


"""simple docstring"""







import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       MODEL_FOR_MASKED_LM_MAPPING
    UpperCAmelCase       :							Any	=       TF_MODEL_FOR_MASKED_LM_MAPPING






    def 	__snake_case							(					self				:    Tuple):
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      if is_torch_available():
        import torch

        torch.cuda.empty_cache()






    @require_tf
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Any      							=			pipeline(task="fill-mask"				, model="sshleifer/tiny-distilroberta-base"				, top_k=2				, framework="tf")
      a						:		Any      							=			unmasker("My name is <mask>")
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				, decimals=6)				, [
              {"sequence": "My name is grouped", "score": 2.1e-05, "token": 38015, "token_str": " grouped"},
              {"sequence": "My name is accuser", "score": 2.1e-05, "token": 25506, "token_str": " accuser"},
          ]				, )

      a						:		Any      							=			unmasker("The largest city in France is <mask>")
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				, decimals=6)				, [
              {
                  "sequence": "The largest city in France is grouped",
                  "score": 2.1e-05,
                  "token": 38015,
                  "token_str": " grouped",
              },
              {
                  "sequence": "The largest city in France is accuser",
                  "score": 2.1e-05,
                  "token": 25506,
                  "token_str": " accuser",
              },
          ]				, )

      a						:		Dict      							=			unmasker("My name is <mask>"				, targets=[" Patrick", " Clara", " Teven"]				, top_k=3)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				, decimals=6)				, [
              {"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
              {"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
              {"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
          ]				, )






    @require_torch
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		str      							=			pipeline(task="fill-mask"				, model="sshleifer/tiny-distilroberta-base"				, top_k=2				, framework="pt")

      a						:		Optional[Any]      							=			unmasker("My name is <mask>")
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				, decimals=6)				, [
              {"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
              {"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
          ]				, )

      a						:		Dict      							=			unmasker("The largest city in France is <mask>")
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				, decimals=6)				, [
              {
                  "sequence": "The largest city in France is Maul",
                  "score": 2.2e-05,
                  "token": 35676,
                  "token_str": " Maul",
              },
              {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
          ]				, )

      a						:		Optional[Any]      							=			unmasker("My name is <mask>"				, targets=[" Patrick", " Clara", " Teven"]				, top_k=3)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				, decimals=6)				, [
              {"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
              {"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
              {"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
          ]				, )

      a						:		Optional[int]      							=			unmasker("My name is <mask> <mask>"				, top_k=2)

      self.assertEqual(
          nested_simplify(__UpperCAmelCase				, decimals=6)				, [
              [
                  {
                      "score": 2.2e-05,
                      "token": 35676,
                      "token_str": " Maul",
                      "sequence": "<s>My name is Maul<mask></s>",
                  },
                  {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
              ],
              [
                  {
                      "score": 2.2e-05,
                      "token": 35676,
                      "token_str": " Maul",
                      "sequence": "<s>My name is<mask> Maul</s>",
                  },
                  {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
              ],
          ]				, )






    @require_torch_gpu
    def 	__snake_case							(					self				:    int):
      a						:		int      							=			pipeline("fill-mask"				, model="hf-internal-testing/tiny-random-distilbert"				, device=0				, framework="pt")

      # convert model to fp16
      pipe.model.half()

      a						:		Tuple      							=			pipe("Paris is the [MASK] of France.")
      # We actually don't care about the result, we just want to make sure
      # it works, meaning the float16 tensor got casted back to float32
      # for postprocessing.
      self.assertIsInstance(__UpperCAmelCase				, __UpperCAmelCase)






    @slow
    @require_torch
    def 	__snake_case							(					self				:    Dict):
      a						:		Any      							=			pipeline(task="fill-mask"				, model="distilroberta-base"				, top_k=2				, framework="pt")
      self.run_large_test(__UpperCAmelCase)






    @slow
    @require_tf
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		List[Any]      							=			pipeline(task="fill-mask"				, model="distilroberta-base"				, top_k=2				, framework="tf")
      self.run_large_test(__UpperCAmelCase)






