






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 			__SCREAMING_SNAKE_CASE    (      UpperCamelCase__						,    unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:				Dict            =    UnCLIPPipeline
						_SCREAMING_SNAKE_CASE		:				Any            =    TEXT_TO_IMAGE_PARAMS - {
						    """negative_prompt""",
						    """height""",
						    """width""",
						    """negative_prompt_embeds""",
						    """guidance_scale""",
						    """prompt_embeds""",
						    """cross_attention_kwargs""",
						}
						_SCREAMING_SNAKE_CASE		:				int            =    TEXT_TO_IMAGE_BATCH_PARAMS
						_SCREAMING_SNAKE_CASE		:				str            =    [
						    """generator""",
						    """return_dict""",
						    """prior_num_inference_steps""",
						    """decoder_num_inference_steps""",
						    """super_res_num_inference_steps""",
						]
						_SCREAMING_SNAKE_CASE		:				int            =    False






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return 32






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return 32






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return self.time_input_dim






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return self.time_input_dim * 4






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return 1_00






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
												return tokenizer






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												torch.manual_seed(0      )
												lowerCAmelCase__			  =  CLIPTextConfig(
												    bos_token_id=0   ,  eos_token_id=2   ,  hidden_size=self.text_embedder_hidden_size   ,  projection_dim=self.text_embedder_hidden_size   ,  intermediate_size=37   ,  layer_norm_eps=1E-05   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  pad_token_id=1   ,  vocab_size=10_00   ,  )
												return CLIPTextModelWithProjection(_UpperCamelCase      )






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												torch.manual_seed(0      )

												lowerCAmelCase__			  =  {
												    'num_attention_heads': 2,
												    'attention_head_dim': 12,
												    'embedding_dim': self.text_embedder_hidden_size,
												    'num_layers': 1,
												}

												lowerCAmelCase__			  =  PriorTransformer(**_UpperCamelCase      )
												return model






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												torch.manual_seed(0      )

												lowerCAmelCase__			  =  {
												    'clip_embeddings_dim': self.text_embedder_hidden_size,
												    'time_embed_dim': self.time_embed_dim,
												    'cross_attention_dim': self.cross_attention_dim,
												}

												lowerCAmelCase__			  =  UnCLIPTextProjModel(**_UpperCamelCase      )
												return model






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												torch.manual_seed(0      )

												lowerCAmelCase__			  =  {
												    'sample_size': 32,
												    # RGB in channels
												    'in_channels': 3,
												    # Out channels is double in channels because predicts mean and variance
												    'out_channels': 6,
												    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
												    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
												    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
												    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    'layers_per_block': 1,
												    'cross_attention_dim': self.cross_attention_dim,
												    'attention_head_dim': 4,
												    'resnet_time_scale_shift': 'scale_shift',
												    'class_embed_type': 'identity',
												}

												lowerCAmelCase__			  =  UNetaDConditionModel(**_UpperCamelCase      )
												return model






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return {
												    "sample_size": 64,
												    "layers_per_block": 1,
												    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
												    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
												    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    "in_channels": 6,
												    "out_channels": 3,
												}






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												torch.manual_seed(0      )

												lowerCAmelCase__			  =  UNetaDModel(**self.dummy_super_res_kwargs      )
												return model






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												torch.manual_seed(1      )

												lowerCAmelCase__			  =  UNetaDModel(**self.dummy_super_res_kwargs      )
												return model






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.dummy_prior
												lowerCAmelCase__			  =  self.dummy_decoder
												lowerCAmelCase__			  =  self.dummy_text_proj
												lowerCAmelCase__			  =  self.dummy_text_encoder
												lowerCAmelCase__			  =  self.dummy_tokenizer
												lowerCAmelCase__			  =  self.dummy_super_res_first
												lowerCAmelCase__			  =  self.dummy_super_res_last

												lowerCAmelCase__			  =  UnCLIPScheduler(
												    variance_type='fixed_small_log'   ,  prediction_type='sample'   ,  num_train_timesteps=10_00   ,  clip_sample_range=5.0   ,  )

												lowerCAmelCase__			  =  UnCLIPScheduler(
												    variance_type='learned_range'   ,  prediction_type='epsilon'   ,  num_train_timesteps=10_00   ,  )

												lowerCAmelCase__			  =  UnCLIPScheduler(
												    variance_type='fixed_small_log'   ,  prediction_type='epsilon'   ,  num_train_timesteps=10_00   ,  )

												lowerCAmelCase__			  =  {
												    'prior': prior,
												    'decoder': decoder,
												    'text_proj': text_proj,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'super_res_first': super_res_first,
												    'super_res_last': super_res_last,
												    'prior_scheduler': prior_scheduler,
												    'decoder_scheduler': decoder_scheduler,
												    'super_res_scheduler': super_res_scheduler,
												}

												return components






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=0      ):






