


'''simple docstring'''







from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin


@dataclass
class 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					42




class 				__lowerCamelCase				( a_       ,      a_ ):
    """simple docstring"""


    @register_to_config
    def __init__(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int = 16   ,							SCREAMING_SNAKE_CASE    :							int = 88   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							int = 1   ,							SCREAMING_SNAKE_CASE    :							float = 0.0   ,							SCREAMING_SNAKE_CASE    :							int = 32   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							str = "geglu"   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							str = "layer_norm"   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							):
           super().__init__()
           _A  :			Optional[int]											=      use_linear_projection
           _A  :			Union[str, Any]											=      num_attention_heads
           _A  :			Optional[int]											=      attention_head_dim
           _A  :			List[str]											=      num_attention_heads * attention_head_dim

           # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
           # Define whether input is continuous or discrete depending on configuration
           _A  :			List[str]											=      (in_channels is not None) and (patch_size is None)
           _A  :			Dict											=      num_vector_embeds is not None
           _A  :			Tuple											=      in_channels is not None and patch_size is not None

           if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                  _A  :			Optional[int]											=      (
                      F'The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'
                      ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
                      ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
                      ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
                      ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
                  )
                  deprecate('norm_type!=num_embeds_ada_norm'   ,							'1.0.0'   ,							SCREAMING_SNAKE_CASE   ,							standard_warn=SCREAMING_SNAKE_CASE)
                  _A  :			List[str]											=      'ada_norm'

           if self.is_input_continuous and self.is_input_vectorized:
                  raise ValueError(
                      F'Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'
                      ' sure that either `in_channels` or `num_vector_embeds` is None.')
           elif self.is_input_vectorized and self.is_input_patches:
                  raise ValueError(
                      F'Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'
                      ' sure that either `num_vector_embeds` or `num_patches` is None.')
           elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
                  raise ValueError(
                      F'Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'
                      F' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.')

           # 2. Define input layers
           if self.is_input_continuous:
                  _A  :			Optional[int]											=      in_channels

                  _A  :			Optional[Any]											=      torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE   ,							num_channels=SCREAMING_SNAKE_CASE   ,							eps=1e-6   ,							affine=SCREAMING_SNAKE_CASE)
                  if use_linear_projection:
                         _A  :			str											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			Dict											=      nn.Convad(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							kernel_size=1   ,							stride=1   ,							padding=0)
           elif self.is_input_vectorized:
                  assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
                  assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

                  _A  :			Dict											=      sample_size
                  _A  :			Any											=      sample_size
                  _A  :			List[str]											=      num_vector_embeds
                  _A  :			Optional[int]											=      self.height * self.width

                  _A  :			int											=      ImagePositionalEmbeddings(
                      num_embed=SCREAMING_SNAKE_CASE   ,							embed_dim=SCREAMING_SNAKE_CASE   ,							height=self.height   ,							width=self.width)
           elif self.is_input_patches:
                  assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

                  _A  :			Union[str, Any]											=      sample_size
                  _A  :			Optional[Any]											=      sample_size

                  _A  :			Optional[int]											=      patch_size
                  _A  :			Any											=      PatchEmbed(
                      height=SCREAMING_SNAKE_CASE   ,							width=SCREAMING_SNAKE_CASE   ,							patch_size=SCREAMING_SNAKE_CASE   ,							in_channels=SCREAMING_SNAKE_CASE   ,							embed_dim=SCREAMING_SNAKE_CASE   ,							)

           # 3. Define transformers blocks
           _A  :			Dict											=      nn.ModuleList(
               [
                   BasicTransformerBlock(
                       SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							dropout=SCREAMING_SNAKE_CASE   ,							cross_attention_dim=SCREAMING_SNAKE_CASE   ,							activation_fn=SCREAMING_SNAKE_CASE   ,							num_embeds_ada_norm=SCREAMING_SNAKE_CASE   ,							attention_bias=SCREAMING_SNAKE_CASE   ,							only_cross_attention=SCREAMING_SNAKE_CASE   ,							upcast_attention=SCREAMING_SNAKE_CASE   ,							norm_type=SCREAMING_SNAKE_CASE   ,							norm_elementwise_affine=SCREAMING_SNAKE_CASE   ,							)
                   for d in range(SCREAMING_SNAKE_CASE)
               ])

