

"""simple docstring"""





import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
   from PIL import Image

   from transformers import MgpstrProcessor




class       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    int,      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    Tuple=False,      UpperCAmelCase__							:    Any=1_3,      UpperCAmelCase__							:    Optional[Any]=(3_2, 1_2_8),      UpperCAmelCase__							:    Union[str, Any]=4,      UpperCAmelCase__							:    Optional[Any]=3,      UpperCAmelCase__							:    Tuple=2_7,      UpperCAmelCase__							:    Optional[int]=3_8,      UpperCAmelCase__							:    Union[str, Any]=9_9,      UpperCAmelCase__							:    Any=9_9,      UpperCAmelCase__							:    str=3_2,      UpperCAmelCase__							:    Any=5,      UpperCAmelCase__							:    Optional[Any]=4,      UpperCAmelCase__							:    List[str]=4.0,      UpperCAmelCase__							:    Optional[Any]=2_5_7,      UpperCAmelCase__							:    Any=None,      ):
    __lowercase			=		parent
    __lowercase			=		is_training
    __lowercase			=		batch_size
    __lowercase			=		image_size
    __lowercase			=		patch_size
    __lowercase			=		num_channels
    __lowercase			=		max_token_length
    __lowercase			=		num_character_labels
    __lowercase			=		num_bpe_labels
    __lowercase			=		num_wordpiece_labels
    __lowercase			=		hidden_size
    __lowercase			=		num_hidden_layers
    __lowercase			=		num_attention_heads
    __lowercase			=		mlp_ratio
    __lowercase			=		patch_embeds_hidden_size
    __lowercase			=		output_hidden_states


 def       _lowercase				(    self							:    int			):
    __lowercase			=		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
    __lowercase			=		self.get_config()
    return config, pixel_values


 def       _lowercase				(    self							:    int			):
    return MgpstrConfig(
        image_size=self.image_size,      patch_size=self.patch_size,      num_channels=self.num_channels,      max_token_length=self.max_token_length,      num_character_labels=self.num_character_labels,      num_bpe_labels=self.num_bpe_labels,      num_wordpiece_labels=self.num_wordpiece_labels,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      mlp_ratio=self.mlp_ratio,      output_hidden_states=self.output_hidden_states,      )


 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    Dict			):
    __lowercase			=		MgpstrForSceneTextRecognition(UpperCAmelCase__			)
    model.to(UpperCAmelCase__			)
    model.eval()
    with torch.no_grad():
       __lowercase			=		model(UpperCAmelCase__			)
    self.parent.assertEqual(
        generated_ids[0][0].shape,      (self.batch_size, self.max_token_length, self.num_character_labels)			)



 def       _lowercase				(    self							:    Any			):
    __lowercase			=		self.prepare_config_and_inputs()
    __lowercase	,__lowercase			=		config_and_inputs
    __lowercase			=		{"pixel_values": pixel_values}
    return config, inputs_dict







@require_torch
class       _lowerCAmelCase						(			lowercase  ,lowercase  ,unittest.TestCase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Dict     	=			(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
 __UpperCAmelCase			:      str     	=			{"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
 __UpperCAmelCase			:      Union[str, Any]     	=			False

 __UpperCAmelCase			:      Union[str, Any]     	=			False
 __UpperCAmelCase			:      int     	=			False
 __UpperCAmelCase			:      List[Any]     	=			False
 __UpperCAmelCase			:      str     	=			False


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		MgpstrModelTester(self			)
    __lowercase			=		ConfigTester(self,      config_class=UpperCAmelCase__,      has_text_modality=UpperCAmelCase__			)


 def       _lowercase				(    self							:    Tuple			):
    self.config_tester.run_common_tests()


 def       _lowercase				(    self							:    Any			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__			)


 @unittest.skip(reason="MgpstrModel does not use inputs_embeds"			)
 def       _lowercase				(    self							:    int			):
    pass


 def       _lowercase				(    self							:    Dict			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase			=		model_class(UpperCAmelCase__			)
       self.assertIsInstance(model.get_input_embeddings(),      (nn.Module)			)
       __lowercase			=		model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCAmelCase__,      nn.Linear			)			)


 def       _lowercase				(    self							:    str			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase			=		model_class(UpperCAmelCase__			)
       __lowercase			=		inspect.signature(model.forward			)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowercase			=		[*signature.parameters.keys()]

