

'''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     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						BigBirdConfig
  SCREAMING_SNAKE_CASE					:						jnp.dtype           =    jnp.floataa
  SCREAMING_SNAKE_CASE					:						bool           =    True






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   super().setup()
   __lowercase													=				nn.Dense(5      ,dtype=self.dtype    )






  def __call__(   self     :  Union[str, Any]      ,*lowercase__     :  int      ,**lowercase__     :  Dict    ):
   __lowercase													=				super().__call__(*UpperCAmelCase__      ,**UpperCAmelCase__    )
   __lowercase													=				self.cls(outputs[2]    )
   return outputs[:2] + (cls_out,)






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    FlaxBigBirdForNaturalQuestionsModule


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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






@dataclass
class     lowercase_			:




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    "google/bigbird-roberta-base"
  SCREAMING_SNAKE_CASE					:						int           =    3_0_0_0
  SCREAMING_SNAKE_CASE					:						int           =    1_0_5_0_0

  SCREAMING_SNAKE_CASE					:						int           =    1_2_8
  SCREAMING_SNAKE_CASE					:						int           =    3

  SCREAMING_SNAKE_CASE					:						int           =    1
  SCREAMING_SNAKE_CASE					:						int           =    5

  # tx_args
  SCREAMING_SNAKE_CASE					:						float           =    3e-5
  SCREAMING_SNAKE_CASE					:						float           =    0.0
  SCREAMING_SNAKE_CASE					:						int           =    2_0_0_0_0
  SCREAMING_SNAKE_CASE					:						float           =    0.0095

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   os.makedirs(self.base_dir      ,exist_ok=UpperCAmelCase__    )
   __lowercase													=				os.path.join(self.base_dir      ,self.save_dir    )
   __lowercase													=				self.batch_size_per_device * jax.device_count()






@dataclass
class     lowercase_			:




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						int
  SCREAMING_SNAKE_CASE					:						int           =    4_0_9_6  # no dynamic padding on TPUs






  def __call__(   self     :  Tuple      ,lowercase__     :  Optional[int]    ):
   __lowercase													=				self.collate_fn(UpperCAmelCase__    )
   __lowercase													=				jax.tree_util.tree_map(UpperCAmelCase__      ,UpperCAmelCase__    )
   return batch






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
   __lowercase													=				self.fetch_inputs(features['''input_ids''']    )
   __lowercase													=				{
       '''input_ids''': jnp.array(UpperCAmelCase__      ,dtype=jnp.intaa    ),
       '''attention_mask''': jnp.array(UpperCAmelCase__      ,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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  list    ):
   __lowercase													=				[self._fetch_inputs(UpperCAmelCase__    ) for ids in input_ids]
   return zip(*UpperCAmelCase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  list    ):
   __lowercase													=				[1 for _ in range(len(UpperCAmelCase__    )    )]
   while len(UpperCAmelCase__    ) < self.max_length:
    input_ids.append(self.pad_id    )
    attention_mask.append(0    )
   return input_ids, attention_mask


def 	_A  (					A__  ,       A__  ,       A__=None   ):






 """simple docstring"""



 if seed is not None:
  __lowercase													=				dataset.shuffle(seed=A__   )
 for i in range(len(A__   ) // batch_size   ):
  __lowercase													=				dataset[i * batch_size : (i + 1) * batch_size]
  yield dict(A__   )


@partial(jax.pmap  ,       axis_name='''batch'''   )
def 	_A  (					A__  ,       A__  ,       **A__   ):






 """simple docstring"""



 def loss_fn(A__   ):
  __lowercase													=				model_inputs.pop('''start_labels'''   )
  __lowercase													=				model_inputs.pop('''end_labels'''   )
  __lowercase													=				model_inputs.pop('''pooled_labels'''   )

  __lowercase													=				state.apply_fn(**A__  ,       params=A__  ,       dropout_rng=A__  ,       train=A__   )
  __lowercase													=				outputs

  return state.loss_fn(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       )

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

 __lowercase													=				state.apply_gradients(grads=A__   )
 return state, metrics, new_drp_rng


@partial(jax.pmap  ,       axis_name='''batch'''   )
def 	_A  (					A__  ,       **A__   ):






 """simple docstring"""



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

 __lowercase													=				state.apply_fn(**A__  ,       params=state.params  ,       train=A__   )
 __lowercase													=				outputs

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






class     lowercase_			(train_state.TrainState   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Callable           =    struct.field(pytree_node=lowerCamelCase__   )






@dataclass
class     lowercase_			:




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Args
  SCREAMING_SNAKE_CASE					:						Callable
  SCREAMING_SNAKE_CASE					:						Callable
  SCREAMING_SNAKE_CASE					:						Callable
  SCREAMING_SNAKE_CASE					:						Callable
  SCREAMING_SNAKE_CASE					:						wandb
  SCREAMING_SNAKE_CASE					:						Callable           =    None






