



"""simple docstring"""
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ConditionalDetrImageProcessor
class     a		(     unittest.TestCase				):



  def __init__(  self       :    int				,						lowerCAmelCase       :    Union[str, Any]				,						lowerCAmelCase       :    Dict=7				,						lowerCAmelCase       :    Union[str, Any]=3				,						lowerCAmelCase       :    int=30				,						lowerCAmelCase       :    List[str]=400				,						lowerCAmelCase       :    List[Any]=True				,						lowerCAmelCase       :    Union[str, Any]=None				,						lowerCAmelCase       :    Union[str, Any]=True				,						lowerCAmelCase       :    Any=[0.5, 0.5, 0.5]				,						lowerCAmelCase       :    int=[0.5, 0.5, 0.5]				,						lowerCAmelCase       :    List[Any]=True				,						lowerCAmelCase       :    Tuple=1 / 255				,						lowerCAmelCase       :    List[str]=True				,						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       int   	=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
     SCREAMING_SNAKE_CASE_:       Dict   	=parent
     SCREAMING_SNAKE_CASE_:       Any   	=batch_size
     SCREAMING_SNAKE_CASE_:       List[Any]   	=num_channels
     SCREAMING_SNAKE_CASE_:       int   	=min_resolution
     SCREAMING_SNAKE_CASE_:       List[Any]   	=max_resolution
     SCREAMING_SNAKE_CASE_:       Any   	=do_resize
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=size
     SCREAMING_SNAKE_CASE_:       int   	=do_normalize
     SCREAMING_SNAKE_CASE_:       int   	=image_mean
     SCREAMING_SNAKE_CASE_:       Tuple   	=image_std
     SCREAMING_SNAKE_CASE_:       List[Any]   	=do_rescale
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=rescale_factor
     SCREAMING_SNAKE_CASE_:       Any   	=do_pad



  def 		lowerCamelCase__				(  self       :    List[str]						)   ->    List[str]:


     '''simple docstring'''




     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }





  def 		lowerCamelCase__				(  self       :    Union[str, Any]				,						lowerCAmelCase       :    Dict				,						lowerCAmelCase       :    Optional[Any]=False						)   ->    Tuple:


     '''simple docstring'''




     if not batched:
        SCREAMING_SNAKE_CASE_:       str   	=image_inputs[0]
        if isinstance(lowerCAmelCase				,						Image.Image						):
           SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Dict   	=image.size
        else:
           SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Any   	=image.shape[1], image.shape[2]
        if w < h:
           SCREAMING_SNAKE_CASE_:       int   	=int(self.size["""shortest_edge"""] * h / w						)
           SCREAMING_SNAKE_CASE_:       Any   	=self.size["""shortest_edge"""]
        elif w > h:
           SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.size["""shortest_edge"""]
           SCREAMING_SNAKE_CASE_:       List[str]   	=int(self.size["""shortest_edge"""] * w / h						)
        else:
           SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.size["""shortest_edge"""]
           SCREAMING_SNAKE_CASE_:       List[Any]   	=self.size["""shortest_edge"""]

     else:
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=[]
        for image in image_inputs:
           SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       List[Any]   	=self.get_expected_values([image]						)
           expected_values.append((expected_height, expected_width)						)
        SCREAMING_SNAKE_CASE_:       Tuple   	=max(lowerCAmelCase				,						key=lambda lowerCAmelCase						: item[0]						)[0]
        SCREAMING_SNAKE_CASE_:       str   	=max(lowerCAmelCase				,						key=lambda lowerCAmelCase						: item[1]						)[1]

     return expected_height, expected_width







@require_torch
@require_vision
class     a		(     __a		,					unittest.TestCase				):
  UpperCamelCase		: List[Any]     =    ConditionalDetrImageProcessor if is_vision_available() else None



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       str   	=ConditionalDetrImageProcessingTester(self						)



  @property
  def 		lowerCamelCase__				(  self       :    int						)   ->    List[str]:


     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(lowerCAmelCase				,						"""image_mean"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase				,						"""image_std"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase				,						"""do_normalize"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase				,						"""do_resize"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase				,						"""size"""						)						)



  def 		lowerCamelCase__				(  self       :    Dict						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size				,						{"""shortest_edge""": 18, """longest_edge""": 1333}						)
     self.assertEqual(image_processor.do_pad				,						lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:       Dict   	=self.image_processing_class.from_dict(
         self.image_processor_dict				,						size=42				,						max_size=84				,						pad_and_return_pixel_mask=lowerCAmelCase						)
     self.assertEqual(image_processor.size				,						{"""shortest_edge""": 42, """longest_edge""": 84}						)
     self.assertEqual(image_processor.do_pad				,						lowerCAmelCase						)



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


     '''simple docstring'''




     pass



  def 		lowerCamelCase__				(  self       :    Optional[int]						)   ->    Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     SCREAMING_SNAKE_CASE_:       str   	=prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowerCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase				,						Image.Image						)

     # Test not batched input
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=image_processing(image_inputs[0]				,						return_tensors="""pt"""						).pixel_values

     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       str   	=self.image_processor_tester.get_expected_values(lowerCAmelCase						)

     self.assertEqual(
         encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

     # Test batched
     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Any   	=self.image_processor_tester.get_expected_values(lowerCAmelCase				,						batched=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:       List[str]   	=image_processing(lowerCAmelCase				,						return_tensors="""pt"""						).pixel_values
     self.assertEqual(
         encoded_images.shape				,						(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,						)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE_:       int   	=prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowerCAmelCase				,						numpify=lowerCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase				,						np.ndarray						)

