





import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device




def 						A_			(  lowercase_  )			->						Tuple:
       _snake_case      :							Optional[int]     				=						{}
       for name in unet.attn_processors.keys():
              _snake_case      :							Union[str, Any]     				=						None if name.endswith('''attn1.processor'''  ) else unet.config.cross_attention_dim
              if name.startswith('''mid_block'''  ):
                     _snake_case      :							Tuple     				=						unet.config.block_out_channels[-1]
              elif name.startswith('''up_blocks'''  ):
                     _snake_case      :							List[str]     				=						int(name[len('''up_blocks.'''  )]  )
                     _snake_case      :							List[Any]     				=						list(reversed(unet.config.block_out_channels  )  )[block_id]
              elif name.startswith('''down_blocks'''  ):
                     _snake_case      :							Dict     				=						int(name[len('''down_blocks.'''  )]  )
                     _snake_case      :							List[str]     				=						unet.config.block_out_channels[block_id]
              _snake_case      :							Optional[int]     				=						(
                  LoRAAttnProcessor2_0 if hasattr(lowercase_      ,		'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
              )
              _snake_case      :							List[Any]     				=						lora_attn_processor_class(
                  hidden_size=lowercase_      ,		cross_attention_dim=lowercase_  )
       _snake_case      :							Optional[int]     				=						AttnProcsLayers(lowercase_  )
       return lora_attn_procs, unet_lora_layers




def 						A_			(  lowercase_  )			->						Union[str, Any]:
       _snake_case      :							Optional[int]     				=						{}
       _snake_case      :							int     				=						(
           LoRAAttnProcessor2_0 if hasattr(lowercase_      ,		'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
       )
       for name, module in text_encoder.named_modules():
              if name.endswith(lowercase_  ):
                     _snake_case      :							Dict     				=						lora_attn_processor_class(
                         hidden_size=module.out_proj.out_features      ,		cross_attention_dim=lowercase_  )
       return text_lora_attn_procs




def 						A_			(  lowercase_  )			->						str:
       _snake_case      :							str     				=						create_text_encoder_lora_attn_procs(lowercase_  )
       _snake_case      :							List[str]     				=						AttnProcsLayers(lowercase_  )
       return text_encoder_lora_layers




def 						A_			(  lowercase_      ,		lowercase_=False  )			->						int:
       for _, attn_proc in text_lora_attn_procs.items():
              # set up.weights
              for layer_name, layer_module in attn_proc.named_modules():
                     if layer_name.endswith('''_lora'''  ):
                            _snake_case      :							int     				=						(
                                torch.randn_like(layer_module.up.weight  )
                                if randn_weight
                                else torch.zeros_like(layer_module.up.weight  )
                            )
                            _snake_case      :							str     				=						torch.nn.Parameter(lowercase_  )

class 				A    (unittest.TestCase				):



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



             '''simple docstring'''

             torch.manual_seed(0					)
             _snake_case      :							Optional[Any]     				=						UNetaDConditionModel(
                 block_out_channels=(32, 64)     ,  layers_per_block=2     ,  sample_size=32     ,  in_channels=4     ,  out_channels=4     ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,  cross_attention_dim=32     ,  )
             _snake_case      :							List[Any]     				=						DDIMScheduler(
                 beta_start=0.0_0085     ,  beta_end=0.012     ,  beta_schedule='''scaled_linear'''     ,  clip_sample=lowercase_     ,  set_alpha_to_one=lowercase_     ,  steps_offset=1     ,  )
             torch.manual_seed(0					)
             _snake_case      :							Tuple     				=						AutoencoderKL(
                 block_out_channels=[32, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,  latent_channels=4     ,  )
             _snake_case      :							Optional[Any]     				=						CLIPTextConfig(
                 bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=32     ,  intermediate_size=37     ,  layer_norm_eps=1E-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=1000     ,  )
             _snake_case      :							Optional[int]     				=						CLIPTextModel(lowercase_					)
             _snake_case      :							Optional[Any]     				=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

             _snake_case				,    _snake_case      :							Dict     				=						create_unet_lora_layers(lowercase_					)
             _snake_case      :							str     				=						create_text_encoder_lora_layers(lowercase_					)

