import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
     from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE_		:







   def __init__(    self	:	Any      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	str=13      ,      lowerCamelCase_	:	Dict=32      ,      lowerCamelCase_	:	List[str]=2      ,      lowerCamelCase_	:	Tuple=3      ,      lowerCamelCase_	:	str=16      ,      lowerCamelCase_	:	Union[str, Any]=[1, 2, 1]      ,      lowerCamelCase_	:	List[Any]=[2, 2, 4]      ,      lowerCamelCase_	:	List[str]=2      ,      lowerCamelCase_	:	Optional[Any]=2.0      ,      lowerCamelCase_	:	Optional[int]=True      ,      lowerCamelCase_	:	List[str]=0.0      ,      lowerCamelCase_	:	Any=0.0      ,      lowerCamelCase_	:	List[str]=0.1      ,      lowerCamelCase_	:	Optional[int]="gelu"      ,      lowerCamelCase_	:	int=False      ,      lowerCamelCase_	:	List[Any]=True      ,      lowerCamelCase_	:	str=0.0_2      ,      lowerCamelCase_	:	Tuple=1E-5      ,      lowerCamelCase_	:	List[Any]=True      ,      lowerCamelCase_	:	Dict=None      ,      lowerCamelCase_	:	Optional[Any]=True      ,      lowerCamelCase_	:	List[Any]=10      ,      lowerCamelCase_	:	List[Any]=8      ,      lowerCamelCase_	:	List[str]=["stage1", "stage2"]      ,      lowerCamelCase_	:	int=[1, 2]      ,      ):





    """simple docstring"""


    UpperCamelCase      =			parent
    UpperCamelCase      =			batch_size
    UpperCamelCase      =			image_size
    UpperCamelCase      =			patch_size
    UpperCamelCase      =			num_channels
    UpperCamelCase      =			embed_dim
    UpperCamelCase      =			depths
    UpperCamelCase      =			num_heads
    UpperCamelCase      =			window_size
    UpperCamelCase      =			mlp_ratio
    UpperCamelCase      =			qkv_bias
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			drop_path_rate
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			use_absolute_embeddings
    UpperCamelCase      =			patch_norm
    UpperCamelCase      =			layer_norm_eps
    UpperCamelCase      =			initializer_range
    UpperCamelCase      =			is_training
    UpperCamelCase      =			scope
    UpperCamelCase      =			use_labels
    UpperCamelCase      =			type_sequence_label_size
    UpperCamelCase      =			encoder_stride
    UpperCamelCase      =			out_features
    UpperCamelCase      =			out_indices







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

    UpperCamelCase      =			self.get_config()

    return config, pixel_values, labels







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    return SwinConfig(
        image_size=self.image_size      ,      patch_size=self.patch_size      ,      num_channels=self.num_channels      ,      embed_dim=self.embed_dim      ,      depths=self.depths      ,      num_heads=self.num_heads      ,      window_size=self.window_size      ,      mlp_ratio=self.mlp_ratio      ,      qkv_bias=self.qkv_bias      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      drop_path_rate=self.drop_path_rate      ,      hidden_act=self.hidden_act      ,      use_absolute_embeddings=self.use_absolute_embeddings      ,      path_norm=self.patch_norm      ,      layer_norm_eps=self.layer_norm_eps      ,      initializer_range=self.initializer_range      ,      encoder_stride=self.encoder_stride      ,      out_features=self.out_features      ,      out_indices=self.out_indices      ,      )







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





    """simple docstring"""


    UpperCamelCase      =			SwinModel(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_							)

    UpperCamelCase      =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
    UpperCamelCase      =			int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

    self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, expected_seq_len, expected_dim)							)







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





    """simple docstring"""


    UpperCamelCase      =			SwinBackbone(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_							)

    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps							)      ,      len(config.out_features							)							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)      ,      [self.batch_size, model.channels[0], 16, 16]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)      ,      len(config.out_features							)							)

    # verify backbone works with out_features=None
    UpperCamelCase      =			None
    UpperCamelCase      =			SwinBackbone(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_							)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps							)      ,      1							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)      ,      [self.batch_size, model.channels[-1], 4, 4]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)      ,      1							)







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





    """simple docstring"""


    UpperCamelCase      =			SwinForMaskedImageModeling(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_							)
    self.parent.assertEqual(
        result.logits.shape      ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

    # test greyscale images
    UpperCamelCase      =			1
    UpperCamelCase      =			SwinForMaskedImageModeling(lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()

    UpperCamelCase      =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
    UpperCamelCase      =			model(lowerCamelCase_							)
    self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, 1, self.image_size, self.image_size)							)







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





    """simple docstring"""


    UpperCamelCase      =			self.type_sequence_label_size
    UpperCamelCase      =			SwinForImageClassification(lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_      ,      labels=lowerCamelCase_							)
    self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)							)

    # test greyscale images
    UpperCamelCase      =			1
    UpperCamelCase      =			SwinForImageClassification(lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()

    UpperCamelCase      =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
    UpperCamelCase      =			model(lowerCamelCase_							)
    self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)							)







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			self.prepare_config_and_inputs()
    (
        (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    (
        UpperCamelCase
    )     ,    
    )      =			config_and_inputs
    UpperCamelCase      =			{"""pixel_values""": pixel_values}
    return config, inputs_dict





@require_torch
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase     ,    __lowerCAmelCase     ,    unittest.TestCase		):
   __lowerCAmelCase											=							(
       (
           SwinModel,
           SwinBackbone,
           SwinForImageClassification,
           SwinForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase											=							(
       {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase											=							True

   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			SwinModelTester(self							)
    UpperCamelCase      =			ConfigTester(self      ,      config_class=lowerCamelCase_      ,      embed_dim=37							)







