






"""simple docstring"""


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 						snake_case  :





    def __init__(							self	:					List[str]				,		A	:					int				,		A	:					List[Any]=1_3				,		A	:					Tuple=3_2				,		A	:					Optional[int]=2				,		A	:					Dict=3				,		A	:					int=1_6				,		A	:					Any=[1, 2, 1]				,		A	:					int=[2, 2, 4]				,		A	:					int=2				,		A	:					Tuple=2.0				,		A	:					int=True				,		A	:					str=0.0				,		A	:					Union[str, Any]=0.0				,		A	:					List[str]=0.1				,		A	:					Any="gelu"				,		A	:					str=False				,		A	:					Tuple=True				,		A	:					Union[str, Any]=0.02				,		A	:					int=1E-5				,		A	:					Optional[int]=True				,		A	:					List[Any]=None				,		A	:					Dict=True				,		A	:					List[Any]=1_0				,		A	:					Dict=8				,		A	:					Dict=["stage1", "stage2"]				,		A	:					str=[1, 2]				,		):


      '''simple docstring'''




      a     :			Union[str, Any]				=   parent
      a     :			Union[str, Any]				=   batch_size
      a     :			int				=   image_size
      a     :			List[Any]				=   patch_size
      a     :			Optional[Any]				=   num_channels
      a     :			str				=   embed_dim
      a     :			Tuple				=   depths
      a     :			Optional[int]				=   num_heads
      a     :			List[Any]				=   window_size
      a     :			int				=   mlp_ratio
      a     :			Any				=   qkv_bias
      a     :			int				=   hidden_dropout_prob
      a     :			str				=   attention_probs_dropout_prob
      a     :			Union[str, Any]				=   drop_path_rate
      a     :			Optional[int]				=   hidden_act
      a     :			List[str]				=   use_absolute_embeddings
      a     :			Optional[int]				=   patch_norm
      a     :			Optional[Any]				=   layer_norm_eps
      a     :			str				=   initializer_range
      a     :			Union[str, Any]				=   is_training
      a     :			Union[str, Any]				=   scope
      a     :			Optional[Any]				=   use_labels
      a     :			str				=   type_sequence_label_size
      a     :			Optional[Any]				=   encoder_stride
      a     :			Optional[int]				=   out_features
      a     :			str				=   out_indices





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			str				=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

      a     :			int				=   self.get_config()

      return config, pixel_values, labels





    def 				lowerCamelCase__      (							self	:					str			):


      '''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	:					Tuple				,		A	:					Union[str, Any]				,		A	:					List[str]				,		A	:					List[Any]			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   SwinModel(config=A			)
      model.to(A			)
      model.eval()
      a     :			Optional[int]				=   model(A			)

      a     :			Tuple				=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
      a     :			List[str]				=   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	:					int				,		A	:					int				,		A	:					Dict				,		A	:					int			):


      '''simple docstring'''




      a     :			int				=   SwinBackbone(config=A			)
      model.to(A			)
      model.eval()
      a     :			Union[str, Any]				=   model(A			)

      # 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], 1_6, 1_6]			)

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

      # verify backbone works with out_features=None
      a     :			Dict				=   None
      a     :			Tuple				=   SwinBackbone(config=A			)
      model.to(A			)
      model.eval()
      a     :			List[Any]				=   model(A			)

      # 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[str]				,		A	:					Optional[int]				,		A	:					Optional[int]				,		A	:					str			):


      '''simple docstring'''




      a     :			Any				=   SwinForMaskedImageModeling(config=A			)
      model.to(A			)
      model.eval()
      a     :			Tuple				=   model(A			)
      self.parent.assertEqual(
          result.logits.shape				,		(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

      # test greyscale images
      a     :			int				=   1
      a     :			Optional[int]				=   SwinForMaskedImageModeling(A			)
      model.to(A			)
      model.eval()

      a     :			Dict				=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a     :			Optional[Any]				=   model(A			)
      self.parent.assertEqual(result.logits.shape				,		(self.batch_size, 1, self.image_size, self.image_size)			)





