


"""simple docstring"""



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 					__magic_name__		:






       def __init__(   self  :    List[str]  ,     snake_case__  :    Tuple  ,     snake_case__  :    List[str]=1_3  ,     snake_case__  :    Optional[Any]=7  ,     snake_case__  :    Tuple=True  ,     snake_case__  :    Any=True  ,     snake_case__  :    Optional[int]=True  ,     snake_case__  :    Dict=True  ,     snake_case__  :    Optional[int]=9_9  ,     snake_case__  :    Optional[Any]=2_4  ,     snake_case__  :    Dict=2  ,     snake_case__  :    List[str]=6  ,     snake_case__  :    Optional[Any]=3_7  ,     snake_case__  :    str="gelu"  ,     snake_case__  :    Tuple=0.1  ,     snake_case__  :    List[str]=0.1  ,     snake_case__  :    Dict=5_1_2  ,     snake_case__  :    List[str]=1_6  ,     snake_case__  :    Optional[int]=2  ,     snake_case__  :    Union[str, Any]=0.02  ,     snake_case__  :    List[str]=3  ,     snake_case__  :    Optional[Any]=None  ,     snake_case__  :    Optional[int]=1_0_0_0  ,     ):

              '''simple docstring'''






              lowercase		:Tuple       							=				parent
              lowercase		:Optional[int]       							=				batch_size
              lowercase		:Union[str, Any]       							=				seq_length
              lowercase		:List[str]       							=				is_training
              lowercase		:int       							=				use_input_mask
              lowercase		:List[str]       							=				use_token_type_ids
              lowercase		:Dict       							=				use_labels
              lowercase		:Optional[int]       							=				vocab_size
              lowercase		:Union[str, Any]       							=				hidden_size
              lowercase		:Union[str, Any]       							=				num_hidden_layers
              lowercase		:Optional[Any]       							=				num_attention_heads
              lowercase		:Optional[int]       							=				intermediate_size
              lowercase		:Optional[int]       							=				hidden_act
              lowercase		:Optional[int]       							=				hidden_dropout_prob
              lowercase		:Any       							=				attention_probs_dropout_prob
              lowercase		:Union[str, Any]       							=				max_position_embeddings
              lowercase		:str       							=				type_vocab_size
              lowercase		:Optional[Any]       							=				type_sequence_label_size
              lowercase		:Tuple       							=				initializer_range
              lowercase		:List[str]       							=				num_labels
              lowercase		:Union[str, Any]       							=				scope
              lowercase		:Dict       							=				range_bbox






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Any       							=				ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

              lowercase		:Optional[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]:
                                   lowercase		:Optional[int]       							=				bbox[i, j, 3]
                                   lowercase		:Any       							=				bbox[i, j, 1]
                                   lowercase		:Tuple       							=				t
                            if bbox[i, j, 2] < bbox[i, j, 0]:
                                   lowercase		:Optional[Any]       							=				bbox[i, j, 2]
                                   lowercase		:Dict       							=				bbox[i, j, 0]
                                   lowercase		:Any       							=				t

              lowercase		:Dict       							=				None
              if self.use_input_mask:
                     lowercase		:Any       							=				ids_tensor([self.batch_size, self.seq_length]  ,     vocab_size=2				)

              lowercase		:List[str]       							=				None
              if self.use_token_type_ids:
                     lowercase		:int       							=				ids_tensor([self.batch_size, self.seq_length]  ,     self.type_vocab_size				)

              lowercase		:List[str]       							=				None
              lowercase		:Dict       							=				None
              if self.use_labels:
                     lowercase		:List[Any]       							=				ids_tensor([self.batch_size]  ,     self.type_sequence_label_size				)
                     lowercase		:Optional[Any]       							=				ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels				)

              lowercase		:Optional[Any]       							=				self.get_config()

              return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              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  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Dict  ,     snake_case__  :    Tuple  ,     snake_case__  :    List[Any]  ,     snake_case__  :    int  ,     snake_case__  :    Any  ,     snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:List[str]       							=				LiltModel(config=snake_case__				)
              model.to(snake_case__				)
              model.eval()
              lowercase		:Dict       							=				model(snake_case__  ,     bbox=snake_case__  ,     attention_mask=snake_case__  ,     token_type_ids=snake_case__				)
              lowercase		:int       							=				model(snake_case__  ,     bbox=snake_case__  ,     token_type_ids=snake_case__				)
              lowercase		:int       							=				model(snake_case__  ,     bbox=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  __snake_case						(   self  :    Dict  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    str  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Tuple  ,     snake_case__  :    Dict  ,     ):

              '''simple docstring'''






              lowercase		:int       							=				self.num_labels
              lowercase		:List[str]       							=				LiltForTokenClassification(config=snake_case__				)
              model.to(snake_case__				)
              model.eval()
              lowercase		:Optional[int]       							=				model(
                  snake_case__  ,     bbox=snake_case__  ,     attention_mask=snake_case__  ,     token_type_ids=snake_case__  ,     labels=snake_case__				)
              self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)				)






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    int  ,     snake_case__  :    str  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Optional[int]  ,     ):

