

import unittest

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

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 (
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
      )


class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=99  ,  _UpperCAmelCase=32  ,  _UpperCAmelCase=5  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=37  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=512  ,  _UpperCAmelCase=16  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=0.0_2  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=1_000  ,  ):
   __UpperCAmelCase	:  Union[str, Any]		=     parent
   __UpperCAmelCase	:  List[Any]		=     batch_size
   __UpperCAmelCase	:  Tuple		=     seq_length
   __UpperCAmelCase	:  List[str]		=     is_training
   __UpperCAmelCase	:  int		=     use_input_mask
   __UpperCAmelCase	:  Dict		=     use_token_type_ids
   __UpperCAmelCase	:  Optional[Any]		=     use_labels
   __UpperCAmelCase	:  str		=     vocab_size
   __UpperCAmelCase	:  str		=     hidden_size
   __UpperCAmelCase	:  Optional[int]		=     num_hidden_layers
   __UpperCAmelCase	:  str		=     num_attention_heads
   __UpperCAmelCase	:  int		=     intermediate_size
   __UpperCAmelCase	:  List[str]		=     hidden_act
   __UpperCAmelCase	:  Union[str, Any]		=     hidden_dropout_prob
   __UpperCAmelCase	:  Tuple		=     attention_probs_dropout_prob
   __UpperCAmelCase	:  str		=     max_position_embeddings
   __UpperCAmelCase	:  Tuple		=     type_vocab_size
   __UpperCAmelCase	:  Optional[Any]		=     type_sequence_label_size
   __UpperCAmelCase	:  str		=     initializer_range
   __UpperCAmelCase	:  Union[str, Any]		=     num_labels
   __UpperCAmelCase	:  int		=     num_choices
   __UpperCAmelCase	:  str		=     scope
   __UpperCAmelCase	:  Optional[Any]		=     range_bbox




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size							)

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

   __UpperCAmelCase	:  Optional[int]		=     None
   if self.use_input_mask:
     __UpperCAmelCase	:  Any		=     ids_tensor([self.batch_size, self.seq_length]  ,  vocab_size=2							)

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

   __UpperCAmelCase	:  Union[str, Any]		=     None
   __UpperCAmelCase	:  int		=     None
   __UpperCAmelCase	:  Any		=     None
   if self.use_labels:
     __UpperCAmelCase	:  List[Any]		=     ids_tensor([self.batch_size]  ,  self.type_sequence_label_size							)
     __UpperCAmelCase	:  List[str]		=     ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels							)
     __UpperCAmelCase	:  List[Any]		=     ids_tensor([self.batch_size]  ,  self.num_choices							)

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

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




 def     a       (			self							):
   return LayoutLMConfig(
       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     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Tuple		=     LayoutLMModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  Dict		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[int]		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)
   __UpperCAmelCase	:  Union[str, Any]		=     model(_UpperCAmelCase  ,  _UpperCAmelCase							)
   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     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Union[str, Any]		=     LayoutLMForMaskedLM(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  List[str]		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)
   self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  str		=     self.num_labels
   __UpperCAmelCase	:  str		=     LayoutLMForSequenceClassification(_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  str		=     model(
       _UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)
   self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  int		=     self.num_labels
   __UpperCAmelCase	:  int		=     LayoutLMForTokenClassification(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  str		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)
   self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Optional[Any]		=     LayoutLMForQuestionAnswering(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  Optional[Any]		=     model(
       _UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  start_positions=_UpperCAmelCase  ,  end_positions=_UpperCAmelCase  ,  )
   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     a       (			self							):
   __UpperCAmelCase	:  int		=     self.prepare_config_and_inputs()
   (
       (
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			
   )	:  Union[str, Any]		=     config_and_inputs
   __UpperCAmelCase	:  Tuple		=     {
       'input_ids': input_ids,
       'bbox': bbox,
       'token_type_ids': token_type_ids,
       'attention_mask': input_mask,
   }
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 _lowerCAmelCase									=	(
     {
         '''document-question-answering''': LayoutLMForQuestionAnswering,
         '''feature-extraction''': LayoutLMModel,
         '''fill-mask''': LayoutLMForMaskedLM,
         '''text-classification''': LayoutLMForSequenceClassification,
         '''token-classification''': LayoutLMForTokenClassification,
         '''zero-shot''': LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowerCAmelCase									=	True




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     LayoutLMModelTester(self							)
   __UpperCAmelCase	:  Tuple		=     ConfigTester(self  ,  config_class=_UpperCAmelCase  ,  hidden_size=37							)




 def     a       (			self							):
   self.config_tester.run_common_tests()




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
     __UpperCAmelCase	:  int		=     type
     self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase							)




 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
 def     a       (			self							):
   pass





def  _snake_case		()							->		Tuple:
  # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  # fmt: off
  __UpperCAmelCase	:  List[str]		=     torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]    ,       device=snake_case_  )  # noqa: E231
  __UpperCAmelCase	:  Union[str, Any]		=     torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]    ,       device=snake_case_  )  # noqa: E231
  __UpperCAmelCase	:  List[Any]		=     torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]    ,       device=snake_case_  )  # noqa: E231
  __UpperCAmelCase	:  Optional[Any]		=     torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]    ,       device=snake_case_  )  # noqa: E231
  # these are sequence labels (i.e. at the token level)
  __UpperCAmelCase	:  Union[str, Any]		=     torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]    ,       device=snake_case_  )  # noqa: E231
  # fmt: on

  return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'							).to(_UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Any		=     model(input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)

