'''simple docstring'''

import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


_A      :				Dict 				=    logging.get_logger(__name__)  # pylint: disable=invalid-name







def 					UpperCamelCase_						( self	:	Tuple						,		snake_case_	:	Union[str, Any]						,		snake_case_	:	Union[str, Any]						,		snake_case_	:	List[str]						,		snake_case_	:	List[Any]				) ->					str:






 '''simple docstring'''

 __lowerCAmelCase    							=						image.to(device=snake_case_				)
 __lowerCAmelCase    							=						image  # take image as image_embeddings
 __lowerCAmelCase    							=						image_embeddings.unsqueeze(1				)

 # duplicate image embeddings for each generation per prompt, using mps friendly method
 __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						image_embeddings.shape
 __lowerCAmelCase    							=						image_embeddings.repeat(1						,		snake_case_						,		1				)
 __lowerCAmelCase    							=						image_embeddings.view(bs_embed * num_images_per_prompt						,		snake_case_						,		-1				)

 if do_classifier_free_guidance:
  __lowerCAmelCase    							=						torch.zeros_like(snake_case_				)

  # For classifier free guidance, we need to do two forward passes.
  # Here we concatenate the unconditional and text embeddings into a single batch
  # to avoid doing two forward passes
  __lowerCAmelCase    							=						torch.cat([uncond_embeddings, image_embeddings]				)

 return image_embeddings







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 def __init__(				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     PriorTransformer				,	SCREAMING_SNAKE_CASE__       :     CLIPTokenizer				,	SCREAMING_SNAKE_CASE__       :     CLIPTextModelWithProjection				,	SCREAMING_SNAKE_CASE__       :     UnCLIPScheduler				,	SCREAMING_SNAKE_CASE__       :     Optional[dict] = None				,	)  ->   Dict:
  super().__init__()

  __lowerCAmelCase    							=						{"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

  __lowerCAmelCase    							=						decoder_pipe_kwargs.get("""torch_dtype"""				,	SCREAMING_SNAKE_CASE__							) or prior.dtype

  __lowerCAmelCase    							=						StableDiffusionImageVariationPipeline.from_pretrained(
      """lambdalabs/sd-image-variations-diffusers"""				,	**SCREAMING_SNAKE_CASE__							)

  # replace `_encode_image` method
  __lowerCAmelCase    							=						types.MethodType(_encode_image				,	self.decoder_pipe							)

  self.register_modules(
      prior=SCREAMING_SNAKE_CASE__				,	tokenizer=SCREAMING_SNAKE_CASE__				,	text_encoder=SCREAMING_SNAKE_CASE__				,	prior_scheduler=SCREAMING_SNAKE_CASE__				,	)
 def a (				self       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[CLIPTextModelOutput, Tuple]] = None				,	SCREAMING_SNAKE_CASE__       :     Optional[torch.Tensor] = None				,	)  ->   List[str]:
  if text_model_output is None:
   __lowerCAmelCase    							=						len(SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else 1
   # get prompt text embeddings
   __lowerCAmelCase    							=						self.tokenizer(
       SCREAMING_SNAKE_CASE__				,	padding="""max_length"""				,	max_length=self.tokenizer.model_max_length				,	return_tensors="""pt"""				,	)
   __lowerCAmelCase    							=						text_inputs.input_ids
   __lowerCAmelCase    							=						text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE__							)

   if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
    __lowerCAmelCase    							=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
    logger.warning(
        """The following part of your input was truncated because CLIP can only handle sequences up to"""
        f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)
    __lowerCAmelCase    							=						text_input_ids[:, : self.tokenizer.model_max_length]

   __lowerCAmelCase    							=						self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE__							)							)

   __lowerCAmelCase    							=						text_encoder_output.text_embeds
   __lowerCAmelCase    							=						text_encoder_output.last_hidden_state

  else:
   __lowerCAmelCase    							=						text_model_output[0].shape[0]
   __lowerCAmelCase							,	__lowerCAmelCase    							=						text_model_output[0], text_model_output[1]
   __lowerCAmelCase    							=						text_attention_mask

  __lowerCAmelCase    							=						text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE__				,	dim=0							)
  __lowerCAmelCase    							=						text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE__				,	dim=0							)
  __lowerCAmelCase    							=						text_mask.repeat_interleave(SCREAMING_SNAKE_CASE__				,	dim=0							)

  if do_classifier_free_guidance:
   __lowerCAmelCase    							=						[""""""] * batch_size

