




'''simple docstring'''



import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

UpperCAmelCase_    :					Optional[int]					      =						logging.get_logger(__name__)







def      _UpperCamelCase	(_lowerCamelCase							:       Dict				,		_lowerCamelCase							:       bool = True		)->				Tuple:




     '''simple docstring'''





     __snake_case         =					{}
     for name in model.attn_processors.keys():
          __snake_case         =					name.endswith('''attn2.processor'''		) and not (
              name.startswith('''transformer_in'''		) or '''temp_attentions''' in name.split('''.'''		)
          )
          __snake_case         =					model.config.cross_attention_dim if has_cross_attention else None
          if name.startswith('''mid_block'''		):
               __snake_case         =					model.config.block_out_channels[-1]
          elif name.startswith('''up_blocks'''		):
               __snake_case         =					int(name[len('''up_blocks.'''		)]		)
               __snake_case         =					list(reversed(model.config.block_out_channels		)		)[block_id]
          elif name.startswith('''down_blocks'''		):
               __snake_case         =					int(name[len('''down_blocks.'''		)]		)
               __snake_case         =					model.config.block_out_channels[block_id]
          elif name.startswith('''transformer_in'''		):
               # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
               __snake_case         =					8 * model.config.attention_head_dim

          __snake_case         =					LoRAAttnProcessor(hidden_size=_lowerCamelCase				,		cross_attention_dim=_lowerCamelCase		)
          __snake_case         =					lora_attn_procs[name].to(model.device		)

          if mock_weights:
               # add 1 to weights to mock trained weights
               with torch.no_grad():
                    lora_attn_procs[name].to_q_lora.up.weight += 1
                    lora_attn_procs[name].to_k_lora.up.weight += 1
                    lora_attn_procs[name].to_v_lora.up.weight += 1
                    lora_attn_procs[name].to_out_lora.up.weight += 1

     return lora_attn_procs







@skip_mps
class       lowerCAmelCase				(	__lowerCAmelCase			,				__lowerCAmelCase			,				unittest.TestCase):
       __lowercase					: Union[str, Any]	    =     UNetaDConditionModel
       __lowercase					: Tuple	    =     '''sample'''


       @property
       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case         =					4
            __snake_case         =					4
            __snake_case         =					4
            __snake_case         =					(32, 32)

            __snake_case         =					floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					torch.tensor([10]					).to(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					floats_tensor((batch_size, 4, 32)					).to(__SCREAMING_SNAKE_CASE					)

            return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}


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



            '''simple docstring'''




            return (4, 4, 32, 32)


       @property
       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            return (4, 4, 32, 32)


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case         =					{
                '''block_out_channels''': (32, 64),
                '''down_block_types''': (
                    '''CrossAttnDownBlock3D''',
                    '''DownBlock3D''',
                ),
                '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
                '''cross_attention_dim''': 32,
                '''attention_head_dim''': 8,
                '''out_channels''': 4,
                '''in_channels''': 4,
                '''layers_per_block''': 1,
                '''sample_size''': 32,
            }
            __snake_case         =					self.dummy_input
            return init_dict, inputs_dict


       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()      ,					reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,					)
       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)

            model.enable_xformers_memory_efficient_attention()

            assert (
                model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
                == "XFormersAttnProcessor"
            ), "xformers is not enabled"


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



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					32

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)

                 if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      __snake_case         =					output.sample

            self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					inputs_dict['''sample'''].shape
            self.assertEqual(output.shape      ,					__SCREAMING_SNAKE_CASE      ,					'''Input and output shapes do not match'''					)


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



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            with torch.no_grad():
                 # Warmup pass when using mps (see #372)
                 if torch_device == "mps" and isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      model(**self.dummy_input					)

                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)
                 if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      __snake_case         =					first.sample

                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)
                 if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      __snake_case         =					second.sample

            __snake_case         =					first.cpu().numpy()
            __snake_case         =					second.cpu().numpy()
            __snake_case         =					out_a[~np.isnan(__SCREAMING_SNAKE_CASE					)]
            __snake_case         =					out_a[~np.isnan(__SCREAMING_SNAKE_CASE					)]
            __snake_case         =					np.amax(np.abs(out_a - out_a					)					)
            self.assertLessEqual(__SCREAMING_SNAKE_CASE      ,					1E-5					)