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    return







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





    """simple docstring"""


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







   @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""							)
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    pass







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





    """simple docstring"""


    super().test_training_gradient_checkpointing()







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


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







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


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







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


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







   @unittest.skip(reason="""Swin does not use inputs_embeds"""							)
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    pass







   @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""							)
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    pass







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     self.assertIsInstance(model.get_input_embeddings()      ,      (nn.Module)							)
     UpperCamelCase      =			model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCamelCase_      ,      nn.Linear							)							)







   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     UpperCamelCase      =			inspect.signature(model.forward							)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     UpperCamelCase      =			[*signature.parameters.keys()]

     UpperCamelCase      =			["""pixel_values"""]
     self.assertListEqual(arg_names[:1]      ,      lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase      =			True

    for model_class in self.all_model_classes:
     UpperCamelCase      =			True
     UpperCamelCase      =			False
     UpperCamelCase      =			True
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.eval()
     with torch.no_grad():
      UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)
     UpperCamelCase      =			outputs.attentions
     UpperCamelCase      =			len(self.model_tester.depths							)
     self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     UpperCamelCase      =			True
     UpperCamelCase      =			config.window_size**2
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.eval()
     with torch.no_grad():
      UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)
     UpperCamelCase      =			outputs.attentions
     self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

     self.assertListEqual(
         list(attentions[0].shape[-3:]							)      ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,      )
     UpperCamelCase      =			len(lowerCamelCase_							)

     # Check attention is always last and order is fine
     UpperCamelCase      =			True
     UpperCamelCase      =			True
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.eval()
     with torch.no_grad():
      UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)

     # also another +1 for reshaped_hidden_states
     UpperCamelCase      =			1 if model_class.__name__ == """SwinBackbone""" else 2
     self.assertEqual(out_len + added_hidden_states      ,      len(lowerCamelCase_							)							)

     UpperCamelCase      =			outputs.attentions

     self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

     self.assertListEqual(
         list(self_attentions[0].shape[-3:]							)      ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,      )







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





    """simple docstring"""


    UpperCamelCase      =			model_class(lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()

    with torch.no_grad():
     UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)

    UpperCamelCase      =			outputs.hidden_states

    UpperCamelCase      =			getattr(
        self.model_tester      ,      """expected_num_hidden_layers"""      ,      len(self.model_tester.depths							) + 1							)
    self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

    # Swin has a different seq_length
    UpperCamelCase      =			(
        config.patch_size
        if isinstance(config.patch_size      ,      collections.abc.Iterable							)
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase      =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

    self.assertListEqual(
        list(hidden_states[0].shape[-2:]							)      ,      [num_patches, self.model_tester.embed_dim]      ,      )

    if not model_class.__name__ == "SwinBackbone":
     UpperCamelCase      =			outputs.reshaped_hidden_states
     self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

     UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			reshaped_hidden_states[0].shape
     UpperCamelCase      =			(
         reshaped_hidden_states[0].view(lowerCamelCase_      ,      lowerCamelCase_      ,      height * width							).permute(0      ,      2      ,      1							)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:]							)      ,      [num_patches, self.model_tester.embed_dim]      ,      )







   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase      =			(
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable							)
        else (self.model_tester.image_size, self.model_tester.image_size)
    )

    for model_class in self.all_model_classes:
     UpperCamelCase      =			True
     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCamelCase      =			True

     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase      =			3

    UpperCamelCase      =			(
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable							)
        else (self.model_tester.image_size, self.model_tester.image_size)
    )
    UpperCamelCase      =			(
        config.patch_size
        if isinstance(config.patch_size      ,      collections.abc.Iterable							)
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase      =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    UpperCamelCase      =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
     UpperCamelCase      =			True
     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      (padded_height, padded_width)							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCamelCase      =			True
     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      (padded_height, padded_width)							)







   @slow
   def 	lowerCamelCase_  (    self	:	List[Any]							):





    """simple docstring"""


    for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCamelCase      =			SwinModel.from_pretrained(lowerCamelCase_							)
     self.assertIsNotNone(lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase      =			_config_zero_init(lowerCamelCase_							)
    for model_class in self.all_model_classes:
     UpperCamelCase      =			model_class(config=lowerCamelCase_							)
     for name, param in model.named_parameters():
      if "embeddings" not in name and param.requires_grad:
       self.assertIn(
           ((param.data.mean() * 1E9).round() / 1E9).item()      ,      [0.0, 1.0]      ,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,      )





@require_vision
@require_torch
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):







   @cached_property
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return (
        AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							)
        if is_vision_available()
        else None
    )







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





    """simple docstring"""


    UpperCamelCase      =			SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							).to(lowerCamelCase_							)
    UpperCamelCase      =			self.default_image_processor

    UpperCamelCase      =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
    UpperCamelCase      =			image_processor(images=lowerCamelCase_      ,      return_tensors="""pt"""							).to(lowerCamelCase_							)

    # forward pass
    with torch.no_grad():
     UpperCamelCase      =			model(**lowerCamelCase_							)

    # verify the logits
    UpperCamelCase      =			torch.Size((1, 1000)							)
    self.assertEqual(outputs.logits.shape      ,      lowerCamelCase_							)
    UpperCamelCase      =			torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]							).to(lowerCamelCase_							)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,      lowerCamelCase_      ,      atol=1E-4							)							)





@require_torch
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase     ,    __lowerCAmelCase		):
   __lowerCAmelCase											=							(SwinBackbone,) if is_torch_available() else ()
   __lowerCAmelCase											=							SwinConfig







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





    """simple docstring"""


    UpperCamelCase      =			SwinModelTester(self							)

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
