



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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				__a       =		filter(lambda _SCREAMING_SNAKE_CASE    : p.requires_grad				,     model.parameters()    )
				__a       =		sum([np.prod(p.size()    ) for p in model_parameters]    )
				return params


lowerCamelCase__			= logging.getLogger(__name__)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				if metric == "rouge2":
								__a       =		"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a       =		"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "em":
								__a       =		"""{val_avg_em:.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."""    )

				__a       =		ModelCheckpoint(
				    dirpath=_SCREAMING_SNAKE_CASE				,     filename=_SCREAMING_SNAKE_CASE				,     monitor=f"val_{metric}"				,     mode="""max"""				,     save_top_k=3				,     every_n_epochs=1				,     )
				return checkpoint_callback

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				return EarlyStopping(
				    monitor=f"val_{metric}"				,     mode="""min""" if """loss""" in metric else """max"""				,     patience=_SCREAMING_SNAKE_CASE				,     verbose=_SCREAMING_SNAKE_CASE				,     )






class    SCREAMING_SNAKE_CASE			(							pl.Callback					):





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
											pl_module.logger.log_metrics(__lowercase	)





							@rank_zero_only
							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		pl.Trainer      ,				__lowercase		:		pl.LightningModule      ,				__lowercase		:		str      ,				__lowercase		:		int=True	):

											'''simple docstring'''
											logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"	)
											__a       =		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
											__a       =		Path(pl_module.hparams.output_dir	)
											if type_path == "test":
															__a       =		od / """test_results.txt"""
															__a       =		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.
															__a       =		od / F"{type_path}_results/{trainer.global_step:05d}.txt"
															__a       =		od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
															results_file.parent.mkdir(exist_ok=__lowercase	)
															generations_file.parent.mkdir(exist_ok=__lowercase	)
											with open(__lowercase      ,				"""a+"""	) as writer:
															for key in sorted(__lowercase	):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__a       =		metrics[key]
																			if isinstance(__lowercase      ,				torch.Tensor	):
																							__a       =		val.item()
																			__a       =		F"{key}: {val:.6f}\n"
																			writer.write(__lowercase	)

											if not save_generations:
															return

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





							@rank_zero_only
							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		List[str]      ,				__lowercase		:		Tuple	):

											'''simple docstring'''
											try:
															__a       =		pl_module.model.model.num_parameters()
											except AttributeError:
															__a       =		pl_module.model.num_parameters()

											__a       =		count_trainable_parameters(__lowercase	)
											# 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 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		pl.Trainer      ,				__lowercase		:		pl.LightningModule	):

											'''simple docstring'''
											save_json(pl_module.metrics      ,				pl_module.metrics_save_path	)
											return self._write_logs(__lowercase      ,				__lowercase      ,				"""test"""	)







							@rank_zero_only
							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		pl.Trainer      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											save_json(pl_module.metrics      ,				pl_module.metrics_save_path	)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