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    Optional[int]):
      a						:		Tuple      							=			unmasker("My name is <mask>")
      self.assertEqual(
          nested_simplify(__UpperCAmelCase)				, [
              {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
              {"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
          ]				, )
      a						:		List[str]      							=			unmasker("The largest city in France is <mask>")
      self.assertEqual(
          nested_simplify(__UpperCAmelCase)				, [
              {
                  "sequence": "The largest city in France is Paris",
                  "score": 0.251,
                  "token": 2201,
                  "token_str": " Paris",
              },
              {
                  "sequence": "The largest city in France is Lyon",
                  "score": 0.214,
                  "token": 12790,
                  "token_str": " Lyon",
              },
          ]				, )

      a						:		int      							=			unmasker("My name is <mask>"				, targets=[" Patrick", " Clara", " Teven"]				, top_k=3)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase)				, [
              {"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
              {"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
              {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
          ]				, )






    @require_torch
    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Any      							=			pipeline(task="fill-mask"				, model="sshleifer/tiny-distilroberta-base"				, framework="pt")
      a						:		Dict      							=			None
      a						:		str      							=			None
      self.run_pipeline_test(__UpperCAmelCase				, [])






    @require_tf
    def 	__snake_case							(					self				:    Tuple):
      a						:		Optional[int]      							=			pipeline(task="fill-mask"				, model="sshleifer/tiny-distilroberta-base"				, framework="tf")
      a						:		Tuple      							=			None
      a						:		Optional[Any]      							=			None
      self.run_pipeline_test(__UpperCAmelCase				, [])






    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Optional[Any]):
      if tokenizer is None or tokenizer.mask_token_id is None:
        self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)")

      a						:		Optional[Any]      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase)
      a						:		Dict      							=			[
          f'''This is another {tokenizer.mask_token} test''',
      ]
      return fill_masker, examples






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Optional[Any]):
      a						:		Union[str, Any]      							=			fill_masker.tokenizer
      a						:		Union[str, Any]      							=			fill_masker.model

      a						:		Union[str, Any]      							=			fill_masker(
          f'''This is a {tokenizer.mask_token}'''				, )
      self.assertEqual(
          __UpperCAmelCase				, [
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
          ]				, )

      a						:		List[Any]      							=			fill_masker([f'''This is a {tokenizer.mask_token}'''])
      self.assertEqual(
          __UpperCAmelCase				, [
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
          ]				, )

      a						:		List[str]      							=			fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.'''])
      self.assertEqual(
          __UpperCAmelCase				, [
              [
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              ],
              [
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              ],
          ]				, )

      with self.assertRaises(__UpperCAmelCase):
        fill_masker([None])
      # No mask_token is not supported
      with self.assertRaises(__UpperCAmelCase):
        fill_masker("This is")

      self.run_test_top_k(__UpperCAmelCase				, __UpperCAmelCase)
      self.run_test_targets(__UpperCAmelCase				, __UpperCAmelCase)
      self.run_test_top_k_targets(__UpperCAmelCase				, __UpperCAmelCase)
      self.fill_mask_with_duplicate_targets_and_top_k(__UpperCAmelCase				, __UpperCAmelCase)
      self.fill_mask_with_multiple_masks(__UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int):
      a						:		str      							=			tokenizer.get_vocab()
      a						:		Any      							=			sorted(vocab.keys())[:2]
      # Pipeline argument
      a						:		List[str]      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase				, targets=__UpperCAmelCase)
      a						:		Optional[int]      							=			fill_masker(f'''This is a {tokenizer.mask_token}''')
      self.assertEqual(
          __UpperCAmelCase				, [
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
          ]				, )
      a						:		int      							=			{vocab[el] for el in targets}
      self.assertEqual({el["token"] for el in outputs}				, __UpperCAmelCase)
      a						:		Tuple      							=			[tokenizer.decode([x]) for x in target_ids]
      self.assertEqual({el["token_str"] for el in outputs}				, set(__UpperCAmelCase))

      # Call argument
      a						:		Union[str, Any]      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase)
      a						:		str      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, targets=__UpperCAmelCase)
      self.assertEqual(
          __UpperCAmelCase				, [
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
          ]				, )
      a						:		Any      							=			{vocab[el] for el in targets}
      self.assertEqual({el["token"] for el in outputs}				, __UpperCAmelCase)
      a						:		List[Any]      							=			[tokenizer.decode([x]) for x in target_ids]
      self.assertEqual({el["token_str"] for el in outputs}				, set(__UpperCAmelCase))

      # Score equivalence
      a						:		Any      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, targets=__UpperCAmelCase)
      a						:		Optional[Any]      							=			[top_mask["token_str"] for top_mask in outputs]
      a						:		List[Any]      							=			[top_mask["score"] for top_mask in outputs]

      # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
      if set(__UpperCAmelCase) == set(__UpperCAmelCase):
        a						:		int      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, targets=__UpperCAmelCase)
        a						:		List[str]      							=			[top_mask["score"] for top_mask in unmasked_targets]
        self.assertEqual(nested_simplify(__UpperCAmelCase)				, nested_simplify(__UpperCAmelCase))

      # Raises with invalid
      with self.assertRaises(__UpperCAmelCase):
        a						:		int      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, targets=[])
      # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
      if "" not in tokenizer.get_vocab():
        with self.assertRaises(__UpperCAmelCase):
          a						:		List[Any]      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, targets=[""])
        with self.assertRaises(__UpperCAmelCase):
          a						:		Union[str, Any]      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, targets="")






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    str):
      a						:		List[str]      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase				, top_k=2)
      a						:		str      							=			fill_masker(f'''This is a {tokenizer.mask_token}''')
      self.assertEqual(
          __UpperCAmelCase				, [
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
          ]				, )

      a						:		Union[str, Any]      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase)
      a						:		Optional[int]      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, top_k=2)
      self.assertEqual(
          __UpperCAmelCase				, [
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
          ]				, )
      self.assertEqual(nested_simplify(__UpperCAmelCase)				, nested_simplify(__UpperCAmelCase))






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    Optional[int]				, __UpperCAmelCase				:    str):
      a						:		Tuple      							=			tokenizer.get_vocab()
      a						:		List[str]      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase)

      # top_k=2, ntargets=3
      a						:		Dict      							=			sorted(vocab.keys())[:3]
      a						:		int      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, top_k=2				, targets=__UpperCAmelCase)

      # If we use the most probably targets, and filter differently, we should still
      # have the same results
      a						:		Union[str, Any]      							=			[el["token_str"] for el in sorted(__UpperCAmelCase				, key=lambda __UpperCAmelCase: x["score"]				, reverse=__UpperCAmelCase)]
      # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
      if set(__UpperCAmelCase).issubset(__UpperCAmelCase):
        a						:		Tuple      							=			fill_masker(f'''This is a {tokenizer.mask_token}'''				, top_k=3				, targets=__UpperCAmelCase)
        # They should yield exactly the same result
        self.assertEqual(nested_simplify(__UpperCAmelCase)				, nested_simplify(__UpperCAmelCase))






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Any):
      a						:		str      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase)
      a						:		str      							=			tokenizer.get_vocab()
      # String duplicates + id duplicates
      a						:		List[Any]      							=			sorted(vocab.keys())[:3]
      a						:		Dict      							=			[targets[0], targets[1], targets[0], targets[2], targets[1]]
      a						:		Union[str, Any]      							=			fill_masker(f'''My name is {tokenizer.mask_token}'''				, targets=__UpperCAmelCase				, top_k=10)

      # The target list contains duplicates, so we can't output more
      # than them
      self.assertEqual(len(__UpperCAmelCase)				, 3)






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    List[Any]):
      a						:		Dict      							=			FillMaskPipeline(model=__UpperCAmelCase				, tokenizer=__UpperCAmelCase)

      a						:		Tuple      							=			fill_masker(
          f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''				, top_k=2)
      self.assertEqual(
          __UpperCAmelCase				, [
              [
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              ],
              [
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              ],
              [
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
                  {"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
              ],
          ]				, )













'''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				( lowercase						:      Dict						)	->	Any:
				_a								= filter(lambda lowercase						: p.requires_grad    ,							model.parameters()						)
				_a								= sum([np.prod(p.size()						) for p in model_parameters]						)
				return params


lowerCAmelCase_					:    int      	=			logging.getLogger(__name__)






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	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}"
				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=lowercase    ,							filename=lowercase    ,							monitor=F'val_{metric}'    ,							mode="max"    ,							save_top_k=3    ,							every_n_epochs=1    ,							)
				return checkpoint_callback






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				return EarlyStopping(
				    monitor=F'val_{metric}'    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase    ,							verbose=lowercase    ,							)



class 				__SCREAMING_SNAKE_CASE  (pl.Callback      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								_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   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					,				__a     :					str					,				__a     :					Tuple=True					):
								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   UpperCamelCase__   (  self     :					int					,				__a     :					List[Any]					,				__a     :					Union[str, Any]					):
								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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								return self._write_logs(__a					,				__a					,				"test"					)




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