




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       tempfile.mkdtemp()

											# fmt: off
											__lowerCAmelCase				   =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
											# fmt: on
											__lowerCAmelCase				   =       os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["vocab_file"]			)
											with open(self.vocab_file		,			"w"		,			encoding="utf-8"			) as vocab_writer:
																vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)

											__lowerCAmelCase				   =       {
											    "do_resize": True,
											    "size": {"height": 1_8, "width": 1_8},
											    "do_normalize": True,
											    "image_mean": [0.5, 0.5, 0.5],
											    "image_std": [0.5, 0.5, 0.5],
											}
											__lowerCAmelCase				   =       os.path.join(self.tmpdirname		,			_A			)
											with open(self.image_processor_file		,			"w"		,			encoding="utf-8"			) as fp:
																json.dump(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			**_A			):




											"""simple docstring"""



											return BertTokenizer.from_pretrained(self.tmpdirname		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			**_A			):




											"""simple docstring"""



											return ViTImageProcessor.from_pretrained(self.tmpdirname		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											shutil.rmtree(self.tmpdirname			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [np.random.randint(2_5_5		,			size=(3, 3_0, 4_0_0)		,			dtype=np.uinta			)]

											__lowerCAmelCase				   =       [Image.fromarray(np.moveaxis(_A		,			0		,			-1			)			) for x in image_inputs]

											return image_inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.get_tokenizer()
											__lowerCAmelCase				   =       self.get_image_processor()

											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor(tokenizer=_A		,			image_processor=_A			)

											processor.save_pretrained(self.tmpdirname			)
											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname			)

											self.assertEqual(processor.tokenizer.get_vocab()		,			tokenizer.get_vocab()			)
											self.assertIsInstance(processor.tokenizer		,			(BertTokenizer, BertTokenizerFast)			)

											self.assertEqual(processor.image_processor.to_json_string()		,			image_processor.to_json_string()			)
											self.assertIsInstance(processor.image_processor		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor(
											    tokenizer=self.get_tokenizer()		,			image_processor=self.get_image_processor()			)
											processor.save_pretrained(self.tmpdirname			)

											__lowerCAmelCase				   =       self.get_tokenizer(bos_token="(BOS)"		,			eos_token="(EOS)"			)
											__lowerCAmelCase				   =       self.get_image_processor(do_normalize=_A		,			padding_value=1.0			)

											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor.from_pretrained(
											    self.tmpdirname		,			bos_token="(BOS)"		,			eos_token="(EOS)"		,			do_normalize=_A		,			padding_value=1.0			)

											self.assertEqual(processor.tokenizer.get_vocab()		,			tokenizer_add_kwargs.get_vocab()			)
											self.assertIsInstance(processor.tokenizer		,			(BertTokenizer, BertTokenizerFast)			)

											self.assertEqual(processor.image_processor.to_json_string()		,			image_processor_add_kwargs.to_json_string()			)
											self.assertIsInstance(processor.image_processor		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.get_image_processor()
											__lowerCAmelCase				   =       self.get_tokenizer()

											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor(tokenizer=_A		,			image_processor=_A			)

											__lowerCAmelCase				   =       self.prepare_image_inputs()

											__lowerCAmelCase				   =       image_processor(_A		,			return_tensors="np"			)
											__lowerCAmelCase				   =       processor(images=_A		,			return_tensors="np"			)

											for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum()		,			input_processor[key].sum()		,			delta=1E-2			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.get_image_processor()
											__lowerCAmelCase				   =       self.get_tokenizer()

											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor(tokenizer=_A		,			image_processor=_A			)

											__lowerCAmelCase				   =       "lower newer"

											__lowerCAmelCase				   =       processor(text=_A			)

											__lowerCAmelCase				   =       tokenizer(_A			)

											for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]		,			encoded_processor[key]			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.get_image_processor()
											__lowerCAmelCase				   =       self.get_tokenizer()

											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor(tokenizer=_A		,			image_processor=_A			)

											__lowerCAmelCase				   =       "lower newer"
											__lowerCAmelCase				   =       self.prepare_image_inputs()

											__lowerCAmelCase				   =       processor(text=_A		,			images=_A			)

											self.assertListEqual(list(inputs.keys()			)		,			["input_ids", "token_type_ids", "attention_mask", "pixel_values"]			)

											# test if it raises when no input is passed
											with self.assertRaises(_A			):
																processor()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.get_image_processor()
											__lowerCAmelCase				   =       self.get_tokenizer()

											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor(tokenizer=_A		,			image_processor=_A			)

											__lowerCAmelCase				   =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											__lowerCAmelCase				   =       processor.batch_decode(_A			)
											__lowerCAmelCase				   =       tokenizer.batch_decode(_A			)

											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.get_image_processor()
											__lowerCAmelCase				   =       self.get_tokenizer()

											__lowerCAmelCase				   =       VisionTextDualEncoderProcessor(tokenizer=_A		,			image_processor=_A			)

											__lowerCAmelCase				   =       "lower newer"
											__lowerCAmelCase				   =       self.prepare_image_inputs()

											__lowerCAmelCase				   =       processor(text=_A		,			images=_A			)

											self.assertListEqual(list(inputs.keys()			)		,			processor.model_input_names			)










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


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					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=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 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					return EarlyStopping(
					    monitor=F"""val_{metric}"""      ,      mode="min" if "loss" in metric else "max"      ,      patience=SCREAMING_SNAKE_CASE_      ,      verbose=SCREAMING_SNAKE_CASE_      ,      )


class 			a__    (					pl.Callback      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A=True			):




											"""simple docstring"""



											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=_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
																					__lowerCAmelCase				   =       metrics[key]
																					if isinstance(_A		,			torch.Tensor			):
																										__lowerCAmelCase				   =       val.item()
																					__lowerCAmelCase				   =       f"""{key}: {val:.6f}\n"""
																					writer.write(_A			)

											if not save_generations:
																return

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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""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")




