size": "n_embd",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}






					def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					str=50400		,lowerCamelCase__		:					Tuple=2048		,lowerCamelCase__		:					int=4096		,lowerCamelCase__		:					Union[str, Any]=28		,lowerCamelCase__		:					int=16		,lowerCamelCase__		:					Union[str, Any]=64		,lowerCamelCase__		:					List[Any]=None		,lowerCamelCase__		:					Any="gelu_new"		,lowerCamelCase__		:					List[str]=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Union[str, Any]=1e-5		,lowerCamelCase__		:					str=0.02		,lowerCamelCase__		:					str=True		,lowerCamelCase__		:					Optional[Any]=50256		,lowerCamelCase__		:					int=50256		,lowerCamelCase__		:					int=False		,**lowerCamelCase__		:					List[str]		,)  ->	str:




											'''simple docstring'''


											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=lowerCamelCase__		,eos_token_id=lowerCamelCase__		,tie_word_embeddings=lowerCamelCase__		,**lowerCamelCase__ )

class 	UpperCamelCase__				(   UpperCamelCase_     ):



					'''simple docstring'''

					def __init__(							self		:					Dict		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Tuple = "default"		,lowerCamelCase__		:					Optional[Any] = None		,lowerCamelCase__		:					Tuple = False		,)  ->	List[Any]:




											'''simple docstring'''


											super().__init__(lowerCamelCase__		,task=lowerCamelCase__		,patching_specs=lowerCamelCase__		,use_past=lowerCamelCase__ )
											if not getattr(self._config		,"""pad_token_id"""		,lowerCamelCase__ ):
																	# TODO: how to do that better?
																	SCREAMING_SNAKE_CASE	=		0






					@property
					def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
											if self.use_past:
																	self.fill_with_past_key_values_(lowerCamelCase__		,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		:					Optional[int] )  ->	int:




											'''simple docstring'''


											return self._config.n_layer






					@property
					def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	int:




											'''simple docstring'''


											return self._config.n_head






					def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					List[str] = -1		,lowerCamelCase__		:					int = -1		,lowerCamelCase__		:					Union[str, Any] = False		,lowerCamelCase__		:					Optional[Any] = None		,)  ->	Mapping[str, Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		super(lowerCamelCase__		,self ).generate_dummy_inputs(
											    lowerCamelCase__		,batch_size=lowerCamelCase__		,seq_length=lowerCamelCase__		,is_pair=lowerCamelCase__		,framework=lowerCamelCase__ )

											# 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	=		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(lowerCamelCase__ ), torch.zeros(lowerCamelCase__ )) 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(lowerCamelCase__		,lowerCamelCase__		,dtype=lowerCamelCase__ )]		,dim=1 )

											return ordered_inputs






					@property
					def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	int:




											'''simple docstring'''


											return 13









import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""				,		set()			)



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      class 	UpperCamelCase__				:



           '''simple docstring'''

           def __init__(							self		:					int		,lowerCamelCase__		:					Dict )  ->	List[Any]:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE	=		metric_id

      class 	UpperCamelCase__				:



           '''simple docstring'''

           __snake_case					:			List[str]				=    [MetricMock(lowerCAmelCase_     ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]






           def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




                 '''simple docstring'''


                 return self._metrics
      monkeypatch.setattr("""datasets.inspect.huggingface_hub"""				,		HfhMock()			)



@pytest.mark.parametrize(
    """func, args"""				,		[(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))]			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      if "tmp_path" in args:
            SCREAMING_SNAKE_CASE	=		tuple(arg if arg != """tmp_path""" else tmp_path for arg in args			)
      with pytest.warns(_SCREAMING_SNAKE_CASE				,		match="""https://huggingface.co/docs/evaluate"""			):
            func(*_SCREAMING_SNAKE_CASE			)