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]=None    ):
   __lowercase													=				model.params
   __lowercase													=				TrainState.create(
       apply_fn=model.__call__      ,params=UpperCAmelCase__      ,tx=UpperCAmelCase__      ,loss_fn=UpperCAmelCase__      ,)
   if ckpt_dir is not None:
    __lowercase													=				restore_checkpoint(UpperCAmelCase__      ,UpperCAmelCase__    )
    __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													=				build_tx(**UpperCAmelCase__    )
    __lowercase													=				train_state.TrainState(
        step=UpperCAmelCase__      ,apply_fn=model.__call__      ,params=UpperCAmelCase__      ,tx=UpperCAmelCase__      ,opt_state=UpperCAmelCase__      ,)
    __lowercase													=				args
    __lowercase													=				data_collator
    __lowercase													=				lr
    __lowercase													=				params
   __lowercase													=				jax_utils.replicate(UpperCAmelCase__    )
   return state






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Optional[int]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Any    ):
   __lowercase													=				self.args
   __lowercase													=				len(UpperCAmelCase__    ) // args.batch_size

   __lowercase													=				jax.random.PRNGKey(0    )
   __lowercase													=				jax.random.split(UpperCAmelCase__      ,jax.device_count()    )
   for epoch in range(args.max_epochs    ):
    __lowercase													=				jnp.array(0      ,dtype=jnp.floataa    )
    __lowercase													=				get_batched_dataset(UpperCAmelCase__      ,args.batch_size      ,seed=UpperCAmelCase__    )
    __lowercase													=				0
    for batch in tqdm(UpperCAmelCase__      ,total=UpperCAmelCase__      ,desc=F"Running EPOCH-{epoch}"    ):
     __lowercase													=				self.data_collator(UpperCAmelCase__    )
     __lowercase													=				self.train_step_fn(UpperCAmelCase__      ,UpperCAmelCase__      ,**UpperCAmelCase__    )
     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(UpperCAmelCase__      ,UpperCAmelCase__    )
      __lowercase													=				{
          '''step''': state_step.item(),
          '''eval_loss''': eval_loss.item(),
          '''tr_loss''': tr_loss,
          '''lr''': lr.item(),
      }
      tqdm.write(str(UpperCAmelCase__    )    )
      self.logger.log(UpperCAmelCase__      ,commit=UpperCAmelCase__    )

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






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






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict    ):
   __lowercase													=				jax_utils.unreplicate(UpperCAmelCase__    )
   print(F"SAVING CHECKPOINT IN {save_dir}"      ,end=''' ... '''    )
   self.model_save_fn(UpperCAmelCase__      ,params=state.params    )
   with open(os.path.join(UpperCAmelCase__      ,'''opt_state.msgpack'''    )      ,'''wb'''    ) as f:
    f.write(to_bytes(state.opt_state    )    )
   joblib.dump(self.args      ,os.path.join(UpperCAmelCase__      ,'''args.joblib'''    )    )
   joblib.dump(self.data_collator      ,os.path.join(UpperCAmelCase__      ,'''data_collator.joblib'''    )    )
   with open(os.path.join(UpperCAmelCase__      ,'''training_state.json'''    )      ,'''w'''    ) as f:
    json.dump({'''step''': state.step.item()}      ,UpperCAmelCase__    )
   print('''DONE'''    )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

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

 __lowercase													=				joblib.load(os.path.join(A__  ,       '''args.joblib'''   )   )
 __lowercase													=				joblib.load(os.path.join(A__  ,       '''data_collator.joblib'''   )   )

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

 print('''DONE'''   )
 return params, opt_state, step, args, data_collator


def 	_A  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



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






def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

 __lowercase													=				scheduler_fn(A__  ,       A__  ,       A__  ,       A__   )

 __lowercase													=				optax.adamw(learning_rate=A__  ,       weight_decay=A__  ,       mask=A__   )
 return tx, lr




'''simple docstring'''






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				hashlib.mda(image.tobytes()   )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]    ):
   __lowercase													=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}      ,lowercase__    )
   import datasets

   __lowercase													=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,'''image'''      ,split='''test'''    )
   __lowercase													=				depth_estimator(
       [
           Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           # RGBA
           dataset[0]['''file'''],
           # LA
           dataset[1]['''file'''],
           # L
           dataset[2]['''file'''],
       ]    )
   self.assertEqual(
       [
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
       ]      ,lowercase__      ,)






  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @slow
  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''Intel/dpt-large'''
   __lowercase													=				pipeline('''depth-estimation'''      ,model=lowercase__    )
   __lowercase													=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
   __lowercase													=				hashimage(outputs['''depth''']    )

   # This seems flaky.
   # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )      ,2_9.3_0_4    )
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )      ,2.6_6_2    )






  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   # This is highly irregular to have no small tests.
   self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