												"""simple docstring"""



												if str(_UpperCamelCase      ).startswith('mps'      ):
																		lowerCAmelCase__			  =  torch.manual_seed(_UpperCamelCase      )
												else:
																		lowerCAmelCase__			  =  torch.Generator(device=_UpperCamelCase      ).manual_seed(_UpperCamelCase      )
												lowerCAmelCase__			  =  {
												    'prompt': 'horse',
												    'generator': generator,
												    'prior_num_inference_steps': 2,
												    'decoder_num_inference_steps': 2,
												    'super_res_num_inference_steps': 2,
												    'output_type': 'numpy',
												}
												return inputs






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  'cpu'

												lowerCAmelCase__			  =  self.get_dummy_components()

												lowerCAmelCase__			  =  self.pipeline_class(**_UpperCamelCase      )
												lowerCAmelCase__			  =  pipe.to(_UpperCamelCase      )

												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												lowerCAmelCase__			  =  pipe(**self.get_dummy_inputs(_UpperCamelCase      )      )
												lowerCAmelCase__			  =  output.images

												lowerCAmelCase__			  =  pipe(
												    **self.get_dummy_inputs(_UpperCamelCase      )   ,  return_dict=_UpperCamelCase   ,  )[0]

												lowerCAmelCase__			  =  image[0, -3:, -3:, -1]
												lowerCAmelCase__			  =  image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												lowerCAmelCase__			  =  np.array(
												    [
												        0.99_97,
												        0.99_88,
												        0.00_28,
												        0.99_97,
												        0.99_84,
												        0.99_65,
												        0.00_29,
												        0.99_86,
												        0.00_25,
												    ]      )

												assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  torch.device('cpu'      )


												class 			__SCREAMING_SNAKE_CASE    :
																		_SCREAMING_SNAKE_CASE		:				str            =    1




												lowerCAmelCase__			  =  self.get_dummy_components()

												lowerCAmelCase__			  =  self.pipeline_class(**_UpperCamelCase      )
												lowerCAmelCase__			  =  pipe.to(_UpperCamelCase      )

												lowerCAmelCase__			  =  components['prior']
												lowerCAmelCase__			  =  components['decoder']
												lowerCAmelCase__			  =  components['super_res_first']
												lowerCAmelCase__			  =  components['tokenizer']
												lowerCAmelCase__			  =  components['text_encoder']

												lowerCAmelCase__			  =  torch.Generator(device=_UpperCamelCase      ).manual_seed(0      )
												lowerCAmelCase__			  =  prior.dtype
												lowerCAmelCase__			  =  1

												lowerCAmelCase__			  =  (batch_size, prior.config.embedding_dim)
												lowerCAmelCase__			  =  pipe.prepare_latents(
												    _UpperCamelCase   ,  dtype=_UpperCamelCase   ,  device=_UpperCamelCase   ,  generator=_UpperCamelCase   ,  latents=_UpperCamelCase   ,  scheduler=DummyScheduler()      )
												lowerCAmelCase__			  =  (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
												lowerCAmelCase__			  =  pipe.prepare_latents(
												    _UpperCamelCase   ,  dtype=_UpperCamelCase   ,  device=_UpperCamelCase   ,  generator=_UpperCamelCase   ,  latents=_UpperCamelCase   ,  scheduler=DummyScheduler()      )

												lowerCAmelCase__			  =  (
												    batch_size,
												    super_res_first.config.in_channels // 2,
												    super_res_first.config.sample_size,
												    super_res_first.config.sample_size,
												)
												lowerCAmelCase__			  =  pipe.prepare_latents(
												    _UpperCamelCase   ,  dtype=_UpperCamelCase   ,  device=_UpperCamelCase   ,  generator=_UpperCamelCase   ,  latents=_UpperCamelCase   ,  scheduler=DummyScheduler()      )

