




'''simple docstring'''







from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFCvtForImageClassification, TFCvtModel
   from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


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



           '''simple docstring'''

           UpperCAmelCase			       =		self.config_class(**self.inputs_dict				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''embed_dim'''				)				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''num_heads'''				)				)






class 			_a  :
       def __init__(	self						:	int   ,				lowercase						:	Tuple   ,				lowercase						:	Optional[int]=13   ,				lowercase						:	Optional[Any]=64   ,				lowercase						:	int=3   ,				lowercase						:	Optional[int]=[16, 48, 96]   ,				lowercase						:	int=[1, 3, 6]   ,				lowercase						:	Union[str, Any]=[1, 2, 10]   ,				lowercase						:	Optional[Any]=[7, 3, 3]   ,				lowercase						:	List[Any]=[4, 2, 2]   ,				lowercase						:	str=[2, 1, 1]   ,				lowercase						:	Union[str, Any]=[2, 2, 2]   ,				lowercase						:	str=[False, False, True]   ,				lowercase						:	Tuple=[0.0, 0.0, 0.0]   ,				lowercase						:	List[str]=0.02   ,				lowercase						:	int=1E-12   ,				lowercase						:	Optional[int]=True   ,				lowercase						:	Dict=True   ,				lowercase						:	Optional[Any]=2   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		image_size
           UpperCAmelCase			       =		patch_sizes
           UpperCAmelCase			       =		patch_stride
           UpperCAmelCase			       =		patch_padding
           UpperCAmelCase			       =		is_training
           UpperCAmelCase			       =		use_labels
           UpperCAmelCase			       =		num_labels
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		embed_dim
           UpperCAmelCase			       =		num_heads
           UpperCAmelCase			       =		stride_kv
           UpperCAmelCase			       =		depth
           UpperCAmelCase			       =		cls_token
           UpperCAmelCase			       =		attention_drop_rate
           UpperCAmelCase			       =		initializer_range
           UpperCAmelCase			       =		layer_norm_eps
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           UpperCAmelCase			       =		None
           if self.use_labels:
               # create a random int32 tensor of given shape
               UpperCAmelCase			       =		ids_tensor([self.batch_size]   ,				self.num_labels				)

           UpperCAmelCase			       =		self.get_config()
           return config, pixel_values, labels
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           return CvtConfig(
               image_size=self.image_size   ,				num_labels=self.num_labels   ,				num_channels=self.num_channels   ,				embed_dim=self.embed_dim   ,				num_heads=self.num_heads   ,				patch_sizes=self.patch_sizes   ,				patch_padding=self.patch_padding   ,				patch_stride=self.patch_stride   ,				stride_kv=self.stride_kv   ,				depth=self.depth   ,				cls_token=self.cls_token   ,				attention_drop_rate=self.attention_drop_rate   ,				initializer_range=self.initializer_range   ,				)
       def     A      (	self						:	str   ,				lowercase						:	Tuple   ,				lowercase						:	Tuple   ,				lowercase						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		TFCvtModel(config=lowercase				)
           UpperCAmelCase			       =		model(lowercase   ,				training=lowercase				)
           UpperCAmelCase			       =		(self.image_size, self.image_size)
           UpperCAmelCase       ,     UpperCAmelCase			       =		image_size[0], image_size[1]
           for i in range(len(self.depth				)				):
               UpperCAmelCase			       =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1				)
               UpperCAmelCase			       =		floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1				)
           self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.embed_dim[-1], height, width)				)
       def     A      (	self						:	str   ,				lowercase						:	int   ,				lowercase						:	str   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		TFCvtForImageClassification(lowercase				)
           UpperCAmelCase			       =		model(lowercase   ,				labels=lowercase   ,				training=lowercase				)
           self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)				)