              '''simple docstring'''






              lowercase		:Tuple       							=				LiltForQuestionAnswering(config=snake_case__				)
              model.to(snake_case__				)
              model.eval()
              lowercase		:str       							=				model(
                  snake_case__  ,     bbox=snake_case__  ,     attention_mask=snake_case__  ,     token_type_ids=snake_case__  ,     start_positions=snake_case__  ,     end_positions=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  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Tuple       							=				self.prepare_config_and_inputs()
              (
                  lowercase  
              )		:Optional[Any]       							=				config_and_inputs
              lowercase		:Dict       							=				{
                  '''input_ids''': input_ids,
                  '''bbox''': bbox,
                  '''token_type_ids''': token_type_ids,
                  '''attention_mask''': input_mask,
              }
              return config, inputs_dict







@require_torch
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   (
           (
               LiltModel,
               LiltForSequenceClassification,
               LiltForTokenClassification,
               LiltForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       __A			:      Tuple        =   (
           {
               "feature-extraction": LiltModel,
               "question-answering": LiltForQuestionAnswering,
               "text-classification": LiltForSequenceClassification,
               "token-classification": LiltForTokenClassification,
               "zero-shot": LiltForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __A			:      Optional[Any]        =   False
       __A			:      List[str]        =   False






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Tuple  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Optional[int]				):

              '''simple docstring'''






              return True






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:str       							=				LiltModelTester(self				)
              lowercase		:List[Any]       							=				ConfigTester(self  ,     config_class=snake_case__  ,     hidden_size=3_7				)






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              self.config_tester.run_common_tests()






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Tuple       							=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*snake_case__				)






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Any       							=				self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                     lowercase		:Optional[Any]       							=				type
                     self.model_tester.create_and_check_model(*snake_case__				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_token_classification(*snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_question_answering(*snake_case__				)







       @slow
       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowercase		:Optional[int]       							=				LiltModel.from_pretrained(snake_case__				)
                     self.assertIsNotNone(snake_case__				)







@require_torch
@slow
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''				).to(snake_case__				)

              lowercase		:List[Any]       							=				torch.tensor([[1, 2]]  ,     device=snake_case__				)
              lowercase		:Optional[Any]       							=				torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]  ,     device=snake_case__				)

              # forward pass
              with torch.no_grad():
                     lowercase		:Optional[Any]       							=				model(input_ids=snake_case__  ,     bbox=snake_case__				)

              lowercase		:Tuple       							=				torch.Size([1, 2, 7_6_8]				)
              lowercase		:Any       							=				torch.tensor(
                  [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]  ,     device=snake_case__  ,     )

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





"""simple docstring"""



import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


UpperCAmelCase      		=			'''sshleifer/mar_enro_6_3_student'''
class 					__magic_name__		(			__UpperCAmelCase     ):






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              super().setUp()

              lowercase		:Union[str, Any]       							=				cached_path(
                  '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''  ,     extract_compressed_file=snake_case__  ,     )
              lowercase		:int       							=				f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              MarianMTModel.from_pretrained(snake_case__				)







       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:str       							=				{
                  '''$MAX_LEN''': 6_4,
                  '''$BS''': 6_4,
                  '''$GAS''': 1,
                  '''$ENRO_DIR''': self.data_dir,
                  '''facebook/mbart-large-cc25''': MARIAN_MODEL,
                  # "val_check_interval=0.25": "val_check_interval=1.0",
                  '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
                  '''--num_train_epochs 6''': '''--num_train_epochs 1''',
              }

              # Clean up bash script
              lowercase		:str       							=				(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''				)[1].strip()
              lowercase		:Any       							=				bash_script.replace('''\\\n'''  ,     ''''''				).strip().replace('''"$@"'''  ,     ''''''				)
              for k, v in env_vars_to_replace.items():
                     lowercase		:Dict       							=				bash_script.replace(snake_case__  ,     str(snake_case__				)				)
              lowercase		:Any       							=				self.get_auto_remove_tmp_dir()

              # bash_script = bash_script.replace("--fp16 ", "")
              lowercase		:Optional[Any]       							=				f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
              # XXX: args.gpus > 1 : handle multi_gpu in the future

              lowercase		:str       							=				['''finetune.py'''] + bash_script.split() + args
              with patch.object(snake_case__  ,     '''argv'''  ,     snake_case__				):
                     lowercase		:Optional[int]       							=				argparse.ArgumentParser()
                     lowercase		:List[Any]       							=				pl.Trainer.add_argparse_args(snake_case__				)
                     lowercase		:Union[str, Any]       							=				SummarizationModule.add_model_specific_args(snake_case__  ,     os.getcwd()				)
                     lowercase		:int       							=				parser.parse_args()
                     lowercase		:int       							=				main(snake_case__				)

              # Check metrics
              lowercase		:int       							=				load_json(model.metrics_save_path				)
              lowercase		:Tuple       							=				metrics['''val'''][0]
              lowercase		:Any       							=				metrics['''val'''][-1]
              self.assertEqual(len(metrics['''val''']				)  ,     (args.max_epochs / args.val_check_interval)				)
              assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]  ,     snake_case__				)

              self.assertGreater(last_step_stats['''val_avg_gen_time''']  ,     0.01				)
              # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
              self.assertLessEqual(last_step_stats['''val_avg_gen_time''']  ,     1.0				)

