
"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    RemBertConfig,
					    RemBertForCausalLM,
					    RemBertForMaskedLM,
					    RemBertForMultipleChoice,
					    RemBertForQuestionAnswering,
					    RemBertForSequenceClassification,
					    RemBertForTokenClassification,
					    RemBertModel,
					)
					from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_=13						,					UpperCAmelCase_=7						,					UpperCAmelCase_=True						,					UpperCAmelCase_=True						,					UpperCAmelCase_=True						,					UpperCAmelCase_=True						,					UpperCAmelCase_=99						,					UpperCAmelCase_=32						,					UpperCAmelCase_=18						,					UpperCAmelCase_=43						,					UpperCAmelCase_=5						,					UpperCAmelCase_=4						,					UpperCAmelCase_=37						,					UpperCAmelCase_="gelu"						,					UpperCAmelCase_=0.1						,					UpperCAmelCase_=0.1						,					UpperCAmelCase_=512						,					UpperCAmelCase_=16						,					UpperCAmelCase_=2						,					UpperCAmelCase_=0.02						,					UpperCAmelCase_=3						,					UpperCAmelCase_=4						,					UpperCAmelCase_=None						,					)						->				Union[str, Any]:
			lowerCamelCase	:     Tuple            =       parent
			lowerCamelCase	:     Any            =       batch_size
			lowerCamelCase	:     List[str]            =       seq_length
			lowerCamelCase	:     Dict            =       is_training
			lowerCamelCase	:     List[str]            =       use_input_mask
			lowerCamelCase	:     Tuple            =       use_token_type_ids
			lowerCamelCase	:     List[str]            =       use_labels
			lowerCamelCase	:     Optional[Any]            =       vocab_size
			lowerCamelCase	:     Any            =       hidden_size
			lowerCamelCase	:     int            =       input_embedding_size
			lowerCamelCase	:     List[str]            =       output_embedding_size
			lowerCamelCase	:     Union[str, Any]            =       num_hidden_layers
			lowerCamelCase	:     str            =       num_attention_heads
			lowerCamelCase	:     Dict            =       intermediate_size
			lowerCamelCase	:     Dict            =       hidden_act
			lowerCamelCase	:     Union[str, Any]            =       hidden_dropout_prob
			lowerCamelCase	:     Union[str, Any]            =       attention_probs_dropout_prob
			lowerCamelCase	:     Tuple            =       max_position_embeddings
			lowerCamelCase	:     List[str]            =       type_vocab_size
			lowerCamelCase	:     List[str]            =       type_sequence_label_size
			lowerCamelCase	:     Union[str, Any]            =       initializer_range
			lowerCamelCase	:     Dict            =       num_labels
			lowerCamelCase	:     Optional[int]            =       num_choices
			lowerCamelCase	:     List[str]            =       scope




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     Optional[int]            =       ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size   )

			lowerCamelCase	:     Any            =       None
			if self.use_input_mask:
					lowerCamelCase	:     str            =       random_attention_mask([self.batch_size, self.seq_length]   )

			lowerCamelCase	:     Optional[Any]            =       None
			if self.use_token_type_ids:
					lowerCamelCase	:     Optional[int]            =       ids_tensor([self.batch_size, self.seq_length]						,					self.type_vocab_size   )

			lowerCamelCase	:     int            =       None
			lowerCamelCase	:     Any            =       None
			lowerCamelCase	:     Tuple            =       None
			if self.use_labels:
					lowerCamelCase	:     List[str]            =       ids_tensor([self.batch_size]						,					self.type_sequence_label_size   )
					lowerCamelCase	:     int            =       ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels   )
					lowerCamelCase	:     str            =       ids_tensor([self.batch_size]						,					self.num_choices   )

			lowerCamelCase	:     Optional[Any]            =       RemBertConfig(
			    vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					input_embedding_size=self.input_embedding_size						,					output_embedding_size=self.output_embedding_size						,					num_hidden_layers=self.num_hidden_layers						,					num_attention_heads=self.num_attention_heads						,					intermediate_size=self.intermediate_size						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					max_position_embeddings=self.max_position_embeddings						,					type_vocab_size=self.type_vocab_size						,					is_decoder=UpperCAmelCase__						,					initializer_range=self.initializer_range						,					)

			return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




	def      _UpperCamelCase       (					self   )						->				Dict:
			(
			    lowerCamelCase						
			)	:     List[Any]            =       self.prepare_config_and_inputs()

			lowerCamelCase	:     Dict            =       True
			lowerCamelCase	:     Tuple            =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
			lowerCamelCase	:     Tuple            =       ids_tensor([self.batch_size, self.seq_length]						,					vocab_size=2   )

