

'''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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     List[str]:
       A_		   = filter(lambda UpperCAmelCase__			: p.requires_grad,    model.parameters()			)
       A_		   = sum([np.prod(p.size()			) for p in model_parameters]			)
       return params


__lowerCamelCase						    =      logging.getLogger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Union[str, Any]:

       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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     int:
       return EarlyStopping(
           monitor=F'''val_{metric}''',    mode="""min""" if """loss""" in metric else """max""",    patience=UpperCAmelCase__,    verbose=UpperCAmelCase__,    )







class    A__ ( pl.Callback     ):



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Optional[int]:





           '''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(A_    )



    @rank_zero_only
    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=True    )  -> None:





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

           if not save_generations:
                  return

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



    @rank_zero_only
    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           try:
                  A_		   = pl_module.model.model.num_parameters()
           except AttributeError:
                  A_		   = pl_module.model.num_parameters()

           A_		   = 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						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           save_json(pl_module.metrics						,			pl_module.metrics_save_path    )
           return self._write_logs(A_						,			A_						,			"""test"""    )







    @rank_zero_only
    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> 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")




'''simple docstring'''







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 CLIPImageProcessor, CLIPProcessor


@require_vision
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> 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(UpperCamelCase__						,			range(len(UpperCamelCase__    )    )    )    )
           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(UpperCamelCase__    ) + """\n"""    )
           with open(self.merges_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(UpperCamelCase__    )    )

           A_		   = {
               """do_resize""": True,
               """size""": 20,
               """do_center_crop""": True,
               """crop_size""": 18,
               """do_normalize""": True,
               """image_mean""": [0.48145466, 0.4578275, 0.40821073],
               """image_std""": [0.26862954, 0.26130258, 0.27577711],
           }
           A_		   = os.path.join(self.tmpdirname						,			UpperCamelCase__    )
           with open(self.image_processor_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  json.dump(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> str:





           '''simple docstring'''
           return CLIPTokenizer.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> Any:





           '''simple docstring'''
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           return CLIPImageProcessor.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           shutil.rmtree(self.tmpdirname    )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = [np.random.randint(255						,			size=(3, 30, 400)						,			dtype=np.uinta    )]

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

           return image_inputs



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





           '''simple docstring'''
           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()
           A_		   = self.get_image_processor()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )
           processor_slow.save_pretrained(self.tmpdirname    )
           A_		   = CLIPProcessor.from_pretrained(self.tmpdirname						,			use_fast=UpperCamelCase__    )

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )
           processor_fast.save_pretrained(self.tmpdirname    )
           A_		   = CLIPProcessor.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						,			UpperCamelCase__    )
           self.assertIsInstance(processor_fast.tokenizer						,			UpperCamelCase__    )

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



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = CLIPProcessor(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=UpperCamelCase__						,			padding_value=1.0    )

           A_		   = CLIPProcessor.from_pretrained(
               self.tmpdirname						,			bos_token="""(BOS)"""						,			eos_token="""(EOS)"""						,			do_normalize=UpperCamelCase__						,			padding_value=1.0    )

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

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



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = self.prepare_image_inputs()

           A_		   = image_processor(UpperCamelCase__						,			return_tensors="""np"""    )
           A_		   = processor(images=UpperCamelCase__						,			return_tensors="""np"""    )

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



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





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = """lower newer"""

           A_		   = processor(text=UpperCamelCase__    )

           A_		   = tokenizer(UpperCamelCase__    )

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



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

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

           A_		   = processor(text=UpperCamelCase__						,			images=UpperCamelCase__    )

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

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



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





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

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

           A_		   = processor.batch_decode(UpperCamelCase__    )
           A_		   = tokenizer.batch_decode(UpperCamelCase__    )

           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )







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





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

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

           A_		   = processor(text=UpperCamelCase__						,			images=UpperCamelCase__    )

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