






"""simple docstring"""



import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
  lowercase							    =       Image.open(requests.get(__snake_case		,					stream=__snake_case    ).raw    ).convert('RGB'    )

  return image
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       []
  # fmt: off

  # vision encoder
  rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')    )
  rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')    )
  rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')    )
  rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')    )
  rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')    )
  rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')    )

  for i in range(config.vision_config.num_hidden_layers    ):
    rename_keys.append((f'visual_encoder.blocks.{i}.norm1.weight', f'vision_model.encoder.layers.{i}.layer_norm1.weight')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.norm1.bias', f'vision_model.encoder.layers.{i}.layer_norm1.bias')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.norm2.weight', f'vision_model.encoder.layers.{i}.layer_norm2.weight')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.norm2.bias', f'vision_model.encoder.layers.{i}.layer_norm2.bias')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.attn.qkv.weight', f'vision_model.encoder.layers.{i}.self_attn.qkv.weight')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.weight', f'vision_model.encoder.layers.{i}.self_attn.projection.weight',)    )
    rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.bias', f'vision_model.encoder.layers.{i}.self_attn.projection.bias')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.weight', f'vision_model.encoder.layers.{i}.mlp.fc1.weight')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.bias', f'vision_model.encoder.layers.{i}.mlp.fc1.bias')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.weight', f'vision_model.encoder.layers.{i}.mlp.fc2.weight')    )
    rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.bias', f'vision_model.encoder.layers.{i}.mlp.fc2.bias')    )

  # QFormer
  rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')    )
  rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')    )

  # fmt: on
  return rename_keys
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  Dict		,					__snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       dct.pop(__snake_case    )
  lowercase							    =       val
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  str    ):





  '''simple docstring'''





  for i in range(config.vision_config.num_hidden_layers    ):
    # read in original q and v biases
    lowercase							    =       state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias'    )
    lowercase							    =       state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias'    )

    # next, set bias in the state dict
    lowercase							    =       torch.cat((q_bias, torch.zeros_like(__snake_case		,					requires_grad=__snake_case    ), v_bias)    )
    lowercase							    =       qkv_bias
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       3_64 if 'coco' in model_name else 2_24
  lowercase							    =       InstructBlipVisionConfig(image_size=__snake_case    ).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "t5-xl" in model_name:
    lowercase							    =       TaConfig.from_pretrained('google/flan-t5-xl'		,					dense_act_fn='gelu'		,					bos_token_id=1    ).to_dict()
  elif "t5-xxl" in model_name:
    lowercase							    =       TaConfig.from_pretrained('google/flan-t5-xxl'		,					dense_act_fn='gelu'		,					bos_token_id=1    ).to_dict()
  elif "vicuna-7b" in model_name:
    lowercase							    =       LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'		,					vocab_size=3_20_01    ).to_dict()
  elif "vicuna-13b" in model_name:
    lowercase							    =       LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'		,					vocab_size=3_20_01    ).to_dict()
  else:
    raise ValueError('Model name not supported'    )

  # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
  lowercase							    =       InstructBlipQFormerConfig(vocab_size=3_05_23    ).to_dict()
  lowercase							    =       InstructBlipConfig(vision_config=__snake_case		,					text_config=__snake_case		,					qformer_config=__snake_case    )

  return config, image_size
@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  List[Any]=None		,					__snake_case			:  Dict=False    ):





