



import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
          TimesformerForVideoClassification,
          TimesformerModel,
      )
      from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from transformers import VideoMAEImageProcessor

class       _SCREAMING_SNAKE_CASE	:





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Any=1_3     ,  UpperCamelCase :							List[Any]=1_0     ,  UpperCamelCase :							List[str]=3     ,  UpperCamelCase :							Any=2     ,  UpperCamelCase :							Tuple=2     ,  UpperCamelCase :							Tuple=True     ,  UpperCamelCase :							List[str]=True     ,  UpperCamelCase :							Any=3_2     ,  UpperCamelCase :							int=5     ,  UpperCamelCase :							Any=4     ,  UpperCamelCase :							int=3_7     ,  UpperCamelCase :							Optional[int]="gelu"     ,  UpperCamelCase :							List[Any]=0.1     ,  UpperCamelCase :							List[str]=0.1     ,  UpperCamelCase :							Optional[Any]=1_0     ,  UpperCamelCase :							List[Any]=0.0_2     ,  UpperCamelCase :							List[Any]="divided_space_time"     ,  UpperCamelCase :							Optional[int]=None     ,  )->List[Any]:
      __SCREAMING_SNAKE_CASE       :    int       =	parent
      __SCREAMING_SNAKE_CASE       :    str       =	batch_size
      __SCREAMING_SNAKE_CASE       :    Dict       =	image_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_channels
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	patch_size
      __SCREAMING_SNAKE_CASE       :    Dict       =	num_frames
      __SCREAMING_SNAKE_CASE       :    Any       =	is_training
      __SCREAMING_SNAKE_CASE       :    Any       =	use_labels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    str       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    str       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    Tuple       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    int       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	attention_type
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	scope
      __SCREAMING_SNAKE_CASE       :    Tuple       =	num_labels

      # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	(image_size // patch_size) ** 2
      __SCREAMING_SNAKE_CASE       :    List[str]       =	(num_frames) * self.num_patches_per_frame + 1





     def 	__snake_case    (       self :							str				)->str:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	floats_tensor(
          [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]				)

      __SCREAMING_SNAKE_CASE       :    Any       =	None
      if self.use_labels:
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	ids_tensor([self.batch_size]     ,  self.num_labels				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_config()

      return config, pixel_values, labels





     def 	__snake_case    (       self :							Optional[int]				)->str:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	TimesformerConfig(
          image_size=self.image_size     ,  patch_size=self.patch_size     ,  num_channels=self.num_channels     ,  num_frames=self.num_frames     ,  hidden_size=self.hidden_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     ,  initializer_range=self.initializer_range     ,  attention_type=self.attention_type     ,  )
      __SCREAMING_SNAKE_CASE       :    str       =	self.num_labels
      return config





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Optional[int]				)->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	TimesformerModel(config=UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	model(UpperCamelCase				)
      self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	TimesformerForVideoClassification(UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.eval()

      __SCREAMING_SNAKE_CASE       :    Tuple       =	model(UpperCamelCase				)

      # verify the logits shape
      __SCREAMING_SNAKE_CASE       :    Tuple       =	torch.Size((self.batch_size, self.num_labels)				)
      self.parent.assertEqual(result.logits.shape     ,  UpperCamelCase				)






     def 	__snake_case    (       self :							Optional[int]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	config_and_inputs
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{"pixel_values": pixel_values}
      return config, inputs_dict






@require_torch
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
     lowerCAmelCase					=			(
         {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
         if is_torch_available()
         else {}
     )

     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False





     def 	__snake_case    (       self :							Dict				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	TimesformerModelTester(self				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	ConfigTester(
          self     ,  config_class=UpperCamelCase     ,  has_text_modality=UpperCamelCase     ,  hidden_size=3_7				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							int=False				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	copy.deepcopy(UpperCamelCase				)

      if return_labels:
       if model_class in get_values(UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.zeros(
            self.model_tester.batch_size     ,  dtype=torch.long     ,  device=UpperCamelCase				)

      return inputs_dict





     def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
      self.config_tester.run_common_tests()