           # 4. Define output layers
           _A  :			Union[str, Any]											=      in_channels if out_channels is None else out_channels
           if self.is_input_continuous:
                  # TODO: should use out_channels for continuous projections
                  if use_linear_projection:
                         _A  :			Union[str, Any]											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			int											=      nn.Convad(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							kernel_size=1   ,							stride=1   ,							padding=0)
           elif self.is_input_vectorized:
                  _A  :			Dict											=      nn.LayerNorm(SCREAMING_SNAKE_CASE)
                  _A  :			Dict											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							self.num_vector_embeds - 1)
           elif self.is_input_patches:
                  _A  :			Union[str, Any]											=      nn.LayerNorm(SCREAMING_SNAKE_CASE   ,							elementwise_affine=SCREAMING_SNAKE_CASE   ,							eps=1e-6)
                  _A  :			Any											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							2 * inner_dim)
                  _A  :			Optional[int]											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							patch_size * patch_size * self.out_channels)


    def      A					(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							torch.Tensor   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.Tensor] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.LongTensor] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.LongTensor] = None   ,							SCREAMING_SNAKE_CASE    :							Dict[str, Any] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.Tensor] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.Tensor] = None   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							):
           # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
           #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
           #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
           # expects mask of shape:
           #   [batch, key_tokens]
           # adds singleton query_tokens dimension:
           #   [batch,                    1, key_tokens]
           # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
           #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
           #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
           if attention_mask is not None and attention_mask.ndim == 2:
                  # assume that mask is expressed as:
                  #   (1 = keep,      0 = discard)
                  # convert mask into a bias that can be added to attention scores:
                  #       (keep = +0,     discard = -10000.0)
                  _A  :			Optional[int]											=      (1 - attention_mask.to(hidden_states.dtype)) * -1_0000.0
                  _A  :			Any											=      attention_mask.unsqueeze(1)

           # convert encoder_attention_mask to a bias the same way we do for attention_mask
           if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                  _A  :			Dict											=      (1 - encoder_attention_mask.to(hidden_states.dtype)) * -1_0000.0
                  _A  :			Optional[Any]											=      encoder_attention_mask.unsqueeze(1)

           # 1. Input
           if self.is_input_continuous:
                  _A						,				_A						,				_A						,				_A  :			Union[str, Any]											=      hidden_states.shape
                  _A  :			Any											=      hidden_states

                  _A  :			Optional[int]											=      self.norm(SCREAMING_SNAKE_CASE)
                  if not self.use_linear_projection:
                         _A  :			str											=      self.proj_in(SCREAMING_SNAKE_CASE)
                         _A  :			Tuple											=      hidden_states.shape[1]
                         _A  :			Optional[Any]											=      hidden_states.permute(0   ,							2   ,							3   ,							1).reshape(SCREAMING_SNAKE_CASE   ,							height * width   ,							SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			int											=      hidden_states.shape[1]
                         _A  :			List[Any]											=      hidden_states.permute(0   ,							2   ,							3   ,							1).reshape(SCREAMING_SNAKE_CASE   ,							height * width   ,							SCREAMING_SNAKE_CASE)
                         _A  :			int											=      self.proj_in(SCREAMING_SNAKE_CASE)
           elif self.is_input_vectorized:
                  _A  :			Any											=      self.latent_image_embedding(SCREAMING_SNAKE_CASE)
           elif self.is_input_patches:
                  _A  :			Optional[Any]											=      self.pos_embed(SCREAMING_SNAKE_CASE)

           # 2. Blocks
           for block in self.transformer_blocks:
                  _A  :			Any											=      block(
                      SCREAMING_SNAKE_CASE   ,							attention_mask=SCREAMING_SNAKE_CASE   ,							encoder_hidden_states=SCREAMING_SNAKE_CASE   ,							encoder_attention_mask=SCREAMING_SNAKE_CASE   ,							timestep=SCREAMING_SNAKE_CASE   ,							cross_attention_kwargs=SCREAMING_SNAKE_CASE   ,							class_labels=SCREAMING_SNAKE_CASE   ,							)