  '''simple docstring'''





  lowercase							    =       AutoTokenizer.from_pretrained('bert-base-uncased'		,					truncation_side='left'    )
  qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}    )

  if "t5" in model_name:
    lowercase							    =       TaTokenizerFast.from_pretrained('google/flan-t5-xl'		,					truncation_side='left'    )
  elif "vicuna" in model_name:
    # the following was used in the original implementation:
    # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
    # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
    # tokenizer.add_special_tokens({"bos_token": "</s>"})
    # tokenizer.add_special_tokens({"eos_token": "</s>"})
    # tokenizer.add_special_tokens({"unk_token": "</s>"})
    lowercase							    =       LlamaTokenizerFast.from_pretrained(
        'huggyllama/llama-7b'		,					truncation_side='left'		,					bos_token='</s>'		,					unk_token='</s>'    )
    tokenizer.add_special_tokens({'pad_token': '[PAD]'}    )

  lowercase    ,     lowercase							    =       get_blipa_config(__snake_case    )
  lowercase							    =       InstructBlipForConditionalGeneration(__snake_case    ).eval()

  lowercase							    =       {
      'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
      'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
      'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
      'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
  }

  lowercase    ,     lowercase							    =       model_name_to_original[model_name]

  # load original model
  print('Loading original model...'    )
  lowercase							    =       'cuda:1' if torch.cuda.is_available() else 'cpu'
  lowercase							    =       'cuda:2' if torch.cuda.is_available() else 'cpu'
  lowercase    ,     lowercase    ,     lowercase							    =       load_model_and_preprocess(
      name=__snake_case		,					model_type=__snake_case		,					is_eval=__snake_case		,					device=__snake_case    )
  original_model.eval()
  print('Done!'    )

  # update state dict keys
  lowercase							    =       original_model.state_dict()
  lowercase							    =       create_rename_keys(__snake_case    )
  for src, dest in rename_keys:
    rename_key(__snake_case		,					__snake_case		,					__snake_case    )

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    lowercase							    =       state_dict.pop(__snake_case    )
    if key.startswith('Qformer.bert'    ):
      lowercase							    =       key.replace('Qformer.bert'		,					'qformer'    )
    if "attention.self" in key:
      lowercase							    =       key.replace('self'		,					'attention'    )
    if "llm_proj" in key:
      lowercase							    =       key.replace('llm_proj'		,					'language_projection'    )
    if "t5_proj" in key:
      lowercase							    =       key.replace('t5_proj'		,					'language_projection'    )
    if key.startswith('llm_model'    ):
      lowercase							    =       key.replace('llm_model'		,					'language_model'    )
    if key.startswith('t5'    ):
      lowercase							    =       key.replace('t5'		,					'language'    )
    lowercase							    =       val

  # read in qv biases
  read_in_q_v_bias(__snake_case		,					__snake_case    )

  # note: weights get loaded in torch.float32 by default
  hf_model.load_state_dict(__snake_case		,					strict=__snake_case    )

  lowercase							    =       load_demo_image()
  lowercase							    =       'What is unusual about this image?'

  # create processor
  lowercase							    =       BlipImageProcessor(
      size={'height': image_size, 'width': image_size}		,					image_mean=__snake_case		,					image_std=__snake_case    )
  lowercase							    =       InstructBlipProcessor(
      image_processor=__snake_case		,					tokenizer=__snake_case		,					qformer_tokenizer=__snake_case		,					)
  lowercase							    =       processor(images=__snake_case		,					text=__snake_case		,					return_tensors='pt'    ).to(__snake_case    )

  # make sure processor creates exact same pixel values
  lowercase							    =       vis_processors['eval'](__snake_case    ).unsqueeze(0    ).to(__snake_case    )
  lowercase							    =       inputs.pixel_values
  assert torch.allclose(original_pixel_values.to(pixel_values.device    )		,					__snake_case    )

  original_model.to(__snake_case    )
  hf_model.to(__snake_case    )
  with torch.no_grad():
    if "vicuna" in model_name:
      lowercase							    =       original_model({'image': original_pixel_values, 'text_input': [prompt]}    ).logits
      lowercase							    =       hf_model(**__snake_case    ).logits
    else:
      lowercase							    =       original_model(
          {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}    ).logits
      lowercase							    =       tokenizer('\n'		,					return_tensors='pt'    ).input_ids.to(__snake_case    )
      lowercase							    =       label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id		,					-1_00    )
      lowercase							    =       hf_model(**__snake_case		,					labels=__snake_case    ).logits

  print('First values of original logits:'		,					original_logits[0, :3, :3]    )
  print('First values of HF logits:'		,					logits[0, :3, :3]    )