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


      '''simple docstring'''




      a     :			Union[str, Any]				=   self.type_sequence_label_size
      a     :			int				=   SwinForImageClassification(A			)
      model.to(A			)
      model.eval()
      a     :			Union[str, Any]				=   model(A				,		labels=A			)
      self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)			)

      # test greyscale images
      a     :			Optional[int]				=   1
      a     :			Tuple				=   SwinForImageClassification(A			)
      model.to(A			)
      model.eval()

      a     :			Union[str, Any]				=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a     :			Any				=   model(A			)
      self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)			)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   self.prepare_config_and_inputs()
      (
          (
          a
      ),     (
          a
      ),     (
          a
      ),     
      )     :			Tuple				=   config_and_inputs
      a     :			Tuple				=   {'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 						snake_case  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     (
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    __magic_name__         =     (
        {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    __magic_name__         =     True

    __magic_name__         =     False
    __magic_name__         =     False
    __magic_name__         =     False





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      a     :			Dict				=   SwinModelTester(self			)
      a     :			Any				=   ConfigTester(self				,		config_class=A				,		embed_dim=3_7			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''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	:					Optional[int]			):


      '''simple docstring'''




      return





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


      '''simple docstring'''




      a     :			Union[str, Any]				=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)





    @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'			)
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      pass





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      super().test_training_gradient_checkpointing()





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


      '''simple docstring'''




      a     :			Dict				=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*A			)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			List[Any]				=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*A			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			int				=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A			)





    @unittest.skip(reason='Swin does not use inputs_embeds'			)
    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      pass





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


      '''simple docstring'''




      pass





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a,     a     :			Optional[int]				=   self.model_tester.prepare_config_and_inputs_for_common()

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





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      a,     a     :			Dict				=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a     :			Any				=   model_class(A			)
        a     :			List[str]				=   inspect.signature(model.forward			)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a     :			int				=   [*signature.parameters.keys()]

        a     :			int				=   ['pixel_values']
        self.assertListEqual(arg_names[:1]				,		A			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a,     a     :			Tuple				=   self.model_tester.prepare_config_and_inputs_for_common()
      a     :			str				=   True

      for model_class in self.all_model_classes:
        a     :			Union[str, Any]				=   True
        a     :			Union[str, Any]				=   False
        a     :			List[Any]				=   True
        a     :			Union[str, Any]				=   model_class(A			)
        model.to(A			)
        model.eval()
        with torch.no_grad():
          a     :			List[Any]				=   model(**self._prepare_for_class(A				,		A			)			)
        a     :			Union[str, Any]				=   outputs.attentions
        a     :			List[str]				=   len(self.model_tester.depths			)
        self.assertEqual(len(A			)				,		A			)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        a     :			Tuple				=   True
        a     :			Optional[Any]				=   config.window_size**2
        a     :			Tuple				=   model_class(A			)
        model.to(A			)
        model.eval()
        with torch.no_grad():
          a     :			Union[str, Any]				=   model(**self._prepare_for_class(A				,		A			)			)
        a     :			List[Any]				=   outputs.attentions
        self.assertEqual(len(A			)				,		A			)

        self.assertListEqual(
            list(attentions[0].shape[-3:]			)				,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,		)
        a     :			Optional[Any]				=   len(A			)

        # Check attention is always last and order is fine
        a     :			List[str]				=   True
        a     :			str				=   True
        a     :			Union[str, Any]				=   model_class(A			)
        model.to(A			)
        model.eval()
        with torch.no_grad():
          a     :			Optional[Any]				=   model(**self._prepare_for_class(A				,		A			)			)

        # also another +1 for reshaped_hidden_states
        a     :			Any				=   1 if model_class.__name__ == 'SwinBackbone' else 2
        self.assertEqual(out_len + added_hidden_states				,		len(A			)			)

        a     :			List[str]				=   outputs.attentions

        self.assertEqual(len(A			)				,		A			)

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





    def 				lowerCamelCase__      (							self	:					Any				,		A	:					Dict				,		A	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]			):


      '''simple docstring'''




      a     :			Tuple				=   model_class(A			)
      model.to(A			)
      model.eval()

      with torch.no_grad():
        a     :			Optional[int]				=   model(**self._prepare_for_class(A				,		A			)			)

      a     :			Optional[Any]				=   outputs.hidden_states

      a     :			int				=   getattr(
          self.model_tester				,		'expected_num_hidden_layers'				,		len(self.model_tester.depths			) + 1			)
      self.assertEqual(len(A			)				,		A			)