             _snake_case      :							List[Any]     				=						{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             _snake_case      :							int     				=						{
                 '''unet_lora_layers''': unet_lora_layers,
                 '''text_encoder_lora_layers''': text_encoder_lora_layers,
                 '''unet_lora_attn_procs''': unet_lora_attn_procs,
             }
             return pipeline_components, lora_components



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



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						1
             _snake_case      :							int     				=						10
             _snake_case      :							List[Any]     				=						4
             _snake_case      :							List[str]     				=						(32, 32)

             _snake_case      :							Dict     				=						torch.manual_seed(0					)
             _snake_case      :							Union[str, Any]     				=						floats_tensor((batch_size, num_channels) + sizes					)
             _snake_case      :							Any     				=						torch.randint(1     ,  lowercase_     ,  size=(batch_size, sequence_length)     ,  generator=lowercase_					)

             _snake_case      :							List[Any]     				=						{
                 '''prompt''': '''A painting of a squirrel eating a burger''',
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''guidance_scale''': 6.0,
                 '''output_type''': '''numpy''',
             }

             return noise, input_ids, pipeline_inputs

             # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							Dict     				=						77

             _snake_case      :							Tuple     				=						torch.randint(2     ,  56     ,  size=(1, max_seq_length)     ,  generator=torch.manual_seed(0					)					)

             _snake_case      :							Optional[int]     				=						{}
             _snake_case      :							Optional[int]     				=						inputs
             return prepared_inputs



      def    __a (					self     ,  lowercase_					)      ->   Tuple:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							List[str]     				=						self.get_dummy_components()
             LoraLoaderMixin.save_lora_weights(
                 save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
             self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Optional[int]     				=						self.get_dummy_components()
             _snake_case      :							Optional[Any]     				=						StableDiffusionPipeline(**lowercase_					)
             _snake_case      :							Optional[Any]     				=						sd_pipe.to(lowercase_					)
             sd_pipe.set_progress_bar_config(disable=lowercase_					)

             _snake_case				,    _snake_case				,    _snake_case      :							Union[str, Any]     				=						self.get_dummy_inputs()

             _snake_case      :							Optional[int]     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							str     				=						original_images[0, -3:, -3:, -1]

             with tempfile.TemporaryDirectory() as tmpdirname:
                    LoraLoaderMixin.save_lora_weights(
                        save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                    sd_pipe.load_lora_weights(lowercase_					)

             _snake_case      :							int     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							Optional[int]     				=						lora_images[0, -3:, -3:, -1]

             # Outputs shouldn't match.
             self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Union[str, Any]     				=						self.get_dummy_components()
             _snake_case      :							Optional[int]     				=						StableDiffusionPipeline(**lowercase_					)
             _snake_case      :							str     				=						sd_pipe.to(lowercase_					)
             sd_pipe.set_progress_bar_config(disable=lowercase_					)

             _snake_case				,    _snake_case				,    _snake_case      :							Any     				=						self.get_dummy_inputs()

             _snake_case      :							Optional[Any]     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							Union[str, Any]     				=						original_images[0, -3:, -3:, -1]

             with tempfile.TemporaryDirectory() as tmpdirname:
                    LoraLoaderMixin.save_lora_weights(
                        save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  safe_serialization=lowercase_     ,  )
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.safetensors'''					)					)					)
                    sd_pipe.load_lora_weights(lowercase_					)

             _snake_case      :							int     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							Optional[int]     				=						lora_images[0, -3:, -3:, -1]

             # Outputs shouldn't match.
             self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Any     				=						self.get_dummy_components()
             _snake_case      :							Union[str, Any]     				=						lora_components['''unet_lora_attn_procs''']
             _snake_case      :							List[Any]     				=						StableDiffusionPipeline(**lowercase_					)
             _snake_case      :							str     				=						sd_pipe.to(lowercase_					)
             sd_pipe.set_progress_bar_config(disable=lowercase_					)

             _snake_case				,    _snake_case				,    _snake_case      :							Tuple     				=						self.get_dummy_inputs()

             _snake_case      :							Optional[int]     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							List[str]     				=						original_images[0, -3:, -3:, -1]

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _snake_case      :							str     				=						sd_pipe.unet
                    unet.set_attn_processor(lowercase_					)
                    unet.save_attn_procs(lowercase_					)
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                    sd_pipe.load_lora_weights(lowercase_					)

             _snake_case      :							str     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							Tuple     				=						lora_images[0, -3:, -3:, -1]