     @unittest.skip(reason="TimeSformer does not use inputs_embeds"				)
     def 	__snake_case    (       self :							Any				)->Optional[Any]:
      pass





     def 	__snake_case    (       self :							Any				)->Any:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Dict       =	model_class(UpperCamelCase				)
       self.assertIsInstance(model.get_input_embeddings()     ,  (nn.Module)				)
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCamelCase     ,  nn.Linear				)				)





     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Any       =	model_class(UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Dict       =	inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __SCREAMING_SNAKE_CASE       :    Dict       =	[*signature.parameters.keys()]

       __SCREAMING_SNAKE_CASE       :    Tuple       =	["pixel_values"]
       self.assertListEqual(arg_names[:1]     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							Any				)->int:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_video_classification(*UpperCamelCase				)





     @slow
     def 	__snake_case    (       self :							Any				)->Union[str, Any]:
      for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __SCREAMING_SNAKE_CASE       :    str       =	TimesformerModel.from_pretrained(UpperCamelCase				)
       self.assertIsNotNone(UpperCamelCase				)





     def 	__snake_case    (       self :							int				)->Tuple:
      if not self.has_attentions:
       pass

      else:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE       :    Dict       =	True

       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.model_tester.seq_length
        __SCREAMING_SNAKE_CASE       :    List[str]       =	self.model_tester.num_frames

        __SCREAMING_SNAKE_CASE       :    List[Any]       =	True
        __SCREAMING_SNAKE_CASE       :    Tuple       =	False
        __SCREAMING_SNAKE_CASE       :    int       =	True
        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model_class(UpperCamelCase				)
        model.to(UpperCamelCase				)
        model.eval()
        with torch.no_grad():
         __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)
        __SCREAMING_SNAKE_CASE       :    Tuple       =	outputs.attentions
        self.assertEqual(len(UpperCamelCase				)     ,  self.model_tester.num_hidden_layers				)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        __SCREAMING_SNAKE_CASE       :    Dict       =	True
        __SCREAMING_SNAKE_CASE       :    Any       =	model_class(UpperCamelCase				)
        model.to(UpperCamelCase				)
        model.eval()
        with torch.no_grad():
         __SCREAMING_SNAKE_CASE       :    Dict       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)
        __SCREAMING_SNAKE_CASE       :    Tuple       =	outputs.attentions
        self.assertEqual(len(UpperCamelCase				)     ,  self.model_tester.num_hidden_layers				)

        # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
        self.assertListEqual(
            list(attentions[0].shape[-3:]				)     ,  [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]     ,  )
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	len(UpperCamelCase				)

        # Check attention is always last and order is fine
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	True
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	True
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	model_class(UpperCamelCase				)
        model.to(UpperCamelCase				)
        model.eval()
        with torch.no_grad():
         __SCREAMING_SNAKE_CASE       :    Tuple       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)

        self.assertEqual(out_len + 1     ,  len(UpperCamelCase				)				)

        __SCREAMING_SNAKE_CASE       :    str       =	outputs.attentions

        self.assertEqual(len(UpperCamelCase				)     ,  self.model_tester.num_hidden_layers				)

        # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
        self.assertListEqual(
            list(self_attentions[0].shape[-3:]				)     ,  [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]     ,  )






     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      def check_hidden_states_output(UpperCamelCase :							int     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Tuple				):
       __SCREAMING_SNAKE_CASE       :    Dict       =	model_class(UpperCamelCase				)
       model.to(UpperCamelCase				)
       model.eval()

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)

       __SCREAMING_SNAKE_CASE       :    int       =	outputs.hidden_states
       __SCREAMING_SNAKE_CASE       :    int       =	self.model_tester.num_hidden_layers + 1
       self.assertEqual(len(UpperCamelCase				)     ,  UpperCamelCase				)