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

      a     :			List[str]				=   (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":
        a     :			str				=   outputs.reshaped_hidden_states
        self.assertEqual(len(A			)				,		A			)

        a,     a,     a,     a     :			Optional[Any]				=   reshaped_hidden_states[0].shape
        a     :			List[Any]				=   (
            reshaped_hidden_states[0].view(A				,		A				,		height * width			).permute(0				,		2				,		1			)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]			)				,		[num_patches, self.model_tester.embed_dim]				,		)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a,     a     :			Dict				=   self.model_tester.prepare_config_and_inputs_for_common()

      a     :			Any				=   (
          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:
        a     :			int				=   True
        self.check_hidden_states_output(A				,		A				,		A				,		A			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a     :			int				=   True

        self.check_hidden_states_output(A				,		A				,		A				,		A			)





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a,     a     :			Any				=   self.model_tester.prepare_config_and_inputs_for_common()
      a     :			List[str]				=   3

      a     :			Dict				=   (
          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)
      )
      a     :			List[str]				=   (
          config.patch_size
          if isinstance(config.patch_size				,		collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      a     :			int				=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      a     :			List[str]				=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
        a     :			Tuple				=   True
        self.check_hidden_states_output(A				,		A				,		A				,		(padded_height, padded_width)			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a     :			Dict				=   True
        self.check_hidden_states_output(A				,		A				,		A				,		(padded_height, padded_width)			)





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


      '''simple docstring'''




      for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a     :			int				=   SwinModel.from_pretrained(A			)
        self.assertIsNotNone(A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      a,     a     :			List[Any]				=   self.model_tester.prepare_config_and_inputs_for_common()

      a     :			Union[str, Any]				=   _config_zero_init(A			)
      for model_class in self.all_model_classes:
        a     :			List[str]				=   model_class(config=A			)
        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 						snake_case  (  unittest.TestCase       ):





    @cached_property
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





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


      '''simple docstring'''




      a     :			int				=   SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			).to(A			)
      a     :			Optional[int]				=   self.default_image_processor

      a     :			Dict				=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      a     :			Optional[Any]				=   image_processor(images=A				,		return_tensors='pt'			).to(A			)

      # forward pass
      with torch.no_grad():
        a     :			List[Any]				=   model(**A			)

      # verify the logits
      a     :			Optional[Any]				=   torch.Size((1, 1_0_0_0)			)
      self.assertEqual(outputs.logits.shape				,		A			)
      a     :			Union[str, Any]				=   torch.tensor([-0.09_48, -0.64_54, -0.09_21]			).to(A			)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]				,		A				,		atol=1E-4			)			)






@require_torch
class 						snake_case  (  unittest.TestCase   , UpperCAmelCase       ):
    __magic_name__         =     (SwinBackbone,) if is_torch_available() else ()
    __magic_name__         =     SwinConfig





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			Dict				=   SwinModelTester(self			)







import unittest

from transformers import ErnieMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        ErnieMForInformationExtraction,
        ErnieMForMultipleChoice,
        ErnieMForQuestionAnswering,
        ErnieMForSequenceClassification,
        ErnieMForTokenClassification,
        ErnieMModel,
    )
    from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase=13							,  lowerCamelCase=7							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase=99							,  lowerCamelCase=32							,  lowerCamelCase=5							,  lowerCamelCase=4							,  lowerCamelCase=37							,  lowerCamelCase="gelu"							,  lowerCamelCase=0.1							,  lowerCamelCase=0.1							,  lowerCamelCase=512							,  lowerCamelCase=16							,  lowerCamelCase=2							,  lowerCamelCase=0.02							,  lowerCamelCase=3							,  lowerCamelCase=4							,  lowerCamelCase=None							,  )						->       Optional[int]:
        snake_case_       						=      parent
        snake_case_       						=      batch_size
        snake_case_       						=      seq_length
        snake_case_       						=      is_training
        snake_case_       						=      use_input_mask
        snake_case_       						=      use_labels
        snake_case_       						=      vocab_size
        snake_case_       						=      hidden_size
        snake_case_       						=      num_hidden_layers
        snake_case_       						=      num_attention_heads
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      type_vocab_size
        snake_case_       						=      type_sequence_label_size
        snake_case_       						=      initializer_range
        snake_case_       						=      num_labels
        snake_case_       						=      num_choices
        snake_case_       						=      scope