       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.prepare_config_and_inputs()
           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		config_and_inputs
           UpperCAmelCase			       =		{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_tf
class 			_a  (	__a							,							__a							,							unittest.TestCase ):
       __a				:     Any   				=					(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
       __a				:     List[str]   				=					(
           {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
           if is_tf_available()
           else {}
       )
       __a				:     Tuple   				=					False
       __a				:     Dict   				=					False
       __a				:     int   				=					False
       __a				:     Dict   				=					False
       __a				:     int   				=					False
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		TFCvtModelTester(self				)
           UpperCAmelCase			       =		TFCvtConfigTester(self   ,				config_class=lowercase   ,				has_text_modality=lowercase   ,				hidden_size=37				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           self.config_tester.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
       @unittest.skip(reason='''Cvt does not output attentions'''				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''Cvt does not use inputs_embeds'''				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''Cvt does not support input and output embeddings'''				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           pass
       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''				)				) == 0   ,				reason='''TF does not support backprop for grouped convolutions on CPU.'''   ,				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           super().test_dataset_conversion()
       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''				)				) == 0   ,				reason='''TF does not support backprop for grouped convolutions on CPU.'''   ,				)
       @slow
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           super().test_keras_fit()
       @unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'''				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		tf.keras.mixed_precision.Policy('''mixed_float16'''				)
           tf.keras.mixed_precision.set_global_policy(lowercase				)
           super().test_keras_fit()
           tf.keras.mixed_precision.set_global_policy('''float32'''				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)
               UpperCAmelCase			       =		inspect.signature(model.call				)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               UpperCAmelCase			       =		[*signature.parameters.keys()]

               UpperCAmelCase			       =		['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,				lowercase				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           def check_hidden_states_output(lowercase						:	Tuple   ,				lowercase						:	Tuple   ,				lowercase						:	Union[str, Any]				):
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)
               UpperCAmelCase			       =		outputs.hidden_states

               UpperCAmelCase			       =		len(self.model_tester.depth				)
               self.assertEqual(len(lowercase				)   ,				lowercase				)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]				)   ,				[
                       self.model_tester.embed_dim[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]   ,				)

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		True
               check_hidden_states_output(lowercase   ,				lowercase   ,				lowercase				)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase			       =		True

               check_hidden_states_output(lowercase   ,				lowercase   ,				lowercase				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase				)







       @slow
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase			       =		TFCvtModel.from_pretrained(lowercase				)
               self.assertIsNotNone(lowercase				)






def 						snake_case_							():
    UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
    return image







@require_tf
@require_vision
class 			_a  (	unittest.TestCase ):
       @cached_property
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)







       @slow
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

           UpperCAmelCase			       =		self.default_image_processor
           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''tf'''				)

           # forward pass
           UpperCAmelCase			       =		model(**lowercase				)

           # verify the logits
           UpperCAmelCase			       =		tf.TensorShape((1, 1_000)				)
           self.assertEqual(outputs.logits.shape   ,				lowercase				)

           UpperCAmelCase			       =		tf.constant([0.9285, 0.9015, -0.3150]				)
           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()   ,				lowercase   ,				atol=1E-4				)				)










'''simple docstring'''







import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


A     =logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A     ={
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
A     ={
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
A     =sorted(arg_to_scheduler.keys())
A     ='{' + ', '.join(arg_to_scheduler_choices) + '}'


class 			_a  (	pl.LightningModule ):
       def __init__(	self						:	List[str]   ,				lowercase						:	argparse.Namespace   ,				lowercase						:	List[Any]=None   ,				lowercase						:	Dict="base"   ,				lowercase						:	Optional[int]=None   ,				lowercase						:	Dict=None   ,				lowercase						:	Tuple=None   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           super().__init__()
           # TODO: move to self.save_hyperparameters()
           # self.save_hyperparameters()
           # can also expand arguments into trainer signature for easier reading

           self.save_hyperparameters(lowercase				)
           UpperCAmelCase			       =		0
           UpperCAmelCase			       =		Path(self.hparams.output_dir				)
           UpperCAmelCase			       =		self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
               UpperCAmelCase			       =		AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path   ,				**({'''num_labels''': num_labels} if num_labels is not None else {})   ,				cache_dir=lowercase   ,				**lowercase   ,				)
           else:
               UpperCAmelCase			       =		config