             # Outputs shouldn't match.
             self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Tuple     				=						self.get_dummy_components()
             _snake_case      :							List[Any]     				=						StableDiffusionPipeline(**lowercase_					)

             _snake_case      :							int     				=						self.get_dummy_tokens()

             # inference without lora
             _snake_case      :							Optional[int]     				=						pipe.text_encoder(**lowercase_					)[0]
             assert outputs_without_lora.shape == (1, 77, 32)

             # create lora_attn_procs with zeroed out up.weights
             _snake_case      :							List[str]     				=						create_text_encoder_lora_attn_procs(pipe.text_encoder					)
             set_lora_up_weights(lowercase_     ,  randn_weight=lowercase_					)

             # monkey patch
             pipe._modify_text_encoder(lowercase_					)

             # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
             del text_attn_procs
             gc.collect()

             # inference with lora
             _snake_case      :							List[str]     				=						pipe.text_encoder(**lowercase_					)[0]
             assert outputs_with_lora.shape == (1, 77, 32)

             assert torch.allclose(
                 lowercase_     ,  lowercase_					), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

             # create lora_attn_procs with randn up.weights
             _snake_case      :							Dict     				=						create_text_encoder_lora_attn_procs(pipe.text_encoder					)
             set_lora_up_weights(lowercase_     ,  randn_weight=lowercase_					)

             # monkey patch
             pipe._modify_text_encoder(lowercase_					)

             # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
             del text_attn_procs
             gc.collect()

             # inference with lora
             _snake_case      :							Dict     				=						pipe.text_encoder(**lowercase_					)[0]
             assert outputs_with_lora.shape == (1, 77, 32)

             assert not torch.allclose(
                 lowercase_     ,  lowercase_					), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Tuple     				=						self.get_dummy_components()
             _snake_case      :							Optional[Any]     				=						StableDiffusionPipeline(**lowercase_					)

             _snake_case      :							int     				=						self.get_dummy_tokens()

             # inference without lora
             _snake_case      :							Optional[Any]     				=						pipe.text_encoder(**lowercase_					)[0]
             assert outputs_without_lora.shape == (1, 77, 32)

             # create lora_attn_procs with randn up.weights
             _snake_case      :							List[Any]     				=						create_text_encoder_lora_attn_procs(pipe.text_encoder					)
             set_lora_up_weights(lowercase_     ,  randn_weight=lowercase_					)

             # monkey patch
             pipe._modify_text_encoder(lowercase_					)

             # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
             del text_attn_procs
             gc.collect()

             # inference with lora
             _snake_case      :							List[Any]     				=						pipe.text_encoder(**lowercase_					)[0]
             assert outputs_with_lora.shape == (1, 77, 32)

             assert not torch.allclose(
                 lowercase_     ,  lowercase_					), "lora outputs should be different to without lora outputs"

             # remove monkey patch
             pipe._remove_text_encoder_monkey_patch()

             # inference with removed lora
             _snake_case      :							str     				=						pipe.text_encoder(**lowercase_					)[0]
             assert outputs_without_lora_removed.shape == (1, 77, 32)

             assert torch.allclose(
                 lowercase_     ,  lowercase_					), "remove lora monkey patch should restore the original outputs"



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Optional[Any]     				=						self.get_dummy_components()
             _snake_case      :							int     				=						StableDiffusionPipeline(**lowercase_					)
             _snake_case      :							List[Any]     				=						sd_pipe.to(lowercase_					)
             sd_pipe.set_progress_bar_config(disable=lowercase_					)

             _snake_case				,    _snake_case				,    _snake_case      :							Union[str, Any]     				=						self.get_dummy_inputs()

             with tempfile.TemporaryDirectory() as tmpdirname:
                    LoraLoaderMixin.save_lora_weights(
                        save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                    sd_pipe.load_lora_weights(lowercase_					)

             _snake_case      :							str     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							Union[str, Any]     				=						lora_images[0, -3:, -3:, -1]

             _snake_case      :							Union[str, Any]     				=						sd_pipe(**lowercase_     ,  cross_attention_kwargs={'''scale''': 0.5}					).images
             _snake_case      :							Optional[int]     				=						lora_images_with_scale[0, -3:, -3:, -1]

             # Outputs shouldn't match.
             self.assertFalse(
                 torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             with tempfile.TemporaryDirectory() as tmpdirname:
                    self.create_lora_weight_file(lowercase_					)