           # 3. Output
           if self.is_input_continuous:
                  if not self.use_linear_projection:
                         _A  :			Dict											=      hidden_states.reshape(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE).permute(0   ,							3   ,							1   ,							2).contiguous()
                         _A  :			List[Any]											=      self.proj_out(SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			List[Any]											=      self.proj_out(SCREAMING_SNAKE_CASE)
                         _A  :			List[str]											=      hidden_states.reshape(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE).permute(0   ,							3   ,							1   ,							2).contiguous()

                  _A  :			List[str]											=      hidden_states + residual
           elif self.is_input_vectorized:
                  _A  :			Optional[int]											=      self.norm_out(SCREAMING_SNAKE_CASE)
                  _A  :			Optional[int]											=      self.out(SCREAMING_SNAKE_CASE)
                  # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                  _A  :			List[Any]											=      logits.permute(0   ,							2   ,							1)

                  # log(p(x_0))
                  _A  :			Any											=      F.log_softmax(logits.double()   ,							dim=1).float()
           elif self.is_input_patches:
                  # TODO: cleanup!
                  _A  :			Any											=      self.transformer_blocks[0].norma.emb(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							hidden_dtype=hidden_states.dtype)
                  _A						,				_A  :			List[Any]											=      self.proj_out_a(F.silu(SCREAMING_SNAKE_CASE)).chunk(2   ,							dim=1)
                  _A  :			Optional[Any]											=      self.norm_out(SCREAMING_SNAKE_CASE) * (1 + scale[:, None]) + shift[:, None]
                  _A  :			Optional[int]											=      self.proj_out_a(SCREAMING_SNAKE_CASE)

                  # unpatchify
                  _A  :			Tuple											=      int(hidden_states.shape[1] ** 0.5)
                  _A  :			Optional[int]											=      hidden_states.reshape(
                      shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
                  _A  :			Union[str, Any]											=      torch.einsum('nhwpqc->nchpwq'   ,							SCREAMING_SNAKE_CASE)
                  _A  :			Union[str, Any]											=      hidden_states.reshape(
                      shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size))

           if not return_dict:
                  return (output,)

           return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE)










'''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   :  Optional[Any]       		=				logging.getLogger(__name__)

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

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


class 				__lowerCamelCase				( pl.LightningModule ):
    """simple docstring"""


    def __init__(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							argparse.Namespace   ,							SCREAMING_SNAKE_CASE    :							Any=None   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]="base"   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=None   ,							SCREAMING_SNAKE_CASE    :							Tuple=None   ,							SCREAMING_SNAKE_CASE    :							str=None   ,							**SCREAMING_SNAKE_CASE    :							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)
           _A  :			Optional[int]											=      0
           _A  :			int											=      Path(self.hparams.output_dir)
           _A  :			str											=      self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
                  _A  :			Dict											=      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:
                  _A  :			PretrainedConfig											=      config

           _A  :			int											=      ('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:
                  _A  :			Tuple											=      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:
                  _A  :			PreTrainedTokenizer											=      tokenizer
           _A  :			List[str]											=      MODEL_MODES[mode]
           if model is None:
                  _A  :			Optional[int]											=      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:
                  _A  :			Dict											=      model


    def      A					(						self    :							int   ,							*SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							**SCREAMING_SNAKE_CASE    :							List[Any]):
           _A  :			List[Any]											=      self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)


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


    def      A					(						self    :							Dict):
           _A  :			Any											=      self.model
           _A  :			List[Any]											=      ['bias', 'LayerNorm.weight']
           _A  :			Union[str, Any]											=      [
               {
                   'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                   'weight_decay': self.hparams.weight_decay,
               },
               {
                   'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                   'weight_decay': 0.0,
               },
           ]
           if self.hparams.adafactor:
                  _A  :			str											=      Adafactor(
                      SCREAMING_SNAKE_CASE   ,							lr=self.hparams.learning_rate   ,							scale_parameter=SCREAMING_SNAKE_CASE   ,							relative_step=SCREAMING_SNAKE_CASE)

           else:
                  _A  :			str											=      AdamW(
                      SCREAMING_SNAKE_CASE   ,							lr=self.hparams.learning_rate   ,							eps=self.hparams.adam_epsilon)
           _A  :			Tuple											=      optimizer

