



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import CLIPSegProcessor, ViTImageProcessor


@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     tempfile.mkdtemp()

								# fmt: off
								A__	  =     ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
								# fmt: on
								A__	  =     dict(zip(_snake_case					,       range(len(_snake_case       )       )       )       )
								A__	  =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
								A__	  =     {'unk_token': '<unk>'}

								A__	  =     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['vocab_file']       )
								A__	  =     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['merges_file']       )
								with open(self.vocab_file					,       'w'					,       encoding='utf-8'       ) as fp:
											fp.write(json.dumps(_snake_case       ) + '\n'       )
								with open(self.merges_file					,       'w'					,       encoding='utf-8'       ) as fp:
											fp.write('\n'.join(_snake_case       )       )

								A__	  =     {
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 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],
								}
								A__	  =     os.path.join(self.tmpdirname					,       _snake_case       )
								with open(self.image_processor_file					,       'w'					,       encoding='utf-8'       ) as fp:
											json.dump(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Union[str, Any]					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								return CLIPTokenizer.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							Union[str, Any]					,       **_snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							List[str]					,       **_snake_case       :							int       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname       )



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     [np.random.randint(2_55					,       size=(3, 30, 4_00)					,       dtype=np.uinta       )]

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

								return image_inputs



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


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

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_slow.save_pretrained(self.tmpdirname       )
								A__	  =     CLIPSegProcessor.from_pretrained(self.tmpdirname					,       use_fast=_snake_case       )

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_fast.save_pretrained(self.tmpdirname       )
								A__	  =     CLIPSegProcessor.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					,       _snake_case       )
								self.assertIsInstance(processor_fast.tokenizer					,       _snake_case       )

								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					,       _snake_case       )
								self.assertIsInstance(processor_fast.image_processor					,       _snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     CLIPSegProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								A__	  =     self.get_tokenizer(bos_token='(BOS)'					,       eos_token='(EOS)'       )
								A__	  =     self.get_image_processor(do_normalize=_snake_case					,       padding_value=1.0       )

								A__	  =     CLIPSegProcessor.from_pretrained(
								    self.tmpdirname					,       bos_token='(BOS)'					,       eos_token='(EOS)'					,       do_normalize=_snake_case					,       padding_value=1.0       )

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

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



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     self.prepare_image_inputs()

								A__	  =     image_processor(_snake_case					,       return_tensors='np'       )
								A__	  =     processor(images=_snake_case					,       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      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'

								A__	  =     processor(text=_snake_case       )

								A__	  =     tokenizer(_snake_case       )

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



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(text=_snake_case					,       images=_snake_case       )

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

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



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     self.prepare_image_inputs()
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(images=_snake_case					,       visual_prompt=_snake_case       )

								self.assertListEqual(list(inputs.keys()       )					,       ['pixel_values', 'conditional_pixel_values']       )

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



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

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

								A__	  =     processor.batch_decode(_snake_case       )
								A__	  =     tokenizer.batch_decode(_snake_case       )

								self.assertListEqual(_snake_case					,       _snake_case       )









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


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			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}'
			elif metric == "loss":
						A__	  =     '{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.'       )

			A__	  =     ModelCheckpoint(
			    dirpath=__UpperCamelCase			,						filename=__UpperCamelCase			,						monitor=f'''val_{metric}'''			,						mode='max'			,						save_top_k=1			,						every_n_epochs=1			,						)

			return checkpoint_callback


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''			,						mode='min' if 'loss' in metric else 'max'			,						patience=__UpperCamelCase			,						verbose=__UpperCamelCase			,						)






class   __lowerCAmelCase  (		pl.Callback   ):
					"""simple docstring"""






					def      _a				(  self       :							Dict					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							str       ):






								"""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(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule					,       _snake_case       :							str					,       _snake_case       :							Optional[Any]=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=_snake_case       )
											generations_file.parent.mkdir(exist_ok=_snake_case       )
								with open(_snake_case					,       'a+'       ) as writer:
											for key in sorted(_snake_case       ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														A__	  =     metrics[key]
														if isinstance(_snake_case					,       torch.Tensor       ):
																	A__	  =     val.item()
														A__	  =     F'''{key}: {val:.6f}\n'''
														writer.write(_snake_case       )

								if not save_generations:
											return

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



					@rank_zero_only
					def      _a				(  self       :							Dict					,       _snake_case       :							List[str]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


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

								A__	  =     count_trainable_parameters(_snake_case       )
								# 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       :							int					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								return self._write_logs(_snake_case					,       _snake_case					,       'test'       )



					@rank_zero_only
					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							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")