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    input_mask,
			    sequence_labels,
			    token_labels,
			    choice_labels,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Union[str, Any]:
			lowerCamelCase	:     Optional[Any]            =       RemBertModel(config=UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     Any            =       model(UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__   )
			lowerCamelCase	:     List[str]            =       model(UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__   )
			lowerCamelCase	:     Tuple            =       model(UpperCAmelCase__   )
			self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				Optional[int]:
			lowerCamelCase	:     str            =       True
			lowerCamelCase	:     str            =       RemBertModel(UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     Tuple            =       model(
			    UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					encoder_hidden_states=UpperCAmelCase__						,					encoder_attention_mask=UpperCAmelCase__						,					)
			lowerCamelCase	:     Optional[int]            =       model(
			    UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					encoder_hidden_states=UpperCAmelCase__						,					)
			lowerCamelCase	:     Union[str, Any]            =       model(UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__   )
			self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				List[Any]:
			lowerCamelCase	:     str            =       RemBertForCausalLM(config=UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     Tuple            =       model(UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					labels=UpperCAmelCase__   )
			self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				List[Any]:
			lowerCamelCase	:     List[Any]            =       RemBertForMaskedLM(config=UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     Tuple            =       model(UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					labels=UpperCAmelCase__   )
			self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				List[str]:
			lowerCamelCase	:     List[Any]            =       True
			lowerCamelCase	:     int            =       True
			lowerCamelCase	:     Optional[Any]            =       RemBertForCausalLM(config=UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()

			# first forward pass
			lowerCamelCase	:     Union[str, Any]            =       model(
			    UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					encoder_hidden_states=UpperCAmelCase__						,					encoder_attention_mask=UpperCAmelCase__						,					use_cache=UpperCAmelCase__						,					)
			lowerCamelCase	:     Optional[Any]            =       outputs.past_key_values

			# create hypothetical multiple next token and extent to next_input_ids
			lowerCamelCase	:     List[str]            =       ids_tensor((self.batch_size, 3)						,					config.vocab_size   )
			lowerCamelCase	:     List[str]            =       ids_tensor((self.batch_size, 3)						,					vocab_size=2   )

			# append to next input_ids and
			lowerCamelCase	:     int            =       torch.cat([input_ids, next_tokens]						,					dim=-1   )
			lowerCamelCase	:     Union[str, Any]            =       torch.cat([input_mask, next_mask]						,					dim=-1   )

			lowerCamelCase	:     List[str]            =       model(
			    UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					encoder_hidden_states=UpperCAmelCase__						,					encoder_attention_mask=UpperCAmelCase__						,					output_hidden_states=UpperCAmelCase__						,					)["""hidden_states"""][0]
			lowerCamelCase	:     Dict            =       model(
			    UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					encoder_hidden_states=UpperCAmelCase__						,					encoder_attention_mask=UpperCAmelCase__						,					past_key_values=UpperCAmelCase__						,					output_hidden_states=UpperCAmelCase__						,					)["""hidden_states"""][0]

			# select random slice
			lowerCamelCase	:     Any            =       ids_tensor((1,)						,					output_from_past.shape[-1]   ).item()
			lowerCamelCase	:     List[str]            =       output_from_no_past[:, -3:, random_slice_idx].detach()
			lowerCamelCase	:     Optional[int]            =       output_from_past[:, :, random_slice_idx].detach()