           UpperCAmelCase			       =		('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
           for p in extra_model_params:
               if getattr(self.hparams   ,				lowercase   ,				lowercase				):
                   assert hasattr(self.config   ,				lowercase				), f"model config doesn't have a `{p}` attribute"
                   setattr(self.config   ,				lowercase   ,				getattr(self.hparams   ,				lowercase				)				)

           if tokenizer is None:
               UpperCAmelCase			       =		AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,				cache_dir=lowercase   ,				)
           else:
               UpperCAmelCase			       =		tokenizer
           UpperCAmelCase			       =		MODEL_MODES[mode]
           if model is None:
               UpperCAmelCase			       =		self.model_type.from_pretrained(
                   self.hparams.model_name_or_path   ,				from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path				)   ,				config=self.config   ,				cache_dir=lowercase   ,				)
           else:
               UpperCAmelCase			       =		model
       def     A      (	self						:	List[Any]   ,				*lowercase						:	List[str]   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_type.from_pretrained(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		arg_to_scheduler[self.hparams.lr_scheduler]
           UpperCAmelCase			       =		get_schedule_func(
               self.opt   ,				num_warmup_steps=self.hparams.warmup_steps   ,				num_training_steps=self.total_steps()				)
           UpperCAmelCase			       =		{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
           return scheduler
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model
           UpperCAmelCase			       =		['''bias''', '''LayerNorm.weight''']
           UpperCAmelCase			       =		[
               {
                   '''params''': [
                       p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)
                   ],  # check this named paramters
                   '''weight_decay''': self.hparams.weight_decay,
               },
               {
                   '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
                   '''weight_decay''': 0.0,
               },
           ]
           if self.hparams.adafactor:
               UpperCAmelCase			       =		Adafactor(
                   lowercase   ,				lr=self.hparams.learning_rate   ,				scale_parameter=lowercase   ,				relative_step=lowercase				)

           else:
               UpperCAmelCase			       =		AdamW(
                   lowercase   ,				lr=self.hparams.learning_rate   ,				eps=self.hparams.adam_epsilon				)
           UpperCAmelCase			       =		optimizer

           UpperCAmelCase			       =		self.get_lr_scheduler()

           return [optimizer], [scheduler]
       def     A      (	self						:	List[Any]   ,				lowercase						:	int   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           return self.validation_step(lowercase   ,				lowercase				)
       def     A      (	self						:	List[Any]   ,				lowercase						:	Tuple				):



           '''simple docstring'''

           return self.validation_end(lowercase				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		max(1   ,				self.hparams.gpus				)  # TODO: consider num_tpu_cores
           UpperCAmelCase			       =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
           return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
       def     A      (	self						:	List[str]   ,				lowercase						:	Any				):



           '''simple docstring'''

           if stage == "test":
               UpperCAmelCase			       =		len(self.test_dataloader().dataset				)
           else:
               UpperCAmelCase			       =		self.get_dataloader('''train'''   ,				self.hparams.train_batch_size   ,				shuffle=lowercase				)
               UpperCAmelCase			       =		len(self.train_dataloader().dataset				)
       def     A      (	self						:	List[str]   ,				lowercase						:	str   ,				lowercase						:	int   ,				lowercase						:	bool = False				):



           '''simple docstring'''

           raise NotImplementedError('''You must implement this for your task'''				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           return self.train_loader
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return self.get_dataloader('''dev'''   ,				self.hparams.eval_batch_size   ,				shuffle=lowercase				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           return self.get_dataloader('''test'''   ,				self.hparams.eval_batch_size   ,				shuffle=lowercase				)
       def     A      (	self						:	Any   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           return os.path.join(
               self.hparams.data_dir   ,				'''cached_{}_{}_{}'''.format(
                   lowercase   ,				list(filter(lowercase   ,				self.hparams.model_name_or_path.split('''/'''				)				)				).pop()   ,				str(self.hparams.max_seq_length				)   ,				)   ,				)
       @pl.utilities.rank_zero_only
       def     A      (	self						:	List[str]   ,				lowercase						:	Dict[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.output_dir.joinpath('''best_tfmr'''				)
           UpperCAmelCase			       =		self.step_count
           self.model.save_pretrained(lowercase				)
           self.tokenizer.save_pretrained(lowercase				)