  # assert values
  assert original_logits.shape == logits.shape
  lowercase							    =       1e-4 if 'vicuna' in model_name else 1e-5
  assert torch.allclose(original_logits.to(logits.device    )		,					__snake_case		,					atol=__snake_case    )
  print('Looks ok!'    )

  print('Generating with original model...'    )
  lowercase							    =       original_model.generate({'image': original_pixel_values, 'prompt': prompt}		,					num_beams=5    )

  # important: we need to cast the weights of the HF model to the appropriate type
  print('Generating with HF model...'    )
  lowercase							    =       hf_model.generate(
      **__snake_case		,					do_sample=__snake_case		,					num_beams=5		,					max_length=2_56		,					min_length=1		,					top_p=0.9		,					repetition_penalty=1.5		,					length_penalty=1.0		,					temperature=1		,					)
  if "vicuna" in model_name:
    # convert output id 0 to 2 (eos_token_id)
    # TODO add this in the generate method?
    lowercase							    =       2
  print('Original generation:'		,					__snake_case    )
  lowercase							    =       processor.batch_decode(__snake_case		,					skip_special_tokens=__snake_case    )
  lowercase							    =       [text.strip() for text in output_text]
  print('HF generation:'		,					__snake_case    )

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(__snake_case    )
    hf_model.save_pretrained(__snake_case    )

  if push_to_hub:
    processor.push_to_hub(f'Salesforce/{model_name}'    )
    hf_model.push_to_hub(f'Salesforce/{model_name}'    )


if __name__ == "__main__":
    _UpperCamelCase		:  List[str]						=		argparse.ArgumentParser()
    _UpperCamelCase		:  str						=		[
        'instructblip-vicuna-7b',
        'instructblip-vicuna-13b',
        'instructblip-flan-t5-xl',
        'instructblip-flan-t5-xxl',
    ]
    parser.add_argument(
        '--model_name',
        default='instructblip-flan-t5-xl',
        choices=choices,
        type=str,
        help='Path to hf config.json of model to convert',
    )
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument(
        '--push_to_hub',
        action='store_true',
        help='Whether to push the model and processor to the hub after converting',
    )

    _UpperCamelCase		:  str						=		parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""



import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class        a     (   a_	):
    UpperCAmelCase_ :     BigBirdConfig
    UpperCAmelCase_ :     jnp.dtype	     =jnp.floataa
    UpperCAmelCase_ :     bool	     =True







    def 			UpperCamelCase_      (			self       ):
      super().setup()
      lowercase							    =       nn.Dense(5							,		dtype=self.dtype       )







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      lowercase							    =       super().__call__(*_lowerCamelCase							,		**_lowerCamelCase       )
      lowercase							    =       self.cls(outputs[2]       )
      return outputs[:2] + (cls_out,)


class        a     (   a_	):
    UpperCAmelCase_ :     str	     =FlaxBigBirdForNaturalQuestionsModule


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  Dict		,					__snake_case			:  Optional[Any]		,					__snake_case			:  Optional[int]		,					__snake_case			:  Tuple		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  def cross_entropy(__snake_case			:  Dict		,					__snake_case			:  str		,					__snake_case			:  Any=None    ):
    lowercase							    =       logits.shape[-1]
    lowercase							    =       (labels[..., None] == jnp.arange(__snake_case    )[None]).astype('f4'    )
    lowercase							    =       jax.nn.log_softmax(__snake_case		,					axis=-1    )
    lowercase							    =       -jnp.sum(labels * logits		,					axis=-1    )
    if reduction is not None:
      lowercase							    =       reduction(__snake_case    )
    return loss

  lowercase							    =       partial(__snake_case		,					reduction=jnp.mean    )
  lowercase							    =       cross_entropy(__snake_case		,					__snake_case    )
  lowercase							    =       cross_entropy(__snake_case		,					__snake_case    )
  lowercase							    =       cross_entropy(__snake_case		,					__snake_case    )
  return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class        a     :
    UpperCAmelCase_ :     str	     ="google/bigbird-roberta-base"
    UpperCAmelCase_ :     int	     =3000
    UpperCAmelCase_ :     int	     =1_0500