     # Test not batched input
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=image_processing(image_inputs[0]				,						return_tensors="""pt"""						).pixel_values

     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       List[Any]   	=self.image_processor_tester.get_expected_values(lowerCAmelCase						)

     self.assertEqual(
         encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

     # Test batched
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=image_processing(lowerCAmelCase				,						return_tensors="""pt"""						).pixel_values

     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Any   	=self.image_processor_tester.get_expected_values(lowerCAmelCase				,						batched=lowerCAmelCase						)

     self.assertEqual(
         encoded_images.shape				,						(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,						)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowerCAmelCase				,						torchify=lowerCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase				,						torch.Tensor						)

     # Test not batched input
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=image_processing(image_inputs[0]				,						return_tensors="""pt"""						).pixel_values

     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Dict   	=self.image_processor_tester.get_expected_values(lowerCAmelCase						)

     self.assertEqual(
         encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

     # Test batched
     SCREAMING_SNAKE_CASE_:       str   	=image_processing(lowerCAmelCase				,						return_tensors="""pt"""						).pixel_values

     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       int   	=self.image_processor_tester.get_expected_values(lowerCAmelCase				,						batched=lowerCAmelCase						)

     self.assertEqual(
         encoded_images.shape				,						(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,						)



  @slow
  def 		lowerCamelCase__				(  self       :    Dict						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""				,						"""r"""						) as f:
        SCREAMING_SNAKE_CASE_:       List[str]   	=json.loads(f.read()						)

     SCREAMING_SNAKE_CASE_:       Any   	={"""image_id""": 3_9769, """annotations""": target}

     # encode them
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""						)
     SCREAMING_SNAKE_CASE_:       int   	=image_processing(images=lowerCAmelCase				,						annotations=lowerCAmelCase				,						return_tensors="""pt"""						)

     # verify pixel values
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=torch.Size([1, 3, 800, 1066]						)
     self.assertEqual(encoding["""pixel_values"""].shape				,						lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:       List[str]   	=torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,						lowerCAmelCase				,						atol=1E-4						)						)

     # verify area
     SCREAMING_SNAKE_CASE_:       Any   	=torch.tensor([5_8_8_7.9_6_0_0, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,						lowerCAmelCase						)						)
     # verify boxes
     SCREAMING_SNAKE_CASE_:       int   	=torch.Size([6, 4]						)
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,						lowerCAmelCase				,						atol=1E-3						)						)
     # verify image_id
     SCREAMING_SNAKE_CASE_:       Tuple   	=torch.tensor([3_9769]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,						lowerCAmelCase						)						)
     # verify is_crowd
     SCREAMING_SNAKE_CASE_:       Any   	=torch.tensor([0, 0, 0, 0, 0, 0]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,						lowerCAmelCase						)						)
     # verify class_labels
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=torch.tensor([75, 75, 63, 65, 17, 17]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,						lowerCAmelCase						)						)
     # verify orig_size
     SCREAMING_SNAKE_CASE_:       List[Any]   	=torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,						lowerCAmelCase						)						)
     # verify size
     SCREAMING_SNAKE_CASE_:       str   	=torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,						lowerCAmelCase						)						)





  @slow
  def 		lowerCamelCase__				(  self       :    str						)   ->    Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""				,						"""r"""						) as f:
        SCREAMING_SNAKE_CASE_:       Any   	=json.loads(f.read()						)

     SCREAMING_SNAKE_CASE_:       str   	={"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

     SCREAMING_SNAKE_CASE_:       List[Any]   	=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

     # encode them
     SCREAMING_SNAKE_CASE_:       List[str]   	=ConditionalDetrImageProcessor(format="""coco_panoptic"""						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=image_processing(images=lowerCAmelCase				,						annotations=lowerCAmelCase				,						masks_path=lowerCAmelCase				,						return_tensors="""pt"""						)

     # verify pixel values
     SCREAMING_SNAKE_CASE_:       List[Any]   	=torch.Size([1, 3, 800, 1066]						)
     self.assertEqual(encoding["""pixel_values"""].shape				,						lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,						lowerCAmelCase				,						atol=1E-4						)						)

     # verify area
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,						lowerCAmelCase						)						)
     # verify boxes
     SCREAMING_SNAKE_CASE_:       Dict   	=torch.Size([6, 4]						)
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       int   	=torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,						lowerCAmelCase				,						atol=1E-3						)						)
     # verify image_id
     SCREAMING_SNAKE_CASE_:       int   	=torch.tensor([3_9769]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,						lowerCAmelCase						)						)
     # verify is_crowd
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=torch.tensor([0, 0, 0, 0, 0, 0]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,						lowerCAmelCase						)						)
     # verify class_labels
     SCREAMING_SNAKE_CASE_:       List[Any]   	=torch.tensor([17, 17, 63, 75, 75, 93]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,						lowerCAmelCase						)						)
     # verify masks
     SCREAMING_SNAKE_CASE_:       List[Any]   	=82_2873
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,						lowerCAmelCase						)
     # verify orig_size
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,						lowerCAmelCase						)						)
     # verify size
     SCREAMING_SNAKE_CASE_:       Any   	=torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,						lowerCAmelCase						)						)









'''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