			self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

			# test that outputs are equal for slice
			self.parent.assertTrue(torch.allclose(UpperCAmelCase__						,					UpperCAmelCase__						,					atol=1E-3   )   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Any:
			lowerCamelCase	:     str            =       RemBertForQuestionAnswering(config=UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     int            =       model(
			    UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					start_positions=UpperCAmelCase__						,					end_positions=UpperCAmelCase__						,					)
			self.parent.assertEqual(result.start_logits.shape						,					(self.batch_size, self.seq_length)   )
			self.parent.assertEqual(result.end_logits.shape						,					(self.batch_size, self.seq_length)   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[Any]:
			lowerCamelCase	:     Optional[int]            =       self.num_labels
			lowerCamelCase	:     int            =       RemBertForSequenceClassification(UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     Union[str, Any]            =       model(UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					labels=UpperCAmelCase__   )
			self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Tuple:
			lowerCamelCase	:     int            =       self.num_labels
			lowerCamelCase	:     List[str]            =       RemBertForTokenClassification(config=UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     str            =       model(UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					labels=UpperCAmelCase__   )
			self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase	:     Dict            =       self.num_choices
			lowerCamelCase	:     Any            =       RemBertForMultipleChoice(config=UpperCAmelCase__   )
			model.to(UpperCAmelCase__   )
			model.eval()
			lowerCamelCase	:     Any            =       input_ids.unsqueeze(1   ).expand(-1						,					self.num_choices						,					-1   ).contiguous()
			lowerCamelCase	:     List[Any]            =       token_type_ids.unsqueeze(1   ).expand(-1						,					self.num_choices						,					-1   ).contiguous()
			lowerCamelCase	:     List[str]            =       input_mask.unsqueeze(1   ).expand(-1						,					self.num_choices						,					-1   ).contiguous()
			lowerCamelCase	:     List[str]            =       model(
			    UpperCAmelCase__						,					attention_mask=UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					labels=UpperCAmelCase__						,					)
			self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_choices)   )




	def      _UpperCamelCase       (					self   )						->				Dict:
			lowerCamelCase	:     Tuple            =       self.prepare_config_and_inputs()
			(
			    lowerCamelCase						
			)	:     List[Any]            =       config_and_inputs
			lowerCamelCase	:     List[Any]            =       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
			return config, inputs_dict






@require_torch
class 			_lowercase  (							__UpperCAmelCase							,			__UpperCAmelCase							,			unittest.TestCase						):
	lowercase_								=      (
	    (
	        RemBertModel,
	        RemBertForMaskedLM,
	        RemBertForCausalLM,
	        RemBertForMultipleChoice,
	        RemBertForQuestionAnswering,
	        RemBertForSequenceClassification,
	        RemBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase_								=      (RemBertForCausalLM,) if is_torch_available() else ()
	lowercase_								=      (
	    {
	        """feature-extraction""": RemBertModel,
	        """fill-mask""": RemBertForMaskedLM,
	        """question-answering""": RemBertForQuestionAnswering,
	        """text-classification""": RemBertForSequenceClassification,
	        """text-generation""": RemBertForCausalLM,
	        """token-classification""": RemBertForTokenClassification,
	        """zero-shot""": RemBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     Any            =       RemBertModelTester(self   )
			lowerCamelCase	:     int            =       ConfigTester(self						,					config_class=UpperCAmelCase__						,					hidden_size=37   )




	def      _UpperCamelCase       (					self   )						->				Tuple:
			self.config_tester.run_common_tests()




	def      _UpperCamelCase       (					self   )						->				Union[str, Any]:
			lowerCamelCase	:     List[str]            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				Dict:
			lowerCamelCase	:     str            =       self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					lowerCamelCase	:     str            =       type
					self.model_tester.create_and_check_model(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				Optional[Any]:
			lowerCamelCase	:     Tuple            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				Dict:
			lowerCamelCase	:     Tuple            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase	:     List[str]            =       self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase	:     Any            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     Any            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				Tuple:
			lowerCamelCase	:     Optional[Any]            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase	:     Dict            =       self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase__   )




	def      _UpperCamelCase       (					self   )						->				Any:
			(
			    lowerCamelCase						
			)	:     List[Any]            =       self.model_tester.prepare_config_and_inputs_for_decoder()

			lowerCamelCase	:     Tuple            =       None

			self.model_tester.create_and_check_model_as_decoder(
			    UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					)




	@slow
	def      _UpperCamelCase       (					self   )						->				int:







			for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase	:     Optional[Any]            =       RemBertModel.from_pretrained(UpperCAmelCase__   )
					self.assertIsNotNone(UpperCAmelCase__   )






@require_torch
class 			_lowercase  (							unittest.TestCase						):




