


import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class 			SCREAMING_SNAKE_CASE__  :
      def __init__(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Dict=1_3					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=7					,							SCREAMING_SNAKE_CASE__    :	Dict=True					,							SCREAMING_SNAKE_CASE__    :	str=True					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					,							SCREAMING_SNAKE_CASE__    :	Any=True					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=9_9					,							SCREAMING_SNAKE_CASE__    :	Dict=2_4					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=6					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=3_7					,							SCREAMING_SNAKE_CASE__    :	Optional[int]="gelu"					,							SCREAMING_SNAKE_CASE__    :	Any=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=5_1_2					,							SCREAMING_SNAKE_CASE__    :	List[Any]=1_6					,							SCREAMING_SNAKE_CASE__    :	int=2					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=0.02					,							SCREAMING_SNAKE_CASE__    :	int=3					,							SCREAMING_SNAKE_CASE__    :	Any=None					,							SCREAMING_SNAKE_CASE__    :	str=1_0_0_0					,							)		->      List[str]:
             a_ :		Optional[int]							=  parent
             a_ :		List[Any]							=  batch_size
             a_ :		Optional[int]							=  seq_length
             a_ :		Tuple							=  is_training
             a_ :		Optional[int]							=  use_input_mask
             a_ :		Tuple							=  use_token_type_ids
             a_ :		Dict							=  use_labels
             a_ :		Optional[Any]							=  vocab_size
             a_ :		Union[str, Any]							=  hidden_size
             a_ :		Optional[Any]							=  num_hidden_layers
             a_ :		Optional[Any]							=  num_attention_heads
             a_ :		Optional[Any]							=  intermediate_size
             a_ :		Tuple							=  hidden_act
             a_ :		Dict							=  hidden_dropout_prob
             a_ :		Union[str, Any]							=  attention_probs_dropout_prob
             a_ :		List[Any]							=  max_position_embeddings
             a_ :		Dict							=  type_vocab_size
             a_ :		Optional[Any]							=  type_sequence_label_size
             a_ :		str							=  initializer_range
             a_ :		int							=  num_labels
             a_ :		List[str]							=  scope
             a_ :		List[str]							=  range_bbox
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Union[str, Any]:
             a_ :		int							=  ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size					)

             a_ :		int							=  ids_tensor([self.batch_size, self.seq_length, 4]					,							self.range_bbox					)
             # Ensure that bbox is legal
             for i in range(bbox.shape[0]					):
                    for j in range(bbox.shape[1]					):
                           if bbox[i, j, 3] < bbox[i, j, 1]:
                                  a_ :		List[Any]							=  bbox[i, j, 3]
                                  a_ :		Union[str, Any]							=  bbox[i, j, 1]
                                  a_ :		Union[str, Any]							=  t
                           if bbox[i, j, 2] < bbox[i, j, 0]:
                                  a_ :		int							=  bbox[i, j, 2]
                                  a_ :		List[str]							=  bbox[i, j, 0]
                                  a_ :		List[Any]							=  t

             a_ :		Dict							=  None
             if self.use_input_mask:
                    a_ :		List[str]							=  ids_tensor([self.batch_size, self.seq_length]					,							vocab_size=2					)

             a_ :		Dict							=  None
             if self.use_token_type_ids:
                    a_ :		int							=  ids_tensor([self.batch_size, self.seq_length]					,							self.type_vocab_size					)

             a_ :		Optional[Any]							=  None
             a_ :		Union[str, Any]							=  None
             if self.use_labels:
                    a_ :		Optional[int]							=  ids_tensor([self.batch_size]					,							self.type_sequence_label_size					)
                    a_ :		Tuple							=  ids_tensor([self.batch_size, self.seq_length]					,							self.num_labels					)

             a_ :		Union[str, Any]							=  self.get_config()