       __SCREAMING_SNAKE_CASE       :    List[str]       =	self.model_tester.seq_length

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]				)     ,  [seq_length, self.model_tester.hidden_size]     ,  )

      __SCREAMING_SNAKE_CASE       :    int       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Dict       =	True
       check_hidden_states_output(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	True

       check_hidden_states_output(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)






def 	_lowerCAmelCase						(				):
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"		,	filename="eating_spaghetti.npy"		,	repo_type="dataset"     )
 __SCREAMING_SNAKE_CASE       :    int       =	np.load(__lowerCamelCase     )
 return list(__lowerCamelCase     )






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     @cached_property
     def 	__snake_case    (       self :							Optional[int]				)->Optional[Any]:
      # logits were tested with a different mean and std, so we use the same here
      return (
          VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,  image_std=[0.5, 0.5, 0.5]				)
          if is_vision_available()
          else None
      )






     @slow
     def 	__snake_case    (       self :							int				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"				).to(
          UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.default_image_processor
      __SCREAMING_SNAKE_CASE       :    Dict       =	prepare_video()
      __SCREAMING_SNAKE_CASE       :    str       =	image_processor(video[:8]     ,  return_tensors="pt"				).to(UpperCamelCase				)

      # forward pass
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    Any       =	model(**UpperCamelCase				)

      # verify the logits
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	torch.Size((1, 4_0_0)				)
      self.assertEqual(outputs.logits.shape     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]				).to(UpperCamelCase				)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  UpperCamelCase     ,  atol=1E-4				)				)











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


_lowerCamelCase						    =  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowerCamelCase						    =  {
    """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
_lowerCamelCase						    =  {
    """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
}
_lowerCamelCase						    =  sorted(arg_to_scheduler.keys())
_lowerCamelCase						    =  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class       _SCREAMING_SNAKE_CASE	(pl.LightningModule ):





     def __init__(       self :							Dict     ,  UpperCamelCase :							argparse.Namespace     ,  UpperCamelCase :							str=None     ,  UpperCamelCase :							Tuple="base"     ,  UpperCamelCase :							Optional[Any]=None     ,  UpperCamelCase :							List[Any]=None     ,  UpperCamelCase :							Optional[int]=None     ,  **UpperCamelCase :							int     ,  )->Dict:
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	0
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	Path(self.hparams.output_dir				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	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=UpperCamelCase     ,  **UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    PretrainedConfig       =	config

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

      if tokenizer is None:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AutoTokenizer.from_pretrained(
           self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    PreTrainedTokenizer       =	tokenizer
      __SCREAMING_SNAKE_CASE       :    str       =	MODEL_MODES[mode]
      if model is None:
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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=UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model





     def 	__snake_case    (       self :							Dict     ,  *UpperCamelCase :							Dict     ,  **UpperCamelCase :							Union[str, Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_type.from_pretrained(*UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	arg_to_scheduler[self.hparams.lr_scheduler]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	get_schedule_func(
          self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{"scheduler": scheduler, "interval": "step", "frequency": 1}
      return scheduler





     def 	__snake_case    (       self :							int				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model
      __SCREAMING_SNAKE_CASE       :    Any       =	["bias", "LayerNorm.weight"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[
          {
              "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:
       __SCREAMING_SNAKE_CASE       :    Dict       =	Adafactor(
           UpperCamelCase     ,  lr=self.hparams.learning_rate     ,  scale_parameter=UpperCamelCase     ,  relative_step=UpperCamelCase				)

      else:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	AdamW(
           UpperCamelCase     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	optimizer

      __SCREAMING_SNAKE_CASE       :    Any       =	self.get_lr_scheduler()

      return [optimizer], [scheduler]





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							Any				)->Optional[Any]:
      return self.validation_step(UpperCamelCase     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Tuple				)->List[Any]:
      return self.validation_end(UpperCamelCase				)





