



'''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


lowercase__  :	str            =      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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

class    lowerCamelCase_				(						pl.LightningModule	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase="base"  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    **__UpperCamelCase  ,    ):
       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(lowerCAmelCase__			)
       UpperCamelCase__	     =      0
       UpperCamelCase__	     =      Path(self.hparams.output_dir			)
       UpperCamelCase__	     =      self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
           UpperCamelCase__	     =      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=lowerCAmelCase__  ,    **lowerCAmelCase__  ,    )
       else:
           UpperCamelCase__	     =      config

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

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


   def 						_UpperCamelCase							(	self  ,    *__UpperCamelCase  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      self.model_type.from_pretrained(*lowerCAmelCase__  ,    **lowerCAmelCase__			)


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


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.model
       UpperCamelCase__	     =      ["bias", "LayerNorm.weight"]
       UpperCamelCase__	     =      [
           {
               "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:
           UpperCamelCase__	     =      Adafactor(
               lowerCAmelCase__  ,    lr=self.hparams.learning_rate  ,    scale_parameter=lowerCAmelCase__  ,    relative_step=lowerCAmelCase__			)

       else:
           UpperCamelCase__	     =      AdamW(
               lowerCAmelCase__  ,    lr=self.hparams.learning_rate  ,    eps=self.hparams.adam_epsilon			)
       UpperCamelCase__	     =      optimizer

       UpperCamelCase__	     =      self.get_lr_scheduler()

       return [optimizer], [scheduler]


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       return self.validation_step(lowerCAmelCase__  ,    lowerCAmelCase__			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       return self.validation_end(lowerCAmelCase__			)


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


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       if mode == "test":
           UpperCamelCase__	     =      len(self.test_dataloader().dataset			)
       else:
           UpperCamelCase__	     =      self.get_dataloader("train"  ,    self.hparams.train_batch_size  ,    shuffle=lowerCAmelCase__			)
           UpperCamelCase__	     =      len(self.train_dataloader().dataset			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase = False			):
       raise NotImplementedError("You must implement this for your task"			)


   def 						_UpperCamelCase							(	self			):
       return self.train_loader


   def 						_UpperCamelCase							(	self			):
       return self.get_dataloader("dev"  ,    self.hparams.eval_batch_size  ,    shuffle=lowerCAmelCase__			)


   def 						_UpperCamelCase							(	self			):
       return self.get_dataloader("test"  ,    self.hparams.eval_batch_size  ,    shuffle=lowerCAmelCase__			)


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


   @pl.utilities.rank_zero_only
   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.output_dir.joinpath("best_tfmr"			)
       UpperCamelCase__	     =      self.step_count
       self.model.save_pretrained(lowerCAmelCase__			)
       self.tokenizer.save_pretrained(lowerCAmelCase__			)







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

class    lowerCamelCase_				(						pl.Callback	):






   '''simple docstring'''

   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      trainer.lr_schedulers[0]["scheduler"]
       UpperCamelCase__	     =      {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
       pl_module.logger.log_metrics(lowerCAmelCase__			)


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







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





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    #  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     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=None       ,	lowerCAmelCase_=True       ,	lowerCAmelCase_=[]       ,	lowerCAmelCase_=None       ,	lowerCAmelCase_=None       ,	**lowerCAmelCase_       ,	):
    pl.seed_everything(args.seed						)

    # init model
    UpperCamelCase__	     =      Path(model.hparams.output_dir						)
    odir.mkdir(exist_ok=lowerCAmelCase_						)

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

    UpperCamelCase__	     =      {}

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

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

    UpperCamelCase__	     =      args.accumulate_grad_batches
    UpperCamelCase__	     =      extra_train_kwargs.get("accelerator"       ,	lowerCAmelCase_						)
    UpperCamelCase__	     =      extra_train_kwargs.get("profiler"       ,	lowerCAmelCase_						)

    UpperCamelCase__	     =      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





"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_weight.chunk(3		,   dim=0   )
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_bias.chunk(3		,   dim=0   )

     snake_case_   			=   pt_attn_layer.out_proj.weight
     snake_case_   			=   pt_attn_layer.out_proj.bias

     snake_case_   			=   q_proj
     snake_case_   			=   q_proj_bias

     snake_case_   			=   k_proj
     snake_case_   			=   k_proj_bias

     snake_case_   			=   v_proj
     snake_case_   			=   v_proj_bias

     snake_case_   			=   out_proj_weights
     snake_case_   			=   out_proj_bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # copy layer norms
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )

     # copy MLP
     copy_mlp(hf_layer.mlp		,   pt_layer.mlp   )

     # copy attn
     copy_attn_layer(hf_layer.self_attn		,   pt_layer.attn   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     snake_case_   			=   pt_model.token_embedding.weight
     snake_case_   			=   pt_model.positional_embedding

     # copy layer norm
     copy_linear(hf_encoder.final_layer_norm		,   pt_model.ln_final   )

     # copy hidden layers
     copy_layers(hf_encoder.encoder.layers		,   pt_model.transformer.resblocks   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

     # copy text encoder
     copy_encoder(hf_model.text_model		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     snake_case_   			=   pt_model.visual.proj.data.T

     # copy layer norms
     copy_linear(hf_model.vision_model.pre_layrnorm		,   pt_model.visual.ln_pre   )
     copy_linear(hf_model.vision_model.post_layernorm		,   pt_model.visual.ln_post   )

     # copy embeds
     snake_case_   			=   pt_model.visual.conva.weight.data
     snake_case_   			=   pt_model.visual.class_embedding
     snake_case_   			=   pt_model.visual.positional_embedding.data

     # copy encoder
     copy_layers(hf_model.vision_model.encoder.layers		,   pt_model.visual.transformer.resblocks   )







@torch.no_grad()
def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     if config_path is not None:
          snake_case_   			=   CLIPConfig.from_pretrained(UpperCAmelCase   )
     else:
          snake_case_   			=   CLIPConfig(projection_dim=512		,   text_config={}		,   vision_config={}   )

     snake_case_   			=   CLIPModel(UpperCAmelCase   ).eval()

     snake_case_			,      snake_case_   			=   load(UpperCAmelCase		,   device='cpu'		,   jit=UpperCAmelCase   )
     snake_case_   			=   pt_model.eval()

     copy_text_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     copy_vison_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   pt_model.logit_scale

     snake_case_   			=   torch.arange(0		,   77   ).unsqueeze(0   )
     snake_case_   			=   torch.randn(1		,   3		,   224		,   224   )

     snake_case_   			=   hf_model(input_ids=UpperCAmelCase		,   pixel_values=UpperCAmelCase		,   return_dict=UpperCAmelCase   )
     snake_case_   			=   hf_outputs.logits_per_image
     snake_case_   			=   hf_outputs.logits_per_text
     snake_case_			,      snake_case_   			=   pt_model(UpperCAmelCase		,   UpperCAmelCase   )

     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )
     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )

     hf_model.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
    __UpperCamelCase										=     argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    __UpperCamelCase										=     parser.parse_args()

    convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