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size   )

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

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

        snake_case_       						=      self.get_config()

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







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size   )

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

        return config, input_ids, input_mask







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        return ErnieMConfig(
            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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Union[str, Any]:
        snake_case_       						=      ErnieMModel(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase							,  return_dict=lowerCamelCase   )
        self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        snake_case_       						=      ErnieMForQuestionAnswering(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(
            lowerCamelCase							,  attention_mask=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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        snake_case_       						=      ErnieMForInformationExtraction(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      torch.ones_like(lowerCamelCase							,  dtype=torch.floataa   )
        snake_case_       						=      model(
            lowerCamelCase							,  attention_mask=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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      self.num_labels
        snake_case_       						=      ErnieMForSequenceClassification(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase							,  attention_mask=lowerCamelCase							,  labels=lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Union[str, Any]:
        snake_case_       						=      self.num_labels
        snake_case_       						=      ErnieMForTokenClassification(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        input_ids.to(lowerCamelCase   )
        input_mask.to(lowerCamelCase   )
        token_labels.to(lowerCamelCase   )

        snake_case_       						=      model(lowerCamelCase							,  attention_mask=lowerCamelCase							,  labels=lowerCamelCase   )

        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        snake_case_       						=      self.num_choices
        snake_case_       						=      ErnieMForMultipleChoice(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      input_ids.unsqueeze(1   ).expand(-1							,  self.num_choices							,  -1   ).contiguous()
        snake_case_       						=      input_mask.unsqueeze(1   ).expand(-1							,  self.num_choices							,  -1   ).contiguous()
        snake_case_       						=      model(
            lowerCamelCase							,  attention_mask=lowerCamelCase							,  labels=lowerCamelCase							,  )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_choices)   )







      def 	lowerCAmelCase_    (     self   )						->       str:
        snake_case_       						=      self.prepare_config_and_inputs()
        (
            (
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				
        )       						=      config_and_inputs
        snake_case_       						=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class  __lowerCamelCase				(	__snake_case  ,							__snake_case  ,							unittest.TestCase	):
      lowerCamelCase_	:      Optional[int]          =					(
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase_	:      List[str]          =					()
      lowerCamelCase_	:      Optional[int]          =					(
          {
              'feature-extraction': ErnieMModel,
              'question-answering': ErnieMForQuestionAnswering,
              'text-classification': ErnieMForSequenceClassification,
              'token-classification': ErnieMForTokenClassification,
              'zero-shot': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCamelCase_	:      List[Any]          =					False







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        if pipeline_test_casse_name == "QAPipelineTests":
          return True

        return False







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_       						=      ErnieMModelTester(self   )
        snake_case_       						=      ConfigTester(self							,  config_class=lowerCamelCase							,  hidden_size=37   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        self.config_tester.run_common_tests()







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          snake_case_       						=      type
          self.model_tester.create_and_check_model(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase   )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       int:
        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case_       						=      ErnieMModel.from_pretrained(lowerCamelCase   )
          self.assertIsNotNone(lowerCamelCase   )







@require_torch
class  __lowerCamelCase				(	unittest.TestCase	):







      @slow
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""   )
        model.eval()
        snake_case_       						=      torch.tensor([[0, 1, 2, 3, 4, 5]]   )
        snake_case_       						=      model(lowerCamelCase   )[0]

        # TODO Replace vocab size
        snake_case_       						=      768

        snake_case_       						=      torch.Size((1, 6, hidden_size)   )
        self.assertEqual(output.shape							,  lowerCamelCase   )

        snake_case_       						=      torch.tensor(
            [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]   )

        self.assertTrue(torch.allclose(output[:, :3, :3]							,  lowerCamelCase							,  atol=1e-3   )   )