   __lowerCAmelCase    							=						self.tokenizer(
       SCREAMING_SNAKE_CASE__				,	padding="""max_length"""				,	max_length=self.tokenizer.model_max_length				,	truncation=SCREAMING_SNAKE_CASE__				,	return_tensors="""pt"""				,	)
   __lowerCAmelCase    							=						uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE__							)							)

   __lowerCAmelCase    							=						uncond_embeddings_text_encoder_output.text_embeds
   __lowerCAmelCase    							=						uncond_embeddings_text_encoder_output.last_hidden_state

   # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

   __lowerCAmelCase    							=						uncond_embeddings.shape[1]
   __lowerCAmelCase    							=						uncond_embeddings.repeat(1				,	SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						uncond_embeddings.view(batch_size * num_images_per_prompt				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						uncond_text_encoder_hidden_states.shape[1]
   __lowerCAmelCase    							=						uncond_text_encoder_hidden_states.repeat(1				,	SCREAMING_SNAKE_CASE__				,	1							)
   __lowerCAmelCase    							=						uncond_text_encoder_hidden_states.view(
       batch_size * num_images_per_prompt				,	SCREAMING_SNAKE_CASE__				,	-1							)
   __lowerCAmelCase    							=						uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE__				,	dim=0							)

   # done duplicates

   # For classifier free guidance, we need to do two forward passes.
   # Here we concatenate the unconditional and text embeddings into a single batch
   # to avoid doing two forward passes
   __lowerCAmelCase    							=						torch.cat([uncond_embeddings, text_embeddings]							)
   __lowerCAmelCase    							=						torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]							)

   __lowerCAmelCase    							=						torch.cat([uncond_text_mask, text_mask]							)

  return text_embeddings, text_encoder_hidden_states, text_mask
 @property
 def a (				self       :     int							)  ->   Optional[Any]:
  if self.device != torch.device("""meta"""							) or not hasattr(self.prior				,	"""_hf_hook"""							):
   return self.device
  for module in self.prior.modules():
   if (
       hasattr(SCREAMING_SNAKE_CASE__				,	"""_hf_hook"""							)
       and hasattr(module._hf_hook				,	"""execution_device"""							)
       and module._hf_hook.execution_device is not None
   ):
    return torch.device(module._hf_hook.execution_device							)
  return self.device
 def a (				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     str							)  ->   List[str]:
  if latents is None:
   __lowerCAmelCase    							=						randn_tensor(SCREAMING_SNAKE_CASE__				,	generator=SCREAMING_SNAKE_CASE__				,	device=SCREAMING_SNAKE_CASE__				,	dtype=SCREAMING_SNAKE_CASE__							)
  else:
   if latents.shape != shape:
    raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
   __lowerCAmelCase    							=						latents.to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						latents * scheduler.init_noise_sigma
  return latents
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[str, torch.device]] = None							)  ->   Dict:
  self.decoder_pipe.to(SCREAMING_SNAKE_CASE__							)
  super().to(SCREAMING_SNAKE_CASE__							)





 @torch.no_grad()
 def __call__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[str, List[str]]] = None				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = None				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = None				,	SCREAMING_SNAKE_CASE__       :     int = 1				,	SCREAMING_SNAKE_CASE__       :     int = 25				,	SCREAMING_SNAKE_CASE__       :     Optional[torch.Generator] = None				,	SCREAMING_SNAKE_CASE__       :     Optional[torch.FloatTensor] = None				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[CLIPTextModelOutput, Tuple]] = None				,	SCREAMING_SNAKE_CASE__       :     Optional[torch.Tensor] = None				,	SCREAMING_SNAKE_CASE__       :     float = 4.0				,	SCREAMING_SNAKE_CASE__       :     float = 8.0				,	SCREAMING_SNAKE_CASE__       :     int = 50				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 1				,	SCREAMING_SNAKE_CASE__       :     float = 0.0				,	SCREAMING_SNAKE_CASE__       :     Optional[str] = "pil"				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	)  ->   List[Any]:
  if prompt is not None:
   if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase    							=						1
   elif isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase    							=						len(SCREAMING_SNAKE_CASE__							)
   else:
    raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE__							)}"""							)
  else:
   __lowerCAmelCase    							=						text_model_output[0].shape[0]