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	max(1     ,  self.hparams.gpus				)  # TODO: consider num_tpu_cores
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Optional[Any]:
      if stage == "test":
       __SCREAMING_SNAKE_CASE       :    List[str]       =	len(self.test_dataloader().dataset				)
      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.get_dataloader("train"     ,  self.hparams.train_batch_size     ,  shuffle=UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Tuple       =	len(self.train_dataloader().dataset				)





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							int     ,  UpperCamelCase :							bool = False				)->List[Any]:
      raise NotImplementedError("You must implement this for your task"				)





     def 	__snake_case    (       self :							List[Any]				)->Union[str, Any]:
      return self.train_loader





     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      return self.get_dataloader("dev"     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]				)->Any:
      return self.get_dataloader("test"     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase				)





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							List[str]				)->int:
      return os.path.join(
          self.hparams.data_dir     ,  "cached_{}_{}_{}".format(
              UpperCamelCase     ,  list(filter(UpperCamelCase     ,  self.hparams.model_name_or_path.split("/"				)				)				).pop()     ,  str(self.hparams.max_seq_length				)     ,  )     ,  )





     @pl.utilities.rank_zero_only
     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Dict[str, Any]				)->None:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.output_dir.joinpath("best_tfmr"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.step_count
      self.model.save_pretrained(UpperCamelCase				)
      self.tokenizer.save_pretrained(UpperCamelCase				)






     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							Any     ,  UpperCamelCase :							Optional[Any]				)->List[str]:
      parser.add_argument(
          "--model_name_or_path"     ,  default=UpperCamelCase     ,  type=UpperCamelCase     ,  required=UpperCamelCase     ,  help="Path to pretrained model or model identifier from huggingface.co/models"     ,  )
      parser.add_argument(
          "--config_name"     ,  default=""     ,  type=UpperCamelCase     ,  help="Pretrained config name or path if not the same as model_name"				)
      parser.add_argument(
          "--tokenizer_name"     ,  default=UpperCamelCase     ,  type=UpperCamelCase     ,  help="Pretrained tokenizer name or path if not the same as model_name"     ,  )
      parser.add_argument(
          "--cache_dir"     ,  default=str(Path(UpperCamelCase				).parent / "test_run" / "cache"				)     ,  type=UpperCamelCase     ,  help="Where do you want to store the pre-trained models downloaded from huggingface.co"     ,  )
      parser.add_argument(
          "--encoder_layerdrop"     ,  type=UpperCamelCase     ,  help="Encoder layer dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument(
          "--decoder_layerdrop"     ,  type=UpperCamelCase     ,  help="Decoder layer dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument(
          "--dropout"     ,  type=UpperCamelCase     ,  help="Dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument(
          "--attention_dropout"     ,  type=UpperCamelCase     ,  help="Attention dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument("--learning_rate"     ,  default=5E-5     ,  type=UpperCamelCase     ,  help="The initial learning rate for Adam."				)
      parser.add_argument(
          "--lr_scheduler"     ,  default="linear"     ,  choices=UpperCamelCase     ,  metavar=UpperCamelCase     ,  type=UpperCamelCase     ,  help="Learning rate scheduler"     ,  )
      parser.add_argument("--weight_decay"     ,  default=0.0     ,  type=UpperCamelCase     ,  help="Weight decay if we apply some."				)
      parser.add_argument("--adam_epsilon"     ,  default=1E-8     ,  type=UpperCamelCase     ,  help="Epsilon for Adam optimizer."				)
      parser.add_argument("--warmup_steps"     ,  default=0     ,  type=UpperCamelCase     ,  help="Linear warmup over warmup_steps."				)
      parser.add_argument("--num_workers"     ,  default=4     ,  type=UpperCamelCase     ,  help="kwarg passed to DataLoader"				)
      parser.add_argument("--num_train_epochs"     ,  dest="max_epochs"     ,  default=3     ,  type=UpperCamelCase				)
      parser.add_argument("--train_batch_size"     ,  default=3_2     ,  type=UpperCamelCase				)
      parser.add_argument("--eval_batch_size"     ,  default=3_2     ,  type=UpperCamelCase				)
      parser.add_argument("--adafactor"     ,  action="store_true"				)






class       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Tuple				)->Tuple:
      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       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Optional[Any]				)->Tuple:
      # print(pl_module.model.rag)
      for name, param in pl_module.model.rag.named_parameters():
       if param.grad is None:
        print(UpperCamelCase				)






class       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Dict				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	trainer.lr_schedulers[0]["scheduler"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
      pl_module.logger.log_metrics(UpperCamelCase				)