   # test the sequence output on [0, :3, :3]
   __UpperCAmelCase	:  str		=     torch.tensor(
       [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]  ,  device=_UpperCAmelCase  ,  )

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

   # test the pooled output on [1, :3]
   __UpperCAmelCase	:  Any		=     torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]  ,  device=_UpperCAmelCase							)

   self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]  ,  _UpperCAmelCase  ,  atol=1E-3							)							)




 @slow
 def     a       (			self							):
   # initialize model with randomly initialized sequence classification head
   __UpperCAmelCase	:  List[str]		=     LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'  ,  num_labels=2							).to(
       _UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Optional[Any]		=     model(
       input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=torch.tensor([1, 1]  ,  device=_UpperCAmelCase							)  ,  )

   # test whether we get a loss as a scalar
   __UpperCAmelCase	:  str		=     outputs.loss
   __UpperCAmelCase	:  List[str]		=     torch.Size([]							)
   self.assertEqual(loss.shape  ,  _UpperCAmelCase							)

   # test the shape of the logits
   __UpperCAmelCase	:  Union[str, Any]		=     outputs.logits
   __UpperCAmelCase	:  int		=     torch.Size((2, 2)							)
   self.assertEqual(logits.shape  ,  _UpperCAmelCase							)




 @slow
 def     a       (			self							):
   # initialize model with randomly initialized token classification head
   __UpperCAmelCase	:  List[Any]		=     LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'  ,  num_labels=13							).to(
       _UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Optional[int]		=     model(
       input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)

   # test the loss calculation to be around 2.65
   # expected_loss = torch.tensor(2.65, device=torch_device)

   # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
   # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

   # test the shape of the logits
   __UpperCAmelCase	:  List[str]		=     outputs.logits
   __UpperCAmelCase	:  Optional[Any]		=     torch.Size((2, 25, 13)							)
   self.assertEqual(logits.shape  ,  _UpperCAmelCase							)




 @slow
 def     a       (			self							):
   # initialize model with randomly initialized token classification head
   __UpperCAmelCase	:  Any		=     LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'							).to(_UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Optional[Any]		=     model(input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)

   # test the shape of the logits
   __UpperCAmelCase	:  List[str]		=     torch.Size((2, 25)							)
   self.assertEqual(outputs.start_logits.shape  ,  _UpperCAmelCase							)
   self.assertEqual(outputs.end_logits.shape  ,  _UpperCAmelCase							)











import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase							=      logging.get_logger(__name__)

_lowerCamelCase							=      {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}


class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Dict     					=    "hubert"
     def __init__(				self ,    _lowerCAmelCase=32 ,    _lowerCAmelCase=768 ,    _lowerCAmelCase=12 ,    _lowerCAmelCase=12 ,    _lowerCAmelCase=3_072 ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.02 ,    _lowerCAmelCase=1E-5 ,    _lowerCAmelCase="group" ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512) ,    _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2) ,    _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2) ,    _lowerCAmelCase=False ,    _lowerCAmelCase=128 ,    _lowerCAmelCase=16 ,    _lowerCAmelCase=False ,    _lowerCAmelCase=True ,    _lowerCAmelCase=0.05 ,    _lowerCAmelCase=10 ,    _lowerCAmelCase=2 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=10 ,    _lowerCAmelCase=0 ,    _lowerCAmelCase="sum" ,    _lowerCAmelCase=False ,    _lowerCAmelCase=False ,    _lowerCAmelCase=256 ,    _lowerCAmelCase=0 ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=2 ,    **_lowerCAmelCase ,    ):
            super().__init__(**_lowerCAmelCase ,    pad_token_id=_lowerCAmelCase ,    bos_token_id=_lowerCAmelCase ,    eos_token_id=_lowerCAmelCase	)
            a			   =hidden_size
            a			   =feat_extract_norm
            a			   =feat_extract_activation
            a			   =list(_lowerCAmelCase	)
            a			   =list(_lowerCAmelCase	)
            a			   =list(_lowerCAmelCase	)
            a			   =conv_bias
            a			   =num_conv_pos_embeddings
            a			   =num_conv_pos_embedding_groups
            a			   =len(self.conv_dim	)
            a			   =num_hidden_layers
            a			   =intermediate_size
            a			   =hidden_act
            a			   =num_attention_heads
            a			   =hidden_dropout
            a			   =attention_dropout
            a			   =activation_dropout
            a			   =feat_proj_layer_norm
            a			   =feat_proj_dropout
            a			   =final_dropout
            a			   =layerdrop
            a			   =layer_norm_eps
            a			   =initializer_range
            a			   =vocab_size
            a			   =do_stable_layer_norm
            a			   =use_weighted_layer_sum
            a			   =classifier_proj_size

            if (
                (len(self.conv_stride	) != self.num_feat_extract_layers)
                or (len(self.conv_kernel	) != self.num_feat_extract_layers)
                or (len(self.conv_dim	) != self.num_feat_extract_layers)
            ):
                   raise ValueError(
                       """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                       """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                       F''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
                       F''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            a			   =apply_spec_augment
            a			   =mask_time_prob
            a			   =mask_time_length
            a			   =mask_time_min_masks
            a			   =mask_feature_prob
            a			   =mask_feature_length
            a			   =mask_feature_min_masks

            # ctc loss
            a			   =ctc_loss_reduction
            a			   =ctc_zero_infinity






     @property
     def        lowerCAmelCase__  (				self	):
            return functools.reduce(operator.mul ,    self.conv_stride ,    1	)