              # test learning requirements:

              # 1. BLEU improves over the course of training by more than 2 pts
              self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']  ,     2				)

              # 2. BLEU finishes above 17
              self.assertGreater(last_step_stats['''val_avg_bleu''']  ,     1_7				)

              # 3. test BLEU and val BLEU within ~1.1 pt.
              self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']				)  ,     1.1				)

              # check lightning ckpt can be loaded and has a reasonable statedict
              lowercase		:Any       							=				os.listdir(snake_case__				)
              lowercase		:int       							=				[x for x in contents if x.endswith('''.ckpt'''				)][0]
              lowercase		:Any       							=				os.path.join(args.output_dir  ,     snake_case__				)
              lowercase		:int       							=				torch.load(snake_case__  ,     map_location='''cpu'''				)
              lowercase		:Any       							=				'''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
              assert expected_key in ckpt["state_dict"]
              assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

              # TODO: turn on args.do_predict when PL bug fixed.
              if args.do_predict:
                     lowercase		:Optional[Any]       							=				{os.path.basename(snake_case__				) for p in contents}
                     assert "test_generations.txt" in contents
                     assert "test_results.txt" in contents
                     # assert len(metrics["val"]) ==  desired_n_evals
                     assert len(metrics['''test''']				) == 1







class 					__magic_name__		(			__UpperCAmelCase     ):






       @timeout_decorator.timeout(6_0_0				)
       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
              lowercase		:List[Any]       							=				{
                  '''--fp16_opt_level=O1''': '''''',
                  '''$MAX_LEN''': 1_2_8,
                  '''$BS''': 1_6,
                  '''$GAS''': 1,
                  '''$ENRO_DIR''': data_dir,
                  '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
                  '''val_check_interval=0.25''': '''val_check_interval=1.0''',
              }

              # Clean up bash script
              lowercase		:Dict       							=				(
                  (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''				)[1].strip()
              )
              lowercase		:Union[str, Any]       							=				bash_script.replace('''\\\n'''  ,     ''''''				).strip().replace('''"$@"'''  ,     ''''''				)
              lowercase		:Dict       							=				bash_script.replace('''--fp16 '''  ,     ''' '''				)

              for k, v in env_vars_to_replace.items():
                     lowercase		:str       							=				bash_script.replace(snake_case__  ,     str(snake_case__				)				)
              lowercase		:Union[str, Any]       							=				self.get_auto_remove_tmp_dir()
              lowercase		:str       							=				bash_script.replace('''--fp16'''  ,     ''''''				)
              lowercase		:Union[str, Any]       							=				6
              lowercase		:str       							=				(
                  ['''distillation.py''']
                  + bash_script.split()
                  + [
                      f"""--output_dir={output_dir}""",
                      '''--gpus=1''',
                      '''--learning_rate=1e-3''',
                      f"""--num_train_epochs={epochs}""",
                      '''--warmup_steps=10''',
                      '''--val_check_interval=1.0''',
                      '''--do_predict''',
                  ]
              )
              with patch.object(snake_case__  ,     '''argv'''  ,     snake_case__				):
                     lowercase		:Optional[int]       							=				argparse.ArgumentParser()
                     lowercase		:Tuple       							=				pl.Trainer.add_argparse_args(snake_case__				)
                     lowercase		:Optional[Any]       							=				SummarizationDistiller.add_model_specific_args(snake_case__  ,     os.getcwd()				)
                     lowercase		:Optional[int]       							=				parser.parse_args()
                     # assert args.gpus == gpus THIS BREAKS for multi_gpu

                     lowercase		:Tuple       							=				distill_main(snake_case__				)

              # Check metrics
              lowercase		:Tuple       							=				load_json(model.metrics_save_path				)
              lowercase		:int       							=				metrics['''val'''][0]
              lowercase		:List[str]       							=				metrics['''val'''][-1]
              assert len(metrics['''val''']				) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

              assert last_step_stats["val_avg_gen_time"] >= 0.01

              assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
              assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
              assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]  ,     snake_case__				)

              # check lightning ckpt can be loaded and has a reasonable statedict
              lowercase		:Union[str, Any]       							=				os.listdir(snake_case__				)
              lowercase		:List[str]       							=				[x for x in contents if x.endswith('''.ckpt'''				)][0]
              lowercase		:Dict       							=				os.path.join(args.output_dir  ,     snake_case__				)
              lowercase		:Optional[int]       							=				torch.load(snake_case__  ,     map_location='''cpu'''				)
              lowercase		:str       							=				'''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
              assert expected_key in ckpt["state_dict"]
              assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

              # TODO: turn on args.do_predict when PL bug fixed.
              if args.do_predict:
                     lowercase		:int       							=				{os.path.basename(snake_case__				) for p in contents}
                     assert "test_generations.txt" in contents
                     assert "test_results.txt" in contents
                     # assert len(metrics["val"]) ==  desired_n_evals
                     assert len(metrics['''test''']				) == 1