    UpperCAmelCase_ :     int	     =128
    UpperCAmelCase_ :     int	     =3

    UpperCAmelCase_ :     int	     =1
    UpperCAmelCase_ :     int	     =5

    # tx_args
    UpperCAmelCase_ :     float	     =3e-5
    UpperCAmelCase_ :     float	     =0.0
    UpperCAmelCase_ :     int	     =2_0000
    UpperCAmelCase_ :     float	     =0.00_95

    UpperCAmelCase_ :     str	     ="bigbird-roberta-natural-questions"
    UpperCAmelCase_ :     str	     ="training-expt"
    UpperCAmelCase_ :     str	     ="data/nq-training.jsonl"
    UpperCAmelCase_ :     str	     ="data/nq-validation.jsonl"







    def 			UpperCamelCase_      (			self       ):
      os.makedirs(self.base_dir							,		exist_ok=_lowerCamelCase       )
      lowercase							    =       os.path.join(self.base_dir							,		self.save_dir       )
      lowercase							    =       self.batch_size_per_device * jax.device_count()


@dataclass
class        a     :
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int	     =4096  # no dynamic padding on TPUs







    def __call__(			self							,		_lowerCamelCase       ):
      lowercase							    =       self.collate_fn(_lowerCamelCase       )
      lowercase							    =       jax.tree_util.tree_map(_lowerCamelCase							,		_lowerCamelCase       )
      return batch







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     lowercase							    =       self.fetch_inputs(features['input_ids']       )
      lowercase							    =       {
          'input_ids': jnp.array(_lowerCamelCase							,		dtype=jnp.intaa       ),
          'attention_mask': jnp.array(_lowerCamelCase							,		dtype=jnp.intaa       ),
          'start_labels': jnp.array(features['start_token']							,		dtype=jnp.intaa       ),
          'end_labels': jnp.array(features['end_token']							,		dtype=jnp.intaa       ),
          'pooled_labels': jnp.array(features['category']							,		dtype=jnp.intaa       ),
      }
      return batch







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       [self._fetch_inputs(_lowerCamelCase       ) for ids in input_ids]
      return zip(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       [1 for _ in range(len(_lowerCamelCase       )       )]
      while len(_lowerCamelCase       ) < self.max_length:
        input_ids.append(self.pad_id       )
        attention_mask.append(0       )
      return input_ids, attention_mask


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  Tuple		,					__snake_case			:  Optional[Any]=None    ):





  '''simple docstring'''





  if seed is not None:
    lowercase							    =       dataset.shuffle(seed=__snake_case    )
  for i in range(len(__snake_case    ) // batch_size    ):
    lowercase							    =       dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(__snake_case    )
@partial(jax.pmap		,					axis_name='batch'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  List[Any]		,					**__snake_case			:  List[Any]    ):





  '''simple docstring'''





  def loss_fn(__snake_case			:  str    ):
    lowercase							    =       model_inputs.pop('start_labels'    )
    lowercase							    =       model_inputs.pop('end_labels'    )
    lowercase							    =       model_inputs.pop('pooled_labels'    )

    lowercase							    =       state.apply_fn(**__snake_case		,					params=__snake_case		,					dropout_rng=__snake_case		,					train=__snake_case    )
    lowercase    ,     lowercase    ,     lowercase							    =       outputs

    return state.loss_fn(
        __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					)

