
"""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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[int]						   =		'''gptj'''
		__lowercase							:  Optional[Any]						   =		{
		    '''max_position_embeddings''': '''n_positions''',
		    '''hidden_size''': '''n_embd''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}




		def __init__(     self							,  lowerCAmelCase__=5_0_4_0_0							,  lowerCAmelCase__=2_0_4_8							,  lowerCAmelCase__=4_0_9_6							,  lowerCAmelCase__=2_8							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=6_4							,  lowerCAmelCase__=None							,  lowerCAmelCase__="gelu_new"							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=1E-5							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=True							,  lowerCAmelCase__=5_0_2_5_6							,  lowerCAmelCase__=5_0_2_5_6							,  lowerCAmelCase__=False							,  **lowerCAmelCase__							,  ):
				__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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = "default"							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  ):
				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 	snake_case_     (     self):
				__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 	snake_case_     (     self):
				return self._config.n_layer




		@property
		def 	snake_case_     (     self):
				return self._config.n_head




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = -1							,  lowerCAmelCase__ = -1							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  ):
				__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       ,__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 	snake_case_     (     self):
				return 1_3







import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
						return lrs







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       []
						for step in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       os.path.join(_UpperCAmelCase				,	"schedule.bin"    )
																								torch.save(scheduler.state_dict()				,	_UpperCAmelCase    )

																								SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.load(_UpperCAmelCase    )
																								scheduler.load_state_dict(_UpperCAmelCase    )
						return lrs



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Tuple):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       AdamW(params=[w]   ,						lr=2E-1   ,						weight_decay=0.0)
									for _ in range(100):
															SCREAMING_SNAKE_CASE_: Dict  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Any  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       Adafactor(
									    params=[w]   ,						lr=1E-2   ,						eps=(1E-30, 1E-3)   ,						clip_threshold=1.0   ,						decay_rate=-0.8   ,						betaa=lowerCAmelCase__   ,						weight_decay=0.0   ,						relative_step=lowerCAmelCase__   ,						scale_parameter=lowerCAmelCase__   ,						warmup_init=lowerCAmelCase__   ,						)
									for _ in range(1000):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)







@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     nn.Linear(50		,       50	) if is_torch_available() else None
			_UpperCAmelCase   :							List[Any]										=     AdamW(m.parameters()		,       lr=10.0	) if is_torch_available() else None
			_UpperCAmelCase   :							Optional[Any]										=     10



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[Any]=None):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__   ,						msg=lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       {"num_warmup_steps": 2, "num_training_steps": 10}
									# schedulers doct format
									# function: (sched_args_dict, expected_learning_rates)
									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    get_constant_schedule: ({}, [10.0] * self.num_steps),
									    get_constant_schedule_with_warmup: (
									        {"num_warmup_steps": 4},
									        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
									    ),
									    get_linear_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
									    ),
									    get_cosine_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
									    ),
									    get_cosine_with_hard_restarts_schedule_with_warmup: (
									        {**common_kwargs, "num_cycles": 2},
									        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
									    ),
									    get_polynomial_decay_schedule_with_warmup: (
									        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
									        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
									    ),
									    get_inverse_sqrt_schedule: (
									        {"num_warmup_steps": 2},
									        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
									    ),
									}

									for scheduler_func, data in scheds.items():
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       data

															SCREAMING_SNAKE_CASE_: List[Any]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															self.assertEqual(len([scheduler.get_lr()[0]])   ,						1)
															SCREAMING_SNAKE_CASE_: int  		=       unwrap_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListAlmostEqual(
															    lowerCAmelCase__   ,						lowerCAmelCase__   ,						tol=1E-2   ,						msg=F"failed for {scheduler_func} in normal scheduler"   ,						)

															SCREAMING_SNAKE_CASE_: List[str]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															if scheduler_func.__name__ != "get_constant_schedule":
																					LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__)  # wrap to test picklability of the schedule
															SCREAMING_SNAKE_CASE_: Tuple  		=       unwrap_and_save_reload_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						msg=F"failed for {scheduler_func} in save and reload")







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       fn



			def __call__( self  :     Optional[int]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     Tuple):
									return self.fn(*lowerCAmelCase__   ,						**lowerCAmelCase__)




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: str  		=       list(map(self   ,						scheduler.lr_lambdas))