												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												lowerCAmelCase__			  =  'this is a prompt example'

												lowerCAmelCase__			  =  torch.Generator(device=_UpperCamelCase      ).manual_seed(0      )
												lowerCAmelCase__			  =  pipe(
												    [prompt]   ,  generator=_UpperCamelCase   ,  prior_num_inference_steps=2   ,  decoder_num_inference_steps=2   ,  super_res_num_inference_steps=2   ,  prior_latents=_UpperCamelCase   ,  decoder_latents=_UpperCamelCase   ,  super_res_latents=_UpperCamelCase   ,  output_type='np'   ,  )
												lowerCAmelCase__			  =  output.images

												lowerCAmelCase__			  =  tokenizer(
												    _UpperCamelCase   ,  padding='max_length'   ,  max_length=tokenizer.model_max_length   ,  return_tensors='pt'   ,  )
												lowerCAmelCase__			  =  text_encoder(text_inputs.input_ids      )
												lowerCAmelCase__			  =  text_inputs.attention_mask

												lowerCAmelCase__			  =  torch.Generator(device=_UpperCamelCase      ).manual_seed(0      )
												lowerCAmelCase__			  =  pipe(
												    generator=_UpperCamelCase   ,  prior_num_inference_steps=2   ,  decoder_num_inference_steps=2   ,  super_res_num_inference_steps=2   ,  prior_latents=_UpperCamelCase   ,  decoder_latents=_UpperCamelCase   ,  super_res_latents=_UpperCamelCase   ,  text_model_output=_UpperCamelCase   ,  text_attention_mask=_UpperCamelCase   ,  output_type='np'   ,  )[0]

												# make sure passing text embeddings manually is identical
												assert np.abs(image - image_from_text      ).max() < 1E-4






						@skip_mps
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  torch_device == 'cpu'

												self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase   ,  expected_max_diff=0.01      )






						@skip_mps
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  torch_device == 'cpu'
												lowerCAmelCase__			  =  True
												lowerCAmelCase__			  =  [
												    'prior_num_inference_steps',
												    'decoder_num_inference_steps',
												    'super_res_num_inference_steps',
												]

												self._test_inference_batch_single_identical(
												    test_max_difference=_UpperCamelCase   ,  relax_max_difference=_UpperCamelCase   ,  additional_params_copy_to_batched_inputs=_UpperCamelCase   ,  )






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  [
												    'prior_num_inference_steps',
												    'decoder_num_inference_steps',
												    'super_res_num_inference_steps',
												]

												if torch_device == "mps":
																		# TODO: MPS errors with larger batch sizes
																		lowerCAmelCase__			  =  [2, 3]
																		self._test_inference_batch_consistent(
																		    batch_sizes=_UpperCamelCase   ,  additional_params_copy_to_batched_inputs=_UpperCamelCase   ,  )
												else:
																		self._test_inference_batch_consistent(
																		    additional_params_copy_to_batched_inputs=_UpperCamelCase      )






						@skip_mps
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return super().test_save_load_local()







						@skip_mps
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return super().test_save_load_optional_components()




@nightly
class 			__SCREAMING_SNAKE_CASE    (      unittest.TestCase):






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/unclip/karlo_v1_alpha_horse_cpu.npy'      )

												lowerCAmelCase__			  =  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'      )
												pipeline.set_progress_bar_config(disable=_UpperCamelCase      )

												lowerCAmelCase__			  =  torch.manual_seed(0      )
												lowerCAmelCase__			  =  pipeline(
												    'horse'   ,  num_images_per_prompt=1   ,  generator=_UpperCamelCase   ,  output_type='np'   ,  )

												lowerCAmelCase__			  =  output.images[0]

												assert image.shape == (2_56, 2_56, 3)
												assert np.abs(expected_image - image      ).max() < 1E-1




@slow
@require_torch_gpu
class 			__SCREAMING_SNAKE_CASE    (      unittest.TestCase):






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/unclip/karlo_v1_alpha_horse_fp16.npy'      )

												lowerCAmelCase__			  =  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   ,  torch_dtype=torch.floataa      )
												lowerCAmelCase__			  =  pipeline.to(_UpperCamelCase      )
												pipeline.set_progress_bar_config(disable=_UpperCamelCase      )

												lowerCAmelCase__			  =  torch.Generator(device='cpu'      ).manual_seed(0      )
												lowerCAmelCase__			  =  pipeline(
												    'horse'   ,  generator=_UpperCamelCase   ,  output_type='np'   ,  )

												lowerCAmelCase__			  =  output.images[0]

												assert image.shape == (2_56, 2_56, 3)

												assert_mean_pixel_difference(_UpperCamelCase   ,  _UpperCamelCase      )