  lowercase    ,     lowercase							    =       jax.random.split(__snake_case    )
  lowercase							    =       jax.value_and_grad(__snake_case    )
  lowercase    ,     lowercase							    =       grad_fn(state.params    )
  lowercase							    =       jax.lax.pmean({'loss': loss}		,					axis_name='batch'    )
  lowercase							    =       jax.lax.pmean(__snake_case		,					'batch'    )

  lowercase							    =       state.apply_gradients(grads=__snake_case    )
  return state, metrics, new_drp_rng
@partial(jax.pmap		,					axis_name='batch'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					**__snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       model_inputs.pop('start_labels'    )
  lowercase							    =       model_inputs.pop('end_labels'    )
  lowercase							    =       model_inputs.pop('pooled_labels'    )

  lowercase							    =       state.apply_fn(**__snake_case		,					params=state.params		,					train=__snake_case    )
  lowercase    ,     lowercase    ,     lowercase							    =       outputs

  lowercase							    =       state.loss_fn(__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case    )
  lowercase							    =       jax.lax.pmean({'loss': loss}		,					axis_name='batch'    )
  return metrics

class        a     (   train_state.TrainState	):
    UpperCAmelCase_ :     Callable	     =struct.field(pytree_node=a_	)


@dataclass
class        a     :
    UpperCAmelCase_ :     Args
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     wandb
    UpperCAmelCase_ :     Callable	     =None







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=None       ):
      lowercase							    =       model.params
      lowercase							    =       TrainState.create(
          apply_fn=model.__call__							,		params=_lowerCamelCase							,		tx=_lowerCamelCase							,		loss_fn=_lowerCamelCase							,		)
      if ckpt_dir is not None:
        lowercase    ,     lowercase    ,     lowercase    ,     lowercase    ,     lowercase							    =       restore_checkpoint(_lowerCamelCase							,		_lowerCamelCase       )
        lowercase							    =       {
            'lr': args.lr,
            'init_lr': args.init_lr,
            'warmup_steps': args.warmup_steps,
            'num_train_steps': num_train_steps,
            'weight_decay': args.weight_decay,
        }
        lowercase    ,     lowercase							    =       build_tx(**_lowerCamelCase       )
        lowercase							    =       train_state.TrainState(
            step=_lowerCamelCase							,		apply_fn=model.__call__							,		params=_lowerCamelCase							,		tx=_lowerCamelCase							,		opt_state=_lowerCamelCase							,		)
        lowercase							    =       args
        lowercase							    =       data_collator
        lowercase							    =       lr
        lowercase							    =       params
      lowercase							    =       jax_utils.replicate(_lowerCamelCase       )
      return state







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self.args
      lowercase							    =       len(_lowerCamelCase       ) // args.batch_size

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )
      for epoch in range(args.max_epochs       ):
        lowercase							    =       jnp.array(0							,		dtype=jnp.floataa       )
        lowercase							    =       get_batched_dataset(_lowerCamelCase							,		args.batch_size							,		seed=_lowerCamelCase       )
        lowercase							    =       0
        for batch in tqdm(_lowerCamelCase							,		total=_lowerCamelCase							,		desc=F'Running EPOCH-{epoch}'       ):
          lowercase							    =       self.data_collator(_lowerCamelCase       )
          lowercase    ,     lowercase    ,     lowercase							    =       self.train_step_fn(_lowerCamelCase							,		_lowerCamelCase							,		**_lowerCamelCase       )
          running_loss += jax_utils.unreplicate(metrics['loss']       )
          i += 1
          if i % args.logging_steps == 0:
            lowercase							    =       jax_utils.unreplicate(state.step       )
            lowercase							    =       running_loss.item() / i
            lowercase							    =       self.scheduler_fn(state_step - 1       )

            lowercase							    =       self.evaluate(_lowerCamelCase							,		_lowerCamelCase       )
            lowercase							    =       {
                'step': state_step.item(),
                'eval_loss': eval_loss.item(),
                'tr_loss': tr_loss,
                'lr': lr.item(),
            }
            tqdm.write(str(_lowerCamelCase       )       )
            self.logger.log(_lowerCamelCase							,		commit=_lowerCamelCase       )