           _A  :			List[Any]											=      self.get_lr_scheduler()

           return [optimizer], [scheduler]


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           return self.validation_step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           return self.validation_end(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple):
           _A  :			Tuple											=      max(1   ,							self.hparams.gpus)  # TODO: consider num_tpu_cores
           _A  :			Optional[int]											=      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    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           if mode == "test":
                  _A  :			Union[str, Any]											=      len(self.test_dataloader().dataset)
           else:
                  _A  :			Optional[int]											=      self.get_dataloader('train'   ,							self.hparams.train_batch_size   ,							shuffle=SCREAMING_SNAKE_CASE)
                  _A  :			Optional[int]											=      len(self.train_dataloader().dataset)


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							bool = False):
           raise NotImplementedError('You must implement this for your task')


    def      A					(						self    :							Optional[int]):
           return self.train_loader


    def      A					(						self    :							Tuple):
           return self.get_dataloader('dev'   ,							self.hparams.eval_batch_size   ,							shuffle=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[Any]):
           return self.get_dataloader('test'   ,							self.hparams.eval_batch_size   ,							shuffle=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           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    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Dict[str, Any]):
           _A  :			List[Any]											=      self.output_dir.joinpath('best_tfmr')
           _A  :			str											=      self.step_count
           self.model.save_pretrained(SCREAMING_SNAKE_CASE)
           self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           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 ):
    """simple docstring"""


    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							str):
           _A  :			Tuple											=      trainer.lr_schedulers[0]['scheduler']
           _A  :			Any											=      {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    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							pl.Trainer   ,							SCREAMING_SNAKE_CASE    :							pl.LightningModule):
           rank_zero_info('***** Validation results *****')
           _A  :			Tuple											=      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    :							int   ,							SCREAMING_SNAKE_CASE    :							pl.Trainer   ,							SCREAMING_SNAKE_CASE    :							pl.LightningModule):
           rank_zero_info('***** Test results *****')
           _A  :			str											=      trainer.callback_metrics
           # Log and save results to file
           _A  :			str											=      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    lowerCAmelCase__ (       lowerCamelCase  :    Tuple						,lowerCamelCase  :    int     ):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       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    lowerCAmelCase__ (       lowerCamelCase  :    BaseTransformer						,lowerCamelCase  :    argparse.Namespace						,lowerCamelCase  :    Union[str, Any]=None						,lowerCamelCase  :    Optional[Any]=True						,lowerCamelCase  :    List[Any]=[]						,lowerCamelCase  :    Dict=None						,lowerCamelCase  :    List[Any]=None						,**lowerCamelCase  :    str						,):
       pl.seed_everything(args.seed     )

       # init model
       _A  :			Optional[Any]											=      Path(model.hparams.output_dir     )
       odir.mkdir(exist_ok=lowerCamelCase     )

       # add custom checkpoints
       if checkpoint_callback is None:
              _A  :			Optional[Any]											=      pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir						,prefix='checkpoint'						,monitor='val_loss'						,mode='min'						,save_top_k=1     )
       if early_stopping_callback:
              extra_callbacks.append(lowerCamelCase     )
       if logging_callback is None:
              _A  :			Dict											=      LoggingCallback()

       _A  :			Union[str, Any]											=      {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _A  :			str											=      16
              _A  :			Optional[int]											=      args.fpaa_opt_level

       if args.gpus > 1:
              _A  :			Union[str, Any]											=      'ddp'

       _A  :			List[Any]											=      args.accumulate_grad_batches
       _A  :			Tuple											=      extra_train_kwargs.get('accelerator'						,lowerCamelCase     )
       _A  :			int											=      extra_train_kwargs.get('profiler'						,lowerCamelCase     )

       _A  :			Union[str, Any]											=      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






