


'''simple docstring'''


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image


lowerCamelCase__					=   ['text', 'image', 'audio']






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Optional[Any]	    =					[]

		for input_type in input_types:
				if input_type == "text":
						inputs.append("Text input"      )
				elif input_type == "image":
						inputs.append(
						    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"      )      ) / "000000039769.png"      ).resize((512, 512)      )      )
				elif input_type == "audio":
						inputs.append(torch.ones(3_000      )      )
				elif isinstance(__lowerCAmelCase					,       __lowerCAmelCase      ):
						inputs.append(create_inputs(__lowerCAmelCase      )      )
				else:
						raise ValueError(F"""Invalid type requested: {input_type}"""      )

		return inputs






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Optional[Any]	    =					[]

		for output in outputs:
				if isinstance(__lowerCAmelCase					,       (str, AgentText)      ):
						output_types.append("text"      )
				elif isinstance(__lowerCAmelCase					,       (Image.Image, AgentImage)      ):
						output_types.append("image"      )
				elif isinstance(__lowerCAmelCase					,       (torch.Tensor, AgentAudio)      ):
						output_types.append("audio"      )
				else:
						raise ValueError(F"""Invalid output: {output}"""      )

		return output_types






@is_tool_test
class 		lowerCAmelCase__	:




			def 							lowerCAmelCase__			(						self     :   Any       )      ->int:



					'''simple docstring'''







					self.assertTrue(hasattr(self.tool		,     "inputs"       )       )
					self.assertTrue(hasattr(self.tool		,     "outputs"       )       )

					_UpperCAmelCase						:				int	    =					self.tool.inputs
					for _input in inputs:
							if isinstance(_input		,     lowerCamelCase__       ):
									for __input in _input:
											self.assertTrue(__input in authorized_types       )
							else:
									self.assertTrue(_input in authorized_types       )

					_UpperCAmelCase						:				Dict	    =					self.tool.outputs
					for _output in outputs:
							self.assertTrue(_output in authorized_types       )




			def 							lowerCAmelCase__			(						self     :   Any       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					create_inputs(self.tool.inputs       )
					_UpperCAmelCase						:				Optional[int]	    =					self.tool(*lowerCamelCase__       )

					# There is a single output
					if len(self.tool.outputs       ) == 1:
							_UpperCAmelCase						:				str	    =					[outputs]

					self.assertListEqual(output_types(lowerCamelCase__       )		,     self.tool.outputs       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Tuple:



					'''simple docstring'''







					self.assertTrue(hasattr(self.tool		,     "description"       )       )
					self.assertTrue(hasattr(self.tool		,     "default_checkpoint"       )       )
					self.assertTrue(self.tool.description.startswith("This is a tool that"       )       )




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					create_inputs(self.tool.inputs       )
					_UpperCAmelCase						:				Dict	    =					self.tool(*lowerCamelCase__       )

					if not isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
							_UpperCAmelCase						:				List[str]	    =					[outputs]

					self.assertEqual(len(lowerCamelCase__       )		,     len(self.tool.outputs       )       )

					for output, output_type in zip(lowerCamelCase__		,     self.tool.outputs       ):
							_UpperCAmelCase						:				Optional[Any]	    =					AGENT_TYPE_MAPPING[output_type]
							self.assertTrue(isinstance(lowerCamelCase__		,     lowerCamelCase__       )       )






			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					create_inputs(self.tool.inputs       )

					_UpperCAmelCase						:				List[Any]	    =					[]

					for _input, input_type in zip(lowerCamelCase__		,     self.tool.inputs       ):
							if isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
									_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input       ) for _input_type in input_type]       )
							else:
									_inputs.append(AGENT_TYPE_MAPPING[input_type](_input       )       )

        # Should not raise an error
					_UpperCAmelCase						:				str	    =					self.tool(*lowerCamelCase__       )

					if not isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
							_UpperCAmelCase						:				Dict	    =					[outputs]

					self.assertEqual(len(lowerCamelCase__       )		,     len(self.tool.outputs       )       )







'''simple docstring'''


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


lowerCamelCase__					=   logging.getLogger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		if metric == "rouge2":
				_UpperCAmelCase						:				List[str]	    =					"{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase						:				int	    =					"{val_avg_bleu:.4f}-{step_count}"
		elif metric == "em":
				_UpperCAmelCase						:				str	    =					"{val_avg_em:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase						:				List[Any]	    =					"{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						:				Any	    =					ModelCheckpoint(
		    dirpath=__lowerCAmelCase					,       filename=__lowerCAmelCase					,       monitor=F"""val_{metric}"""					,       mode="max"					,       save_top_k=1					,       every_n_epochs=1					,       )

		return checkpoint_callback






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






class 		lowerCAmelCase__	(	pl.Callback		):




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					{F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
					pl_module.logger.log_metrics(lowerCamelCase__       )




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Union[str, Any]=True       )      ->None:



					'''simple docstring'''







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

					if not save_generations:
							return

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




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->Any:



					'''simple docstring'''







					try:
							_UpperCAmelCase						:				Tuple	    =					pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase						:				int	    =					pl_module.model.num_parameters()

					_UpperCAmelCase						:				int	    =					count_trainable_parameters(lowerCamelCase__       )
					# 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     :   Dict		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule       )      ->int:



					'''simple docstring'''







					save_json(pl_module.metrics		,     pl_module.metrics_save_path       )
					return self._write_logs(lowerCamelCase__		,     lowerCamelCase__		,     "test"       )






			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   Union[str, Any]       )      ->Union[str, 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")