	@slow
	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     Dict            =       RemBertModel.from_pretrained('google/rembert'   )
			lowerCamelCase	:     List[str]            =       torch.tensor([[312, 56498, 313, 2125, 313]]   )
			lowerCamelCase	:     Dict            =       torch.tensor([[0, 0, 0, 1, 1]]   )
			with torch.no_grad():
					lowerCamelCase	:     List[str]            =       model(UpperCAmelCase__						,					token_type_ids=UpperCAmelCase__						,					output_hidden_states=UpperCAmelCase__   )

			lowerCamelCase	:     Optional[int]            =       1152

			lowerCamelCase	:     Union[str, Any]            =       torch.Size((1, 5, hidden_size)   )
			self.assertEqual(output['last_hidden_state'].shape						,					UpperCAmelCase__   )

			lowerCamelCase	:     List[Any]            =       torch.tensor(
			    [
			        [
			            [0.0754, -0.2022, 0.1904],
			            [-0.3354, -0.3692, -0.4791],
			            [-0.2314, -0.6729, -0.0749],
			            [-0.0396, -0.3105, -0.4234],
			            [-0.1571, -0.0525, 0.5353],
			        ]
			    ]   )

			# Running on the original tf implementation gives slightly different results here.
			# Not clear why this variations is present
			# TODO: Find reason for discrepancy
			# expected_original_implementation = [[
			#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
			#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
			#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
			#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
			#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
			# ]]

			self.assertTrue(torch.allclose(output['last_hidden_state'][:, :, :3]						,					UpperCAmelCase__						,					atol=1E-4   )   )









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 import save_json






def        __lowercase   (  _A      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   Dict	  = filter(lambda _A      : p.requires_grad    ,     model.parameters()      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = sum([np.prod(p.size()      ) for p in model_parameters]      )
      return params


UpperCAmelCase__			:  List[Any]					     =     logging.getLogger(__name__)







class 							a__							( pl.Callback							):







  """simple docstring"""




  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Any		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
        pl_module.logger.log_metrics(UpperCAmelCase__		)


  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Tuple=True		)       ->None:
        """simple docstring"""







        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"		)
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = Path(pl_module.hparams.output_dir		)
        if type_path == "test":
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = od / """test_results.txt"""
              SCREAMING_SNAKE_CASE			:   int	  = 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.
              SCREAMING_SNAKE_CASE			:   Any	  = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
              SCREAMING_SNAKE_CASE			:   int	  = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
              results_file.parent.mkdir(exist_ok=UpperCAmelCase__		)
              generations_file.parent.mkdir(exist_ok=UpperCAmelCase__		)
        with open(UpperCAmelCase__					,					"""a+"""		) as writer:
              for key in sorted(UpperCAmelCase__		):
                    if key in ["log", "progress_bar", "preds"]:
                          continue
                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = metrics[key]
                    if isinstance(UpperCAmelCase__					,					torch.Tensor		):
                          SCREAMING_SNAKE_CASE			:   Optional[int]	  = val.item()
                    SCREAMING_SNAKE_CASE			:   List[str]	  = f"{key}: {val:.6f}\n"
                    writer.write(UpperCAmelCase__		)

        if not save_generations:
              return

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


  @rank_zero_only
  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->int:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE			:   int	  = pl_module.model.model.num_parameters()
        except AttributeError:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE			:   Dict	  = count_trainable_parameters(UpperCAmelCase__		)
        # 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 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule		)       ->Optional[int]:
        """simple docstring"""







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




  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """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")






def        __lowercase   (  _A    ,     _A    ,     _A=1    ,     _A=False      )  ->    Tuple:
      if metric == "rouge2":
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            SCREAMING_SNAKE_CASE			:   Tuple	  = """{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "loss":
            SCREAMING_SNAKE_CASE			:   Any	  = """{val_avg_loss:.4f}-{step_count}"""
      else:
            raise NotImplementedError(
                F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
                """ this function."""      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = ModelCheckpoint(
          dirpath=_A    ,     filename=_A    ,     monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     save_top_k=_A    ,     )
      return checkpoint_callback







def        __lowercase   (  _A    ,     _A      )  ->    Optional[int]:
      return EarlyStopping(
          monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     patience=_A    ,     verbose=_A    ,     )