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							pl.Trainer     ,  UpperCamelCase :							pl.LightningModule				)->Union[str, Any]:
      rank_zero_info("***** Validation results *****"				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	trainer.callback_metrics
      # Log results
      for key in sorted(UpperCamelCase				):
       if key not in ["log", "progress_bar"]:
        rank_zero_info("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)






     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							pl.Trainer     ,  UpperCamelCase :							pl.LightningModule				)->Optional[Any]:
      rank_zero_info("***** Test results *****"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	trainer.callback_metrics
      # Log and save results to file
      __SCREAMING_SNAKE_CASE       :    List[str]       =	os.path.join(pl_module.hparams.output_dir     ,  "test_results.txt"				)
      with open(UpperCamelCase     ,  "w"				) as writer:
       for key in sorted(UpperCamelCase				):
        if key not in ["log", "progress_bar"]:
         rank_zero_info("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)
         writer.write("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 parser.add_argument(
     "--output_dir"		,	default=str(Path(__lowerCamelCase     ).parent / "test_run" / "model_checkpoints"     )		,	type=__lowerCamelCase		,	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=__lowerCamelCase		,	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=__lowerCamelCase     )
 parser.add_argument("--max_grad_norm"		,	dest="gradient_clip_val"		,	default=1.0		,	type=__lowerCamelCase		,	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=__lowerCamelCase		,	default=1		,	help="Number of updates steps to accumulate before performing a backward/update pass."		,	)
 parser.add_argument("--seed"		,	type=__lowerCamelCase		,	default=42		,	help="random seed for initialization"     )
 parser.add_argument(
     "--data_dir"		,	default=str(Path(__lowerCamelCase     ).parent / "test_run" / "dummy-train-data"     )		,	type=__lowerCamelCase		,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,	)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     BaseTransformer		,	__lowerCamelCase   :     argparse.Namespace		,	__lowerCamelCase   :     Union[str, Any]=None		,	__lowerCamelCase   :     List[Any]=True		,	__lowerCamelCase   :     List[Any]=[]		,	__lowerCamelCase   :     Any=None		,	__lowerCamelCase   :     Optional[int]=None		,	**__lowerCamelCase   :     Any		,	):



 """simple docstring"""





 pl.seed_everything(args.seed     )

 # init model
 __SCREAMING_SNAKE_CASE       :    List[str]       =	Path(model.hparams.output_dir     )
 odir.mkdir(exist_ok=__lowerCamelCase     )

 # add custom checkpoints
 if checkpoint_callback is None:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	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(__lowerCamelCase     )
 if logging_callback is None:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	LoggingCallback()

 __SCREAMING_SNAKE_CASE       :    str       =	{}

 if args.fpaa:
  __SCREAMING_SNAKE_CASE       :    Any       =	16

 if args.gpus > 1:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"auto"
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"ddp"

 __SCREAMING_SNAKE_CASE       :    List[str]       =	args.accumulate_grad_batches
 __SCREAMING_SNAKE_CASE       :    List[str]       =	None
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"auto"

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	pl.Trainer.from_argparse_args(
     __lowerCamelCase		,	weights_summary=__lowerCamelCase		,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,	logger=__lowerCamelCase		,	val_check_interval=1		,	num_sanity_val_steps=2		,	**__lowerCamelCase		,	)

 if args.do_train:
  trainer.fit(__lowerCamelCase     )

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






