
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__				(				__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


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


_A   =     logging.getLogger(__name__)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Optional[int]				):




			"""simple docstring"""


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

			lowerCAmelCase_						=	ModelCheckpoint(
			    dirpath=snake_case__    ,							filename=snake_case__    ,							monitor=F"""val_{metric}"""    ,							mode="max"    ,							save_top_k=3    ,							every_n_epochs=1    ,							)
			return checkpoint_callback






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Optional[int]				):




			"""simple docstring"""


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





class 	_lowerCAmelCase					(	pl.Callback       ):




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Tuple:
							lowerCAmelCase_						=	{f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
							pl_module.logger.log_metrics(_UpperCamelCase     )




				@rank_zero_only
				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=True     )				->      None:
							logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
							lowerCAmelCase_						=	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
							lowerCAmelCase_						=	Path(pl_module.hparams.output_dir     )
							if type_path == "test":
										lowerCAmelCase_						=	od / '''test_results.txt'''
										lowerCAmelCase_						=	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.
										lowerCAmelCase_						=	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
										lowerCAmelCase_						=	od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
										results_file.parent.mkdir(exist_ok=_UpperCamelCase     )
										generations_file.parent.mkdir(exist_ok=_UpperCamelCase     )
							with open(_UpperCamelCase				,				"a+"     ) as writer:
										for key in sorted(_UpperCamelCase     ):
													if key in ["log", "progress_bar", "preds"]:
																continue
													lowerCAmelCase_						=	metrics[key]
													if isinstance(_UpperCamelCase				,				torch.Tensor     ):
																lowerCAmelCase_						=	val.item()
													lowerCAmelCase_						=	f"""{key}: {val:.6f}\n"""
													writer.write(_UpperCamelCase     )

							if not save_generations:
										return

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




				@rank_zero_only
				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:






							try:
										lowerCAmelCase_						=	pl_module.model.model.num_parameters()
							except AttributeError:
										lowerCAmelCase_						=	pl_module.model.num_parameters()

							lowerCAmelCase_						=	count_trainable_parameters(_UpperCamelCase     )
							# 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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Tuple:
							save_json(pl_module.metrics				,				pl_module.metrics_save_path     )
							return self._write_logs(_UpperCamelCase				,				_UpperCamelCase				,				"test"     )




				@rank_zero_only
				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							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'''




import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Union[str, Any]					=						[]
         A       :   Union[str, Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Any					=						self.in_channels if i == 0 else self.out_channels

             A       :   Optional[Any]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   Optional[int]					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						resnets
         A       :   Union[str, Any]					=						attentions

         if self.add_downsample:
             A       :   int					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						()

         for resnet, attn in zip(self.resnets						,			self.attentions	):
             A       :   int					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Dict					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[Any]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Optional[Any]					=						self.in_channels if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						resnets

         if self.add_downsample:
             A       :   Dict					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[Any]:






         """simple docstring"""






         A       :   str					=						()

         for resnet in self.resnets:
             A       :   Optional[int]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[int]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						[]
         A       :   Optional[int]					=						[]

         for i in range(self.num_layers	):
             A       :   str					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   Dict					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						resnets
         A       :   Optional[Any]					=						attentions

         if self.add_upsample:
             A       :   Optional[int]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[int]:






         """simple docstring"""






         for resnet, attn in zip(self.resnets						,			self.attentions	):
             # pop res hidden states
             A       :   List[str]					=						res_hidden_states_tuple[-1]
             A       :   int					=						res_hidden_states_tuple[:-1]
             A       :   List[str]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Tuple					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   Dict					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   int					=						[]

         for i in range(self.num_layers	):
             A       :   List[Any]					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   List[str]					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   str					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						resnets

         if self.add_upsample:
             A       :   Optional[Any]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Tuple:






         """simple docstring"""






         for resnet in self.resnets:
             # pop res hidden states
             A       :   Optional[int]					=						res_hidden_states_tuple[-1]
             A       :   Optional[Any]					=						res_hidden_states_tuple[:-1]
             A       :   List[Any]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Optional[Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   List[str]					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   str					=						[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
         ]

         A       :   List[Any]					=						[]

         for _ in range(self.num_layers	):
             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.in_channels						,			n_heads=self.num_attention_heads						,			d_head=self.in_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

             A       :   Union[str, Any]					=						FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						resnets
         A       :   List[str]					=						attentions





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Dict:






         """simple docstring"""






         A       :   Optional[Any]					=						self.resnets[0](SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         for attn, resnet in zip(self.attentions						,			self.resnets[1:]	):
             A       :   Optional[int]					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         return hidden_states