  __lowerCAmelCase    							=						self._execution_device

  __lowerCAmelCase    							=						batch_size * num_images_per_prompt

  __lowerCAmelCase    							=						prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

  __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						self._encode_prompt(
      SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

  # prior

  self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				,	device=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						self.prior_scheduler.timesteps

  __lowerCAmelCase    							=						self.prior.config.embedding_dim

  __lowerCAmelCase    							=						self.prepare_latents(
      (batch_size, embedding_dim)				,	text_embeddings.dtype				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	self.prior_scheduler				,	)

  for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__							)							):
   # expand the latents if we are doing classifier free guidance
   __lowerCAmelCase    							=						torch.cat([prior_latents] * 2							) if do_classifier_free_guidance else prior_latents

   __lowerCAmelCase    							=						self.prior(
       SCREAMING_SNAKE_CASE__				,	timestep=SCREAMING_SNAKE_CASE__				,	proj_embedding=SCREAMING_SNAKE_CASE__				,	encoder_hidden_states=SCREAMING_SNAKE_CASE__				,	attention_mask=SCREAMING_SNAKE_CASE__				,	).predicted_image_embedding

   if do_classifier_free_guidance:
    __lowerCAmelCase							,	__lowerCAmelCase    							=						predicted_image_embedding.chunk(2							)
    __lowerCAmelCase    							=						predicted_image_embedding_uncond + prior_guidance_scale * (
        predicted_image_embedding_text - predicted_image_embedding_uncond
    )

   if i + 1 == prior_timesteps_tensor.shape[0]:
    __lowerCAmelCase    							=						None
   else:
    __lowerCAmelCase    							=						prior_timesteps_tensor[i + 1]

   __lowerCAmelCase    							=						self.prior_scheduler.step(
       SCREAMING_SNAKE_CASE__				,	timestep=SCREAMING_SNAKE_CASE__				,	sample=SCREAMING_SNAKE_CASE__				,	generator=SCREAMING_SNAKE_CASE__				,	prev_timestep=SCREAMING_SNAKE_CASE__				,	).prev_sample

  __lowerCAmelCase    							=						self.prior.post_process_latents(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						prior_latents

  __lowerCAmelCase    							=						self.decoder_pipe(
      image=SCREAMING_SNAKE_CASE__				,	height=SCREAMING_SNAKE_CASE__				,	width=SCREAMING_SNAKE_CASE__				,	num_inference_steps=SCREAMING_SNAKE_CASE__				,	guidance_scale=SCREAMING_SNAKE_CASE__				,	generator=SCREAMING_SNAKE_CASE__				,	output_type=SCREAMING_SNAKE_CASE__				,	return_dict=SCREAMING_SNAKE_CASE__				,	num_images_per_prompt=SCREAMING_SNAKE_CASE__				,	eta=SCREAMING_SNAKE_CASE__				,	)
  return output







'''simple docstring'''

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_A      :				str 				=    logging.getLogger(__name__)

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

_A      :				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
_A      :				List[str] 				=    {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_A      :				Optional[Any] 				=    sorted(arg_to_scheduler.keys())
_A      :				Any 				=    '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class     _lowercase					(    pl.LightningModule      ):


 '''simple docstring'''






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

  self.save_hyperparameters(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						0
  __lowerCAmelCase    							=						Path(self.hparams.output_dir							)
  __lowerCAmelCase    							=						self.hparams.cache_dir if self.hparams.cache_dir else None
  if config is None:
   __lowerCAmelCase    							=						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:
   __lowerCAmelCase    							=						config

  __lowerCAmelCase    							=						("""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:
   __lowerCAmelCase    							=						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:
   __lowerCAmelCase    							=						tokenizer
  __lowerCAmelCase    							=						MODEL_MODES[mode]
  if model is None:
   __lowerCAmelCase    							=						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:
   __lowerCAmelCase    							=						model
 def a (				self       :     Dict				,	*SCREAMING_SNAKE_CASE__       :     List[Any]				,	**SCREAMING_SNAKE_CASE__       :     int							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__							)
 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						arg_to_scheduler[self.hparams.lr_scheduler]
  __lowerCAmelCase    							=						get_schedule_func(
      self.opt				,	num_warmup_steps=self.hparams.warmup_steps				,	num_training_steps=self.total_steps()							)
  __lowerCAmelCase    							=						{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
  return scheduler
 def a (				self       :     Union[str, Any]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.model
  __lowerCAmelCase    							=						["""bias""", """LayerNorm.weight"""]
  __lowerCAmelCase    							=						[
      {
          """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:
   __lowerCAmelCase    							=						Adafactor(
       SCREAMING_SNAKE_CASE__				,	lr=self.hparams.learning_rate				,	scale_parameter=SCREAMING_SNAKE_CASE__				,	relative_step=SCREAMING_SNAKE_CASE__							)

  else:
   __lowerCAmelCase    							=						AdamW(
       SCREAMING_SNAKE_CASE__				,	lr=self.hparams.learning_rate				,	eps=self.hparams.adam_epsilon							)
  __lowerCAmelCase    							=						optimizer