       @staticmethod
       def     A      (	lowercase						:	Optional[int]   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           parser.add_argument(
               '''--model_name_or_path'''   ,				default=lowercase   ,				type=lowercase   ,				required=lowercase   ,				help='''Path to pretrained model or model identifier from huggingface.co/models'''   ,				)
           parser.add_argument(
               '''--config_name'''   ,				default=''''''   ,				type=lowercase   ,				help='''Pretrained config name or path if not the same as model_name'''				)
           parser.add_argument(
               '''--tokenizer_name'''   ,				default=lowercase   ,				type=lowercase   ,				help='''Pretrained tokenizer name or path if not the same as model_name'''   ,				)
           parser.add_argument(
               '''--cache_dir'''   ,				default=str(Path(lowercase				).parent / '''test_run''' / '''cache'''				)   ,				type=lowercase   ,				help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''   ,				)
           parser.add_argument(
               '''--encoder_layerdrop'''   ,				type=lowercase   ,				help='''Encoder layer dropout probability (Optional). Goes into model.config'''   ,				)
           parser.add_argument(
               '''--decoder_layerdrop'''   ,				type=lowercase   ,				help='''Decoder layer dropout probability (Optional). Goes into model.config'''   ,				)
           parser.add_argument(
               '''--dropout'''   ,				type=lowercase   ,				help='''Dropout probability (Optional). Goes into model.config'''   ,				)
           parser.add_argument(
               '''--attention_dropout'''   ,				type=lowercase   ,				help='''Attention dropout probability (Optional). Goes into model.config'''   ,				)
           parser.add_argument('''--learning_rate'''   ,				default=5E-5   ,				type=lowercase   ,				help='''The initial learning rate for Adam.'''				)
           parser.add_argument(
               '''--lr_scheduler'''   ,				default='''linear'''   ,				choices=lowercase   ,				metavar=lowercase   ,				type=lowercase   ,				help='''Learning rate scheduler'''   ,				)
           parser.add_argument('''--weight_decay'''   ,				default=0.0   ,				type=lowercase   ,				help='''Weight decay if we apply some.'''				)
           parser.add_argument('''--adam_epsilon'''   ,				default=1E-8   ,				type=lowercase   ,				help='''Epsilon for Adam optimizer.'''				)
           parser.add_argument('''--warmup_steps'''   ,				default=0   ,				type=lowercase   ,				help='''Linear warmup over warmup_steps.'''				)
           parser.add_argument('''--num_workers'''   ,				default=4   ,				type=lowercase   ,				help='''kwarg passed to DataLoader'''				)
           parser.add_argument('''--num_train_epochs'''   ,				dest='''max_epochs'''   ,				default=3   ,				type=lowercase				)
           parser.add_argument('''--train_batch_size'''   ,				default=32   ,				type=lowercase				)
           parser.add_argument('''--eval_batch_size'''   ,				default=32   ,				type=lowercase				)
           parser.add_argument('''--adafactor'''   ,				action='''store_true'''				)






class 			_a  (	pl.Callback ):
       def     A      (	self						:	Dict   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[Any]				):



           '''simple docstring'''

           if (
               trainer.is_global_zero and trainer.global_rank == 0
           ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 			_a  (	pl.Callback ):
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Any				):



           '''simple docstring'''

           for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                   print(lowercase				)






class 			_a  (	pl.Callback ):
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Optional[int]   ,				lowercase						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		trainer.lr_schedulers[0]['''scheduler''']
           UpperCAmelCase			       =		{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
           pl_module.logger.log_metrics(lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	pl.Trainer   ,				lowercase						:	pl.LightningModule				):



           '''simple docstring'''

           rank_zero_info('''***** Validation results *****'''				)
           UpperCAmelCase			       =		trainer.callback_metrics
           # Log results
           for key in sorted(lowercase				):
               if key not in ["log", "progress_bar"]:
                   rank_zero_info('''{} = {}\n'''.format(lowercase   ,				str(metrics[key]				)				)				)







       def     A      (	self						:	Dict   ,				lowercase						:	pl.Trainer   ,				lowercase						:	pl.LightningModule				):