             return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:
             return LiltConfig(
                 vocab_size=self.vocab_size					,							hidden_size=self.hidden_size					,							num_hidden_layers=self.num_hidden_layers					,							num_attention_heads=self.num_attention_heads					,							intermediate_size=self.intermediate_size					,							hidden_act=self.hidden_act					,							hidden_dropout_prob=self.hidden_dropout_prob					,							attention_probs_dropout_prob=self.attention_probs_dropout_prob					,							max_position_embeddings=self.max_position_embeddings					,							type_vocab_size=self.type_vocab_size					,							initializer_range=self.initializer_range					,							)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							)		->      Any:
             a_ :		List[Any]							=  LiltModel(config=_SCREAMING_SNAKE_CASE					)
             model.to(_SCREAMING_SNAKE_CASE					)
             model.eval()
             a_ :		str							=  model(_SCREAMING_SNAKE_CASE					,							bbox=_SCREAMING_SNAKE_CASE					,							attention_mask=_SCREAMING_SNAKE_CASE					,							token_type_ids=_SCREAMING_SNAKE_CASE					)
             a_ :		List[str]							=  model(_SCREAMING_SNAKE_CASE					,							bbox=_SCREAMING_SNAKE_CASE					,							token_type_ids=_SCREAMING_SNAKE_CASE					)
             a_ :		Dict							=  model(_SCREAMING_SNAKE_CASE					,							bbox=_SCREAMING_SNAKE_CASE					)

             self.parent.assertEqual(result.last_hidden_state.shape					,							(self.batch_size, self.seq_length, self.hidden_size)					)
             self.parent.assertEqual(result.pooler_output.shape					,							(self.batch_size, self.hidden_size)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							)		->      Optional[int]:
             a_ :		List[str]							=  self.num_labels
             a_ :		str							=  LiltForTokenClassification(config=_SCREAMING_SNAKE_CASE					)
             model.to(_SCREAMING_SNAKE_CASE					)
             model.eval()
             a_ :		Union[str, Any]							=  model(
                 _SCREAMING_SNAKE_CASE					,							bbox=_SCREAMING_SNAKE_CASE					,							attention_mask=_SCREAMING_SNAKE_CASE					,							token_type_ids=_SCREAMING_SNAKE_CASE					,							labels=_SCREAMING_SNAKE_CASE					)
             self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.seq_length, self.num_labels)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	str					,							)		->      Any:
             a_ :		int							=  LiltForQuestionAnswering(config=_SCREAMING_SNAKE_CASE					)
             model.to(_SCREAMING_SNAKE_CASE					)
             model.eval()
             a_ :		List[Any]							=  model(
                 _SCREAMING_SNAKE_CASE					,							bbox=_SCREAMING_SNAKE_CASE					,							attention_mask=_SCREAMING_SNAKE_CASE					,							token_type_ids=_SCREAMING_SNAKE_CASE					,							start_positions=_SCREAMING_SNAKE_CASE					,							end_positions=_SCREAMING_SNAKE_CASE					,							)
             self.parent.assertEqual(result.start_logits.shape					,							(self.batch_size, self.seq_length)					)
             self.parent.assertEqual(result.end_logits.shape					,							(self.batch_size, self.seq_length)					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             a_ :		Optional[int]							=  self.prepare_config_and_inputs()
             (
                 (
                 a_ 
             )							,		(
                 a_ 
             )							,		(
                 a_ 
             )							,		(
                 a_ 
             )							,		(
                 a_ 
             )							,		(
                 a_ 
             )							,		(
                 a_ 
             )							,		 
             ) :		Tuple							=  config_and_inputs
             a_ :		Union[str, Any]							=  {
                 'input_ids': input_ids,
                 'bbox': bbox,
                 'token_type_ids': token_type_ids,
                 'attention_mask': input_mask,
             }
             return config, inputs_dict
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__						,   unittest.TestCase      ):
      snake_case__    :					Dict					       =						(
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      snake_case__    :					str					       =						(
          {
              "feature-extraction": LiltModel,
              "question-answering": LiltForQuestionAnswering,
              "text-classification": LiltForSequenceClassification,
              "token-classification": LiltForTokenClassification,
              "zero-shot": LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__    :					str					       =						False
      snake_case__    :					Tuple					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Tuple:
             return True
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Any:
             a_ :		Union[str, Any]							=  LiltModelTester(self					)
             a_ :		Dict							=  ConfigTester(self					,							config_class=_SCREAMING_SNAKE_CASE					,							hidden_size=3_7					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Optional[int]:
             self.config_tester.run_common_tests()
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             a_ :		List[str]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Union[str, Any]:
             a_ :		Dict							=  self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    a_ :		Optional[Any]							=  type
                    self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
             a_ :		List[str]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Union[str, Any]:
             a_ :		Union[str, Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Optional[Any]:
             for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_ :		Union[str, Any]							=  LiltModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)
@require_torch
@slow
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Any:
             a_ :		List[str]							=  LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'					).to(_SCREAMING_SNAKE_CASE					)

             a_ :		List[str]							=  torch.tensor([[1, 2]]					,							device=_SCREAMING_SNAKE_CASE					)
             a_ :		int							=  torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]					,							device=_SCREAMING_SNAKE_CASE					)