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            model.set_attention_slice('''auto'''					)
            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)
            assert output is not None

            model.set_attention_slice('''max'''					)
            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)
            assert output is not None

            model.set_attention_slice(2					)
            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)
            assert output is not None


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)

            # make sure we can set a list of attention processors
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            # test that attn processors can be set to itself
            model.set_attn_processor(model.attn_processors					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.0}					).sample
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.5}					).sample
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.5}					).sample

            assert (samplea - samplea).abs().max() < 3E-3
            assert (samplea - samplea).abs().max() < 3E-3

            # sample 2 and sample 3 should be different
            assert (samplea - samplea).abs().max() > 3E-3


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.5}					).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__SCREAMING_SNAKE_CASE					)
                 self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE      ,					'''pytorch_lora_weights.bin'''					)					)					)
                 torch.manual_seed(0					)
                 __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
                 new_model.to(__SCREAMING_SNAKE_CASE					)
                 new_model.load_attn_procs(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					new_model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.5}					).sample

            assert (sample - new_sample).abs().max() < 1E-3

            # LoRA and no LoRA should NOT be the same
            assert (sample - old_sample).abs().max() > 1E-4


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.5}					).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__SCREAMING_SNAKE_CASE      ,					safe_serialization=__SCREAMING_SNAKE_CASE					)
                 self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE      ,					'''pytorch_lora_weights.safetensors'''					)					)					)
                 torch.manual_seed(0					)
                 __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
                 new_model.to(__SCREAMING_SNAKE_CASE					)
                 new_model.load_attn_procs(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					new_model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.5}					).sample

            assert (sample - new_sample).abs().max() < 3E-3

            # LoRA and no LoRA should NOT be the same
            assert (sample - old_sample).abs().max() > 1E-4


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



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE      ,					mock_weights=__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__SCREAMING_SNAKE_CASE					)
                 self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE      ,					'''pytorch_lora_weights.bin'''					)					)					)
                 torch.manual_seed(0					)
                 __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
                 new_model.to(__SCREAMING_SNAKE_CASE					)
                 new_model.load_attn_procs(__SCREAMING_SNAKE_CASE      ,					weight_name='''pytorch_lora_weights.bin'''					)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE      ,					mock_weights=__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__SCREAMING_SNAKE_CASE					)
                 self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE      ,					'''pytorch_lora_weights.bin'''					)					)					)
                 torch.manual_seed(0					)
                 __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
                 new_model.to(__SCREAMING_SNAKE_CASE					)
                 with self.assertRaises(__SCREAMING_SNAKE_CASE					) as e:
                      new_model.load_attn_procs(__SCREAMING_SNAKE_CASE      ,					use_safetensors=__SCREAMING_SNAKE_CASE					)
                 self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''      ,					str(e.exception					)					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.0}					).sample

            model.set_attn_processor(AttnProcessor()					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            assert (sample - new_sample).abs().max() < 1E-4
            assert (sample - old_sample).abs().max() < 3E-3


       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()      ,					reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,					)
       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					4

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

            # default
            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

                 model.enable_xformers_memory_efficient_attention()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

                 model.disable_xformers_memory_efficient_attention()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            assert (sample - on_sample).abs().max() < 1E-4
            assert (sample - off_sample).abs().max() < 1E-4




       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()
            __snake_case         =					32

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)[0]

            model.enable_forward_chunking()
            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)[0]

            self.assertEqual(output.shape      ,					output_a.shape      ,					'''Shape doesn\'t match'''					)
            assert np.abs(output.cpu() - output_a.cpu()					).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.












'''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


UpperCAmelCase_    :					Tuple					      =						logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

UpperCAmelCase_    :					List[str]					      =						{
    '''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
UpperCAmelCase_    :					Any					      =						{
    '''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
}
UpperCAmelCase_    :					Dict					      =						sorted(arg_to_scheduler.keys())
UpperCAmelCase_    :					List[Any]					      =						'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class       lowerCAmelCase				(	pl.LightningModule):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE="base"      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					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(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					0
            __snake_case         =					Path(self.hparams.output_dir					)
            __snake_case         =					self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 __snake_case         =					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=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					config