           '''simple docstring'''

           rank_zero_info('''***** Test results *****'''				)
           UpperCAmelCase			       =		trainer.callback_metrics
           # Log and save results to file
           UpperCAmelCase			       =		os.path.join(pl_module.hparams.output_dir   ,				'''test_results.txt'''				)
           with open(lowercase   ,				'''w'''				) as writer:
               for key in sorted(lowercase				):
                   if key not in ["log", "progress_bar"]:
                       rank_zero_info('''{} = {}\n'''.format(lowercase   ,				str(metrics[key]				)				)				)
                       writer.write('''{} = {}\n'''.format(lowercase   ,				str(metrics[key]				)				)				)






def 						snake_case_							(_a	:							int    ,   _a	:							Optional[Any]     ):
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        '''--output_dir'''    ,   default=str(Path(_a     ).parent / '''test_run''' / '''model_checkpoints'''     )    ,   type=_a    ,   help='''The output directory where the model predictions and checkpoints will be written.'''    ,   )
    parser.add_argument(
        '''--fp16'''    ,   action='''store_true'''    ,   help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''    ,   )

    parser.add_argument(
        '''--fp16_opt_level'''    ,   type=_a    ,   default='''O2'''    ,   help=(
            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
            '''See details at https://nvidia.github.io/apex/amp.html'''
        )    ,   )
    parser.add_argument('''--n_tpu_cores'''    ,   dest='''tpu_cores'''    ,   type=_a     )
    parser.add_argument('''--max_grad_norm'''    ,   dest='''gradient_clip_val'''    ,   default=1.0    ,   type=_a    ,   help='''Max gradient norm'''     )
    parser.add_argument('''--do_train'''    ,   action='''store_true'''    ,   help='''Whether to run training.'''     )
    parser.add_argument('''--do_predict'''    ,   action='''store_true'''    ,   help='''Whether to run predictions on the test set.'''     )
    parser.add_argument(
        '''--gradient_accumulation_steps'''    ,   dest='''accumulate_grad_batches'''    ,   type=_a    ,   default=1    ,   help='''Number of updates steps to accumulate before performing a backward/update pass.'''    ,   )
    parser.add_argument('''--seed'''    ,   type=_a    ,   default=4_2    ,   help='''random seed for initialization'''     )
    parser.add_argument(
        '''--data_dir'''    ,   default=str(Path(_a     ).parent / '''test_run''' / '''dummy-train-data'''     )    ,   type=_a    ,   help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,   )







def 						snake_case_							(_a	:							BaseTransformer    ,   _a	:							argparse.Namespace    ,   _a	:							List[Any]=None    ,   _a	:							Tuple=True    ,   _a	:							int=[]    ,   _a	:							Any=None    ,   _a	:							int=None    ,   **_a	:							Optional[Any]    ,   ):
    pl.seed_everything(args.seed     )

    # init model
    UpperCAmelCase			       =		Path(model.hparams.output_dir     )
    odir.mkdir(exist_ok=_a     )

    # add custom checkpoints
    if checkpoint_callback is None:
        UpperCAmelCase			       =		pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir    ,   prefix='''checkpoint'''    ,   monitor='''val_loss'''    ,   mode='''min'''    ,   save_top_k=1     )
    if early_stopping_callback:
        extra_callbacks.append(_a     )
    if logging_callback is None:
        UpperCAmelCase			       =		LoggingCallback()

    UpperCAmelCase			       =		{}

    if args.fpaa:
        UpperCAmelCase			       =		1_6

    if args.gpus > 1:
        UpperCAmelCase			       =		'''auto'''
        UpperCAmelCase			       =		'''ddp'''

    UpperCAmelCase			       =		args.accumulate_grad_batches
    UpperCAmelCase			       =		None
    UpperCAmelCase			       =		'''auto'''

    UpperCAmelCase			       =		pl.Trainer.from_argparse_args(
        _a    ,   weights_summary=_a    ,   callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]    ,   logger=_a    ,   val_check_interval=1    ,   num_sanity_val_steps=2    ,   **_a    ,   )

    if args.do_train:
        trainer.fit(_a     )

    else:
        print('''RAG modeling tests with new set functions successfuly executed!'''     )
    return trainer




