
import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def 							A__       (    lowerCamelCase    )	->						Union[str, Any]:
				UpperCamelCase_:				Tuple						=    filter(lambda lowerCamelCase    : p.requires_grad  ,     model.parameters()    )
				UpperCamelCase_:				List[str]						=    sum([np.prod(p.size()    ) for p in model_parameters]    )
				return params


lowerCamelCase_    :			Tuple    =	logging.getLogger(__name__)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Tuple:
				if metric == "rouge2":
								UpperCamelCase_:				Tuple						=    """{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								UpperCamelCase_:				int						=    """{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "em":
								UpperCamelCase_:				Union[str, Any]						=    """{val_avg_em:.4f}-{step_count}"""
				elif metric == "loss":
								UpperCamelCase_:				Tuple						=    """{val_avg_loss:.4f}-{step_count}"""
				else:
								raise NotImplementedError(
								    F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
								    """ function."""    )

				UpperCamelCase_:				int						=    ModelCheckpoint(
				    dirpath=lowerCamelCase  ,     filename=lowerCamelCase  ,     monitor=F'''val_{metric}'''  ,     mode="""max"""  ,     save_top_k=1  ,     every_n_epochs=1  ,     )

				return checkpoint_callback







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[Any]:
				return EarlyStopping(
				    monitor=F'''val_{metric}'''  ,     mode="""min""" if """loss""" in metric else """max"""  ,     patience=lowerCamelCase  ,     verbose=lowerCamelCase  ,     )





class 							_UpperCamelCase					(       pl.Callback	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							str    ,						snake_case_			:							Union[str, Any]   ):
							UpperCamelCase_:				str						=    {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
							pl_module.logger.log_metrics(_A   )

			@rank_zero_only
			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							Dict    ,						snake_case_			:							str    ,						snake_case_			:							List[Any]    ,						snake_case_			:							List[str]=True   ):
							logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''   )
							UpperCamelCase_:				List[Any]						=    trainer.callback_metrics
							trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}   )
							# Log results
							UpperCamelCase_:				int						=    Path(pl_module.hparams.output_dir   )
							if type_path == "test":
											UpperCamelCase_:				Tuple						=    od / """test_results.txt"""
											UpperCamelCase_:				List[str]						=    od / """test_generations.txt"""
							else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											UpperCamelCase_:				int						=    od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
											UpperCamelCase_:				str						=    od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=_A   )
											generations_file.parent.mkdir(exist_ok=_A   )
							with open(_A    ,						"""a+"""   ) as writer:
											for key in sorted(_A   ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															UpperCamelCase_:				int						=    metrics[key]
															if isinstance(_A    ,						torch.Tensor   ):
																			UpperCamelCase_:				Optional[int]						=    val.item()
															UpperCamelCase_:				Union[str, Any]						=    f'''{key}: {val:.6f}\n'''
															writer.write(_A   )

							if not save_generations:
											return

							if "preds" in metrics:
											UpperCamelCase_:				int						=    """\n""".join(metrics["""preds"""]   )
											generations_file.open("""w+"""   ).write(_A   )

			@rank_zero_only
			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							List[str]    ,						snake_case_			:							Tuple   ):







							try:
											UpperCamelCase_:				Any						=    pl_module.model.model.num_parameters()
							except AttributeError:
											UpperCamelCase_:				Any						=    pl_module.model.num_parameters()

							UpperCamelCase_:				Any						=    count_trainable_parameters(_A   )
							# mp stands for million parameters
							trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}   )

			@rank_zero_only
			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							Optional[Any]   ):
							save_json(pl_module.metrics    ,						pl_module.metrics_save_path   )
							return self._write_logs(_A    ,						_A    ,						"""test"""   )




			@rank_zero_only
			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Optional[int]   ):
							save_json(pl_module.metrics    ,						pl_module.metrics_save_path   )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")













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

__magic_name__       :					Any						=			{
    """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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[int]										=  """gptj"""
			__lowerCAmelCase     :				Optional[Any]										=  {
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}







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






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			vocab_size
									UpperCamelCase				:		Any				   =			n_positions
									UpperCamelCase				:		List[str]				   =			n_embd
									UpperCamelCase				:		List[str]				   =			n_layer
									UpperCamelCase				:		Optional[int]				   =			n_head
									UpperCamelCase				:		int				   =			n_inner
									UpperCamelCase				:		Optional[Any]				   =			rotary_dim
									UpperCamelCase				:		Optional[int]				   =			activation_function
									UpperCamelCase				:		str				   =			resid_pdrop
									UpperCamelCase				:		Union[str, Any]				   =			embd_pdrop
									UpperCamelCase				:		Optional[Any]				   =			attn_pdrop
									UpperCamelCase				:		Optional[int]				   =			layer_norm_epsilon
									UpperCamelCase				:		Any				   =			initializer_range
									UpperCamelCase				:		Optional[int]				   =			use_cache

									UpperCamelCase				:		List[Any]				   =			bos_token_id
									UpperCamelCase				:		List[str]				   =			eos_token_id

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






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = "default"						,   _A = None						,   _A = False						,   ):






									'''simple docstring'''







									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?
															UpperCamelCase				:		Optional[Any]				   =			0







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
									if self.use_past:
															self.fill_with_past_key_values_(_A						,   direction="""inputs"""    )
															UpperCamelCase				:		Any				   =			{0: """batch""", 1: """past_sequence + sequence"""}
									else:
															UpperCamelCase				:		List[Any]				   =			{0: """batch""", 1: """sequence"""}

									return common_inputs







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._config.n_layer







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._config.n_head







			def   _a      (      self						,   _A						,   _A = -1						,   _A = -1						,   _A = False						,   _A = None						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			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()
									UpperCamelCase				:		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

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

									UpperCamelCase				:		str				   =			common_inputs["""attention_mask"""]
									if self.use_past:
															UpperCamelCase				:		Any				   =			ordered_inputs["""attention_mask"""].dtype
															UpperCamelCase				:		List[Any]				   =			torch.cat(
															    [ordered_inputs["""attention_mask"""], torch.ones(_A						,   _A						,   dtype=_A    )]						,   dim=1    )

									return ordered_inputs







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return 1_3