                    _snake_case				,    _snake_case      :							str     				=						self.get_dummy_components()
                    _snake_case      :							Optional[Any]     				=						StableDiffusionPipeline(**lowercase_					)
                    _snake_case      :							Any     				=						sd_pipe.to(lowercase_					)
                    sd_pipe.set_progress_bar_config(disable=lowercase_					)

                    # check if vanilla attention processors are used
                    for _, module in sd_pipe.unet.named_modules():
                           if isinstance(lowercase_     ,  lowercase_					):
                                  self.assertIsInstance(module.processor     ,  (AttnProcessor, AttnProcessor2_0)					)

            # load LoRA weight file
                    sd_pipe.load_lora_weights(lowercase_					)

                    # check if lora attention processors are used
                    for _, module in sd_pipe.unet.named_modules():
                           if isinstance(lowercase_     ,  lowercase_					):
                                  _snake_case      :							str     				=						(
                                      LoRAAttnProcessor2_0 if hasattr(lowercase_     ,  '''scaled_dot_product_attention'''					) else LoRAAttnProcessor
                                  )
                                  self.assertIsInstance(module.processor     ,  lowercase_					)



      @unittest.skipIf(torch_device != '''cuda'''     ,  '''This test is supposed to run on GPU'''					)
      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             with tempfile.TemporaryDirectory() as tmpdirname:
                    self.create_lora_weight_file(lowercase_					)

                    _snake_case				,    _snake_case      :							Any     				=						self.get_dummy_components()
                    _snake_case      :							int     				=						StableDiffusionPipeline(**lowercase_					)
                    _snake_case      :							List[Any]     				=						sd_pipe.to(lowercase_					)
                    sd_pipe.set_progress_bar_config(disable=lowercase_					)

                    # enable XFormers
                    sd_pipe.enable_xformers_memory_efficient_attention()

                    # check if xFormers attention processors are used
                    for _, module in sd_pipe.unet.named_modules():
                           if isinstance(lowercase_     ,  lowercase_					):
                                  self.assertIsInstance(module.processor     ,  lowercase_					)

            # load LoRA weight file
                    sd_pipe.load_lora_weights(lowercase_					)

                    # check if lora attention processors are used
                    for _, module in sd_pipe.unet.named_modules():
                           if isinstance(lowercase_     ,  lowercase_					):
                                  self.assertIsInstance(module.processor     ,  lowercase_					)



      @unittest.skipIf(torch_device != '''cuda'''     ,  '''This test is supposed to run on GPU'''					)
      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Tuple     				=						self.get_dummy_components()
             _snake_case      :							List[Any]     				=						StableDiffusionPipeline(**lowercase_					)
             _snake_case      :							Dict     				=						sd_pipe.to(lowercase_					)
             sd_pipe.set_progress_bar_config(disable=lowercase_					)

             _snake_case				,    _snake_case				,    _snake_case      :							Dict     				=						self.get_dummy_inputs()

             # enable XFormers
             sd_pipe.enable_xformers_memory_efficient_attention()

             _snake_case      :							List[Any]     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							Tuple     				=						original_images[0, -3:, -3:, -1]

             with tempfile.TemporaryDirectory() as tmpdirname:
                    LoraLoaderMixin.save_lora_weights(
                        save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                    sd_pipe.load_lora_weights(lowercase_					)

             _snake_case      :							Tuple     				=						sd_pipe(**lowercase_					).images
             _snake_case      :							Dict     				=						lora_images[0, -3:, -3:, -1]

             # Outputs shouldn't match.
             self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)













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 				A    (pl.LightningModule				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_="base"     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=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_					)
             _snake_case      :							Union[str, Any]     				=						0
             _snake_case      :							Any     				=						Path(self.hparams.output_dir					)
             _snake_case      :							Optional[Any]     				=						self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                    _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=lowercase_     ,  **lowercase_     ,  )
             else:
                    _snake_case      :							PretrainedConfig     				=						config

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



      def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(*lowercase_     ,  **lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

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



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



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model
             _snake_case      :							List[Any]     				=						['''bias''', '''LayerNorm.weight''']
             _snake_case      :							Optional[Any]     				=						[
                 {
                     '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
                     '''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:
                    _snake_case      :							Optional[int]     				=						Adafactor(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  scale_parameter=lowercase_     ,  relative_step=lowercase_					)

             else:
                    _snake_case      :							Optional[Any]     				=						AdamW(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon					)
             _snake_case      :							Optional[int]     				=						optimizer