          if i % args.save_steps == 0:
            self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}'							,		state=_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       get_batched_dataset(_lowerCamelCase							,		self.args.batch_size       )
      lowercase							    =       len(_lowerCamelCase       ) // self.args.batch_size
      lowercase							    =       jnp.array(0							,		dtype=jnp.floataa       )
      lowercase							    =       0
      for batch in tqdm(_lowerCamelCase							,		total=_lowerCamelCase							,		desc='Evaluating ... '       ):
        lowercase							    =       self.data_collator(_lowerCamelCase       )
        lowercase							    =       self.val_step_fn(_lowerCamelCase							,		**_lowerCamelCase       )
        running_loss += jax_utils.unreplicate(metrics['loss']       )
        i += 1
      return running_loss / i







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       jax_utils.unreplicate(_lowerCamelCase       )
      print(F'SAVING CHECKPOINT IN {save_dir}'							,		end=' ... '       )
      self.model_save_fn(_lowerCamelCase							,		params=state.params       )
      with open(os.path.join(_lowerCamelCase							,		'opt_state.msgpack'       )							,		'wb'       ) as f:
        f.write(to_bytes(state.opt_state       )       )
      joblib.dump(self.args							,		os.path.join(_lowerCamelCase							,		'args.joblib'       )       )
      joblib.dump(self.data_collator							,		os.path.join(_lowerCamelCase							,		'data_collator.joblib'       )       )
      with open(os.path.join(_lowerCamelCase							,		'training_state.json'       )							,		'w'       ) as f:
        json.dump({'step': state.step.item()}							,		_lowerCamelCase       )
      print('DONE'       )


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  print(f'RESTORING CHECKPOINT FROM {save_dir}'		,					end=' ... '    )
  with open(os.path.join(__snake_case		,					'flax_model.msgpack'    )		,					'rb'    ) as f:
    lowercase							    =       from_bytes(state.params		,					f.read()    )

  with open(os.path.join(__snake_case		,					'opt_state.msgpack'    )		,					'rb'    ) as f:
    lowercase							    =       from_bytes(state.opt_state		,					f.read()    )

  lowercase							    =       joblib.load(os.path.join(__snake_case		,					'args.joblib'    )    )
  lowercase							    =       joblib.load(os.path.join(__snake_case		,					'data_collator.joblib'    )    )

  with open(os.path.join(__snake_case		,					'training_state.json'    )		,					'r'    ) as f:
    lowercase							    =       json.load(__snake_case    )
  lowercase							    =       training_state['step']

  print('DONE'    )
  return params, opt_state, step, args, data_collator
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  str		,					__snake_case			:  Any		,					__snake_case			:  Any    ):





  '''simple docstring'''





  lowercase							    =       num_train_steps - warmup_steps
  lowercase							    =       optax.linear_schedule(init_value=__snake_case		,					end_value=__snake_case		,					transition_steps=__snake_case    )
  lowercase							    =       optax.linear_schedule(init_value=__snake_case		,					end_value=1e-7		,					transition_steps=__snake_case    )
  lowercase							    =       optax.join_schedules(schedules=[warmup_fn, decay_fn]		,					boundaries=[warmup_steps]    )
  return lr



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]		,					__snake_case			:  str		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  def weight_decay_mask(__snake_case			:  Tuple    ):
    lowercase							    =       traverse_util.flatten_dict(__snake_case    )
    lowercase							    =       {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
    return traverse_util.unflatten_dict(__snake_case    )

  lowercase							    =       scheduler_fn(__snake_case		,					__snake_case		,					__snake_case		,					__snake_case    )

  lowercase							    =       optax.adamw(learning_rate=__snake_case		,					weight_decay=__snake_case		,					mask=__snake_case    )
  return tx, lr


