





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
  )







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=False       ,      snake_case=True       ,      snake_case="None"       ,      snake_case=3       ,      snake_case=4       ,      snake_case=None       ,      )					->	int:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size   )

      a__						:    Union[str, Any]   		=	None
      if self.use_input_mask:
         a__						:    Any   		=	random_attention_mask([self.batch_size, self.seq_length]   )

      a__						:    Optional[Any]   		=	None
      if self.use_token_type_ids:
         a__						:    str   		=	ids_tensor([self.batch_size, self.seq_length]       ,      self.type_vocab_size   )

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

      a__						:    List[str]   		=	DebertaVaConfig(
          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       ,      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   )					->	List[Any]:
      """simple docstring"""





      a__						:    Dict   		=	TFDebertaVaModel(config=snake_case   )
      a__						:    Union[str, Any]   		=	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

      a__						:    Union[str, Any]   		=	[input_ids, input_mask]
      a__						:    Any   		=	model(snake_case   )

      a__						:    Optional[Any]   		=	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   )					->	Any:
      """simple docstring"""





      a__						:    Optional[Any]   		=	TFDebertaVaForMaskedLM(config=snake_case   )
      a__						:    Optional[Any]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      a__						:    List[str]   		=	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   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	self.num_labels
      a__						:    int   		=	TFDebertaVaForSequenceClassification(config=snake_case   )
      a__						:    Optional[Any]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      a__						:    int   		=	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   )					->	Dict:
      """simple docstring"""





      a__						:    Dict   		=	self.num_labels
      a__						:    str   		=	TFDebertaVaForTokenClassification(config=snake_case   )
      a__						:    Dict   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      a__						:    Tuple   		=	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   )					->	List[str]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	TFDebertaVaForQuestionAnswering(config=snake_case   )
      a__						:    Optional[int]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      a__						:    List[Any]   		=	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   )					->	List[str]:
      """simple docstring"""





      a__						:    List[str]   		=	self.prepare_config_and_inputs()
      (
          (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  
      )						:    int   		=	config_and_inputs
      a__						:    Any   		=	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_tf
class 	__lowerCAmelCase      (       _UpperCamelCase		,_UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    int  		=					(
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   _UpperCamelCase			:    List[str]  		=					(
       {
           """feature-extraction""": TFDebertaVaModel,
           """fill-mask""": TFDebertaVaForMaskedLM,
           """question-answering""": TFDebertaVaForQuestionAnswering,
           """text-classification""": TFDebertaVaForSequenceClassification,
           """token-classification""": TFDebertaVaForTokenClassification,
           """zero-shot""": TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   _UpperCamelCase			:    str  		=					False
   _UpperCamelCase			:    List[str]  		=					False





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Any   		=	TFDebertaVaModelTester(self   )
      a__						:    int   		=	ConfigTester(self       ,      config_class=snake_case       ,      hidden_size=37   )





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      self.config_tester.run_common_tests()





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Dict   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    List[str]   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Tuple   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Any   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    int   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*snake_case   )





   @slow
   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    Tuple   		=	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
      self.assertIsNotNone(snake_case   )







@require_tf
class 	__lowerCAmelCase      (       unittest.TestCase	):





   @unittest.skip(reason="Model not available yet"   )
   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      pass





   @slow
   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
      a__						:    Any   		=	tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   )
      a__						:    List[str]   		=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
      a__						:    Tuple   		=	model(snake_case       ,      attention_mask=snake_case   )[0]

      a__						:    List[Any]   		=	tf.constant(
          [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]   )
      tf.debugging.assert_near(output[:, 1:4, 1:4]       ,      snake_case       ,      atol=1E-4   )








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE__							:      List[str]         =    Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

SCREAMING_SNAKE_CASE__							:      Any         =    """zero2"""
SCREAMING_SNAKE_CASE__							:      Dict         =    """zero3"""
SCREAMING_SNAKE_CASE__							:      str         =    [ZEROa, ZEROa]






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # customize the test name generator function as we want both params to appear in the sub-test
   # name, as by default it shows only the first param
   a__						:    Optional[Any]   		=	parameterized.to_safe_name("_".join(str(lowerCamelCase ) for x in param.args ) )
   return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 	__lowerCAmelCase      (       _UpperCamelCase	):





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	str:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      pass





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = True       ,      snake_case = True       ,      snake_case = True       ,      )					->	str:
      """simple docstring"""





      a__						:    Tuple   		=	models[model]

      a__						:    int   		=	self.run_trainer(
          stage=snake_case       ,      model_name=snake_case       ,      eval_steps=snake_case       ,      num_train_epochs=1       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )

      self.do_checks(snake_case   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = 1       ,      snake_case = True       ,      snake_case = True       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    str   		=	self.get_auto_remove_tmp_dir("./xxx"       ,      after=snake_case   )
      a__						:    List[Any]   		=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case   )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

      if fpaa:
         args.extend(["--fp16"]   )

      # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
      # hence the separate config files
      a__						:    str   		=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
      a__						:    Dict   		=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
      a__						:    Optional[int]   		=	self.get_launcher(snake_case   )

      a__						:    Optional[int]   		=	launcher + script + args + ds_args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(snake_case       ,      env=self.get_env()   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case=False   )					->	List[Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	min(2       ,      get_gpu_count()   ) if distributed else 1
      return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