						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowerCAmelCase__			  =  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   ,  torch_dtype=torch.floataa      )
												lowerCAmelCase__			  =  pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )
												pipe.enable_attention_slicing()
												pipe.enable_sequential_cpu_offload()

												lowerCAmelCase__			  =  pipe(
												    'horse'   ,  num_images_per_prompt=1   ,  prior_num_inference_steps=2   ,  decoder_num_inference_steps=2   ,  super_res_num_inference_steps=2   ,  output_type='np'   ,  )

												lowerCAmelCase__			  =  torch.cuda.max_memory_allocated()
												# make sure that less than 7 GB is allocated
												assert mem_bytes < 7 * 10**9











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


lowercase__      :			List[Any]           =					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase__      :			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
lowercase__      :			Union[str, 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
}
lowercase__      :			Any           =					sorted(arg_to_scheduler.keys())
lowercase__      :			Union[str, Any]           =					"{" + ", ".join(arg_to_scheduler_choices) + "}"




class 			a__       (					pl.LightningModule      ):







     def __init__(	self  ,		A  ,		A=None  ,		A="base"  ,		A=None  ,		A=None  ,		A=None  ,		**A  ,		)  ->    Any:



          '''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(A )
          a      =      0
          a      =      Path(self.hparams.output_dir )
          a      =      self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
               a      =      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=A  ,		**A  ,		)
          else:
               a      =      config

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

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







     def 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    Dict:



          '''simple docstring'''



          a      =      self.model_type.from_pretrained(*A  ,		**A )







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



          '''simple docstring'''



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







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



          '''simple docstring'''



          a      =      self.model
          a      =      ["bias", "LayerNorm.weight"]
          a      =      [
              {
                  "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:
               a      =      Adafactor(
                   A  ,		lr=self.hparams.learning_rate  ,		scale_parameter=A  ,		relative_step=A )

          else:
               a      =      AdamW(
                   A  ,		lr=self.hparams.learning_rate  ,		eps=self.hparams.adam_epsilon )
          a      =      optimizer

          a      =      self.get_lr_scheduler()

          return [optimizer], [scheduler]







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



          return self.validation_step(A  ,		A )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          return self.validation_end(A )







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          a      =      max(1  ,		self.hparams.gpus )  # TODO: consider num_tpu_cores
          a      =      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  ,		A )  ->    List[Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A = False )  ->    int:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Optional[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=A )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          return self.get_dataloader("test"  ,		self.hparams.eval_batch_size  ,		shuffle=A )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Optional[int]:



          '''simple docstring'''



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







     @pl.utilities.rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A )  ->    None:



          '''simple docstring'''



          a      =      self.output_dir.joinpath("best_tfmr" )
          a      =      self.step_count
          self.model.save_pretrained(A )
          self.tokenizer.save_pretrained(A )







     @staticmethod
     def 			lowerCAmelCase_				(	A  ,		A )  ->    Any:



          '''simple docstring'''



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







class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Tuple:



          '''simple docstring'''



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







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     None:
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir"		,					default=__UpperCamelCase		,					type=__UpperCamelCase		,					required=__UpperCamelCase		,					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=__UpperCamelCase		,					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=__UpperCamelCase)
     parser.add_argument("--max_grad_norm"		,					dest="gradient_clip_val"		,					default=1.0		,					type=__UpperCamelCase		,					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=__UpperCamelCase		,					default=1		,					help="Number of updates steps to accumulate before performing a backward/update pass."		,					)
     parser.add_argument("--seed"		,					type=__UpperCamelCase		,					default=42		,					help="random seed for initialization")
     parser.add_argument(
         "--data_dir"		,					default=__UpperCamelCase		,					type=__UpperCamelCase		,					required=__UpperCamelCase		,					help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,					)





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

     # init model
     a      =      Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=__UpperCamelCase)

     # add custom checkpoints
     if checkpoint_callback is None:
          a      =      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(__UpperCamelCase)
     if logging_callback is None:
          a      =      LoggingCallback()

     a      =      {}

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

     if args.gpus > 1:
          a      =      "ddp"

     a      =      args.accumulate_grad_batches
     a      =      extra_train_kwargs.get("accelerator"		,					__UpperCamelCase)
     a      =      extra_train_kwargs.get("profiler"		,					__UpperCamelCase)

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

     if args.do_train:
          trainer.fit(__UpperCamelCase)

     return trainer