       __lowercase			=		["pixel_values"]
       self.assertListEqual(arg_names[:1],      UpperCAmelCase__			)


 @unittest.skip(reason="MgpstrModel does not support feedforward chunking"			)
 def       _lowercase				(    self							:    str			):
    pass


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       if not model_class.supports_gradient_checkpointing:
          continue

       __lowercase			=		True
       __lowercase			=		model_class(UpperCAmelCase__			)
       self.assertTrue(model.is_gradient_checkpointing			)


 def       _lowercase				(    self							:    Any			):
    def check_hidden_states_output(UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    Tuple			):
       __lowercase			=		model_class(UpperCAmelCase__			)
       model.to(UpperCAmelCase__			)
       model.eval()

       with torch.no_grad():
          __lowercase			=		model(**self._prepare_for_class(UpperCAmelCase__,      UpperCAmelCase__			)			)

       __lowercase			=		outputs.hidden_states

       __lowercase			=		getattr(
           self.model_tester,      "expected_num_hidden_layers",      self.model_tester.num_hidden_layers + 1			)
       self.assertEqual(len(UpperCAmelCase__			),      UpperCAmelCase__			)

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]			),      [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size],      )

    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase			=		True
       check_hidden_states_output(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __lowercase			=		True

       check_hidden_states_output(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[str]			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    __lowercase			=		_config_zero_init(UpperCAmelCase__			)
    for model_class in self.all_model_classes:
       __lowercase			=		model_class(config=UpperCAmelCase__			)
       for name, param in model.named_parameters():
          if isinstance(UpperCAmelCase__,      (nn.Linear, nn.Convad, nn.LayerNorm)			):
             if param.requires_grad:
                self.assertIn(
                    ((param.data.mean() * 1E9).round() / 1E9).item(),      [0.0, 1.0],      msg=F"""Parameter {name} of model {model_class} seems not properly initialized""",      )



 @unittest.skip(reason="Retain_grad is tested in individual model tests"			)
 def       _lowercase				(    self							:    int			):
    pass







def 						_A						(						)			->		List[Any]:
   '''simple docstring'''


   __lowercase			=		"https://i.postimg.cc/ZKwLg2Gw/367-14.png"
   __lowercase			=		Image.open(requests.get(UpperCamelCase_,    stream=UpperCamelCase_).raw).convert("RGB")
   return im




@require_vision
@require_torch
class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 @slow
 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		"alibaba-damo/mgp-str-base"
    __lowercase			=		MgpstrForSceneTextRecognition.from_pretrained(UpperCAmelCase__			).to(UpperCAmelCase__			)
    __lowercase			=		MgpstrProcessor.from_pretrained(UpperCAmelCase__			)

    __lowercase			=		prepare_img()
    __lowercase			=		processor(images=UpperCAmelCase__,      return_tensors="pt"			).pixel_values.to(UpperCAmelCase__			)

    # forward pass
    with torch.no_grad():
       __lowercase			=		model(UpperCAmelCase__			)

    # verify the logits
    self.assertEqual(outputs.logits[0].shape,      torch.Size((1, 2_7, 3_8)			)			)

    __lowercase			=		processor.batch_decode(outputs.logits			)
    __lowercase			=		"ticket"

    self.assertEqual(out_strs["generated_text"][0],      UpperCAmelCase__			)

    __lowercase			=		torch.tensor(
        [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]],      device=UpperCAmelCase__,      )

    self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4],      UpperCAmelCase__,      atol=1E-4			)			)







"""simple docstring"""





import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
   print('#################### AVAILABLE DEVICES ####################')
   print(jax.devices())
   print('###########################################################')

   # setup for wandb sweep
   _a				   =							Args()
   _a				   =							wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _a				   =							dict(logger.config)
   del wandb_args["batch_size"]
   _a				   =							replace(args, **wandb_args)
   _a				   =							args.base_dir + '-' + wandb.run.id
   _a				   =							replace(args, base_dir=base_dir)
   print(args)

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       'lr': args.lr,
       'init_lr': args.init_lr,
       'warmup_steps': args.warmup_steps,
       'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
       'weight_decay': args.weight_decay,
   }
   _a   ,		_a				   =							build_tx(**tx_args)

   _a				   =							Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )

   _a				   =							None
   _a				   =							trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
   try:
      trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
      print('Oooops; TRAINING STOPPED UNFORTUNATELY')

   print('SAVING WEIGHTS IN `final-weights`')
   _a				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)