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

            if tokenizer is None:
                 __snake_case         =					AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,					cache_dir=__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					tokenizer
            __snake_case         =					MODEL_MODES[mode]
            if model is None:
                 __snake_case         =					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=__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					model


       def  lowerCAmelCase (				self      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					self.model
            __snake_case         =					['''bias''', '''LayerNorm.weight''']
            __snake_case         =					[
                {
                    '''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         =					Adafactor(
                     __SCREAMING_SNAKE_CASE      ,					lr=self.hparams.learning_rate      ,					scale_parameter=__SCREAMING_SNAKE_CASE      ,					relative_step=__SCREAMING_SNAKE_CASE					)

            else:
                 __snake_case         =					AdamW(
                     __SCREAMING_SNAKE_CASE      ,					lr=self.hparams.learning_rate      ,					eps=self.hparams.adam_epsilon					)
            __snake_case         =					optimizer

            __snake_case         =					self.get_lr_scheduler()

            return [optimizer], [scheduler]


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




            return self.validation_step(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            return self.validation_end(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					max(1      ,					self.hparams.gpus					)  # TODO: consider num_tpu_cores
            __snake_case         =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




            if mode == "test":
                 __snake_case         =					len(self.test_dataloader().dataset					)
            else:
                 __snake_case         =					self.get_dataloader('''train'''      ,					self.hparams.train_batch_size      ,					shuffle=__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					len(self.train_dataloader().dataset					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = False					) ->					str:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            return self.train_loader


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



            '''simple docstring'''




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


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



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            return os.path.join(
                self.hparams.data_dir      ,					'''cached_{}_{}_{}'''.format(
                    __SCREAMING_SNAKE_CASE      ,					list(filter(__SCREAMING_SNAKE_CASE      ,					self.hparams.model_name_or_path.split('''/'''					)					)					).pop()      ,					str(self.hparams.max_seq_length					)      ,					)      ,					)


       @pl.utilities.rank_zero_only
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					self.output_dir.joinpath('''best_tfmr'''					)
            __snake_case         =					self.step_count
            self.model.save_pretrained(__SCREAMING_SNAKE_CASE					)
            self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE					)




       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




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





class       lowerCAmelCase				(	pl.Callback):


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					trainer.lr_schedulers[0]['''scheduler''']
            __snake_case         =					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
            pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




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




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




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







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       Optional[Any]		)->				None:




     '''simple docstring'''





     parser.add_argument(
         '''--output_dir'''				,		default=_lowerCamelCase				,		type=_lowerCamelCase				,		required=_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=_lowerCamelCase				,		type=_lowerCamelCase				,		required=_lowerCamelCase				,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,		)







def      _UpperCamelCase	(_lowerCamelCase							:       BaseTransformer				,		_lowerCamelCase							:       argparse.Namespace				,		_lowerCamelCase							:       Any=None				,		_lowerCamelCase							:       Union[str, Any]=True				,		_lowerCamelCase							:       Union[str, Any]=[]				,		_lowerCamelCase							:       int=None				,		_lowerCamelCase							:       Any=None				,		**_lowerCamelCase							:       Tuple				,		)->				Optional[Any]:




     '''simple docstring'''





     pl.seed_everything(args.seed		)

     # init model
     __snake_case         =					Path(model.hparams.output_dir		)
     odir.mkdir(exist_ok=_lowerCamelCase		)

     # add custom checkpoints
     if checkpoint_callback is None:
          __snake_case         =					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:
          __snake_case         =					LoggingCallback()

     __snake_case         =					{}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          __snake_case         =					16
          __snake_case         =					args.fpaa_opt_level

     if args.gpus > 1:
          __snake_case         =					'''ddp'''

     __snake_case         =					args.accumulate_grad_batches
     __snake_case         =					extra_train_kwargs.get('''accelerator'''				,		_lowerCamelCase		)
     __snake_case         =					extra_train_kwargs.get('''profiler'''				,		_lowerCamelCase		)

     __snake_case         =					pl.Trainer.from_argparse_args(
         _lowerCamelCase				,		weights_summary=_lowerCamelCase				,		callbacks=[logging_callback] + extra_callbacks				,		logger=_lowerCamelCase				,		checkpoint_callback=_lowerCamelCase				,		**_lowerCamelCase				,		)

     if args.do_train:
          trainer.fit(_lowerCamelCase		)

     return trainer