             _snake_case      :							Tuple     				=						self.get_lr_scheduler()

             return [optimizer], [scheduler]



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Any:



             '''simple docstring'''

             return self.validation_step(lowercase_     ,  lowercase_					)



      def    __a (					self     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             return self.validation_end(lowercase_					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						max(1     ,  self.hparams.gpus					)  # TODO: consider num_tpu_cores
             _snake_case      :							Dict     				=						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     ,  lowercase_					)      ->   int:



             '''simple docstring'''

             if stage == "test":
                    _snake_case      :							Optional[Any]     				=						len(self.test_dataloader().dataset					)
             else:
                    _snake_case      :							Any     				=						self.get_dataloader('''train'''     ,  self.hparams.train_batch_size     ,  shuffle=lowercase_					)
                    _snake_case      :							Union[str, Any]     				=						len(self.train_dataloader().dataset					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = False					)      ->   Union[str, Any]:



             '''simple docstring'''

             raise NotImplementedError('''You must implement this for your task'''					)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return self.train_loader



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             return self.get_dataloader('''dev'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             return self.get_dataloader('''test'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)



      def    __a (					self     ,  lowercase_					)      ->   Dict:



             '''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     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.output_dir.joinpath('''best_tfmr'''					)
             _snake_case      :							str     				=						self.step_count
             self.model.save_pretrained(lowercase_					)
             self.tokenizer.save_pretrained(lowercase_					)



      @staticmethod
      def    __a (					lowercase_     ,  lowercase_					)      ->   Dict:



             '''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=''''''     ,  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     ,  lowercase_     ,  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     ,  lowercase_     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						trainer.lr_schedulers[0]['''scheduler''']
             _snake_case      :							Any     				=						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
             pl_module.logger.log_metrics(lowercase_					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[int]:



             '''simple docstring'''

             rank_zero_info('''***** Validation results *****'''					)
             _snake_case      :							Tuple     				=						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     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             rank_zero_info('''***** Test results *****'''					)
             _snake_case      :							int     				=						trainer.callback_metrics
             # Log and save results to file
             _snake_case      :							Dict     				=						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 						A_			(  lowercase_      ,		lowercase_  )			->						None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           '''--output_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		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=lowercase_      ,		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=lowercase_  )
       parser.add_argument('''--max_grad_norm'''      ,		dest='''gradient_clip_val'''      ,		default=1.0      ,		type=lowercase_      ,		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=lowercase_      ,		default=1      ,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''      ,		)
       parser.add_argument('''--seed'''      ,		type=lowercase_      ,		default=42      ,		help='''random seed for initialization'''  )
       parser.add_argument(
           '''--data_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''      ,		)




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=True      ,		lowercase_=None      ,		lowercase_=[]      ,		lowercase_=None      ,		lowercase_=None      ,		**lowercase_      ,		)			->						Optional[Any]:
       pl.seed_everything(args.seed  )

       # init model
       _snake_case      :							int     				=						Path(model.hparams.output_dir  )
       odir.mkdir(exist_ok=lowercase_  )

       # add custom checkpoints
       if checkpoint_callback is None:
              _snake_case      :							Union[str, Any]     				=						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(lowercase_  )
       if logging_callback is None:
              _snake_case      :							Optional[int]     				=						LoggingCallback()

       _snake_case      :							Union[str, Any]     				=						{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _snake_case      :							Any     				=						16
              # train_params["amp_level"] = args.fp16_opt_level

       if args.gpus > 1:
              _snake_case      :							List[str]     				=						'''auto'''  # "ddp"
              _snake_case      :							Dict     				=						'''ddp'''

       _snake_case      :							Optional[int]     				=						args.accumulate_grad_batches
       _snake_case      :							Optional[Any]     				=						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       _snake_case      :							Tuple     				=						'''auto'''

       _snake_case      :							int     				=						pl.Trainer.from_argparse_args(
           lowercase_      ,		weights_summary=lowercase_      ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,		logger=lowercase_      ,		**lowercase_      ,		)

       if args.do_train:
              trainer.fit(lowercase_  )

       return trainer






