


"""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	(		_UpperCamelCase      ):

			'''simple docstring'''




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


A     :   List[str]			    =			logging.getLogger(__name__)


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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=_UpperCamelCase    , filename=_UpperCamelCase    , monitor=f"val_{metric}"    , mode="max"    , save_top_k=3    , every_n_epochs=1    , )
			return checkpoint_callback


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


class   _UpperCamelCase  (   pl.Callback		):
							'''simple docstring'''







							def 			snake_case				(     self    ,   __a    ,   __a	):
										__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 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a=True	):
										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 			snake_case				(     self    ,   __a    ,   __a	):
										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 			snake_case				(     self    ,   __a    ,   __a	):
										save_json(pl_module.metrics    ,   pl_module.metrics_save_path	)
										return self._write_logs(__a    ,   __a    ,   "test"	)



							@rank_zero_only
							def 			snake_case				(     self    ,   __a    ,   __a	):
										save_json(pl_module.metrics    ,   pl_module.metrics_save_path	)
										# Uncommenting this will save val generations
										# return self._write_logs(trainer, pl_module, "valid")









import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   tempfile.mkdtemp()

						a					     =   [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "的",
						    "价",
						    "格",
						    "是",
						    "15",
						    "便",
						    "alex",
						    "##andra",
						    "，",
						    "。",
						    "-",
						    "t",
						    "shirt",
						]
						a					     =   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]		)		)

						a					     =   {
						    "do_resize": True,
						    "size": {"height": 224, "width": 224},
						    "do_center_crop": True,
						    "crop_size": {"height": 18, "width": 18},
						    "do_normalize": True,
						    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
						    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
						    "do_convert_rgb": True,
						}
						a					     =   os.path.join(self.tmpdirname		,       lowerCamelCase_		)
						with open(self.image_processor_file		,       "w"		,       encoding="utf-8"		) as fp:
											json.dump(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return BertTokenizerFast.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						shutil.rmtree(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   [np.random.randint(255		,       size=(3, 30, 400)		,       dtype=np.uinta		)]

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

						return image_inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_tokenizer()
						a					     =   self.get_rust_tokenizer()
						a					     =   self.get_image_processor()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_slow.save_pretrained(self.tmpdirname		)
						a					     =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,       use_fast=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_fast.save_pretrained(self.tmpdirname		)
						a					     =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

						self.assertEqual(processor_slow.tokenizer.get_vocab()		,       tokenizer_slow.get_vocab()		)
						self.assertEqual(processor_fast.tokenizer.get_vocab()		,       tokenizer_fast.get_vocab()		)
						self.assertEqual(tokenizer_slow.get_vocab()		,       tokenizer_fast.get_vocab()		)
						self.assertIsInstance(processor_slow.tokenizer		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.tokenizer		,       lowerCamelCase_		)

						self.assertEqual(processor_slow.image_processor.to_json_string()		,       image_processor.to_json_string()		)
						self.assertEqual(processor_fast.image_processor.to_json_string()		,       image_processor.to_json_string()		)
						self.assertIsInstance(processor_slow.image_processor		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.image_processor		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						a					     =   self.get_tokenizer(cls_token="(CLS)"		,       sep_token="(SEP)"		)
						a					     =   self.get_image_processor(do_normalize=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname		,       cls_token="(CLS)"		,       sep_token="(SEP)"		,       do_normalize=lowerCamelCase_		)

						self.assertEqual(processor.tokenizer.get_vocab()		,       tokenizer_add_kwargs.get_vocab()		)
						self.assertIsInstance(processor.tokenizer		,       lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   self.prepare_image_inputs()

						a					     =   image_processor(lowerCamelCase_		,       return_tensors="np"		)
						a					     =   processor(images=lowerCamelCase_		,       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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"

						a					     =   processor(text=lowerCamelCase_		)

						a					     =   tokenizer(lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"
						a					     =   self.prepare_image_inputs()

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

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

						# test if it raises when no input is passed
						with pytest.raises(lowerCamelCase_		):
											processor()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

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

						a					     =   processor.batch_decode(lowerCamelCase_		)
						a					     =   tokenizer.batch_decode(lowerCamelCase_		)

						self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"
						a					     =   self.prepare_image_inputs()

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

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