  __lowerCAmelCase    							=						self.get_lr_scheduler()

  return [optimizer], [scheduler]
 def a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     List[Any]							)  ->   Dict:
  return self.validation_step(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]							)  ->   Any:
  return self.validation_end(SCREAMING_SNAKE_CASE__							)
 def a (				self       :     List[str]							)  ->   int:
  __lowerCAmelCase    							=						max(1				,	self.hparams.gpus							)  # TODO: consider num_tpu_cores
  __lowerCAmelCase    							=						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       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Tuple							)  ->   Tuple:
  if mode == "test":
   __lowerCAmelCase    							=						len(self.test_dataloader().dataset							)
  else:
   __lowerCAmelCase    							=						self.get_dataloader("""train"""				,	self.hparams.train_batch_size				,	shuffle=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						len(self.train_dataloader().dataset							)
 def a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     bool = False							)  ->   str:
  raise NotImplementedError("""You must implement this for your task"""							)
 def a (				self       :     Union[str, Any]							)  ->   Tuple:
  return self.train_loader
 def a (				self       :     Tuple							)  ->   Optional[int]:
  return self.get_dataloader("""dev"""				,	self.hparams.eval_batch_size				,	shuffle=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Tuple							)  ->   int:
  return self.get_dataloader("""test"""				,	self.hparams.eval_batch_size				,	shuffle=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   int:
  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 a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     Dict[str, Any]							)  ->   None:
  __lowerCAmelCase    							=						self.output_dir.joinpath("""best_tfmr"""							)
  __lowerCAmelCase    							=						self.step_count
  self.model.save_pretrained(SCREAMING_SNAKE_CASE__							)
  self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)





 @staticmethod
 def a (				SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]							)  ->   Any:
  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     _lowercase					(    pl.Callback      ):


 '''simple docstring'''






 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Dict							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						trainer.lr_schedulers[0]["""scheduler"""]
  __lowerCAmelCase    							=						{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
  pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     pl.Trainer				,	SCREAMING_SNAKE_CASE__       :     pl.LightningModule							)  ->   Dict:
  rank_zero_info("""***** Validation results *****"""							)
  __lowerCAmelCase    							=						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 a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     pl.Trainer				,	SCREAMING_SNAKE_CASE__       :     pl.LightningModule							)  ->   List[str]:
  rank_zero_info("""***** Test results *****"""							)
  __lowerCAmelCase    							=						trainer.callback_metrics
  # Log and save results to file
  __lowerCAmelCase    							=						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_						( snake_case_	:	Tuple						,		snake_case_	:	Optional[int]				) ->					None:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	BaseTransformer						,		snake_case_	:	argparse.Namespace						,		snake_case_	:	Optional[int]=None						,		snake_case_	:	List[Any]=True						,		snake_case_	:	Optional[Any]=[]						,		snake_case_	:	List[str]=None						,		snake_case_	:	int=None						,		**snake_case_	:	Optional[int]						,		) ->					Dict:






 '''simple docstring'''

 pl.seed_everything(args.seed				)

 # init model
 __lowerCAmelCase    							=						Path(model.hparams.output_dir				)
 odir.mkdir(exist_ok=snake_case_				)

 # add custom checkpoints
 if checkpoint_callback is None:
  __lowerCAmelCase    							=						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(snake_case_				)
 if logging_callback is None:
  __lowerCAmelCase    							=						LoggingCallback()

 __lowerCAmelCase    							=						{}

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

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

 __lowerCAmelCase    							=						args.accumulate_grad_batches
 __lowerCAmelCase    							=						extra_train_kwargs.get("""accelerator"""						,		snake_case_				)
 __lowerCAmelCase    							=						extra_train_kwargs.get("""profiler"""						,		snake_case_				)

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

 if args.do_train:
  trainer.fit(snake_case_				)

 return trainer






