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 SCREAMING_SNAKE_CASE_		:







   def __init__(    self	:	str      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Tuple=13      ,      lowerCamelCase_	:	Any=7      ,      lowerCamelCase_	:	Optional[int]=True      ,      lowerCamelCase_	:	Dict=True      ,      lowerCamelCase_	:	Dict=True      ,      lowerCamelCase_	:	List[Any]=True      ,      lowerCamelCase_	:	str=99      ,      lowerCamelCase_	:	Dict=32      ,      lowerCamelCase_	:	Tuple=5      ,      lowerCamelCase_	:	Tuple=4      ,      lowerCamelCase_	:	Dict=37      ,      lowerCamelCase_	:	Any="gelu"      ,      lowerCamelCase_	:	Any=0.1      ,      lowerCamelCase_	:	Union[str, Any]=0.1      ,      lowerCamelCase_	:	Optional[Any]=512      ,      lowerCamelCase_	:	int=16      ,      lowerCamelCase_	:	Optional[Any]=2      ,      lowerCamelCase_	:	Any=0.0_2      ,      lowerCamelCase_	:	Any=3      ,      lowerCamelCase_	:	Union[str, Any]=4      ,      lowerCamelCase_	:	Any=None      ,      lowerCamelCase_	:	Tuple=1000      ,      ):





    """simple docstring"""


    UpperCamelCase      =			parent
    UpperCamelCase      =			batch_size
    UpperCamelCase      =			seq_length
    UpperCamelCase      =			is_training
    UpperCamelCase      =			use_input_mask
    UpperCamelCase      =			use_token_type_ids
    UpperCamelCase      =			use_labels
    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			hidden_size
    UpperCamelCase      =			num_hidden_layers
    UpperCamelCase      =			num_attention_heads
    UpperCamelCase      =			intermediate_size
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			max_position_embeddings
    UpperCamelCase      =			type_vocab_size
    UpperCamelCase      =			type_sequence_label_size
    UpperCamelCase      =			initializer_range
    UpperCamelCase      =			num_labels
    UpperCamelCase      =			num_choices
    UpperCamelCase      =			scope
    UpperCamelCase      =			range_bbox







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


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

    UpperCamelCase      =			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      =			bbox[i, j, 3]
       UpperCamelCase      =			bbox[i, j, 1]
       UpperCamelCase      =			t
      if bbox[i, j, 2] < bbox[i, j, 0]:
       UpperCamelCase      =			bbox[i, j, 2]
       UpperCamelCase      =			bbox[i, j, 0]
       UpperCamelCase      =			t

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

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

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

    UpperCamelCase      =			self.get_config()

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







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    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 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	List[Any]      ,      lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	Union[str, Any]      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Any							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMModel(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_      ,      lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_							)
    UpperCamelCase      =			model(lowerCamelCase_      ,      lowerCamelCase_      ,      token_type_ids=lowerCamelCase_							)
    UpperCamelCase      =			model(lowerCamelCase_      ,      lowerCamelCase_							)
    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 	lowerCamelCase_  (    self	:	Tuple      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	Union[str, Any]      ,      lowerCamelCase_	:	List[Any]      ,      lowerCamelCase_	:	Optional[Any]      ,      lowerCamelCase_	:	Union[str, Any]      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMForMaskedLM(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_      ,      lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_      ,      labels=lowerCamelCase_							)
    self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Dict      ,      lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Any							):





    """simple docstring"""


    UpperCamelCase      =			self.num_labels
    UpperCamelCase      =			LayoutLMForSequenceClassification(lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(
        lowerCamelCase_      ,      lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_      ,      labels=lowerCamelCase_							)
    self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)							)







   def 	lowerCamelCase_  (    self	:	int      ,      lowerCamelCase_	:	List[Any]      ,      lowerCamelCase_	:	List[Any]      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	Optional[Any]      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	Optional[Any]      ,      lowerCamelCase_	:	Dict      ,      lowerCamelCase_	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			self.num_labels
    UpperCamelCase      =			LayoutLMForTokenClassification(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_      ,      lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_      ,      labels=lowerCamelCase_							)
    self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	Optional[Any]      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	List[str]      ,      lowerCamelCase_	:	List[Any]      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMForQuestionAnswering(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(
        lowerCamelCase_      ,      bbox=lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_      ,      start_positions=lowerCamelCase_      ,      end_positions=lowerCamelCase_      ,      )
    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 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			self.prepare_config_and_inputs()
    (
        (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    
    )      =			config_and_inputs
    UpperCamelCase      =			{
        """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     ,    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 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMModelTester(self							)
    UpperCamelCase      =			ConfigTester(self      ,      config_class=lowerCamelCase_      ,      hidden_size=37							)