             # forward pass
             with torch.no_grad():
                    a_ :		List[Any]							=  model(input_ids=_SCREAMING_SNAKE_CASE					,							bbox=_SCREAMING_SNAKE_CASE					)

             a_ :		List[Any]							=  torch.Size([1, 2, 7_6_8]					)
             a_ :		Optional[Any]							=  torch.tensor(
                 [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]					,							device=_SCREAMING_SNAKE_CASE					,							)

             self.assertTrue(outputs.last_hidden_state.shape					,							_SCREAMING_SNAKE_CASE					)
             self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]					,							_SCREAMING_SNAKE_CASE					,							atol=1E-3					)					)






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Any    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		str							=  filter(lambda __A    : p.requires_grad  ,   model.parameters()    )
       a_ :		List[Any]							=  sum([np.prod(p.size()    ) for p in model_parameters]    )
       return params


UpperCAmelCase_				:			Dict   =	logging.getLogger(__name__)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		List[str]:

       """simple docstring"""

       if metric == "rouge2":
              a_ :		Dict							=  '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              a_ :		Dict							=  '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              a_ :		Union[str, Any]							=  '{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              a_ :		str							=  '{val_avg_loss:.4f}-{step_count}'
       else:
              raise NotImplementedError(
                  F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
                  ' function.'    )

       a_ :		Dict							=  ModelCheckpoint(
           dirpath=__A  ,   filename=__A  ,   monitor=F"""val_{metric}"""  ,   mode='max'  ,   save_top_k=1  ,   every_n_epochs=1  ,   )

       return checkpoint_callback







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      List[Any]    )     ->		int:

       """simple docstring"""

       return EarlyStopping(
           monitor=F"""val_{metric}"""  ,   mode='min' if 'loss' in metric else 'max'  ,   patience=__A  ,   verbose=__A  ,   )
class 			SCREAMING_SNAKE_CASE__  (    pl.Callback      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Any:
             a_ :		int							=  {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
             pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					)		->      None:
             logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""					)
             a_ :		List[str]							=  trainer.callback_metrics
             trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}					)
             # Log results
             a_ :		Optional[int]							=  Path(pl_module.hparams.output_dir					)
             if type_path == "test":
                    a_ :		Tuple							=  od / 'test_results.txt'
                    a_ :		Optional[int]							=  od / 'test_generations.txt'
             else:
                    # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
                    # If people want this it will be easy enough to add back.
                    a_ :		str							=  od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
                    a_ :		Optional[Any]							=  od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                    results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
                    generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
             with open(SCREAMING_SNAKE_CASE__					,							'a+'					) as writer:
                    for key in sorted(SCREAMING_SNAKE_CASE__					):
                           if key in ["log", "progress_bar", "preds"]:
                                  continue
                           a_ :		int							=  metrics[key]
                           if isinstance(SCREAMING_SNAKE_CASE__					,							torch.Tensor					):
                                  a_ :		List[str]							=  val.item()
                           a_ :		int							=  F"""{key}: {val:.6f}\n"""
                           writer.write(SCREAMING_SNAKE_CASE__					)

             if not save_generations:
                    return

             if "preds" in metrics:
                    a_ :		Optional[Any]							=  '\n'.join(metrics['preds']					)
                    generations_file.open('w+'					).write(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Dict:
             try:
                    a_ :		Any							=  pl_module.model.model.num_parameters()
             except AttributeError:
                    a_ :		List[str]							=  pl_module.model.num_parameters()

             a_ :		Any							=  count_trainable_parameters(SCREAMING_SNAKE_CASE__					)
             # mp stands for million parameters
             trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					)		->      Optional[int]:
             save_json(pl_module.metrics					,							pl_module.metrics_save_path					)
             return self._write_logs(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							'test'					)

      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      Optional[Any]:
             save_json(pl_module.metrics					,							pl_module.metrics_save_path					)
             # Uncommenting this will save val generations
             # return self._write_logs(trainer, pl_module, "valid")


