



from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDebertaForMaskedLM,
     TFDebertaForQuestionAnswering,
     TFDebertaForSequenceClassification,
     TFDebertaForTokenClassification,
     TFDebertaModel,
 )





class  __lowerCAmelCase			:




    def __init__(							self						,    _snake_case						,    _snake_case=13						,    _snake_case=7						,    _snake_case=True						,    _snake_case=True						,    _snake_case=True						,    _snake_case=True						,    _snake_case=99						,    _snake_case=32						,    _snake_case=2						,    _snake_case=4						,    _snake_case=37						,    _snake_case="gelu"						,    _snake_case=0.1						,    _snake_case=0.1						,    _snake_case=512						,    _snake_case=16						,    _snake_case=2						,    _snake_case=0.02						,    _snake_case=3						,    _snake_case=4						,    _snake_case=None						,    ):



         """simple docstring"""





         _lowerCAmelCase      =   parent
         _lowerCAmelCase      =   13
         _lowerCAmelCase      =   7
         _lowerCAmelCase      =   True
         _lowerCAmelCase      =   True
         _lowerCAmelCase      =   True
         _lowerCAmelCase      =   True
         _lowerCAmelCase      =   99
         _lowerCAmelCase      =   32
         _lowerCAmelCase      =   2
         _lowerCAmelCase      =   4
         _lowerCAmelCase      =   37
         _lowerCAmelCase      =   """gelu"""
         _lowerCAmelCase      =   0.1
         _lowerCAmelCase      =   0.1
         _lowerCAmelCase      =   512
         _lowerCAmelCase      =   16
         _lowerCAmelCase      =   False
         _lowerCAmelCase      =   -1
         _lowerCAmelCase      =   True
         _lowerCAmelCase      =   2
         _lowerCAmelCase      =   0.02
         _lowerCAmelCase      =   3
         _lowerCAmelCase      =   4
         _lowerCAmelCase      =   None




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size		)

         _lowerCAmelCase      =   None
         if self.use_input_mask:
              _lowerCAmelCase      =   random_attention_mask([self.batch_size, self.seq_length]		)

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

         _lowerCAmelCase      =   None
         _lowerCAmelCase      =   None
         _lowerCAmelCase      =   None
         if self.use_labels:
              _lowerCAmelCase      =   ids_tensor([self.batch_size]						,    self.type_sequence_label_size		)
              _lowerCAmelCase      =   ids_tensor([self.batch_size, self.seq_length]						,    self.num_labels		)

         _lowerCAmelCase      =   DebertaConfig(
             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						,    relative_attention=self.relative_attention						,    max_relative_positions=self.max_relative_positions						,    position_biased_input=self.position_biased_input						,    initializer_range=self.initializer_range						,    return_dict=_snake_case						,    )

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




    def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   TFDebertaModel(config=_snake_case		)
         _lowerCAmelCase      =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

         _lowerCAmelCase      =   [input_ids, input_mask]
         _lowerCAmelCase      =   model(_snake_case		)

         _lowerCAmelCase      =   model(_snake_case		)

         self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)		)




    def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   TFDebertaForMaskedLM(config=_snake_case		)
         _lowerCAmelCase      =   {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         _lowerCAmelCase      =   model(_snake_case		)
         self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)		)




    def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   self.num_labels
         _lowerCAmelCase      =   TFDebertaForSequenceClassification(config=_snake_case		)
         _lowerCAmelCase      =   {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         _lowerCAmelCase      =   model(_snake_case		)
         self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)		)




    def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   self.num_labels
         _lowerCAmelCase      =   TFDebertaForTokenClassification(config=_snake_case		)
         _lowerCAmelCase      =   {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         _lowerCAmelCase      =   model(_snake_case		)
         self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.num_labels)		)




    def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   TFDebertaForQuestionAnswering(config=_snake_case		)
         _lowerCAmelCase      =   {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         _lowerCAmelCase      =   model(_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		):



         """simple docstring"""





         _lowerCAmelCase      =   self.prepare_config_and_inputs()
         (
             (
             _lowerCAmelCase     
         )     ,     (
             _lowerCAmelCase     
         )     ,     (
             _lowerCAmelCase     
         )     ,     (
             _lowerCAmelCase     
         )     ,     (
             _lowerCAmelCase     
         )     ,     (
             _lowerCAmelCase     
         )     ,     (
             _lowerCAmelCase     
         )     ,          
         )      =   config_and_inputs
         _lowerCAmelCase      =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict





@require_tf
class  __lowerCAmelCase			(					lowerCamelCase__	,						lowerCamelCase__	,						unittest.TestCase	):
    __lowerCamelCase			=    (
        (
            TFDebertaModel,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase			=    (
        {
            '''feature-extraction''': TFDebertaModel,
            '''fill-mask''': TFDebertaForMaskedLM,
            '''question-answering''': TFDebertaForQuestionAnswering,
            '''text-classification''': TFDebertaForSequenceClassification,
            '''token-classification''': TFDebertaForTokenClassification,
            '''zero-shot''': TFDebertaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    __lowerCamelCase			=    False
    __lowerCamelCase			=    False




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   TFDebertaModelTester(self		)
         _lowerCAmelCase      =   ConfigTester(self						,    config_class=_snake_case						,    hidden_size=37		)




    def 							snake_case       (							self		):



         """simple docstring"""





         self.config_tester.run_common_tests()




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_snake_case		)




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_snake_case		)




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_snake_case		)




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_snake_case		)




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_snake_case		)




    @slow
    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""		)
         self.assertIsNotNone(_snake_case		)





@require_tf
class  __lowerCAmelCase			(					unittest.TestCase	):




    @unittest.skip(reason="""Model not available yet"""		)
    def 							snake_case       (							self		):



         """simple docstring"""





         pass




    @slow
    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""		)
         _lowerCAmelCase      =   tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
         _lowerCAmelCase      =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
         _lowerCAmelCase      =   model(_snake_case						,    attention_mask=_snake_case		)[0]

         _lowerCAmelCase      =   tf.constant(
             [
                 [
                     [-0.5985_5896, -0.8055_2566, -0.846_2135],
                     [1.448_4025, -0.9348_3794, -0.8059_3085],
                     [0.312_2741, 0.0031_6059, -1.413_1377],
                 ]
             ]		)
         tf.debugging.assert_near(output[:, 1:4, 1:4]						,    _snake_case						,    atol=1e-4		)








import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


A__     				=	torch.cuda.is_available()
A__     				=	[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A__     				=	["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A__     				=	{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-0_8,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   """\n""".join(snake_case      )
					Path(snake_case      ).open("""w"""      ).writelines(snake_case      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(snake_case     ,						F'{split}.source'      )     ,						snake_case      )
										_dump_articles(os.path.join(snake_case     ,						F'{split}.target'      )     ,						snake_case      )
					return tmp_dir





class  __lowerCAmelCase			(					lowerCamelCase__	):




				@classmethod
				def 							snake_case       (							cls		):



									"""simple docstring"""





									return cls




				@require_torch_multi_gpu
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(_snake_case						,    check_contents=_snake_case		)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case=True		):



									"""simple docstring"""





									_lowerCAmelCase      =   {
									    """label_smoothing""": 0.0,
									    """early_stopping_patience""": -1,
									    """train_batch_size""": 1,
									    """eval_batch_size""": 2,
									    """max_epochs""": 2,
									    """alpha_mlm""": 0.2,
									    """alpha_ce""": 0.8,
									    """do_predict""": True,
									    """model_name_or_path""": """sshleifer/tinier_bart""",
									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
									    """val_check_interval""": 0.5,
									}
									default_updates.update(_snake_case		)
									_lowerCAmelCase      =   CHEAP_ARGS.copy()
									_lowerCAmelCase      =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									_lowerCAmelCase      =   self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=_snake_case						,    output_dir=_snake_case						,    **_snake_case		)

									def convert(_snake_case						,    _snake_case		):
														if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																			return ""
														if v is False or v is None:
																			return ""
														if v is True:  # or len(str(v))==0:
																			return F'--{k}'
														return F'--{k}={v}'

									_lowerCAmelCase      =   [x for x in (convert(_snake_case						,    _snake_case		) for k, v in args_d.items()) if len(_snake_case		)]
									_lowerCAmelCase      =   [sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
									execute_subprocess_async(_snake_case						,    env=self.get_env()		)

									_lowerCAmelCase      =   os.listdir(_snake_case		)
									_lowerCAmelCase      =   {os.path.basename(_snake_case		) for p in contents}
									_lowerCAmelCase      =   [p for p in contents if p.endswith("""ckpt"""		)]
									assert len(_snake_case		) > 0

									self.assertIn("""test_generations.txt"""						,    _snake_case		)
									self.assertIn("""test_results.txt"""						,    _snake_case		)

									# get the following from the module, (we don't have access to `model` here)
									_lowerCAmelCase      =   os.path.join(_snake_case						,    """metrics.json"""		)
									_lowerCAmelCase      =   """rouge2"""

									_lowerCAmelCase      =   load_json(_snake_case		)
									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
									print(_snake_case		)
									_lowerCAmelCase      =   metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]						,    0.01		)
									self.assertIsInstance(last_step_stats[F'val_avg_{val_metric}']						,    _snake_case		)
									self.assertEqual(len(metrics["""test"""]		)						,    1		)
									_lowerCAmelCase      =   int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1		)
									self.assertEqual(len(metrics["""val"""]		)						,    _snake_case		)