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    self.config_tester.run_common_tests()







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     UpperCamelCase      =			type
     self.model_tester.create_and_check_model(*lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_							)







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





    """simple docstring"""


    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_							)







   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    pass





def  lowercase(  )		->  Optional[Any]:







 '''simple docstring'''
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 UpperCamelCase      =			torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]       ,				device=UpperCamelCase_					)  # noqa: E231
 UpperCamelCase      =			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=UpperCamelCase_					)  # noqa: E231
 UpperCamelCase      =			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=UpperCamelCase_					)  # noqa: E231
 UpperCamelCase      =			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=UpperCamelCase_					)  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 UpperCamelCase      =			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=UpperCamelCase_					)  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):







   @slow
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""							).to(lowerCamelCase_							)

    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCamelCase      =			model(input_ids=lowerCamelCase_      ,      bbox=lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_							)

    # test the sequence output on [0, :3, :3]
    UpperCamelCase      =			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=lowerCamelCase_      ,      )

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

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

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







   @slow
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,      num_labels=2							).to(
        lowerCamelCase_							)

    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCamelCase      =			model(
        input_ids=lowerCamelCase_      ,      bbox=lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_      ,      labels=torch.tensor([1, 1]      ,      device=lowerCamelCase_							)      ,      )

    # test whether we get a loss as a scalar
    UpperCamelCase      =			outputs.loss
    UpperCamelCase      =			torch.Size([]							)
    self.assertEqual(loss.shape      ,      lowerCamelCase_							)

    # test the shape of the logits
    UpperCamelCase      =			outputs.logits
    UpperCamelCase      =			torch.Size((2, 2)							)
    self.assertEqual(logits.shape      ,      lowerCamelCase_							)







   @slow
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,      num_labels=13							).to(
        lowerCamelCase_							)

    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCamelCase      =			model(
        input_ids=lowerCamelCase_      ,      bbox=lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_      ,      labels=lowerCamelCase_							)

    # 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      =			outputs.logits
    UpperCamelCase      =			torch.Size((2, 25, 13)							)
    self.assertEqual(logits.shape      ,      lowerCamelCase_							)







   @slow
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    UpperCamelCase      =			LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""							).to(lowerCamelCase_							)

    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCamelCase      =			model(input_ids=lowerCamelCase_      ,      bbox=lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      token_type_ids=lowerCamelCase_							)

    # test the shape of the logits
    UpperCamelCase      =			torch.Size((2, 25)							)
    self.assertEqual(outputs.start_logits.shape      ,      lowerCamelCase_							)
    self.assertEqual(outputs.end_logits.shape      ,      lowerCamelCase_							)

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
     import transformer_engine.pytorch as te





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True					)		->  int:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""					)
 for name, module in model.named_children():
  if isinstance(UpperCamelCase_       ,				nn.Linear					) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape					):
    return
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			te.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				nn.LayerNorm					) and to_transformer_engine and _convert_ln:
   UpperCamelCase      =			te.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.Linear					) and not to_transformer_engine and _convert_linear:
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			nn.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.LayerNorm					) and not to_transformer_engine and _convert_ln:
   UpperCamelCase      =			nn.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  else:
   convert_model(
       UpperCamelCase_       ,				to_transformer_engine=UpperCamelCase_       ,				_convert_linear=_convert_linear       ,				_convert_ln=_convert_ln       ,				)





def  lowercase(  UpperCamelCase_					)		->  Any:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""					)
 for m in model.modules():
  if isinstance(UpperCamelCase_       ,				(te.LayerNorm, te.Linear)					):
   return True
 return False
